Coupled Model Intercomparison Project Phase 5 Models Research Articles

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

AbstractClimate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

Differences Between the CMIP5 and CMIP6 Antarctic Sea Ice Concentration Budgets

AbstractCompared to the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models, the Antarctic sea ice area (SIA) has been improved in Phase 6 (CMIP6). However, the lack of knowledge about the reliability of sea ice dynamic and thermodynamic processes in the CMIP6 models still limits the accuracy of Antarctic sea ice projections. Here, by using a novel and systematic statistical metric, the performance of CMIP5 and CMIP6 models with near‐realistic SIAs was assessed. We found improvements in CMIP6 models relative to CMIP5. Moreover, forcing the sea ice‐ocean model with atmospheric reanalysis led to excessive ice convergence compared to the fully coupled ocean‐sea ice‐atmosphere model, although the SIA bias could be much smaller. This prevalent insufficient ice divergence in the models is highly correlated with the negative ice thickness bias, highlighting the importance of ice thickness in the correct simulation of sea ice dynamics.

Climate Change Will Increase Biomass Proportion of Global Forest Carbon Stocks Under an SSP5–8.5 Climate Trajectory

AbstractA large amount of carbon is stored in global forests. However, the fraction of carbon stored as plant biomass versus soil organic carbon (SOC) varies among forest types, and potential changes over the 21st century are uncertain. Here, we used extensive data derived from inventories and remote sensing and Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine the current and 21st century dynamics in the proportion of biomass and SOC across global forests. We found that precipitation, elevation, soil, and wildfire were the primary controls of these differences in carbon pools. Under the SSP5–8.5 climate scenario, CMIP6 models project that the ratio of biomass to ecosystem carbon in global forests will increase across the 21st century, with the largest increases in boreal forests (95 ± 37%) compared to moist tropical forests (16 ± 15%). Changes in forest carbon pools resulting in greater biomass fraction will affect disturbance, and ecosystem carbon and energy balances, all of which interact with the climate system.

Assessing Arctic wetting: Performances of CMIP6 models and projections of precipitation changes

The Arctic region is experiencing a notable increase in precipitation, known as Arctic wetting, amidst the backdrop of Arctic warming. This phenomenon has implications for the Arctic hydrological cycle and numerous socio-ecological systems. However, the ability of climate models to accurately simulate changes in Arctic wetting has not been thoroughly assessed. In this study, we analyze total precipitation in the Arctic using station data, multiple reanalyses, and 35 models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). By employing the moisture budget equation and an evaluation method for model performance with ERA5 reanalysis as a reference, we evaluated the models' capability to reproduce past Arctic wetting patterns. Our findings indicate that most reanalyses and models are able to replicate Arctic wetting. However, the CMIP6 models generally exhibit an overestimation of Arctic wetting during the warm season and an underestimation during the cold season from 1979 to 2014 when compared to the ERA5 reanalysis. Further investigation reveals that the overestimation of wetting during the warm season is largest over the Arctic Ocean's northern part, specifically the Canadian Arctic Archipelago, and is associated with an overestimation of atmospheric moisture transport. Conversely, the models significantly underestimate wetting over the Barents-Kara Sea during the cold season, which can be attributed to an underestimation of evaporation resulting from the models' inadequate representation of sea ice reduction in that region. The models with the best performance in simulating historical Arctic wetting indicate a projected intensification of Arctic wetting, and optimal models significantly reduce uncertainties in future projections compared to the original models, particularly in the cold season and oceanic regions. Our study highlights significant biases in the CMIP6 models' simulation of Arctic precipitation, and improving the model's ability to simulate historical Arctic precipitation could reduce uncertainties in future projections.

The Misrepresentation of the Southern African Easterly Jet in Models and Its Implications for Aerosol, Clouds, and Precipitation Distributions

Abstract The southern African easterly jet (AEJ-S) is an important midtropospheric feature critical to understanding the tropical convective system over central Africa and the aerosol–cloud interactions over the southeast Atlantic Ocean. However, it remains unclear how well models represent the AEJ-S and its influence on aerosol transport, clouds, and precipitation distribution. Here, we use ground- and satellite-based observations and reanalysis datasets to assess the representation of AEJ-S in the Coupled Model Intercomparison Project phase 6 (CMIP6) models between September and October during the peak of midtropospheric winds, aerosol transport, clouds, and precipitation. We find that most CMIP6 models have difficulty accurately simulating the strength, position, and spatial distribution of the AEJ-S. Specifically, the AEJ-S is relatively weaker and at a slightly lower altitude in the ensemble of CMIP6 models than represented by observation and reanalysis datasets. To assess the influence of the misrepresented the AEJ-S on CMIP6-simulated aerosol, clouds, and precipitation distributions, we performed composite analyses using models with low and high biases based on the estimates of their midtropospheric easterly wind speed. We find that the misrepresentation of the AEJ-S in CMIP6 models is associated with the overestimation of clouds and precipitation over central Africa, the underestimation of clouds over the southeast Atlantic Ocean, and the limitation of aerosol transport over the continent or the deviation of its spatial distribution from the typical zonal transport over the Atlantic Ocean. Because aerosols, clouds, and precipitation are important components of the regional climate system, we conclude that accurate representation of the AEJ-S is essential over central Africa and the southeast Atlantic Ocean.

Evaluation of Summer Non‐Uniform Multidecadal Temperature Variations Over Eurasia in CMIP6 Models

AbstractThe summer surface air temperatures (SAT) over Eurasia exhibit a non‐uniform multidecadal pattern since the 1990s, with amplified warming over Europe–West Asia (EWA) and Northeast Asia (NEA) but weak warming over Central Asia. However, current climate models may inadequately capture this regional pattern. In this study, we evaluated the performance of 44 coupled models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the decadal variability of Eurasian temperatures. Results show that all the CMIP6 models generally reproduce the largest SAT trends from 1985 to 2020 over EWA and NEA. However, the models fail to capture the non‐uniform warming pattern and the SAT magnitudes are underestimated in most CMIP6 models. We compare “good” and “poor” ensemble, which represent the best and worst simulations of the non‐uniform warming, in order to understand the inter‐model spread. The BMME models capture the teleconnection wave trains from northwest Europe to East Asia, and the associated summer North Atlantic Oscillation like circulation changes. In contrast, the WMME models fail to reproduce such teleconnections. The contrast between the BMME and WMME simulations give a measure of confidence that the non‐uniform warming pattern over Eurasia is tightly coupled with the upper‐tropospheric wave‐like pattern over Eurasia. If such atmospheric circulation changes cannot be reproduced, the non‐uniform warming pattern over Eurasia will not be captured. Our results suggest a fundamental pattern of atmospheric circulation changes that determine the non‐uniform warming variations in model simulations.

Evaluation of Extreme Climate Indices over the Three Northeastern Provinces of China Based on CMIP6 Models Outputs

This study evaluates the performance of Global Climate Models (GCMs) in simulating extreme climate in three northeastern provinces of China (TNPC). A total of 23 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were selected and compared with observations from 1961 to 2010, using the 12 extreme climate indices defined by the Expert Team on Climate Change Detection and Indicators. The Interannual Variability Skill Score (IVS), Taylor diagrams and Taylor Skill Scores (S) were used as evaluation tools to compare the outputs of these 23 GCMs with the observations. The results show that the monthly minimum of daily minimum temperature (TNn) is overestimated in 55.7% of the regional grids, while the percentage of time when the daily minimum temperature is below the 10th percentile (TN10p) and the monthly mean difference between the daily maximum and minimum temperatures (DTR) are underestimated in more than 95% of the regional grids. The monthly maximum value of the daily maximum temperature (TXx) and the annual count when there are at least six consecutive days of the minimum temperature below the 10th percentile (CSDI) have relatively low regional spatial biases of 1.17 °C and 1.91 d, respectively. However, the regional spatial bias of annual count when the daily minimum temperature is below 0 °C (FD) is relatively high at 9 d. The GCMs can efficiently capture temporal variations in CSDI and TN10p (IVS < 0.5), as well as the spatial patterns of TNn and FD (S > 0.8). For the extreme precipitation indices, GCMs overestimate the annual total precipitation from days greater than the 95th percentile (R95p) and the annual count when precipitation is greater than or equal to 10 mm (R10 mm) in more than 90% of the regional grids. The maximum number of consecutive days when precipitation is below 1 mm (CDD) and the ratio of annual total precipitation to the number of wet days (greater than or equal to 1 mm) (SDII) are underestimated in more than 80% and 54% of the regional grids, respectively. The regional spatial bias of the monthly maximum consecutive 5-day precipitation (RX5day) is relatively small at 10.66%. GCMs are able to better capture temporal variations in the monthly maximum 1-day precipitation (RX1day) and SDII (IVS < 0.6), as well as spatial patterns in R95p and R10mm (S > 0.7). The findings of this study can provide a reference that can inform climate hazard risk management and mitigation strategies for the TNPC.

Temperature Thresholds for Carbon Flux Variation and Warming‐Induced Changes

AbstractThe response of net ecosystem exchange (NEE) to environmental changes is strongly nonlinear, characterized with threshold behaviors that are not well understood. Here, we investigated the threshold behaviors in the relationship between NEE and surface air temperature based on FLUXNET2015 observations, Community Land Model simulations, and Coupled Model Intercomparison Project Phase 6 (CMIP6) model outputs. An air temperature threshold of 16.4°C, corresponding to the maximum carbon sink (Tsink), was identified for all 205 FLUXNET2015 sites combined. For deciduous broadleaf and mixed forests, wetlands and wheat‐barley croplands, and rice‐maize‐soybean croplands, we identified a carbon‐source threshold (Tsource) of 6.8, 5.0, and 18.0°C, respectively, beyond which the ecosystem becomes less of a carbon source. Five cold climate types mainly encompassing these plant functional types showed a clear carbon‐source Tsource of 12.2°C. Six CMIP6 models project a threshold temperature increase of 1.0–2.8°C by the 2090s, which results primarily from a shift of the optimum temperature for gross primary production. Not accounting for the warming‐induced threshold changes may lead to an estimated time of the average summer air temperature passing Tsink that is earlier by 4.5–6.7 and 6.4–12.2 years at low (15°N–15°S) and high (≥60°S or ≥ 60°N) latitudes, respectively.

High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections

We introduce Version 2 of our widely used 1-km Köppen-Geiger climate classification maps for historical and future climate conditions. The historical maps (encompassing 1901–1930, 1931–1960, 1961–1990, and 1991–2020) are based on high-resolution, observation-based climatologies, while the future maps (encompassing 2041–2070 and 2071–2099) are based on downscaled and bias-corrected climate projections for seven shared socio-economic pathways (SSPs). We evaluated 67 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6) and kept a subset of 42 with the most plausible CO2-induced warming rates. We estimate that from 1901–1930 to 1991–2020, approximately 5% of the global land surface (excluding Antarctica) transitioned to a different major Köppen-Geiger class. Furthermore, we project that from 1991–2020 to 2071–2099, 5% of the land surface will transition to a different major class under the low-emissions SSP1-2.6 scenario, 8% under the middle-of-the-road SSP2-4.5 scenario, and 13% under the high-emissions SSP5-8.5 scenario. The Köppen-Geiger maps, along with associated confidence estimates, underlying monthly air temperature and precipitation data, and sensitivity metrics for the CMIP6 models, can be accessed at www.gloh2o.org/koppen.

Compound wind and precipitation extremes at a global scale based on CMIP6 models: Evaluation, projection and uncertainty

AbstractCompound wind and precipitation extremes (CWPEs) amplify risk to human health, socio‐economic and ecological systems relative to their single extreme meteorological events. Given the rise in weather and climate extremes resulting from global warming, it is crucial to evaluate the ability of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to capture this bivariate compound event and explore projected changes of CWPEs in the future under different climate‐change scenarios—the Shared Socioeconomic Pathway (SSP) scenarios. In this study, we first evaluate 14 CMIP6 models at a global scale using the ERA5 reanalysis data set spanning 1979–2014. Overall, some of the CMIP6 models, especially the multi‐model ensemble mean (MMEM), can reasonably capture CWPEs during the historical period, with more CWPEs in the northern and southern hemispheres during their respective cold seasons. However, the MMEM tends to overestimate CWPEs in some land areas and show underestimation in some oceanic regions. Then we compute projected changes of CWPEs in periods 1 (2041–2070) and 2 (2071–2100) under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios. Low emission scenarios effectively mitigate the long‐term increase in future CWPEs. The occurrence of CWPEs will change significantly with the increase of emissions during period 2, particularly in polar regions. Finally, we quantify the uncertainty for global future projections of CWPE changes. The main sources of uncertainty are internal variability and model uncertainty, but the contribution of scenario uncertainty will increase as time progresses. Overall, our results provide useful information to cope with CWPEs' global impact, emphasizing the importance of incorporating the compound nature of weather and climate extremes in future climate projections.

Delayed onset of the tropical Asian summer monsoon in CMIP6 can be linked to the cold bias over the Tibetan Plateau

Most global circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulate a delayed onset of the tropical Asian summer monsoon of 3–6 pentads when compared with the observations. However, a clear explanation of this model bias has yet to be developed. This study indicates that 23 of the 31 of CMIP6 models generate both the Tibetan Plateau (TP) cold bias and the delayed monsoon onset across tropical Asia. The aloft TP cold air temperature associated with these models tends to reduce the land–sea thermal contrast and monsoon circulation, and hence it generates a delayed onset for the tropical summer monsoon. Two sensitivity experiments based on a coupled ocean–atmosphere–land GCM, together with additional data analysis, further confirm the underlying connection between monsoon onset and temperature anomaly over the TP. Therefore, it is of great importance that we attempt to reduce the model bias associated with the simulation of monsoon onset by improving the physical process parameterization scheme related to the TP temperatures.

Climate risks and vulnerabilities of the Arabica coffee in Brazil under current and future climates considering new CMIP6 models

The susceptibility to climate change concerns the coffee market worldwide due to possible severe productivity losses. Brazil is the world's largest Arabica coffee producer and has crops in regions considered persistent climate change hotspots. Our study analyzed risks, vulnerabilities, and susceptibilities to pests and diseases in these regions under current and future climates and outlined adaptive measures to reduce future vulnerabilities. Ten risk indicators based on Arabica coffee requirements were proposed: water supply (Iw), base (TIB) and maximum temperature stresses (TImax), which delimit the temperature range where Arabica coffee grows and productivity is penalized outside both ranges, frost stress (TIfrost), diseases such as rust (DIrust), brown eye spot (DIbrown), and Phoma leaf spot (DIphoma), pests such as coffee berry borer (PIberry), coffee leaf miner (PIminer), and yield loss due to water stress (Iyg). Daily near-surface air temperature (minimum, mean, and maximum), relative humidity, precipitation, and global solar radiation were used from 16 General Circulation Models (GCMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in three Shared Socioeconomic Pathways scenarios (SSP245, SSP370 and SSP585). All risk indicators were calculated for the current climate (1995–2014) and projected for the near (2041–2060), intermediate (2061–2080), and far future (2081–2100) in three SSPs and then classified into five risk classes (very low, low, moderate, high and very high). Our results indicated that due to increases in TImax and Iyg indicators, with high to very high risk in area and magnitude, Arabica coffee plantations will be negatively affected and economically unfeasible for about 35 % to 75 % of the studied area throughout the 21st century. Furthermore, the rust and the leaf miner will remain a concern in future climates due to increased temperatures and reduced relative humidity. The future of Arabica coffee crops in Brazil will depend on adopting effective adaptive measures and prudent agricultural strategies to address anticipated risks, including shifting crops to higher altitude areas, introducing more climate-resilient coffee cultivars/varieties, using agroforestry or intercropping systems, planting in closer spacing or higher density planting, and employing dripper or partial root-zone irrigation techniques.

Constraining the First Year of Ice‐Free Arctic: Importance of Regional Perspective

AbstractThe ice‐free Arctic in summer not only symbolizes human‐induced climate change but also highlights the need to critically consider climate change adaptation policies. To constrain projections when the Arctic Ocean will first become ice‐free, studies have typically combined the historical observations of the Arctic sea ice area (SIA) with future emission scenario simulations. However, these studies primarily relied on the historical climatology and trend of the Arctic SIA, without considering regional variations. In this study, we analyze September SIA projections using the Coupled Model Intercomparison Project Phase 6 (CMIP6) model simulations, while considering Arctic sub‐regions. Additionally, we assess the impact of incorporating sub‐region September SIA when constraining the first year of ice‐free Arctic in September. CMIP6 models generally overestimate the historical September SIA decreasing trend in the central Arctic, whereas they underestimate this trend in the surrounding shelf seas. The central Arctic, where the region expected to retain sea ice for the longest period in the future, holds particular significance for projecting when the Arctic will first become ice‐free. Consequently, when we employ the historical trend of September SIA in the central Arctic as a constraint, observationally‐constrained projections suggest a delay of 12 years (2056) for the first ice‐free September compared to raw/unconstrained projections (2044 in model average) under a high‐emission scenario. These findings underscore the importance of considering model biases in central Arctic SIA when constraining projections of the first year of ice‐free Arctic, which may occur later than previously projected in many studies.

Assessing CMIP6 simulations of Arctic sea ice drift: Role of near-surface wind and surface ocean current in model performance

Sea ice drift is a critical parameter for understanding the rapid changes in Arctic sea ice. Since the release of the Coupled Model Intercomparison Project Phase 6 (CMIP6), there has been a lack of quantitative analysis regarding CMIP6's simulation of Arctic sea ice drift. This study aims to assess the simulated Arctic sea ice drift from 1979 to 2014 by fifteen CMIP6 models against recent satellite retrievals, utilizing various quantitative indices. Additionally, the influence of near-surface wind and surface ocean current on model performance is further analyzed. The CMIP6 models capture several aspects of the observed Arctic sea ice drift climatology and variability. The seasonal patterns of sea ice drift speed in all models exhibit similarities with the observed data, and the models agree with the evaluation datasets, indicating that the seasonal evolution of sea ice drift corresponds to near-surface wind patterns. However, notable discrepancies are identified. All models overestimate sea ice drift speed, exceeding the observational data by 36%–97%. Fourteen out of fifteen models display larger seasonal variability (ranging from 0.74 to 1.28 km d−1) compared to the observed data (0.54 km d−1). Seven out of fifteen models exhibit a significant increasing trend in annual sea ice drift speed, similar to the observed trend of 0.58 km d−1 per decade, but with weaker trends (ranging from 0.11 to 0.33 km d−1 per decade). The remaining eight models reveal no statistically significant trend. The potential causes of such biases were further explored in this study. It suggests that the overestimation of sea ice drift speed in the models might be primarily attributed to the overestimation of near-surface wind speeds and their influence on sea ice drift speed. The models' overestimation of seasonal variability in near-surface wind speeds may account for the overestimation of seasonal variability in sea ice drift. The models' inability to represent the trend in sea ice drift speed may result from their failure to simulate an increasing trend in surface ocean current speed.

Evaluation of the Performance of CMIP6 Climate Models in Simulating Rainfall over the Philippines

The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to simulate the observed rainfall using the latest state-of-the-art model is essential for reliable forecasting. Based on this background, this paper objectively aims at assessing and ranking the capabilities of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed rainfall over the Philippines. The Global Precipitation Climatology Project (GPCP) v2.3 was used as a proxy to gauge the performance of 11 CMIP6 models in simulating the annual and rainy-season rainfall during 1980–2014. Several statistical metrics (mean, standard deviation, normalized root means square error, percentage bias, Pearson correlation coefficient, Mann–Kendall test, Theil–Sen slope estimator, and skill score) and geospatial measures were assessed. The results show that that CMIP6 historical simulations exhibit satisfactory effectiveness in simulating the annual cycle, though some models display wet/dry biases. The CMIP6 models generally underestimate rainfall on the land but overestimate it over the ocean. The trend analysis shows that rainfall over the country is insignificantly increasing both annually and during the rainy seasons. Notably, most of the models could correctly simulate the trend sign but over/underestimate the magnitude. The CMIP6 historical rainfall simulating models significantly agree on simulating the mean annual cycle but diverge in temporal ability simulation. The performance of the models remarkably differs from one metric to another and among different time scales. Nevertheless, the models may be ranked from the best to the least best at simulating the Philippines’ rainfall in the order GFDL, NOR, ACCESS, ENS, MRI, CMCC, NESM, FIO, MIROC, CESM, TAI, and CAN. The findings of this study form a good basis for the selection of models to be used in robust future climate projection and impact studies regarding the Philippines. The climate model developers may use the documented shortcoming of these models and improve their physical parametrization for better performance in the future.

Simulated responses of soil carbon to climate change in CMIP6 Earth system models: the role of false priming

Abstract. Reliable estimates of soil carbon change are required to determine the carbon budgets consistent with the Paris Agreement climate targets. This study evaluates projections of soil carbon during the 21st century in Coupled Model Intercomparison Project Phase 6 (CMIP6) Earth system models (ESMs) under a range of atmospheric composition scenarios. In general, we find a reduced spread of changes in global soil carbon (ΔCs) in CMIP6 compared to the previous CMIP5 model generation. However, similar reductions were not seen in the derived contributions to ΔCs due to both increases in plant net primary productivity (NPP, named ΔCs,NPP) and reductions in the effective soil carbon turnover time (τs, named ΔCs,τ). Instead, we find a strong relationship across the CMIP6 models between these NPP and τs components of ΔCs, with more positive values of ΔCs,NPP being correlated with more negative values of ΔCs,τ. We show that the concept of “false priming” is likely to be contributing to this emergent relationship, which leads to a decrease in the effective soil carbon turnover time as a direct result of NPP increase and occurs when the rate of increase in NPP is relatively fast compared to the slower timescales of a multi-pool soil carbon model. This finding suggests that the structure of soil carbon models within ESMs in CMIP6 has likely contributed towards the reduction in the overall model spread in future soil carbon projections since CMIP5.

Projected Changes in Precipitation Based on the CMIP6 Optimal Multi-Model Ensemble in the Pearl River Basin, China

Precipitation fluctuations in the Pearl River Basin (PRB) have a significant impact on river runoff, causing huge economic losses and casualties. However, future precipitation variations in the PRB remain unclear. Therefore, we explored the projected changes in precipitation in the PRB based on the coupled model intercomparison project phase 6 (CMIP6) model via three shared socio-economic pathways scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). In our study, the optimal ensemble of global climate models in the PRB was identified using the comprehensive rating index (CRI), which is based on climatology, spatial variation, and interannual variability, and it was used to analyze potential precipitation changes in the basin in the period 2025–2100. The results showed that the CMIP6 models underestimated precipitation in the PRB; the consistency between the observations and the multi-model ensemble mean of the four best models was higher than those of any other ensembles, and the CRI value was highest (0.92). The annual precipitation in the PRB shows a significant increasing trend under three scenarios from 2025 to 2100 (p < 0.01), with the highest rate of precipitation increase being seen under the high-emission scenario. By the end of the 21st century, the regional mean precipitation in the PRB will increase by 13%, 9.4%, and 20.1% under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, respectively. Spatially, the entire basin is projected to become wetter, except for a slight decrease of less than 6% in the central part of the basin and the Pearl River Delta in the near term in the 21st century, and the highest increases are projected to occur in the Xijiang River basin.

Atmospheric response to cold wintertime Tibetan Plateau conditions over eastern Asia in climate models

Abstract. Central Asian orography (namely the Tibetan and Mongolian plateaux) sets important features of the winter climate over eastern Asia and the Pacific. By deflecting the mid-latitude jet polewards it contributes to the formation of the Siberian high and, on the lee side, to the advection of dry cold continental air over the eastern Asian coast and the Pacific Ocean, where atmospheric instability and cyclogenesis thrive. While the mechanic forcing by the orography is assessed in a number of modelling studies, it is still not clear how near-surface temperature over the two most prominent orographic barriers of the central Asian continent – the Tibetan and Mongolian plateaux – influences the winter climate. The problem is particularly relevant in view of a well-known cold bias in state-of-the-art climate models in proximity to the Tibetan Plateau, likely related to the modelling of land processes and land–atmosphere interaction over complex orography. Here we take advantage of the large spread in near-surface temperature over the central Asian plateaux within the Coupled Model Intercomparison Project Phase 6 (CMIP6) to study how colder-than-average Asian plateau temperatures impact the atmospheric circulation. Based on composites of the CMIP6 models' climatologies showing the coldest Tibetan Plateau conditions, we find that such negative temperature anomalies appear to amplify the atmospheric response to orography, with an intensification of the eastern Asian winter monsoon and of the equatorward flank of the Pacific jet. The results of the CMIP6 composite analysis are supported by experiments run with an intermediate-complexity atmospheric model, forced by a similar pattern of cold surface temperatures over the central Asian plateaux. Within this setting, the relative influence of the Tibetan and the Mongolian Plateau surface conditions is analysed. Based on the results reported in this work we project that advances in the modelling of the land energy budget over the elevated regions of central Asia could improve the simulation of the climate in the Asian–Pacific sector climate, together with the reliability of climate projections and the performance of shorter-term forecasts.

Spatiotemporal Projections of Precipitation in the Lancang–Mekong River Basin Based on CMIP6 Models

The Lancang–Mekong River Basin (LMRB) is the largest international river in Southeast Asia, supporting a population of about 70 million people. Precipitation is the main source of water resources in the basin, with significant impacts on ecology, production, and livelihoods in the basin. In this study, future precipitation was projected using the Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models. The initial bias of each model was corrected using the daily bias-correction (DBC) method, and then the models were ensembled using the Bayesian model-averaging (BMA) method. The evaluation, based on metrics such as climatology bias, root-mean-square error (RMSE), mean absolute error (MAE), and correlation coefficient (COR), showed that the ensemble precipitation performs better than the individual models. Precipitation under four future Shared Socioeconomic Pathway scenarios (SSP126, SSP245, SSP370, SSP585) displayed an increasing trend throughout the LMRB. The anomalies in annual precipitation in 2061–2090 under each scenario are 136 mm, 142 mm, 114 mm, and 227 mm, in that order. Precipitation in spring and winter shows a trend of increasing in the northern LMRB and decreasing in the southern LMRB, and precipitation in summer and autumn shows a significant trend of increasing in almost the whole basin (significance level 0.05). Spring precipitation in the Mekong Delta decreases in all scenarios. The ratio of wet-season precipitation to dry-season precipitation shows an increasing trend for all scenarios, indicating that the difference between wet-season precipitation and dry-season precipitation will increase in the future. For daily precipitation, the Lancang River Basin (LRB) is dominated by a 3–5% increase in the number of days with 5–10 mm/d of precipitation and the Mekong River Basin (MRB) by a 3–5% increase in the number of days with 10–20 mm/d of precipitation under four SSP scenarios in 2061–2090. There are important changes in the spatial distribution of future precipitation, with the 2500 mm isohyet expanding outwards in a circular pattern and the center of the 1500 mm isohyet moving westwards; i.e., areas with annual precipitation exceeding 2500 mm and 1500 mm will expand. For dry-season precipitation, the 500 mm isohyet shrinks, mainly in a circular pattern towards the center, while the 300 mm isohyet moves mainly towards the east, indicating that areas of dry-season precipitation below 500 mm and 300 mm will expand. In the future, the LMRB will generally become wetter in the wet season and drier in the dry season.

Revelation and Projection of Historic and Future Precipitation Characteristics in the Haihe River Basin, China

Precipitation, as one of the main components of the hydrological cycle, is known to be significantly impacted by global climate change. In recent years, the frequency of extreme precipitation has increased, resulting in greater destructiveness. Atmospheric circulation has a significant impact on extreme precipitation in a region. This study aims to investigate the prospective changes in extreme precipitation and their relationship with large-scale atmospheric circulation in the Haihe River Basin. The Haihe River Basin is located in the North China Plain. Mountains and plains can be found in both the eastern and western parts of the study region. The summer seasons experience the most precipitation. The monthly and extreme precipitation (based on daily precipitation) results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models were evaluated using observed precipitation data, which was utilized as a reference. The CMIP6 models were used to assess future changes in the characteristics of extreme precipitation in the study region. The relationship between extreme precipitation and large-scale atmospheric circulation was also analyzed using historical observation data. Remote sensing results regarding land cover and soil erosion were used to analyze the risks of extreme precipitation and their influences in the study region. According to the results, their multi-model ensembles (MME) and BCC-CSM2-MR models, respectively, outperformed all other CMIP6 models in simulating monthly and extreme (based on daily precipitation) precipitation over the study region. Extreme precipitation demonstrated a rising degree of contribution and future risk under numerous scenarios. The degrees of contribution of R95p and R99p are anticipated to increase in the future. BCC-CSM2-MR predicted that Rx1day and Rx5day would decline in the future. Generally, extreme precipitation increased to a greater degree under SSP585 than under SSP245. Both the El Niño–Southern Oscillation and the Pacific Decadal Oscillation displayed substantial resonance with the extreme precipitation from 1962 to 1980 and around 1995, respectively. This study not only improves our understanding of the occurrence of extreme precipitation, but it also serves as a reference for flood control and waterlogging prevention in the Haihe River Basin.