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3. North Atlantic Simulations in Coordinated Ocean-Ice Reference Experiments Phase II (CORE-II) Part II: Inter-Annual to Decadal Variability

Author

Gokhan Danabasoglu, Steve G Yeager, Who M Kim, Erik Behrens, Mats Bentsen, Daohua Bi, Arne Biastoch, Rainer Bleck, Claus Boening, Alexandra Bozec, Vittorio M Canuto, Christophe Cassou, Eric Chassignet, Andrew C Coward, Sergey Danilov, Nikolay Diansky, Helge Drange, Riccardo Farneti, Elodie Fernandez, Pier Giuseppe Fogli, Gael Forget, Yosuke Fujii, Stephen M Griffies, Anatoly Gusev, Patrick Heimbach, Armando M Howard, Mehmet Ilicak, Thomas Jung, Alicia R Karspeck, Maxwell Kelley, William G Large, Anthony Leboissetier, Jianhua Lu, Gurvan Madec, Simon J Marsland, Simona Masina, Antonio Navarra, A J George Nurser, Anna Pirani, Anastasia Romanou, David Salas y Melia, Bonita L Samuels, Markus Scheinert, Dmitry Sidorenko, Shan Sun, Anne-Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Sophie Valcke, Aurore Voldoire, Qiang Wang, and Igor Yashayaev

Subjects
Oceanography
Abstract

Simulated inter-annual to decadal variability and trends in the North Atlantic for the 1958−2007 period from twenty global ocean - sea-ice coupled models are presented. These simulations are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The study is Part II of our companion paper (Danabasoglu et al., 2014) which documented the mean states in the North Atlantic from the same models. A major focus of the present study is the representation of Atlantic meridional overturning circulation (AMOC) variability in the participating models. Relationships between AMOC variability and those of some other related variables, such as subpolar mixed layer depths, the North Atlantic Oscillation (NAO), and the Labrador Sea upper-ocean hydrographic properties, are also investigated. In general, AMOC variability shows three distinct stages. During the first stage that lasts until the mid- to late-1970s, AMOC is relatively steady, remaining lower than its long-term (1958−2007) mean. Thereafter, AMOC intensifies with maximum transports achieved in the mid- to late-1990s. This enhancement is then followed by a weakening trend until the end of our integration period. This sequence of low frequency AMOC variability is consistent with previous studies. Regarding strengthening of AMOC between about the mid-1970s and the mid-1990s, our results support a previously identified variability mechanism where AMOC intensification is connected to increased deep water formation in the subpolar North Atlantic, driven by NAO-related surface fluxes. The simulations tend to show general agreement in their representations of, for example, AMOC, sea surface temperature (SST), and subpolar mixed layer depth variabilities. In particular, the observed variability of the North Atlantic SSTs is captured well by all models. These findings indicate that simulated variability and trends are primarily dictated by the atmospheric datasets which include the influence of ocean dynamics from nature superimposed onto anthropogenic effects. Despite these general agreements, there are many differences among the model solutions, particularly in the spatial structures of variability patterns. For example, the location of the maximum AMOC variability differs among the models between Northern and Southern Hemispheres.

Published
2015
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4. Competitive Association of Antibiotics with a Clay Mineral and Organoclay Derivatives as a Control of Their Lifetimes in the Environment

Author

Régis Guégan, Minoru Sohmiya, Yoshiyuki Sugahara, Mohammed Boussafir, Laëtitia Fougère, Elodie Fernandez, Tiago De Oliveira, Emilie Destandau, Guégan, Régis, Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Biogéosystèmes Continentaux - UMR7327, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique - CERFACS (CERFACS), Institut de Chimie Organique et Analytique (ICOA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université d'Orléans (UO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Department of Materials and Life Science, Seikei University, Department of Applied Chemistry, Waseda University, Waseda University [Tokyo, Japan], Université d'Orléans (UO), CERFACS, Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut de Chimie du CNRS (INC)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Waseda University

Subjects
General Chemical Engineering, 02 engineering and technology, 010501 environmental sciences, 01 natural sciences, Article, [PHYS.PHYS.PHYS-CHEM-PH] Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph], lcsh:Chemistry, chemistry.chemical_compound, Adsorption, Pulmonary surfactant, Cation-exchange capacity, Organoclay, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, 0105 earth and related environmental sciences, [CHIM.MATE] Chemical Sciences/Material chemistry, Chemistry, Cationic polymerization, [CHIM.MATE]Chemical Sciences/Material chemistry, General Chemistry, 021001 nanoscience & nanotechnology, [SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces, environment, 6. Clean water, Addition/Correction, lcsh:QD1-999, Degradation (geology), Ammonium chloride, [PHYS.PHYS.PHYS-CHEM-PH]Physics [physics]/Physics [physics]/Chemical Physics [physics.chem-ph], 0210 nano-technology, Clay minerals, Nuclear chemistry
Abstract

International audience; A Na-smectite clay mineral (Na-Mt) was exchanged with two concentrations of benzyldimethyltetradecyl ammonium chloride cationic surfactant up to one time the cation exchange capacity. Nonionic organoclay was prepared with polyoxyethylene (20) oleyl ether (Brij-O20) nonionic surfactant at one concentration. The resulting organoclays displayed lateral layer organization of the surfactants within their interlayer space.. The adsorption properties of these organoclays and the starting raw clay mineral were evaluated for three extensively used antibiotic pharmaceutical products: the amoxicillin (AMX), the sulfamethoxazole (SMX), and the trimethoprim (TRI), recognized as recalcitrant compounds to conventional water treatments and to display a complex behavior for different pH and temperature experimental conditions. Besides showing short half-life time with possible degradation by UV radiation, these antibiotics associated with mineral phases cause serious environmental issues of which the toxic effect can be exacerbated in the presence of other chemical compounds. From the set of data obtained by complementary techniques: UV and Fourier transform infrared spectroscopy, high-performance liquid chromatography coupled with mass spectrometry, and X-ray diffraction, it appears that the nonionic organoclay shows its versatility for the adsorption of individual molecules as well as a pool of antibiotics. The mixing of the three antibiotics showing different electric charged species (cations, anions, and zwitterions) mimics the natural context drives to a deep modification of the adsorption behavior onto the different materials that can act as possible carrier mineral phases in aquatic environment. These competition effects can be measured through the significant decrease of the K F Freundlich constants for AMX in the presence of other molecules (or electrolytes), whereas TRI and SMX, by their possible association, create a synergistic effect that favors their adsorption on the whole layered materials.

Published
2018
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5. North Atlantic Simulations in Coordinated Ocean-Ice Reference Experiments Phase II (CORE-II). Part I: Mean States

Author

Gokhan Danabasoglu, Steve G Yeager, David Bailey, Erik Behrens, Mats Bentsen, Daohua Bi, Arne Biastoch, Claus Boning, Alexandra Bozec, Vittorio M Canuto, Christophe Cassou, Eric Chassignet, Andrew C Coward, Sergey Danilov, Nikolay Diansky, Helge Drange, Riccardo Farneti, Elodie Fernandez, Pier Giuseppe Fogli, Gael Forget, Yosuke Fujii, Stephen M Griffies, Anatoly Gusev, Patrick Heimbach, Armando M Howard, Thomas Jung, Maxwell Kelley, William G Large, Anthony Leboissetier, Jianhua Lu, Gurvan Madec, Simon J Marsland, Simona Masina, Antonio Navarra, A J George Nurser, Anna Pirani, David Salas y Melia, Bonita L Samuels, Markus Scheinert, Dmitry Sidorenko, Anne-Marie Treguier, Hiroyuki Tsujino, Petteri Uotila, Sophie Valcke, Aurore Voldoire, and Qiang Wang

Subjects
Earth Resources And Remote Sensing, Meteorology And Climatology
Abstract

Simulation characteristics from eighteen global ocean-sea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60-year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Oceanice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort. atmospheric forcing atmospheric temperatures

Published
2013
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6. An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part II: Liquid freshwater

Author

Sergey Danilov, Christophe Cassou, Rüdiger Gerdes, Claus W. Böning, Mats Bentsen, Yosuke Fujii, Eric P. Chassignet, Hiroyuki Tsujino, Jianhua Lu, Christina Roth, Gokhan Danabasoglu, David A. Bailey, Doroteaciro Iovino, Steve G. Yeager, Yevgeny Aksenov, Helge Drange, A. J. George Nurser, Craig M. Lee, Thomas Jung, Elodie Fernandez, Bonita L. Samuels, Andrew C. Coward, Stephen M. Griffies, William G. Large, Qiang Wang, Benjamin Rabe, Arne Biastoch, Alexandra Bozec, Beth Curry, Xuezhu Wang, Mehmet Ilicak, Simona Masina, David Salas y Mélia, Aurore Voldoire, Pier Giuseppe Fogli, Sophie Valcke, Camille Lique, Paul Spence, and Alexandra Jahn

Subjects
geography, Atmospheric Science, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, 010505 oceanography, Sea ice, Flux, Spatial distribution, Geotechnical Engineering and Engineering Geology, Oceanography, 01 natural sciences, The arctic, Marine Sciences, Freshwater, CORE II atmospheric forcing, 13. Climate action, Climatology, Phase (matter), Arctic Ocean, Computer Science (miscellaneous), Environmental science, Model development, 14. Life underwater, 0105 earth and related environmental sciences
Abstract

The Arctic Ocean simulated in 14 global ocean-sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II) is analyzed in this study. The focus is on the Arctic liquid freshwater (FW) sources and freshwater content (FWC). The models agree on the interannual variability of liquid FW transport at the gateways where the ocean volume transport determines the FW transport variability. The variation of liquid FWC is induced by both the surface FW flux (associated with sea ice production) and lateral liquid FW transport, which are in phase when averaged on decadal time scales. The liquid FWC shows an increase starting from the mid-1990s, caused by the reduction of both sea ice formation and liquid FW export, with the former being more significant in most of the models. The mean state of the FW budget is less consistently simulated than the temporal variability. The model ensemble means of liquid FW transport through the Arctic gateways compare well with observations. On average, the models have too high mean FWC, weaker upward trends of FWC in the recent decade than the observation, and low consistency in the temporal variation of FWC spatial distribution, which needs to be further explored for the purpose of model development.

Published
2016
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7. An assessment of a multi-model ensemble of decadal climate predictions

Author

Christophe Cassou, E. Sanchez, Doug Smith, D. Salas y Melia, Klaus Wyser, Shuting Yang, Alessio Bellucci, Mihaela Caian, Agathe Germe, Johann H. Jungclaus, Panos Athanasiadis, Laurent Terray, Rein Haarsma, Jürgen Kröger, Daniela Matei, Silvio Gualdi, Elodie Fernandez, Wolfgang A. Müller, and Holger Pohlmann

Subjects
Earth system science, Atmospheric Science, Coupled model intercomparison project, Data assimilation, Meteorology, Anomaly (natural sciences), Climatology, Range (statistics), Initialization, Environmental science, Forecast skill, Climate model, Physics::Atmospheric and Oceanic Physics
Abstract

A multi-model ensemble of decadal prediction experiments, performed in the framework of the EU-funded COMBINE (Comprehensive Modelling of the Earth System for Better Climate Prediction and Projection) Project following the 5th Coupled Model Intercomparison Project protocol is examined. The ensemble combines a variety of dynamical models, initialization and perturbation strategies, as well as data assimilation products employed to constrain the initial state of the system. Taking advantage of the multi-model approach, several aspects of decadal climate predictions are assessed, including predictive skill, impact of the initialization strategy and the level of uncertainty characterizing the predicted fluctuations of key climate variables. The present analysis adds to the growing evidence that the current generation of climate models adequately initialized have significant skill in predicting years ahead not only the anthropogenic warming but also part of the internal variability of the climate system. An important finding is that the multi-model ensemble mean does generally outperform the individual forecasts, a well-documented result for seasonal forecasting, supporting the need to extend the multi-model framework to real-time decadal predictions in order to maximize the predictive capabilities of currently available decadal forecast systems. The multi-model perspective did also allow a more robust assessment of the impact of the initialization strategy on the quality of decadal predictions, providing hints of an improved forecast skill under full-value (with respect to anomaly) initialization in the near-term range, over the Indo-Pacific equatorial region. Finally, the consistency across the different model predictions was assessed. Specifically, different systems reveal a general agreement in predicting the near-term evolution of surface temperatures, displaying positive correlations between different decadal hindcasts over most of the global domain.

Published
2014
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8. Analog Downscaling of Seasonal Rainfall Forecasts in the Murray Darling Basin

Author

Harry H. Hendon, Bertrand Timbal, Andrew Charles, and Elodie Fernandez

Subjects
Model output statistics, Atmospheric Science, Climatology, General Circulation Model, Correlation analysis, Environmental science, Precipitation, Structural basin, Prediction system, Predictability, Downscaling
Abstract

Seasonal predictions based on coupled atmosphere–ocean general circulation models (GCMs) provide useful predictions of large-scale circulation but lack the conditioning on topography required for locally relevant prediction. In this study a statistical downscaling model based on meteorological analogs was applied to continental-scale GCM-based seasonal forecasts and high quality historical site observations to generate a set of downscaled precipitation hindcasts at 160 sites in the South Murray Darling Basin region of Australia. Large-scale fields from the Predictive Ocean–Atmosphere Model for Australia (POAMA) 1.5b GCM-based seasonal prediction system are used for analog selection. Correlation analysis indicates modest levels of predictability in the target region for the selected predictor fields. A single best-match analog was found using model sea level pressure, meridional wind, and rainfall fields, with the procedure applied to 3-month-long reforecasts, initialized on the first day of each month from 1980 to 2006, for each model day of 10 ensemble members. Assessment of the total accumulated rainfall and number of rainy days in the 3-month reforecasts shows that the downscaling procedure corrects the local climate variability with no mean effect on predictive skill, resulting in a smaller magnitude error. The amount of total rainfall and number of rain days in the downscaled output is significantly improved over the direct GCM output as measured by the difference in median and tercile thresholds between station observations and downscaled rainfall. Confidence in the downscaled output is enhanced by strong consistency between the large-scale mean of the downscaled and direct GCM precipitation.

Published
2013
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9. An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part III: Hydrography and fluxes

Author

Hiroyuki Tsujino, Jianhua Lu, Christina Roth, David A. Bailey, Paul Spence, Rüdiger Gerdes, Elodie Fernandez, Stephen M. Griffies, Alexandra Jahn, Claus W. Böning, Yevgeny Aksenov, Mats Bentsen, Xuezhu Wang, Doroteaciro Iovino, David Salas y Mélia, Aurore Voldoire, Craig M. Lee, Gokhan Danabasoglu, Alexandra Bozec, Eric P. Chassignet, Sergey Danilov, Christophe Cassou, Helge Drange, Bonita L. Samuels, Qiang Wang, Arne Biastoch, Thomas Jung, Yosuke Fujii, William G. Large, Sophie Valcke, Camille Lique, A. J. George Nurser, Beth Curry, Simona Masina, Pier Giuseppe Fogli, Mehmet Ilicak, Steve G. Yeager, and Andrew C. Coward

Subjects
Arctic sea ice decline, Atmospheric Science, 010504 meteorology & atmospheric sciences, Oceanography, St. Anna Trough, 01 natural sciences, Arctic Ocean, Computer Science (miscellaneous), CORE-II atmospheric forcing, 14. Life underwater, 0105 earth and related environmental sciences, Coupled model intercomparison project, Arctic dipole anomaly, 010505 oceanography, Density currents, Atlantic Water, Geotechnical Engineering and Engineering Geology, Arctic geoengineering, Marine Sciences, Arctic, 13. Climate action, Climatology, Climate model, Thermohaline circulation, Ocean heat content, Geology
Abstract

Highlights: • We compare the simulated Arctic Ocean in 15 global ocean–sea ice models. • There is a large spread in temperature bias in the Arctic Ocean between the models. • Warm bias models have a strong temperature anomaly of inflow of Atlantic Water. • Dense outflows formed on Arctic shelves are not captured accurately in the models. In this paper we compare the simulated Arctic Ocean in 15 global ocean-sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE-II). Most of these models are the ocean and sea-ice components of the coupled climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments. We mainly focus on the hydrography of the Arctic interior, the state of Atlantic Water layer and heat and volume transports at the gateways of the Davis Strait, the Bering Strait, the Fram Strait and the Barents Sea Opening. We found that there is a large spread in temperature in the Arctic Ocean between the models, and generally large differences compared to the observed temperature at intermediate depths. Warm bias models have a strong temperature anomaly of inflow of the Atlantic Water entering the Arctic Ocean through the Fram Strait. Another process that is not represented accurately in the CORE-II models is the formation of cold and dense water, originating on the eastern shelves. In the cold bias models, excessive cold water forms in the Barents Sea and spreads into the Arctic Ocean through the St. Anna Through. There is a large spread in the simulated mean heat and volume transports through the Fram Strait and the Barents Sea Opening. The models agree more on the decadal variability, to a large degree dictated by the common atmospheric forcing. We conclude that the CORE-II model study helps us to understand the crucial biases in the Arctic Ocean. The current coarse resolution state-of-the-art ocean models need to be improved in accurate representation of the Atlantic Water inflow into the Arctic and density currents coming from the shelves.

Published
2016
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10. A comparison of multi-site daily rainfall downscaling techniques under Australian conditions

Author

Andrew Frost, Dewi Kirono, Francis H. S. Chiew, Richard E. Chandler, David Kent, Rajeshwar Mehrotra, Stephen P. Charles, Kim Nguyen, Bertrand Timbal, Guobin Fu, Elodie Fernandez, and John L. McGregor

Subjects
Meteorology, Markov chain, Climatology, Range (statistics), Linear model, Environmental science, Forcing (mathematics), Precipitation, Atmospheric model, Standard deviation, Water Science and Technology, Downscaling
Abstract

Summary Six methods of downscaling GCM simulations to multi-site daily precipitation were applied to a set of 30 rain gauges located within South-Eastern Australia. The methods were tested at reproducing a range of statistics important within hydrological studies including inter-annual variability and spatial coherency using both NCEP/NCAR reanalysis and GCM predictors, thus testing the validity of GCM downscaled predictions. The methods evaluated, all having found application in Australia previously, are: (1) the dynamical downscaling Conformal-Cubic Atmospheric Model (CCAM) of McGregor (2005) ; the historical data based (2) Scaling method applied by Chiew et al. (2009) and (3) Analogue method of Timbal (2004) ; and three stochastic methods, (4) the GLIMCLIM (Generalised Linear Model for daily Climate time series) software package ( Chandler, 2002 ), (5) the Non-homogeneous Hidden Markov Model (NHMM) of Charles et al. (1999), and (6) the modified Markov model–kernel probability density estimation (MMM–KDE) downscaling technique of Mehrotra and Sharma (2007) . The results showed that the simple Scaling approach provided relatively robust results for a range of statistics when GCM forcing data was used, and was therefore recommended for regional water availability and planning studies (subject to certain limitations as mentioned in conclusion section). The stochastic methods better capture changes to a fuller range of rainfall statistics and are recommended for detailed catchment modelling studies. In particular, the stochastic methods show better results for daily extreme rainfall (e.g. flooding/low flow) as the simulations are not based purely on temporal/spatial rainfall patterns observed in the past, and a hybrid GLIMCLIM occurrence-KDE amounts model is recommended based on performance for individual statistics. For GCM downscaled simulations, biases in annual mean and standard deviation of ±5% and −30% were observed typically, and no single model performed well over all timescales/statistics, suggesting that the user beware of model limitations when applying downscaling methods for various purposes. A brief demonstration of predictor biases is presented, highlighting that biases observed in GCM predictors can cause poorer performance during GCM validation, and that investigation of these biases should inform choice of GCMs, GCM predictors, and the downscaling methods that use them.

Published
2011
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11. An assessment of the Arctic Ocean in a suite of interannual CORE-II simulations. Part I: Sea ice and solid freshwater

Author

Thomas Jung, A. J. George Nurser, Hiroyuki Tsujino, Sergey Danilov, Jianhua Lu, Christophe Cassou, Rüdiger Gerdes, Craig M. Lee, Steve G. Yeager, Yosuke Fujii, Helge Drange, Bonita L. Samuels, Xuezhu Wang, Paul Spence, Qiang Wang, William G. Large, Yevgeny Aksenov, Arne Biastoch, David Salas y Mélia, Mats Bentsen, Doroteaciro Iovino, Alexandra Bozec, Alexandra Jahn, Andrew C. Coward, Claus W. Böning, Gokhan Danabasoglu, Benjamin Rabe, Beth Curry, Simona Masina, Aurore Voldoire, Pier Giuseppe Fogli, Stephen M. Griffies, Christina Roth, Eric P. Chassignet, David A. Bailey, Sophie Valcke, Camille Lique, Elodie Fernandez, and Mehmet Ilicak

Subjects
Arctic sea ice decline, Atmospheric Science, 010504 meteorology & atmospheric sciences, Sea ice, Antarctic sea ice, Oceanography, 01 natural sciences, Freshwater, Arctic Ocean, Computer Science (miscellaneous), Cryosphere, 14. Life underwater, Sea ice concentration, 0105 earth and related environmental sciences, Drift ice, geography, geography.geographical_feature_category, 010505 oceanography, Geotechnical Engineering and Engineering Geology, Arctic ice pack, Marine Sciences, CORE II atmospheric forcing, 13. Climate action, Climatology, Sea ice thickness, Environmental science
Abstract

The Arctic Ocean simulated in fourteen global ocean-sea ice models in the framework of the Coordinated Ocean-ice Reference Experiments, phase II (CORE II) is analyzed. The focus is on the Arctic sea ice extent, the solid freshwater (FW) sources and solid freshwater content (FWC). Available observations are used for model evaluation. The variability of sea ice extent and solid FW budget is more consistently reproduced than their mean state in the models. The descending trend of September sea ice extent is well simulated in terms of the model ensemble mean. Models overestimating sea ice thickness tend to underestimate the descending trend of September sea ice extent. The models underestimate the observed sea ice thinning trend by a factor of two. When averaged on decadal time scales, the variation of Arctic solid FWC is contributed by those of both sea ice production and sea ice transport, which are out of phase in time. The solid FWC decreased in the recent decades, caused mainly by the reduction in sea ice thickness. The models did not simulate the acceleration of sea ice thickness decline, leading to an underestimation of solid FWC trend after 2000. The common model behavior, including the tendency to underestimate the trend of sea ice thickness and March sea ice extent, remains to be improved.

Published
2016
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12. North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part II: Inter-annual to decadal variability

Author

Patrick Heimbach, Aurore Voldoire, Anatoly Gusev, Alicia Karspeck, Eric P. Chassignet, Rainer Bleck, Dmitry Sidorenko, Alexandra Bozec, David Salas y Mélia, Anastasia Romanou, Who M. Kim, Gael Forget, Daohua Bi, Vittorio Canuto, Simon J. Marsland, Gokhan Danabasoglu, Simona Masina, Hiroyuki Tsujino, Stephen M. Griffies, Sophie Valcke, Jianhua Lu, Mats Bentsen, Claus W. Böning, Pier Giuseppe Fogli, Gurvan Madec, Anthony Leboissetier, Mehmet Ilicak, Anna Pirani, Thomas Jung, Igor Yashayaev, Andrew C. Coward, Maxwell Kelley, Shan Sun, Yosuke Fujii, A. J. George Nurser, Petteri Uotila, Sergey Danilov, Elodie Fernandez, Steve G. Yeager, Christophe Cassou, Nikolay Diansky, A. M. Howard, William G. Large, Helge Drange, Anne-Marie Tréguier, Bonita L. Samuels, Erik Behrens, Qiang Wang, Arne Biastoch, Riccardo Farneti, Markus Scheinert, Antonio Navarra, National Center for Atmospheric Research [Boulder] (NCAR), Helmholtz Centre for Ocean Research [Kiel] (GEOMAR), Uni Research Climate, Uni Research Ltd, Centre for Australian Weather and Climate Research (CAWCR), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University [Tallahassee] (FSU), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), National Oceanography Centre (NOC), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), Institute of Numerical Mathematics [Moscou] (INM-RAS), Russian Academy of Sciences [Moscow] (RAS), University of Bergen (UiB), Abdus Salam International Centre for Theoretical Physics [Trieste] (ICTP), Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna] (CMCC), Massachusetts Institute of Technology (MIT), Meteorological Research Institute [Tsukuba] (MRI), Japan Meteorological Agency (JMA), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric Administration (NOAA), Pacific Northwest National Laboratory (PNNL), 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), Dipartimento di Matematica e Informatica [Perugia] (DMI), Università degli Studi di Perugia = University of Perugia (UNIPG), National Oceanography Centre [Southampton] (NOC), University of Southampton, International CLIVAR, Princeton University, 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), Leibniz-Institut für Meereswissenschaften (IFM-GEOMAR), Laboratoire de physique des océans (LPO), 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), Biomedical Research Imaging Center [North Carolina] (BRIC), University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC)-University of North Carolina System (UNC), Danabasoglu G, Yeager SG, Kim WM, Behrens E, Bentsen M, Bi DH, Biastoch A, Bleck R, Boning C, Bozec A, Canuto VM, Cassou C, Chassignet E, Coward AC, Danilov S, Diansky N, Drange H, Farneti R, Fernandez E, Fogli PG, Forget G, Fujii Y, Griffies SM, Gusev A, Heimbach P, Howard A, Ilicak M, Jung T, Karspeck AR, Kelley M, Large WG, Leboissetier A, Lu JH, Madec G, Marsland SJ, Masina S, Navarra A, Nurser AJG, Pirani A, Romanou A, Melia DSY, Samuels BL, Scheinert M, Sidorenko D, Sun S, Treguier AM, Tsujino H, Uotila P, Valcke S, Voldoire A, Wang Q, Yashayaev I, CERFACS [Toulouse], Institut national des sciences de l'Univers (INSU - CNRS)-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)), 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)), Università degli Studi di Perugia (UNIPG), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), and CERFACS

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Mixed layer, Phase (waves), [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Oceanography, 01 natural sciences, Ocean model comparisons, Computer Science (miscellaneous), 14. Life underwater, Atmospheric forcing, Variability in the North Atlantic, 0105 earth and related environmental sciences, Inter-annual to decadal variability and mechanisms, Atlantic meridional overturning circulation variability, 010505 oceanography, Global ocean - sea-ice modelling, Geotechnical Engineering and Engineering Geology, Deep water, Ocean dynamics, Marine Sciences, Sea surface temperature, 13. Climate action, North Atlantic oscillation, Climatology, Global ocean – sea-ice modelling, Ocean model comparisons, Atmospheric forcing, Hydrography, Global ocean – sea-ice modelling, Geology
Abstract

Simulated inter-annual to decadal variability and trends in the North Atlantic for the 1958-2007 period from twenty global ocean - sea-ice coupled models are presented. These simulations are performed as contributions to the second phase of the Coordinated Ocean-ice Reference Experiments (CORE-II). The study is Part II of our companion paper (Danabasoglu et al., 2014) which documented the mean states in the North Atlantic from the same models. A major focus of the present study is the representation of Atlantic meridional overturning circulation (AMOC) variability in the participating models. Relationships between AMOC variability and those of some other related variables, such as subpolar mixed layer depths, the North Atlantic Oscillation (NAO), and the Labrador Sea upper-ocean hydrographic properties, are also investigated. In general, AMOC variability shows three distinct stages. During the first stage that lasts until the mid-to late-1970s, AMOC is relatively steady, remaining lower than its long-term (1958-2007) mean. Thereafter, AMOC intensifies with maximum transports achieved in the mid-to late-1990s. This enhancement is then followed by a weakening trend until the end of our integration period. This sequence of low frequency AMOC variability is consistent with previous studies. Regarding strengthening of AMOC between about the mid-1970s and the mid-1990s, our results support a previously identified variability mechanism where AMOC intensification is connected to increased deep water formation in the subpolar North Atlantic, driven by NAO-related surface fluxes. The simulations tend to show general agreement in their temporal representations of, for example, AMOC, sea surface temperature (SST), and subpolar mixed layer depth variabilities. In particular, the observed variability of the North Atlantic SSTs is captured well by all models. These findings indicate that simulated variability and trends are primarily dictated by the atmospheric datasets which include the influence of ocean dynamics from nature superimposed onto anthropogenic effects. Despite these general agreements, there are many differences among the model solutions, particularly in the spatial structures of variability patterns. For example, the location of the maximum AMOC variability differs among the models between Northern and Southern Hemispheres. (C) 2015 Elsevier Ltd. All rights reserved.

Published
2016
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13. An assessment of global and regional sea level for years 1993-2007 in a suite of interannual CORE-II simulations

Author

Sophie Valcke, David M. Holland, Michael Winton, Xuebin Zhang, David Salas y Mélia, Gokhan Danabasoglu, Mats Bentsen, Sergey Danilov, Richard J. Greatbatch, Eric P. Chassignet, William G. Large, Hiroyuki Tsujino, Jianhua Lu, Stephen M. Griffies, Paul Goddard, A. J. George Nurser, Jens Schröter, Daohua Bi, Riccardo Farneti, Simon J. Marsland, Jianjun Yin, Petteri Uotila, Helge Drange, Anne-Marie Tréguier, Akhilesh Mishra, Katja Lorbacher, Bonita L. Samuels, Erik Behrens, Qiang Wang, Arne Biastoch, Catia M. Domingues, Aurore Voldoire, Yu-Heng Tseng, Paul J. Durack, Mehmet Ilicak, Franziska U. Schwarzkopf, Dmitry Sidorenko, Alexandra Bozec, Susan C. Bates, Jaime B. Palter, Elodie Fernandez, Claus W. Böning, NOAA Geophysical Fluid Dynamics Laboratory ( GFDL ), National Oceanic and Atmospheric Administration ( NOAA ), Department of Geosciences, University of Arizona, Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, National Center for Atmospheric Research [Boulder] ( NCAR ), GEOMAR - Helmholtz Centre for Ocean Research [Kiel] ( GEOMAR ), Uni Research Climate, Uni Research Ltd, Center for Australian Weather and Climate Research, Bureau of Meteorolgy, Center for Acean-Atmospheric Prediction Studies ( COAPS ), Florida State University, Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung ( AWI ), University of Bergen ( UIB ), International Centre for Theoretical Physics, Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique ( CERFACS ), CERFACS, New York University [New York], National Oceanography Centre [Southampton] ( NOC ), University of Southampton [Southampton], Groupe d'étude de l'atmosphère météorologique ( CNRM-GAME ), Institut national des sciences de l'Univers ( INSU - CNRS ) -Météo France-Centre National de la Recherche Scientifique ( CNRS ), McGill University, Laboratoire de physique des océans ( LPO ), 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 ), Meteorological Research Institute [Tsukuba] ( MRI ), Japan Meteorological Agency ( JMA ), CSIRO Marine and Atmospheric Research ( CSIRO ), Commonwealth Scientific and Industrial Research Organisation [Canberra] ( CSIRO ), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric Administration (NOAA), Department of Geosciences [Tucson], Lawrence Livermore National Laboratory (LLNL), National Center for Atmospheric Research [Boulder] (NCAR), Helmholtz Centre for Ocean Research [Kiel] (GEOMAR), Centre for Australian Weather and Climate Research (CAWCR), Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University [Tallahassee] (FSU), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), University of Bergen (UiB), Abdus Salam International Centre for Theoretical Physics [Trieste] (ICTP), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), New York University [New York] (NYU), NYU System (NYU), National Oceanography Centre [Southampton] (NOC), University of Southampton, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), McGill University = Université McGill [Montréal, Canada], Laboratoire de physique des océans (LPO), 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), Meteorological Research Institute [Tsukuba] (MRI), Japan Meteorological Agency (JMA), CSIRO Marine and Atmospheric Research (CSIRO), and Commonwealth Scientific and Industrial Research Organisation [Canberra] (CSIRO)

Subjects
Arctic sea ice decline, Atmospheric Science, 010504 meteorology & atmospheric sciences, Oceanography, 01 natural sciences, CORE global ocean-ice simulations, Ocean gyre, Steric sea level, Computer Science (miscellaneous), Sea level, 14. Life underwater, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, 0105 earth and related environmental sciences, Momentum (technical analysis), geography, geography.geographical_feature_category, [ SDU.STU.OC ] Sciences of the Universe [physics]/Earth Sciences/Oceanography, 010505 oceanography, Advection, Global sea level, Geotechnical Engineering and Engineering Geology, Ocean heat content, 13. Climate action, North Atlantic oscillation, Climatology, Environmental science, Thermohaline circulation
Abstract

Highlights: • Global mean sea level simulated in interannual CORE simulations. • Regional sea level patterns simulated in interannual CORE simulations. • Theoretical foundation for analysis of global mean sea level and regional patterns. Abstract: We provide an assessment of sea level simulated in a suite of global ocean-sea ice models using the interannual CORE atmospheric state to determine surface ocean boundary buoyancy and momentum fluxes. These CORE-II simulations are compared amongst themselves as well as to observation-based estimates. We focus on the final 15 years of the simulations (1993–2007), as this is a period where the CORE-II atmospheric state is well sampled, and it allows us to compare sea level related fields to both satellite and in situ analyses. The ensemble mean of the CORE-II simulations broadly agree with various global and regional observation-based analyses during this period, though with the global mean thermosteric sea level rise biased low relative to observation-based analyses. The simulations reveal a positive trend in dynamic sea level in the west Pacific and negative trend in the east, with this trend arising from wind shifts and regional changes in upper 700 m ocean heat content. The models also exhibit a thermosteric sea level rise in the subpolar North Atlantic associated with a transition around 1995/1996 of the North Atlantic Oscillation to its negative phase, and the advection of warm subtropical waters into the subpolar gyre. Sea level trends are predominantly associated with steric trends, with thermosteric effects generally far larger than halosteric effects, except in the Arctic and North Atlantic. There is a general anti-correlation between thermosteric and halosteric effects for much of the World Ocean, associated with density compensated changes.

Published
2014
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14. North Atlantic simulations in Coordinated Ocean-ice Reference Experiments phase II (CORE-II). Part I: Mean states

Author

Hiroyuki Tsujino, Jianhua Lu, Anatoly Gusev, David A. Bailey, Simon J. Marsland, Yosuke Fujii, Elodie Fernandez, Nikolay Diansky, Claus W. Böning, Gael Forget, Helge Drange, Anne-Marie Tréguier, Andrew C. Coward, Gokhan Danabasoglu, Mats Bentsen, Eric P. Chassignet, William G. Large, Sergey Danilov, A. J. George Nurser, Christophe Cassou, Riccardo Farneti, Bonita L. Samuels, Patrick Heimbach, David Salas y Mélia, Erik Behrens, Stephen M. Griffies, Gurvan Madec, Qiang Wang, Anthony Leboissetier, Arne Biastoch, Maxwell Kelley, Vittorio Canuto, Markus Scheinert, Aurore Voldoire, A. M. Howard, Steve G. Yeager, Thomas Jung, Petteri Uotila, Simona Masina, Pier Giuseppe Fogli, Daohua Bi, Sophie Valcke, Dmitry Sidorenko, Alexandra Bozec, Antonio Navarra, Anna Pirani, National Center for Atmospheric Research [Boulder] (NCAR), Lawrence Berkeley National Laboratory [Berkeley] (LBNL), Uni Research Climate, Uni Research Ltd, Centre for Australian Weather and Climate Research (CAWCR), Leibniz-Institut für Meereswissenschaften (IFM-GEOMAR), Center for Ocean-Atmospheric Prediction Studies (COAPS), Florida State University [Tallahassee] (FSU), NASA Goddard Institute for Space Studies (GISS), NASA Goddard Space Flight Center (GSFC), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), CERFACS, National Oceanography Centre (NOC), Massachusetts Institute of Technology (MIT), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), National Oceanic and Atmospheric Administration (NOAA), Nucleus for European Modeling of the Ocean (NEMO R&D ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), 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)), 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), Istituto Nazionale di Geofisica e Vulcanologia - Sezione di Bologna (INGV), Istituto Nazionale di Geofisica e Vulcanologia, International CLIVAR, Princeton University, Laboratoire de physique des océans (LPO), 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), Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), 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)), 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), Danabasoglu G, Yeager SG, Bailey D, Behrens E, Bentsen M, Bi D, Biastoch A, Boning C, Bozec A, Canuto VM, Cassou C, Chassignet E, Coward AC, Danilov S, Diansky N, Drange H, Farneti R, Fernandez E, Fogli PG, Forget G, Fujii Y, Griffies SM, Gusev A, Heimbach P, Howard A, Jung T, Kelley M, Large WG, Leboissetier A, Lu J, Madec G, Marsland SJ, Masina S, Navarra A, Nurser AJG, Pirani A, Melia DSY, Samuels BL, Scheinert M, Sidorenko D, Treguier AM, Tsujino H, Uotila P, Valcke S, Voldoire A, and Wangi Q

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Mixed layer, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SDE.MCG]Environmental Sciences/Global Changes, Atlantic meridional overturning circulation, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], Oceanography, 01 natural sciences, Ocean model comparisons, Computer Science (miscellaneous), Sea ice, Potential temperature, Hindcast, 14. Life underwater, Atmospheric forcing, 0105 earth and related environmental sciences, geography, geography.geographical_feature_category, 010505 oceanography, Global ocean–sea-ice modelling, Ocean model comparisons, Atmospheric forcing, Experimental design, Atlantic meridional overturning circulation, North Atlantic simulations, Albedo, Geotechnical Engineering and Engineering Geology, Snow, Experimental design, Sea surface temperature, 13. Climate action, Meridional flow, Climatology, Environmental science, Global ocean-sea-ice modelling
Abstract

Simulation characteristics from eighteen global ocean-sea-ice coupled models are presented with a focus on the mean Atlantic meridional overturning circulation (AMOC) and other related fields in the North Atlantic. These experiments use inter-annually varying atmospheric forcing data sets for the 60-year period from 1948 to 2007 and are performed as contributions to the second phase of the Coordinated Oceanice Reference Experiments (CORE-II). The protocol for conducting such CORE-II experiments is summarized. Despite using the same atmospheric forcing, the solutions show significant differences. As most models also differ from available observations, biases in the Labrador Sea region in upper-ocean potential temperature and salinity distributions, mixed layer depths, and sea-ice cover are identified as contributors to differences in AMOC. These differences in the solutions do not suggest an obvious grouping of the models based on their ocean model lineage, their vertical coordinate representations, or surface salinity restoring strengths. Thus, the solution differences among the models are attributed primarily to use of different subgrid scale parameterizations and parameter choices as well as to differences in vertical and horizontal grid resolutions in the ocean models. Use of a wide variety of sea-ice models with diverse snow and sea-ice albedo treatments also contributes to these differences. Based on the diagnostics considered, the majority of the models appear suitable for use in studies involving the North Atlantic, but some models require dedicated development effort. (C) 2013 Elsevier Ltd. All rights reserved.

Published
2014
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15. The CNRM-CM5.1 global climate model: description and basic evaluation

Author

Sophie Valcke, Marie-Pierre Moine, Julie Deshayes, S. Tyteca, Ramdane Alkama, D. Salas y Melia, Serge Planton, Christophe Cassou, Alain Braun, Aurore Voldoire, Fabrice Chauvin, Elodie Fernandez, Sophie Szopa, Emilia Sanchez-Gomez, Bertrand Decharme, Eric Maisonnave, Michel Déqué, I. Beau, Hervé Douville, Gurvan Madec, Laure Coquart, S. Belamari, David Saint-Martin, Stephane Sénési, Antoinette Alias, Matthieu Chevallier, Groupe d'étude de l'atmosphère météorologique (CNRM-GAME), Institut national des sciences de l'Univers (INSU - CNRS)-Météo France-Centre National de la Recherche Scientifique (CNRS), Centre Européen de Recherche et de Formation Avancée en Calcul Scientifique (CERFACS), CERFACS, Météo-France [Paris], Météo France, Laboratoire de physique des océans (LPO), 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), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), 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), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), 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), Modélisation du climat (CLIM), 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), Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), 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), Météo-France, 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 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)-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 Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), 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)-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), Institut de Physique Nucléaire de Lyon (IPNL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-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), Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire Midi-Pyrénées (OMP), and Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)

Subjects
Atmospheric Science, 010504 meteorology & atmospheric sciences, Meteorology, [SDU.STU.GP]Sciences of the Universe [physics]/Earth Sciences/Geophysics [physics.geo-ph], [SDE.MCG]Environmental Sciences/Global Changes, 0207 environmental engineering, [PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], 02 engineering and technology, Atmospheric model, 01 natural sciences, Atmosphere, Troposphere, Global climate modelling, Radiative transfer, Sea ice, CMIP5, 14. Life underwater, Precipitation, 020701 environmental engineering, 0105 earth and related environmental sciences, Coupled model intercomparison project, geography, geography.geographical_feature_category, Lead (sea ice), GCM, 13. Climate action, Climatology, Environmental science
Abstract

A new version of the general circulation model CNRM-CM has been developed jointly by CNRM-GAME (Centre National de Recherches Météorologiques-Groupe d'Etudes de l'AtmosphSre Météorologique) and Cerfacs (Centre Européen de Recherche et de Formation Avancée) in order to contribute to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The purpose of the study is to describe its main features and to provide a preliminary assessment of its mean climatology. CNRM-CM5.1 includes the atmospheric model ARPEGE-Climat (v5.2), the ocean model NEMO (v3.2), the land surface scheme ISBA and the sea ice model GELATO (v5) coupled through the OASIS (v3) system. The main improvements since CMIP3 are the following. Horizontal resolution has been increased both in the atmosphere (from 2.8A degrees to 1.4A degrees) and in the ocean (from 2A degrees to 1A degrees). The dynamical core of the atmospheric component has been revised. A new radiation scheme has been introduced and the treatments of tropospheric and stratospheric aerosols have been improved. Particular care has been devoted to ensure mass/water conservation in the atmospheric component. The land surface scheme ISBA has been externalised from the atmospheric model through the SURFEX platform and includes new developments such as a parameterization of sub-grid hydrology, a new freezing scheme and a new bulk parameterisation for ocean surface fluxes. The ocean model is based on the state-of-the-art version of NEMO, which has greatly progressed since the OPA8.0 version used in the CMIP3 version of CNRM-CM. Finally, the coupling between the different components through OASIS has also received a particular attention to avoid energy loss and spurious drifts. These developments generally lead to a more realistic representation of the mean recent climate and to a reduction of drifts in a preindustrial integration. The large-scale dynamics is generally improved both in the atmosphere and in the ocean, and the bias in mean surface temperature is clearly reduced. However, some flaws remain such as significant precipitation and radiative biases in many regions, or a pronounced drift in three dimensional salinity.

Published
2013
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16. Drift dynamics in a coupled model initialized for decadal forecasts

Author

Emilia Sanchez-Gomez, Elodie Fernandez, Yohan Ruprich-Robert, Laurent Terray, and Christophe Cassou

Subjects
Water mass, geography, Atmospheric Science, geography.geographical_feature_category, 010504 meteorology & atmospheric sciences, Atmospheric circulation, Mode (statistics), Initialization, 010502 geochemistry & geophysics, 01 natural sciences, North Atlantic oscillation, Ocean gyre, Climatology, Thermocline, Geology, 0105 earth and related environmental sciences, Teleconnection
Abstract

Drifts are always present in models when initialized from observed conditions because of intrinsic model errors; those potentially affect any type of climate predictions based on numerical experiments. Model drifts are usually removed through more or less sophisticated techniques for skill assessment, but they are rarely analysed. In this study, we provide a detailed physical and dynamical description of the drifts in the CNRM-CM5 coupled model using a set of decadal retrospective forecasts produced within CMIP5. The scope of the paper is to give some physical insights and lines of approach to, on one hand, implement more appropriate techniques of initialisation that minimize the drift in forecast mode, and on the other hand, eventually reduce the systematic biases of the models. We first document a novel protocol for ocean initialization adopted by the CNRM-CERFACS group for forecasting purpose in CMIP5. Initial states for starting dates of the predictions are obtained from a preliminary integration of the coupled model where full-field ocean surface temperature and salinity are restored everywhere to observations through flux derivative terms and full-field subsurface fields (below the prognostic ocean mixed layer) are nudged towards NEMOVAR reanalyses. Nudging is applied only outside the 15°S–15°N band allowing for dynamical balance between the depth and tilt of the tropical thermocline and the model intrinsic biased wind. A sensitivity experiment to the latitudinal extension of no-nudging zone (1°S–1°N instead of 15°, hereafter referred to as NOEQ) has been carried out. In this paper, we concentrate our analyses on two specific regions: the tropical Pacific and the North Atlantic basins. In the Pacific, we show that the first year of the forecasts is characterized by a quasi-systematic excitation of El Nino-Southern Oscillation (ENSO) warm events whatever the starting dates. This, through ocean-to-atmosphere heat transfer materialized by diabatic heating, can be viewed for the coupled model as an efficient way to rapidly adjust to its own biased climate mean state. Weak cold ENSO events tend to occur the second year of the forecast due to the so-called discharge–recharge mechanism while the spurious oscillatory behavior is progressively damped. The latter mechanism is much more pronounced in retrospective forecasts initialized from the NOEQ configuration for which the ENSO flip-flop is still detectable at leadtime 4 year. Associated atmospheric teleconnections interfere worldwide with regional drifts, especially in the North Pacific and more remotely in the North Atlantic. In the latter basin, the drift can be interpreted as the model response to intrinsic atmospheric circulation biases found in the stand-alone atmosphere component of the model, which project onto the negative phase of the North Atlantic Oscillation. A fast adjustment (up to ~5-year leadtime) occurs leading to a rapid slackening of both the vertical (Atlantic meridional overturning circulation) and horizontal circulations, especially in the subpolar gyre. Slower adjustment of the entire water masses distribution in the North Atlantic then takes over involving several mechanisms. We show that a weak feedback is locally present between the atmospheric circulation and the ocean drift that controls the timescale of the setting of the coupled model biases.

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