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Meteorological Research Institute-Earth System Model Version 1 (MRI-ESM1) — Model Description —
Abstract and Figures
The Meteorological Research Institute (MRI) of Japan developed the Earth System Model MRI-ESM1 to enable us to simulate both the climate system and global material transport, along with their interaction. Its core component, the atmosphere–ocean coupled global climate model MRI-CGCM3, represents a substantial advance from the previous model, MRI-CGCM2.3, which made important contributions to the fourth assessment report of the Intergovernmental Panel on Climate Change. The global atmospheric model GSMUV, used as the atmospheric component of MRI-CGCM3, incorporates various new physical parameterizations, including a cumulus convection scheme, a high-accuracy radiation scheme, a two-moment bulk cloud model that explicitly represents aerosol effects on clouds, and a new, sophisticated land-surface model, into the dynamics framework by a conservative semi-Lagrange method. MRI.COM3, also newly developed at MRI, is used for the global ocean-ice component of MRI-CGCM3. We adopted for MRI.COM3 a tripolar grid coordinate system, in which the North Pole is not a singular point, because MRI.COM3 supports general orthogonal curvilinear coordinates. The sea-ice model has also been updated; it now represents the sub-grid ice-thickness distribution by thickness categories, and incorporates ice rheology dynamics in addition to detailed thermodynamics. The MASINGAR mk-2 aerosol model takes into account five kinds of atmospheric aerosols, sulfate, black and organic carbon, mineral dust, and sea salt. The MRI-CCM2 atmospheric chemistry climate model (ozone model) is used to treat chemical reactions and the transport of atmospheric species associated with both tropospheric and stratospheric ozone. To represent the global carbon cycle, terrestrial ecosystem carbon cycle and ocean biogeochemical carbon cycle processes are incorporated into the land-surface model and the ocean model, respectively. The Scup coupler developed at MRI is used to integrate each component model, the atmospheric, ocean, aerosol, and ozone models, into MRI-ESM1. This flexible coupler can couple models with different resolutions and grid coordinates with variable coupling intervals. This advantage not only leads to efficient execution of the earth system model but also allows the efficient and independent development of the component models.
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... AOD data at 550 nm were obtained through three aerosol reanalyses: (a) the Japanese Reanalysis for Aerosols v1.0 (JRAero; Yumimoto et al., 2017); (b) the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA; Inness et al., 2019); and (c) Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2; Buchard et al., 2017;Randles et al., 2017). The configuration of each reanalysis is summarized in Table S1 of the Supporting Information S1. Yukimoto et al., 2011) comprising the atmospheric general circulation model (MRI-AGCM3) and the Model of Aerosol Species In the Global AtmospheRe mk-2 (MASINGAR mk-2; Tanaka et al., 2003). Horizontal wind and temperature predicted by MRI-AGCM3 are nudged into the 6-hr JMA operational global analysis (GANAL/ JMA), with MASINGAR mk-2 then calculating the emission, transport, reaction, and deposition of five major aerosol species (sulfate, hydrophobic and hydrophilic BC and organic carbon (OC), mineral dust, and sea salt (SS)) through atmospheric fields. ...
... JRAero provides AOD data with a TL159 horizontal intervals (∼1.1° × 1.1°) and 48 vertical levels. Further details of JRAero, MRI-ESM1, and MASINGAR mk-2 are provided by Yumimoto et al. (2017), Yukimoto et al. (2011Yukimoto et al. ( , 2012, Tanaka et al. (2003), and Tanaka and Chiba (2005). ...
Atmospheric aerosol affects the radiation budget, cloud cover and properties, and surface albedo of sea ice and snow over the Arctic with obvious climatological significance. However, observations are scarce and have large uncertainties. Aerosol optical depth (AOD) variability in the Arctic and its relationship with atmospheric disturbances on synoptic timescales were investigated on the basis of the JRAero, CAMSRA, and MERRA2 reanalyses. Total AODs of the three reanalyses were spatiotemporally consistent in summer, although contributions of individual aerosol species differed. Summertime AOD variability was strongly correlated with observations in all reanalyses. The predominance of organic carbon aerosol indicates that aerosols are derived mainly from biomass burning over northern Eurasia and northern North America. Differences in composites of atmospheric fields between high‐ and low‐loading days indicate that locations of synoptic disturbances are related to high‐AOD events in the Arctic. Furthermore, empirical orthogonal function analysis indicates that the first‐ and second‐largest AOD variabilities in synoptic timescales occur over northern Eurasia. This AOD variability is related to two different types of Arctic cyclone, the development of which is important in aerosol transport, aging, and deposition in summer.
Impact models used in water, ecology, and agriculture require accurate climatic data to simulate observed impacts. Some of these models emphasize the distribution of precipitation within a month or season rather than the overall amount. To meet this requirement , a study applied three bias correction techniques-scaled distribution mapping (SDM), quantile distribution mapping (QDM), and QDM with a separate treatment for precipitation below and above the 95th percentile threshold (QDM95)-to daily precipitation data from eleven Coupled Model Intercomparison Project Phase 6 (CMIP6) models, using the Climate Hazards Group Infrared Precipitation with Station version 2 (CHIRPS) as a reference. This study evaluated the performance of all bias-corrected CMIP6 models over Southern Africa from 1982 to 2014 in replicating the spatial and temporal patterns of precipitation across the region against three observational datasets, CHIRPS, the Climatic Research Unit (CRU), and the Global Precipitation Climatology Centre (GPCC), using standard statistical metrics. The results indicate that all bias-corrected precipitation generally performs better than native model precipitation in replicating the observed December-February (DJF) mean and seasonal cycle. The probability density function (PDF) of the bias-corrected regional precipitation indicates that bias correction enhances model performance, particularly for precipitation in the range of 3-35 mm/day. However, both corrected and uncorrected models underestimate higher extremes. The pattern correlations of the bias-corrected precipitation with CHIRPS, the GPCC, and the CRU, as compared to the correlations of native precipitation with the three datasets, have improved from 0.76-0.89 to 0.97-0.99, 0.73-0.87 to 0.94-0.97, and 0.74-0.89 to 0.97-0.99, respectively. Additionally, the Taylor skill scores of the models for replicating the CHIRPS, GPCC, and CRU precipitation spatial patterns over Southern Africa have improved from 0.57-0.80 to 0.79-0.95, 0.55-0.76 to 0.80-0.91, and 0.54-0.75 to 0.81-0.91, respectively. Overall, among the three bias correction techniques, QDM consistently demonstrated better performance than both QDM95 and SDM across various metrics. The implementation of distribution-based bias correction resulted in a significant reduction in bias and improved the spatial consistency between models and observations over the region.
Permafrost is undergoing rapid changes due to climate warming, potentially exposing a vast reservoir of carbon to be released to the atmosphere, causing a positive feedback cycle. Despite the importance of this feedback, its specifics remain poorly constrained, because representing permafrost dynamics still poses a significant challenge for Earth System Models (ESMs). This review assesses the current state of permafrost representation in land surface models (LSMs) used in ESMs and offline permafrost models, highlighting both the progress made and the remaining gaps.
We identify several key physical processes crucial for permafrost dynamics, including soil thermal regimes, freeze–thaw cycles, and soil hydrology, which are underrepresented in many models. While some LSMs have advanced significantly in incorporating these processes, others lack fundamental elements such as latent heat of freeze–thaw, deep soil columns, and Arctic vegetation dynamics. Offline permafrost models provide valuable insights, offering detailed process testing and aiding the prioritization of improvements in coupled LSMs.
Our analysis reveals that while significant progress has been made in incorporating permafrost‐related processes into coupled LSMs, many small‐scale processes crucial for permafrost dynamics remain underrepresented. This is particularly important for capturing the complex interactions between physical and biogeochemical processes required to model permafrost carbon dynamics. We recommend leveraging advancements from offline permafrost models and progressively integrating them into LSMs, while recognizing the computational and technical challenges that may arise in coupled simulations. We highlight the importance of enhancing the representation of physical processes, including through improvements in model resolution and complexity, as this is a fundamental precursor to accurately incorporate biogeochemical processes and capture the permafrost carbon feedback.
Permafrost and ground freezing/thawing processes are physically and eco-climatologically important factors in the terrestrial cryosphere. The model reproducibility of frozen ground affects the certainty and reliability of simulated eco-climate conditions in cold regions as well as on a global scale. This study evaluated the variations and their attributes in the model performance developed and employed in the recent decade regarding the subsurface thermal state using outputs from Japanese and international model intercomparison projects and reanalysis data. The simulated surface and subsurface physical states were compared at four Arctic sites under different frozen ground conditions (Fairbanks, Kevo, Tiksi, and Yakutsk). The results showed that despite large variations in the modeled permafrost temperature, all the models, including the reanalysis data, successfully reproduced the permafrost conditions for the continuous permafrost sites. In contrast, some models failed to reproduce the presence of permafrost for the sites in the discontinuous to isolated permafrost zones. Evaluations of near-surface ground temperature variability revealed that the overall wellness of the simulated ground thermal states relied on winter reproducibility. The importance of snowpack metamorphosis for adequate thermal insulation was confirmed and demonstrated. The results at the coastal tundra site imply the importance of snow cover redistribution and wind crust formation owing to strong winds, the lack of which resulted in overestimations of thermal insulation and overcooled near-surface ground by most models.
Simulation of the carbon cycle in climate models is important due to its impact on climate change, but many weaknesses in its reproduction were found in previous models. Improvements in the representation of the land carbon cycle in Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include the interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2-concentration- and CO2-emission-driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle but remaining for models without one. This points to the importance of including nutrient limitation. Simulating the leaf area index (LAI) remains challenging, with a large model spread in both CMIP5 and CMIP6. In ESMs, the global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. However, this is the result of an underestimation of NBP in the Northern Hemisphere, which is compensated by an overestimation in the Southern Hemisphere and the tropics. Carbon stocks remain a large uncertainty in the models. While vegetation carbon content is slightly better represented in CMIP6, the inter-model range of soil carbon content remains the same between CMIP5 and CMIP6. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 compared to their CMIP5 models. This improvement is not as significant in the multi-model means due to more new models in CMIP6, especially those using older versions of the Community Land Model (CLM). Emission-driven simulations perform just as well as the concentration-driven models, despite the added process realism. Due to this, we recommend that ESMs in future Coupled Model Intercomparison Project (CMIP) phases perform emission-driven simulations as the standard so that climate–carbon cycle feedbacks are fully active. The inclusion of the nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Possible benefits when including further limiting nutrients such as phosphorus should also be considered.
Changes in climate in the South Atlantic region and adjacent regions have been described in numerous works using projections from global climate models from CMIP5 and CMIP6. This paper presents an evaluation of the ability of these models to reproduce the atmospheric circulation patterns (weather types) and their seasonal and inter‐annual variability. The analyses are performed based on the probability of occurrence of weather types in the historical period and in future projections. The scatter index and the relative entropy are the statistical parameters used to evaluate the models' performance in the historical period. Future projections consist of RCP2.6, 4.5 and 8.5 scenarios for the CMIP5 models and the SSP126, 245, 370 and 585 scenarios for the CMIP6 and are assessed at different time intervals: short term (2015–2039), mid‐term (2040–2069) and long term (2070–2100). The performance of projections is measured by analysing their consistency, that is, based on the similarity between projections of the same scenario in different models. The results show that the reproduction of the probability of occurrence of historical weather types and their seasonal and interannual variability was better performed by ACCESS1‐0, HadGEM2‐ES, HadGEM2‐CC, CMCC‐CM and MPI‐ESM‐P when assessing the models from CMIP5, and by HadGEM3‐GC31‐MM, ACCESS‐ESM1‐5, ACCESS‐ CM2 and MRI‐ESM‐P when assessing the models from CMIP6. As for future projections, only the BESM‐AO2‐5, GFDL‐ESM4 and HadGEM3‐GC31‐MM models showed inconsistency in one or more scenarios.
High clouds play an important role in climate variations by shading the sun and contributing to the greenhouse effect. It has been suggested that these variations are strongly controlled by atmospheric circulations; however, the relationship between high clouds and circulations in global simulations has been confirmed only for a limited number of general circulation models (GCMs). Increasing our understanding of this relationship requires a more complete dataset of state-of-the-art GCMs. This study quantifies the relationship between high clouds and circulations by spatial and temporal correlation using data from 28 atmospheric GCMs along with a global nonhydrostatic model, a new-type of GCM, and reveals a robust and strong relationship in most models. This study also finds that the sensitivity, which is defined as the slope of the relationship between high clouds and mass fluxes of circulations, is highly model-dependent. This suggests that a similar change of circulations does not guarantee that of high clouds, which could be one reason for the complexity in projecting high-cloud changes due to warming. Moreover, this study confirms in both observation and models that the relationship with circulations is stronger for medium-thickness and thick high clouds than for thin clouds. Furthermore, the relationship between high clouds and circulations is more convincing in near-equatorial regions between 15°S–15°N, the ascending branch of the Hadley circulation, where deep convection is especially active.
Improvements in the representation of the land carbon cycle in Earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2 concentration and emission driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle, but remaining for models without one. This points to the importance of including nutrient limitation. Simulating the leaf area index (LAI) remains challenging with a large model spread in both CMIP5 and CMIP6. In ESMs, global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. However, this is the result of an underestimation of NBP in the northern hemisphere, which is compensated by an overestimation in the southern hemisphere and the tropics. Carbon stocks remain a large uncertainty in the models. While vegetation carbon content is slightly better represented in CMIP6, the inter-model range of soil carbon content remains the same between CMIP5 and CMIP6. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 compared to their CMIP5 models. This improvement is not as significant in the multi-model means due to more new models in CMIP6, especially those using older versions of the Community Land Model (CLM). Emission driven simulations perform just as well as concentration driven models despite the added process-realism. Due to this we recommend ESMs in future CMIP phases to perform emission driven simulations as the standard so that climate-carbon cycle feedbacks are fully active. The inclusion of nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Possible benefits when including further limiting nutrients such as phosphorus should also be considered.
The Medieval Climate Anomaly (MCA; ca. 950–1250 CE) and the Little Ice Age (LIA; ca. 1450–1850 CE) were periods generally characterized by respectively higher and lower temperatures in many regions. However, they have also been associated with drier and wetter conditions in areas around the Intertropical Convergence Zone (ITCZ) and the Asian Monsoon region and in areas impacted by large-scale climatic modes like the Northern Annular Mode and Southern Annular Mode (NAM and SAM respectively). To analyze coordinated changes in large-scale hydroclimate patterns and whether similar changes also extend to other periods of the Last Millennium (LM) outside the MCA and the LIA, reconstruction-based products have been analyzed. This includes the collection of tree-ring-based drought atlases (DAs), the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR). These analyses have shown coherent changes in the hydroclimate of tropical and extratropical regions, such as northern and central South America, East Africa, western North America, western Europe, the Middle East, Southeast Asia, and the Indo-Pacific, during the MCA, the LIA and other periods of the LM. Comparisons with model simulations from the Community Earth System Model – Last Millennium Ensemble (CESM-LME) and phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) show that both external forcing and internal variability contributed to these changes, with the contribution of internal variability being particularly important in the Indo-Pacific basin and that of external forcing in the Atlantic basin. These results may help to identify not only those areas showing coordinated changes, but also those regions more impacted by the internal variability, where forced model simulations would not be expected to successfully reproduce the evolution of past actual hydroclimate changes.
The processes governing tropospheric stratification alterations during the Last Glacial Maximum (LGM) are investigated using the Paleoclimate Modeling Intercomparison Project Phase 3/4 (PMIP3/PMIP4) simulations. The results demonstrate a decrease in static stability in the tropics during both December‐January‐February (DJF) and June‐July‐August (JJA), while an increase is observed in the extratropics during DJF. Further analysis reveals that the rise in static stability over high‐latitude ocean is driven by shifts in surface frozen lines, whereas the increased static stability over ice sheet margins is due to the cooling effect of ice sheet slopes. The study finds that the dry static stability change in ice sheet‐covered areas remains significant and robust in both PMIP3 and PMIP4. However, a weaker stabilization signal is detected in the North Atlantic in PMIP4. These findings provide valuable insights into the processes shaping tropospheric stratification during the LGM and underscore the importance of accounting for ice sheet effects in paleoclimate simulations.
Characteristics of the equatorial intraseasonal oscillation are studied with the use of a general circulation model which includes the Arakawa-Schubert (abbreviated as AS hereafter) model of penetrative cumulus convections. The AS model is modified by introducing the minimum value of cumulus entrainment rate of the environmental air, μmin, as μmin=α/D, where D is the depth of the planetary boundary layer (PBL) and a is a non-negative constant. The introduction of a positive α in the AS model suppresses the activity of deep penetrative cumulus in the area where D is not sufficiently thick, which allows, in turn, an accumulation of moist air in the large-scale low level convergence zone. This process is essential in maintaining the equatorial 30-60 day oscillations, and also in simulating the Pacific subtropical high during the northern summer. Experiments are performed by changing α from 0 (ie., the original AS model), to ∞ (i.e., no penetrative cumulus convection) under an aqua-planet condition.
When α=0, the 30-60 day oscillation does not appear in the tropics. Instead, there exists a quasi -10 day eastward propagating oscillation with zonal wavenumber 1, which resembles a neutral Kelvin wave. Moist air is not accumulated in the 'low level east-west convergence longitude associated with the flow of zonal wavenumber 1 (LLCL)' due to the rapid response of the AS model to the evaporation and moisture flux convergence by small scale motions and also due to the resulting upward transport of water vapor by penetrative cumuli to the west of the LLCL before the moist air can be accumulated in the LLCL.
When α=0.1, a quasi-30 day eastward propagating oscillation with zonal wavenumber 1 grows in the model, with the moist air and the major heating found around the LLCL. The change in the heating is mostly due to the increase of middle-level convection (i.e., moist convection between adjacent vertical layers within the free atmosphere) and to the decrease of deep penetrative cumuli to the west of the LLCL. Overall characteristics of the mode are close to the observed ones. When α=∞, a quasi-45 day eastward propagating oscillation grows in the model. The structure of the heating associated with the oscillation is similar to that of the quasi-30 day oscillation.
Associated with the increase of α, the static stability decreases in the low latitudes. The maximum level of the zonally averaged heating lowers due to the suppression of deep penetrative cumulus and the increase in both the middle-level convection and large-scale condensation. The maximum amplitude level of the heating associated with the equatorial intraseasonal oscillation also lowers from 300mb for α=0 to 500mb for α=0.1-∞. These changes seem to provide favorable conditions for the occurrence of the equatorial intraseasonal oscillation, in agreement with linear stability studies of a CISK model.
A 3-dimensional, anelastic cloud model is applied to the simulation of the July 19, 1981 Cooperative Convective Precipitation Experiment (CCOPE) case study cloud. The model utilizes the bulk water microphysical parameterization technique where number concentrations of cloud ice and snow are taken into account in addition to the mixing ratios of six water species (water vapor, cloud water, cloud ice, rain, snow and graupel/hail). Cloud ice is initiated only by primary nucleation processes (deposition/sorption and heterogeneous and homogeneous freezing of cloud droplets) in the present model. The timing reference was established between the simulation and observations based on a remarkable change in the rise rates of both the observed and simulated cloud tops, and the model results are compared with the observations as a function of time and space.
The general features of the cloud (such as cloud top height, cloud size, arrival time of precipitation at the cloud base, radar first echo, etc.) seem to have been well reproduced. Furthermore, the model cloud simulated quite well the location of hydrometeors with respect to the updraft/downdraft structure, the number concentration of precipitating ice particles, updraft velocity and cloud water content along the King Air's penetrating pass.
The main features which are not accurately reproduced are the cloud base height, the rise rate of the cloud top and the radar echo near the ground. The cloud base height is too low, which is attributed to the lack of representativeness in the input data taken from the closest radiosonde sounding, while the too rapid rise rate of the cloud top seems to be attributable to the way in which convection is initiated. The rapid decrease in radar reflectivity of the simulated cloud seems to be attributable to inadequate parameterization for rain and graupel.
This paper describes the status of the 2004 edition of the HITRAN molecular spectroscopic database. The HITRAN compilation consists of several components that serve as input for radiative transfer calculation codes: individual line parameters for the microwave through visible spectra of molecules in the gas phase; absorption cross-sections for molecules having dense spectral features, i.e., spectra in which the individual lines are unresolvable; individual line parameters and absorption cross-sections for bands in the ultra-violet; refractive indices of aerosols; tables and files of general properties associated with the database; and database management software. The line-by-line portion of the database contains spectroscopic parameters for 39 molecules including many of their isotopologues. The format of the section of the database on individual line parameters of HITRAN has undergone the most extensive enhancement in almost two decades. It now lists the Einstein A-coefficients, statistical weights of the upper and lower levels of the transitions, a better system for the representation of quantum identifications, and enhanced referencing and uncertainty codes. In addition, there is a provision for making corrections to the broadening of line transitions due to line mixing.
Current numerical models of oceanic circulation differentiate between the eddy diffusion and viscosity transport along the geopotential horizontal and vertical directions only. In order to model the effect of anisotropic turbulence as diffusive transport along and across density surfaces, the isopycnal mixing tensor has been transformed from a diagonal second-rank tensor in the isopycnal coordinate system to a tensor containing off-diagonal elements in the geopotential coordinate system.