Global Climate Simulations Research Articles

Divergent Transformation of Wet to Cold Bias on the Tibetan Plateau in Climate Models During Snow Season

AbstractWet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio‐temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP.

A more quiescent deep ocean under global warming

The ocean is a magnificent reservoir of kinetic energy possessed by currents at diverse spatio-temporal scales. These currents transport heat and material, regulating the regional and global climate. It is generally thought that large-scale ocean circulations should become more energetic under global warming, especially in the ocean’s upper layer. However, using high-resolution global climate simulations, here we demonstrate that the total ocean kinetic energy is projected to be significantly reduced in a warming climate, despite overall acceleration of large-scale ocean circulations in the upper layer. This reduction is primarily attributed to weakened ocean mesoscale eddies in the deep ocean. Enhanced vertical stratification under global warming reduces the available potential energy stored in large-scale ocean circulations, diminishing its conversion into eddy kinetic energy. Our findings reveal a more quiescent deep ocean under global warming and suggest a crucial role of mesoscale eddies in determining the anthropogenic change of total ocean kinetic energy.

Global climate modeling of the Jupiter troposphere and effect of dry and moist convection on jets

Aims. The atmosphere of Jupiter is characterized by banded jets, including an equatorial super-rotating jet, by an intense moist con-vective activity, and by perturbations exerted by vortices, waves, and turbulence. Even after space exploration missions to Jupiter and detailed numerical modeling of Jupiter, questions remain about the mechanisms underlying the banded jets and the role played by dry and moist convection in maintaining these jets. Methods. We report three-dimensional simulations of the Jupiter weather layer using a global climate model (GCM) called Jupiter-DYNAMICO, which couples hydrodynamical integrations on an icosahedral grid with detailed radiative transfer computations. We added a thermal plume model for Jupiter that emulates the effect of mixing of heat, momentum, and tracers by dry and moist convec-tive plumes that are left unresolved in the GCM mesh spacing with a physics-based approach. Results. Our Jupiter-DYNAMICO global climate simulations show that the large-scale Jovian flow, in particular the jet structure, could be highly sensitive to the water abundance in the troposphere and that an abundance threshold exists at which equatorial super-rotation develops. In contrast to our dry (or weakly moist) simulations, simulations that include the observed amount of tropospheric water exhibit a clear-cut super-rotating eastward jet at the equator and a dozen eastward mid-latitude jets that do not migrate poleward. The magnitudes agree with the observations. The convective activity simulated by our thermal plume model is weaker in the equatorial regions than in mid to high latitudes, as indicated by lightning observations. Regardless of whether they are dry or moist, our simulations exhibit the observed inverse energy cascade from small (eddies) to large scales (jets) in a zonostrophic regime.

Machine learning and CORDEX-Africa regional model for assessing the impact of climate change on the Gilgel Gibe Watershed, Ethiopia

Climate change is one of the most pressing challenges of our time, profoundly impacting global water resources and sustainability. This study aimed to predict the long-term effects of climate change on the Gilgel Gibe watershed by integrating machine learning (ML) methods and climate model scenarios. Utilizing an ensemble mean of four regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX) Africa project, we forecast future climatic conditions. Although global and regional climate simulations offer valuable insights, their limitations necessitate alternative approaches, such as ML, for improved accuracy. Employing an ensemble ML model with Random Forest (RF), Extra Tree (ET), and CatBoost (CB) algorithms, we assessed various bias-correction methods using historical data from 1993 to 2009. Our results highlight the effectiveness of distribution mapping (DM) in capturing temperature variability and precipitation patterns, using the power transpiration (PT) method to represent precipitation variability. Projections indicate a decline in future precipitation under the RCP 8.5 (−32.2%) and SSP 4.5 (−88.8%) for 2024–2049, with further decreases expected for 2050–2099. Conversely, temperatures will rise under RCP 4.5 (TMAX 0.67 °C) and RCP 8.5 (TMAX 0.25 °C and TMIN 1.11 °C) in the near term, exacerbated by higher emissions under SSP 4.5 and 8.5. By leveraging an ensemble mean of four observed RCMs in an ML framework, our study successfully reproduced future Coupled Model Intercomparison Project (CMIP5) and (CMIP6) climatic datasets, with the CB model demonstrating superior performance in predicting future precipitation and temperature trends. These findings offer valuable insights for shaping future climate scenarios and informing policy decisions for the Gilgel Gibe Watershed, thereby enhancing water resource management in the basin and its environs.

Ensemble of global climate simulations for temperature in historical, 1.5 °C and 2.0 °C scenarios from HadAM4

Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files.

In-situ observations reveal weak hygroscopicity in the Southern Tibetan Plateau: implications for aerosol activation and indirect effects

As a precursor process to cloud formation, aerosol activation over the Tibetan Plateau (TP) plays a pivotal role in regional cloud-precipitation and global climate. However, its characteristics remain unclear due to the absence of targeted observations in the TP. We conducted a ground in-situ aerosol-cloud-precipitation experiment in the southern TP (GACPE-STP), thereby unveiling the aerosol activation characteristics in this crucial region. Our findings reveal a weak aerosol activation capacity with low hygroscopicity (κ) values of less than 0.1 through multi-method κ measurements, contrasting starkly with the widely recommended κ value of 0.3 for continental regions. A κ parameterization is developed for predicting aerosol activation in this region, which can significantly reduce the overestimations of cloud droplet number concentration and hence aerosol indirect effects caused by using the recommended κ of 0.3. These findings address the unclear characteristics of aerosol activation in the TP region, contributing to the enhancement of regional cloud precipitation and global climate simulations.

Contrasting latitudinal evolution of East Asian monsoonal precipitation during the Last Interglacial (130–120 ka)

Evolution of East Asian monsoonal precipitation across the Last Interglacial (LIG) remains controversial, owing to the discrepancies between various proxies and their low temporal resolution. Through a transient high-resolution global climate simulation covering the interval of 130–120 ka, we illustrate a long-term increasing (decreasing) trend in summer precipitation over south China (northeast Asia) during the LIG (i.e. 130–120 ka). The out-of-phase precipitation evolution across latitudes were coherently regulated by the weakened monsoonal circulation, southward moved western North Pacific high, and southward displaced East Asian westerly jet from the early to late LIG. These atmospheric circulation variations were in turn determined by sea surface temperature anomalies over the Pacific and the propagation of extratropical Rossby waves originating from North Africa. Our results may provide important insights for reconciling discrepancies between precipitation proxies during the LIG and for precipitation behavior in a warmer-than-present world.

Climate change increases the number of landslides at the juncture of the Alpine, Pannonian and Mediterranean regions

During the next few decades, changes in rainfall frequency and magnitude are expected to have major impacts on landscape evolution, social, and economic aspects of human society. We focus on seasonal rainfall variations by the end of the twenty-first century to define affected landslide-prone areas, future landslide alerts and the impact of landslides on landscape development in the juncture of the Alpine, Pannonian, and Mediterranean region. A moderate and a worst-case climate scenario from CMIP5 global climate simulations were considered to determine the impact of rainfall on the two most common types of landslides in region, shallow and deep-seated landslides. The observed changes in the occurrence of shallow landslides are significant, especially in the winter months, where we can expect more landslide-prone areas compared to the baseline period. Shallow landslides will have a greater impact on the landscape in spring and summer than deep-seated landslides, especially in vineyards.

A Refined Understanding of the Ice Cloud Longwave Scattering Effects in Climate Model

AbstractBecause longwave (LW) absorption by greenhouse gases and clouds is more significant than the LW scattering effect by clouds, most climate models neglect cloud LW scattering to save computational costs. Ignoring cloud LW scattering directly overestimates outgoing longwave radiation (OLR). This study included ice‐cloud LW scattering treatment in the Exascale Energy Earth System Model (E3SM) version 2 and ran fully‐coupled simulations, prescribed sea surface temperature simulations, and offline radiative transfer calculations to comprehensively assess the impact of ice‐cloud LW scattering on global climate simulation. The instantaneous effect due to ice‐cloud LW scattering reduces the OLR by ∼1 W/m2 on the global average and 2 W/m2 on the tropical average. Tropospheric warming and high cloud amount reduction act to partially compensate for such instantaneous OLR reduction caused by the inclusion of LW scattering. When the simulation reaches the equilibrium, the surface warms by 0.66 K on average with respect to the simulation without LW scattering, with the Arctic surface temperature differences more than twice as large as that of the global mean. The impact of including LW scattering on the simulated climate change in response to 4 × CO2 is also assessed. While including the cloud LW scattering does not significantly modify radiative forcing and total radiative feedback under such a scenario, it results in a 10% more positive cloud feedback.

Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability

Abstract. The widely used open-source community Noah with multi-parameterization options (Noah-MP) land surface model (LSM) is designed for applications ranging from uncoupled land surface hydrometeorological and ecohydrological process studies to coupled numerical weather prediction and decadal global or regional climate simulations. It has been used in many coupled community weather, climate, and hydrology models. In this study, we modernize and refactor the Noah-MP LSM by adopting modern Fortran code standards and data structures, which substantially enhance the model modularity, interoperability, and applicability. The modernized Noah-MP is released as the version 5.0 (v5.0), which has five key features: (1) enhanced modularization as a result of re-organizing model physics into individual process-level Fortran module files, (2) an enhanced data structure with new hierarchical data types and optimized variable declaration and initialization structures, (3) an enhanced code structure and calling workflow as a result of leveraging the new data structure and modularization, (4) enhanced (descriptive and self-explanatory) model variable naming standards, and (5) enhanced driver and interface structures to be coupled with the host weather, climate, and hydrology models. In addition, we create a comprehensive technical documentation of the Noah-MP v5.0 and a set of model benchmark and reference datasets. The Noah-MP v5.0 will be coupled to various weather, climate, and hydrology models in the future. Overall, the modernized Noah-MP allows a more efficient and convenient process for future model developments and applications.

Altitudinal dependence of projected changes in occurrence of extreme events in the Great Alpine Region

AbstractClimate change has a strong impact on the environment in mountain areas, where ecosystems have adapted to climatic conditions that change with elevation. In this study, the response of temperature and precipitation climatic indices in the complex orography setting of the Great Alpine Region is discussed. The high‐resolution gridded dataset that is presented has been produced with the Weather Research Forecasting (WRF) convection permitting regional model. Two 30‐year periods have been considered (1979–2008, 2039–2068), obtained by downscaling global climate simulations with historical setting and RCP8.5 emission scenario. Both daily temperature and precipitation statistics have been found to be significantly different in the two periods, consisting in an overall projected warming and drying of the region. The dependence of the projected changes on elevation is highlighted, indicating a larger warming at medium and high elevations as well as a limited or nonexisting drying at high elevations. Physical mechanisms at the base of those differences are presented and discussed.

Multidecadal Oceanic Modulation of Summer Precipitation in North China in 1200-Year Global Climate Simulations

Abstract The multidecadal decrease of precipitation in North China since the 1960s has caused a significant impact on the ecological environment and social development. It has become a key concern for meteorologists and national decision-makers to understand what is driving this. To better understand the oceanic impact on the interdecadal variation of summer precipitation in North China, we analyze two 1200-yr global climate simulations from state-of-the-art coupled models with an explainable convolutional neural network (CNN). The accuracy of the CNN model in correctly diagnosing the increased and decreased summer precipitation anomaly in North China reaches 98% and 96.6% in two CMIP6 models, respectively. We find that a sea surface temperature anomaly (SSTA) in the northern Pacific, associated with the Pacific decadal oscillation (PDO), and an SSTA in the northern Atlantic, associated with the Atlantic multidecadal oscillation (AMO), play essential roles in the interdecadal variation of summer precipitation in North China. The modulations of the PDO and AMO on summer precipitation in North China are consistent with the results derived from limited observations and simulations, which means these mechanisms are robust. In particular, when the PDO is in a negative phase and the AMO is in a positive phase, the positive anomaly of precipitation and the associated circulation anomalies are intensified. This study also confirms that with the explainable deep learning method, a convolutional neural network can be used to identify multidecadal variability in North China summer precipitation, although limited datasets such as observational records make it more challenging to apply deep learning methods.

Climate shifts orchestrated hominin interbreeding events across Eurasia.

When, where, and how often hominin interbreeding happened is largely unknown. We study the potential for Neanderthal-Denisovan admixture using species distribution models that integrate extensive fossil, archaeological, and genetic data with transient coupled general circulation model simulations of global climate and biomes. Our Pleistocene hindcast of past hominins' habitat suitability reveals pronounced climate-driven zonal shifts in the main overlap region of Denisovans and Neanderthals in central Eurasia. These shifts, which influenced the timing and intensity of potential interbreeding events, can be attributed to the response of climate and vegetation to past variations in atmospheric carbon dioxide and Northern Hemisphere ice-sheet volume. Therefore, glacial-interglacial climate swings likely played an important role in favoring gene flow between archaic humans.

Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme

AbstractA new spectral convection scheme, the stochastic unified convection scheme (stochastic UNICON), is implemented in a general circulation model. The global climate simulation using stochastic UNICON is evaluated and compared with UNICON, focusing on the simulation of the Madden–Julian oscillation (MJO). Stochastic UNICON extends the original UNICON by randomly sampling convective updrafts from the joint probability density function constructed at the near‐surface, generating a spectrum of convective updrafts in a physically based manner. The performances of UNICON and stochastic UNICON on simulating observed mean climates are comparable, while stochastic UNICON slightly reduces the mean bias of climate variables. For the simulation of intraseasonal variabilities, stochastic UNICON outperforms UNICON in many aspects. Stochastic UNICON improves the simulation of the intensity and propagation patterns of boreal winter MJO, which are too weakly simulated in UNICON. The coherency between MJO‐related convection and large‐scale circulation is also enhanced, which many climate models underestimate. The improvement of MJO simulation by stochastic UNICON is related to a better representation of the relationship between moisture and convection in the model. The increased frequency of shallow convection in stochastic UNICON leads to stronger moisture convergence that precedes convection activity peak and results in the more robust development of organized convection and more frequent intense precipitation. A precipitation budget analysis reveals that the moisture tendencies due to horizontal advection and convective process are consistently enhanced during MJO developing periods by stochastic UNICON.

Performance evaluation of a global CMIP6 single forcing, multi wave model ensemble of wave climate simulations

A performance evaluation is conducted for a state-of-the-art Coupled Model Intercomparison Project Phase 6 (CMIP6)-derived ensemble of global wave climate simulations. A single-model (forcing), single-scenario approach is considered to build the ensemble, where the differentiating factor between each member is the wave model or physics parameterization used to simulate waves. The 7-member ensemble is evaluated for the 1995-2014 historical period, highlighting the impact of the multiple source terms on its robustness. The ensemble’s ability to accurately represent the present wave climate is assessed through an extensive comparison with long-term ERA5 reanalysis and in-situ observational data. Relevant aspects such as the depiction of extremes and natural wave climate variability are analyzed, and inter-member uncertainties are quantified. Overall, the results indicate that the ensemble is able to accurately simulate the global wave climate, regarding the significant wave height (HS), mean and peak wave periods (Tm and Tp, respectively) and mean wave direction (MWD). However, we show that using multiple wave models and parameterizations should be cautiously considered when building ensembles, even under the same forcing conditions. Model-parameterization-induced ensemble spreads during the historical period are found to be high, compromising the robustness of projected changes in wave parameters towards the end of the 21st century across several areas of the global ocean.

Tropical Indian Ocean Mixed Layer Bias in CMIP6 CGCMs Primarily Attributed to the AGCM Surface Wind Bias

Abstract The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupled general circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulations of regional and global climate. However, the cause of the bias is debated because the bias is strongly model dependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanic GCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found that OGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale cold mixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting a set of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind bias in the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs (AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The bias in surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as vertical mixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvement in the capability of AGCM in simulating the climatological surface winds. Significance Statement We aimed to discover the cause of temperature bias in the Indian Ocean in CMIP6 models. The bias was separated into oceanic model and ocean-model-independent bias to correspond exactly to bias caused by the oceanic model and by the atmospheric model and coupling, respectively. Oceanic bias contributes little to bias in CMIP6, but ocean-model-independent bias explains the CMIP6 bias throughout the year. We ran oceanic model experiments to show that surface wind bias causes ocean-model-independent temperature bias in the entire TIO and subsurface temperature bias in some areas of the Indian Ocean. We further found that 89% of surface wind bias originates from the atmospheric model. The results improve our understanding of the cause of the bias in the Indian Ocean and show that our method of bias separation is effective for attributing the source of bias to different proposed mechanisms.

Greenhouse Warming Weakens the Seasonal Cycle of the Eastern Boundary Upwelling

AbstractThe response of upwelling in the eastern boundary upwelling systems (EBUSs) to anthropogenic climate change has attracted much scientific attention due to its core role in nourishing marine ecosystems. A decade‐old hypothesis suggests that greenhouse warming may intensify upwelling‐favorable winds and subsequently the upwelling in EBUSs, but the impact of greenhouse warming on the seasonal cycle of upwelling remains unknown. Using the recent generation of global climate simulations under a high carbon emission scenario, we show a universal weakening of the upwelling seasonal cycle in the major EBUSs. This is mainly ascribed to the projected weakened seasonal cycle of the upwelling‐favorable winds. In addition, long‐term changes in geostrophic transport exert a nonnegligible contribution to the changes in the upwelling seasonal cycle. Our study suggests that the upwelling in the EBUSs is likely to have a more complicated response to greenhouse warming than previously thought.

Interpretation of Net Surface Heat Fluxes and Meridional Overturning Circulations in Global Coupled HadGEM3 Climate Simulations

Abstract The annual mean net surface heat fluxes (NSHFs) from the ocean to the atmosphere generated by historical forcing simulations using the HadGEM3-GC3.1 coupled climate model are shown to be relatively independent of resolution, for model horizontal grid spacings between 1° and 1/12°, and to agree well with those based on the DEEP-C (Diagnosing Earth’s Energy Pathways in the Climate System) analyses. Interpretations of the geographical patterns of the NSHFs are suggested that use basic ideas extracted from the theory of the ventilated thermocline and planetary geostrophic layer models. As a step toward investigation of the validity of the assumptions underlying the interpretations, we examine the contributions to the rate of change of the active tracers from the main terms in their prognostic equations as a function of the active tracer and latitude. We find that, consistent with our assumptions, the main contributions from vertical diffusion occur in “near-surface” layers. We also find that, except at high latitudes, the sum of the NSHF and vertical diffusion is mainly balanced by time-mean advection of potential temperature. A corresponding statement holds for potential density but not salinity. We also show that the heat input by latitude bands is dominated by the NSHFs, the time-mean advection, and the equatorial Pacific. It is usually assumed that global integrals of tracer tendencies due to advection as a function of the tracer should be identically zero. We show that nonnegligible contributions to them arise from net freshwater surface fluxes. Significance Statement Our aim is to understand better how the heat and freshwater that are input into the ocean from the atmosphere are then redistributed within the ocean and released back into the atmosphere. We show that the geographical patterns of the heat that is input to or released from the ocean surface in coupled climate models agree well with observations. We outline a dynamically based interpretation of these surface fluxes and provide evidence that supports some of its assumptions. This work might in future help us to understand how the patterns of the surface fluxes will respond to changes in greenhouse gas forcing.

Climate Impacts of Convective Cloud Microphysics in NCAR CAM5

Abstract We improved the treatments of convective cloud microphysics in the NCAR Community Atmosphere Model version 5.3 (CAM5.3) by 1) implementing new terminal velocity parameterizations for convective ice and snow particles, 2) adding graupel microphysics, 3) considering convective snow detrainment, and 4) enhancing rain initiation and generation rate in warm clouds. We evaluated the impacts of improved microphysics on simulated global climate, focusing on simulated cloud radiative forcing, graupel microphysics, convective cloud ice amount, and tropical precipitation. Compared to CAM5.3 with the default convective microphysics, the too-strong cloud shortwave radiative forcing due primarily to excessive convective cloud liquid is largely alleviated over the tropics and midlatitudes after rain initiation and generation rate is enhanced, in better agreement with the CERES-EBAF estimates. Geographic distributions of graupel occurrence are reasonably simulated over continents; whereas the graupel occurrence remains highly uncertain over the oceanic storm-track regions. When evaluated against the CloudSat–CALIPSO estimates, the overestimation of convective ice mass is alleviated with the improved convective ice microphysics, among which adding graupel microphysics and the accompanying increase in hydrometeor fall speed play the most important role. The probability distribution function (PDF) of rainfall intensity is sensitive to warm rain processes in convective clouds, and enhancement in warm rain production shifts the PDF toward heavier precipitation, which agrees better with the TRMM observations. Common biases of overestimating the light rain frequency and underestimating the heavy rain frequency in GCMs are mitigated.

North Atlantic subtropical mode water formation controlled by Gulf Stream fronts.

The North Atlantic Ocean hosts the largest volume of global subtropical mode waters (STMWs) in the world, which serve as heat, carbon and oxygen silos in the ocean interior. STMWs are formed in the Gulf Stream region where thermal fronts are pervasive and result in feedback with the atmosphere. However, their roles in STMW formation have been overlooked. Using eddy-resolving global climate simulations, we find that suppressing local frontal-scale ocean-to-atmosphere (FOA) feedback leads to STMW formation being reduced almost by half. This is because FOA feedback enlarges STMW outcropping, attributable to the mixed layer deepening associated with cumulative excessive latent heat loss due to higher wind speeds and greater air-sea humidity contrast driven by the Gulf Stream fronts. Such enhanced heat loss overshadows the stronger restratification induced by vertical eddies and turbulent heat transport, making STMW colder and heavier. With more realistic representation of FOA feedback, the eddy-present/rich coupled global climate models reproduce the observed STMWs much better than the eddy-free ones. Such improvement in STMW production cannot be achieved, even with the oceanic resolution solely refined but without coupling to the overlying atmosphere in oceanic general circulation models. Our findings highlight the need to resolve FOA feedback to ameliorate the common severe underestimation of STMW and associated heat and carbon uptakes in earth system models.