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1. Gathering users and developers to shape together the next-generation ocean reanalyses: Ocean reanalyses workshop of the European Copernicus Marine Service

Author

Yang, Chunxue, primary, Bourdallé-Badie, Romain, additional, Drevillon, Marie, additional, Amaya, Dillon, additional, Aouf, Lotfi, additional, Aydogdu, Ali, additional, Barton, Benjamin, additional, Bell, Mike, additional, Boyer, Tim, additional, Blauw, Anouk, additional, Carton, James, additional, Candela, Tony, additional, Cossarini, Gianpiero, additional, Dabrowski, Tomasz, additional, de Boisseson, Eric, additional, de Mora, Lee, additional, Fablet, Ronan, additional, Forget, Gaël, additional, Fujii, Yosuke, additional, Garric, Gilles, additional, Giunta, Valentina, additional, Salamon, Peter, additional, Hersbach, Hans, additional, Juza, Mélanie, additional, Sommer, Julien Le, additional, Martin, Matthew, additional, McAdam, Ronan, additional, Garcia, Melisa Menendez, additional, Morim, Joao, additional, Nicolì, Dario, additional, Reppucci, Antonio, additional, Samuelsen, Annette, additional, Sauzède, Raphaëlle, additional, Slivinski, Laura, additional, Specq, Damien, additional, Storto, Andrea, additional, Tuomi, Laura, additional, Vandenbulcke, Luc, additional, Aznar, Roland, additional, Beuvier, Jonathan, additional, Cipollone, Andrea, additional, Clementi, Emanuela, additional, Di Biagio, Valeria, additional, Escudier, Romain, additional, Giesen, Rianne, additional, Greiner, Eric, additional, Guihou, Karen, additional, Korabel, Vasily, additional, Lamouroux, Julien, additional, Chune, Stephane Law, additional, Lellouche, Jean- Michel, additional, Levier, Bruno, additional, Lima, Leonardo, additional, Mangin, Antoine, additional, Mayer, Michael, additional, Melet, Angelique, additional, Miraglio, Pietro, additional, Oikonomou, Charikleia, additional, Pfeffer, Julia, additional, Renshaw, Richard, additional, Ringgaard, Ida, additional, Thual, Sulian, additional, Titaud, Olivier, additional, Tonani, Marina, additional, van Gennip, Simon, additional, von Schuckmann, Karina, additional, Drillet, Yann, additional, and Traon, Pierre-Yves Le, additional

Published
2024
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3. Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models.

Author

Lee, Younjoo, Matrai, Patricia, Friedrichs, Marjorie, Saba, Vincent, Aumont, Olivier, Babin, Marcel, Buitenhuis, Erik, Chevallier, Matthieu, de Mora, Lee, Dessert, Morgane, Dunne, John, Ellingsen, Ingrid, Feldman, Doron, Frouin, Robert, Gehlen, Marion, Gorgues, Thomas, Ilyina, Tatiana, Jin, Meibing, John, Jasmin, Lawrence, Jon, Manizza, Manfredi, Menkes, Christophe, Perruche, Coralie, Le Fouest, Vincent, Popova, Ekaterina, Romanou, Anastasia, Samuelsen, Annette, Schwinger, Jörg, Séférian, Roland, Stock, Charles, Tjiputra, Jerry, Tremblay, L, Ueyoshi, Kyozo, Vichi, Marcello, Yool, Andrew, and Zhang, Jinlun

Abstract

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Published
2016

7. Scenario choice impacts carbon allocation projection at global warming levels

Author

de Mora, Lee, Swaminathan, Ranjini, Allan, Richard P., Blackford, Jeremy C., Kelley, Douglas I., Harris, Phil, Jones, Chris D., Jones, Colin G., Liddicoat, Spencer, Parker, Robert J., Quaife, Tristan, Walton, Jeremy, Yool, Andrew, de Mora, Lee, Swaminathan, Ranjini, Allan, Richard P., Blackford, Jeremy C., Kelley, Douglas I., Harris, Phil, Jones, Chris D., Jones, Colin G., Liddicoat, Spencer, Parker, Robert J., Quaife, Tristan, Walton, Jeremy, and Yool, Andrew

Abstract

•We show that the distribution of anthropogenic carbon between the atmosphere, land surface, and ocean differs with the choice of projection scenario even for identical changes in mean global surface temperature. Warming thresholds occur later in lower-CO2-emissions scenarios and with less carbon in the three main reservoirs than in higher-CO2-emissions scenarios. At 2 ∘C of warming, the mean carbon allocation differs by up to 63 PgC between scenarios, which is equivalent to approximately 6 years of the current global total emissions. At the same warming level, higher-CO2-concentration scenarios have a lower combined ocean and land carbon allocation fraction of the total carbon compared to lower-CO2-concentration scenarios. •The warming response to CO2, quantified as the equilibrium climate sensitivity, ECS, directly impacts the global warming level exceedance year and hence the carbon allocation. Low-ECS models have more carbon than high-ECS models at a given warming level because the warming threshold occurs later, allowing more emissions to accumulate. •These results are important for carbon budgets and mitigation strategies as they impact how much carbon the ocean and land surface could absorb at a given warming level. Carbon budgeting will be key to reducing the impacts of anthropogenic climate change, and these findings could have critical consequences for policies aimed at reaching net zero.

Published
2023

8. UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model

Author

Mulcahy, Jane P., Jones, Colin G., Rumbold, Steven T., Kuhlbrodt, Till, Dittus, Andrea J., Blockley, Edward W., Yool, Andrew, Walton, Jeremy, Hardacre, Catherine, Andrews, Timothy, Bodas-Salcedo, Alejandro, Stringer, Marc, de Mora, Lee, Harris, Phil, Hill, Richard, Kelley, Doug, Robertson, Eddy, Tang, Yongming, Mulcahy, Jane P., Jones, Colin G., Rumbold, Steven T., Kuhlbrodt, Till, Dittus, Andrea J., Blockley, Edward W., Yool, Andrew, Walton, Jeremy, Hardacre, Catherine, Andrews, Timothy, Bodas-Salcedo, Alejandro, Stringer, Marc, de Mora, Lee, Harris, Phil, Hill, Richard, Kelley, Doug, Robertson, Eddy, and Tang, Yongming

Abstract

Many Coupled Model Intercomparison Project phase 6 (CMIP6) models have exhibited a substantial cold bias in the global mean surface temperature (GMST) in the latter part of the 20th century. An overly strong negative aerosol forcing has been suggested as a leading contributor to this bias. An updated configuration of UK Earth System Model (UKESM) version 1, UKESM1.1, has been developed with the aim of reducing the historical cold bias in this model. Changes implemented include an improved representation of SO2 dry deposition, along with several other smaller modifications to the aerosol scheme and a retuning of some uncertain parameters of the fully coupled Earth system model. The Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, a six-member historical ensemble and a subset of future scenario simulations are completed. In addition, the total anthropogenic effective radiative forcing (ERF), its components and the effective and transient climate sensitivities are also computed. The UKESM1.1 preindustrial climate is warmer than UKESM1 by up to 0.75 K, and a significant improvement in the historical GMST record is simulated, with the magnitude of the cold bias reduced by over 50 %. The warmer climate increases ocean heat uptake in the Northern Hemisphere oceans and reduces Arctic sea ice, which is in better agreement with observations. Changes to the aerosol and related cloud properties are a driver of the improved GMST simulation despite only a modest reduction in the magnitude of the negative aerosol ERF (which increases by +0.08 W m−2). The total anthropogenic ERF increases from 1.76 W m−2 in UKESM1 to 1.84 W m−2 in UKESM1.1. The effective climate sensitivity (5.27 K) and transient climate response (2.64 K) remain largely unchanged from UKESM1 (5.36 and 2.76 K respectively).

Published
2023

9. Choice of Forecast Scenario Impacts the Carbon Allocation at the Same Global Warming Levels

Author

de Mora, Lee, primary, Swaminathan, Ranjini, additional, Allan, Richard P., additional, Blackford, Jeremy, additional, Kelley, Douglas I., additional, Harris, Phil, additional, Jones, Chris D., additional, Jones, Colin G., additional, Liddicoat, Spencer, additional, Parker, Robert J., additional, Quaife, Tristan, additional, Walton, Jeremy, additional, and Yool, Andrew, additional

Published
2023
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10. Supplementary material to "UKESM1.1: Development and evaluation of an updated configuration of the UK Earth System Model"

Author

Mulcahy, Jane Patricia, primary, Jones, Colin G., additional, Rumbold, Steven T., additional, Kuhlbrodt, Till, additional, Dittus, Andrea J., additional, Blockley, Edward W., additional, Yool, Andrew, additional, Walton, Jeremy, additional, Hardacre, Catherine, additional, Andrews, Timothy, additional, Bodas-Salcedo, Alejandro, additional, Stringer, Marc, additional, de Mora, Lee, additional, Harris, Phil, additional, Hill, Richard, additional, Kelley, Doug, additional, Robertson, Eddy, additional, and Tang, Yongming, additional

Published
2022
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11. UKESM1.1: Development and evaluation of an updated configuration of the UK Earth System Model

Author

Mulcahy, Jane Patricia, primary, Jones, Colin G., additional, Rumbold, Steven T., additional, Kuhlbrodt, Till, additional, Dittus, Andrea J., additional, Blockley, Edward W., additional, Yool, Andrew, additional, Walton, Jeremy, additional, Hardacre, Catherine, additional, Andrews, Timothy, additional, Bodas-Salcedo, Alejandro, additional, Stringer, Marc, additional, de Mora, Lee, additional, Harris, Phil, additional, Hill, Richard, additional, Kelley, Doug, additional, Robertson, Eddy, additional, and Tang, Yongming, additional

Published
2022
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12. The physical climate at global warming thresholds as seen in the U.K. Earth System Model

Author

Swaminathan, Ranjini, Parker, Robert J., Jones, Colin G., Allan, Richard P., Quaife, Tristan, Kelley, Douglas I., De Mora, Lee, Walton, Jeremy, Swaminathan, Ranjini, Parker, Robert J., Jones, Colin G., Allan, Richard P., Quaife, Tristan, Kelley, Douglas I., De Mora, Lee, and Walton, Jeremy

Abstract

A key goal of the 2015 Paris Climate Agreement is to keep global mean temperature change at 2°C and if possible under 1.5°C by the end of the century. To investigate the likelihood of achieving this target, we calculate the year of exceedance of a given global warming threshold (GWT) temperature across 32 CMIP6 models for Shared Socioeconomic Pathway (SSP) and radiative forcing combinations included in the Tier 1 ScenarioMIP simulations. Threshold exceedance year calculations reveal that a majority of CMIP6 models project warming beyond 2°C by the end of the century under every scenario or pathway apart from the lowest emission scenarios considered, SSP1–1.9 and SSP1–2.6, which is largely a function of the ScenarioMIP experiment design. The U.K. Earth System Model (UKESM1) ScenarioMIP projections are analyzed in detail to assess the regional and seasonal variations in climate at different warming levels. The warming signal emerging by midcentury is identified as significant and distinct from internal climate variability in all scenarios considered and includes warming summers in the Mediterranean, drying in the Amazon, and heavier Indian monsoons. Arctic sea ice depletion results in prominent amplification of warming and tropical warming patterns emerge that are distinct from interannual variability. Climate changes projected for a 2°C warmer world are in almost all cases exacerbated with further global warming (e.g., to a 4°C warmer world).

Published
2022

13. UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model.

Author

Mulcahy, Jane P., Jones, Colin G., Rumbold, Steven T., Kuhlbrodt, Till, Dittus, Andrea J., Blockley, Edward W., Yool, Andrew, Walton, Jeremy, Hardacre, Catherine, Andrews, Timothy, Bodas-Salcedo, Alejandro, Stringer, Marc, de Mora, Lee, Harris, Phil, Hill, Richard, Kelley, Doug, Robertson, Eddy, and Tang, Yongming

Subjects
GLOBAL warming, CLIMATE sensitivity, RADIATIVE forcing, SEA ice, SURFACE temperature, AEROSOLS
Abstract

Many Coupled Model Intercomparison Project phase 6 (CMIP6) models have exhibited a substantial cold bias in the global mean surface temperature (GMST) in the latter part of the 20th century. An overly strong negative aerosol forcing has been suggested as a leading contributor to this bias. An updated configuration of UK Earth System Model (UKESM) version 1, UKESM1.1, has been developed with the aim of reducing the historical cold bias in this model. Changes implemented include an improved representation of SO2 dry deposition, along with several other smaller modifications to the aerosol scheme and a retuning of some uncertain parameters of the fully coupled Earth system model. The Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, a six-member historical ensemble and a subset of future scenario simulations are completed. In addition, the total anthropogenic effective radiative forcing (ERF), its components and the effective and transient climate sensitivities are also computed. The UKESM1.1 preindustrial climate is warmer than UKESM1 by up to 0.75 K , and a significant improvement in the historical GMST record is simulated, with the magnitude of the cold bias reduced by over 50 %. The warmer climate increases ocean heat uptake in the Northern Hemisphere oceans and reduces Arctic sea ice, which is in better agreement with observations. Changes to the aerosol and related cloud properties are a driver of the improved GMST simulation despite only a modest reduction in the magnitude of the negative aerosol ERF (which increases by +0.08 Wm-2). The total anthropogenic ERF increases from 1.76 Wm-2 in UKESM1 to 1.84 Wm-2 in UKESM1.1. The effective climate sensitivity (5.27 K) and transient climate response (2.64 K) remain largely unchanged from UKESM1 (5.36 and 2.76 K respectively). [ABSTRACT FROM AUTHOR]

Published
2023
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14. Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations

Author

Yool, Andrew, Palmiéri, Julien, Jones, Colin G., De Mora, Lee, Kuhlbrodt, Till, Popova, Ekatarina E., Nurser, A. J. George, Hirschi, Joel, Blaker, Adam T., Coward, Andrew C., Blockley, Edward W., Sellar, Alistair A., Yool, Andrew, Palmiéri, Julien, Jones, Colin G., De Mora, Lee, Kuhlbrodt, Till, Popova, Ekatarina E., Nurser, A. J. George, Hirschi, Joel, Blaker, Adam T., Coward, Andrew C., Blockley, Edward W., and Sellar, Alistair A.

Abstract

The ocean plays a key role in modulating the climate of the Earth system (ES). At the present time it is also a major sink both for the carbon dioxide (CO2) released by human activities and for the excess heat driven by the resulting atmospheric greenhouse effect. Understanding the ocean's role in these processes is critical for model projections of future change and its potential impacts on human societies. A necessary first step in assessing the credibility of such future projections is an evaluation of their performance against the present state of the ocean. Here we use a range of observational fields to validate the physical and biogeochemical performance of the ocean component of UKESM1, a new Earth system model (ESM) for CMIP6 built upon the HadGEM3-GC3.1 physical climate model. Analysis focuses on the realism of the ocean's physical state and circulation, its key elemental cycles, and its marine productivity. UKESM1 generally performs well across a broad spectrum of properties, but it exhibits a number of notable biases. Physically, these include a global warm bias inherited from model spin-up, excess northern sea ice but insufficient southern sea ice and sluggish interior circulation. Biogeochemical biases found include shallow remineralization of sinking organic matter, excessive iron stress in regions such as the equatorial Pacific, and generally lower surface alkalinity that results in decreased surface and interior dissolved inorganic carbon (DIC) concentrations. The mechanisms driving these biases are explored to identify consequences for the behaviour of UKESM1 under future climate change scenarios and avenues for model improvement. Finally, across key biogeochemical properties, UKESM1 improves in performance relative to its CMIP5 precursor and performs well alongside its fellow members of the CMIP6 ensemble.

Published
2021
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15. Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations

Author

Yool, Andrew, primary, Palmiéri, Julien, additional, Jones, Colin G., additional, de Mora, Lee, additional, Kuhlbrodt, Till, additional, Popova, Ekatarina E., additional, Nurser, A. J. George, additional, Hirschi, Joel, additional, Blaker, Adam T., additional, Coward, Andrew C., additional, Blockley, Edward W., additional, and Sellar, Alistair A., additional

Published
2021
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16. UKESM1.1: Development and evaluation of an updated configuration of the UK Earth System Model.

Author

Mulcahy, Jane P., Jones, Colin G., Rumbold, Steven T., Kuhlbrodt, Till, Dittus, Andrea J., Blockley, Edward W., Yool, Andrew, Walton, Jeremy, Hardacre, Catherine, Andrews, Timothy, Bodas-Salcedo, Alejandro, Stringer, Marc, de Mora, Lee, Harris, Phil, Hill, Richard, Kelley, Doug, Robertson, Eddy, and Yongming Tang

Subjects
CLIMATE sensitivity, RADIATIVE forcing, SEA ice, SURFACE temperature, AEROSOLS, TWENTIETH century
Abstract

Many CMIP6 models exhibit a substantial cold bias in global mean surface temperature (GMST) in the latter part of the 20th century. An overly strong negative aerosol forcing has been suggested as a leading contributor to this bias. An updated configuration of UKESM1, UKESM1.1, has been developed with the aim of reducing the historical cold bias in this model. Changes implemented include an improved representation of SO2 dry deposition along with several other smaller modifications to the aerosol scheme and a retuning of some uncertain parameters of the fully coupled Earth System Model. The Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, a 6-member historical ensemble and a subset of future scenario simulations are completed. In addition, the total anthropogenic effective radiative forcing (ERF), its components and the effective and transient climate sensitivities are also computed. The UKESM1.1 pre-industrial climate is warmer than UKESM1 by up to 0.75K and a significant improvement in the historical GMST record is simulated with the magnitude of the cold bias reduced by over 50%. The warmer climate increases ocean heat uptake in the northern hemisphere oceans and reduces Arctic sea ice in better agreement with observations. Changes to the aerosol and related cloud properties are the key drivers of the improved GMST simulation despite only a modest change in aerosol ERF (+0.08Wm-2). The total anthropogenic ERF increases from 1.76Wm-2 in UKESM1 to 1.84Wm-2 in UKESM1.1. The effective climate sensitivity (5.27K) and transient climate response (2.64K) remain largely unchanged from UKESM1 (5.36K and 2.76K respectively). [ABSTRACT FROM AUTHOR]

Published
2022
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17. Earth System Model Evaluation Tool (ESMValTool) v2.0 - An extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Author

Eyring, Veronika, Bock, Lisa, Lauer, Axel, Righi, Mattia, Schlund, Manuel, Andela, Bouwe, Arnone, Enrico, Bellprat, Omar, Carvalhais, Nuno, Cionni, Irene, Cortesi, Nicola, Crezee, Bas, L. Davin, Edouard, Davini, Paolo, Debeire, Kevin, De Mora, Lee, Deser, Clara, Docquier, David, Earnshaw, Paul, Ehbrecht, Carsten, K. Gier, Bettina, Gonzalez-Reviriego, Nube, Goodman, Paul, Hagemann, Stefan, Hardiman, Steven, Hassler, Birgit, Hunter, Alasdair, Kadow, Christopher, Kindermann, Stephan, Koirala, Sujan, Koldunov, Nikolay, Lejeune, Quentin, Lembo, Valerio, Lovato, Tomas, Lucarini, Valerio, Müller, Benjamin, Pandde, Amarjiit, Phillips, Adam, Predoi, Valeriu, Russell, Joellen, Sellar, Alistair, Serva, Federico, Stacke, Tobias, Swaminathan, Ranjini, Vegas-Regidor, Javier, Von Hardenberg, Jost, Weigel, Katja, Zimmermann, Klaus, DCEA - Departamento de Ciências e Engenharia do Ambiente, Eyring, V., Bock, L., Lauer, A., Righi, M., Schlund, M., Andela, B., Arnone, E., Bellprat, O., Carvalhais, N., Cionni, I., Cortesi, N., Crezee, B., L. Davin, E., Davini, P., Debeire, K., De Mora, L., Deser, C., Docquier, D., Earnshaw, P., Ehbrecht, C., K. Gier, B., Gonzalez-Reviriego, N., Goodman, P., Hagemann, S., Hardiman, S., Hassler, B., Hunter, A., Kadow, C., Kindermann, S., Koirala, S., Koldunov, N., Lejeune, Q., Lembo, V., Lovato, T., Lucarini, V., Muller, B., Pandde, A., Phillips, A., Predoi, V., Russell, J., Sellar, A., Serva, F., Stacke, T., Swaminathan, R., Vegas-Regidor, J., Von Hardenberg, J., Weigel, K., and Zimmermann, K.

Subjects
COUPLED MODEL, LAND-COVER CHANGES, INTERNAL VARIABILITY, Earth and Planetary Sciences(all), VERSION 2, NORTH-ATLANTIC, PART I, Modelling and Simulation, GLOBAL CLIMATE MODEL, VEGETATION COVER, SDG 13 - Climate Action, MIDLATITUDE ATMOSPHERIC VARIABILITY, WEATHER REGIMES, SDG 15 - Life on Land
Abstract

824084 grant no. 274762653 grant no. CMIP6-DICAD The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of (1) an easy-to-install, well-documented Python package providing the core functionalities (ESMValCore) that performs common preprocessing operations and (2) a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability, the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top-down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klimarechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5. publishersversion published

Published
2020
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18. Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview

Author

Righi, Mattia, Andela, Bouwe, Eyring, Veronika, Lauer, Axel, Predoi, Valeriu, Schlund, Manuel, Vegas-Regidor, Javier, Bock, Lisa, Brötz, Björn, de Mora, Lee, Diblen, Faruk, Dreyer, Laura, Drost, Niels, Earnshaw, Paul, Hassler, Birgit, Koldunov, Nikolay, Little, Bill, Loosveldt Tomas, Saskia, and Zimmermann, Klaus

Subjects
Institut für Physik der Atmosphäre, Climate Research, Earth System Modelling, Erdsystemmodell -Evaluation und -Analyse, CMIP, Evaluation, Klimatforskning
Abstract

This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of 3. The performance can be further improved, up to a factor of more than 30, when the newly introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, the setting of user-configurable options based on modern language formats, and high code quality standards following the best practices for software development.

Published
2020

19. ESMValTool v2.0 Extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Author

Eyring, Veronika, Bock, Lisa, Lauer, Axel, Righi, Mattia, Schlund, Manuel, Andela, Bouwe, Arnone, Enrico, Bellprat, Omar, Brötz, Björn, Caron, Louis-Philippe, Carvalhais, Nuno, Cionni, Irene, Cortesi, Nicola, Crezee, Bas, Davin, Edouard, Davini, Paolo, Debeire, Kevin, de Mora, Lee, Deser, Clara, Docquier, David, Earnshaw, Paul, Ehbrecht, Carsten, Gier, Bettina, Gonzalez-Reviriego, Nube, Goodman, Paul, Hagemann, Stefan, Hardiman, Steven, Hassler, Birgit, Hunter, Alasdair, Kadow, Christopher, Kindermann, Stephan, Koirala, Sujan, Koldunov, Nikolay, Lejuene, Quentin, Lembo, Valerio, Lovato, Tomas, Lucarini, Valerio, Massonnet, Francois, Müller, Benjamin, Pandde, Amarjiit, Pérez-Zanón, Nuria, Phillips, Adam S., Predoi, Valeriu, Russell, Joellen L., Sellar, Alistair, Serva, Federico, Stacke, Tobias, Swaminathan, Ranjini, Torralba, Véronica, Vegas-Regidor, Javier, von Hardenberg, Jost, Weigel, Katja, and Zimmermann, Klaus

Subjects
evaluation, diagnostics, Earth System Models (ESMs), Erdsystemmodell -Evaluation und -Analyse, Coupled Model intercomparison Project (CMIP), ESMValTool v2.0
Abstract

The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility. It consists of an easy-to-install, well documented Python package providing the core functionalities (ESMValCore) that performs common pre-processing operations and a diagnostic part that includes tailored diagnostics and performance metrics for specific scientific applications. Here we describe large-scale diagnostics of the second major release of the tool that supports the evaluation of ESMs participating in CMIP Phase 6 (CMIP6). ESMValTool v2.0 includes a large collection of diagnostics and performance metrics for atmospheric, oceanic, and terrestrial variables for the mean state, trends, and variability. ESMValTool v2.0 also successfully reproduces figures from the evaluation and projections chapters of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and incorporates updates from targeted analysis packages, such as the NCAR Climate Variability Diagnostics Package for the evaluation of modes of variability the Thermodynamic Diagnostic Tool (TheDiaTo) to evaluate the energetics of the climate system, as well as parts of AutoAssess that contains a mix of top-down performance metrics. The tool has been fully integrated into the Earth System Grid Federation (ESGF) infrastructure at the Deutsches Klima Rechenzentrum (DKRZ) to provide evaluation results from CMIP6 model simulations shortly after the output is published to the CMIP archive. A result browser has been implemented that enables advanced monitoring of the evaluation results by a broad user community at much faster timescales than what was possible in CMIP5.

Published
2020

20. Implementation of U.K. Earth system models for CMIP6

Author

Sellar, Alistair A., Walton, Jeremy, Jones, Colin G., Wood, Richard, Abraham, Nathan Luke, Andrejczuk, Miroslaw, Andrews, Martin B., Andrews, Timothy, Archibald, Alex T., de Mora, Lee, Dyson, Harold, Elkington, Mark, Ellis, Richard, Florek, Piotr, Good, Peter, Gohar, Laila, Haddad, Stephen, Hardiman, Steven C., Hogan, Emma, Iwi, Alan, Jones, Christopher D., Johnson, Ben, Kelley, Douglas I., Kettleborough, Jamie, Knight, Jeff R., Köhler, Marcus O., Kuhlbrodt, Till, Liddicoat, Spencer, Linova‐Pavlova, Irina, Mizielinski, Matthew S., Morgenstern, Olaf, Mulcahy, Jane, Neininger, Erica, O'Connor, Fiona M., Petrie, Ruth, Ridley, Jeff, Rioual, Jean‐Christophe, Roberts, Malcolm, Robertson, Eddy, Rumbold, Steven, Seddon, Jon, Shepherd, Harry, Shim, Sungbo, Stephens, Ag, Teixiera, Joao C., Tang, Yongming, Williams, Jonny, Wiltshire, Andy, Griffiths, Paul T., Sellar, Alistair A., Walton, Jeremy, Jones, Colin G., Wood, Richard, Abraham, Nathan Luke, Andrejczuk, Miroslaw, Andrews, Martin B., Andrews, Timothy, Archibald, Alex T., de Mora, Lee, Dyson, Harold, Elkington, Mark, Ellis, Richard, Florek, Piotr, Good, Peter, Gohar, Laila, Haddad, Stephen, Hardiman, Steven C., Hogan, Emma, Iwi, Alan, Jones, Christopher D., Johnson, Ben, Kelley, Douglas I., Kettleborough, Jamie, Knight, Jeff R., Köhler, Marcus O., Kuhlbrodt, Till, Liddicoat, Spencer, Linova‐Pavlova, Irina, Mizielinski, Matthew S., Morgenstern, Olaf, Mulcahy, Jane, Neininger, Erica, O'Connor, Fiona M., Petrie, Ruth, Ridley, Jeff, Rioual, Jean‐Christophe, Roberts, Malcolm, Robertson, Eddy, Rumbold, Steven, Seddon, Jon, Shepherd, Harry, Shim, Sungbo, Stephens, Ag, Teixiera, Joao C., Tang, Yongming, Williams, Jonny, Wiltshire, Andy, and Griffiths, Paul T.

Abstract

We describe the scientific and technical implementation of two models for a core set of experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models used are the physical atmosphere‐land‐ocean‐sea ice model HadGEM3‐GC3.1 and the Earth system model UKESM1 which adds a carbon‐nitrogen cycle and atmospheric chemistry to HadGEM3‐GC3.1. The model results are constrained by the external boundary conditions (forcing data) and initial conditions. We outline the scientific rationale and assumptions made in specifying these. Notable details of the implementation include an ozone redistribution scheme for prescribed ozone simulations (HadGEM3‐GC3.1) to avoid inconsistencies with the model's thermal tropopause, and land use change in dynamic vegetation simulations (UKESM1) whose influence will be subject to potential biases in the simulation of background natural vegetation. We discuss the implications of these decisions for interpretation of the simulation results. These simulations are expensive in terms of human and CPU resources and will underpin many further experiments; we describe some of the technical steps taken to ensure their scientific robustness and reproducibility.

Published
2020

21. A system of metrics for the assessment and improvement of aquatic ecosystem models

Author

Biological Sciences, Hipsey, Matthew R., Gal, Gideon, Arhonditsis, George B., Carey, Cayelan C., Elliott, J. Alex, Frassl, Marieke A., Janse, Jan H., de Mora, Lee, Robson, Barbara J., Biological Sciences, Hipsey, Matthew R., Gal, Gideon, Arhonditsis, George B., Carey, Cayelan C., Elliott, J. Alex, Frassl, Marieke A., Janse, Jan H., de Mora, Lee, and Robson, Barbara J.

Abstract

In this paper, we introduce the CSPS framework for the hierarchical assessment of aquatic ecosystem models built on a range of metrics and characteristic signatures relevant to aquatic ecosystem condition. The framework is comprised of four levels: 0) conceptual validation; 1) comparison of simulated state variables with observations ('state validation'); 2) comparison of fluxes with measured process rates ('process validation'); and 3) assessment of system-level emergent properties, patterns and relationships ('system validation'). Of these, only levels 0 and 1 are routinely undertaken at present. To highlight a diverse range of contexts relevant to the aquatic ecosystem modelling community, we present several case studies of improved validation approaches using the level 0-3 assessment hierarchy. We envision that the community-driven adoption of these metrics will lead to more rigorously assessed models, ultimately accelerating advances in model structure and function, and improved confidence in model predictions.

Published
2020

22. Supplementary material to "Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 Historical simulations"

Author

Yool, Andrew, primary, Palmiéri, Julien, additional, Jones, Colin G., additional, de Mora, Lee, additional, Kuhlbrodt, Till, additional, Popova, Ekatarina E., additional, Nurser, A. J. George, additional, Hirschi, Joel, additional, Blaker, Adam T., additional, Coward, Andrew C., additional, Blockley, Edward W., additional, and Sellar, Alistair A., additional

Published
2020
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23. Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 Historical simulations

Author

Yool, Andrew, primary, Palmiéri, Julien, additional, Jones, Colin G., additional, de Mora, Lee, additional, Kuhlbrodt, Till, additional, Popova, Ekatarina E., additional, Nurser, A. J. George, additional, Hirschi, Joel, additional, Blaker, Adam T., additional, Coward, Andrew C., additional, Blockley, Edward W., additional, and Sellar, Alistair A., additional

Published
2020
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28. Implementation of U.K. Earth System Models for CMIP6

Author

Sellar, Alistair A., primary, Walton, Jeremy, additional, Jones, Colin G., additional, Wood, Richard, additional, Abraham, Nathan Luke, additional, Andrejczuk, Miroslaw, additional, Andrews, Martin B., additional, Andrews, Timothy, additional, Archibald, Alex T., additional, de Mora, Lee, additional, Dyson, Harold, additional, Elkington, Mark, additional, Ellis, Richard, additional, Florek, Piotr, additional, Good, Peter, additional, Gohar, Laila, additional, Haddad, Stephen, additional, Hardiman, Steven C., additional, Hogan, Emma, additional, Iwi, Alan, additional, Jones, Christopher D., additional, Johnson, Ben, additional, Kelley, Douglas I., additional, Kettleborough, Jamie, additional, Knight, Jeff R., additional, Köhler, Marcus O., additional, Kuhlbrodt, Till, additional, Liddicoat, Spencer, additional, Linova‐Pavlova, Irina, additional, Mizielinski, Matthew S., additional, Morgenstern, Olaf, additional, Mulcahy, Jane, additional, Neininger, Erica, additional, O'Connor, Fiona M., additional, Petrie, Ruth, additional, Ridley, Jeff, additional, Rioual, Jean‐Christophe, additional, Roberts, Malcolm, additional, Robertson, Eddy, additional, Rumbold, Steven, additional, Seddon, Jon, additional, Shepherd, Harry, additional, Shim, Sungbo, additional, Stephens, Ag, additional, Teixiera, Joao C., additional, Tang, Yongming, additional, Williams, Jonny, additional, Wiltshire, Andy, additional, and Griffiths, Paul T., additional

Published
2020
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30. ESMValCore: analyzing CMIP data made easy

Author

Andela, Bouwe, primary, Bock, Lisa, additional, Brötz, Björn, additional, Diblen, Faruk, additional, Dreyer, Laura, additional, Drost, Niels, additional, Earnshaw, Paul, additional, Eyring, Veronika, additional, Hassler, Birgit, additional, Koldunov, Nikolay, additional, Lauer, Axel, additional, Little, Bill, additional, Loosveldt-Tomas, Saskia, additional, de Mora, Lee, additional, Predoi, Valeriu, additional, Righi, Mattia, additional, Schlund, Manuel, additional, Vegas-Regidor, Javier, additional, and Zimmermann, Klaus, additional

Published
2020
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31. Earth System Model Evaluation Tool (ESMValTool) v2.0 – technical overview

Author

Righi, Mattia, primary, Andela, Bouwe, additional, Eyring, Veronika, additional, Lauer, Axel, additional, Predoi, Valeriu, additional, Schlund, Manuel, additional, Vegas-Regidor, Javier, additional, Bock, Lisa, additional, Brötz, Björn, additional, de Mora, Lee, additional, Diblen, Faruk, additional, Dreyer, Laura, additional, Drost, Niels, additional, Earnshaw, Paul, additional, Hassler, Birgit, additional, Koldunov, Nikolay, additional, Little, Bill, additional, Loosveldt Tomas, Saskia, additional, and Zimmermann, Klaus, additional

Published
2020
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36. CMIP6 Evaluation with the ESMValTool

Author

Bock, Lisa, Eyring, Veronika, Lauer, Axel, Righi, Mattia, Andela, Bouwe, Little, Bill, de Mora, Lee, Pedroi, Valeriu, Vegas-Regidor, Javier, Brötz, Björn, Hassler, Birgit, and Schlund, Manuel

Subjects
model evaluation, ESMValTool, CMIP6
Published
2019

37. UKESM1: description and evaluation of the U.K. Earth System Model

Author

Sellar, Alistair A., Jones, Colin G., Mulcahy, Jane P., Tang, Yongming, Yool, Andrew, Wiltshire, Andy, O'Connor, Fiona M., Stringer, Marc, Hill, Richard, Palmieri, Julien, Woodward, Stephanie, de Mora, Lee, Kuhlbrodt, Till, Rumbold, Steven T., Kelley, Douglas I., Ellis, Rich, Johnson, Colin E., Walton, Jeremy, Abraham, Nathan Luke, Andrews, Martin B., Andrews, Timothy, Archibald, Alex T., Berthou, Segolene, Burke, Eleanor, Blockley, Ed, Carslaw, Ken, Dalvi, Mohit, Edwards, John, Folberth, Gerd A., Gedney, Nicola, Griffiths, Paul T., Harper, Anna B., Hendry, Maggie A., Hewitt, Alan J., Johnson, Ben, Jones, Andy, Jones, Chris D., Keeble, James, Liddicoat, Spencer, Mordenstern, Olaf, Parker, Robert J., Predoi, Valeriu, Robertson, Eddy, Siahaan, Antony, Smith, Robin S., Swaminathan, Ranjini, Woodhouse, Matthew T., Zeng, Guang, Zerroukat, Mohamed, Sellar, Alistair A., Jones, Colin G., Mulcahy, Jane P., Tang, Yongming, Yool, Andrew, Wiltshire, Andy, O'Connor, Fiona M., Stringer, Marc, Hill, Richard, Palmieri, Julien, Woodward, Stephanie, de Mora, Lee, Kuhlbrodt, Till, Rumbold, Steven T., Kelley, Douglas I., Ellis, Rich, Johnson, Colin E., Walton, Jeremy, Abraham, Nathan Luke, Andrews, Martin B., Andrews, Timothy, Archibald, Alex T., Berthou, Segolene, Burke, Eleanor, Blockley, Ed, Carslaw, Ken, Dalvi, Mohit, Edwards, John, Folberth, Gerd A., Gedney, Nicola, Griffiths, Paul T., Harper, Anna B., Hendry, Maggie A., Hewitt, Alan J., Johnson, Ben, Jones, Andy, Jones, Chris D., Keeble, James, Liddicoat, Spencer, Mordenstern, Olaf, Parker, Robert J., Predoi, Valeriu, Robertson, Eddy, Siahaan, Antony, Smith, Robin S., Swaminathan, Ranjini, Woodhouse, Matthew T., Zeng, Guang, and Zerroukat, Mohamed

Abstract

We document the development of the first version of the United Kingdom Earth System Model UKESM1. The model represents a major advance on its predecessor HadGEM2‐ES, with enhancements to all component models and new feedback mechanisms. These include: a new core physical model with a well‐resolved stratosphere; terrestrial biogeochemistry with coupled carbon and nitrogen cycles and enhanced land management; tropospheric‐stratospheric chemistry allowing the holistic simulation of radiative forcing from ozone, methane and nitrous oxide; two‐moment, five‐species, modal aerosol; and ocean biogeochemistry with two‐way coupling to the carbon cycle and atmospheric aerosols. The complexity of coupling between the ocean, land and atmosphere physical climate and biogeochemical cycles in UKESM1 is unprecedented for an Earth system model. We describe in detail the process by which the coupled model was developed and tuned to achieve acceptable performance in key physical and Earth system quantities, and discuss the challenges involved in mitigating biases in a model with complex connections between its components. Overall the model performs well, with a stable pre‐industrial state, and good agreement with observations in the latter period of its historical simulations. However, global mean surface temperature exhibits stronger‐than‐observed cooling from 1950 to 1970, followed by rapid warming from 1980 to 2014. Metrics from idealised simulations show a high climate sensitivity relative to previous generations of models: equilibrium climate sensitivity (ECS) is 5.4 K, transient climate response (TCR) ranges from 2.68 K to 2.85 K, and transient climate response to cumulative emissions (TCRE) is 2.49 K/TtC to 2.66 K/TtC.

Published
2019

38. ESMValTool v2.0 – Extended set of large-scale diagnostics for quasi-operational and comprehensive evaluation of Earth system models in CMIP

Author

Eyring, Veronika, primary, Bock, Lisa, additional, Lauer, Axel, additional, Righi, Mattia, additional, Schlund, Manuel, additional, Andela, Bouwe, additional, Arnone, Enrico, additional, Bellprat, Omar, additional, Brötz, Björn, additional, Caron, Louis-Phillippe, additional, Carvalhais, Nuno, additional, Cionni, Irene, additional, Cortesi, Nicola, additional, Crezee, Bas, additional, Davin, Edouard, additional, Davini, Paolo, additional, Debeire, Kevin, additional, de Mora, Lee, additional, Deser, Clara, additional, Docquier, David, additional, Earnshaw, Paul, additional, Ehbrecht, Carsten, additional, Gier, Bettina K., additional, Gonzalez-Reviriego, Nube, additional, Goodman, Paul, additional, Hagemann, Stefan, additional, Hardiman, Steven, additional, Hassler, Birgit, additional, Hunter, Alasdair, additional, Kadow, Christopher, additional, Kindermann, Stephan, additional, Koirala, Sujan, additional, Koldunov, Nikolay V., additional, Lejeune, Quentin, additional, Lembo, Valerio, additional, Lovato, Tomas, additional, Lucarini, Valerio, additional, Massonnet, Francois, additional, Müller, Benjamin, additional, Pandde, Amarjiit, additional, Pérez-Zanón, Nuria, additional, Phillips, Adam, additional, Predoi, Valeriu, additional, Russell, Joellen, additional, Sellar, Alistair, additional, Serva, Federico, additional, Stacke, Tobias, additional, Swaminathan, Ranjini, additional, Torralba, Verónica, additional, Vegas-Regidor, Javier, additional, von Hardenberg, Jost, additional, Weigel, Katja, additional, and Zimmermann, Klaus, additional

Published
2019
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39. Supplementary material to "ESMValTool v2.0 – Technical overview"

Author

Righi, Mattia, primary, Andela, Bouwe, additional, Eyring, Veronika, additional, Lauer, Axel, additional, Predoi, Valeriu, additional, Schlund, Manuel, additional, Vegas-Regidor, Javier, additional, Bock, Lisa, additional, Brötz, Björn, additional, de Mora, Lee, additional, Diblen, Faruk, additional, Dreyer, Laura, additional, Drost, Niels, additional, Earnshaw, Paul, additional, Hassler, Birgit, additional, Koldunov, Nikolay, additional, Little, Bill, additional, Loosveldt Tomas, Saskia, additional, and Zimmermann, Klaus, additional

Published
2019
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40. ESMValTool v2.0 – Technical overview

Author

Righi, Mattia, primary, Andela, Bouwe, additional, Eyring, Veronika, additional, Lauer, Axel, additional, Predoi, Valeriu, additional, Schlund, Manuel, additional, Vegas-Regidor, Javier, additional, Bock, Lisa, additional, Brötz, Björn, additional, de Mora, Lee, additional, Diblen, Faruk, additional, Dreyer, Laura, additional, Drost, Niels, additional, Earnshaw, Paul, additional, Hassler, Birgit, additional, Koldunov, Nikolay, additional, Little, Bill, additional, Loosveldt Tomas, Saskia, additional, and Zimmermann, Klaus, additional

Published
2019
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41. AccessLab: Workshops to broaden access to scientific research

Author

Griffiths, Amber G. F., primary, Modinou, Ivvet, additional, Heslop, Clio, additional, Brand, Charlotte, additional, Weatherill, Aidan, additional, Baker, Kate, additional, Hughes, Anna E., additional, Lewis, Jen, additional, de Mora, Lee, additional, Mynott, Sara, additional, Roberts, Katherine E., additional, and Griffiths, David J., additional

Published
2019
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42. BGC-val: a model- and grid-independent Python toolkit to evaluate marine biogeochemical models

Author

de Mora, Lee, Yool, Andrew, Palmieri, Julien, Sellar, Alistair, Kuhlbrodt, Till, Popova, Ekaterina, Jones, Colin, Allen, J. Icarus, de Mora, Lee, Yool, Andrew, Palmieri, Julien, Sellar, Alistair, Kuhlbrodt, Till, Popova, Ekaterina, Jones, Colin, and Allen, J. Icarus

Abstract

The biogeochemical evaluation toolkit, BGC-val, is a model- and grid-independent Python toolkit that has been built to evaluate marine biogeochemical models using a simple interface. Here, we present the ideas that motivated the development of the BGC-val software framework, introduce the code structure, and show some applications of the toolkit using model results from the Fifth Climate Model Intercomparison Project (CMIP5). A brief outline of how to access and install the repository is presented in Appendix , but the specific details on how to use the toolkit are kept in the code repository. The key ideas that directed the toolkit design were model and grid independence, front-loading analysis functions and regional masking, interruptibility, and ease of use. We present each of these goals, why they were important, and what we did to address them. We also present an outline of the code structure of the toolkit illustrated with example plots produced by the toolkit. After describing BGC-val, we use the toolkit to investigate the performance of the marine physical and biogeochemical quantities of the CMIP5 models and highlight some predictions about the future state of the marine ecosystem under a business-as-usual CO2 concentration scenario (RCP8.5).

Published
2018

43. Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 Historical simulations.

Author

Yool, Andrew, Palmiéri, Julien, Jones, Colin G., de Mora, Lee, Kuhlbrodt, Till, Popova, Ekatarina E., Nurser, A. J. George, Hirschi, Joel, Blaker, Adam T., Coward, Andrew C., Blockley, Edward W., and Sellar, Alistair A.

Subjects
GREENHOUSE effect, CARBON dioxide sinks, OCEAN, PHYSICAL mobility, MARINE productivity
Abstract

The ocean plays a key role in modulating the climate of the Earth system (ES). At the present time it is also a major sink both for the carbon dioxide (CO2) released by human activities as well as for the excess heat driven by the resulting atmospheric greenhouse effect. Understanding the ocean's role in these processes is critical for model projections of future change and its potential impacts on human societies. A necessary first step in assessing the credibility of such future projections is an evaluation of their performance against the present state of the ocean. Here we use a range of observational properties to validate the physical and biogeochemical performance of the ocean component of UKESM1, a new Earth system (ESM) for CMIP6 built upon the HadGEM3 physical climate model. Analysis focuses on the realism of the ocean's physical state and circulation, its key elemental cycles, and its marine productivity. UKESM1 generally performs well across a broad spectrum of properties, but it exhibits a number of notable biases. Physically, these include a global warm bias inherited from model spin-up, excess northern sea-ice but insufficient southern sea-ice, and sluggish interior circulation. Biogeochemical biases found include shallow remineralisation of sinking organic matter, excessive iron stress in regions such as the Equatorial Pacific, and generally lower surface alkalinity that results in decreased surface and interior dissolved inorganic carbon (DIC) concentrations. The mechanisms driving these biases are explored to identify consequences for the behaviour of UKESM1 under future climate scenarios, and avenues for model improvement. Finally, across key biogeochemical properties, UKESM1 improves in performance relative to its CMIP5 precursor, and compares favourably to fellow members of the CMIP6 ensemble. [ABSTRACT FROM AUTHOR]

Published
2020
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47. Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Author

Lee, Younjoo J., Matrai, Patricia A., Friedrichs, Marjorie A.M., Saba, Vincent S., Aumount, Olivier, Babin, Marcel, Buitenhuis, Erik T., Chevallier, Matthieu, de Mora, Lee, Dessert, Morgane, Dunne, John P., Ellingsen, Ingrid H., Feldman, Doron, Frouin, Robert, Gehlen, Marion, Gorguess, Thomas, Ilyina, Tatiana, Jin, Meibing, John, Jasmin G., Lawrence, Jon, Manizza, Manfredi, Menkes, Christophe E., Perruche, Coralie, Le Fouest, Vincent, Popova, Ekaterina E., Romanou, Anastasia, Samuelsen, Annette, Schwinger, Jörg, Séférian, Roland, Stock, Charles A., Tjiputra, Jerry, Tremblay, L. Bruno, Ueyoshi, Kyozo, Vichi, Marcello, Yool, Andrew, Zhang, Jinlun, Bigelow Laboratory for Ocean Sciences, Virginia Institute of Marine Science (VIMS), NOAA National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Nucleus for European Modeling of the Ocean (NEMO R&D ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Takuvik Joint International Laboratory ULAVAL-CNRS, Université Laval [Québec] (ULaval)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), School of Environmental Sciences [Norwich], University of East Anglia [Norwich] (UEA), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Plymouth Marine Laboratory (PML), Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), SINTEF Fisheries and Aquaculture, NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Scripps Institution of Oceanography (SIO - UC San Diego), University of California [San Diego] (UC San Diego), University of California (UC)-University of California (UC), Modelling the Earth Response to Multiple Anthropogenic Interactions and Dynamics (MERMAID), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Max Planck Institute for Meteorology (MPI-M), Max-Planck-Gesellschaft, International Arctic Research Center (IARC), University of Alaska [Fairbanks] (UAF), National Oceanography Centre [Southampton] (NOC), University of Southampton, PaleoEnvironnements et PaleobioSphere (PEPS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Mercator Océan, Société Civile CNRS Ifremer IRD Météo-France SHOM, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), LIttoral ENvironnement et Sociétés (LIENSs), La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Nansen Environmental and Remote Sensing Center [Bergen] (NERSC), Bjerknes Centre for Climate Research (BCCR), Department of Biological Sciences [Bergen] (BIO / UiB), University of Bergen (UiB)-University of Bergen (UiB), Department of Atmospheric and Oceanic Sciences [Montréal], McGill University = Université McGill [Montréal, Canada], Department of Oceanography [Cape Town], University of Cape Town, Applied Physics Laboratory [Seattle] (APL-UW), University of Washington [Seattle], Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Centre National de la Recherche Scientifique (CNRS)-Université Laval [Québec] (ULaval), Météo France-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Plymouth Marine Laboratory, Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Scripps Institution of Oceanography (SIO), University of California-University of California, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), LIttoral ENvironnement et Sociétés - UMRi 7266 (LIENSs), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS), Naval Postgraduate School (U.S.), and Oceanography

Subjects
Arctic models underestimated net primary productivity (NPP) but overestimated nitrate, mixed layer depth, and euphotic depth, [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry, Arctic NPP model skill was greatest in low production regions, Arctic NPP model skill was constrained by different environmental factors in different Arctic Ocean regions, Article, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract

The article of record as published may be found at http://dx.doi.org/10.1002/2016JC011993 Upon publication, the in situ data will be available for academic purposes through the NASA SeaWiFS Bio-optical Archive and Storage System (http:// seabass.gsfc.nasa.gov/), including NPP, NO3, and Zeu. The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling. National Aeronautics and Space Agency (NASA) Ocean Biology and Biogeochemistry (OBB) The project ‘‘Green Mercator’’ National Program CNRS/LEFE/INSU. NSF Office of Polar Programs FP7 MyOcean2 PAVE (Polish-Norwegian Research Program) Norwegian Supercomputing Project (NOTUR2) Research Council of Norway funded project ORGANIC NASA Cryosphere program CNRM-CM climate model Météo-France/DSI supercomputing Ocean Biology and Biogeochemistry (OBB) NNX13AE81G NSF Office of Polar Programs PLR- 1417925 NSF Office of Polar Programs PLR-1416920 FP7 MyOcean2 (project number 283367) Research Council of Norway funded project ORGANIC (239965/RU) NASA Cryosphere program (NNX15AG68G).

Published
2016
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48. Expanding Aquatic Observations through Recreation

Author

Brewin, Robert J. W., primary, Hyder, Kieran, additional, Andersson, Andreas J., additional, Billson, Oliver, additional, Bresnahan, Philip J., additional, Brewin, Thomas G., additional, Cyronak, Tyler, additional, Dall'Olmo, Giorgio, additional, de Mora, Lee, additional, Graham, George, additional, Jackson, Thomas, additional, and Raitsos, Dionysios E., additional

Published
2017
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49. ESMValTool v2.0 - Technical overview.

Author

Righi, Mattia, Andela, Bouwe, Eyring, Veronika, Lauer, Axel, Predoi, Valeriu, Schlund, Manuel, Vegas-Regidor, Javier, Bock, Lisa, Brötz, Björn, de Mora, Lee, Diblen, Faruk, Dreyer, Laura, Drost, Niels, Earnshaw, Paul, Hassler, Birgit, Koldunov, Nikolay, Little, Bill, Tomas, Saskia Loosveldt, and Zimmermann, Klaus

Subjects
COMPUTER software development, KEY performance indicators (Management), INFORMATION storage & retrieval systems, QUALITY standards, MODERN languages
Abstract

This paper describes the second major release of the Earth System Model Evaluation Tool (ESMValTool), a community diagnostic and performance metrics tool for the evaluation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). Compared to version 1.0, released in 2016, ESMValTool version 2.0 (v2.0) features a brand new design, with an improved interface and a revised preprocessor. It also features a significantly enhanced diagnostic part that is described in three companion papers. The new version of the ESMValTool has been specifically developed to target the increased data volume of CMIP Phase 6 (CMIP6) and the related challenges posed by the analysis and the evaluation of output from multiple high-resolution or complex ESMs. The new version takes advantage of state-of-the-art computational libraries and methods to deploy an efficient and user-friendly data processing. Common operations on the input data (such as regridding or computation of multi-model statistics) are centralized in a highly optimized preprocessor, which allows applying a series of preprocessing functions before diagnostics scripts are applied for in-depth scientific analysis of the model output. Performance tests conducted on a set of standard diagnostics show that the new version is faster than its predecessor by about a factor of three. The performance can be further improved, up to a factor of more than 30, when the newly-introduced task-based parallelization options are used, which enable the efficient exploitation of much larger computing infrastructures. ESMValTool v2.0 also includes a revised and simplified installation procedure, setting of user configurable options based on modern language formats, and high code quality standards following the best practices for software development. [ABSTRACT FROM AUTHOR]

Published
2019
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50. Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models

Author

Naval Postgraduate School (U.S.), Oceanography, Lee, Younjoo J., Matrai, Patricia A., Friedrichs, Marjorie A.M., Saba, Vincent S., Aumount, Olivier, Babin, Marcel, Buitenhuis, Erik T., Chevallier, Matthieu, de Mora, Lee, Dessert, Morgane, Dunne, John P., Ellingsen, Ingrid H., Feldman, Doron, Frouin, Robert, Gehlen, Marion, Gorguess, Thomas, Ilyina, Tatiana, Jin, Meibing, John, Jasmin G., Lawrence, Jon, Manizza, Manfredi, Menkes, Christophe E., Perruche, Coralie, Le Fouest, Vincent, Popova, Ekaterina E., Romanou, Anastasia, Samuelsen, Annette, Schwinger, Jörg, Séférian, Roland, Stock, Charles A., Tjiputra, Jerry, Tremblay, L. Bruno, Ueyoshi, Kyozo, Vichi, Marcello, Yool, Andrew, Zhang, Jinlun, Naval Postgraduate School (U.S.), Oceanography, Lee, Younjoo J., Matrai, Patricia A., Friedrichs, Marjorie A.M., Saba, Vincent S., Aumount, Olivier, Babin, Marcel, Buitenhuis, Erik T., Chevallier, Matthieu, de Mora, Lee, Dessert, Morgane, Dunne, John P., Ellingsen, Ingrid H., Feldman, Doron, Frouin, Robert, Gehlen, Marion, Gorguess, Thomas, Ilyina, Tatiana, Jin, Meibing, John, Jasmin G., Lawrence, Jon, Manizza, Manfredi, Menkes, Christophe E., Perruche, Coralie, Le Fouest, Vincent, Popova, Ekaterina E., Romanou, Anastasia, Samuelsen, Annette, Schwinger, Jörg, Séférian, Roland, Stock, Charles A., Tjiputra, Jerry, Tremblay, L. Bruno, Ueyoshi, Kyozo, Vichi, Marcello, Yool, Andrew, and Zhang, Jinlun

Abstract

The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959–2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.

Published
2016

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