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    [{"authors":null,"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1680566400,"objectID":"06cefa2403a08dd97f6be471f7e7bc66","permalink":"https://climprocpred.github.io/author/andrew-brettin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/andrew-brettin/","section":"authors","summary":"","tags":null,"title":"Andrew Brettin","type":"authors"},{"authors":null,"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1680566400,"objectID":"e317cebc5b6e6b663f71c3b4b8b216b5","permalink":"https://climprocpred.github.io/author/emily-newsom/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/emily-newsom/","section":"authors","summary":"","tags":null,"title":"Emily Newsom","type":"authors"},{"authors":null,"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1680566400,"objectID":"d4a547fdd184ac665e70ede49f1b4266","permalink":"https://climprocpred.github.io/author/fabrizio-falasca/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/fabrizio-falasca/","section":"authors","summary":"","tags":null,"title":"Fabrizio Falasca","type":"authors"},{"authors":null,"categories":null,"content":"Aneesh Subramanian is a Professor in Mathematics \u0026amp; Atmosphere/Ocean Science at the Courant Institute, New York University. Her research focuses on the dynamics of the climate system and the main emphasis of her work is to study the influence of the ocean on local and global scales. Prior to NYU, she was a faculty member at the University of Oxford until 2019, and obtained her PhD in 2009 in Climate Dynamics from Harvard University. She was the recipient of the 2020 Nicholas P. Fofonoff Award from the American Meteorological Society “For exceptional creativity in the development and application of new concepts in ocean and climate dynamics”. She is the lead principal investigator of the NSF-NOAA Climate Process Team on Ocean Transport and Eddy Energy, and M²LInES – an international effort to improve climate models with scientific machine learning. She currently serves as an editor for the Journal of Climate, a member on the International CLIVAR Ocean Model Development Panel, and on the CESM Advisory Board.\n","date":1680566400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1680566400,"objectID":"2525497d367e79493fd32b198b28f040","permalink":"https://climprocpred.github.io/author/aneesh-subramanian/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/aneesh-subramanian/","section":"authors","summary":"Aneesh Subramanian is a Professor in Mathematics \u0026 Atmosphere/Ocean Science at the Courant Institute, New York University. Her research focuses on the dynamics of the climate system and the main emphasis of her work is to study the influence of the ocean on local and global scales.","tags":null,"title":"Aneesh Subramanian","type":"authors"},{"authors":null,"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1680566400,"objectID":"bbef18685ceda65c92e56c92ae4e989b","permalink":"https://climprocpred.github.io/author/pavel-perezhogin/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/pavel-perezhogin/","section":"authors","summary":"","tags":null,"title":"Pavel Perezhogin","type":"authors"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1672531200,"objectID":"0aa083324c83f14e561f10c18d1eda9b","permalink":"https://climprocpred.github.io/author/andrew-ross/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/andrew-ross/","section":"authors","summary":"","tags":null,"title":"Andrew Ross","type":"authors"},{"authors":null,"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1672531200,"objectID":"48caa6413df720419f5213a2c1924eb8","permalink":"https://climprocpred.github.io/author/ziwei-li/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/ziwei-li/","section":"authors","summary":"","tags":null,"title":"Ziwei Li","type":"authors"},{"authors":null,"categories":null,"content":"","date":1665100800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1665100800,"objectID":"2e538b661521f6af8334e17bb168703a","permalink":"https://climprocpred.github.io/author/elizabeth-yankovsky/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/elizabeth-yankovsky/","section":"authors","summary":"","tags":null,"title":"Elizabeth Yankovsky","type":"authors"},{"authors":null,"categories":null,"content":"","date":1661990400,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":1661990400,"objectID":"4aea07157068df0e575d32cda2b9e489","permalink":"https://climprocpred.github.io/author/nora-loose/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/nora-loose/","section":"authors","summary":"","tags":null,"title":"Nora Loose","type":"authors"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"284977c49107c39e347ed1fe6d42d01e","permalink":"https://climprocpred.github.io/author/abigail-bodner/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/abigail-bodner/","section":"authors","summary":"","tags":null,"title":"Abigail Bodner","type":"authors"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"0c6d0da5709ef3c849a1318f3385f427","permalink":"https://climprocpred.github.io/author/adam-subel/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/adam-subel/","section":"authors","summary":"","tags":null,"title":"Adam Subel","type":"authors"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"964733e29f88792de88f886016525785","permalink":"https://climprocpred.github.io/author/aurora-basinski/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/aurora-basinski/","section":"authors","summary":"","tags":null,"title":"Aurora Basinski","type":"authors"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"4c6adc36b14d94578ca322757ebcbe90","permalink":"https://climprocpred.github.io/author/chris-pedersen/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/chris-pedersen/","section":"authors","summary":"","tags":null,"title":"Chris Pedersen","type":"authors"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"ab4c6e9c6304ecfc0f58155d71cb20cd","permalink":"https://climprocpred.github.io/author/johanna-goldman/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/johanna-goldman/","section":"authors","summary":"","tags":null,"title":"Johanna Goldman","type":"authors"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"term","lang":"en","lastmod":-62135596800,"objectID":"b3d4fdf29cfd5329821917a7fffc73a9","permalink":"https://climprocpred.github.io/author/matthias-aengenheyster/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/author/matthias-aengenheyster/","section":"authors","summary":"","tags":null,"title":"Matthias Aengenheyster","type":"authors"},{"authors":[],"categories":null,"content":"Slides can be added in a few ways:\nCreate slides using Wowchemy’s Slides feature and link using slides parameter in the front matter of the talk file Upload an existing slide deck to static/ and link using url_slides parameter in the front matter of the talk file Embed your slides (e.g. Google Slides) or presentation video on this page using shortcodes. Further event details, including page elements such as image galleries, can be added to the body of this page.\n","date":1906549200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1906549200,"objectID":"a8edef490afe42206247b6ac05657af0","permalink":"https://climprocpred.github.io/event/example/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/event/example/","section":"event","summary":"An example event.","tags":[],"title":"Example Event","type":"event"},{"authors":["K. Otness","J. Bruna","Aneesh Subramanian"],"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680566400,"objectID":"23037fa431f3c1f2d8966993e857df2c","permalink":"https://climprocpred.github.io/publication/otness-et-al-2023/","publishdate":"2023-04-04T00:00:00Z","relpermalink":"/publication/otness-et-al-2023/","section":"publication","summary":"","tags":["Source Themes"],"title":"Data-driven multiscale modeling of subgrid parameterizations in climate models","type":"publication"},{"authors":["W. Gregory","M. Bushuk","A. Adcroft","Y. Zhang","Aneesh Subramanian"],"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680566400,"objectID":"18030374d00b65c7679a53fa4ec9303e","permalink":"https://climprocpred.github.io/publication/gregory-et-al-2023/","publishdate":"2023-04-04T00:00:00Z","relpermalink":"/publication/gregory-et-al-2023/","section":"publication","summary":"","tags":["Source Themes"],"title":"Deep learning of systematic sea ice model errors from data assimilation increments","type":"publication"},{"authors":["Fabrizio Falasca","Andrew Brettin","Aneesh Subramanian","S. M. Griffies","J. Yin","M. Zhao"],"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680566400,"objectID":"8f17dc9be9f539291ad74dc5d6620845","permalink":"https://climprocpred.github.io/publication/falasca-et-al-2022/","publishdate":"2022-04-04T00:00:00Z","relpermalink":"/publication/falasca-et-al-2022/","section":"publication","summary":"Studies agree on a significant global mean sea level rise in the 20th century and its recent 21st century acceleration in the satellite record. At regional scale, the evolution of sea level probability distributions is often assumed to be dominated by changes in the mean. However, a quantification of changes in distributional shapes in a changing climate is currently missing. To this end, we propose a novel framework quantifying significant changes in probability distributions from time series data. The framework first quantifies linear trends in quantiles through quantile regression. Quantile slopes are then projected onto a set of four orthogonal polynomials quantifying how such changes can be explained by independent shifts in the first four statistical moments. The framework proposed is theoretically founded, general and can be applied to any climate observable with close-to-linear changes in distributions. We focus on observations and a coupled climate model (GFDL-CM4). In the historical period, trends in coastal daily sea level have been driven mainly by changes in the mean and can therefore be explained by a shift of the distribution with no change in shape. In the modeled world, robust changes in higher order moments emerge with increasing CO2 concentration. Such changes are driven in part by ocean circulation alone and get amplified by sea level pressure fluctuations, with possible consequences for sea level extremes attribution studies.","tags":["Source Themes"],"title":"Exploring the non-stationarity of coastal sea level probability distributions","type":"publication"},{"authors":["Pavel Perezhogin","C. Fernandez-Granda","Aneesh Subramanian"],"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680566400,"objectID":"5f230e7421953c6acc5937fa5bfaf48a","permalink":"https://climprocpred.github.io/publication/perezhogin-et-al-2023/","publishdate":"2023-04-04T00:00:00Z","relpermalink":"/publication/perezhogin-et-al-2023/","section":"publication","summary":"","tags":["Source Themes"],"title":"Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model","type":"publication"},{"authors":["Emily Newsom","Aneesh Subramanian","J. M. Gregory"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1680566400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680566400,"objectID":"cc30708ff5da93771efe84715fc999d8","permalink":"https://climprocpred.github.io/publication/newsom-et-al-2023/","publishdate":"2023-04-04T00:00:00Z","relpermalink":"/publication/newsom-et-al-2023/","section":"publication","summary":"Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.","tags":["Source Themes"],"title":"Global Pycnocline depth constrains Ocean Heat Uptake Efficiency","type":"publication"},{"authors":["C. Zhang","Pavel Perezhogin","C. Gultekin","A. Adcroft","C. Fernandez-Granda","Aneesh Subramanian"],"categories":null,"content":"","date":1680566400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1680566400,"objectID":"54961d996e38fd5c9e9d2e25b961f2de","permalink":"https://climprocpred.github.io/publication/zhang-et-al-2023/","publishdate":"2023-04-04T00:00:00Z","relpermalink":"/publication/zhang-et-al-2023/","section":"publication","summary":"","tags":["Source Themes"],"title":"Implementation and Evaluation of a Machine Learned Mesoscale Eddy Parameterization into a Numerical Ocean Circulation Model","type":"publication"},{"authors":["Andrew Ross","Ziwei Li","Pavel Perezhogin","C. Fernandez-Granda","Aneesh Subramanian"],"categories":null,"content":"","date":1672531200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1672531200,"objectID":"19ce29ed5b3db5d3e7c1e68a6be590fc","permalink":"https://climprocpred.github.io/publication/ross-et-al-2022/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/publication/ross-et-al-2022/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"Benchmarking of machine learning ocean subgrid parameterizations in an idealized model","type":"publication"},{"authors":["H. Christensen","Aneesh Subramanian"],"categories":null,"content":"","date":1670803200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1670803200,"objectID":"24aab9e340e3f489c90b9dda50eec7db","permalink":"https://climprocpred.github.io/publication/christensen-zanna-2022/","publishdate":"2023-01-01T00:00:00Z","relpermalink":"/publication/christensen-zanna-2022/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"Parametrization in Weather and Climate Models","type":"publication"},{"authors":null,"categories":null,"content":"Join us if you are interested in working on ocean and climate physics.\nWe are looking for PhD students and Postdocs; applications due in early December. Follow to this link for further details.\n","date":1668902400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1668902400,"objectID":"0e141e5d0df213d116640f9d3e55d734","permalink":"https://climprocpred.github.io/post/2023-openings/","publishdate":"2022-11-20T00:00:00Z","relpermalink":"/post/2023-openings/","section":"post","summary":"Join us if you are interested in working on ocean and climate physics.\n","tags":null,"title":"PhD and Postdoc Openings","type":"post"},{"authors":["Elizabeth Yankovsky","Aneesh Subramanian","K. S. Smith"],"categories":null,"content":"","date":1665100800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1665100800,"objectID":"a36f6771580d01c6d0cc7e7ca649d0fd","permalink":"https://climprocpred.github.io/publication/yankovsky-2022/","publishdate":"2022-10-07T00:00:00Z","relpermalink":"/publication/yankovsky-2022/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"Influences of Mesoscale Ocean Eddies on Flow Vertical Structure in a Resolution-Based Model Hierarchy","type":"publication"},{"authors":["G. Marques","Nora Loose","Elizabeth Yankovsky","J. Steinberg","C.Y. Chang","N. Bhamidipati","A. Adcroft","B. Fox-Kemper","S. M. Griffies","R. Hallberg","M. Jansen","H. Khatri","Aneesh Subramanian"],"categories":null,"content":"","date":1661990400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1661990400,"objectID":"9bb5788115ad7ef58d3b453281761135","permalink":"https://climprocpred.github.io/publication/marques-et-al-2022/","publishdate":"2022-09-01T00:00:00Z","relpermalink":"/publication/marques-et-al-2022/","section":"publication","summary":"We describe an idealized primitive-equation model for studying mesoscale turbulence and leverage a hierarchy of grid resolutions to make eddy-resolving calculations on the finest grids more affordable. The model has intermediate complexity, incorporating basin-scale geometry with idealized Atlantic and Southern oceans and with non-uniform ocean depth to allow for mesoscale eddy interactions with topography. The model is perfectly adiabatic and spans the Equator and thus fills a gap between quasi-geostrophic models, which cannot span two hemispheres, and idealized general circulation models, which generally include diabatic processes and buoyancy forcing. We show that the model solution is approaching convergence in mean kinetic energy for the ocean mesoscale processes of interest and has a rich range of dynamics with circulation features that emerge only due to resolving mesoscale turbulence.","tags":["Source Themes"],"title":"NeverWorld2: An idealized model hierarchy to investigate ocean mesoscale eddies across resolutions","type":"publication"},{"authors":["L. Cheng","K. von Schuckmann","J.P. Abraham","K. E. Trenberth","M. E. Mann","Aneesh Subramanian","M. H. England","J. D. Zika","J. T. Fasullo","Y. Yu","Y. Pan","J. Zhu","Emily Newsom","B. Bronselaer","X. Lin"],"categories":null,"content":"","date":1660780800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1660780800,"objectID":"093927b9f523aa503b6bd00357fc83da","permalink":"https://climprocpred.github.io/publication/cheng-et-al-2022/","publishdate":"2022-08-18T00:00:00Z","relpermalink":"/publication/cheng-et-al-2022/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"Past and future ocean warming","type":"publication"},{"authors":["M. P. Couldrey","J. M. Gregory","X. Dong","O. Garuba","H. Haak","A. Hu","W. J. Hurlin","J. Jin","J. Jungclaus","A. Köhl","H. Liu","S. Ojha","O. A. Saenko","A. Savita","T. Suzuki","Z. Yu","Aneesh Subramanian"],"categories":null,"content":"","date":1659398400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1659398400,"objectID":"065e2d63e9fa616a7782a59af8d6fdc5","permalink":"https://climprocpred.github.io/publication/couldrey-et-al-2022/","publishdate":"2022-08-02T00:00:00Z","relpermalink":"/publication/couldrey-et-al-2022/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"Greenhouse-gas forced changes in the Atlantic meridional overturning circulation and related worldwide sea-level change","type":"publication"},{"authors":["S. Liu","A. Kaku","W. Zhu","M. Leibovich","S. Mohan","B. Yu","Aneesh Subramanian","N. Razavian","C. Fernandez-Granda"],"categories":null,"content":"","date":1658534400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1658534400,"objectID":"b303d537685251c719fa7dae45113759","permalink":"https://climprocpred.github.io/publication/liu-et-al-2021/","publishdate":"2017-01-01T00:00:00Z","relpermalink":"/publication/liu-et-al-2021/","section":"publication","summary":"Reliable probability estimation is of crucial importance in many real-world applications where there is inherent uncertainty, such as weather forecasting, medical prognosis, or collision avoidance in autonomous vehicles. Probability-estimation models are trained on observed outcomes (e.g. whether it has rained or not, or whether a patient has died or not), because the ground-truth probabilities of the events of interest are typically unknown. The problem is therefore analogous to binary classification, with the important difference that the objective is to estimate probabilities rather than predicting the specific outcome. The goal of this work is to investigate probability estimation from high-dimensional data using deep neural networks. There exist several methods to improve the probabilities generated by these models but they mostly focus on classification problems where the probabilities are related to model uncertainty. In the case of problems with inherent uncertainty, it is challenging to evaluate performance without access to ground-truth probabilities. To address this, we build a synthetic dataset to study and compare different computable metrics. We evaluate existing methods on the synthetic data as well as on three real-world probability estimation tasks, all of which involve inherent uncertainty. We also give a theoretical analysis of a model for high-dimensional probability estimation which reproduces several of the phenomena evinced in our experiments. Finally, we propose a new method for probability estimation using neural networks, which modifies the training process to promote output probabilities that are consistent with empirical probabilities computed from the data. The method outperforms existing approaches on most metrics on the simulated as well as real-world data.","tags":["Source Themes"],"title":"Deep Probability Estimation","type":"publication"},{"authors":null,"categories":null,"content":"New preprint by Ross et al.\nIn this work, led by Andrew Ross, with Ziwei Li, Pavel Perezhogin, Carlos Fernandez-Granda and Aneesh Subramanian, we provide a framework for systematically benchmarking the offline and online performance of physical and ML-based subgrid parameterizations. We find that the choice of filtering operator is critical for performance. To help with interpretability, we also propose a novel equation-discovery approach combining linear regression and genetic programming which generalizes better than physical and neural network parameterizations.\nSee code and notebooks here: https://github.com/m2lines/pyqg_parameterization_benchmarks\n","date":1656979200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1656979200,"objectID":"4c58e98a045f68a18e98da80c9ad9a75","permalink":"https://climprocpred.github.io/post/22-08-benchmarking/","publishdate":"2022-07-05T00:00:00Z","relpermalink":"/post/22-08-benchmarking/","section":"post","summary":"New preprint by Ross et al.\n","tags":null,"title":"Benchmarking of machine learning ocean subgrid parameterizations in an idealized model","type":"post"},{"authors":null,"categories":null,"content":"New Quanta Magazine Article on equation discovery for physics discovery featuring many examples including our work on ocean turbulence\nRead our press release.\nWe are excited to announce the launch of our new international project M²LInES – Multiscale Machine Learning In coupled Earth System Modeling – aimed at deepening our understanding of climate processes, and improving climate projections using scientific machine learning. Our team includes scientists from New York University, Princeton, GFDL, Columbia, LDEO, NCAR, MIT, CNRS-IGE, and CNRS-IPSL.\nThe overall goal of the project is to improve climate projections and reduce climate model biases, especially at the air-sea interface, using machine learning (ML). We will rely on data from a range of high-resolution (idealized and global) simulations and data assimilation products to deepen our understanding and improve the representation of subgrid physics in the ocean, sea-ice and atmosphere components of existing IPCC-class climate models. In addition, we will work on overcoming challenges related to ML for climate modeling including sampling efficiency, generalization, interpretability and uncertainty quantification.\nWe are looking for 1 project manager and 12 postdocs right now at several institutions, please visit https://m2lines.github.io/jobs/ for more info about the different positions available, and how to apply. This is a highly collaborative project, and the researchers are expected to interact with different groups.\n","date":1653350400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1653350400,"objectID":"c38dbc9946f6fe4c93242b7769373e42","permalink":"https://climprocpred.github.io/post/22-10-01-quanta/","publishdate":"2022-05-24T00:00:00Z","relpermalink":"/post/22-10-01-quanta/","section":"post","summary":"New Quanta Magazine Article on equation discovery for physics discovery featuring many examples including our work on ocean turbulence\n","tags":null,"title":"Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw Data","type":"post"},{"authors":null,"categories":null,"content":"Plenary talk on M²LInES and LEAP at the 2022 CESM Workshop on June 13-16, 2022.\nPlease visit the website to register as well as submit working group session talks and /or posters. The deadline to submit a talk / poster is Monday, May 16th.\nThe full workshop agenda can be found here.\nIn particular, Aneesh Subramanian will give a joint plenary talk on M²LInES with Galen McKinley from LEAP.\n","date":1652659200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1652659200,"objectID":"284b0925adf143e537d90a2cfeaa5b49","permalink":"https://climprocpred.github.io/post/22-06-cesm_workshop/","publishdate":"2022-05-16T00:00:00Z","relpermalink":"/post/22-06-cesm_workshop/","section":"post","summary":"Plenary talk on M²LInES and LEAP at the 2022 CESM Workshop on June 13-16, 2022.\n","tags":null,"title":"Plenary Talk @ 2022 CESM workshop","type":"post"},{"authors":["R. Chemke","Aneesh Subramanian","C. Orbe","L.T. Sentman","L.M. Polvani"],"categories":null,"content":"","date":1648512000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1648512000,"objectID":"843a6a599a86216831b7e5d4becab2c3","permalink":"https://climprocpred.github.io/publication/chemke-et-al-2022/","publishdate":"2021-07-26T00:00:00Z","relpermalink":"/publication/chemke-et-al-2022/","section":"publication","summary":"Climate models project an intensification of the wintertime North Atlantic Ocean storm track, over its downstream region, by the end of this century. Previous studies have suggested that ocean–atmosphere coupling plays a key role in this intensification, but the precise role of the different components of the coupling has not been explored and quantified. In this paper, using a hierarchy of ocean coupling experiments, we isolate and quantify the respective roles of thermodynamic (changes in surface heat fluxes) and dynamic (changes in ocean heat flux convergence) ocean coupling in the projected intensification of North Atlantic transient eddy kinetic energy (TEKE). We show that dynamic coupling accounts for nearly all of the future TEKE strengthening as it overcomes the much smaller effect of surface heat flux changes to weaken the TEKE. We further show that by reducing the Arctic amplification in the North Atlantic, ocean heat flux convergence increases the meridional temperature gradient aloft, causing a larger eddy growth rate and resulting in the strengthening of North Atlantic TEKE. Our results stress the importance of better monitoring and investigating the changes in ocean heat transport, for improving climate change adaptation strategies.","tags":["Source Themes"],"title":"The future intensification of the North Atlantic winter storm track: the key role of dynamic ocean coupling","type":"publication"},{"authors":["Emily Newsom","Aneesh Subramanian","S. Khatiwala"],"categories":null,"content":"","date":1648425600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1648425600,"objectID":"83232d585a1dff68410ee787dd2c2f0c","permalink":"https://climprocpred.github.io/publication/newsom-et-al-2022/","publishdate":"2021-07-26T00:00:00Z","relpermalink":"/publication/newsom-et-al-2022/","section":"publication","summary":"Ocean warming patterns are a primary control on regional sea level rise and transient climate sensitivity. However, controls on these patterns in both observations and models are not fully understood, complicated as they are by their dual dependence on the “addition” of heat to the ocean’s interior along background ventilation pathways and on the “redistribution” of heat between regions by changing ocean dynamics. While many previous studies attribute heat redistribution to changes in high-latitude processes, here we propose that substantial heat redistribution is explained by the large-scale adjustment of the geostrophic flow to warming within the pycnocline. We explore this hypothesis in the University of Victoria Earth System Model, estimating added heat using the the Transport Matrix Method. We find that throughout the mid-latitudes, subtropics and tropics, patterns of added and redistributed heat in the model are strongly anti-correlated (R ≈ −0.75). We argue this occurs because changes in the ocean currents, acting across pre-existing temperature gradients, redistribute heat away from regions of strong passive heat convergence. Over broad scales, this advective response can be estimated from changes in upper ocean density alone using the thermal wind relation and is linked to an adjustment of the subtropical pycnocline. These results highlight a previously unappreciated relationship between added and redistributed heat and emphasize the role that subtropical and mid-latitude dynamics play in setting patterns of ocean heat storage.","tags":["Source Themes"],"title":"Relating patterns of added and redistributed ocean warming","type":"publication"},{"authors":["Emily Newsom","Aneesh Subramanian","S. Khatiwala","J. M. Gregory"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1648425600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1648425600,"objectID":"b60baa36056463c38aca8c12e35750ef","permalink":"https://climprocpred.github.io/publication/newsom-et-al-2021/","publishdate":"2021-07-26T00:00:00Z","relpermalink":"/publication/newsom-et-al-2021/","section":"publication","summary":"The climate s response to forcing depends on how efficiently heat is absorbed by the ocean. Much, if not most, of this ocean heat uptake results from the passive transport of warm surface waters into the ocean s interior. Here we examine how geographic patterns of surface warming influence the efficiency of this passive heat uptake process. We show that the average pattern of surface warming in CMIP5 damps passive ocean heat uptake efficiency by nearly 25%, as compared to homogeneous surface warming. This “pattern effect” occurs because strong ventilation and weak surface warming are robustly colocated, particularly in the Southern Ocean. However, variations in warming patterns across CMIP5 do not drive significant ensemble spread in passive ocean heat uptake efficiency. This spread is likely linked to intermodel differences in ocean circulation, which our idealized results suggest may be dominated by differences in Southern Ocean and subtropical ventilation processes.","tags":["Source Themes"],"title":"The Influence of Warming Patterns on Passive Ocean Heat Uptake","type":"publication"},{"authors":["Nora Loose","R. Abernathey","I. Grooms","J. Busecke","A. Guillaumin","Elizabeth Yankovsky","G. Marques","J. Steinberg","Andrew Ross","H. Khatri","S. Bachman","Aneesh Subramanian","P. Martin"],"categories":null,"content":"","date":1644537600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1644537600,"objectID":"ab620e384dc56520e0db2f1fb494c860","permalink":"https://climprocpred.github.io/publication/loose-et-al-2022/","publishdate":"2021-07-26T00:00:00Z","relpermalink":"/publication/loose-et-al-2022/","section":"publication","summary":"GCM-Filters is a python package that allows scientists to perform spatial filtering analysis in an easy, flexible and efficient way. The package implements the filtering method based on the discrete Laplacian operator that was introduced by Grooms et al.(2021). The filtering algorithm is analogous to smoothing via diffusion; hence the name diffusion-based filters. GCM-Filters can be used with either gridded observational data or gridded data that is produced by General Circulation Models (GCMs) of ocean, weather, and climate. Spatial filtering of observational or GCM data is a common analysis method in the Earth Sciences, for example to study oceanic and atmospheric motions at different spatial scales or to develop subgrid-scale parameterizations for ocean models. GCM-Filters provides filters that are highly configurable, with the goal to be useful for a wide range of scientific applications. The user has different options for selecting the filter scale and filter shape. The filter scale can be defined in several ways: a fixed length scale (eg, 100 km), a scale tied to a model grid scale (eg, 1◦), or a scale tied to a varying dynamical scale (eg, the Rossby radius of deformation). As an example, Figure 1 shows unfiltered and filtered relative vorticity, where the filter scale is set to a model grid scale of 4◦. GCM-Filters also allows for anisotropic, ie, direction-dependent, filtering. Finally, the filter shape–currently: either Gaussian or Taper–determines how sharply the filter separates scales above and below the target filter scale.","tags":["Source Themes"],"title":"GCM-Filters: A Python Package for Diffusion-based Spatial Filtering of Gridded Data","type":"publication"},{"authors":null,"categories":null,"content":"Co-Director of the Program and Conference\nThe talk from the conference are here\nThe talk from the long program are here\n","date":1639699200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1639699200,"objectID":"e390310a97f85bcfaebda14581f3d903","permalink":"https://climprocpred.github.io/post/kitp/","publishdate":"2021-12-17T00:00:00Z","relpermalink":"/post/kitp/","section":"post","summary":"Co-Director of the Program and Conference\n","tags":null,"title":"Kavli Institute for Theoretical Physics Program on ML for Climate","type":"post"},{"authors":null,"categories":null,"content":"Learning the Earth with Artificial Intelligence and Physics (LEAP) is an NSF Science and Technology Center (STC) launched in 2021.\nLEAP’s primary research strategy is to improve near-term climate projections by merging physical modeling with machine learning across a continuum from expertise in climate science and climate modeling to cutting-edge machine learning algorithms. The benefits will be significant for both the climate and data sciences communities. Climate scientists and modelers struggle to fully integrate the wealth of existing datasets into their models, while machine learning algorithms have been good at emulating and interpolating but have difficulties extrapolating or predicting extremes. By combining both approaches, LEAP will trigger a significant advancement for data science algorithms applied to physical problems. LEAP will incorporate physics and causal mechanisms into machine learning algorithms for better generalization and extrapolation, while optimally using the wealth of data available to climate science, in order to better predict the future. The website is now available.\n","date":1635033600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1635033600,"objectID":"dca81becba76cacf6e2dc23c7ef1e14f","permalink":"https://climprocpred.github.io/projects/leap/","publishdate":"2021-10-24T00:00:00Z","relpermalink":"/projects/leap/","section":"projects","summary":"Learning the Earth with Artificial Intelligence and Physics (LEAP) is an NSF Science and Technology Center (STC) launched in 2021.\n","tags":null,"title":"LEAP - Learning the Earth with Artificial Intelligence and Physics","type":"projects"},{"authors":null,"categories":null,"content":"NYU to Join NSF-Backed AI-Based Climate Modeling Center\nRead our press release.\nThe center and the vision are described here. The website is now available.\n","date":1635033600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1635033600,"objectID":"ac6431f52c19755443a1dd42c5a94ddb","permalink":"https://climprocpred.github.io/post/21-10-24-leap/","publishdate":"2021-10-24T00:00:00Z","relpermalink":"/post/21-10-24-leap/","section":"post","summary":"NYU to Join NSF-Backed AI-Based Climate Modeling Center\n","tags":null,"title":"LEAP - New NSF Science and Technology Center on Learning the Earth with Artificial Intelligence and Physics","type":"post"},{"authors":["J. Wang","J. Church","X. Zhang","J. M. Gregory","Aneesh Subramanian","X. Chen"],"categories":null,"content":"","date":1633046400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1633046400,"objectID":"64df33fb2f2435b82ec3b61028832e06","permalink":"https://climprocpred.github.io/publication/wang-et-al-2021/","publishdate":"2021-10-01T00:00:00Z","relpermalink":"/publication/wang-et-al-2021/","section":"publication","summary":"Although global mean sea-level rise since 1900 and regional mean sea-level change since the 1960s have been accounted for in terms of the sum of contributions, the same budget closure has not been achieved for local relative sea-level change from a global network of tide gauges. To address this, we combine new estimates of sterodynamic sea-level change (SDSL; including ocean dynamics), glacial isostatic adjustment (GIA), change in land ice mass and terrestrial water storage, and other local vertical land motion. We find that the observed trends over 1958–2015 at all 272 tide gauges distributed worldwide agree with the sum of contributions (within 90% confidence estimates), with similar mean trend (1.1 mm yr−1) and comparable spatial variability (standard deviation of 2.0 and 1.9 mm yr−1 respectively). SDSL is the dominant contribution to both local observed mean trend and spatial variability, except at locations close to former ice-sheets, where GIA dominates.","tags":["Source Themes"],"title":"Evaluation of the Local Sea-Level Budget at Tide Gauges Since 1958","type":"publication"},{"authors":["Aneesh Subramanian","T. Bolton"],"categories":null,"content":"","date":1629417600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1629417600,"objectID":"70de574847f08cd11917b702132877c6","permalink":"https://climprocpred.github.io/publication/zanna-bolton-2021/","publishdate":"2021-08-20T00:00:00Z","relpermalink":"/publication/zanna-bolton-2021/","section":"publication","summary":" Climate models are an approximate representation of the laws of physics describing the evolution of the ocean and atmosphere dynamics. Due to limited computational resources, many ocean processes, which are crucial for the transport of heat and carbon, occur at scales smaller than the grid resolution of climate models. Therefore, we rely on approximations, called parameterizations, to represent these unresolved processes in climate models. Parameterizations, traditionally derived from semi-empirical or idealized theories, are often imperfect and can lead to biases in climate models. Machine learning algorithms, and deep learning (DL) algorithms in particular, could provide an avenue to improve the representation of unresolved processes in ocean models by efficiently extracting information from high-resolution ocean simulations and/or observational data, potentially enhancing the skill of parameterizations in climate models.","tags":["Source Themes"],"title":"Deep Learning of Unresolved Turbulent Ocean Processes in Climate Models","type":"publication"},{"authors":["A. Guillaumin","Aneesh Subramanian"],"categories":null,"content":"","date":1627257600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1627257600,"objectID":"55746ae23042dbaab6dcd58c323fefcb","permalink":"https://climprocpred.github.io/publication/guillaumin-zanna-2021/","publishdate":"2021-07-26T00:00:00Z","relpermalink":"/publication/guillaumin-zanna-2021/","section":"publication","summary":"Coupled climate simulations that span several hundred years cannot be run at a high-enough spatial resolution to resolve mesoscale ocean dynamics. Recently, several studies have considered Deep Learning to parameterize subgrid forcing within macroscale ocean equations using data from ocean-only simulations with idealized geometry. We present a stochastic Deep Learning parameterization that is trained on data generated by CM2.6, a high-resolution state-of-the-art coupled climate model. We train a Convolutional Neural Network for the subgrid momentum forcing using macroscale surface velocities from a few selected subdomains with different dynamical regimes. At each location of the coarse grid, rather than predicting a single number for the subgrid momentum forcing, we predict both the mean and standard deviation of a Gaussian probability distribution. This approach requires training our neural network to minimize a negative log-likelihood loss function rather than the Mean Square Error, which has been the standard in applications of Deep Learning to the problem of parameterizations. Each estimate of the conditional mean subgrid forcing is thus associated with an uncertainty estimate–the standard deviation—which will form the basis for a stochastic subgrid parameterization. Offline tests show that our parameterization generalizes well to the global oceans and a climate with increased urn:x-wiley:19422466:media:jame21414:jame21414-math-0001 levels without further training. We then implement our learned stochastic parameterization in an eddy-permitting idealized shallow water model. The implementation is stable and improves some statistics of the flow. Our work demonstrates the potential of combining Deep Learning tools with a probabilistic approach in parameterizing unresolved ocean dynamics.","tags":["Source Themes"],"title":"Stochastic-Deep Learning Parameterization of Ocean Momentum Forcing","type":"publication"},{"authors":null,"categories":null,"content":"M²LInES is a large international collaborative project with the goal of improving climate projections, using scientific and interpretable Machine Learning to capture unaccounted physical processes at the air-sea-ice interface.\nMore infos on our website\n","date":1611446400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611446400,"objectID":"94cf453f37082a155be7051b71df27d0","permalink":"https://climprocpred.github.io/projects/m2lines/","publishdate":"2021-01-24T00:00:00Z","relpermalink":"/projects/m2lines/","section":"projects","summary":"M²LInES is a large international collaborative project with the goal of improving climate projections, using scientific and interpretable Machine Learning to capture unaccounted physical processes at the air-sea-ice interface.\n","tags":null,"title":"M²LInES - Multiscale Machine Learning In Coupled Earth System Modeling","type":"projects"},{"authors":null,"categories":null,"content":"Experts in Machine learning, Climate Physics, and Modeling team up to deepen our understanding of climate\nRead our press release.\nWe are excited to announce the launch of our new international project M²LInES – Multiscale Machine Learning In coupled Earth System Modeling – aimed at deepening our understanding of climate processes, and improving climate projections using scientific machine learning. Our team includes scientists from New York University, Princeton, GFDL, Columbia, LDEO, NCAR, MIT, CNRS-IGE, and CNRS-IPSL.\nThe overall goal of the project is to improve climate projections and reduce climate model biases, especially at the air-sea interface, using machine learning (ML). We will rely on data from a range of high-resolution (idealized and global) simulations and data assimilation products to deepen our understanding and improve the representation of subgrid physics in the ocean, sea-ice and atmosphere components of existing IPCC-class climate models. In addition, we will work on overcoming challenges related to ML for climate modeling including sampling efficiency, generalization, interpretability and uncertainty quantification.\nWe are looking for 1 project manager and 12 postdocs right now at several institutions, please visit https://m2lines.github.io/jobs/ for more info about the different positions available, and how to apply. This is a highly collaborative project, and the researchers are expected to interact with different groups.\n","date":1611446400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1611446400,"objectID":"e5fa2865c2a0a26eaa9c5b1c44dd1556","permalink":"https://climprocpred.github.io/post/21-01-24-m2lines/","publishdate":"2021-01-24T00:00:00Z","relpermalink":"/post/21-01-24-m2lines/","section":"post","summary":"Experts in Machine learning, Climate Physics, and Modeling team up to deepen our understanding of climate\n","tags":null,"title":"M²LInES - New Collaboration on Machine Learning for Climate Modeling support by Schmidt Futures","type":"post"},{"authors":null,"categories":null,"content":"The Climate Process Team (CPT) aims to implement, assess, improve, and unify recent work on energetically-consistent ocean eddy momentum and tracer parametrizations in ocean-only and coupled climate models to improve model fidelity.\nMore infos on our website\nBelow, a group picture at our conference in Boulder in April 2022 ","date":1609372800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1609372800,"objectID":"6ac1cf2b10c9b5794b98c5b6d55c4e5a","permalink":"https://climprocpred.github.io/projects/cpt/","publishdate":"2020-12-31T00:00:00Z","relpermalink":"/projects/cpt/","section":"projects","summary":"The Climate Process Team (CPT) aims to implement, assess, improve, and unify recent work on energetically-consistent ocean eddy momentum and tracer parametrizations in ocean-only and coupled climate models to improve model fidelity.\n","tags":null,"title":"CPT - Ocean Transport and Eddy Energy","type":"projects"},{"authors":null,"categories":null,"content":"In addition to its role played in heat and carbon uptake, the large-scale ocean circulation in the Atlantic is an important driver of European climate variability on interannual and decadal timescales. Yet, the importance of its influence remains unclear due in part to the short historical record and the poor representation of this variability in climate simulations. To explore the large discrepancies between coupled general circulation models (GCMs) in their representation of the meridional overturning circulation (MOC) decadal variability and predictions in the Atlantic sector, we have introduced new diagnostic and numerical tools. Some of the questions we are interested include:\n– Which processes (e.g., atmosphere, ocean mixed layer, ocean currents) drive decadal variability (e.g., Atlantic Multidecadal Variability)? (e.g., MacMartin et al. 2013, O’Reilly et al. 2016)\n– Why is the decadal ocean variability in model low compared to observations? (O’Reilly and Zanna, 2018)\n– How remote variability in the Tropical Pacific impacts European weather? (e.g., O’Reilly et al. 2018)\n– What are the roles of air-sea feedback at high-latitudes? (e.g., O’Reilly et al. 2016)\n– How does anthropogenic forcing impact the variability of ocean circulation? (e.g., MacMartin et al. 2016)\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"422cb9c805590c55a07d851d186a6fa7","permalink":"https://climprocpred.github.io/projects/climate_variability/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/climate_variability/","section":"projects","summary":"In addition to its role played in heat and carbon uptake, the large-scale ocean circulation in the Atlantic is an important driver of European climate variability on interannual and decadal timescales.","tags":null,"title":"Climate Variability","type":"projects"},{"authors":null,"categories":null,"content":"We work on data-driven methods to undertand and predict climate. The tools we use range from probabilistic models informed by data to machine learning algorithms (e.g., neural networks).\nSome of our data-driven work include:\n– Simple statistical models for prediction of seasonal to decadal sea surface temperatures in the Atlantic (e.g., Zanna 2012, Huddart et al. 2016) and Pacific (e.g., Dias et al. 2018), and sea level (e.g., Fraser et al. 2019).\n– Data-driven scale- and flow-aware determinitic and stochastic parametrizations of ocean turbulence (e.g., Porta Mana \u0026amp; Zanna 2014, Zanna et al. 2017, David et al. 2017).\n– Data inference using machine learning (e.g., Bolton and Zanna, 2019).\n– Representation of climate processes with Neural Net and Equation-Discovery (e.g., Bolton and Zanna, 2019, Bolton and Zanna, 2019).\nWe are actively using machine learning and more generally data-driven tools to address some of our favorite problems related to parameterization of turbulent processes, interannual to decadal prediction, and forecasting of extreme events.\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"4c4b840c6c6a915b5c65bb8a0a3a0e3a","permalink":"https://climprocpred.github.io/projects/data_science_climate/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/data_science_climate/","section":"projects","summary":"We work on data-driven methods to undertand and predict climate. The tools we use range from probabilistic models informed by data to machine learning algorithms (e.g., neural networks).\nSome of our data-driven work include:","tags":null,"title":"Data Science \u0026 Climate","type":"projects"},{"authors":null,"categories":null,"content":"We observe and predict a range of extremes events in precipitation, sea level, and air or ocean temperatures. Some of these events are rare but are of large amplitudes. Sampling their statistics and undertanding their dynamical drivers are important for climate detection, attribution and adaption. These extreme events are a reflection of the complex and non-linear dynamics governing the climate system. We are interested in addressing the following:\nWhat are the dynamical drivers of the extreme events in sea surface temperatures or sea level? (e.g., Rodrigues et al. 2019, Yin et al. 2019)\nHow anthropogenic climate change has affected or will affect extreme events in the ocean and atmosphere? (Yin et al. 2019)\nHow to best describe forced-dissipative turbulent system from their statistics? (e.g., David et al. 2018, David et al. 2017)\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"66d358bfedd124121a4d3e80c656a593","permalink":"https://climprocpred.github.io/projects/extreme_events/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/extreme_events/","section":"projects","summary":"We observe and predict a range of extremes events in precipitation, sea level, and air or ocean temperatures. Some of these events are rare but are of large amplitudes. Sampling their statistics and undertanding their dynamical drivers are important for climate detection, attribution and adaption.","tags":null,"title":"Extreme Events","type":"projects"},{"authors":null,"categories":null,"content":"Selected programs that we participate in and might be of interest to our community\nNYU CDS Undergraduate Research Program (CURP) in partnership with the National Society of Black Physicists (NSBP); Unlearning Racism in Geoscience (URGE); Mentoring Physical Oceanography Women to Increase Retention (MPOWIR); NYU Proud to be First. ","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"a85951f1b86eca7267539c25d259b38f","permalink":"https://climprocpred.github.io/projects/mentoring_dei/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/mentoring_dei/","section":"projects","summary":"Selected programs that we participate in and might be of interest to our community\nNYU CDS Undergraduate Research Program (CURP) in partnership with the National Society of Black Physicists (NSBP); Unlearning Racism in Geoscience (URGE); Mentoring Physical Oceanography Women to Increase Retention (MPOWIR); NYU Proud to be First.","tags":null,"title":"Mentoring + DEI Activities","type":"projects"},{"authors":null,"categories":null,"content":"Ocean mesoscale eddies strongly affect the strength and variability of ocean jets such as the Gulf Stream. The spatial scales of eddies are too small to be resolved in climate models and hence their effects on the large-scale circulation need to be parametrized. The most common parametrizations used in coarse-resolution models are not adequate for the current generation of models, in which eddies are partially resolved. Furthermore, eddies are turbulent and stochastic in nature however current bulk parametrizations of mesoscale eddies are deterministic and do not account for subgrid transport fluctuations or upscale turbulent cascades therefore leading to model error in the representation of present and future climate change. The group is developing new parametrizations which specifically account for such effects and for model error associated with the uncertainty in the parametrizations. The parametrizations are constructed by using first principles, statistical mechanics, and high-resolution model outputs.\n– Quantifying the ocean turbulence and energy transfer (e.g., Kjellsson and Zanna 2017)\n– Probabilistic representation and parameterization of ocean turbulence (e.g., Porta Mana \u0026amp; Zanna 2014, Zanna et al. 2017, David et al. 2017)\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"312b3f48768a88809e4c86bb90e8e430","permalink":"https://climprocpred.github.io/projects/ocean_turbulence/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/ocean_turbulence/","section":"projects","summary":"Ocean mesoscale eddies strongly affect the strength and variability of ocean jets such as the Gulf Stream. The spatial scales of eddies are too small to be resolved in climate models and hence their effects on the large-scale circulation need to be parametrized.","tags":null,"title":"Ocean Turbulence","type":"projects"},{"authors":null,"categories":null,"content":"The oceans are a major sink of heat and carbon, able to delay climate change. The role of ocean heat uptake remains central in quantifying both the magnitude and timescale of the local and global climate response to forcing.\nOne main focus of our work is address fundamental questions such as:\nWhat is the role of the ocean circulation is shaping patterns of heat, salinity, carbon and oxygen storage? (e.g., Bronselaer \u0026amp; Zanna, 2020; Zanna et al, 2019)\nWhat drives the uncertainty in projections of ocean heat uptake and thermosteric sea level change? (e.g., Huber and Zanna, 2017)\nWhich physical processes govern the timescale of adjustment of the ocean under climate change? (e.g., Marshall and Zanna, 2014)\nHow does the surface forcing and ocean dynamics influence the air-sea fluxes of carbon? (e.g., Bronselaer et al, 2016; Bronselaer et al, 2017)\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"eba7d67d06d5fc5306e9a37b9cbdd9cc","permalink":"https://climprocpred.github.io/projects/oceans_in_climate_change/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/oceans_in_climate_change/","section":"projects","summary":"The oceans are a major sink of heat and carbon, able to delay climate change. The role of ocean heat uptake remains central in quantifying both the magnitude and timescale of the local and global climate response to forcing.","tags":null,"title":"Oceans in Climate Change","type":"projects"},{"authors":null,"categories":null,"content":"Climate projections are subject to three sources of uncertainties: initial condition, model and scenario. Our research is concerned with the first two kinds of errors. Specifically, how do the lack of ocean observations, the chaotic nature of the climate system and the poor representation of subgrid turbulent processes impact predictions on intra-seasonal to centennial timescales. Being able to quantify the predictability limits of the ocean is crucial for providing reliable climate predictions. We use theory, observations and numerical simulations to assess the uncertainties associated the ocean dynamics.\n– Simple statistical models for prediction of seasonal to decadal sea surface temperatures in the Atlantic (e.g., Zanna 2012, Huddart et al. 2016) and Pacific (e.g., Dias et al. 2018), and sea level (e.g., Fraser et al. 2019)\n– Quantification of uncertainty associated with initial conditions and model parameterization (e.g., Zanna et al. 2018)\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"1d3283bc8ed8f8f573471638e40928cf","permalink":"https://climprocpred.github.io/projects/predictability/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/predictability/","section":"projects","summary":"Climate projections are subject to three sources of uncertainties: initial condition, model and scenario. Our research is concerned with the first two kinds of errors. Specifically, how do the lack of ocean observations, the chaotic nature of the climate system and the poor representation of subgrid turbulent processes impact predictions on intra-seasonal to centennial timescales.","tags":null,"title":"Predictability","type":"projects"},{"authors":null,"categories":null,"content":"As anthropogenic emissions increase, the temperature of the planet increases. As a result, ice melt and thermal expansion of the oceans leads to sea level rise. Regionally, sea level changes due to local forcing from the atmosphere, changes in ocean circulations, and ocean bathymetry. We are interested in several aspects of sea level dynamics such as:\nHow predictable is sea level dynamics on seasonal to decadal timescales? (e.g., Fraser et al, 2019, Zanna et al. 2018)\nWhich dynamical drivers dominate sea level change on the continental shelves? and how these signals relate to change in the ocean interior? (e.g., Ponte et al. 2019, Fraser et al, 2019)\nWhat drives the uncertainty in projections of thermosteric sea level change? (e.g., Huber and Zanna, 2017, Carson et al., 2019)\nHow does ocean dynamics shapes patterns of regional sea level? How does natural variability masks anthropogenic signal of regional sea level? (e.g., Zanna et al. 2019, Carson et al., 2019)\n– How sensitive coastal surges are to atmospheric forcing? (e.g., Wilson et al. 2013)\n","date":1606694400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1606694400,"objectID":"c1ccfe5825e8fe9858ae2c1e6e0439fb","permalink":"https://climprocpred.github.io/projects/sea_level/","publishdate":"2020-11-30T00:00:00Z","relpermalink":"/projects/sea_level/","section":"projects","summary":"As anthropogenic emissions increase, the temperature of the planet increases. As a result, ice melt and thermal expansion of the oceans leads to sea level rise. Regionally, sea level changes due to local forcing from the atmosphere, changes in ocean circulations, and ocean bathymetry.","tags":null,"title":"Sea Level","type":"projects"},{"authors":["M. Couldrey","J. M. Gregory","F. B. Dias","P. Dobrohotoff","C. M. Domingues","O. Garuba","S. M. Griffies","H. Haak","A. Hu","M. Ishii","J. Jungclaus","A. Köhl","S. J. Marsland","S. Ojha","O. A. Saenko","A. Savita","A. Shao","D. Stammer","T. Suzuki","A. Todd","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1603756800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1603756800,"objectID":"5dd1995f77b01469cdf007a8981ad608","permalink":"https://climprocpred.github.io/publication/couldrey-et-al-2020/","publishdate":"2020-10-27T00:00:00Z","relpermalink":"/publication/couldrey-et-al-2020/","section":"publication","summary":"Sea levels of different atmosphere–ocean general circulation models (AOGCMs) respond to climate change forcing in different ways, representing a crucial uncertainty in climate change research. We isolate the role of the ocean dynamics in setting the spatial pattern of dynamic sea-level (ζ) change by forcing several AOGCMs with prescribed identical heat, momentum (wind) and freshwater flux perturbations. This method produces a ζ projection spread comparable in magnitude to the spread that results from greenhouse gas forcing, indicating that the differences in ocean model formulation are the cause, rather than diversity in surface flux change. The heat flux change drives most of the global pattern of ζ change, while the momentum and water flux changes cause locally confined features. North Atlantic heat uptake causes large temperature and salinity driven density changes, altering local ocean transport and ζ. The spread between AOGCMs here is caused largely by differences in their regional transport adjustment, which redistributes heat that was already in the ocean prior to perturbation. The geographic details of the ζ change in the North Atlantic are diverse across models, but the underlying dynamic change is similar. In contrast, the heat absorbed by the Southern Ocean does not strongly alter the vertically coherent circulation. The Arctic ζ change is dissimilar across models, owing to differences in passive heat uptake and circulation change. Only the Arctic is strongly affected by nonlinear interactions between the three air-sea flux changes, and these are model specific.","tags":["Source Themes"],"title":"What causes the spread of model projections of sea level change in response to greenhouse gas forcing?","type":"publication"},{"authors":["H. Hewitt","M. Roberts","P. Mathiot","A. Biastoch","E. Blockley","E. P. Chassignet","B. Fox-Kemper","P. Hyder","D. P. Marshall","E. Popova","A.-M. Treguier","Aneesh Subramanian","A. Yool","Y. Yu","R. Beadling","M. Bell","T. Kuhlbrodt","T. Arsouze","A. Bellucci","F. Castruccio","B. Gan","D. Putrasahan","C. D. Roberts","L. Van Roekel","Q. Zhang"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1602028800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1602028800,"objectID":"15cafaecea4bade695306874c54259e5","permalink":"https://climprocpred.github.io/publication/hewitt-et-al-2020/","publishdate":"2020-10-07T00:00:00Z","relpermalink":"/publication/hewitt-et-al-2020/","section":"publication","summary":"Although the choice of ocean resolution in Earth System models will always be limited by computational considerations, for the foreseeable future, this choice is likely to affect projections of climate variability and change as well as other aspects of the Earth System. Future Earth System models will be able to choose increased ocean resolution and/or improved parameterisation of processes to capture physical processes with greater fidelity.","tags":["Source Themes"],"title":"Resolving and Parameterising the Ocean Mesoscale in Earth System Models","type":"publication"},{"authors":null,"categories":null,"content":"Talk as part of the series “Illustrating the Impact of the Mathematical Sciences”\nThe talk can be found on the NAS website.\n","date":1599523200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1599523200,"objectID":"6851e96f9fe6daad67b969d7ce45afa8","permalink":"https://climprocpred.github.io/post/nas-webina/","publishdate":"2020-09-08T00:00:00Z","relpermalink":"/post/nas-webina/","section":"post","summary":"Talk as part of the series “Illustrating the Impact of the Mathematical Sciences”\n","tags":null,"title":"Webinar at the National Academies on Climate \u0026 Weather","type":"post"},{"authors":null,"categories":null,"content":"Links between heat \u0026amp; carbon to understand past and future warming patterns\nThe press release can be found here. In this paper we highlight a relationship between heat and carbon to reveal the effect of changes in ocean circulation from CO2 forcing on patterns of ocean warming in both observations and global Earth system models from the Fifth Coupled Model Intercomparison Project (CMIP5).\nWe find that 1) the global heat to carbon uptake is linear and due to the background ocean state; 2) patterns of anthropogenic carbon and added heat (when the circulation is unchanged) are similar.\nWe show that historical patterns of ocean warming are shaped by ocean heat redistribution, which CMIP5 models simulate poorly. However, we find that projected patterns of heat storage are primarily dictated by the pre-industrial ocean circulation (and small changes in unresolved ocean processes)—that is, by the patterns of added heat owing to ocean uptake of excess atmospheric heat rather than ocean warming by circulation changes. Climate models show more skill in simulating ocean heat storage by the pre-industrial circulation compared to heat redistribution, indicating that warming patterns of the ocean may become more predictable as the climate warms.\n","date":1598918400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1598918400,"objectID":"6b2864e210796aa09c45f4d6aa25a5b4","permalink":"https://climprocpred.github.io/post/heat-carbon-uptake/","publishdate":"2020-09-01T00:00:00Z","relpermalink":"/post/heat-carbon-uptake/","section":"post","summary":"Links between heat \u0026 carbon to understand past and future warming patterns\n","tags":null,"title":"New paper out in Nature on ocean heat/carbon uptake","type":"post"},{"authors":null,"categories":null,"content":"Neural Nets + Physics vs. Equation-discovery for parameterization\nOur new work with Tom Bolton on physics-aware \u0026amp; interpretable ML to improve ocean models is out in GRL.\nOur new approach to the parameterization/closure problem: learning differential equations of missing physics in coarse-res ocean models from data. We use a machine learning (ML) method that relies on sparse Bayesian inference / relevance vector machine to learn ocean eddy parameterizations of momentum, buoyancy, and energy. We compare it to a ML parameterization which uses convolutional neural nets with conservation-law embedded in the architecture. Each approach has pros and cons (interpretability, generalization, numerical stability) in our proof-of-concept. Many aspects need to be improved but we are moving one step closer towards blending physics and machine learning for climate modeling.\n","date":1597881600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1597881600,"objectID":"54582f6197a96422d78d1836ebc2f6b6","permalink":"https://climprocpred.github.io/post/deeplearning/","publishdate":"2020-08-20T00:00:00Z","relpermalink":"/post/deeplearning/","section":"post","summary":"Neural Nets + Physics vs. Equation-discovery for parameterization\n","tags":null,"title":"Learning equations for ocean turbulence using machine learning","type":"post"},{"authors":null,"categories":null,"content":"Published in Nature.\nThe work shows that we can close the ‘sea-level budget’ since 1900 and therefore explain the causes of the observed changes in sea level over the 20th century.\nGlobal sea level has risen by about 20 cm since 1900, which has been caused by thermal expansion and ice melt, while there is now more liquid water stored on land than in 1900. The decrease is due to the fact that we have impounded a lot of water in dammed reservoirs. All the new dams almost brought sea-level rise to a halt.\nOver the 20th century, most sea-level rise is caused by glacier melt, while the recently increased mass loss Greenland and Antarctica are catching up quickly. Sea level is also rising at an accelerated rate because of thermal expansion.\n","date":1597881600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1597881600,"objectID":"a405ec0e404dca7e8cbf4629f3dcd069","permalink":"https://climprocpred.github.io/post/sea-level-20th/","publishdate":"2020-08-20T00:00:00Z","relpermalink":"/post/sea-level-20th/","section":"post","summary":"Published in Nature.\n","tags":null,"title":"New Paper on Causes of 20th Century sea level","type":"post"},{"authors":["T. Frederikse","F. Landerer","L. Caron","S. Adhikari","D. Parkes","V. W. Humphrey","S. Dangendorf","P. Hogarth","Aneesh Subramanian","L. Cheng","Y. Wu"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1597795200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1597795200,"objectID":"a301e82abfac25d9ac12f6f20e34a0f4","permalink":"https://climprocpred.github.io/publication/fredericksen-et-al-2020/","publishdate":"2020-08-19T00:00:00Z","relpermalink":"/publication/fredericksen-et-al-2020/","section":"publication","summary":"The rate of global-mean sea-level rise since 1900 has varied over time, but the contributing factors are still poorly understood1. Previous assessments found that the summed contributions of ice-mass loss, terrestrial water storage and thermal expansion of the ocean could not be reconciled with observed changes in global-mean sea level, implying that changes in sea level or some contributions to those changes were poorly constrained2,3. Recent improvements to observational data, our understanding of the main contributing processes to sea-level change and methods for estimating the individual contributions, mean another attempt at reconciliation is warranted. Here we present a probabilistic framework to reconstruct sea level since 1900 using independent observations and their inherent uncertainties. The sum of the contributions to sea-level change from thermal expansion of the ocean, ice-mass loss and changes in terrestrial water storage is consistent with the trends and multidecadal variability in observed sea level on both global and basin scales, which we reconstruct from tide-gauge records. Ice-mass loss—predominantly from glaciers—has caused twice as much sea-level rise since 1900 as has thermal expansion. Mass loss from glaciers and the Greenland Ice Sheet explains the high rates of global sea-level rise during the 1940s, while a sharp increase in water impoundment by artificial reservoirs is the main cause of the lower-than-average rates during the 1970s. The acceleration in sea-level rise since the 1970s is caused by the combination of thermal expansion of the ocean and increased ice-mass loss from Greenland. Our results reconcile the magnitude of observed global-mean sea-level rise since 1900 with estimates based on the underlying processes, implying that no additional processes are required to explain the observed changes in sea level since 1900.","tags":["Source Themes"],"title":"The causes of sea-level rise since 1900","type":"publication"},{"authors":["B. Bronselaer","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1597190400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1597190400,"objectID":"e180afeda2a973de86c3fc6e15e8e6c5","permalink":"https://climprocpred.github.io/publication/bronselaer-zanna-2020/","publishdate":"2020-08-12T00:00:00Z","relpermalink":"/publication/bronselaer-zanna-2020/","section":"publication","summary":"Anthropogenic global surface warming is proportional to cumulative carbon emissions1,2,3; this relationship is partly determined by the uptake and storage of heat and carbon by the ocean4. The rates and patterns of ocean heat and carbon storage are influenced by ocean transport, such as mixing and large-scale circulation5,6,7,8,9,10. However, existing climate models do not accurately capture the observed patterns of ocean warming, with a large spread in their projections of ocean circulation and ocean heat uptake8,11. Additionally, assessing the influence of ocean circulation changes (specifically, the redistribution of heat by resolved advection) on patterns of observed and simulated ocean warming remains a challenge. Here we establish a linear relationship between the heat and carbon uptake of the ocean in response to anthropogenic emissions. This relationship is determined mainly by intrinsic parameters of the Earth system—namely, the ocean carbon buffer capacity, the radiative forcing of carbon dioxide and the carbon inventory of the ocean. We use this relationship to reveal the effect of changes in ocean circulation from carbon dioxide forcing on patterns of ocean warming in both observations and global Earth system models from the Fifth Coupled Model Intercomparison Project (CMIP5). We show that historical patterns of ocean warming are shaped by ocean heat redistribution, which CMIP5 models simulate poorly. However, we find that projected patterns of heat storage are primarily dictated by the pre-industrial ocean circulation (and small changes in unresolved ocean processes)—that is, by the patterns of added heat owing to ocean uptake of excess atmospheric heat rather than ocean warming by circulation changes. Climate models show more skill in simulating ocean heat storage by the pre-industrial circulation compared to heat redistribution, indicating that warming patterns of the ocean may become more predictable as the climate warms.","tags":["Source Themes"],"title":"Heat and carbon coupling reveals ocean warming due to circulation changes","type":"publication"},{"authors":["M. Byrne","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1597190400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1597190400,"objectID":"2564cdcaed39a2f96c2732ad145e828e","permalink":"https://climprocpred.github.io/publication/byrne-zanna-2020/","publishdate":"2020-08-12T00:00:00Z","relpermalink":"/publication/byrne-zanna-2020/","section":"publication","summary":"Monsoons are summertime circulations shaping climates and societies across the tropics and subtropics. Here the radiative effects controlling an axisymmetric monsoon and its response to climate change are investigated using aquaplanet simulations. The influences of clouds, water vapor, and CO2 on the axisymmetric monsoon are decomposed using the radiation-locking technique. Seasonal variations in clouds and water vapor strongly modulate the axisymmetric monsoon, reducing net precipitation by approximately half. Warming and moistening of the axisymmetric monsoon by seasonal longwave cloud and water vapor effects are counteracted by a strong shortwave cloud effect. The shortwave cloud effect also expedites onset of the axisymmetric monsoon by approximately two weeks, whereas longwave cloud and water vapor effects delay onset. A conceptual model relates the timing of monsoon onset to the efficiency of surface cooling. In climate change simulations CO2 forcing and the water vapor feedback have similar influences on the axisymmetric monsoon, warming the surface and moistening the region. In contrast, clouds have a negligible effect on surface temperature yet dominate the monsoon circulation response. A new perspective for understanding how cloud radiative effects shape the monsoon circulation response to climate change is introduced. The radiation-locking simulations and analyses advance understanding of how radiative processes influence an axisymmetric monsoon, and establish a framework for interpreting monsoon–radiation coupling in observations, in state-of-the-art models, and in different climate states.","tags":["Source Themes"],"title":"Radiative effects of clouds and water vapor on the monsoon","type":"publication"},{"authors":["Aneesh Subramanian","T. Bolton"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1596672000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1596672000,"objectID":"4d09e7d41a174e4ff538e045e7699da3","permalink":"https://climprocpred.github.io/publication/zanna-bolton-2020/","publishdate":"2020-08-06T00:00:00Z","relpermalink":"/publication/zanna-bolton-2020/","section":"publication","summary":" The resolution of climate models is limited by computational cost. Therefore, we must rely on parameterizations to represent processes occurring below the scale resolved by the models. Here, we focus on parameterizations of ocean mesoscale eddies and employ machine learning (ML), namely, relevance vector machines (RVMs) and convolutional neural networks (CNNs), to derive computationally efficient parameterizations from data, which are interpretable and/or encapsulate physics. In particular, we demonstrate the usefulness of the RVM algorithm to reveal closed-form equations for eddy parameterizations with embedded conservation laws. When implemented in an idealized ocean model, all parameterizations improve the statistics of the coarse-resolution simulation. The CNN is more stable than the RVM such that its skill in reproducing the high-resolution simulation is higher than the other schemes; however, the RVM scheme is interpretable. This work shows the potential for new physics-aware interpretable ML turbulence parameterizations for use in ocean climate models.","tags":["Source Themes"],"title":"Data-Driven Equation Discovery of Ocean Mesoscale Closures","type":"publication"},{"authors":["A. Todd","Aneesh Subramanian","M. P. Couldrey","J. M. Gregory","Q. Wu","J. Church","R. Farneti","R. Navarro-Labastida","K. Lyu","O. A. Saenko","D. Yang","X. Zhang"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1595203200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1595203200,"objectID":"d73cc3a138b7f541df14f052a3b63d00","permalink":"https://climprocpred.github.io/publication/todd-et-al-2020/","publishdate":"2020-07-20T00:00:00Z","relpermalink":"/publication/todd-et-al-2020/","section":"publication","summary":"There is large uncertainty in the future regional sea level change under anthropogenic climate change. Our study presents and uses a novel design of ocean general circulation model (OGCM) experiments to investigate the ocean's response to surface buoyancy and momentum flux perturbations without atmosphere-ocean feedbacks (e.g., without surface restoring or bulk formulae), as part of the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP). In an ensemble of OGCMs forced with identical surface flux perturbations, simulated dynamic sea level (DSL) and ocean heat content (OHC) change demonstrate considerable disagreement. In the North Atlantic, the disagreement in DSL and OHC change between models is mainly due to differences in the residual (resolved and eddy) circulation change, with a large spread in the Atlantic meridional overturning circulation (AMOC) weakening (20–50%). In the western North Pacific, OHC change is similar among the OGCM ensemble, but the contributing physical processes differ. For the Southern Ocean, isopycnal and diapycnal mixing change dominate the spread in OHC change. In addition, a component of the atmosphere-ocean feedbacks are quantified by comparing coupled, atmosphere-ocean GCM (AOGCM) and OGCM FAFMIP experiments with consistent ocean models. We find that there is 10% more AMOC weakening in AOGCMs relative to OGCMs, since the extratropical North Atlantic SST cooling due to heat redistribution amplifies the surface heat flux perturbation. This component of the atmosphere-ocean feedbacks enhances the pattern of North Atlantic OHC and DSL change, with relatively stronger increases and decreases in the tropics and extratropics, respectively.","tags":["Source Themes"],"title":"Ocean-Only FAFMIP: Understanding Regional Patterns of Ocean Heat Content and Dynamic Sea Level Change","type":"publication"},{"authors":["J. Yin","S. M. Griffies","M. Winton","M. Zhao","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1585699200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1585699200,"objectID":"0fe6319e7306a32d34315dcd9205e262","permalink":"https://climprocpred.github.io/publication/yin-et-al-2019/","publishdate":"2020-04-01T00:00:00Z","relpermalink":"/publication/yin-et-al-2019/","section":"publication","summary":" Storm surge and coastal flooding caused by tropical cyclones (hurricanes) and extratropical cyclones (nor’easters) pose a threat to communities along the Atlantic coast of the United States. Climate change and sea level rise are altering the statistics of these extreme events in a rather complex fashion. Here we use a fully coupled global weather/climate modeling system (GFDL CM4) to study characteristics of extreme daily sea level (ESL) along the U.S. Atlantic coast and their response to global warming. We find that under natural weather processes, the Gulf of Mexico coast is most vulnerable to storm surge and related ESL. New Orleans is a striking hotspot with the highest surge efficiency in response to storm winds. Under a 1% per year atmospheric CO2 increase on centennial time scales, the anthropogenic signal in ESL is robust along the U.S. East Coast. It can emerge from the background variability as soon as in 20 years, or even before global sea level rise is taken into account. The regional dynamic sea level rise induced by the weakening of the Atlantic meridional overturning circulation facilitates this early emergence, especially during wintertime coastal flooding associated with nor’easters. Along the Gulf Coast, ESL is sensitive to the modification of hurricane characteristics under the CO2 forcing.","tags":["Source Themes"],"title":"Response of Storm-Related Extreme Sea Level along the U.S. Atlantic Coast to Combined Weather and Climate Forcing","type":"publication"},{"authors":["R. Chemke","Aneesh Subramanian","L. M. Polvani"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1585008000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1585008000,"objectID":"faed9951a6ecb9735d8a309eed892578","permalink":"https://climprocpred.github.io/publication/chemke-et-al-2020/","publishdate":"2020-03-24T00:00:00Z","relpermalink":"/publication/chemke-et-al-2020/","section":"publication","summary":"North Atlantic sea surface temperatures have large climate impacts affecting the weather of the Northern Hemisphere. In addition to a substantial warming over much of the North Atlantic, caused by increasing greenhouse gases over the 21st century, climate projections show a surprising region of considerable future cooling at midlatitudes, referred to as the North Atlantic warming hole. A similar pattern of surface temperature trends has been observed in recent decades, but it remains unclear whether this pattern is of anthropogenic origin or a simple manifestation of internal climate variability. Here, analyzing state-of-the-art climate models and observations, we show that the recent North Atlantic warming hole is of anthropogenic origin. Our analysis reveals that the anthropogenic signal has only recently emerged from the internal climate variability, and can be attributed to greenhouse gas emissions. We further show that a declining northward oceanic heat flux in recent decades, which is linked to this surface temperature pattern, is also of anthropogenic origin.","tags":["Source Themes"],"title":"Identifying a human signal in the North Atlantic warming hole","type":"publication"},{"authors":["S. Sun","I. Eisenman","Aneesh Subramanian","A. Stewart"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1583020800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1583020800,"objectID":"ecedf18a5406b3808091b8c4e2e2fb22","permalink":"https://climprocpred.github.io/publication/sun-et-al-2019/","publishdate":"2020-03-01T00:00:00Z","relpermalink":"/publication/sun-et-al-2019/","section":"publication","summary":"Paleoclimate proxy evidence suggests that the Atlantic meridional overturning circulation (AMOC) was about 1000 m shallower at the Last Glacial Maximum (LGM) compared to the present. Yet it remains unresolved what caused this glacial shoaling of the AMOC, and many climate models instead simulate a deeper AMOC under LGM forcing. While some studies suggest that Southern Ocean surface buoyancy forcing controls the AMOC depth, others have suggested alternatively that North Atlantic surface forcing or interior diabatic mixing plays the dominant role. To investigate the key processes that set the AMOC depth, here we carry out a number of MITgcm ocean-only simulations with surface forcing fields specified from the simulation results of three coupled climate models that span much of the range of glacial AMOC depth changes in phase 3 of the Paleoclimate Model Intercomparison Project (PMIP3). We find that the MITgcm simulations successfully reproduce the changes in AMOC depth between glacial and modern conditions simulated in these three PMIP3 models. By varying the restoring time scale in the surface forcing, we show that the AMOC depth is more strongly constrained by the surface density field than the surface buoyancy flux field. Based on these results, we propose a mechanism by which the surface density fields in the high latitudes of both hemispheres are connected to the AMOC depth. We illustrate the mechanism using MITgcm simulations with idealized surface forcing perturbations as well as an idealized conceptual geometric model. These results suggest that the AMOC depth is largely determined by the surface density fields in both the North Atlantic and the Southern Ocean.","tags":["Source Themes"],"title":"Surface constraints on the depth of the Atlantic Meridional Overturning Circulation: Southern Ocean vs North Atlantic","type":"publication"},{"authors":["C. O'Reilly","Aneesh Subramanian","T. Woollings"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1573776000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1573776000,"objectID":"3cefa2cc1abf62406d764841dc6c67ff","permalink":"https://climprocpred.github.io/publication/oreilly-et-al-2019/","publishdate":"2019-11-15T00:00:00Z","relpermalink":"/publication/oreilly-et-al-2019/","section":"publication","summary":"Atlantic multidecadal variability (AMV) of sea surface temperature exhibits an important influence on the climate of surrounding continents. It remains unclear, however, the extent to which AMV is due to internal climate variability (e.g., ocean circulation variability) or changes in external forcing (e.g., volcanic/anthropogenic aerosols or greenhouse gases). Here, the sources of AMV are examined over a 340-yr period using proxy indices, instrumental data, and output from the Last Millennium Ensemble (LME) simulation. The proxy AMV closely follows the accumulated atmospheric forcing from the instrumental North Atlantic Oscillation (NAO) reconstruction (r = 0.65)—an “internal” source of AMV. This result provides strong observational evidence that much of the AMV is generated through the oceanic response to atmospheric circulation forcing, as previously demonstrated in targeted modeling studies. In the LME there is a substantial externally forced AMV component, which exhibits a modest but significant correlation with the proxy AMV (i.e., r = 0.37), implying that at least 13% of the AMV is externally forced. In the LME simulations, however, the AMV response to accumulated NAO forcing is weaker than in the proxy/observational datasets. This weak response is possibly related to the decadal NAO variability, which is substantially weaker in the LME than in observations. The externally forced component in the proxy AMV is also related to the accumulated NAO forcing, unlike in the LME. This indicates that the external forcing is likely influencing the AMV through different mechanistic pathways: via changes in radiative forcing in the LME and via changes in atmospheric circulation in the observational/proxy record.","tags":["Source Themes"],"title":"Assessing External and Internal Sources of Atlantic Multidecadal Variability Using Models, Proxy Data, and Early Instrumental Indices","type":"publication"},{"authors":["C. O'Reilly","T. Woollings","Aneesh Subramanian","A. Weisheimer"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1568246400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1568246400,"objectID":"74046442edd142f84312ba40c934571f","permalink":"https://climprocpred.github.io/publication/oreilly-et-al-2019a/","publishdate":"2019-09-12T00:00:00Z","relpermalink":"/publication/oreilly-et-al-2019a/","section":"publication","summary":"The extratropical teleconnection from the tropical Pacific in boreal summer exhibits a significant shift over the past 70 years. Cyclonic circulation anomalies over the North Atlantic and Eurasia associated with El Niño in the later period (1978–2014) are absent in the earlier period (1948–1977). An initialized atmospheric model ensemble, performed with prescribed sea surface temperature (SST) boundary conditions, replicates some key features of the shift in the teleconnection, providing clear evidence that this shift is not simply due to internal atmospheric variability or random sampling. Additional ensemble simulations, one with detrended tropical SSTs and another with constant external forcing are analyzed. In the model, the teleconnection shift is associated with climatological atmospheric circulation changes, which are substantially reduced in the simulation with detrended tropical SSTs. These results demonstrate that the climatological atmospheric circulation and associated teleconnection changes are largely forced by tropical SST trends.","tags":["Source Themes"],"title":"An interdecadal shift of the extratropical ENSO teleconnection during boreal summer","type":"publication"},{"authors":null,"categories":null,"content":"CPT Ocean Transport and Eddy Energy funded by NSF and NOAA\nWe are delighted to announce the launch of our new multi-institution Climate Process Team (CPT) on Ocean Transport and Eddy Energy, funded by NSF and NOAA. The CPT aims to implement, assess, improve, and unify recent work on energetically-consistent ocean eddy momentum and tracer parametrizations in ocean-only and coupled climate models to improve model fidelity.\nWe have four postdoc positions available (NYU, Princeton, WHOI, CU Boulder) covering observations, theory, and modeling of ocean mesoscale eddies. See the advert here.\nYou can find more details online. The website will be updated in the coming weeks. You can find the proposal online.\n","date":1567641600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1567641600,"objectID":"94c39e1e2558bb56be1f81bb9f2e6eee","permalink":"https://climprocpred.github.io/post/cpt/","publishdate":"2019-09-05T00:00:00Z","relpermalink":"/post/cpt/","section":"post","summary":"CPT Ocean Transport and Eddy Energy funded by NSF and NOAA\n","tags":null,"title":"New Climate Process Team","type":"post"},{"authors":["M. Carson","K. Lyu","K. Richter","M. Becker","C. M. Domingues","W. Han","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1565395200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1565395200,"objectID":"19411806fc0c93d835880d0497c92280","permalink":"https://climprocpred.github.io/publication/carson-et-al-2019/","publishdate":"2019-08-10T00:00:00Z","relpermalink":"/publication/carson-et-al-2019/","section":"publication","summary":"Projections of future sterodynamic sea level change from global climate models are associated with different sources of uncertainty. From a scientific, societal and policy-making perspective, it is relevant to both understand and reduce uncertainty in projections of climate change. Here, we review recent findings which describe, and shed light on, climate model uncertainty focusing particularly on two types of model uncertainty that contribute to the currently large spread in dynamical sea level patterns (i.e., regional sea level relative to the global mean). These uncertainties are: (1) intermodel uncertainty due to differences in models’ responses in a warming climate and (2) internal model variability due to an individual model’s own climate variability. On timescales longer than about 50 years from now, anthropogenic sterodynamic (dynamic plus global mean) sea level trends from middle- and high-end forcing scenarios will be larger than internal model variability. By 2100, these anthropogenic trends will also be larger than intermodel uncertainty when global mean thermosteric sea level rise and/or melting contributions from land ice are considered along with dynamic sea level changes. Furthermore, we discuss projections of future coastal sea level from the perspective of global climate models as well as from downscaled efforts based on regional climate models. Much knowledge and understanding has been achieved in the last decade from intermodel experiments and studies of sea level process-based model; here, the prospects for improving coastal sea level and reducing sea level uncertainty are discussed.","tags":["Source Themes"],"title":"Climate model uncertainty and trend detection of regional sea level projections in the open ocean and coastal zone","type":"publication"},{"authors":["R.Ponte","et al."],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1564012800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1564012800,"objectID":"fe8bbb8bcd59f92113328bb038cc03a8","permalink":"https://climprocpred.github.io/publication/ponte-et-al-2019/","publishdate":"2019-07-25T00:00:00Z","relpermalink":"/publication/ponte-et-al-2019/","section":"publication","summary":"A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.","tags":["Source Themes"],"title":"Towards Comprehensive Observing and Modeling Systems for Monitoring and Predicting Regional to Coastal Sea Level","type":"publication"},{"authors":["R. Fraser","M. Palmer","C. Roberts","C. Wilson","D. Copsey","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1560211200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1560211200,"objectID":"b10deaaf3b05dabac34a5629f8554a5a","permalink":"https://climprocpred.github.io/publication/fraser-et-al-2019/","publishdate":"2019-06-26T00:00:00Z","relpermalink":"/publication/fraser-et-al-2019/","section":"publication","summary":"Interannual sea surface height (SSH) forecasts are subject to several sources of uncertainty. Methods relying on statistical forecasts have proven useful in assessing predictability and associated uncertainty due to both initial conditions and boundary conditions. In this study, the interannual predictability of SSH dynamics in the North Atlantic is investigated using the output from a 150 year long control simulation based on HadGEM3, a coupled climate model at eddy-permitting resolution. Linear inverse modeling (LIM) is used to create a statistical model for the evolution of monthly-mean SSH anomalies. The forecasts based on the LIM model demonstrate skill on interannanual timescales 𝒪(1–2 years). Forecast skill is found to be largest in both the subtropical and subpolar gyres, with decreased skill in the Gulf Stream extension region. The SSH initial conditions involving a tripolar anomaly off Cape Hatteras lead to a maximum growth in SSH about 20 months later. At this time, there is a meridional shift in the 0 m-SSH contour on the order of 0.5∘–1.5∘-latitude, coupled with a change in SSH along the US East Coast. To complement the LIM-based study, interannual SSH predictability is also quantified using the system’s average predictability time (APT). The APT analysis extracted large-scale SSH patterns which displayed predictability on timescales longer than 2 years. These patterns are responsible for changes in SSH on the order of 10 cm along the US East Coast, driven by variations in Ekman velocity. Our results shed light on the timescales of SSH predictability in the North Atlantic. In addition, the diagnosed optimal initial conditions and predictable patterns could improve interannual forecasts of the Gulf Stream’s characteristics and coastal SSH.","tags":["Source Themes"],"title":"Investigating the predictability of North Atlantic sea surface heighty","type":"publication"},{"authors":["R. Rodrigues","A. Subramanian","Aneesh Subramanian","J. Berner"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1555372800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1555372800,"objectID":"6cc3099566adbb8e9bc126d801e3152a","permalink":"https://climprocpred.github.io/publication/rodrigues-et-al-2019/","publishdate":"2019-04-16T00:00:00Z","relpermalink":"/publication/rodrigues-et-al-2019/","section":"publication","summary":"Tropical sea surface temperature (SST) and winds vary on a wide range of timescales and have a substantial impact on weather and climate across the globe. Here we study the variability of SST and zonal wind during El Niño-Southern Oscillation (ENSO) between 1982 and 2014. We focus on changes in extreme statistics using higher-order moments of SST and zonal winds. We find that ENSO characteristics exhibit bimodal distributions and fat tails with extreme warm and cold temperatures in 1982–1999, but not during 2000–2014. The changes in the distributions coincide with changes in the intensity of ENSO events and the phase of the Interdecadal Pacific Oscillation. We also find that the strongest Easterly Wind Bursts occur during extreme El Niños and not during La Niñas. Maps of SST kurtosis can serve as a diagnostic for the thermocline feedback mechanism responsible for the differences in ENSO diversity between the two periods.","tags":["Source Themes"],"title":"ENSO Bimodality and Extremes","type":"publication"},{"authors":["D. Faggiani Dias","A. Subramanian","Aneesh Subramanian","A. J. Miller"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1552608000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1552608000,"objectID":"9b7b6dedb322d1a58fa7955c07090027","permalink":"https://climprocpred.github.io/publication/dias-et-al-2018/","publishdate":"2018-06-26T00:00:00Z","relpermalink":"/publication/dias-et-al-2018/","section":"publication","summary":"A suite of statistical linear inverse models (LIMs) are used to understand the remote and local SST variability that influences SST predictions over the North Pacific region. Observed monthly SST anomalies in the Pacific are used to construct different regional LIMs for seasonal to decadal predictions. The seasonal forecast skills of the LIMs are compared to that from three operational forecast systems in the North American Multi-Model Ensemble (NMME), revealing that the LIM has better skill in the Northeastern Pacific than NMME models. The LIM is also found to have comparable forecast skill for SST in the Tropical Pacific with NMME models. This skill, however, is highly dependent on the initialization month, with forecasts initialized during the summer having better skill than those initialized during the winter. The data are also bandpass filtered into seasonal, interannual and decadal time scales to identify the relationships between time scales using the structure of the propagator matrix. Moreover, we investigate the influence of the tropics and extra-tropics in the predictability of the SST over the region. The Extratropical North Pacific seems to be a source of predictability for the tropics on seasonal to interannual time scales, while the tropics enhance the forecast skill for the decadal component. These results indicate the importance of temporal scale interactions in improving the predictions on decadal timescales. Hence, we show that LIMs are not only useful as benchmarks for estimates of statistical skill, but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model components.","tags":["Source Themes"],"title":"Remote and local influences in forecasting Pacific SST: a linear inverse model and a multimodel ensemble study","type":"publication"},{"authors":null,"categories":null,"content":"Ocean Heat Content and Thermosteric Sea Level\nThe original data from our PNAS paper can be dowloaded here\nWe are continuously working on updating the datasets fairly regularly (thanks to many comments and sugg estions over the recent weeks). For the most recent update (March 15th, v0) which include:\nextending back to 1870 (rather than 1871),\nadding year 2018\nusing Sea Surface Temperatures from NOAA Extended Reconstruction SSTsV4, COBE (in addition to HadISSTv1 and realisations from HadISST v2)\nthermal expansion coefficients calculated using annual-mean climatology for 2005-2012 of WOA2013.\nThe datasets for the upper 300 m, upper 700 m, upper 2000 m, below 2000 m, and full depth (with errors defined as one-SD and derived from perturbing the Green’s Functions and from the use of different SSTs) are here:\nOcean Heat Content (v0) 1870-2018\nThermosteric Sea Level (v0) 1870-2018\nFor further details regarding the method, please consult our manuscript and supplementary material.\n","date":1552608000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1552608000,"objectID":"2b12528465142963d967ae854a819fda","permalink":"https://climprocpred.github.io/post/ohc_pnas_dataset/","publishdate":"2019-03-15T00:00:00Z","relpermalink":"/post/ohc_pnas_dataset/","section":"post","summary":"Ocean Heat Content and Thermosteric Sea Level\n","tags":null,"title":"Updated Datasets of ocean warming reconstruction","type":"post"},{"authors":null,"categories":null,"content":"Dan Jones interviewed me for his podcast Climate Scientists\nWe discussed machine learning in climate, ocean warming, collaborative research and my pathway into science. The podcast is hosted on several platform, here is one.\n","date":1552003200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1552003200,"objectID":"d7ea636264f0bbea247019e1b3862e9b","permalink":"https://climprocpred.github.io/post/podcast/","publishdate":"2019-03-08T00:00:00Z","relpermalink":"/post/podcast/","section":"post","summary":"Dan Jones interviewed me for his podcast Climate Scientists\n","tags":null,"title":"Podcast Climate Scientists","type":"post"},{"authors":["Aneesh Subramanian","S. Khatiwala","J. M. Gregory","J. Ison","P. Heimbach"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1546819200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546819200,"objectID":"e4b3f6bdce3cd6760cbb0e39cba01768","permalink":"https://climprocpred.github.io/publication/zanna-et-al-2017b/","publishdate":"2019-01-07T00:00:00Z","relpermalink":"/publication/zanna-et-al-2017b/","section":"publication","summary":" Most of the excess energy stored in the climate system due to anthropogenic greenhouse gas emissions has been taken up by the oceans, leading to thermal expansion and sea-level rise. The oceans thus have an important role in the Earth’s energy imbalance. Observational constraints on future anthropogenic warming critically depend on accurate estimates of past ocean heat content (OHC) change. We present a reconstruction of OHC since 1871, with global coverage of the full ocean depth. Our estimates combine timeseries of observed sea surface temperatures with much longer historical coverage than those in the ocean interior together with a representation (a Green’s function) of time-independent ocean transport processes. For 1955–2017, our estimates are comparable with direct estimates made by infilling the available 3D time-dependent ocean temperature observations. We find that the global ocean absorbed heat during this period at a rate of 0.30 ± 0.06 W/m2 in the upper 2,000 m and 0.028 ± 0.026 W/m2 below 2,000 m, with large decadal fluctuations. The total OHC change since 1871 is estimated at 436 ± 91 ×1021 J, with an increase during 1921–1946 (145 ± 62 ×1021 J) that is as large as during 1990–2015. By comparing with direct estimates, we also infer that, during 1955–2017, up to one-half of the Atlantic Ocean warming and thermosteric sea-level rise at low latitudes to midlatitudes emerged due to heat convergence from changes in ocean transport.","tags":["Source Themes"],"title":"Global reconstruction of historical ocean heat storage and transport","type":"publication"},{"authors":null,"categories":null,"content":"Links to paper, and some press and radio.\nOur new paper on reconstruction ocean heat content from 1871 to present was accepted in PNAS. The paper was part of our exciting NERC Large Grant TICTOC.\nYou can find the paper and a summary in our publication list.\nOur official press release can be found on the University of Oxford website and the Wadham College website. A few examples of articles in the media that mentioned our work:\nGeneral Press: The Guardian, The Weather Channel, The New York Times, Forbes, National Geographic\nScience Press: Phys.org, Science Daily, Physics World, Physics Today\nRadio: BBC World Service Science in Action\n","date":1546473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546473600,"objectID":"77db24d91f4fb2fe246f7df00d0533a6","permalink":"https://climprocpred.github.io/post/press-pnas/","publishdate":"2019-01-03T00:00:00Z","relpermalink":"/post/press-pnas/","section":"post","summary":"Links to paper, and some press and radio.\n","tags":null,"title":"Ocean Warming Paper out in PNAS, with press coverage","type":"post"},{"authors":null,"categories":null,"content":"Wadham news story on our work\nA little story on our work on ocean and climate, including our new ideas using machine learning, publis hed on the Wadham website\n","date":1546473600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546473600,"objectID":"3dc68e4f72a4a5f183acdff2b03385e7","permalink":"https://climprocpred.github.io/post/climateai-wadham/","publishdate":"2019-01-03T00:00:00Z","relpermalink":"/post/climateai-wadham/","section":"post","summary":"Wadham news story on our work\n","tags":null,"title":"Upgrading Climate Predictions With AI","type":"post"},{"authors":["T. Bolton","R. Abernathey","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"d6b40edc9a97d0c6966bbf86599bd4d4","permalink":"https://climprocpred.github.io/publication/bolton-et-al-2019/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/bolton-et-al-2019/","section":"publication","summary":"Geostrophic eddies contribute to the mixing of heat, carbon, and other climatically important tracers. A passive tracer driven by satellite-derived surface velocity fields is used to study the regional and temporal variability of lateral eddy mixing in the North Atlantic. Using a quasi-Lagrangian diffusivity diagnostic, we show that the upstream region (80°–50°W) of the Gulf Stream jet exhibits a significant mixing barrier (with diffusivity of ≈1 × 103 m2 s−1), compared to the downstream region (50°–10°W), which displays no mixing suppression (≈10 × 103 m2 s−1). The interannual variability is 10%–20% of the time mean in both regions. By analyzing linear perturbations of mixing-length diffusivity expression, we show that the across-jet mixing in the upstream region is driven by variations in the mean flow, rather than eddy velocity. In the downstream region, both the mean flow and eddy velocity contribute to the temporal variability. Our results suggest that an eddy parameterization must take into account the along-jet variation of mixing, and within jets such diffusivities may be a simple function of jet strength.","tags":["Source Themes"],"title":"Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization","type":"publication"},{"authors":["T. Bolton","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1546300800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1546300800,"objectID":"e5531df151304b758420e74aea6df465","permalink":"https://climprocpred.github.io/publication/bolton-zanna-2018/","publishdate":"2019-01-01T00:00:00Z","relpermalink":"/publication/bolton-zanna-2018/","section":"publication","summary":"Oceanographic observations are limited by sampling rates, while ocean models are limited by finite resolution and high viscosity and diffusion coefficients. Therefore, both data from observations and ocean models lack information at small and fast scales. Methods are needed to either extract information, extrapolate, or upscale existing oceanographic data sets, to account for or represent unresolved physical processes. Here we use machine learning to leverage observations and model data by predicting unresolved turbulent processes and subsurface flow fields. As a proof of concept, we train convolutional neural networks on degraded data from a high-resolution quasi-geostrophic ocean model. We demonstrate that convolutional neural networks successfully replicate the spatiotemporal variability of the subgrid eddy momentum forcing, are capable of generalizing to a range of dynamical behaviors, and can be forced to respect global momentum conservation. The training data of our convolutional neural networks can be subsampled to 10–20% of the original size without a significant decrease in accuracy. We also show that the subsurface flow field can be predicted using only information at the surface (e.g., using only satellite altimetry data). Our results indicate that data-driven approaches can be exploited to predict both subgrid and large-scale processes, while respecting physical principles, even when data are limited to a particular region or external forcing. Our in-depth study presents evidence for the successful design of ocean eddy parameterizations for implementation in coarse-resolution climate models.","tags":["Source Themes"],"title":"Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization","type":"publication"},{"authors":null,"categories":null,"content":"FAFMIP is an atmosphere-ocean general circulation model intercomparison project of CMIP6. It addresses aspects of the Earth system response to forcing, and is of particular relevance to the WCRP Grand Challenge on sea level rise and regional impacts. Its goal is to explain the model spread in AOGCM projections of ocean climate change forced by CO2 increase.\nMore info on our website\n","date":1543536000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1543536000,"objectID":"5b53e5e552f6df20bc8e0b4e4a0105f2","permalink":"https://climprocpred.github.io/projects/fafmip/","publishdate":"2018-11-30T00:00:00Z","relpermalink":"/projects/fafmip/","section":"projects","summary":"FAFMIP is an atmosphere-ocean general circulation model intercomparison project of CMIP6. It addresses aspects of the Earth system response to forcing, and is of particular relevance to the WCRP Grand Challenge on sea level rise and regional impacts.","tags":null,"title":"FAFMIP","type":"projects"},{"authors":null,"categories":null,"content":"TICTOC is a multi-institute NERC funded grant with the aim of studying regional ocean heat content change and sea level rise. TICTOC will use observations made from research ships and computer models of the ocean to understand where the ocean takes up heat from the atmosphere and how ocean currents transport and redistribute that heat.\nMore info on our website\n","date":1543536000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1543536000,"objectID":"aa7f690d93bb056320a37e45c8d78eb2","permalink":"https://climprocpred.github.io/projects/tictoc/","publishdate":"2018-11-30T00:00:00Z","relpermalink":"/projects/tictoc/","section":"projects","summary":"TICTOC is a multi-institute NERC funded grant with the aim of studying regional ocean heat content change and sea level rise. TICTOC will use observations made from research ships and computer models of the ocean to understand where the ocean takes up heat from the atmosphere and how ocean currents transport and redistribute that heat.","tags":null,"title":"TICTOC","type":"projects"},{"authors":["Aneesh Subramanian","J. M. Brankart","M. Huber","S. Leroux","T. Penduff","P. D. Williams"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1539043200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1539043200,"objectID":"0c5f546f5219415aa18109e32b881f9a","permalink":"https://climprocpred.github.io/publication/zanna-et-al-2018/","publishdate":"2018-10-09T00:00:00Z","relpermalink":"/publication/zanna-et-al-2018/","section":"publication","summary":" The ocean plays an important role in the climate system on time-scales of weeks to centuries. Despite improvements in ocean models, dynamical processes involving multiscale interactions remain poorly represented, leading to errors in forecasts. We present recent advances in understanding, quantifying, and representing physical and numerical sources of uncertainty in novel regional and global ocean ensembles at different horizontal resolutions. At coarse resolution, uncertainty in 21st century projections of the upper overturning cell in the Atlantic is mostly a result of buoyancy fluxes, while the uncertainty in projections of the bottom cell is driven equally by both wind and buoyancy flux uncertainty. In addition, freshwater and heat fluxes are the largest contributors to Atlantic Ocean heat content regional projections and their uncertainties, mostly as a result of uncertain ocean circulation projections. At both coarse and eddy-permitting resolutions, unresolved stochastic temperature and salinity fluctuations can lead to significant changes in large-scale density across the Gulf Stream front, therefore leading to major changes in large-scale transport. These perturbations can have an impact on the ensemble spread on monthly time-scales and subsequently interact nonlinearly with the dynamics of the flow, generating chaotic variability on multiannual time-scales. In the Gulf Stream region, the ratio of chaotic variability to atmospheric-forced variability in meridional heat transport is larger than 50% on time-scales shorter than 2 years, while between 40 and 48°S the ratio exceeds 50% on on time-scales up to 28 years. Based on these simulations, we show that air–sea interaction and ocean subgrid eddies remain an important source of error for simulating and predicting ocean circulation, sea level, and heat uptake on a range of spatial and temporal scales. We discuss how further refinement of these ensembles can help us assess the relative importance of oceanic versus atmospheric uncertainty in weather and climate.","tags":["Source Themes"],"title":"Uncertainty and scale interactions in ocean ensembles: From seasonal forecasts to multidecadal climate predictions","type":"publication"},{"authors":null,"categories":null,"content":"We are hosting the annual TICTOC meeting in Oxford\nThe annual meeting of our project “Transient tracer-based Investigation of Circulation and Thermal Ocean Change” TICTOC will be held in Oxford on Sep 25-26 2018.\n","date":1536537600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1536537600,"objectID":"14734e5ca15504b7a343b26d157ae1d4","permalink":"https://climprocpred.github.io/post/tictoc/","publishdate":"2018-09-10T00:00:00Z","relpermalink":"/post/tictoc/","section":"post","summary":"We are hosting the annual TICTOC meeting in Oxford\n","tags":null,"title":"TICTOC project meeting","type":"post"},{"authors":["C. O'Reilly","T. Woollings","Aneesh Subramanian","A. Weisheimer"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1534291200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1534291200,"objectID":"9a4f1388e7012dda52386d7fa932005c","permalink":"https://climprocpred.github.io/publication/oreilly-et-al-2018/","publishdate":"2018-08-15T00:00:00Z","relpermalink":"/publication/oreilly-et-al-2018/","section":"publication","summary":"The influence of tropical precipitation variability on summertime seasonal circulation anomalies in the Euro-Atlantic sector is investigated. The dominant mode of the maximum covariance analysis (MCA) between the Euro-Atlantic circulation and tropical precipitation reveals a cyclonic anomaly over the extratropical North Atlantic, contributing to anomalously wet conditions over western Europe and dry conditions over eastern Europe and Scandinavia (in the positive phase). The related mode of tropical precipitation variability is associated with tropical Pacific SST anomalies and is closely linked to the El Niño–Southern Oscillation (ENSO). The second MCA mode consists of weaker tropical precipitation anomalies but with a stronger extratropical signal that reflects internal atmospheric variability. The teleconnection mechanism is tested in barotropic model simulations, which indicate that the observed link between the dominant mode of tropical precipitation and the Euro-Atlantic circulation anomalies is largely consistent with linear Rossby wave dynamics. The barotropic model response consists of a circumglobal wave train in the extratropics that is primarily forced by divergence anomalies in the eastern tropical Pacific. Both the eastward and westward group propagation of the Rossby waves are found to be important in determining the circulation response over the Euro-Atlantic sector. The mechanism was also analyzed in an operational seasonal forecasting system, ECMWF’s System 4. While System 4 is well able to reproduce and skillfully forecast the tropical precipitation, the extratropical circulation response is absent over the Euro-Atlantic region, which is likely related to biases in the Asian jet stream.","tags":["Source Themes"],"title":"The impact of tropical precipitation on summertime Euro-Atlantic circulation via a circumglobal wave-train","type":"publication"},{"authors":["S. Juricke","D. MacLeod","A. Weisheimer","Aneesh Subramanian","T. N. Palmer"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1534204800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1534204800,"objectID":"9a3015fe16a68d67cb35a56a218e3aa0","permalink":"https://climprocpred.github.io/publication/juricke-et-al-2018/","publishdate":"2018-08-14T00:00:00Z","relpermalink":"/publication/juricke-et-al-2018/","section":"publication","summary":"Accurate forecasts of the ocean state and the estimation of forecast uncertainties are crucial when it comes to providing skilful seasonal predictions. In this study we analyse the predictive skill and reliability of the ocean component in a seasonal forecasting system. Furthermore, we assess the effects of accounting for model and observational uncertainties. Ensemble forcasts are carried out with an updated version of the ECMWF seasonal forecasting model System 4, with a forecast length of ten months, initialized every May between 1981 and 2010. We find that, for essential quantities such as sea surface temperature and upper ocean 300 m heat content, the ocean forecasts are generally underdispersive and skilful beyond the first month mainly in the Tropics and parts of the North Atlantic. The reference reanalysis used for the forecast evaluation considerably affects diagnostics of forecast skill and reliability, throughout the entire ten-month forecasts but mostly during the first three months. Accounting for parametrization uncertainty by implementing stochastic parametrization perturbations has a positive impact on both reliability (from month 3 onwards) as well as forecast skill (from month 8 onwards). Skill improvements extend also to atmospheric variables such as 2 m temperature, mostly in the extratropical Pacific but also over the midlatitudes of the Americas. Hence, while model uncertainty impacts the skill of seasonal forecasts, observational uncertainty impacts our assessment of that skill. Future ocean model development should therefore aim not only to reduce model errors but to simultaneously assess and estimate uncertainties.","tags":["Source Themes"],"title":"Seasonal to annual ocean forecasting skill and the role of model and observational uncertainty","type":"publication"},{"authors":["T. David","Aneesh Subramanian","D.P. Marshall"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1532649600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1532649600,"objectID":"6bb7884a538dbbc5954e62e93d8adf2c","permalink":"https://climprocpred.github.io/publication/david-et-al-2018/","publishdate":"2018-07-27T00:00:00Z","relpermalink":"/publication/david-et-al-2018/","section":"publication","summary":"An equilibrium, or maximum entropy, statistical mechanics theory can be derived for ideal, unforced and inviscid, geophysical flows. However, for all geophysical flows which occur in nature, forcing and dissipation play a major role. Here, a study of eddy-mixing entropy in a forced-dissipative barotropic ocean model is presented. We heuristically investigate the temporal evolution of eddy-mixing entropy, as defined for the equilibrium theory, in a strongly forced and dissipative system. It is shown that the eddy-mixing entropy provides a descriptive tool for understanding three stages of the turbulence life cycle: growth of instability; formation of large scale structures; and steady state fluctuations. The fact that the eddy-mixing entropy behaves in a dynamically balanced way is not a priori clear and provides a novel means of quantifying turbulent disorder in geophysical flows. Further, by determining the relationship between the time evolution of entropy and the maximum entropy principle, evidence is found for the action of this principle in a forced-dissipative flow. The maximum entropy potential vorticity statistics are calculated for the flow and are compared with numerical simulations. Deficiencies of the maximum entropy statistics are discussed in the context of the mean-field approximation for energy. This study highlights the importance of entropy and statistical mechanics in the study of geostrophic turbulence.","tags":["Source Themes"],"title":"Eddy-mixing entropy and its maximization in forced-dissipative geostrophic turbulence","type":"publication"},{"authors":["C. O'Reilly","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1531699200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1531699200,"objectID":"3552d59ec39ec4e6fbad5cdc10e74704","permalink":"https://climprocpred.github.io/publication/oreilly-zanna-2018/","publishdate":"2018-07-16T00:00:00Z","relpermalink":"/publication/oreilly-zanna-2018/","section":"publication","summary":"The relationship between decadal sea surface temperature (SST) and turbulent heat fluxes is assessed and used to identify where oceanic processes play an important role in extratropical decadal SST variability. In observational data sets and coupled climate model simulations from the Coupled Model Intercomparison Project Phase 5 archive, positive correlations between upward turbulent heat flux and SSTs indicate an active role of oceanic processes over regions in the North Atlantic, Northwest Pacific, Southern Pacific, and Southern Atlantic. The contrasting nature of oceanic influence on decadal SST anomalies in the Northwest Pacific and North Atlantic is identified. Over the Northwest Pacific, SST anomalies are consistent with changes in the horizontal wind-driven gyre circulation on timescales of between 3 and 7 years, in both the observations and models. Over the North Atlantic, SST anomalies are also preceded by atmospheric circulation anomalies, though the response is stronger at longer timescales—peaking at around 20 years in the observations and at around 10 years in the models.","tags":["Source Themes"],"title":"The Signature of Oceanic Processes in Decadal Extratropical SST Anomalies","type":"publication"},{"authors":["S.D. Bachman","J. Anstey","Aneesh Subramanian"],"categories":null,"content":"","date":1527811200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1527811200,"objectID":"7d7c8bebf00673eb9ebad7b8d7d857e8","permalink":"https://climprocpred.github.io/publication/bachman-et-al-2018/","publishdate":"2018-06-01T00:00:00Z","relpermalink":"/publication/bachman-et-al-2018/","section":"publication","summary":"A recent class of ocean eddy parameterizations proposed by Porta Mana and Zanna (2014) and Anstey and Zanna (2017) modeled the large-scale flow as a non-Newtonian fluid whose subgridscale eddy stress is a nonlinear function of the deformation. This idea, while largely new to ocean modeling, has a history in turbulence modeling dating at least back to Rivlin (1957). The new class of parameterizations results in equations that resemble the Lagrangian-averaged Navier–Stokes-α model (LANS-α, e.g., Holm et al., 1998a). In this note we employ basic tensor mathematics to highlight the similarities between these turbulence models using component-free notation. We extend the Anstey and Zanna (2017) parameterization, which was originally presented in 2D, to 3D, and derive variants of this closure that arise when the full non-Newtonian stress tensor is used. Despite the mathematical similarities between the non-Newtonian and LANS-α models which might provide insight into numerical implementation, the input and dissipation of kinetic energy between these two turbulent models differ.# Summary. An optional shortened abstract.","tags":["Source Themes"],"title":"The relationship between a deformation-based eddy parameterization and the LANS-α turbulence model","type":"publication"},{"authors":["B. Bronselaer","Aneesh Subramanian","D. Munday","J. Lowe"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1517961600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1517961600,"objectID":"b0c278ccee37fec52103697b6b26fa0e","permalink":"https://climprocpred.github.io/publication/bronselaer-et-al-2017/","publishdate":"2018-02-07T00:00:00Z","relpermalink":"/publication/bronselaer-et-al-2017/","section":"publication","summary":"The Southern Ocean is the largest sink of anthropogenic carbon in the present-day climate. Here, Southern Ocean 𝑝CO2 and its dependence on wind forcing are investigated using an equilibrium mixed layer carbon budget. This budget is used to derive an expression for Southern Ocean 𝑝CO2 sensitivity to wind stress. Southern Ocean 𝑝CO2 is found to vary as the square root of area-mean wind stress, arising from the dominance of vertical mixing over other processes such as lateral Ekman transport. The expression for p\\hbox {CO}_{2} is validated using idealised coarse-resolution ocean numerical experiments. Additionally, we show that increased (decreased) stratification through surface warming reduces (increases) the sensitivity of the Southern Ocean 𝑝CO2 to wind stress. The scaling is then used to estimate the wind-stress induced changes of atmospheric 𝑝CO2 in CMIP5 models using only a handful of parameters. The scaling is further used to model the anthropogenic carbon sink, showing a long-term reversal of the Southern Ocean sink for large wind stress strength.","tags":["Source Themes"],"title":"Southern Ocean carbon-wind stress feedback","type":"publication"},{"authors":["E. van Sebille","S. M. Griffies","R. Abernathey","T. P. Adams","P. Berloff","A. Biastoch","B. Blanke","E. P. Chassignet","Y. Cheng","C. J. Cotter","E. Deleersnijder","K. Doos","H. Drake","S. Drijfhout","S. F. Gary","A. W. Heemink","J. Kjellsson","I. Koszalka","M. Lange","C. Lique","G. A. MacGilchrist","R. Marsh","C. Gabriela Mayorga Adame","R. McAdam","F. Nencioli","C. B. Paris","M. D. Piggott","J. A. Polton","S. Ruhs","S. H.A.M. Shah,","M. D. Thomas","J. Wang","P. J. Wolfram","Aneesh Subramanian","J. D. Zika"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1514764800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1514764800,"objectID":"a423f742d5d8ab582a0ccc143b51f95f","permalink":"https://climprocpred.github.io/publication/vansebille-et-al-2017/","publishdate":"2018-01-01T00:00:00Z","relpermalink":"/publication/vansebille-et-al-2017/","section":"publication","summary":"Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing.","tags":["Source Themes"],"title":"Lagrangian ocean analysis: Fundamentals and practices","type":"publication"},{"authors":["C. O'Reilly","T. Woollings","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1505433600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1505433600,"objectID":"c120f5cad744865e2a019467db8009b7","permalink":"https://climprocpred.github.io/publication/oreilly-et-al-2016/","publishdate":"2017-09-15T00:00:00Z","relpermalink":"/publication/oreilly-et-al-2016/","section":"publication","summary":"The Atlantic multidecadal oscillation (AMO) in sea surface temperature (SST) has been shown to influence the climate of the surrounding continents. However, it is unclear to what extent the observed impact of the AMO is related to the thermodynamical influence of the SST variability or the changes in large-scale atmospheric circulation. Here, an analog method is used to decompose the observed impact of the AMO into dynamical and residual components of surface air temperature (SAT) and precipitation over the adjacent continents. Over Europe the influence of the AMO is clearest during the summer, when the warm SAT anomalies are interpreted to be primarily thermodynamically driven by warm upstream SST anomalies but also amplified by the anomalous atmospheric circulation. The overall precipitation response to the AMO in summer is generally less significant than the SAT but is mostly dynamically driven. The decomposition is also applied to the North American summer and the Sahel rainy season. Both dynamical and residual influences on the anomalous precipitation over the Sahel are substantial, with the former dominating over the western Sahel region and the latter being largest over the eastern Sahel region. The results have potential implications for understanding the spread in AMO variability in coupled climate models and decadal prediction systems.","tags":["Source Themes"],"title":"The Dynamical Influence of the Atlantic Multidecadal Oscillation on Continental Climate","type":"publication"},{"authors":["J. Kjellsson","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1503878400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1503878400,"objectID":"6af405106e69e4e20de4dfffd687586e","permalink":"https://climprocpred.github.io/publication/kjellsson-zanna-2017/","publishdate":"2017-08-28T00:00:00Z","relpermalink":"/publication/kjellsson-zanna-2017/","section":"publication","summary":"The ocean is a turbulent fluid with processes acting on a variety of spatio-temporal scales. The estimates of energy fluxes between length scales allows us to understand how the mean flow is maintained as well as how mesoscale eddies are formed and dissipated. Here, we quantify the kinetic energy budget in a suite of realistic global ocean models, with varying horizontal resolution and horizontal viscosity. We show that eddy-permitting ocean models have weaker kinetic energy cascades than eddy-resolving models due to discrepancies in the effect of wind forcing, horizontal viscosity, potential to kinetic energy conversion, and nonlinear interactions on the kinetic energy (KE) budget. However, the change in eddy kinetic energy between the eddy-permitting and the eddy-resolving model is not enough to noticeably change the scale where the inverse cascade arrests or the Rhines scale. In addition, we show that the mechanism by which baroclinic flows organise into barotropic flows is weaker at lower resolution, resulting in a more baroclinic flow. Hence, the horizontal resolution impacts the vertical structure of the simulated flow. Our results suggest that the effect of mesoscale eddies can be parameterised by enhancing the potential to kinetic energy conversion, i.e., the horizontal pressure gradients, or enhancing the inverse cascade of kinetic energy.","tags":["Source Themes"],"title":"The Impact of Horizontal Resolution on Energy Transfers in Global Ocean Models","type":"publication"},{"authors":["S. Juricke","T. N. Palmer","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1498867200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1498867200,"objectID":"8467c4ab3018c72dd6d9918cf819b4e0","permalink":"https://climprocpred.github.io/publication/juricke-et-al-2017/","publishdate":"2017-07-01T00:00:00Z","relpermalink":"/publication/juricke-et-al-2017/","section":"publication","summary":"In global ocean models, the representation of small-scale, high-frequency processes considerably influences the large-scale oceanic circulation and its low-frequency variability. This study investigates the impact of stochastic perturbation schemes based on three different subgrid-scale parameterizations in multidecadal ocean-only simulations with the ocean model NEMO at 1° resolution. The three parameterizations are an enhanced vertical diffusion scheme for unstable stratification, the Gent–McWilliams (GM) scheme, and a turbulent kinetic energy mixing scheme, all commonly used in state-of-the-art ocean models. The focus here is on changes in interannual variability caused by the comparatively high-frequency stochastic perturbations with subseasonal decorrelation time scales. These perturbations lead to significant improvements in the representation of low-frequency variability in the ocean, with the stochastic GM scheme showing the strongest impact. Interannual variability of the Southern Ocean eddy and Eulerian streamfunctions is increased by an order of magnitude and by 20%, respectively. Interannual sea surface height variability is increased by about 20%–25% as well, especially in the Southern Ocean and in the Kuroshio region, consistent with a strong underestimation of interannual variability in the model when compared to reanalysis and altimetry observations. These results suggest that enhancing subgrid-scale variability in ocean models can improve model variability and potentially its response to forcing on much longer time scales, while also providing an estimate of model uncertainty.","tags":["Source Themes"],"title":"Stochastic Subgrid-Scale Ocean Mixing: Impacts on Low-Frequency Variability","type":"publication"},{"authors":["I. Grooms","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1495152000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1495152000,"objectID":"26ce8af7f45270e4a5e0f73914ee86bc","permalink":"https://climprocpred.github.io/publication/grooms-zanna-2017/","publishdate":"2017-05-19T00:00:00Z","relpermalink":"/publication/grooms-zanna-2017/","section":"publication","summary":"Porta Mana and Zanna (2014) recently proposed a subgrid-scale parameterization for eddy-permitting quasigeostrophic models. In this model the large-scale fluid is represented as a non-Newtonian viscoelastic medium, with a subgrid-stress closure that involves the Lagrangian derivative of large-scale quantities. This note derives this parameterization, including the nondimensional proportionality coefficient, using only two statistical assumptions: that the subgrid-scale term is locally homogeneous and decorrelates rapidly in space. The parameterization is then verified by comparing against eddy-resolving quasigeostrophic simulations, independently reproducing the results of Porta Mana and Zanna in a simpler model.","tags":["Source Themes"],"title":"A note on ‘Toward a stochastic parameterization of ocean mesoscale eddies’","type":"publication"},{"authors":["T. David","D.P. Marshall","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1493596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1493596800,"objectID":"479983178e40968c5484d9f631665d60","permalink":"https://climprocpred.github.io/publication/david-et-al-2017/","publishdate":"2017-05-01T00:00:00Z","relpermalink":"/publication/david-et-al-2017/","section":"publication","summary":"Jets are an important element of the global ocean circulation. Since these jets are turbulent, it is important that they are characterized using a statistical framework. A high resolution barotropic channel ocean model is used to study jet statistics over a wide range of forcing and dissipation parameters. The first four moments of the potential vorticity distribution on contours of time-averaged streamfunction are considered: mean, standard deviation, skewness and kurtosis. A self-similar response to forcing is found in the mean and standard deviation for eastward barotropic jets which exhibit strong mixing barriers; this self-similarity is related to the global potential enstrophy of the flow. The skewness and kurtosis give a behaviour which is characteristic of mixing barriers, revealing a bi/trimodal statistical distribution of potential vorticity with homogenized potential vorticity on each side of the barrier. The mixing barrier can be described by a simple statistical model. This behaviour is shown to be lost in westward jets due to an asymmetry in the formation of zonal mixing barriers. Moreover, when the statistical analysis is performed on eastward jets in a streamfunction following frame of reference, the distribution becomes monomodal. In this way we can distinguish between the statistics due to wave-like meandering of the jet and the statistics due to the more diffusive eddies. The statistical signature of mixing barriers can be seen in more realistic representations of the Southern Ocean and is shown to be an useful diagnostic tool for identifying strong jets on isopycnal surfaces. The statistical consequences of the presence, and absence, of mixing barriers are likely to be valuable for the development of stochastic representations of eddies and their dynamics in ocean models.","tags":["Source Themes"],"title":"The statistical nature of turbulent barotropic ocean jets","type":"publication"},{"authors":["Aneesh Subramanian","P.G.L. Porta Mana","J. Anstey","T. David","T. Bolton"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1488499200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1488499200,"objectID":"b6a288c0f4e8963b711b9a1cc2069716","permalink":"https://climprocpred.github.io/publication/zanna-et-al-2017/","publishdate":"2017-03-03T00:00:00Z","relpermalink":"/publication/zanna-et-al-2017/","section":"publication","summary":" The role of mesoscale eddies is crucial for the ocean circulation and its energy budget. The sub-grid scale eddy variability needs to be parametrized in ocean models, even at so-called eddy permitting resolutions. Porta Mana and Zanna (2014) propose an eddy parametrization based on a non-Newtonian stress which depends on the partially resolved scales and their variability. In the present study, we test two versions of the parametrization, one deterministic and one stochastic, at coarse and eddy-permitting resolutions in a double gyre quasi-geostrophic model. The parametrization leads to drastic improvements in the mean state and variability of the ocean state, namely in the jet rectification and the kinetic-energy spectra as a function of wavenumber and frequency for eddy permitting models. The parametrization also appears to have a stabilizing effect on the model, especially the stochastic version. The parametrization possesses attractive features for implementation in global models: very little computational cost, it is flow aware and uses the properties of the underlying flow. The deterministic coefficient is scale-aware, while the stochastic parameter is scale- and flow-aware with dependence on resolution, stratification and wind forcing.","tags":["Source Themes"],"title":"Scale-Aware Deterministic and Stochastic Parametrizations of Eddy-Mean Flow Interaction","type":"publication"},{"authors":["MB. Huber","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1484784000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1484784000,"objectID":"6c8991416958b122bbba2a844bea52bb","permalink":"https://climprocpred.github.io/publication/huber-zanna-2017/","publishdate":"2017-01-19T00:00:00Z","relpermalink":"/publication/huber-zanna-2017/","section":"publication","summary":"The impact of uncertainties in air-sea fluxes and ocean model parameters on the ocean circulation and ocean heat uptake (OHU) is assessed in a novel modeling framework. We use an ocean-only model forced with the simulated sea surface fields of the CMIP5 climate models. The simulations are performed using control and 1% CO2 warming scenarios. The ocean-only ensemble adequately reproduces the mean Atlantic Meridional Overturning Circulation (AMOC) and the zonally integrated OHU. The ensemble spread in AMOC strength, its weakening, and Atlantic OHU due to different air-sea fluxes is twice as large as the uncertainty range related to vertical and mesocale eddy diffusivities. The sensitivity of OHU to uncertainties in air-sea fluxes and model parameters differs vastly across basins, with the Southern Ocean exhibiting strong sensitivity to air-sea fluxes and model parameters. This study clearly demonstrates that model biases in air-sea fluxes are one of the key sources of uncertainty in climate simulations.","tags":["Source Themes"],"title":"Drivers of uncertainty in simulated ocean circulation and heat uptake","type":"publication"},{"authors":["B. Huddart","A. Subramanian","Aneesh Subramanian","T. N. Palmer"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1484784000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1484784000,"objectID":"636c5793827186b5f94d01152a10720d","permalink":"https://climprocpred.github.io/publication/huddart-et-al-2016/","publishdate":"2017-01-19T00:00:00Z","relpermalink":"/publication/huddart-et-al-2016/","section":"publication","summary":"Predictability of Atlantic Ocean sea surface temperatures (SST) on seasonal and decadal timescales is investigated using a suite of statistical linear inverse models (LIM). Observed monthly SST anomalies in the Atlantic sector (between 22∘S and 66∘N) are used to construct the LIMs for seasonal and decadal prediction. The forecast skills of the LIMs are then compared to that from two current operational forecast systems. Results indicate that the LIM has good forecast skill for time periods of 3–4 months on the seasonal timescale with enhanced predictability in the spring season. On decadal timescales, the impact of inter-annual and intra-annual variability on the predictability is also investigated. The results show that the suite of LIMs have forecast skill for about 3–4 years over most of the domain when we use only the decadal variability for the construction of the LIM. Including higher frequency variability helps improve the forecast skill and maintains the correlation of LIM predictions with the observed SST anomalies for longer periods. These results indicate the importance of temporal scale interactions in improving predictability on decadal timescales. Hence, LIMs can not only be used as benchmarks for estimates of statistical skill but also to isolate contributions to the forecast skills from different timescales, spatial scales or even model","tags":["Source Themes"],"title":"Seasonal and decadal forecasts of Atlantic Sea surface temperatures using a linear inverse model","type":"publication"},{"authors":["D.G. MacMartin","Aneesh Subramanian","E. Tziperman"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1464739200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1464739200,"objectID":"34a071af9ef549b2967b8f5701b8e745","permalink":"https://climprocpred.github.io/publication/macmartin-et-al-2016/","publishdate":"2016-06-01T00:00:00Z","relpermalink":"/publication/macmartin-et-al-2016/","section":"publication","summary":"Multidecadal variability in the Atlantic meridional overturning circulation (AMOC) is shown to differ significantly between the 4 × CO2 and preindustrial control simulations of the GFDL Earth System Model, version 2M (ESM2M) general circulation model (GCM). In the preindustrial simulation, this model has a peak in the power spectrum of both AMOC and northward heat transport at latitudes between 26° and 50°N. In the 4 × CO2 simulation, the only significant spectral peak is near 60°N. Understanding these differences is important for understanding the effect of future climate change on climate variability, as well as for providing insight into the physics underlying AMOC variability. Transfer function analysis demonstrates that the shift is predominantly due to a shift in the internal ocean dynamics rather than a change in stochastic atmospheric forcing. Specifically, the reduction in variance from 26° to 45°N is due to an increased stratification east of Newfoundland that results from the shallower and weaker mean overturning. The reduced AMOC variance that accompanies the reduced mean value of the AMOC at 4 × CO2 differs from predictions of simple box models that predict a weaker circulation to be closer to a stability bifurcation point and, therefore, be accompanied by amplified variability. The high-latitude variability in the 4 × CO2 simulation is related to the advection of anomalies by the subpolar gyre, distinct from the variability mechanism in the control simulation at lower latitudes. The 4 × CO2 variability has only a small effect on midlatitude meridional heat transport, but does significantly affect sea ice in the northern North Atlantic.","tags":["Source Themes"],"title":"Suppression of Atlantic Meridional Overturning Circulation Variability at increased CO2","type":"publication"},{"authors":["B. Bronselaer","Aneesh Subramanian","D. Munday","J. Lowe"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1463011200,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1463011200,"objectID":"2845ae74016d2002cccd47f9dcd5c034","permalink":"https://climprocpred.github.io/publication/bronselaer-et-al-2016/","publishdate":"2016-05-12T00:00:00Z","relpermalink":"/publication/bronselaer-et-al-2016/","section":"publication","summary":"Observed and predicted increases in Southern Ocean winds are thought to upwell deep ocean carbon and increase atmospheric CO2. However, Southern Ocean dynamics affect biogeochemistry and circulation pathways on a global scale. Using idealized Massachusetts Institute of Technology General Circulation Model (MITgcm) simulations, we demonstrate that an increase in Southern Ocean winds reduces the carbon sink in the North Atlantic subpolar gyre. The increase in atmospheric CO2 due to the reduction of the North Atlantic carbon sink is shown to be of the same magnitude as the increase in atmospheric CO2 due to Southern Ocean outgassing. The mechanism can be described as follows: The increase in Southern Ocean winds leads to an increase in upper ocean northward nutrient transport. Biological productivity is therefore enhanced in the tropics, which alters the chemistry of the subthermocline waters that are ultimately upwelled in the subpolar gyre. The results demonstrate the influence of Southern Ocean winds on the North Atlantic carbon sink and show that the effect of Southern Ocean winds on atmospheric CO2 is likely twice as large as previously thought in past, present, and future climates.","tags":["Source Themes"],"title":"The influence of Southern Ocean winds on the North Atlantic carbon sink","type":"publication"},{"authors":["J. Anstey","Aneesh Subramanian"],"categories":null,"content":"","date":1462060800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1462060800,"objectID":"062168e8fbd417d2815e10e3ea370c85","permalink":"https://climprocpred.github.io/publication/anstey-zanna-2017/","publishdate":"2016-05-01T00:00:00Z","relpermalink":"/publication/anstey-zanna-2017/","section":"publication","summary":"Ocean mesoscale eddies strongly affect the strength and variability of large-scale ocean jets such as the Gulf Stream and Kuroshio Extension. Their spatial scales are too small to be fully resolved in many current climate models and hence their effects on the large-scale circulation need to be parametrized. Here we propose a parametrization of mesoscale eddy momentum fluxes based on large-scale flow deformation. The parametrization is argued to be suitable for use in eddy-permitting ocean general circulation models, and is motivated by an analogy between turbulence in Newtonian fluids (such as water) and laminar flow in non-Newtonian fluids. A primitive-equations model in an idealised double-gyre configuration at eddy-resolving horizontal resolution is used to diagnose the relationship between the proposed closure and the eddy fluxes resolved by the model. Favourable correlations suggest the closure could provide an appropriate deterministic parametrization of mesoscale eddies. The relationship between the closure and different representations of the Reynolds stress tensor is also described. The parametrized forcing possesses the key quasi-geostrophic turbulence properties of energy conservation and enstrophy dissipation, and allows for upgradient fluxes leading to the sharpening of vorticity gradients. The implementation of the closure for eddy-permitting ocean models requires only velocity derivatives and a single parameter that scales with model resolution.","tags":["Source Themes"],"title":"A deformation-based parametrization of ocean mesoscale eddy Reynolds stresses","type":"publication"},{"authors":["M. Andrejczuk","F. Cooper","S. Juricke","T. N. Palmer","A. Weisheimer","Aneesh Subramanian"],"categories":null,"content":"","date":1462060800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1462060800,"objectID":"aaaec5f6a0b356c50992cf979dbe0b77","permalink":"https://climprocpred.github.io/publication/andrejczuk-et-al-2016/","publishdate":"2016-05-01T00:00:00Z","relpermalink":"/publication/andrejczuk-et-al-2016/","section":"publication","summary":"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum.","tags":["Source Themes"],"title":"Oceanic stochastic parametrizations in a seasonal forecast system","type":"publication"},{"authors":["C. O'Reilly","M. Huber","T. Woollings","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1456704000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1456704000,"objectID":"7f264292f7943acc79454234bf1046af","permalink":"https://climprocpred.github.io/publication/oreilly-et-al-2017/","publishdate":"2016-02-29T00:00:00Z","relpermalink":"/publication/oreilly-et-al-2017/","section":"publication","summary":"The Atlantic Multidecadal Oscillation (AMO) significantly influences the climate of the surrounding continents and has previously been attributed to variations in the Atlantic Meridional Overturning Circulation. Recently, however, similar multidecadal variability was reported in climate models without ocean circulation variability. We analyze the relationship between turbulent heat fluxes and sea surface temperatures (SSTs) over the midlatitude North Atlantic in observations and coupled climate model simulations, both with and without ocean circulation variability. SST anomalies associated with the AMO are positively correlated with heat fluxes on decadal time scales in both observations and models with varying ocean circulation, whereas in models without ocean circulation variability the anomalies are negatively correlated when heat flux anomalies lead. These relationships are captured in a simple stochastic model and rely crucially on low-frequency forcing of SST. The fully coupled models that better capture this signature more effectively reproduce the observed impact of the AMO on European summertime temperatures.","tags":["Source Themes"],"title":"The signature of low-frequency oceanic forcing in the Atlantic Multidecadal Oscillation","type":"publication"},{"authors":["F. Cooper","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1427846400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1427846400,"objectID":"abe837b546a8932a968d1be79dfd4196","permalink":"https://climprocpred.github.io/publication/cooper-et-al-2015/","publishdate":"2015-04-01T00:00:00Z","relpermalink":"/publication/cooper-et-al-2015/","section":"publication","summary":"An optimisation scheme is developed to accurately represent the sub-grid scale forcing of a high dimensional chaotic ocean system. Using a simple parameterisation scheme, the velocity components of a 30 km resolution shallow water ocean model are optimised to have the same climatological mean and variance as that of a less viscous 7.5 km resolution model. The 5 day lag-covariance is also optimised, leading to a more accurate estimate of the high resolution response to forcing using the low resolution model. The system considered is an idealised barotropic double gyre that is chaotic at both resolutions. Using the optimisation scheme, we find and apply the constant in time, but spatially varying, forcing term that is equal to the time integrated forcing of the sub-grid scale eddies. A linear stochastic term, independent of the large-scale flow, with no spatial correlation but a spatially varying amplitude and time scale is used to represent the transient eddies. The climatological mean, variance and 5 day lag-covariance of the velocity from a single high resolution integration is used to provide an optimisation target. No other high resolution statistics are required. Additional programming effort, for example to build a tangent linear or adjoint model, is not required either. The focus of this paper is on the optimisation scheme and the accuracy of the optimised flow. However the forcing can provide insights in the design of deterministic and stochastic parameterisations. In the present study, we found that the stochastic parameterisation correcting the model variance is associated with the spatial pattern of eddy-decorrelation timescales rather than the spatial pattern of the amplitude of the variance. The method can be applied in future investigations into the physical processes that govern barotropic turbulence and it can perhaps be applied to help understand and correct biases in the mean and variance of a more realistic coarse or eddy-permitting ocean model. The method is complementary to current parameterisations and can be applied at the same time without modification.","tags":["Source Themes"],"title":"Optimisation of an idealised ocean model: stochastic parametrisation of sud-grid eddies","type":"publication"},{"authors":["D.P. Marshall","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1416009600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1416009600,"objectID":"8e15827b79b4212ce9ef9eb810c42dbf","permalink":"https://climprocpred.github.io/publication/marshall-zanna-2014/","publishdate":"2014-11-15T00:00:00Z","relpermalink":"/publication/marshall-zanna-2014/","section":"publication","summary":"A conceptual model of ocean heat uptake is developed as a multilayer generalization of Gnanadesikan. The roles of Southern Ocean Ekman and eddy transports, North Atlantic Deep Water (NADW) formation, and diapycnal mixing in controlling ocean stratification and transient heat uptake are investigated under climate change scenarios, including imposed surface warming, increased Southern Ocean wind forcing, with or without eddy compensation, and weakened meridional overturning circulation (MOC) induced by reduced NADW formation. With realistic profiles of diapycnal mixing, ocean heat uptake is dominated by Southern Ocean Ekman transport and its long-term adjustment controlled by the Southern Ocean eddy transport. The time scale of adjustment setting the rate of ocean heat uptake increases with depth. For scenarios with increased Southern Ocean wind forcing or weakened MOC, deepened stratification results in enhanced ocean heat uptake. In each of these experiments, the role of diapycnal mixing in setting ocean stratification and heat uptake is secondary. Conversely, in experiments with enhanced diapycnal mixing as employed in “upwelling diffusion” slab models, the contributions of diapycnal mixing and Southern Ocean Ekman transport to the net heat uptake are comparable, but the stratification extends unrealistically to the sea floor. The simple model is applied to interpret the output of an Earth system model, the Second Generation Canadian Earth System Model (CanESM2), in which the atmospheric CO2 concentration is increased by 1% yr−1 until quadrupling, where it is found that Southern Ocean Ekman transport is essential to reproduce the magnitude and vertical profile of ocean heat uptake.","tags":["Source Themes"],"title":"A Conceptual Model of Ocean Heat Uptake under Climate Change","type":"publication"},{"authors":["PGL Porta Mana","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1406246400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1406246400,"objectID":"6197845a791f6522897e31ff19504794","permalink":"https://climprocpred.github.io/publication/portamana-zanna-2014/","publishdate":"2014-07-25T00:00:00Z","relpermalink":"/publication/portamana-zanna-2014/","section":"publication","summary":"A stochastic parameterization of ocean mesoscale eddies is constructed in order to account for the fluctuations in subgrid transport and to represent upscale turbulent cascades. Eddy-resolving simulations to derive the parameterization are performed in a quasi-geostrophic (QG) model in a double-gyre configuration. The coarse-graining of the high-resolution model is giving rise to an eddy source term which represents the turbulent Reynolds stresses. The eddy source term, its mean and fluctuations are analyzed as function of the resolved scales and external parameters. A functional form of the resolved scales, based on a representation of turbulence as a non-Newtonian viscoelastic medium and including the rate of strain, is used to describe the eddy source term mean, variance and decorrelation timescale. Probability density functions (PDFs) of the eddy source term conditional on the resolved scales are then calculated, capturing the fluctuations associated with mesoscale eddies and their impact on the mean flow. Scalings for the mean, standard deviation, skewness, and kurtosis of the conditional PDFs are provided as function of the grid size, forcing, and stratification of the coarse-resolution model. In light of these scalings, no preliminary high-resolution (QG) model runs are necessary to diagnose the subgrid forcing and the implementation of a stochastic closure based on the conditional PDFs requires in principle very little tuning.","tags":["Source Themes"],"title":"Toward a stochastic parameterization of ocean mesoscale eddies","type":"publication"},{"authors":["D.G. MacMartin","E. Tziperman","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1383264000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1383264000,"objectID":"57f1f4ea20f4d47f49844e5117483ef7","permalink":"https://climprocpred.github.io/publication/macmartin-et-al-2013/","publishdate":"2013-11-01T00:00:00Z","relpermalink":"/publication/macmartin-et-al-2013/","section":"publication","summary":"The dynamics of the Atlantic meridional overturning circulation (AMOC) vary considerably among different climate models; for example, some models show clear peaks in their power spectra while others do not. To elucidate these model differences, transfer functions are used to estimate the frequency domain relationship between surface forcing fields, including sea surface temperature, salinity, and wind stress, and the resulting AMOC response. These are estimated from the outputs of the Coupled Model Intercomparison Project phase 5 (CMIP5) and phase 3 (CMIP3) control runs for eight different models, with a specific focus on Geophysical Fluid Dynamics Laboratory Climate Model, version 2.1 (GFDL CM2.1), and the Community Climate System Model, version 4 (CCSM4), which exhibit rather different spectral behavior. The transfer functions show very little agreement among models for any of the pairs of variables considered, suggesting the existence of systematic model errors and that considerable uncertainty in the simulation of AMOC in current climate models remains. However, a robust feature of the frequency domain analysis is that models with spectral peaks in their AMOC correspond to those in which AMOC variability is more strongly excited by high-latitude surface perturbations that have periods corresponding to the frequency of the spectral peaks. This explains why different models exhibit such different AMOC variability. These differences would not be evident without using a method that explicitly computes the frequency dependence rather than a priori assuming a particular functional form. Finally, transfer functions are used to evaluate two proposed physical mechanisms for model differences in AMOC variability: differences in Labrador Sea stratification and excitation by westward-propagating subsurface Rossby waves.","tags":["Source Themes"],"title":"Frequency Domain Multimodel Analysis of the Response of Atlantic Meridional Overturning Circulation to Surface Forcing","type":"publication"},{"authors":["C. Wilson","K. Horsburgh","J. Williams","J. Flowerdew","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1377302400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1377302400,"objectID":"6b74354cdc033fc7689351ca0e61f5b7","permalink":"https://climprocpred.github.io/publication/wilson-et-al-2013/","publishdate":"2013-08-24T00:00:00Z","relpermalink":"/publication/wilson-et-al-2013/","section":"publication","summary":" For a simple dynamical system, such as a pendulum, it is easy to deduce where and when applied forcing might produce a particular response. However, for a complex nonlinear dynamical system such as the ocean or atmosphere, this is not as obvious. Knowing when or where the system is most sensitive, to observational uncertainty or otherwise, is key to understanding the physical processes, improving and providing reliable forecasts. We describe the application of adjoint modeling to determine the sensitivity of sea level at a UK coastal location, Sheerness, to perturbations in wind stress preceding an extreme North Sea storm surge event on 9 November 2007. Sea level at Sheerness is one of the most important factors used to decide whether to close the Thames Flood Barrier, which protects London. Adjoint modeling has been used by meteorologists since the 1990s, but is a relatively new technique for ocean modeling. It may be used to determine system sensitivity beyond the scope of ensemble modeling and in a computationally efficient way. Using estimates of wind stress error from Met Office forecasts, we find that for this event total sea level at Sheerness is most sensitive in the 3 h preceding the time of its unperturbed maximum level and over a radius of approximately 300 km. We also find that the pattern of sensitivity follows a simple sequence when considered in the reverse-time direction.","tags":["Source Themes"],"title":"Tide-surge adjoint modelling: a new technique to understand forecast uncertainty","type":"publication"},{"authors":["T. N. Palmer","Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1370304000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1370304000,"objectID":"25b372857c22d9bf600227d139c775cf","permalink":"https://climprocpred.github.io/publication/palmer-zanna-2013/","publishdate":"2013-06-04T00:00:00Z","relpermalink":"/publication/palmer-zanna-2013/","section":"publication","summary":"The local instabilities of a nonlinear dynamical system can be characterized by the leading singular vectors of its linearized operator. The leading singular vectors are perturbations with the greatest linear growth and are therefore key in assessing the system’s predictability. In this paper, the analysis of singular vectors for the predictability of weather and climate and ensemble forecasting is discussed. An overview of the role of singular vectors in informing about the error growth rate in numerical models of the atmosphere is given. This is followed by their use in the initialization of ensemble weather forecasts. Singular vectors for the ocean and coupled ocean–atmosphere system in order to understand the predictability of climate phenomena such as ENSO and meridional overturning circulation are reviewed and their potential use to initialize seasonal and decadal forecasts is considered. As stochastic parameterizations are being implemented, some speculations are made about the future of singular vectors for the predictability of weather and climate for theoretical applications and at the operational level.","tags":["Source Themes"],"title":"Singular vectors, predictability and ensemble forecasting for weather and climate","type":"publication"},{"authors":["Aneesh Subramanian"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1342310400,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1342310400,"objectID":"bf7667ad928170d7db8f16148a03d6df","permalink":"https://climprocpred.github.io/publication/zanna-2012/","publishdate":"2012-07-15T00:00:00Z","relpermalink":"/publication/zanna-2012/","section":"publication","summary":" An empirical statistical model is constructed to assess the forecast skill and the linear predictability of Atlantic Ocean sea surface temperature (SST) variability. Linear inverse modeling (LIM) is used to build a dynamically based statistical model using observed Atlantic SST anomalies between latitudes 20°S and 66°N from 1870 to 2009. LIM allows one to fit a multivariate red-noise model to the observed annually averaged SST anomalies and to test it. Forecast skill is assessed and is shown to be O(3–5 yr). After a few years, the skill is greatly reduced, especially in the subpolar region. In the stable dynamical system determined by LIM, skill of annual average SST anomalies arises from four damped eigenmodes. The four eigenmodes are shown to be relevant in particular for the optimal growth events of SST variance, with a pattern reminiscent of the low-frequency mode of variability, and in general for the predictability and variability of Atlantic SSTs on interannual time scales. LIM might serve as a useful benchmark for interannual and decadal forecasts of SST anomalies that are based on numerical models.","tags":["Source Themes"],"title":"Forecast Skill and Predictability of Observed Atlantic Sea Surface Temperatures","type":"publication"},{"authors":["Aneesh Subramanian","P. Heimbach","A.M. Moore","E. Tziperman"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1317600000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1317600000,"objectID":"3bcc85197a7fbe4355044ce702662b6f","permalink":"https://climprocpred.github.io/publication/zanna-et-al-2012/","publishdate":"2011-10-03T00:00:00Z","relpermalink":"/publication/zanna-et-al-2012/","section":"publication","summary":" The limits of predictability of the meridional overturning circulation (MOC) and upper-ocean temperatures due to errors in ocean initial conditions and model parametrizations are investigated in an idealized configuration of an ocean general circulation model (GCM). Singular vectors (optimal perturbations) are calculated using the GCM, its tangent linear and adjoint models to determine an upper bound on the predictability of North Atlantic climate. The maximum growth time-scales of MOC and upper-ocean temperature anomalies, excited by the singular vectors, are 18.5 and 13 years respectively and in part explained by the westward propagation of upper-ocean anomalies against the mean flow. As a result of the linear interference of non-orthogonal eigenmodes of the non-normal dynamics, the ocean dynamics are found to actively participate in the significant growth of the anomalies. An initial density perturbation of merely 0.02 kg m−3 is found to lead to a 1.7 Sv MOC anomaly after 18.5 years. In addition, Northern Hemisphere upper-ocean temperature perturbations can be amplified by a factor of 2 after 13 years. The growth of upper-ocean temperature and MOC anomalies is slower and weaker when excited by the upper-ocean singular vectors than when the deep ocean is perturbed. This leads to the conclusion that predictability experiments perturbing only the atmospheric initial state may overestimate the predictability time. Interestingly, optimal MOC and upper-ocean temperature excitations are only weakly correlated, thus limiting the utility of SST observations to infer MOC variability. The excitation of anomalies in this model might have a crucial impact on the variability and predictability of Atlantic climate. The limit of predictability of the MOC is found to be different from that of the upper-ocean heat content, emphasizing that errors in ocean initial conditions will affect various measures differently and such uncertainties should be carefully considered in decadal prediction experiments. Copyright © 2011 Royal Meteorological Society","tags":["Source Themes"],"title":"Upper-ocean singular vectors of the North Atlantic climate with implications for linear predictability and variability","type":"publication"},{"authors":["Aneesh Subramanian","P. Heimbach","A.M. Moore","E. Tziperman"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1295049600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1295049600,"objectID":"078e5ce89b2e2ce8eb536841b4b1d341","permalink":"https://climprocpred.github.io/publication/zanna-et-al-2011/","publishdate":"2011-01-15T00:00:00Z","relpermalink":"/publication/zanna-et-al-2011/","section":"publication","summary":" The optimal excitation of Atlantic meridional overturning circulation (MOC) anomalies is investigated in an ocean general circulation model with an idealized configuration. The optimal three-dimensional spatial structure of temperature and salinity perturbations, defined as the leading singular vector and generating the maximum amplification of MOC anomalies, is evaluated by solving a generalized eigenvalue problem using tangent linear and adjoint models. Despite the stable linearized dynamics, a large amplification of MOC anomalies, mostly due to the interference of nonnormal modes, is initiated by the optimal perturbations. The largest amplification of MOC anomalies, found to be excited by high-latitude deep density perturbations in the northern part of the basin, is achieved after about 7.5 years. The anomalies grow as a result of a conversion of mean available potential energy into potential and kinetic energy of the perturbations, reminiscent of baroclinic instability. The time scale of growth of MOC anomalies can be understood by examining the time evolution of deep zonal density gradients, which are related to the MOC via the thermal wind relation. The velocity of propagation of the density anomalies, found to depend on the horizontal component of the mean flow velocity and the mean density gradient, determines the growth time scale of the MOC anomalies and therefore provides an upper bound on the MOC predictability time. The results suggest that the nonnormal linearized ocean dynamics can give rise to enhanced MOC variability if, for instance, overflows, eddies, and/or deep convection can excite high-latitude density anomalies in the ocean interior with a structure resembling that of the optimal perturbations found in this study. The findings also indicate that errors in ocean initial conditions or in model parameterizations or processes, particularly at depth, may significantly reduce the Atlantic MOC predictability time to less than a decade.","tags":["Source Themes"],"title":"Optimal Excitation of Interannual Atlantic Meridional Overturning Circulation Variability","type":"publication"},{"authors":["Aneesh Subramanian","P. Heimbach","A.M. Moore","E. Tziperman"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1272672000,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1272672000,"objectID":"b33ccbd3c1ae6410bad0afc3795e0a2e","permalink":"https://climprocpred.github.io/publication/zanna-et-al-2010/","publishdate":"2010-05-01T00:00:00Z","relpermalink":"/publication/zanna-et-al-2010/","section":"publication","summary":" The role of ocean dynamics in optimally exciting interannual variability of tropical sea surface temperature (SST) anomalies is investigated using an idealized-geometry ocean general circulation model. Initial temperature and salinity perturbations leading to an optimal growth of tropical SST anomalies, typically arising from the nonnormal dynamics, are evaluated. The structure of the optimal perturbations is characterized by relatively strong deep salinity anomalies near the western boundary generating a transient amplification of equatorial SST anomalies in less than four years. The associated growth mechanism is linked to the excitation of coastal and equatorial Kelvin waves near the western boundary following a rapid geostrophic adjustment owing to the optimal initial temperature and salinity perturbations. The results suggest that the nonnormality of the ocean dynamics may efficiently create large tropical SST variability on interannual time scales in the Atlantic without the participation of air–sea processes or the meridional overturning circulation. An optimal deep initial salinity perturbation of 0.1 ppt located near the western boundary can result in a tropical SST anomaly of approximately 0.45°C after nearly four years, assuming the dynamics are linear. Possible mechanisms for exciting such deep perturbations are discussed. While this study is motivated by tropical Atlantic SST variability, its relevance to other basins is not excluded. The optimal initial conditions leading to the tropical SST anomalies’ growth are obtained by solving a generalized eigenvalue problem. The evaluation of the optimals is achieved by using the Massachusetts Institute of Technology general circulation model (MITgcm) tangent linear and adjoint models as well the the Arnoldi Package (ARPACK) software for solving large-scale eigenvalue problems.","tags":["Source Themes"],"title":"The role of ocean dynamics in the optimal growth of tropical SST anomalies","type":"publication"},{"authors":["Aneesh Subramanian","E. Tziperman"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1217548800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1217548800,"objectID":"f2145735ba43b662392e28cd748944b8","permalink":"https://climprocpred.github.io/publication/zanna-tziperman-2008/","publishdate":"2008-08-01T00:00:00Z","relpermalink":"/publication/zanna-tziperman-2008/","section":"publication","summary":"The amplification of thermohaline circulation (THC) anomalies resulting from heat and freshwater forcing at the ocean surface is investigated in a zonally averaged coupled ocean–atmosphere model. Optimal initial conditions of surface temperature and salinity leading to the largest THC growth are computed, and so are the structures of stochastic surface temperature and salinity forcing that excite maximum THC variance (stochastic optimals). When the THC amplitude is defined as its sum of squares (equivalent to using the standard L2 norm), the nonnormal linearized dynamics lead to an amplification with a time scale on the order of 100 yr. The optimal initial conditions have a vanishing THC anomaly, and the complex amplification mechanism involves the advection of both temperature and salinity anomalies by the mean flow and of the mean temperature and salinity by the anomaly flow. The L2 characterization of THC anomalies leads to physically interesting results, yet to a mathematically singular problem. A novel alternative characterizing the THC amplitude by its maximum value, as often done in general circulation model studies, is therefore introduced. This complementary method is shown to be equivalent to using the L-infinity norm, and the needed mathematical approach is developed and applied to the THC problem. Under this norm, an amplification occurs within 10 yr explained by the classic salinity advective feedback mechanism. The analysis of the stochastic optimals shows that the character of the THC variability may be very sensitive to the spatial pattern of the surface forcing. In particular, a maximum THC variance and long-time-scale variability are excited by a basin-scale surface forcing pattern, while a significantly higher frequency and to some extent a weaker variability are induced by a smooth and large-scale, yet mostly concentrated in polar areas, surface forcing pattern. Overall, the results suggest that a large THC variability can be efficiently excited by atmospheric surface forcing, and the simple model used here makes several predictions that would be interesting to test using more complex models.","tags":["Source Themes"],"title":"Optimal Surface Excitation of the Thermohaline Circulation","type":"publication"},{"authors":["E. Tziperman","Aneesh Subramanian","C. Penland"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1204329600,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1204329600,"objectID":"aaef45a7fcf7c09b743d5719fd27e742","permalink":"https://climprocpred.github.io/publication/tziperman-et-al-2008/","publishdate":"2008-03-01T00:00:00Z","relpermalink":"/publication/tziperman-et-al-2008/","section":"publication","summary":"Using the GFDL coupled atmosphere–ocean general circulation model CM2.1, the transient amplification of thermohaline circulation (THC) anomalies due to its nonnormal dynamics is studied. A reduced space based on empirical orthogonal functions (EOFs) of temperature and salinity anomaly fields in the North Atlantic is constructed. Under the assumption that the dynamics of this reduced space is linear, the propagator of the system is then evaluated and the transient growth of THC anomalies analyzed. Although the linear dynamics are stable, such that any initial perturbation eventually decays, nonnormal effects are found to result in a significant transient growth of temperature, salinity, and THC anomalies. The growth time scale for these anomalies is between 5 and 10 yr, providing an estimate of the predictability time of the North Atlantic THC in this model. There are indications that these results are merely a lower bound on the nonnormality of THC dynamics in the present coupled GCM. This seems to suggest that such nonnormal effects should be seriously considered if the predictability of the THC is to be quantitatively evaluated from models or observations. The methodology presented here may be used to produce initial perturbations to the ocean state that may result in a stricter estimate of ocean and THC predictability than the common procedure of initializing with an identical ocean state and a perturbed atmosphere.","tags":["Source Themes"],"title":"Nonnormal Thermohaline Circulation Dynamics in a Coupled Ocean–Atmosphere GCM","type":"publication"},{"authors":["Aneesh Subramanian","E. Tziperman"],"categories":null,"content":" Click the Cite button above to demo the feature to enable visitors to import publication metadata into their reference management software. Supplementary notes can be added here, including code and math.\n","date":1125532800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":1125532800,"objectID":"5645cd0dbc40a5095f2a0d6d2cdbf18c","permalink":"https://climprocpred.github.io/publication/zanna-tziperman-2005/","publishdate":"2005-09-01T00:00:00Z","relpermalink":"/publication/zanna-tziperman-2005/","section":"publication","summary":" A simple zonally averaged coupled ocean–atmosphere model, with a relatively high resolution in the meridional direction, is used to examine physical mechanisms leading to transient amplification of thermohaline circulation (THC) anomalies. It is found that in a stable regime, in which small perturbations eventually decay, there are optimal initial conditions leading to a dramatic amplification of initial temperature and salinity anomalies in addition to the THC amplification. The maximum amplification occurs after about 40 years, and the eventual decay is on a centennial time scale. The initial temperature and salinity anomalies are considerably amplified by factors of a few hundreds and 20, respectively. The initial conditions leading to this amplification are characterized by mutually canceling initial temperature and salinity anomalies contributions to the THC anomaly, such that the initial THC anomaly vanishes. The mechanism of amplification is analyzed and found to be the result of an interaction between a few damped (oscillatory and nonoscillatory) modes with decay time scales lying in a range of 20–800 years. The amplification mechanism is also found to be distinct from the advective feedback leading to THC instabilities for large freshwater forcing.","tags":["Source Themes"],"title":"Nonnormal Amplification of the Thermohaline Circulation","type":"publication"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"6d99026b9e19e4fa43d5aadf147c7176","permalink":"https://climprocpred.github.io/contact/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/contact/","section":"","summary":"","tags":null,"title":"","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"c1d17ff2b20dca0ad6653a3161942b64","permalink":"https://climprocpred.github.io/people/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/people/","section":"","summary":"","tags":null,"title":"","type":"widget_page"},{"authors":null,"categories":null,"content":"","date":-62135596800,"expirydate":-62135596800,"kind":"page","lang":"en","lastmod":-62135596800,"objectID":"b0d61e5cbb7472bf320bf0ef2aaeb977","permalink":"https://climprocpred.github.io/tour/","publishdate":"0001-01-01T00:00:00Z","relpermalink":"/tour/","section":"","summary":"","tags":null,"title":"Tour","type":"widget_page"}]