Coupled Model Intercomparison Project Phase Research Articles

Consistency of global carbon budget between concentration- and emission-driven historical experiments simulated by CMIP6 Earth system models and suggestions for improved simulation of CO2 concentration

Abstract. Anthropogenically emitted CO2 from fossil fuel use and land use change is partly absorbed by terrestrial ecosystems and the ocean, while the remainder retained in the atmosphere adds to the ongoing increase in atmospheric CO2 concentration. Earth system models (ESMs) can simulate such dynamics of the global carbon cycle and consider its interaction with the physical climate system. The ESMs that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6) performed historical simulations to reproduce past climate–carbon cycle dynamics. This study investigated the cause of CO2 concentration biases in ESMs and identified how they might be reduced. First, we compared simulated historical carbon budgets in two types of experiments: one with prescribed CO2 emissions (the emission-driven experiment, “E-HIST”) and the other with a prescribed CO2 concentration (the concentration-driven experiment, “C-HIST”). Because the design of CMIP7 is being considered, it is important to explore any differences or implications associated with such variations. The findings of this confirmed that the multi-model means of the carbon budgets simulated by one type of experiment generally showed good agreement with those simulated by the other. However, the multi-model average of cumulative compatible fossil fuel emission diagnosed from the C-HIST experiment was lower by 35 PgC than that used as the prescribed input data to drive the E-HIST experiment; the multi-model average of the simulated CO2 concentration for 2014 in E-HIST was higher by 7 ppmv than that used to drive C-HIST. Regarding individual models, some showed a distinctly different magnitude of ocean carbon uptake from C-HIST because the E-HIST setting allows ocean carbon fluxes to be dependent on land carbon fluxes via CO2 concentration. Second, we investigated the potential linkages of two types of carbon cycle indices: simulated CO2 concentration in E-HIST and compatible fossil fuel emission in C-HIST. It was confirmed quantitatively that the two indices are reasonable indicators of overall model performance in the context of carbon cycle feedbacks, although most models cannot accurately reproduce the cumulative compatible fossil fuel emission and thus cannot reproduce the CO2 concentration precisely. Third, analysis of the atmospheric CO2 concentration in five historical eras enabled the identification of periods that caused the concentration bias in individual models. Fourth, it is suggested that this non-CO2 effect is likely to be the reason why the magnitude of the natural land carbon sink in historical simulations is difficult to explain based on analysis of idealized experiments. Finally, accurate reproduction of land use change emission is critical for better reproduction of the global carbon budget and CO2 concentration. The magnitude of simulated land use change emission not only affects the level of net land carbon uptake but also determines the magnitude of the ocean carbon sink in the emission-driven experiment. This study confirmed that E-HIST enables an evaluation of the full span of the uncertainty range covering the entire carbon–climate system and allows for an explicit simulation of the interlinking process of the carbon cycle between land and ocean. By isolating the forced responses and feedback processes of the carbon cycle processes, the usefulness of C-HIST in elucidating climate–carbon cycle systems and in identifying the cause of CO2 biases was confirmed.

Constrained Projections of Extreme Low Temperatures in Eastern China

AbstractThe latest two generations of climate models (CMIP5 and CMIP6, Coupled Model Intercomparison Projects Phase 5 and 6) show a clear discrepancy in the projected future changes in mean temperature. Different methods have been proposed to reduce this difference, however, very limited studies are focused on extreme low temperatures (ELT). Here we propose a new method to constrain the projection of ELT changes by establishing their quasi‐linear relationships with mean temperature (Tmean) in Eastern China. The results show that the Tmean weighting coefficients considering model performance and independence can effectively reduce the uncertainty range of future ELT. Before constraint, there are substantial differences in the projected ranges of Tmean and ELT between CMIP5 and CMIP6 models. After constraint, the projected ranges of CMIP6 models are considerably reduced, particularly at the warmer end, thus showing better consistency with CMIP5. Under the SSP5‐8.5 scenario, at the end of the 21st century (2081–2100), the original projected changes in Tmean are constrained from 5.1 (3.5–7.9)°C to 5.0 (3.5–6.5)°C, with the warmer end of the projected range decreased by 1.4°C. For the ELT, the decreases of the warmer ends are 1.9°C and 1.2°C for the annual minima of daily minimum (TNn) and maximum temperature (TXn), respectively. The reliability evaluation shows that the differences between pseudo‐observations represented by two large ensemble models and constrained projections are smaller than those for unconstrained projections, thereby confirming the reliability of the weighted method employed in this study.

Surface ozone pollution-driven risks for the yield of major food crops under future climate change scenarios in India.

Surface ozone pollution-driven risks for the yield of major food crops under future climate change scenarios in India.

High temporal variability not trend dominates Mediterranean precipitation

State-of-the-art climate models project a substantial decline in precipitation for the Mediterranean region in the future1. Supporting this notion, several studies based on observed precipitation data spanning recent decades have suggested a decrease in Mediterranean precipitation2, 3–4, with some attributing a large fraction of this change to anthropogenic influences3,5. Conversely, certain researchers have underlined that Mediterranean precipitation exhibits considerable spatiotemporal variability driven by atmospheric circulation patterns6,7 maintaining stationarity over the long term8,9. These conflicting perspectives underscore the need for a comprehensive assessment of precipitation changes in this region, given the profound social, economic and environmental implications. Here we show that Mediterranean precipitation has largely remained stationary from 1871 to 2020, albeit with significant multi-decadal and interannual variability. This conclusion is based on the most comprehensive dataset available for the region, encompassing over 23,000 stations across 27 countries. While trends can be identified for some periods and subregions, our findings attribute these trends primarily to atmospheric dynamics, which would be mostly linked to internal variability. Furthermore, our assessment reconciles the observed precipitation trends with Coupled Model Intercomparison Project Phase 6 model simulations, neither of which indicate a prevailing past precipitation trend in the region. The implications of our results extend to environmental, agricultural and water resources planning in one of the world’s prominent climate change hotspots10.

Characterization of heatwave pattern and its long-run predictions using CMIP6 model in western and north-western climatic zones of Bangladesh.

Heatwaves, characterized by prolonged high temperatures, have become a recurrent and severe phenomenon in Bangladesh, posing significant risks to public health, agriculture, and socioeconomic stability. This study analyzed the characterization and long-term projections of heatwave patterns in Bangladesh's western and northwestern climatic zones, addressing a critical gap in understanding regional vulnerabilities to extreme temperature events. Heatwave thresholds were defined using the Bangladesh Meteorological Department Criterion, a percentile-based criterion, and the Indian Meteorological Department Criterion, providing a comprehensive framework for assessing heatwave intensity and frequency. Using nine bias-corrected general circulation models from CMIP6 (Coupled Model Intercomparison Project Phase 6), the study examined historical heatwave patterns (1990-2014) across five cities, Bogra, Chuadanga, Dinajpur, Ishwardi, and Rajshahi, and evaluated future projections for three epochs: Epoch (2026-2050), Epoch (2051-2075), and Epoch (2076-2100). The findings revealed a significant increase in heatwave days under the high-emission Shared Socioeconomic Pathway (SSP ) scenario compared to SSP , with SSP projected to have 46.7% more heatwave days on average across the three epochs. The largest disparities are observed in Chuadanga (49.4%) and Rajshahi (44.5%), with Chuadanga emerging as the most vulnerable region, experiencing 1629 cumulative heatwave days under SSP in Epoch , compared to 1301 under SSP -a 25.2% increase. These findings underscore the profound impact of human activities on heatwave frequency and emphasize the urgent need for climate change mitigation. By offering a novel approach to heatwave characterization, this study provides critical insights to inform regional climate resilience planning and develop targeted adaptation measures.

Leveraging ML to predict climate change impact on rice crop disease in Eastern India.

Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for current and future scenarios. The nine biophysical parameters are precipitation (Pr), maximum temperature (Tmax), minimum temperature (Tmin), soil texture (ST), available water capacity (AWC), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), normalized difference chlorophyll index (NDCI), and normalized difference moisture index (NDMI) by Random forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), Artificial Neural Net (ANN), and Support vector Machine (SVM). The multicollinearity test Boruta feature selection techniques that assessed interdependency and prioritized the factors impacting crop disease. However, climatic change scenarios were created using the most recent Climate Coupled Model Intercomparison Project Phase 6 (CMIP6) Shared Socioeconomic Pathways (SSP) 2-4.5 and SSP5-8.5 datasets. The rice crop disease validation was accomplished using 1105 field-based farmer observation recordings. According to the current findings, Purba Bardhaman district experienced a 96.72% spread of rice brown spot disease due to weather conditions. In contrast, rice blast diseases are prevalent in the north-western region of Birbhum district, affecting 72.38% of rice plants due to high temperatures, water deficits, and low soil moisture. Rice tungro disease affects 63.45% of the rice plants in Bankura district due to nitrogen and zinc deficiencies. It was discovered that the link between NDMI and NDVI is robust and positive, with values ranging from 0.8 to 1. According to SHAP analysis, Pr, Tmin, and Tmax are the top three climatic variables impacting all types of disease cases. The study's findings could have a substantial impact on precision crop protection and meeting the United Nations Sustainable Development Goals.

Projections of major climate change indicators over Iran from 2021 to 2080.

This study addresses the impact of climate change (2021-2080) on the seasonal distribution of rainfall, temperature, and season lengths over Iran under three Shared Socioeconomic Pathways (SSPs) of the low (SSP1-1.9), moderate (SSP2-4.5), and high (SSP5-8.5) levels of global warming, based on the 29 model ensemble of the Coupled Model Inter-comparison Project Phase 6 (CMIP6). Results reveal that spring and autumn lengths are ~ 90days during the baseline period (1980-2014), while summer (~ 97days) is longer than winter (~ 87days) by 10days. However, global warming will result in longer summer and winter and shorter spring and autumn seasons in the future. The temperature will increase during all four seasons of spring and autumn (1.5-2.7 ), winter (1.1-2.2 ), and summer (2.0-3.2 ) consistently with the level of global warming scenarios. Meanwhile, minimum and maximum temperature enhancement will occur during winter and summer, respectively, under a given climate change scenario. Rainfall over Iran will increase during all seasons (6-36%) under SSP1-1.9 but will reduce under warmer scenarios SSP2-4.5 (12-24%) and SSP5-8.5 (8-24%). The contributions of the spring, summer, autumn, and winter seasons to the annual rainfall are 32%, 5%, 20%, and 43%, respectively, implying winter and spring as wet seasons during the baseline period. However, climate change may shift the wet season from winter to spring or autumn, depending on the station and SSP, under climate change. Water managers and policymakers need to consider the highlighted issues for future sustainable management in Iran.

Spatiotemporal Distribution of Wine Grape Under Climate Change in Northwestern China.

The favorable terroir of China's northwest region provides an ideal environment for the cultivation and thriving growth of grapes. However, climate change threatens to alter the optimal grape-growing regions, presenting considerable challenges to the local wine making industry. To enhance the utilization of regional climate resources and refine the wine grape industry's spatial distribution, this study assessed the performance of nine climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in Northwestern China, simulated the future spatiotemporal distribution of wine grape. Results showed that EC-Earth3 performed well in simulating temperature, precipitation, and interannual changes. Under the historical periods (1980-2014), the highly suitable areas for wine grapes were predominantly found in the eastern foothills of Helan Mountain in Ningxia, along the Yellow River in Wuhai and Linhe of Inner Mongolia, along the Qilian Mountains in Wuwei, Zhangye and Jiayuguan of Gansu, and along rivers and oases in the northern foothills of Tianshan Mountains, Ili River Valley, Tuha Basin, Yanqi Basin, Aksu, Muzart, and Kashgar of Xinjiang. Compared with historical periods, the highly and moderately suitable areas were expected to expand under SSP245 and SSP585. Nevertheless, the overall pattern of spatial distribution was not anticipated to experience substantial alterations. In the next 50 years (2055-2085), the suitable areas under SSP245 scenario would be higher than SSP585. Precipitation from July to September (pr79), soil pH (ph), elevation (dem), and near-surface air temperature in January (tas1) were the main factors affecting the suitable areas of wine grapes. Further analysis revealed that a certain level of the near-surface air temperature in January (tas1) contributed positively to the expansion of suitable areas. However, excessively high average temperatures in January and July tended to have a detrimental effect. A rise in winter temperature can foster a more favorable environment for wine grapes to overwinter. However, frequent summer heat waves and high winter temperatures caused by climate warming may have adverse effects.

Energetic Electron Precipitation From the Radiation Belts: Geomagnetic and Solar Wind Proxies for Precipitation Flux Magnitudes

AbstractPreviously the geomagnetic Ap index has been used as a proxy to produce empirical energetic electron precipitation (EEP) forcing representations suitable for incorporation into coupled‐climate model runs. The long‐running Ap index has the advantage that it allows descriptions of EEP to be made for periods outside the current satellite era, but its suitability has not been checked against other reasonable proxies. In this study three different satellite electron precipitation data sets (DEMETER, POES, and SAMPEX) are used to examine the suitability of a variety of geomagnetic and solar wind proxies to represent EEP flux in different energy ranges. Analysis was undertaken using indices at their fundamental timescales (typically minutes or hours). For medium energy electron precipitation (i.e., >100 keV), the best proxy is found to be either Ap or Dst. For relativistic energy electron precipitation (i.e., >700 keV), the best proxy is Kp or AE, the latter suggesting a connection to substorm activity. The identification of the Ap index as one of the best proxies for medium energy EEP supports the approach taken by van de Kamp et al. (2016), https://doi.org/10.1002/2015jd024212. An EEP forcing capability based on Ap was developed by those authors for inclusion as a solar forcing factor in the Coupled Model Intercomparison Project Phase 6 of the World Climate Research Program.

Future MJO Change and Its Impact on Extreme Precipitation and Temperature Over the Western US in CMIP6

AbstractThe Madden‐Julian oscillation (MJO) has remarkable impacts on global weather and climate systems. This study examines the future changes in MJO projected by 23 Coupled Model Intercomparison Project Phase 6 (CMIP6) models that produce a realistic MJO propagation in their historical runs. Results from the multi‐model mean show a ∼17% increase in MJO precipitation amplitude, a ∼11%–14% increase in MJO circulation amplitude, a ∼9% increase in propagation speed, a ∼2‐day decrease in MJO period, and a ∼5° eastward extension. Analysis of the lower tropospheric moisture budget suggests the dominant role of an increased meridional advection of mean moisture caused by the steepening of mean moisture gradient over the Indo‐Pacific warm pool in a warming climate in the majority of models. The enhanced zonal moisture advection plays a secondary or comparable role in some models. The stronger anticyclonic gyres to the east of the MJO convection center along with the enhanced moisture gradient favor an enhanced export of moisture away from the Equator and a local moistening at the flanks of the MJO convection center. This leads to a stronger positive moisture tendency to the east of MJO convection and hence an enhanced eastward MJO propagation with strengthened amplitude and faster speed. As the MJO changes in the future, its relationships with the precipitation and temperature extremes over the western United States (US) are found to change accordingly. The MJO impacts on precipitation tend to be stronger with the stronger MJO amplitude, whereas its impacts on temperature are generally weaker.

Comparing traditional hydrological forecasting models with CatBoost algorithm: insights from CMIP6 climate scenarios

ABSTRACT Hydrological prediction is crucial for managing water resources, and innovations like machine learning (ML) present an opportunity to enhance predictive modeling capabilities. The aim of this study is to compare the usage of ML algorithms, such as CatBoost, with traditional techniques such as ridge regression, support vector machines (SVMs), and gene expression programming (GEP) in climate projection. In order to assess the accuracy of the best models, statistical measures such as root mean squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE) were used. The investigation found that CatBoost was superior to conventional models in the testing period, with RMSE 3.78 m3/s, MAE 2.613 m3/s, Kling–Gupta efficiency (KGE) 0.650, root mean square error to standard deviation ratio (RSR) 0.611 and NSE 0.626. After it was proven that the best-performing model is CatBoost, future projections according to the NorESM2-MM scenarios were calculated using this model. Climate projections are based on simulations from the Coupled Model Intercomparison Project Phase 6 model, utilizing shared socioeconomic pathway (SSP) scenarios. The results show that SSP3-7.0 and SSP5-8.5 scenarios indicate an increasing trend between 2015 and 2100, while SSP1-2.6 and SSP2-4.5 expect a balancing tendency. This suggests that climate change has little effect on the measuring station and its basin and that the flow is increasing positively.

Integrating Land Use/Land Cover and Climate Change Projections to Assess Future Hydrological Responses: A CMIP6-Based Multi-Scenario Approach in the Omo–Gibe River Basin, Ethiopia

It is imperative to assess and comprehend the hydrological processes of the river basin in light of the potential effects of land use/land cover and climate changes. The study’s main objective was to evaluate hydrologic response of water balance components to the projected land use/land cover (LULC) and climate changes in the Omo–Gibe River Basin, Ethiopia. The study employed historical precipitation, maximum and minimum temperature data from meteorological stations, projected LULC change from module for land use simulation and evaluation (MOLUSCE) output, and climate change scenarios from coupled model intercomparison project phase 6 (CMIP6) global climate models (GCMs). Landsat thematic mapper (TM) (2007) enhanced thematic mapper plus (ETM+) (2016), and operational land imager (OLI) (2023) image data were utilized for LULC change analysis and used as input in MOLUSCE simulation to predict future LULC changes for 2047, 2073, and 2100. The predictive capacity of the model was evaluated using performance evaluation metrics such as Nash–Sutcliffe Efficiency (NSE), the coefficient of determination (R2), and percent bias (PBIAS). The bias correction and downscaling of CMIP6 GCMs was performed via CMhyd. According to the present study’s findings, rainfall will drop by up to 24% in the 2020s, 2050s, and 2080s while evapotranspiration will increase by 21%. The findings of this study indicate that in the 2020s, 2050s, and 2080s time periods, the average annual Tmax will increase by 5.1, 7.3, and 8.7%, respectively under the SSP126 scenario, by 5.2, 10.5, and 14.9%, respectively under the SSP245 scenario, by 4.7, 11.3, and 20.7%, respectively, under the SSP585 scenario while Tmin will increase by 8.7, 13.1, and 14.6%, respectively, under the SSP126 scenario, by 1.5, 18.2, and 27%, respectively, under the SSP245 scenario, and by 4.7, 30.7, and 48.2%, respectively, under the SSP585 scenario. Future changes in the annual average Tmax, Tmin, and precipitation could have a significant effect on surface and subsurface hydrology, reservoir sedimentation, hydroelectric power generation, and agricultural production in the OGRB. Considering the significant and long-term effects of climate and LULC changes on surface runoff, evapotranspiration, and groundwater recharge in the Omo–Gibe River Basin, the following recommendations are essential for efficient water resource management and ecological preservation. National, regional, and local governments, as well as non-governmental organizations, should develop and implement a robust water resources management plan, promote afforestation and reforestation programs, install high-quality hydrological and meteorological data collection mechanisms, and strengthen monitoring and early warning systems in the Omo–Gibe River Basin.

Stormwater Treatment in Future Tropical and Sub-Tropical Climates

Stormwater treatment systems play an integral part in achieving sustainable urban development. The performance of these systems is likely to be impacted by potential changes in climatic patterns, including precipitation. This project investigates the simulated impacts of climate change on the performance of stormwater treatment systems used as a part of Water-Sensitive Urban Design (WSUD). Townsville and the Gold Coast of Queensland, Australia, were selected for the study to investigate tropical and sub-tropical climates experienced by cities across the globe adjoining sensitive coastal environments such as wetlands and coral reefs. The daily precipitation output projected by Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models was downscaled to pluviograph input into the Model for Urban Improvement Conceptualisation (MUSIC). The treatment performance of bioretention systems and constructed wetlands was variable across both locations, with some models showing little to no change or improvement. Worsening of treatment performance was more prominent in the tropical climate, with numerous models reaching a decline of up to 16%. However, the highest observed reduction from a single model output occurred in the sub-tropical climate location. To make the WSUD treatment system effective under the future climate scenarios, physical modification is necessary to increase the treatment area or depth. Increasing the area in the worst-case scenario could incur a cost increase of 20% to 30% and present challenges due to development constraints. Increasing the depth could be a viable alternative for bioretention systems but is likely impractical for constructed wetlands.

Climate change projections and hydrological modelling to predict the streamflow in Berach-Banas catchment, Rajasthan

This study investigates the impact of climate change on streamflow dynamics in the Berach-Banas catchment of Rajasthan through climate projections and hydrological modeling. This study employs the MIKE Hydro River and NAM (Nedbor-Afstromings Model) Rainfall-Runoff modules, integrating data from 14-meteorological stations and two streamflow stations (Chittorgarh and Bigod) for period 2000–2022. Climate projections are derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) under the SSP2-4.5 (Shared Socioeconomic Pathways) scenario for the period 1951–2100. Sixteen downscaled Global Climate Models (GCMs) from various institutes are utilized to simulate future conditions for 2030, 2050, and 2090. The hydrological model incorporates ten water storage structures and delineates the catchments into 13 sub-catchments. The calibration period (2011–2015) demonstrated strong model performance at Chittorgarh (R2 = 0.92 with a water balance error (WBL) of 1.41%) and Bigod (R2 = 0.95, WBL of 0.99%). Similarly, the validation period (2017–2022) exhibited good performance at Chittorgarh (R2 = 0.91, WBL = 1.64%) and Bigod (R2 = 0.94, WBL = 1.13%). Sensitivity analysis identified CQOF (overland flow runoff coefficient), CK1,2 (time constants for routing overland flow), and Lmax (maximum water content in root zone storage) as critical parameters, consistent with findings from previous studies on Indian river basins. The climate change impact analysis indicated a consistent increase in streamflow rates for 2030, 2050, and 2090 compared to 2022, likely driven by rising temperatures and changes in precipitation patterns. The projected increase in streamflow rates underscore potential future challenges for water management, highlighting the need for effective adaptation strategies. The novelty of the study lies in its comprehensive integration of future climate scenarios with hydrological modeling, offering valuable insights for sustainable water resource planning in the region. The results highlight the substantial hydrological changes anticipated in the coming decades, enhancing the overall understanding of climate change impacts on water systems.

Underestimated Ural blocking events lead to weakened spring Arctic warming in CMIP6 simulations

Historical Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations underestimate spring Arctic warming due to insufficient representation of Ural blocking events. Observations reveal that strong winter warming over the Barents–Kara Sea (BKS) reduces the meridional potential vorticity gradient, thereby enhancing atmospheric blocking events and driving Arctic surface warming in spring. Many CMIP6 members, which exhibit lower sensitivity to climate forcing, involve the use of initial fields with warmer climate conditions (e.g. reduced sea ice cover). Consequently, these models simulate less sea ice melting and minimal winter BKS warming, leading to weaker Ural blocking events and underestimation of spring Arctic surface warming. While recent global surface temperature trends among CMIP6 models are relatively consistent, their future projections diverge due to their varying climate sensitivities, potentially underestimating global warming across different scenarios.

Can CMIP6 Models Accurately Reproduce Terrestrial Evapotranspiration Across China?

ABSTRACTTerrestrial evapotranspiration (ET) plays a fundamental role in the climate system. The Coupled Model Intercomparison Project Phase 6 (CMIP6) provides a valuable framework for assessing global climate model performance, but gaps remain in evaluating its ET estimates, particularly in China. To fill this gap, we employed the Global Land Evaporation Amsterdam Model (GLEAM) and the water balance ET method to validate the CMIP6 ET outputs from 1980 to 2014 at both national and river basin scales. Key findings include: (1) GLEAM ET performs comparably to the water balance method, making it reliable for validating CMIP6 ET outputs. From 1980 to 2014, the annual mean ET in GLEAM for China ranges from 355 to 411 mm/year. In contrast, most CMIP6 models overestimate ET, with the multi‐model ensemble (MME) mean ranging from 524 to 542 mm/year, showing considerable variation among models. Spatially, the MME overestimates ET across over 90% of China. Bayesian model averaging (BMA) results align closely with reference data, with overestimation concentrated in southwest China. (2) At the national scale, CMIP6 trends range from −0.36 to 0.58 mm/year2, which contrasts sharply with the GLEAM trend of 1.27 mm/year2. At the basin scale, most models overestimate annual ET compared to GLEAM, with discrepancies particularly evident in the major river basins. The smallest difference in ET trend simulation occurs in the Northwest River basin, where model distributions are more concentrated, while the largest discrepancies appear in the Pearl River basin, where model performance is more scattered. Furthermore, signal‐to‐noise ratio (SNR) analysis reveals high ensemble consistency in regions such as the Haihe, Yellow, Yangtze, Pearl and Songliao River basins, indicating more reliable model performance in these areas. This study contributes to enhancing the reliability and accuracy of climate projections, which is essential for informed decision‐making and policy formulation in atmospheric science.

Benchmarking Uninitialized CMIP6 Simulations for Inter-Annual Surface Wind Predictions

This study investigates the potential of uninitialized global climate projections for providing 12-month (inter-annual) wind forecasts in Europe in light of the increasing demand for long-term climate predictions. This is important in a context where models based on the past climate may not fully account for the implications for climate variability of current warming trends, and where initialized 12-month forecasts are still not widely available (i.e., seasonal forecasts) and/or consolidated (i.e., decadal predictions). To this aim, we use two types of simulations: uninitialized climate projections from CMIP6 (Coupled Model Intercomparison Project Phase 6) and initialized 6-month seasonal forecasts (ECMWF’s SEAS5), using the latter as a benchmark. All the predictions are bias-corrected with five distinct approaches (quantile delta mapping, empirical quantile mapping, quantile delta mapping, scaling bias-adjustment and a proprietary quantile mapping) and verified against weather observations from the ECA&D E-OBS project (684 weather stations across Europe). It is observed that the quantile-mapping techniques outperform the other bias-correction algorithm in adjusting the cumulative distribution function (CDF) to the reference weather stations and, also, in reducing the mean bias error closer to zero. However, a simple bias -correction by scaling improves the time-series predictive accuracy (root mean square error, anomaly correlation coefficient and mean absolute scaled error) of CMIP6 simulations over quantile-mapping bias corrections. Thus, the results suggest that CMIP6 projections may provide a valuable preliminary framework for comprehending climate wind variations over the ensuing 12-month period. Finally, while baseline methods like climatology could still outperform the presented methods in terms of time-series accuracy (i.e., root mean square error), our approach highlights a key advantage: climatology is static, whereas CMIP6 offers a dynamic, evolving view of climatology. The combination of dynamism and bias correction makes CMIP6 projections a valuable starting point for understanding wind climate variations over the next 12 months. Furthermore, using workload schedulers within high-performance computing frameworks is essential for effectively handling these complex and ever-evolving datasets, highlighting the critical role of advanced computational methods in fully realizing the potential of CMIP6 for climate analysis.

Future Snow Scenarios for Northern Europe Based on Coupled Model Intercomparison Project Phase 6 Data

ABSTRACTThe ongoing climate change alters the snow conditions. This paper evaluates these changes in Northern Europe including Fennoscandia and the Baltic Sea region, based on data from the newest generation of global climate models (Coupled Model Intercomparison Project phase 6; CMIP6). Thirteen CMIP6 models are selected for the analysis based on the availability of daily snow data and the models' performance in simulating global and Northern European climate and snow conditions in Finland. The analysis focuses on four quantities: the largest daily value of snow water equivalent during the winter SWEmax, and the length, start day and end day of the longest continuous snow period. The models project an overall shift towards less snowy conditions with progressing warming: reduced SWEmax and shorter snow seasons that start later and end earlier. This is seen already in recent (1951–2023) trends, with largest simulated trends in southern Fennoscandia and in the Baltic countries and smaller trends in the northern inland regions. ERA5‐Land reanalysis data mainly agree with this spatial pattern, although with some notable differences. The decrease of snow continues into the future (2023–2100), with larger trends projected for Shared Socioeconomic Pathways (SSP) scenarios with larger radiative forcing. Also, larger changes are projected for southern than northern Fennoscandia. For example, for the moderate emission scenario SSP245, snow seasons around 2090 are projected to be nearly 50 days shorter than in 1981–2010 in southern Finland but only 30 days shorter in Finnish Lapland. However, there is substantial quantitative uncertainty in the trends in snow conditions, even for a fixed emission scenario. For example, for SSP245, the one‐sigma uncertainty due to natural variability alone is estimated to be at least 30%–50% of the multi‐model mean trends in 2023–2100 for all snow‐season metrics considered.

Deciphering the Intermodel Spread in Projections of the Impacts of Indian Summer Monsoon on ENSO Under Global Warming

AbstractThe Indian summer monsoon (ISM) is intricately linked to the El Niño‐Southern Oscillation (ENSO) on interannual timescale. Although previous studies have explored ENSO's effects on the ISM, the reverse influence, particularly under global warming, remains unclear. This study examines the projected changes in the ISM's impacts on ENSO under the SSP5‐8.5 emission scenario using 34 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that reasonably simulate the monsoon's effects on ENSO. A significant spread is found in the projections across the models, with approximately half of the models projecting an enhancing influence of ISM on ENSO, whereas the other half indicates a weakening effect. The intermodel spread is primarily associated with the projected changes in the strength of the feedback between precipitation and low‐level circulation over the tropical northwest Pacific, which is crucial for generating ISM‐induced anomalous circulation over the region. Models projecting an enhanced precipitation‐circulation feedback simulate larger ISM‐driven rainfall and circulation anomalies over the tropical northwest Pacific in a warmer climate, leading to more pronounced zonal wind anomalies near the equator along the southern side of the anomalous circulation and vice versa. As a result, the larger zonal wind anomalies caused by abnormal monsoons exert intensified effects on the subsequent ENSO evolution by significantly suppressing or amplifying the atmosphere‐ocean coupling processes related to ENSO development.

Ambient temperature and female infertility prevalence: an ecological study based on the 2019 global burden of disease study

BackgroundThe impact of climate change on human health is well established; however, its effect on the prevalence of female infertility is poorly understood. In this study, we aimed to investigate the association between ambient temperature changes and the prevalence of female infertility.MethodsIn this ecological study, 174 countries and regions were included. We utilized 2000–2019 data on the age-standardized prevalence rate (ASPR) of female infertility and temperature data from Global Burden of Disease, ERA5 (fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis for the global climate), and Coupled Model Intercomparison Project Phase 6 databases. Temperature over 12 months was averaged to express the annual temperature estimates, and the deviance percentage of temperature (DPT) was calculated based on the 20-year average temperature. Three-node restricted cubic spline curves were used to evaluate the association between temperature and the ASPR of female infertility. Linear mixed-effects models, with country code as a random effect, were used to estimate the effect size (β) and 95% confidence interval (CI) for DPT and the ASPR of female infertility. Adjusted linear mixed-effects models were used to predict the impact of future temperature changes (2020–2030) on the ASPR of female infertility.ResultsBetween 2000 and 2019, a U-shaped relationship was observed between temperature and the ASPR of female infertility, with the lowest ASPR occurring at 15 ℃. Increased DPT was associated with an increased ASPR of female infertility, with an adjusted β (95% CI) of 78.952 (10.514, 147.710). Future temperature increases will further elevate the ASPR of female infertility.ConclusionGlobally, temperature changes may be associated with an increase in the ASPR of female infertility.