2021 Vol. 38, No. 2
Display Method:
2021, 38(2): 157-167.
doi: 10.1007/s00376-020-0178-4
Abstract:
Anthropogenic climate forcing will cause the global mean sea level to rise over the 21st century. However, regional sea level is expected to vary across ocean basins, superimposed by the influence of natural internal climate variability. Here, we address the detection of dynamic sea level (DSL) changes by combining the perspectives of a single and a multi-model ensemble approach (the 50-member CanESM5 and a 27-model ensemble, respectively, all retrieved from the CMIP6 archive), under three CMIP6 projected scenarios: SSP1-2.6, SSP3-7.0 and SSP5-8.5. The ensemble analysis takes into account four key metrics: signal (S), noise (N), S/N ratio, and time of emergence (ToE). The results from both sets of ensembles agree in the fact that regions with higher S/N (associated with smaller uncertainties) also reflect earlier ToEs. The DSL signal is projected to emerge in the Southern Ocean, Southeast Pacific, Northwest Atlantic, and the Arctic. Results common for both sets of ensemble simulations show that while S progressively increases with increased projected emissions, N, in turn, does not vary substantially among the SSPs, suggesting that uncertainty arising from internal climate variability has little dependence on changes in the magnitude of external forcing. Projected changes are greater and quite similar for the scenarios SSP3-7.0 and SSP5-8.5 and considerably smaller for the SSP1-2.6, highlighting the importance of public policies towards lower emission scenarios and of keeping emissions below a certain threshold.
Anthropogenic climate forcing will cause the global mean sea level to rise over the 21st century. However, regional sea level is expected to vary across ocean basins, superimposed by the influence of natural internal climate variability. Here, we address the detection of dynamic sea level (DSL) changes by combining the perspectives of a single and a multi-model ensemble approach (the 50-member CanESM5 and a 27-model ensemble, respectively, all retrieved from the CMIP6 archive), under three CMIP6 projected scenarios: SSP1-2.6, SSP3-7.0 and SSP5-8.5. The ensemble analysis takes into account four key metrics: signal (S), noise (N), S/N ratio, and time of emergence (ToE). The results from both sets of ensembles agree in the fact that regions with higher S/N (associated with smaller uncertainties) also reflect earlier ToEs. The DSL signal is projected to emerge in the Southern Ocean, Southeast Pacific, Northwest Atlantic, and the Arctic. Results common for both sets of ensemble simulations show that while S progressively increases with increased projected emissions, N, in turn, does not vary substantially among the SSPs, suggesting that uncertainty arising from internal climate variability has little dependence on changes in the magnitude of external forcing. Projected changes are greater and quite similar for the scenarios SSP3-7.0 and SSP5-8.5 and considerably smaller for the SSP1-2.6, highlighting the importance of public policies towards lower emission scenarios and of keeping emissions below a certain threshold.
2021, 38(2): 168-186.
doi: 10.1007/s00376-020-0213-5
Abstract:
Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.
Snow depth over sea ice is an essential variable for understanding the Arctic energy budget. In this study, we evaluate snow depth over Arctic sea ice during 1993–2014 simulated by 31 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) against recent satellite retrievals. The CMIP6 models capture some aspects of the observed snow depth climatology and variability. The observed variability lies in the middle of the models’ simulations. All the models show negative trends of snow depth during 1993–2014. However, substantial spatiotemporal discrepancies are identified. Compared to the observation, most models have late seasonal maximum snow depth (by two months), remarkably thinner snow for the seasonal minimum, an incorrect transition from the growth to decay period, and a greatly underestimated interannual variability and thinning trend of snow depth over areas with frequent occurrence of multi-year sea ice. Most models are unable to reproduce the observed snow depth gradient from the Canadian Arctic to the outer areas and the largest thinning rate in the central Arctic. Future projections suggest that snow depth in the Arctic will continue to decrease from 2015 to 2099. Under the SSP5-8.5 scenario, the Arctic will be almost snow-free during the summer and fall and the accumulation of snow starts from January. Further investigation into the possible causes of the issues for the simulated snow depth by some models based on the same family of models suggests that resolution, the inclusion of a high-top atmospheric model, and biogeochemistry processes are important factors for snow depth simulation.
2021, 38(2): 187-202.
doi: 10.1007/s00376-020-0250-0
Abstract:
Using the World Meteorological Organization definition and a threshold-based classification technique, simulations of vortex displacement and split sudden stratospheric warmings (SSWs) are evaluated for four Chinese models (BCC-CSM2-MR, FGOALS-f3-L, FGOALS-g3, and NESM3) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the Japanese 55-year reanalysis (JRA-55) as a baseline. Compared with six or seven SSWs in a decade in JRA-55, three models underestimate the SSW frequency by ~50%, while NESM3 doubles the SSW frequency. SSWs mainly appear in midwinter in JRA-55, but one-month climate drift is simulated in the models. The composite of splits is stronger than displacements in both the reanalysis and most models due to the longer pulse of positive eddy heat flux before onset of split SSWs. A wavenumber-1-like temperature anomaly pattern (cold Eurasia, warm North America) before onset of displacement SSWs is simulated, but cold anomalies are mainly confined to North America after displacement SSWs. Although the lower tropospheric temperature also displays a wavenumber-1-like pattern before split SSWs, most parts of Eurasia and North America are covered by cold anomalies after split SSWs in JRA-55. The models have different degrees of fidelity for the temperature anomaly pattern before split SSWs, but the wavenumber-2-like temperature anomaly pattern is well simulated after split SSWs. The center of the negative height anomalies in the Pacific sector before SSWs is sensitive to the SSW type in both JRA-55 and the models. A negative North Atlantic Oscillation is simulated after both types of SSWs in the models, although it is only observed for split SSWs.
Using the World Meteorological Organization definition and a threshold-based classification technique, simulations of vortex displacement and split sudden stratospheric warmings (SSWs) are evaluated for four Chinese models (BCC-CSM2-MR, FGOALS-f3-L, FGOALS-g3, and NESM3) from phase 6 of the Coupled Model Intercomparison Project (CMIP6) with the Japanese 55-year reanalysis (JRA-55) as a baseline. Compared with six or seven SSWs in a decade in JRA-55, three models underestimate the SSW frequency by ~50%, while NESM3 doubles the SSW frequency. SSWs mainly appear in midwinter in JRA-55, but one-month climate drift is simulated in the models. The composite of splits is stronger than displacements in both the reanalysis and most models due to the longer pulse of positive eddy heat flux before onset of split SSWs. A wavenumber-1-like temperature anomaly pattern (cold Eurasia, warm North America) before onset of displacement SSWs is simulated, but cold anomalies are mainly confined to North America after displacement SSWs. Although the lower tropospheric temperature also displays a wavenumber-1-like pattern before split SSWs, most parts of Eurasia and North America are covered by cold anomalies after split SSWs in JRA-55. The models have different degrees of fidelity for the temperature anomaly pattern before split SSWs, but the wavenumber-2-like temperature anomaly pattern is well simulated after split SSWs. The center of the negative height anomalies in the Pacific sector before SSWs is sensitive to the SSW type in both JRA-55 and the models. A negative North Atlantic Oscillation is simulated after both types of SSWs in the models, although it is only observed for split SSWs.
2021, 38(2): 203-220.
doi: 10.1007/s00376-020-0177-5
Abstract:
To meet the low warming targets proposed in the 2015 Paris Agreement, substantial reduction in carbon emissions is needed in the future. It is important to know how surface climates respond under low warming targets. The present study investigates the surface temperature changes under the low-forcing scenario of Representative Concentration Pathways (RCP2.6) and its updated version (Shared Socioeconomic Pathways, SSP1-2.6) by the Flexible Global Ocean–Atmosphere–Land System (FGOALS) models participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). In both scenarios, radiative forcing (RF) first increases to a peak of 3 W m−2 around 2045 and then decreases to 2.6 W m−2 by 2100. Global mean surface air temperature rises in all FGOALS models when RF increases (RF increasing stage) and declines or holds nearly constant when RF decreases (RF decreasing stage). The surface temperature change is distinct in its sign and magnitude between the RF increasing and decreasing stages over the land, Arctic, North Atlantic subpolar region, and Southern Ocean. Besides, the regional surface temperature change pattern displays pronounced model-to-model spread during both the RF increasing and decreasing stages, mainly due to large intermodel differences in climatological surface temperature, ice-albedo feedback, natural variability, and Atlantic Meridional Overturning Circulation change. The pattern of tropical precipitation change is generally anchored by the spatial variations of relative surface temperature change (deviations from the tropical mean value) in the FGOALS models. Moreover, the projected changes in the updated FGOALS models are closer to the multi-model ensemble mean results than their predecessors, suggesting that there are noticeable improvements in the future projections of FGOALS models from CMIP5 to CMIP6.
To meet the low warming targets proposed in the 2015 Paris Agreement, substantial reduction in carbon emissions is needed in the future. It is important to know how surface climates respond under low warming targets. The present study investigates the surface temperature changes under the low-forcing scenario of Representative Concentration Pathways (RCP2.6) and its updated version (Shared Socioeconomic Pathways, SSP1-2.6) by the Flexible Global Ocean–Atmosphere–Land System (FGOALS) models participating in phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6, respectively). In both scenarios, radiative forcing (RF) first increases to a peak of 3 W m−2 around 2045 and then decreases to 2.6 W m−2 by 2100. Global mean surface air temperature rises in all FGOALS models when RF increases (RF increasing stage) and declines or holds nearly constant when RF decreases (RF decreasing stage). The surface temperature change is distinct in its sign and magnitude between the RF increasing and decreasing stages over the land, Arctic, North Atlantic subpolar region, and Southern Ocean. Besides, the regional surface temperature change pattern displays pronounced model-to-model spread during both the RF increasing and decreasing stages, mainly due to large intermodel differences in climatological surface temperature, ice-albedo feedback, natural variability, and Atlantic Meridional Overturning Circulation change. The pattern of tropical precipitation change is generally anchored by the spatial variations of relative surface temperature change (deviations from the tropical mean value) in the FGOALS models. Moreover, the projected changes in the updated FGOALS models are closer to the multi-model ensemble mean results than their predecessors, suggesting that there are noticeable improvements in the future projections of FGOALS models from CMIP5 to CMIP6.
2021, 38(2): 221-236.
doi: 10.1007/s00376-020-0147-y
Abstract:
El Niño–Southern Oscillation (ENSO) events have a strong influence on East Asian summer rainfall (EASR). This paper investigates the simulated ENSO–EASR relationship in CMIP6 models and compares the results with those in CMIP3 and CMIP5 models. In general, the CMIP6 models show almost no appreciable progress in representing the ENSO–EASR relationship compared with the CMIP5 models. The correlation coefficients in the CMIP6 models are relatively smaller and exhibit a slightly greater intermodel diversity than those in the CMIP5 models. Three physical processes related to the delayed effect of ENSO on EASR are further analyzed. Results show that, firstly, the relationships between ENSO and the tropical Indian Ocean (TIO) sea surface temperature (SST) in the CMIP6 models are more realistic, stronger, and have less intermodel diversity than those in the CMIP3 and CMIP5 models. Secondly, the teleconnections between the TIO SST and Philippine Sea convection (PSC) in the CMIP6 models are almost the same as those in the CMIP5 models, and stronger than those in the CMIP3 models. Finally, the CMIP3, CMIP5, and CMIP6 models exhibit essentially identical capabilities in representing the PSC–EASR relationship. Almost all the three generations of models underestimate the ENSO–EASR, TIO SST–PSC, and PSC–EASR relationships. Moreover, almost all the CMIP6 models that successfully capture the significant TIO SST–PSC relationship realistically simulate the ENSO–EASR relationship and vice versa, which is, however, not the case in the CMIP5 models.
El Niño–Southern Oscillation (ENSO) events have a strong influence on East Asian summer rainfall (EASR). This paper investigates the simulated ENSO–EASR relationship in CMIP6 models and compares the results with those in CMIP3 and CMIP5 models. In general, the CMIP6 models show almost no appreciable progress in representing the ENSO–EASR relationship compared with the CMIP5 models. The correlation coefficients in the CMIP6 models are relatively smaller and exhibit a slightly greater intermodel diversity than those in the CMIP5 models. Three physical processes related to the delayed effect of ENSO on EASR are further analyzed. Results show that, firstly, the relationships between ENSO and the tropical Indian Ocean (TIO) sea surface temperature (SST) in the CMIP6 models are more realistic, stronger, and have less intermodel diversity than those in the CMIP3 and CMIP5 models. Secondly, the teleconnections between the TIO SST and Philippine Sea convection (PSC) in the CMIP6 models are almost the same as those in the CMIP5 models, and stronger than those in the CMIP3 models. Finally, the CMIP3, CMIP5, and CMIP6 models exhibit essentially identical capabilities in representing the PSC–EASR relationship. Almost all the three generations of models underestimate the ENSO–EASR, TIO SST–PSC, and PSC–EASR relationships. Moreover, almost all the CMIP6 models that successfully capture the significant TIO SST–PSC relationship realistically simulate the ENSO–EASR relationship and vice versa, which is, however, not the case in the CMIP5 models.
2021, 38(2): 237-252.
doi: 10.1007/s00376-020-0167-7
Abstract:
This study provides a comprehensive evaluation of historical surface soil moisture simulation (1979–2012) over Eurasia at annual and seasonal time scales between two medium-resolution versions of the Beijing Climate Center Climate System Model (BCC-CSM)—one that is currently participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), i.e., BCC-CSM2-MR, and the other, BCC-CSM1.1m, which participated in CMIP5. We show that BCC-CSM2-MR is more skillful in reproducing the climate mean states and standard deviations of soil moisture, with pattern correlations increased and biases reduced significantly. BCC-CSM2-MR performs better in capturing the first two primary patterns of soil moisture anomalies, where the period of the corresponding time series is closer to that of reference data. Comparisons show that BCC-CSM2-MR performs at a high level among multiple models of CMIP6 in terms of centered pattern correlation and “amplitude of variation” (relative standard deviation). In general, the centered pattern correlation of BCC-CSM2-MR, ranging from 0.61 to 0.87, is higher than the multi-model mean of CMIP6, and the relative standard deviation is 0.75, which surmounts the overestimations in most of the CMIP6 models. Due to the vital role played by precipitation in land–atmosphere interaction, possible causes of the improvement of soil moisture simulation are further related to precipitation in BCC-CSM2-MR. The results indicate that a better description of the relationship between soil moisture and precipitation and a better reproduction of the climate mean precipitation by the model may result in the improved performance of soil moisture simulation.
This study provides a comprehensive evaluation of historical surface soil moisture simulation (1979–2012) over Eurasia at annual and seasonal time scales between two medium-resolution versions of the Beijing Climate Center Climate System Model (BCC-CSM)—one that is currently participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), i.e., BCC-CSM2-MR, and the other, BCC-CSM1.1m, which participated in CMIP5. We show that BCC-CSM2-MR is more skillful in reproducing the climate mean states and standard deviations of soil moisture, with pattern correlations increased and biases reduced significantly. BCC-CSM2-MR performs better in capturing the first two primary patterns of soil moisture anomalies, where the period of the corresponding time series is closer to that of reference data. Comparisons show that BCC-CSM2-MR performs at a high level among multiple models of CMIP6 in terms of centered pattern correlation and “amplitude of variation” (relative standard deviation). In general, the centered pattern correlation of BCC-CSM2-MR, ranging from 0.61 to 0.87, is higher than the multi-model mean of CMIP6, and the relative standard deviation is 0.75, which surmounts the overestimations in most of the CMIP6 models. Due to the vital role played by precipitation in land–atmosphere interaction, possible causes of the improvement of soil moisture simulation are further related to precipitation in BCC-CSM2-MR. The results indicate that a better description of the relationship between soil moisture and precipitation and a better reproduction of the climate mean precipitation by the model may result in the improved performance of soil moisture simulation.
2021, 38(2): 253-267.
doi: 10.1007/s00376-020-0182-8
Abstract:
Extreme high temperature (EHT) events are among the most impact-related consequences related to climate change, especially for China, a nation with a large population that is vulnerable to the climate warming. Based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), this study assesses future EHT changes across China at five specific global warming thresholds (1.5°C–5°C). The results indicate that global mean temperature will increase by 1.5°C/2°C before 2030/2050 relative to pre-industrial levels (1861–1900) under three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5. Under SSP5-8.5, global warming will eventually exceed 5°C by 2100, while under SSP1-2.6, it will stabilize around 2°C after 2050. In China, most of the areas where warming exceeds global average levels will be located in Tibet and northern China (Northwest China, North China and Northeast China), covering 50%–70% of the country. Furthermore, about 0.19–0.44 billion people (accounting for 16%–41% of the national population) will experience warming above the global average. Compared to present-day (1995–2014), the warmest day (TXx) will increase most notably in northern China, while the number of warm days (TX90p) and warm spell duration indicator (WSDI) will increase most profoundly in southern China. For example, relative to the present-day, TXx will increase by 1°C–5°C in northern China, and TX90p (WSDI) will increase by 25–150 (10–80) days in southern China at 1.5°C–5°C global warming. Compared to 2°C–5°C, limiting global warming to 1.5°C will help avoid about 36%–87% of the EHT increases in China.
Extreme high temperature (EHT) events are among the most impact-related consequences related to climate change, especially for China, a nation with a large population that is vulnerable to the climate warming. Based on the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), this study assesses future EHT changes across China at five specific global warming thresholds (1.5°C–5°C). The results indicate that global mean temperature will increase by 1.5°C/2°C before 2030/2050 relative to pre-industrial levels (1861–1900) under three future scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and warming will occur faster under SSP5-8.5 compared to SSP1-2.6 and SSP2-4.5. Under SSP5-8.5, global warming will eventually exceed 5°C by 2100, while under SSP1-2.6, it will stabilize around 2°C after 2050. In China, most of the areas where warming exceeds global average levels will be located in Tibet and northern China (Northwest China, North China and Northeast China), covering 50%–70% of the country. Furthermore, about 0.19–0.44 billion people (accounting for 16%–41% of the national population) will experience warming above the global average. Compared to present-day (1995–2014), the warmest day (TXx) will increase most notably in northern China, while the number of warm days (TX90p) and warm spell duration indicator (WSDI) will increase most profoundly in southern China. For example, relative to the present-day, TXx will increase by 1°C–5°C in northern China, and TX90p (WSDI) will increase by 25–150 (10–80) days in southern China at 1.5°C–5°C global warming. Compared to 2°C–5°C, limiting global warming to 1.5°C will help avoid about 36%–87% of the EHT increases in China.
2021, 38(2): 268-284.
doi: 10.1007/s00376-020-0173-9
Abstract:
This paper introduces the experimental designs and outputs of the Diagnostic, Evaluation and Characterization of Klima (DECK), historical, Scenario Model Intercomparison Project (MIP), and Paleoclimate MIP (PMIP) experiments from the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3). Results show that NESM3 reasonably simulates the modern climate and the major internal modes of climate variability. In the Scenario MIP experiment, changes in the projected surface air temperature (SAT) show robust “Northern Hemisphere (NH) warmer than Southern Hemisphere (SH)” and “land warmer than ocean” patterns, as well as an El Niño-like warming over the tropical Pacific. Changes in the projected precipitation exhibit “NH wetter than SH” and “eastern hemisphere gets wetter and western hemisphere gets drier” patterns over the tropics. These precipitation patterns are driven by circulation changes owing to the inhomogeneous warming patterns. Two PMIP experiments show enlarged seasonal cycles of SAT and precipitation over the NH due to the seasonal redistribution of solar radiation. Changes in the climatological mean SAT, precipitation, and ENSO amplitudes are consistent with the results from PMIP4 models. The NESM3 outputs are available on the Earth System Grid Federation nodes for data users.
This paper introduces the experimental designs and outputs of the Diagnostic, Evaluation and Characterization of Klima (DECK), historical, Scenario Model Intercomparison Project (MIP), and Paleoclimate MIP (PMIP) experiments from the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3). Results show that NESM3 reasonably simulates the modern climate and the major internal modes of climate variability. In the Scenario MIP experiment, changes in the projected surface air temperature (SAT) show robust “Northern Hemisphere (NH) warmer than Southern Hemisphere (SH)” and “land warmer than ocean” patterns, as well as an El Niño-like warming over the tropical Pacific. Changes in the projected precipitation exhibit “NH wetter than SH” and “eastern hemisphere gets wetter and western hemisphere gets drier” patterns over the tropics. These precipitation patterns are driven by circulation changes owing to the inhomogeneous warming patterns. Two PMIP experiments show enlarged seasonal cycles of SAT and precipitation over the NH due to the seasonal redistribution of solar radiation. Changes in the climatological mean SAT, precipitation, and ENSO amplitudes are consistent with the results from PMIP4 models. The NESM3 outputs are available on the Earth System Grid Federation nodes for data users.
2021, 38(2): 285-295.
doi: 10.1007/s00376-020-0171-y
Abstract:
This paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences (CAMS) climate system model (CAMS-CSM), which are contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). The model description, experiment design and model outputs are presented. Three members’ historical experiments are conducted by CAMS-CSM, with two members starting from different initial conditions, and one excluding the stratospheric aerosol to identify the effect of volcanic eruptions. The outputs of the historical experiments are also validated using observational data. It is found that the model can reproduce the climatological mean states and seasonal cycle of the major climate system quantities, including the surface air temperature, precipitation, and the equatorial thermocline. The long-term trend of air temperature and precipitation is also reasonably captured by CAMS-CSM. There are still some biases in the model that need further improvement. This paper can help the users to better understand the performance and the datasets of CAMS-CSM.
This paper describes the historical simulations produced by the Chinese Academy of Meteorological Sciences (CAMS) climate system model (CAMS-CSM), which are contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). The model description, experiment design and model outputs are presented. Three members’ historical experiments are conducted by CAMS-CSM, with two members starting from different initial conditions, and one excluding the stratospheric aerosol to identify the effect of volcanic eruptions. The outputs of the historical experiments are also validated using observational data. It is found that the model can reproduce the climatological mean states and seasonal cycle of the major climate system quantities, including the surface air temperature, precipitation, and the equatorial thermocline. The long-term trend of air temperature and precipitation is also reasonably captured by CAMS-CSM. There are still some biases in the model that need further improvement. This paper can help the users to better understand the performance and the datasets of CAMS-CSM.
2021, 38(2): 296-306.
doi: 10.1007/s00376-020-0188-2
Abstract:
The second version of the Chinese Academy of Sciences Earth System Model (CAS-ESM2.0) is participating in the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The purpose of FAFMIP is to understand and reduce the uncertainty of ocean climate changes in response to increased CO2 forcing in atmosphere-ocean general circulation models (AOGCMs), including the simulations of ocean heat content (OHC) change, ocean circulation change, and sea level rise due to thermal expansion. FAFMIP experiments (including faf-heat, faf-stress, faf-water, faf-all, faf-passiveheat, faf-heat-NA50pct and faf-heat-NA0pct) have been conducted. All of the experiments were integrated over a 70-year period and the corresponding data have been uploaded to the Earth System Grid Federation data server for CMIP6 users to download. This paper describes the experimental design and model datasets and evaluates the preliminary results of CAS-ESM2.0 simulations of ocean climate changes in the FAFMIP experiments. The simulations of the changes in global ocean temperature, Atlantic Meridional Overturning Circulation (AMOC), OHC, and dynamic sea level (DSL), are all reasonably reproduced.
The second version of the Chinese Academy of Sciences Earth System Model (CAS-ESM2.0) is participating in the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The purpose of FAFMIP is to understand and reduce the uncertainty of ocean climate changes in response to increased CO2 forcing in atmosphere-ocean general circulation models (AOGCMs), including the simulations of ocean heat content (OHC) change, ocean circulation change, and sea level rise due to thermal expansion. FAFMIP experiments (including faf-heat, faf-stress, faf-water, faf-all, faf-passiveheat, faf-heat-NA50pct and faf-heat-NA0pct) have been conducted. All of the experiments were integrated over a 70-year period and the corresponding data have been uploaded to the Earth System Grid Federation data server for CMIP6 users to download. This paper describes the experimental design and model datasets and evaluates the preliminary results of CAS-ESM2.0 simulations of ocean climate changes in the FAFMIP experiments. The simulations of the changes in global ocean temperature, Atlantic Meridional Overturning Circulation (AMOC), OHC, and dynamic sea level (DSL), are all reasonably reproduced.
2021, 38(2): 307-316.
doi: 10.1007/s00376-020-0150-3
Abstract:
As a member of the Chinese modeling groups, the coupled ocean–ice component of the Chinese Academy of Sciences’ Earth System Model, version 2.0 (CAS-ESM2.0), is taking part in the Ocean Model Intercomparison Project Phase 1 (OMIP1) experiment of phase 6 of the Coupled Model Intercomparison Project (CMIP6). The simulation was conducted, and monthly outputs have been published on the ESGF (Earth System Grid Federation) data server. In this paper, the experimental dataset is introduced, and the preliminary performances of the ocean model in simulating the global ocean temperature, salinity, sea surface temperature, sea surface salinity, sea surface height, sea ice, and Atlantic Meridional Overturning Circulation (AMOC) are evaluated. The results show that the model is at quasi-equilibrium during the integration of 372 years, and performances of the model are reasonable compared with observations. This dataset is ready to be downloaded and used by the community in related research, e.g., multi-ocean–sea-ice model performance evaluation and interannual variation in oceans driven by prescribed atmospheric forcing.
As a member of the Chinese modeling groups, the coupled ocean–ice component of the Chinese Academy of Sciences’ Earth System Model, version 2.0 (CAS-ESM2.0), is taking part in the Ocean Model Intercomparison Project Phase 1 (OMIP1) experiment of phase 6 of the Coupled Model Intercomparison Project (CMIP6). The simulation was conducted, and monthly outputs have been published on the ESGF (Earth System Grid Federation) data server. In this paper, the experimental dataset is introduced, and the preliminary performances of the ocean model in simulating the global ocean temperature, salinity, sea surface temperature, sea surface salinity, sea surface height, sea ice, and Atlantic Meridional Overturning Circulation (AMOC) are evaluated. The results show that the model is at quasi-equilibrium during the integration of 372 years, and performances of the model are reasonable compared with observations. This dataset is ready to be downloaded and used by the community in related research, e.g., multi-ocean–sea-ice model performance evaluation and interannual variation in oceans driven by prescribed atmospheric forcing.
2021, 38(2): 317-328.
doi: 10.1007/s00376-020-0151-2
Abstract:
BCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model, and is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved. All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted. The DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations have also been run as the entry card of CMIP6. The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies. To facilitate the use of the BCC-ESM1 datasets, this study describes the experiment settings and summarizes the model outputs in detail. Preliminary evaluations of BCC-ESM1 are also presented, revealing that: the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5; the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured, despite some common precipitation biases as in CMIP5 and CMIP6 models; a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1, as in most other ESMs; and the mean states of surface sulfate concentrations can also be reasonably reproduced, as well as their temporal evolution at regional scales. These datasets have been archived on the Earth System Grid Federation (ESGF) node for atmospheric chemistry studies.
BCC-ESM1 is the first version of the Beijing Climate Center’s Earth System Model, and is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The Aerosol Chemistry Model Intercomparison Project (AerChemMIP) is the only CMIP6-endorsed MIP in which BCC-ESM1 is involved. All AerChemMIP experiments in priority 1 and seven experiments in priorities 2 and 3 have been conducted. The DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations have also been run as the entry card of CMIP6. The AerChemMIP outputs from BCC-ESM1 have been widely used in recent atmospheric chemistry studies. To facilitate the use of the BCC-ESM1 datasets, this study describes the experiment settings and summarizes the model outputs in detail. Preliminary evaluations of BCC-ESM1 are also presented, revealing that: the climate sensitivities of BCC-ESM1 are well within the likely ranges suggested by IPCC AR5; the spatial structures of annual mean surface air temperature and precipitation can be reasonably captured, despite some common precipitation biases as in CMIP5 and CMIP6 models; a spurious cooling bias from the 1960s to 1990s is evident in BCC-ESM1, as in most other ESMs; and the mean states of surface sulfate concentrations can also be reasonably reproduced, as well as their temporal evolution at regional scales. These datasets have been archived on the Earth System Grid Federation (ESGF) node for atmospheric chemistry studies.
2021, 38(2): 329-339.
doi: 10.1007/s00376-020-0112-9
Abstract:
The datasets for the tier-1 Scenario Model Intercomparison Project (ScenarioMIP) experiments from the Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System model, finite-volume version 3 (CAS FGOALS-f3-L) are described in this study. ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Considering future CO2, CH4, N2O and other gases’ concentrations, as well as land use, the design of ScenarioMIP involves eight pathways, including two tiers (tier-1 and tier-2) of priority. Tier-1 includes four combined Shared Socioeconomic Pathways (SSPs) with radiative forcing, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6, 4.5, 7.0 and 8.5 W m−2, respectively. This study provides an introduction to the ScenarioMIP datasets of this model, such as their storage location, sizes, variables, etc. Preliminary analysis indicates that surface air temperatures will increase by about 1.89°C, 3.07°C, 4.06°C and 5.17°C by around 2100 under these four scenarios, respectively. Meanwhile, some other key climate variables, such as sea-ice extension, precipitation, heat content, and sea level rise, also show significant long-term trends associated with the radiative forcing increases. These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.
The datasets for the tier-1 Scenario Model Intercomparison Project (ScenarioMIP) experiments from the Chinese Academy of Sciences (CAS) Flexible Global Ocean–Atmosphere–Land System model, finite-volume version 3 (CAS FGOALS-f3-L) are described in this study. ScenarioMIP is one of the core MIP experiments in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Considering future CO2, CH4, N2O and other gases’ concentrations, as well as land use, the design of ScenarioMIP involves eight pathways, including two tiers (tier-1 and tier-2) of priority. Tier-1 includes four combined Shared Socioeconomic Pathways (SSPs) with radiative forcing, i.e., SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, in which the globally averaged radiative forcing at the top of the atmosphere around the year 2100 is approximately 2.6, 4.5, 7.0 and 8.5 W m−2, respectively. This study provides an introduction to the ScenarioMIP datasets of this model, such as their storage location, sizes, variables, etc. Preliminary analysis indicates that surface air temperatures will increase by about 1.89°C, 3.07°C, 4.06°C and 5.17°C by around 2100 under these four scenarios, respectively. Meanwhile, some other key climate variables, such as sea-ice extension, precipitation, heat content, and sea level rise, also show significant long-term trends associated with the radiative forcing increases. These datasets will help us understand how the climate will change under different anthropogenic and radiative forcings.
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