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The diurnal temperature range (DTR) serves as a vital indicator reflecting both natural climate variability and anthropogenic climate change. This study investigates the historical and projected multitemporal DTR variations over the Tibetan Plateau. It assesses 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) using CN05.1 observational data as validation, evaluating their ability to simulate DTR over the Tibetan Plateau. Then, the evolution of DTR over the Tibetan Plateau under different shared socioeconomic pathway (SSP) scenarios for the near, middle, and long term of future projection are analyzed using 11 selected robustly performing models. Key findings reveal: (1) Among the models examined, BCC-CSM2-MR, EC-Earth3, EC-Earth3-CC, EC-Earth3-Veg, EC-Earth3-Veg-LR, FGOALS-g3, FIO-ESM-2-0, GFDL-ESM4, MPI-ESM1-2-HR, MPI- ESM1-2-LR, and INM-CM5-0 exhibit superior integrated simulation capability for capturing the spatiotemporal variability of DTR over the Tibetan Plateau. (2) Projection indicates a slightly increasing trend in DTR on the Tibetan Plateau in the SSP1-2.6 scenario, and decreasing trends in the SSP2-4.5, SSP3-7.0, and SPP5-8.5 scenarios. In certain areas, such as the southeastern edge of the Tibetan Plateau, western hinterland of the Tibetan Plateau, southern Kunlun, and the Qaidam basins, the changes in DTR are relatively large. (3) Notably, the warming rate of maximum temperature under SSP2-4.5, SSP3-7.0, and SPP5-8.5 is slower compared to that of minimum temperature, and it emerges as the primary contributor to the projected decrease in DTR over the Tibetan Plateau in the future. [ABSTRACT FROM AUTHOR]

Assessing and selecting climate models with lower uncertainty is necessary to predict future climate and hydrological risks at the watershed scale. In this study, we integrated stepwise cluster analysis (SCA) to propose a multi-model ensemble downscaling framework aimed at reducing the uncertainty of GCM-based precipitation projections in large-scale watersheds. The Pearl River Basin (PRB) in southern China was selected as the study area to validate the reliability of this framework. Spatially, we investigated the features of terrain-related spatial heterogeneity in precipitation simulation of different climate models using a stepwise cluster zoning approach. The spatial performance of most CMIP6 models was effective in capturing the annual mean precipitation from the source region to the downstream of the PRB. To further evaluate the model's skill in simulating precipitation patterns, we conducted a seasonal analysis for different periods throughout the year. However, the seasonal precipitation cycle exhibited a wet bias during cold seasons, and the most significant deviation of precipitation percentage intervals occurred during winter. The TSS ranking of CMIP6 models was used to select the top-performing models to construct an improved multi-model ensemble mean (MEM5), resulting in a more accurate precipitation simulation for PRB. Results showed consistent precipitation increases (p < 0.05) for all scenarios in the PRB, with the middle and lower reaches being the most sensitive to changes in precipitation. The improved MEM5 can serve as a valuable reference for accurately simulating hydrological regimes and extreme weather events in the PRB. The proposed multi-model ensemble downscaling framework, which incorporates SCA, offers a new approach for high-resolution and low-uncertainty climate simulations in other large-scale watersheds. [ABSTRACT FROM AUTHOR]

Amidst global concerns about land use change and its far-reaching impact on biodiversity and human well-being, there is a growing need to understand how land use stock and flow changes over time through land use accounting. While existing studies on land accounting have focused on historical land changes, little attention has been paid to future transitions. This study assessed historical patterns and projected future shifts in land use dynamics from 1987 to 2050 across Japan by combining high-resolution land use and land cover datasets, land change simulations, and land accounting. In the analyses, particular attention was paid to the historical and future trends of farmland abandonment by leveraging data at 100-m resolution built on national vegetation surveys. High-resolution analysis of farmland abandonment issue with national scale in Japan is a novelty. From 1987 to 1998, the land stock analysis results showed a pronounced marked increase in residential land (10.4%) and grassland (16.9%); the flow analysis results showed that urban residential sprawl expansion was mainly formed by secondary (32.6%) and plantation (21.1%) forest areas, coinciding with increasing population and economic growth. Projections from 2010 to 2050 indicate a marked increase in abandoned farmland (67.2% per decade), a trend influenced by rapid population decline and presumably agricultural policies, especially significant in regions such as Hokkaido and Kyushu. The findings of this study are crucial for shaping policy and decision-making, underlining the need for sustainable land management strategies that effectively balance urban growth, agricultural productivity, and environmental preservation in Japan. [ABSTRACT FROM AUTHOR]

The persistent occurrence of extreme precipitation events presents considerable challenges to the well-being, assets, and sustainable progress of the population in China. During this century, the trends, change points, and spatial variability of population exposure to precipitation extremes remain unclear. This study assesses how the exposure changes in future decades, using climate predictions from CMIP6 models and population projections under the SSP2-4.5 and SSP5-8.5 scenarios. We identify the initial decade and duration when exposure exceeds dangerous level, and also reveal the movements of the geographical centroids in different regions. It is found that under the SSP5-8.5 scenario, both precipitation extremes and population exposure to these extremes would be more severe, in contrast to the SSP2-4.5 scenario. Under the SSP2-4.5 scenario, 47.88% of the study area is projected to experience dangerously high levels of population exposure to precipitation extremes compared to the baseline. Under the SSP5-8.5 scenario, the areas experiencing dangerous exposure levels would increase by 4.11% by the 2030s, with 18.76% of areas experiencing earlier occurrences. In the North China Plain, the geographic centroid of population exposure is expected to move toward southward. These findings aid governmental authorities in identifying crucial timeframes, devising effective response strategies, and adjusting spatial disaster reduction resource allocation plans. [ABSTRACT FROM AUTHOR]

Abstract Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model’s skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change.

Abstract Background Falls among older individuals contribute significantly to the rise in ambulance transport use. To recognize the importance of future countermeasures, we estimated the projected number and percentage of fall-related emergency medical service (EMS) calls. Methods We examined the sex, age group, and location of falls among patients aged ≥ 65 years who contacted emergency services in Sapporo City from 2013 to 2021. Annual fall-related calls per population subgroup were calculated, and trends were analyzed. Four models were used to estimate the future number of fall-related calls from the 2025–2060 projected population: (1) based on the 2022 data, estimates from the 2013–2022 data using (2) Poisson progression, (3) neural network, (4) estimates from the 2013–2019 data using neural network. The number of all EMS calls was also determined using the same method to obtain the ratio of all EMS calls. Results During 2013–2022, 70,262 fall-related calls were made for those aged ≥ 65 years. The rate was higher indoors among females and outdoor among males in most age groups and generally increased with age. After adjusting for age, the rate increased by year. Future estimates of the number of fall calls are approximately double the number in 2022 in 2040 and three times in 2060, with falls accounting for approximately 11% and 13% of all EMS calls in 2040 and 2060, respectively. Conclusion The number of fall-related EMS calls among older people is expected to increase in the future, and the percentage of EMS calls will also increase; therefore, countermeasures are urgently needed.

Bomb cyclones, or explosive cyclones, are rapidly-intensifying extratropical weather systems that are often associated with extreme wind and precipitation. The projection of bomb cyclones is crucial for climate change risk assessment given their severe impacts on society and environment. This study investigates the projected changes in bomb cyclone activity between historical (1950–2014) and future (2015–2050) periods using the HighResMIP-PRIMAVERA multimodel ensemble. In the North Pacific (NP), the ensemble projects a 0.40° northward shift of bomb cyclone activity, with notably less events around Japan. An eastward shift of bomb cyclone activity is projected in the North Atlantic (NA) with a marked decline in the northwestern part. In the Southern Hemisphere (SH), bomb cyclones are projected to shift poleward by 0.49°, with little change in the total number of events. In all three analysis regions, the projected changes in bomb cyclone activity are associated with changes in lower-tropospheric baroclinicity, with the major contributor being vertical wind shear and static stability in the NP and the SH, respectively. Furthermore, we identify close relationships between the projected bomb cyclone changes and mean-flow changes in the NP and SH, with the shift of mid-latitude jet latitude significantly correlated with the projected decline and poleward shift of bomb cyclone activity. In the SH, the cooling of the polar lower stratosphere also contributes to the projected poleward movement. These results are not strongly dependent on model horizontal resolution, although higher-resolution models generally have a larger number of bomb cyclones. [ABSTRACT FROM AUTHOR]

Anthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under‐investigated. This study explored the impacts of forest management‐induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management‐induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management‐induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (−7.5%) and the smallest decrease under SSP3 (−0.7%) in 2100. Among land use types, considering forest management‐induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management‐induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections. Plain Language Summary: Land use and land cover change has shown widespread social, economic, and environmental impacts. However, the influence of forest management on forest productivity change and subsequent land use and land cover change projections remains under‐investigated. This study explored how changes in forest productivity caused by forest management practices impact land use and land cover throughout the 21st century under various social and economic scenarios. The results show that more intense forest management generally leads to more productive forests. This, in turn, results in smaller changes in land use and land cover. Increasing forest management reduces the need to expand managed forests and helps preserve natural lands by 2100. Ignoring the impact of forest management on forest productivity could lead to biases in projecting forest expansion and wood production and potentially induce uncertainties in carbon cycling and biodiversity. Our results emphasize the need to account for forest management in future projections of land use and land cover changes. Key Points: Forest management‐induced productivity change has a significant impact on future land use and land cover change (LULCC)Neglecting such impact could overestimate the managed forest expansion and natural land reduction, especially under SSP1 and SSP5We advocate considering such impacts during LULCC projection to constrain the uncertainty [ABSTRACT FROM AUTHOR]

Simulating and predicting Arctic sea ice accurately remains an academic focus due to the complex and unclear mechanisms of Arctic sea ice variability and model biases. Meanwhile, the relevant forecasting and monitoring authorities are searching for models to meet practical needs. Given the previous ideal performance of cGENIE model in other fields and notable features, we evaluated the model's skill in simulating Arctic sea ice using multiple methods and it demonstrates great potential and combined advantages. On this basis, we examined the direct drivers of sea-ice variability and predicted the future spatio-temporal changes of Arctic sea ice using the model under different Representative Concentration Pathways (RCP) scenarios. Further studies also found that Arctic sea ice concentration shows large regional differences under RCP 8.5, while the magnitude of the reduction in Arctic sea ice thickness is generally greater compared to concentration, showing a more uniform consistency of change. [ABSTRACT FROM AUTHOR]

In this study, a new skill score (SS) is proposed to evaluate the performance of climatological East Asian summer precipitation (EASP) in the Coupled Model Intercomparison Project Phase 6 (CMIP6) over the historical period. By applying the empirical orthogonal function (EOF) to the EASP bias of CMIP6 models, the intermodel spread of EASP bias is revealed to be dominated by the first two modes: the uniform precipitation bias pattern and the north–south dipole precipitation bias pattern. Then the SS is constructed by the weighted‐average model‐observation distances regarding different EOF modes, where the model‐observation distance in a certain EOF mode is defined as the difference between their principal components, and the weight is the corresponding percentage variance. The perfect‐models ensemble based on the SS shows a spatial magnitude close to the observation, indicating that the SS effectively depicts the models' historical performance. However, no robust relationship is found between the model's historical performance and future projection regarding the EASP. This is because they are governed by different physical factors. The historical EASM is determined by the thermal responses to a specific radiative forcing, while the future change in EASP is associated with the warming rate along with the increased radiative forcing. [ABSTRACT FROM AUTHOR]

The present study analyzes the projected changes of extreme climate indices over Central Asia using regional climate model (RCM) simulations from the Coordinated Regional Climate Downscaling Experiment (CORDEX) - Coordinated Output for Regional Evaluations (CORE). The extreme indices are based on precipitation and temperature and are inspected for present (1981–2005) and future periods - near- (2031–2055) and far-future (2071–2095) - to assess the long-term climate change under the representative concentration pathway RCP8.5. Projected changes are analyzed for three different model ensembles. These ensembles are based on CORDEX-Central Asia (ENS_CAS, four ensemble members) and CORDEX-East Asia (ENS_EAS, six ensemble members), and a combination of both (ENS, ten ensemble members) for our study area centered over high mountain Asia, called Central East Asia (CEAS). For precipitation indices, an increase of consecutive dry days (CDD) in ENS_EAS and a slight to moderate decrease in northern parts in ENS_CAS during near-future is observed. Consecutive wet days (CWD), very heavy precipitation events (R20mm), maximum one-day precipitation (RX1day), and very wet days (R95p) are projected to increase in most areas. All indices show a further intensification towards the end of the century over large parts of the domain, e.g., + 7.8% / +5.6 days for CDD, + 96.6% / +0.26 days for R20mm, and + 19.7% for RX1day as median of ENS over CEAS. For temperature indices, the ensembles project a strong increase over the high mountain regions and southern parts for consecutive summer days (CSU, + 108.5% / +38.3 days), heat wave duration index (HWDI, + 1379.1% / +91.37 days), and the percentage of very hot days (TX90p, + 391.1% / +34.54 days). Accordingly, the number of consecutive frost days (CFD, -43.7% / -25.2 days) and the percentage of very cold days (TX10p, -83.4% / -8.13 days) are projected to decrease. The first-time usage of CORDEX-CORE and the larger ensemble size by considering simulations from overlapping domains increase the robustness of the findings from earlier studies. However, some discrepancies in the projected changes prevail among the different RCMs being part of the two CORDEX-domains and in specific landscapes like complex mountainous or lake areas. These uncertainties may be tackled by further model development with improved land-surface processes and potentially higher spatial resolution. [ABSTRACT FROM AUTHOR]

Temperature extremes exert a significant influence on terrestrial ecosystems, but the precise levels at which these extremes trigger adverse shifts in vegetation productivity have remained elusive. In this study, we have derived two critical thresholds, using standard deviations (SDs) of growing‐season temperature and satellite‐based vegetation productivity as key indicators. Our findings reveal that, on average, vegetation productivity experiences rapid suppression when confronted with temperature anomalies exceeding 1.45 SD above the mean temperature during 2001–2018. Furthermore, at temperatures exceeding 2.98 SD above the mean, we observe the maximum level of suppression, particularly in response to the most extreme high‐temperature events. When Earth System Models are driven by a future medium emission scenario, they project that mean temperatures will routinely surpass both of these critical thresholds by approximately the years 2050 and 2070, respectively. However, it is important to note that the timing of these threshold crossings exhibits spatial variation and will appear much earlier in tropical regions. Our finding highlights that restricting global warming to just 1.5°C can increase safe areas for vegetation growth by 13% compared to allowing warming to reach 2°C above preindustrial levels. This mitigation strategy helps avoid exposure to detrimental extreme temperatures that breach these thresholds. Our study underscores the pivotal role of climate mitigation policies in fostering the sustainable development of terrestrial ecosystems in a warming world. [ABSTRACT FROM AUTHOR]

Background: Falls among older individuals contribute significantly to the rise in ambulance transport use. To recognize the importance of future countermeasures, we estimated the projected number and percentage of fall-related emergency medical service (EMS) calls. Methods: We examined the sex, age group, and location of falls among patients aged ≥ 65 years who contacted emergency services in Sapporo City from 2013 to 2021. Annual fall-related calls per population subgroup were calculated, and trends were analyzed. Four models were used to estimate the future number of fall-related calls from the 2025–2060 projected population: (1) based on the 2022 data, estimates from the 2013–2022 data using (2) Poisson progression, (3) neural network, (4) estimates from the 2013–2019 data using neural network. The number of all EMS calls was also determined using the same method to obtain the ratio of all EMS calls. Results: During 2013–2022, 70,262 fall-related calls were made for those aged ≥ 65 years. The rate was higher indoors among females and outdoor among males in most age groups and generally increased with age. After adjusting for age, the rate increased by year. Future estimates of the number of fall calls are approximately double the number in 2022 in 2040 and three times in 2060, with falls accounting for approximately 11% and 13% of all EMS calls in 2040 and 2060, respectively. Conclusion: The number of fall-related EMS calls among older people is expected to increase in the future, and the percentage of EMS calls will also increase; therefore, countermeasures are urgently needed. [ABSTRACT FROM AUTHOR]

High temperature warning indicators play a pivotal role in meteorological departments, serving as crucial criteria for issuing warnings that guide both social production and daily life. Despite their importance, limited studies have explored the relationship between different global warming levels and changes in high temperature warning indicators. In this study, we analyze data from 2,419 meteorological stations over China and utilize the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to examine historical changes in high temperature warning indicators used by the China Meteorological Administration. We evaluate model performance and estimate future changes in these indicators using an annual cycle bias correction method. The results indicate that since 1961, the number of high temperature days (TX35d and TX40d) and length of season (TX40d and TX40l) with daily maximum temperature reaching or exceeding 35°C and 40°C have increased over China. The intensity of high temperatures (TXx) has strengthened and the geographical extent affected by high temperatures has expanded. In 2022, the occurrence of 40°C high temperatures surges, with Eastern China experiencing a two-day increase in TX40d and an extended seasonal length in TX40l by over five days. While CMIP6 models have underestimated the high temperature indictors associated with 35°C during historical periods, notable difference is not observed between the models and observations for TX40d and TX40l, given their rare occurrence. However, future projections, after bias correction, indicate that the increasing trends for 35°C and 40°C high temperature days and length of season become more pronounced than the raw projection, suggesting a more severe increase than that anticipated originally. As global warming intensifies, the high temperature days and length of season are projected to increase non-linearly, while the intensity of high temperatures is expected to increase linearly. For every 1°C increase in global temperature, the intensity is projected to rise by approximately 1.4°C. The impact of high temperatures is expanding, with the major hotspot for China located in the eastern and northwestern regions. Under 5°C global warming, certain regions in China may experience prolonged extreme high temperatures. For instance, 40°C high temperature days in areas like North China and the Yangtze River Basin could increase by about 32 d, and the length of season could extend by approximately 100 d. [ABSTRACT FROM AUTHOR]

Evidence of climate change can be observed in multiple climate variables, including air temperature rises and precipitation pattern changes. To manage water resources and agriculture effectively, it's important to project climate variables' changes at the local level, as these changes can vary depending on the specific area. The baseline weather data trend was analyzed using the percentage change (PC) method and the Innovative Trend Analysis (ITA) technique at three cluster levels: cluster 1 (PC: 0–20%), cluster 2 (PC: 21–80%), and cluster 3 (PC: 81–100%). The precipitation (Prec.), maximum (Tmax), and minimum (Tmin) temperatures showed downward trends in 9, 4, and 6 stations out of 24 stations, respectively. The SDSM model performed best in predicting Prec., while the LARS-WG model was more effective in predicting Tmax, Tmin, and solar radiation (SR). The average monthly Prec. percentage change shows both rising and falling trends in different weather areas for all three time periods (2040, 2060, and 2090) and for both RCPs (RCP4.5 and RCP8.5). In contrast to precipitation, both Tmax and Tmin consistently showed an upward trend across all meteorological stations for both RCPs and three-time frames. Across the four distinct plain regions, the overall projection suggests a slight increase in precipitation. The study predicts the highest increase in precipitation to occur in June across all meteorological stations. Seasonally, the greatest increase in precipitation is projected during summer (JJA) by 5.10%, while the largest decrease is expected during winter (DJF) by 3.29%. Additionally, precipitation variability shows an increase from RCP4.5 to RCP8.5 and from near-term (2040) to long-term (2090), with the northern Jiangsu Plain exhibiting the highest variation. The biggest rise in Tmax/Tmin was observed at RCP 8.5, by 2.69/2.39 °C, and in the long term (2090), by 3.25/2.86 °C. This was compared to RCP 4.5 by 1.73/1.51 °C, in the near term (2040) by 1.24/1.08 °C, and in the mid-term (2060) by 2.14/1.90 °C. The highest increase in Tmax is expected compared to Tmin, leading to the highest diurnal temperature (DTR) at all three periods and both RCPs. Seasonally, the highest increase is projected in the autumn for both Tmax and Tmin. Similar to Tmax and Tmin, the longest time period (2090) exhibits the highest increase in solar radiation, followed by the midterm (2060) and then the short term (2040). Unlike Tmax and Tmin, the highest increase in SR is predicted during the summer season (JJA), while the lowest increase is projected during the winter season (DJF). The future projections highlight the expectation of a wettest and hottest summer, along with the driest and coldest winter. These findings provide valuable insights for water resource planners, agricultural managers, and policymakers, as these climate variables play a significant role in crop production and water allocation decisions. [ABSTRACT FROM AUTHOR]

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRIESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios. [ABSTRACT FROM AUTHOR]

Wetland ecosystems play a pivotal role in terrestrial carbon and water cycles, thereby possessing great potential to regulate terrestrial water use efficiency (WUE), which is calculated as the ratio of gross primary productivity (GPP) to evapotranspiration (ET). However, it remains unclear the possible changes in wetland WUE under present and future climate conditions. In this research, WUE variations in a Phragmites australis-dominated freshwater wetland were determined by the eddy covariance method during 2020–2023. Further, we projected future GPP, ET, and WUE under four Representative Concentration Pathway (RCP) scenarios based on five Earth System Models. The 3-year average field observation suggested that the P. australis marsh exhibited high GPP (1149 g C m−2), but consumed large amounts of water through ET (611 mm H2O), resulting in relatively low WUE (1.89 g C mm−1 H2O). During wet years, the studied marsh consumed much more water through evaporation than through transpiration, thus exhibiting lower WUE. Contrarily, large amounts of water were utilized to maintain high primary productivity through transpiration in 2021–2022 dry year, leading to higher WUE. In future scenarios, GPP in the P. australis marsh shows consistently faster uptrends compared to ET from 2020 to 2100, consequently yielding persistent growths of WUE. Future growths of WUE indicate that P. australis marsh tends consume more water for maintaining productivity levels rather than loss via evaporation under future climate conditions, thereby intensifying the carbon–water interaction. Driven by the most rapid growth of environmental drivers, the RCP8.5 scenario shows the fastest increasing trends in GPP, ET, and WUE among four RCP scenarios, whereas the reverse ocurres under RCP2.6 scenario. Overall, our study advocates for more comprehensive research encompassing field observation and model simulation to address the current knowledge gap regarding the response of wetland WUE to contemporary and future climate change.

The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin. HIGHLIGHTS The study evaluated the biases of 23 global climate models (GCMs) from observations and the spatial distribution of the biases.; Interannual variability score, Taylor diagram, and Taylor skill score were used to evaluate CMIP6 GCMs.; The future changes of the Yellow River Basin and its subregions extreme precipitation under SSP2-4.5 and SSP5-8.5 scenarios were projected by using six ETCCDI indices.;

Diurnal temperature range (DTR), as a core indicator of the Earth system, exhibits obvious temporal and spatial variations, which is not entirely consistent over global. The historical simulation capabilities of 19 Coupled Model Intercomparison Project 6 (CMIP6) models for DTR were firstly evaluated against CRU_TS v4.04 data. Future changes under three shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP5-8.5) in DTR were projected using a multi-model ensemble mean (MME), and its interannual variations were seasonally explored through empirical orthogonal function (EOF) analysis. The results indicated that CMIP6 models could reflect the decreasing trend of DTR during 1901–2014, with the global spatial correlation coefficient between models and observation ranging from 0.4 to 0.7. MME outperformed individual models in both spatial and temporal variations, indicating higher accuracy and reliability. The future changes of DTR exhibited significantly decrease across the northern hemisphere and increase in the South America, and change magnitude enlarged with time extension and emission intensity, especially by more than 0.4 °C under SSP5-8.5. The decreasing trend of global DTR was kept in SSP2-4.5 and SSP5-8.5, while SSP1-2.6 changed increasing trend during 2015–2100. DTR showed seasonal variations and was mainly influenced by colder months. The dominant modes of interannual DTR and their relationship with the 500 hPa geopotential height, the 200 hPa U wind, and the outgoing longwave radiation showed higher features in tropical regions. The highly positive correlation between the first mode of DTR and the Niño3.4 index in December/January/February (DJF) is 0.67, indicating significant influence of the El Niño-Southern Oscillation on DTR. [ABSTRACT FROM AUTHOR]

The Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS. Plain Language Summary: In recent decades, the densely populated Northeast United States has faced the most rapid increase in the frequency of extreme rainfall within the US. Here, we examine the causes of the increase in extreme rainfall over the Northeast US in both current and future climates. We find that the surge in extreme rainfall since the 1990s is primarily linked to events associated with tropical cyclones. In a future warming climate, based on projections from a high‐resolution climate model, our research shows that we can expect more frequent occurrences of extreme rainfall related to both atmospheric rivers and tropical cyclones. However, the increase in extreme rainfall linked to atmospheric rivers is projected to outpace that associated with tropical cyclones. Given the distinct spatial patterns of extreme rainfall resulting from atmospheric rivers and tropical cyclones, changes in their relative contributions could have profound implications for flood prevention and mitigation strategies. Key Points: The autumn extreme precipitation trend over the Northeast US is primarily attributed to tropical cyclone‐related events since the 1990sIn future projections, extreme precipitation linked to atmospheric rivers increases faster than that associated with tropical cyclonesDespite fewer projected tropical cyclones in the future, extreme precipitation associated with them is projected to increase [ABSTRACT FROM AUTHOR]

Wind and solar energy are crucial for meeting the growing energy demand and mitigating the impact of climate change, and their sources show a climate‐dependence. Here, based on the outputs from two regional climate models (RCMs) driven by three global climate models within the Coordinated Regional Climate Downscaling Experiments‐East Asia (CORDEX‐EA‐II), the effects of future climate change on wind power density (WPD) and photovoltaic power potential (PVP) in China under the Representative Concentration Pathway (RCP) 2.6 scenario and RCP8.5 scenario are comprehensively investigated, and the sources of uncertainty are also quantified. Results show that all RCMs can reproduce the observed WPD and PVP in China by employing the Quantile Delta Mapping method for wind speed simulations. For the future projections, the annual averages of WPD and PVP in China tend to decrease by −11.67% to −1.7% and −4.6% to −1.12%, respectively, with more significant reduction under the RCP8.5 than under the RCP2.6. Note that uncertainties exist among the RCMs' simulations in terms of future projections and long‐term trends. Further analysis reveals that the uncertainty in both WPD and PVP projections are primarily driven by the internal variability in most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. This study highlights the potential benefits of controlling greenhouse gas emissions in enhancing and stabilizing renewable energy in China. Plain Language Summary: China has set an ambitious goal of achieving carbon neutrality by 2060, which requires an accelerated energy transition toward wind power and photovoltaic. However, the sources of wind and solar energy are sensitive to weather and climate, and future potential of wind and solar energy in China under a warming climate remains uncertain, posing a significant concern for the decision‐makers. Based on high‐resolution multiple regional climate models (RCMs) projections, we found that the annual average of wind power density (WPD) and photovoltaic power potential (PVP) are expected to decrease in the future by approximately −11.67% to −1.7% and −4.6% to −1.12%, respectively. There are notable uncertainties among the multi‐RCM projections, and the internal variability is the main source of uncertainty for most sub‐regions except the Tibetan Plateau, where GCM uncertainty and emission scenarios play the dominant role in the uncertainty of WPD and PVP projection, respectively. Our results highlight the advantages of reducing greenhouse gas emissions to enhance and stabilize renewable energy sources in China. Key Points: The annual average wind and solar energy in China are expected to decrease in the future based on a multi‐RCM ensembleThe internal variability plays a dominated role in the uncertainty of WPD and PVP projections in most sub‐regionsThe uncertainty in WPD and PVP projections over the TP is driven primarily by the different GCMs and emission scenarios, respectively [ABSTRACT FROM AUTHOR]

Information about wind variations and future wind conditions is essential for a monsoon domain such as the Northwest Pacific (NWP) region. This study utilizes 10 Generalized Circulation Models (GCM) from CMIP6 to evaluate near-future wind changes in the NWP under various climate warming scenarios. Evaluation against the ERA5 reanalysis dataset for the historical period 1985–2014 reveals a relatively small error with an average of no more than 1 m/s, particularly in the East Asian Marginal Seas (EAMS). Future projections (2026–2050) indicate intensified winds, with a 5–8% increase in the summer season in the EAMS, such as the Yellow Sea, East Sea, and East China Sea, while slight decreases are observed in the winter period. Climate mode influences show that winter El Niño tends to decrease wind speeds in the southern study domain, while intensifying winds are observed in the northern part, particularly under SSP5-8.5. Conversely, summer El Niño induces higher positive anomalous wind speeds in the EAMS, observed in SSP2-4.5. These conditions are likely linked to El Niño-induced SST anomalies. For the application of CMIP6 surface winds, the findings are essential for further investigations focusing on the oceanic consequences of anticipated wind changes such as the ocean wave climate, which can be studied through model simulations. [ABSTRACT FROM AUTHOR]

The quantitative assessment of the uncertainty components of future climate projections is critical for decision-makers and organizations to establish climate change adaptation and mitigation strategies at regional or local scales. This is the first study in which the changes in the uncertainty components of future temperature and precipitation projections are quantitatively evaluated using multiple regional climate models over East Asia, vulnerable to future climate change. For temperature, internal variability and model uncertainty were the main factors affecting the near-term projections. The scenario uncertainty continued to increase and was estimated to be the dominant factor affecting the uncertainty after the mid-term projections. Although precipitation has the same main uncertainty factors as the temperature in the near-term projections, it considerably differs from temperature because the internal variability notably contributes to the fraction to the total variance, even in the long-term projections. The internal variability of the temperature and precipitation in the near-term projections were predicted to be larger in Korea than that in East Asia. This was confirmed by regional climate models as well as previous studies using global climate models as to the importance of internal variability at smaller regional scales during the near-term projections. This study is meaningful because it provides new possibilities with respect to the consideration of climate uncertainties to the establishment of climate change policies in more detail on the regional scale. [ABSTRACT FROM AUTHOR]

Abstract Anthropogenic land use and land cover change (LULCC) is projected to continue in the future. However, the influence of forest management on forest productivity change and subsequent LULCC projections remains under‐investigated. This study explored the impacts of forest management‐induced change in forest productivity on LULCC throughout the 21st century. Specifically, we developed a framework to softly couple the Global Change Analysis Model and Global Timber Model to consider forest management‐induced forest productivity change and projected future LULCC across the five Shared Socioeconomic Pathways (SSPs). We found future increases in forest management intensity overall drive the increase of forest productivity. The forest management‐induced forest productivity change shows diverse responses across all SSPs, with a global increase from 2015 to 2100 ranging from 3.9% (SSP3) to 8.8% (SSP1). This further leads to an overall decrease in the total area with a change of land use types, with the largest decrease under SSP1 (−7.5%) and the smallest decrease under SSP3 (−0.7%) in 2100. Among land use types, considering forest management‐induced change significantly reduces the expansion of managed forest and also reduces the loss of natural land in 2100 across SSPs. This suggests that ignoring forest management‐induced forest productivity change underestimates the efficiency of wood production, overestimates the managed forest expansion required to meet the future demand, and consequently, potentially introduces uncertainties into relevant analyses, for example, carbon cycle and biodiversity. Thus, we advocate to better account for the impacts of forest management in future LULCC projections.

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRI-ESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios.

As a climate-sensitive region， precipitation over the Qinghai-Xizang （Tibetan） Plateau significantly impacts the water cycle and the climate of East Asia.Therefore， it is important to study its changes.Precipitation is an important variable in the global hydrological cycle and one of the major climate systems affected by climate change.To investigate the ability of the global climate models to simulate precipitation over the Qinghai-Xizang （Tibetan） Plateau and examine possible changes in future precipitation under the new model and scenarios， this paper uses the latest monthly precipitation data from the 31 climate models of the Coupled Model Intercomparison Project 6 （CMIP6） and the CN05.1 precipitation observation data set provided by the National Climate Center to evaluate the ability of the CMIP6 model to simulate precipitation over the Qinghai-Xizang （Tibetan） Plateau.Furthermore， better models are selected to project the future precipitation of the Qinghai-Xizang （Tibetan） Plateau under different Shared Socioeconomic Pathway （SSP） scenarios.The results show that the model distribution of observed precipitation over the Qinghai-Xizang （Tibetan） Plateau from 1995 to 2014 is characterized by a decrease from southeast to northwest and a summer precipitation concentration.Most of the models can simulate the precipitation distribution and seasonal trend， but almost all of them overestimate the precipitation phenomenon， and the average precipitation of multiple modes is 102% higher than that observed.In general， the latest model of CMIP6 has a poor ability to simulate precipitation over the Qinghai-Xizang （Tibetan） Plateau， and the average relative deviation index of the model from the observation is 102%， indicating that most of the models are not satisfactory， and EC-Earth3-Veg-LR， MPI-ESM1-2-LR， EC-Earth3-Veg， and MRI-ESM2-0 are selected as the better modes after quantitative analysis of all the models， which can roughly reflect the precipitation characteristics of the Qinghai-Xizang （Tibetan） Plateau.Climate models show the slowest increase of precipitation over the Qinghai-Xizang （Tibetan） Plateau under the SSP1-2.6 scenario and the fastest increase under SSP5-8.5.From SSP1-2.6 in the small radiative forcing scenario to SSP5-8.5 in the large scenario， the recent （from 2021 to 2040） precipitation increase on the plateau is difficult to find a large difference in each scenario， but there is a significant increase in the mid （from 2041 to 2060） and late （from 2081 to 2100） scenarios， indicating that carbon emission intensity has a small impact in the short term and a large impact in the long term.The future increase in precipitation mainly occurs in the area south of the Nianqing Tanggula Mountains， from a seasonal point of view， the summer increase is the largest， followed by spring and autumn， the smallest increase is in winter， so we should pay attention to the future summer and spring precipitation changes over the Qinghai-Xizang （Tibetan） Plateau and take coping measures.

Heatwave exposure has increased dramatically because of climate warming and population growth, along with their interactive effects. However, effective adaptation measures can reduce these impacts. Nonetheless, the dynamic changes, regional inequality in adaptive capacity and their potential contributions to reducing exposure in the future remain unclear. This study quantifies the impact of adaptive capacity and underscores regional variations in heatwave magnitudes, population exposure and adaptation levels in China. We projected the future adaptive capacity using air-conditioner penetration, factoring in climate cooling requirements and individuals’ purchasing power. Utilising population and gross domestic product (GDP) data from four Shared Socioeconomic Pathways (SSP1, SSP2, SSP3 and SSP5) and daily temperature data from four SSP-based emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5), we estimated heatwave duration, population exposure and avoided impacts through adaptation across China and its sub-regions. Results show a substantial increase in heatwave duration in Southwest and Southern China, especially under the SSP5-8.5 scenario, with a projection of 163.2 ± 36.7 d during 2081–2100. Under the SSP3|SSP3-7.0 scenario, total exposure reaches 156.4 ± 76.8 billion person d per year, which is the highest among all scenarios and 23 times greater than that in 1986–2005 without adaptation. Upon considering adaptation measures, a noteworthy reduction in population exposure is observed, especially in the SSP3|SSP3-7.0 and SSP5|SSP5-8.5 scenarios, with reductions of (62.6 ± 3.9) % and (65.8 ± 5.1) %, respectively, compared with the scenario without adaptation during 2081–2100. Remarkable regional disparities in avoided impacts are also evident, with variations of up to 50% across different regions. The implementation of effective and environmentally friendly adaptation measures can notably address climate change, thereby alleviating the profound threats posed to human well-being.

Global climate models provide useful tools to forecast large‐scale anthropogenic trends and the impacts on ocean physics and marine biology and chemistry. Due to coarse spatial resolution, they typically lack the ability to represent important regional processes while underestimating mesoscale variability and vertical mixing. This means they provide limited value when it comes to regional climate projections. We developed a regional submesoscale‐permitting physical/biogeochemical model to dynamically downscale the output of a CMIP6 Earth System Model for three different Socioeconomic Pathways for the main Hawaiian Islands. We describe the methodology for downscaling the CMIP6 ocean physics and biogeochemistry along with atmospheric conditions in order to offline nest a regional model. We expect the large‐scale spatial and temporal features of the global model to be retained by the regional model, while adding representation of the regional processes that are crucial to understanding climate change on a local scale. We compare the regional model representation against both observed data and a regional reanalysis over the first two decades of the century. We show that the regional model maintains the large‐scale trends and interannual variability provided by the CMIP6 model while well‐representing the regional dynamics that drive the short‐term variability. To better illustrate the benefit of the downscaling, we present preliminary analysis of the downscaled results to examine climate impacts on the island corals that are not resolved by the global models. This analysis reveals that coastal corals are likely to experience unprecedented ocean acidification and substantial warming over the course of the century. Plain Language Summary: Climate models play an important role in projecting the changes in the ocean that may result from human‐induced climate change; however, these models lack the ability to represent critical features important to regional localities. This means that high‐resolution regional models are required to understand the impacts of climate change important to local communities. We describe how to downscale climate model solutions such that they can be used by high‐resolution regional models to project local impacts of climate change. Our application is for the main Hawaiian Islands in the Pacific ocean that are highly dynamic but are missing from climate models. We also provide preliminary results of these projections that show regional impact on the island corals due to climate change. Key Points: The first dynamically downscaled submesoscale permitting future climate projections for the main Hawaiian Islands are presentedA careful treatment of global model output is critical to capture essential local oceanographic characteristicsHawai'i's near‐surface ocean is likely to experience unprecedented acidification and substantial warming over the course of the century [ABSTRACT FROM AUTHOR]

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Previous evaluations of coupled climate models have indicated that under the influence of global warming precipitation extremes, including those over Indonesia, are expected to intensify. Here, we examine the most recent future projections of extreme rainfall in Indonesia using 24 global climate models from the Coupled Model Intercomparison Project Phase 6, consisting of 11 low resolution (LR) and 13 medium resolution (MR) models. The performance of these models is evaluated against Climate Hazards Group InfraRed Precipitation with Station data observations and realistically reproduce Indonesia's mean climatology over the period assessed (1987–2014). Overall, both LR and MR multi‐model ensemble means (MMEM) outperform individual models. Interestingly, extreme rainfall projections vary across different seasons, time periods, spatial resolutions and climate change scenarios. The LR and MR MMEMs project a continuous increase in wet extremes (R95p and Rx5d) during the wet season across most of Indonesia over the 21st century. Conversely, dry extremes (consecutive dry days [CDD]) are projected to increase (decrease) over the south (north) during the wet season but increase countrywide during the dry season. We show that future extreme wet and dry events are projected to be more frequent and intense, with upper extreme values surpassing historical records. The MR models project smaller changes in extreme wet indices than the LR models but simulate a more prolonged extreme dry index. Key findings here indicate that forthcoming instances of extreme rainfall present substantial hazards, necessitating the implementation of adequate preventative measures by policymakers, particularly within densely inhabited regions, such as Java. [ABSTRACT FROM AUTHOR]

The increased rate of urbanization has led to the transformation of landscapes at a higher rate, making the accurate assessment of land use and land cover changes (LULCC) imperative for sustainable urban planning and environmental management. This study presents the geospatial assessment of LULCC in the FCT (Federal Capital Territory) of Nigeria over a 35-year period, selecting the following years: 1987, 2002, 2011, and 2022, based on low cloud cover using remote sensing imagery and GIS techniques. By incorporating integrated cellular automata (CA)-Markov module from IDRISI-TerrSet software, the year 2050 was predicted. The overall accuracy for the four years (1987, 2002, 2011, and 2022) was 89.76%, 88.93%, 87.97%, and 86.54%, respectively. The Kappa coefficients were 0.88, 0.85, 0.86, and 0.83, respectively, which is considered acceptable. From 1987–2022, built-up land gained 881.74 km² in area, barren land gained 73.93 km² in area, forest lost 1,761.85 km² in area, wetland lost 97.44 km² in area, water body gained 1.75 km², shrub land lost 905.77 km², and grassland gained 1,806.47 km². The results of the predicted year (2050) showed that from 2022–2050, built-up land will gain 182.93 km² in area, barren land will gain 119.20 km² in area, forest will lose 39.78 km² in area, wetland will lose 99.94 km² in area, water bodies will gain 1.75 km², shrub land will lose 499.51 km², and grassland will gain 237.91 km². Results from the study will provide insight for urban planners, policymakers, and environmentalists in making decisions geared towards sustainable development and resilient urban growth in the FCT. [ABSTRACT FROM AUTHOR]

It is one of the major challenges in climate science to project future changes in extreme rainfall. To overcome this challenge, in this study, four typical synoptic patterns (SPs) triggering summer extreme rainfall over the middle and lower reaches of Yangtze River (MLYR) are identified through hierarchical clustering. These typical SPs share common characteristics of intensified Mei‐yu trough and Western Pacific Subtropical High but differ in terms of mid‐latitudes disturbances, such as an intensified ridge (Cluster 1, Cluster 2) or trough (Cluster 3, Cluster 4) near Lake Baikal (Cluster 1, Cluster 3) or Northeast China (Cluster 2, Cluster 4). The linkage between extreme rainfall and typical SPs is verified at various time scales. The typical SPs associated with extreme rainfall are substantially different from the circulation patterns found on ordinary days, and their frequency is significantly correlated with that of extreme rainfall across the interannual scales. Furthermore, the distinct changes in different typical SPs serve as a "bridge" for understanding the long‐term impact of circulation changes on local extreme rainfall, even though the two do not appear to be connected at first sight. Specifically, the circulation changes imply more (less) frequent SP‐Cluster 1 (SP‐Cluster 3), which tends to produce more extreme rainfall to the south (north) of the Yangtze River within MLYR. To project future changes in extreme rainfall, we utilize a weighting method for the multi‐model ensemble based on each model's capability to capture the observed typical SPs. This method effectively narrows the inter‐model spread. Plain Language Summary: Extreme rainfall has a huge impact on human society and ecosystem, but the large uncertainty of extreme rainfall simulation hinders the use of climate models in future risk assessment. To reduce the uncertainty, we identify critical physical mechanisms for extreme rainfall formation in the real world and then utilize them as metrics to assess climate models and give more weight to models with better performances. We first identified four types of weather configurations as the critical physical mechanisms for extreme rainfall formation, which are further verified to be closely connected with extreme rainfall at different time scales (i.e., daily, interannual, and several decades). Then, we assess how well each model captures these weather configurations and then generate weighted multi‐model ensembles based on the model performances. This method effectively reduces the uncertainty in future projections. This assessment and correction procedure has great potential to provide reliable projections of extreme events in other regions and seasons. Key Points: The typical synoptic patterns (SPs) triggering summer extreme rainfall (Rx) are differed by mid‐latitudes disturbancesThe connection between long‐term changes in circulation and Rx emerges when considering various trends of different typical SPsBased on the model's capability to capture the typical SPs, a weighting method could narrow the inter‐model spread of future projections [ABSTRACT FROM AUTHOR]

This study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate. [ABSTRACT FROM AUTHOR]

In summer 2020, several watersheds in China monsoon region experienced historically unusually heavy precipitation. The flooding associated with these heavy precipitation events led to devastating impacts on human life, infrastructure, agriculture and economy. Using historical climate simulations from the HadGEM3‐GA6 and CMIP6 models under influences of natural and/or anthropogenic forcings, we conducted a comparative study on the attribution of extremely heavy precipitation events in summer 2020 in the mid‐lower reaches of the Yangtze River and the mid‐lower reaches of the Yellow River‐Hai River Basin which locates respectively in the south and north parts of the East China monsoon region. The potential contributions of anthropogenic forcings to monthly‐scale extreme precipitation and daily maximum precipitation (RX1day) were examined. The results suggest that human activities have decreased the probability of month‐scale extreme precipitation events in the south part of the East China monsoon region. For RX1day extreme precipitation events, anthropogenic factors have increased their probability in both regions. However, the influence of anthropogenic forcings on month‐scale extreme precipitation events in the north part of the East China monsoon region is not robust among the two attribution systems. Further analyses indicated that anthropogenic aerosols (AER) make both month‐scale extreme precipitation events and extreme RX1day less likely to occur in summer 2020, and greenhouse gases (GHG) increase the likelihood of both. GHG influences on Rx1day overwhelm AER influences, leading to an increase of the probability of Rx1day similar to the 2020 events in both regions. In contrast, the decrease in the probability of month‐scale precipitation in the south part of the East China monsoon region is predominantly due to aerosol forcing. The model projections show that the likelihood of both monthly and daily extreme precipitation events in both regions will increase in the future. Accordingly, the recurrence period of extreme precipitation events will be shortened by the end of the 21st century, which is more significant under the high‐emission scenario. [ABSTRACT FROM AUTHOR]

Abstract The Northeast United States (NEUS) has faced the most rapidly increasing occurrences of extreme precipitation within the US in the past few decades. Understanding the physics leading to long‐term trends in regional extreme precipitation is essential but the progress is limited partially by the horizontal resolution of climate models. The latest fully coupled 25‐km GFDL (Geophysical Fluid Dynamics Laboratory) SPEAR (Seamless system for Prediction and EArth system Research) simulations provide a good opportunity to study changes in regional extreme precipitation and the relevant physical processes. Here, we focus on the contributions of changes in synoptic‐scale events, including atmospheric rivers (AR) and tropical cyclone (TC)‐related events, to the trend of extreme precipitation in the fall season over the Northeast US in both the recent past and future projections using the 25‐km GFDL‐SPEAR. In observations, increasing extreme precipitation over the NEUS since the 1990s is mainly linked to TC‐related events, especially those undergoing extratropical transitions. In the future, both AR‐related and TC‐related extreme precipitation over the NEUS are projected to increase, even though the numbers of TCs in the North Atlantic are projected to decrease in the SPEAR simulations using the SSP5‐8.5 projection of future radiative forcing. Factors such as enhancing TC intensity, strengthening TC‐related precipitation, and/or westward shift in Atlantic TC tracks may offset the influence of declining Atlantic TC numbers in the model projections, leading to more frequent TC‐related extreme precipitation over the NEUS.

Study region: The Upper Mekong River Basin (UMRB), Southwest China. Study focus: With climate change unfolding and climate change knowledge evolving over time, it is imperative to investigate whether the latest CMIP6 models offer enhanced utility in climate change impact studies compared to their predecessors. This study strengthens the comparison between CMIP5 and CMIP6 models in assessing hydrological responses to future climate change. This was achieved utilizing the Soil and Water Assessment Tool, driven by downscaled CMIP5/CMIP6 model outputs under RCP8.5/SSP5–8.5. Both streamflow and sediment responses, encompassing the spatial and temporal changes, and the relationships between streamflow and sediment loads, were carefully evaluated and compared between CMIP5 and CMIP6. New hydrological insights for the region: CMIP6 indicates a stronger warming in 2071–2100 over the UMRB compared to CMIP5. Mean annual precipitation/streamflow is projected to increase by 22.7%/26.3% using CMIP5 and 28.4%/34.4% using CMIP6. Mean annual sediment load changes, however, show a discrepancy between CMIP5 (−3.7%) and CMIP6 (+13.8%). CMIP6 exhibits larger inter-model variability in both climate and hydrological projections. Regarding future spatial distributions of mean annual water and sediment yields, a considerable agreement is demonstrated between CMIP5 and CMIP6, despite CMIP6 showing larger projections over most subbasins. Additionally, both ensembles exhibit approximate relationships between streamflow and sediment loads, indicating a comparable decline in watershed sediment generation and transport capacity under future climate change. Overall, CMIP6 suggests more severe climate change impacts on streamflow and sediment loads in the UMRB than CMIP5, highlighting the need to update climate change adaptation and mitigation policies based on the latest insights derived from CMIP6.

Snow algal blooms have a remarkable climatic or environmental effect through influencing the snow–albedo feedback, accelerating the melting of surface snow, and amplifying global warming. Snow algal blooms occurred frequently on King George Island, Antarctic, during the recent six austral summers (December to next February) through 2017–2022. Based on an assessment of satellite images, this study found that the range and amount of snow algal blooms in the summers of 2018, 2020, 2021 and 2022 are relatively larger than in the summers of 2017 and 2019. Whether meteorological conditions have shaped the year-to-year variation of algal bloom intensities is analyzed through observational composite. The results suggest that during the strong bloom summers there exist prevailing northerly or northwesterly wind anomalies which advect warm and humid airmass from the southern ocean into the island, increasing surface air temperature and humidity; the warmer and more humid surface favors melting of snow and an increase of low cloud cover, subsequently enhancing the atmospheric downward long-wave radiation and amplifying surface warmth; the increased low cloud cover reflects more ultraviolet rays back to space and weakens the short-wave radiation reaching the surface. All these factors together favor to a stronger bloom. In comparison, during 2017 and 2019 there exist weak southerly wind anomalies which induce the northward advection of cold and dry air from the Antarctic Continent and favor the cooler surface. Consequently, it is unfavorable for the snow algal bloom. Based on these results, a snow algal bloom potential index (API) integrating the meteorological conditions is constructed, and its future trend is projected based on the EC-Earth3 run attending the CMIP6 under SSP245 and SSP585. A significant increasing trend is projected especially under SSP585. Thus snow algal bloom on King George Island will become more frequent and stronger in future. This implies a potential accelerate melting of ice shelf over Antarctic Peninsula.

This study investigates the future wave climate-driven longshore sediment transport (LST) and shoreline change on the Karasu Coast, situated on the southwestern coast of the Black Sea, under the RCP4.5 and RCP8.5 wave climate scenarios. Within the scope of this study, hourly deep sea wave data between 2021 and 2100, according to the RCP4.5 and RCP8.5, were used in order to predict future LST processes. Net and gross LST rates were computed using various empirical and numerical methods based on hourly wave parameters. By the conclusion of the study period after 80 years, the average net LST rates were obtained as 48,000 and 51,500 m3/year in the RCP4.5 and RCP8.5, respectively, while the gross LST rates were 250,000 and 255,000 m3/year. Due to the increase in wave height and period in both climate scenarios compared to the historical data, the average gross LST rates are projected to rise in the future. The reduction in swell wave heights, coupled with an increase in wind wave heights, compared to the past has led to a reduction in net LST. The results show that, after 80 years, LST will have increased 2.5 times more in the near future in comparison with the middle future for both scenarios.

Abstract Global climate models provide useful tools to forecast large‐scale anthropogenic trends and the impacts on ocean physics and marine biology and chemistry. Due to coarse spatial resolution, they typically lack the ability to represent important regional processes while underestimating mesoscale variability and vertical mixing. This means they provide limited value when it comes to regional climate projections. We developed a regional submesoscale‐permitting physical/biogeochemical model to dynamically downscale the output of a CMIP6 Earth System Model for three different Socioeconomic Pathways for the main Hawaiian Islands. We describe the methodology for downscaling the CMIP6 ocean physics and biogeochemistry along with atmospheric conditions in order to offline nest a regional model. We expect the large‐scale spatial and temporal features of the global model to be retained by the regional model, while adding representation of the regional processes that are crucial to understanding climate change on a local scale. We compare the regional model representation against both observed data and a regional reanalysis over the first two decades of the century. We show that the regional model maintains the large‐scale trends and interannual variability provided by the CMIP6 model while well‐representing the regional dynamics that drive the short‐term variability. To better illustrate the benefit of the downscaling, we present preliminary analysis of the downscaled results to examine climate impacts on the island corals that are not resolved by the global models. This analysis reveals that coastal corals are likely to experience unprecedented ocean acidification and substantial warming over the course of the century.

Extreme precipitation intensification due to global warming has received significant attention; however, large uncertainties remain regarding how much it will change in the future, even under a given greenhouse gas emissions pathway. Our constraining analysis provides smaller future extreme precipitation intensification with reduced uncertainties than raw/unconstrained model simulation projections from the global to continental scales and also several sub-continental areas. Historical warming trends in climate model simulations present strong positive inter-model correlations with future annual maximum daily precipitation intensification from the global to continental scales. This emergent relationship is employed for constraining analysis. The proposed emergent constraints are mostly associated with thermodynamics (atmospheric moisture changes), with limited contribution from dynamics (atmospheric circulation variations). This constraining framework has various advantages, including lower observation uncertainty, and more robust theoretical interpretation than previously suggested constraints for extreme precipitation frequency projections. CMIP6 models generally overestimate historically observed warming, which resultantly generates weaker extreme precipitation intensification following constraining analysis than unconstrained model simulation projections from the global to individual continents, and even several mid- and high-latitude sub-continental areas. Additionally, constrained projections exhibit narrower uncertainty ranges in future extreme precipitation projections. During the late 21st century (2070–2099), under the high-emission scenario (Shared Socioeconomic Pathway 5–8.5, characterized by fossil fueled development and a radiative forcing of 8.5 W m−2 in 2100), our results present reduced global average intensity and uncertainty for future annual maximum daily precipitation intensification by 25% and 27%, respectively.

Domestication is an ongoing well‐described process. However, while many have studied the changes domestication causes in plant genetics, few have explored its impact on the portion of the geographic landscape in which the plants exist. Therefore, the goal of this study was to understand how the process of domestication changed the geographic space suitable for chile pepper (Capsicum annuum) in its center of origin (domestication). C. annuum is a major crop species globally whose center of domestication, Mexico, has been well‐studied. It provides a unique opportunity to explore the degree to which ranges of different domestication classes diverged and how these ranges might be altered by climate change. To this end, we created ecological niche models for four domestication classes (wild, semiwild, landrace, modern cultivar) based on present climate and future climate scenarios for 2050, 2070, and 2090. Considering present environment, we found substantial overlap in the geographic niches of all the domestication classes. Yet, environmental and geographic aspects of the current ranges did vary among classes. Wild and commercial varieties could grow in desert conditions, while landraces could not. With projections into the future, habitat was lost asymmetrically, with wild, semiwild, and landraces at greater risk of territorial declines than modern cultivars. Further, we identified areas where future suitability overlap between landraces and wilds is expected to be lost. While range expansion is widely associated with domestication, we found little support of a constant niche expansion (either in environmental or geographical space) throughout the domestication gradient in chile peppers in Mexico. Instead, particular domestication transitions resulted in loss, followed by capturing or recapturing environmental or geographic space. The differences in environmental characterization among domestication gradient classes and their future potential range shifts increase the need for conservation efforts to preserve landraces and semiwild genotypes. [ABSTRACT FROM AUTHOR]

Reliable projections of monsoon mean and extreme precipitation are vital to water resource management, disaster mitigation as well as policy makings. Since the climate models have better capability in projecting mean precipitation, the relationship between mean and extreme precipitation is a crucial clue for reliable projections of extreme precipitation. However, selections of optimal models in reproducing historical mean/extreme precipitation and their relationship were rarely reported. Here, more credible projections of mean/extreme precipitation and their connections over Northern Hemisphere land monsoon domain (NHLMD) were explored after a systematical assessment of the models from Coupled Model Intercomparsion Project Phase 6 (CMIP6) and CMIP5. Results indicated that most models could well reproduce the climatology and the variation of mean precipitation over NHLMD, while large portions have underestimated the extreme precipitation threshold (EPTH) and overestimated the extreme precipitation days (EPDs). However, nearly all models captured the observed robust relationships between mean and extreme precipitation. The projected mean precipitation and EPDs exhibit coherent variations with increase trend over Asian‐North African monsoon, and decrease trend over North American monsoon. The significant positive correlations between mean precipitation and EPDs will continue during 21st century. Considering the relatively low skills and large uncertainty in the simulation and the projection of EPDs, the tight connection could be used to reliably project the future extreme precipitation over the NHLMD using the projection of monsoon mean precipitation via the selected models. [ABSTRACT FROM AUTHOR]

Simulating the features of the African Easterly Waves (AEWs), such as their westward propagation off the east Atlantic coast, is challenging for coarse-resolution climate models. In this study, we use High-Resolution Atmospheric Model (HiRAM) to simulate AEWs and analyze their future projections by the end of the twenty first century. The simulations are performed globally at a horizontal resolution of ∼ 25 km. The model uses shallow convective parameterization for moist convection and stratiform cloudiness. Future projections are conducted using representative concentration pathway 8.5. The AEWs are separated with respect to their periods as 3–5- and 6–9-day period AEWs, and bandpass filtering is used to filter the waves from the mean flow. HiRAM simulates structure and propagation of the waves well; however, it tends to overestimate the associated precipitation. In the future, the AEW precipitation and intensity of the circulation will considerably increase. The northward extent of the AEW track also shows a significant increase in the future. Enhanced baroclinic overturning and eddy available potential energy generated due to diabatic heating is also observed in the future. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) represents a key process of human‐Earth system interaction and has profound impacts on terrestrial ecosystem carbon cycling. As a key input for ecosystem models, future gridded LULCC data is typically spatially downscaled from regional LULCC projections by integrated assessment models, such as the Global Change Analysis Model (GCAM). The uncertainty associated with the different spatial downscaling methods and its impacts on the subsequent model projections have been historically ignored and rarely examined. This study investigated this problem using two representative spatial downscaling methods and focused on their impacts on the carbon cycle over the Arctic‐Boreal Vulnerability Experiment (ABoVE) domain, where extensive LULCC is expected. Specifically, we used the Future Land Use Simulation model (FLUS) and the Demeter model to generate 0.25° gridded LULCC data (i.e., LULCCFLUS and LULCCDemeter, respectively) with the same input of regional LULCC projections from GCAM, under both the low (i.e., SSP126) and high (i.e., SSP585) greenhouse gas emission scenarios. The two sets of downscaled LULCC were used to drive the Community Land Model version 5 and prognostically simulate the terrestrial carbon cycle dynamics over the 21st century. The results suggest large spatial‐temporal differences between LULCCFLUS and LULCCDemeter, and the spatial distributions of the needleleaf evergreen boreal tree, broadleaf deciduous boreal tree, broadleaf deciduous boreal shrub, and C3 arctic grass are particularly different under both SSP126 and SSP585. Additionally, the spatiotemporal differences are larger under SSP126 than SSP585, due to more intensive LULCC under SSP126 than SSP585 from GCAM projection. The differences in LULCC further lead to large discrepancies in the spatial patterns of projected gross primary productivity, ecosystem respiration, and net ecosystem exchange, which represent more than 79% of the contributions of future LULCC in 2100. Additionally, the difference in carbon flux under SSP126 is generally larger than those under SSP585. This study highlights the importance of considering the uncertainties induced by the spatial downscaling process in future LULCC projections and carbon cycle simulations. Plain Language Summary: Land use and land cover change (LULCC) affects the carbon cycle in ecosystems. To predict future LULCC and carbon cycle changes, scientists use spatial downscaling methods to create detailed LULCC maps. However, different methods can lead to different results and can impact carbon cycle projections. Our study found that using different spatial downscaling methods can contribute to a large portion of the uncertainty in future projections of LULCC and carbon cycle over the Arctic‐Boreal region. It is important to consider these uncertainties when studying future changes in land use and carbon cycle. Key Points: We identified a traditionally ignored source of uncertainty in model projected carbon cycle from the future land use and land cover change (LULCC) dataSpatial downscaling is a necessary step for generating gridded LULCC data, but different downscaling methods may lead to results with large spatial differencesThe impacts of using different spatial downscaling methods are more than 79% of the contributions of future LULCC to carbon cycle projections in 2100 [ABSTRACT FROM AUTHOR]

Mountains play a vital role in shaping regional and global climate, altering atmospheric circulation and precipitation patterns. To this end, identifying projected changes in mountain precipitation is significantly challenging due to topographic complexity. This study explains how mountain precipitation could respond to rising greenhouse gases. Using a series of century‐long fully coupled high‐resolution simulations conducted with the Community Earth System Model, we aim to disentangle future changes in mountain precipitation in response to atmospheric carbon dioxide (CO2) perturbations. Our research findings indicate that the warming observed in global mountains is more pronounced when compared to the mean warming rates experienced globally and in the ocean under elevated CO2. We identify five low‐latitude mountain ranges with elevation‐dependent precipitation response, including New Guinea, East Africa, Eastern Himalayas, Central America, and Central Andes. Those mountains are expected to have a mixture of increasing and decreasing precipitation in response to CO2‐induced warming, especially over the summit and steep topography. To elucidate the mechanisms controlling future changes in mountain precipitation, we propose "Orographic moisture omega feedback" in which an increase in low‐level relative humidity enhances local precipitation by strengthening the upward motion through moist processes for the wetting response and vice versa for the drying response. The effects of Mountain precipitation changes can be extended to hydrology and could lead to significant consequences for human societies and ecosystems. Plain Language Summary: Recently, Mountain warming with increasing greenhouse warming has taken attention. However, predicting changes in mountain rainfall is hard and limitedly explored. There is a need to connect predominately idealized studies of change in orographic precipitation with warming to State‐of‐the‐art global climate model simulation (GCM). Therefore, we ran experiment simulations to study how mountain rainfall will change in the future due to the increase in atmospheric carbon dioxide. The impact of warming on mountain precipitation is complex and could result in a combination of results, such as increased and decreased precipitation. To our knowledge, this is the first kind of study that connects the change in mountain precipitation with future greenhouse warming using a 25 km high‐resolution GCM. This study found that the changes in rainfall are expected in high‐elevation mountains located in low‐latitude regions with steep slopes. This analysis concentrates on five major mountain ranges: New Guinea, East Africa, the Himalayas, Central America, and Central Andes. Furthermore, the feedback mechanism could explain how low‐level humidity affects Mountain rainfall and atmospheric convection. By studying mountain meteorology, we can gain insights into the complex interactions between mountains and the atmosphere, contributing to the overall understanding of our earth system. Key Points: This study uses a high‐resolution model experiment to explain how mountain precipitation could respond to rising atmospheric carbon dioxide (CO2) concentrationProjected precipitation changes are dominated over low‐latitude mountains, especially the summit and steep topographyWe propose a mechanism, "Orographic moisture omega feedback," that explains the unprecedented anomalous changes in the mountain climate [ABSTRACT FROM AUTHOR]

Attributions and predictions of gross primary productivity (GPP) under climate change is of great significance for facilitating a deeper understanding of the global and regional terrestrial carbon cycle and assessing ecosystem health. In this study, we have designed a novel approach to simulate GPP based on the satellite and meteorological data compiling the advantages of the light use efficiency model with regression methods (LUE-RE model), which overcomes the limitation of the satellite-based method in GPP simulation and projection in the future time without satellite data. Based on the proposed method, results show that GPP in the Yangtze River Basin shows a significant increase trend in the historical period. Elevated CO2 dominates the changes of GPP in the Yangtze River Basin. In the future, with the increase in elevated CO2 and climate change, the trend of GPP growth is more obvious. The growth slopes under different scenarios are 2.65 gCm−2year−1a−1, 12.34 gCm−2year−1a−1, 24.91 gCm−2year−1a−1, and 39.62 gCm−2year−1a−1. There are obvious seasonal differences in the future changes of GPP in the Yangtze River Basin, of which the GPP changes mostly in spring. The spatial patterns show that higher GPP is concentrated in the upper stream, while the low values are mainly concentrated in the middle reaches. This study contributes a new method to project GPP and highlights that stakeholders should pay more attention to the significant GPP increases in spring in the future. [ABSTRACT FROM AUTHOR]

As a semi-arid to semi-humid transitional zone, the Loess Plateau is sensitive to climate change due to its fragile ecological environment and geographic features. This study assesses the performance of six historical experiments from the Coordinated Regional Climate Downscaling Experiment (CORDEX) in this region during 1980–2005. In addition, projected future changes in surface air temperature and precipitation are investigated under the representative concentration pathways (RCP) 2.6 and 8.5 during three periods in the 21st century: the early future (2011–2040), middle future (2041–2070), and late future (2071–2099). Results show that experiments reasonably reproduce the spatial pattern of 2m temperature and precipitation for all seasons, yet with a slight warm bias and prominent wet bias. In the future, the area-averaged magnitude of change will be 1.1 °C, 1.4 °C, and 1.4 °C under RCP2.6 and 1.3 °C, 2.7 °C, and 4.5 °C under RCP8.5 for the early, middle, and late periods, respectively. The warming effect is greater in elevated areas. Precipitation change in future periods is more complex, with both increasing and decreasing trends, depending on the season, location, and scenario. The results are expected to provide regional climate information for decision makers and benefit applications such as agriculture, ecological environment protection, and water resource management. [ABSTRACT FROM AUTHOR]

Global circulation models (GCMs) are routinely used to project future climate conditions worldwide, such as temperature and precipitation. However, inputs with a finer resolution are required to drive impact‐related models at local scales. The non‐homogeneous hidden Markov model (NHMM) is a widely used algorithm for the precipitation statistical downscaling for GCMs. To improve the accuracy of the traditional NHMM in reproducing spatiotemporal precipitation features of specific geographic sites, especially extreme precipitation, we developed a new precipitation downscaling framework. This hierarchical model includes two levels: (1) establishing an ensemble learning model to predict the occurrence probabilities for different levels of daily precipitation aggregated at multiple sites and (2) constructing a NHMM downscaling scheme of daily amount at the scale of a single rain gauge using the outputs of ensemble learning model as predictors. As the results obtained for the case study in the central‐eastern China (CEC), show that our downscaling model is highly efficient and performs better than the NHMM in simulating precipitation variability and extreme precipitation. Finally, our projections indicate that CEC may experience increased precipitation in the future. Compared with ~26 years (1990–2015), the extreme precipitation frequency and amount would significantly increase by 21.9%–48.1% and 12.3%–38.3%, respectively, by the late century (2075–2100) under the Shared Socioeconomic Pathway 585 climate scenario. [ABSTRACT FROM AUTHOR]