Simulation of modern climate with the new version of the INM RAS climate model

Volodin, E. M.; Mortikov, Evgeny; Kostrykin, S. V.; Galin, V. Ya.; Lykosov, V. N.; Gritsun, Andrey; Diansky, N. A.; Гусев, А. В.; Yakovlev, N.

doi:10.1134/s0001433817020128

Cited by 50 publications

(14 citation statements)

References 24 publications

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“…Discussion started: 17 July 2020 c Author(s) 2020.CC BY 4.0 License. al., 2017a;Volodin et al, 2017b) INM-CM5-0(Volodin et al, 2017a; Volodin et al (Park et al, 2019) UKESM1-0-LL(Sellar et al, 2019)…”

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confidence: 99%

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Schlund¹,

Lauer²,

Gentine³

et al. 2020

Preprint

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Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS) which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which stem the major source of uncertainty in ECS. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here has been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a large decrease of the correlation coefficients for most emergent relationships, indicating that nearly all of the constraints are less skillful in predicting ECS than they were in CMIP5. Many do not appear sufficiently skillful to be useful in constraining ECS, and several of them do not pass a significance test. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble the emergent constraint was originally derived from. The emergent-constrained best estimate of ECS increased from CMIP5 to CMIP6, with a best estimate range of 2.97–3.88 K for CMIP5 and 3.41–4.36 K for CMIP6. This can be at least partly explained by the increased number of high-ECS models in CMIP6 with an ECS above 4.5 K without a corresponding change in the constraint predictors. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that emergent constraints focusing on specific processes contributing to ECS are more promising than emergent constraints based on statistical model analysis.

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confidence: 99%

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Schlund¹,

Lauer²,

Gentine³

et al. 2020

Preprint

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show abstract

“…The MPI-ESM global joint model [6] was used as the mathematical model. Since this model is necessary only as a tool of investigation and is not an object of analysis; the peculiarities of its structure and detailed description are not presented here; only necessary information on its configuration and the main parameters is provided.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…The state-of-the-art models of circulation have appeared and are being developed; they include not only the ocean and sea dynamics, but also describe the joint ocean/atmosphere/Earth/ice/carbon/solar -radiation exchange processes, i.e., all physical processes that influence directly or indirectly on the medium-and longterm ocean dynamics. Let us note the most significant recent studies concerning the pure ocean circulation [5] and modeling with the use of joint ocean-atmosphere models [6]. In these studies the problems of describing the processes of ocean and ocean-atmosphere dynamics are solved at a high scientific level by using supercomputers and parallel programming methods.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Meridional Heat and Mass Transport in the South Atlantic by Using the Joint Atmosphere and Ocean Circulation Model with Data Assimilation and Visualization Facilities

Belyaev¹,

Кулешов²,

Tuchkova³

2019

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The joint ocean-Earth-atmosphere model of the Max Planck Institute for Meteorology with application of original data assimilation methods are used to estimate the meridional heat and mass flows. The CTD-sections of temperature and salinity across the Atlantic Ocean for 1991-1995 obtained in the course of the WOCE (The World Ocean Circulation Experiment) international experiment are used as observation data. The sections contain about 650 stations; each of them provides from 100 to 3000 measurements from the sea surface to the bottom. In our study both the control estimates for the velocities of currents and those with assimilation of the data of these experiments are obtained. Using visualization facilities, we have shown how data assimilation changes the model characteristics, including velocities of currents and temperature fields at different sea levels. The graphs demonstrate the influence of the change in the initial field after data assimilation on the results of the model calculations after 6 and 11 months of integration for both the observed model characteristics and those which are not observed directly. The Generalized Kalman Filter (GKF) method which coincides in a particular case with the classical Ensemble Kalman Filter (EnKF) is used as the data assimilation method. The results of calculations of the heat and mass flows are analyzed and compared with those of control calculations with no data assimilation. The calculations were performed on the supercomputers-Mistral‖ in DKRZ (Deutsches Klimarechenzentrum,

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“…В 2020 г. завершена шестая фаза CMIP6 этого проекта, в которой были сопоставлены 108 моделей, отличавшихся пространственным разрешением, выбором переменных и параметризацией процессов, происходящих в климатической системе и многим другим. На всех этапах проекта CMIP отмечалось, что при значительном прогрессе в описании многих процессов в атмосфере, океане и на суше, достигнуты значительные успехи при воспроизведении термического режима [6] Интерполяция значений средней месячной температуры воздуха и относительной влажности воздуха выполнена методом сплайновой поверхности [10], а атмосферных осадков -методом кригинга [11]. Современный этап развития МОЦАиО не позволяет интерпретиро-вать модельные результаты отдельно в каждом узле сетки.…”

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Applicability of GCMs for evaluation of agro-climatic properties of local territories

Lemeshko¹,

Evstigneev²,

Morozov³

et al. 2021

MSoE

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The analysis of reliability and accuracy reproduction of air temperature, precipitation, and relative humidity of the air by 18 ocean-atmosphere general circulation models (GCMs) included in CMIP6 (Coupled Model Intercomparison Project) is performed. Based on statistical criteria, the best models that most accurately reproduce empirical data are selected. On the basis of these models, an ensemble of models is compiled. The calculation of several agro-climatic indicators for the European part of Russia is performed using an ensemble approach. The comparison of agro-climatic indicators calculated on the basis of the ensemble of models and observational data is carried out for the territory of the Upper Volga including Yaroslavl, Kostroma, Vologda, Novgorod and Tver regions. The feasibility of using the ensemble of models to assess agro-climatic conditions of the region is shown.

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Simulation of modern climate with the new version of the INM RAS climate model

Cited by 50 publications

References 24 publications

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Emergent constraints on Equilibrium Climate Sensitivity in CMIP5: do they hold for CMIP6?

Estimation of the Meridional Heat and Mass Transport in the South Atlantic by Using the Joint Atmosphere and Ocean Circulation Model with Data Assimilation and Visualization Facilities

Applicability of GCMs for evaluation of agro-climatic properties of local territories

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