Multi-model Simulations Research Articles

Light Precipitation rather than Total Precipitation Determines Aerosol Wet Removal.

Precipitation scavenging is an important sink for both organic and inorganic pollutants in the atmosphere. However, there has been controversy over the relative importance of different precipitation characteristics (i.e., the precipitation amount, intensity, frequency, and duration) in the removal of air pollutants. It is critical to reach a consensus on which precipitation characteristics are most significant for aerosol wet removal. In this study, the analysis of multisource in situ observations of aerosol wet deposition worldwide indicates that the precipitation frequency plays a first-order role in scavenging aerosols. This is because large amounts of air pollutants are efficiently removed at precipitation initiation. As the duration and amount of precipitation increase, the scavenging efficiency decreases sharply. Consequently, it is featured that light precipitation, due to its high frequency of occurrence, rather than total precipitation, determines climatological aerosol wet deposition. To further confirm this, a state-of-the-art global climate model with convection resolved is modified to reduce the light precipitation frequency in both stratiform and convective cloud regimes. Results show widespread increases in aerosol optical depth (AOD) due to the reduced wet deposition. The spatial distribution of aerosol wet deposition changes resembles that of changes in the light precipitation frequency rather than that of total precipitation changes. These findings are consistent with those from the Coupled Model Intercomparison Project Phase 6 multimodel simulations, which show that the intermodel uncertainty in simulated AOD correlates more strongly with the uncertainty in light precipitation frequency than with that in total precipitation.

Detection and Attribution of Changes in Precipitation Extremes in China and Its Different Climate Zones

Abstract Based on the observations and the phase 6 of Coupled Model Intercomparison Project (CMIP6) multimodel simulations, we conducted a detection and attribution analysis for the observed changes in intensity and frequency indices of extreme precipitation during 1961–2014 over the whole of China and within distinct climate regions across the country. A space–time analysis is simultaneously applied in detection so that spatial structure on the signals is considered. Results show that the CMIP6 models can simulate the observed general increases of extreme precipitation indices during the historical period except for the drying trends from southwestern to northeastern China. The anthropogenic (ANT) signal is detectable and attributable to the observed increase of extreme precipitation over China, with human-induced greenhouse gas (GHG) increases being the dominant contributor. Additionally, we also detected the ANT and GHG signals in China’s temperate continental, subtropical–tropical monsoon, and plateau mountain climate zones, demonstrating the role of human activity in historical extreme precipitation changes on much smaller spatial scales. Significance Statement The observed intensification of extreme precipitation globally has been attributed to human influences. Here, we demonstrate that anthropogenic forcing has discernably intensified extreme precipitation over the period 1961–2014, over China and in three of its four climate zones, with human-induced greenhouse gas increases being the dominant contributor. Our results strengthen the body of evidence that greenhouse gas increases are intensifying extreme precipitation by quantifying their role in observed changes at smaller regional scales than previously reported.

Winter Wheat Experiments to Optimize Sowing Dates and Densities in a High-Yielding Environment in New Zealand: Field Experiments and AgMIP-Wheat Multi-Model Simulations

This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. The data includes one winter wheat cultivar (Wakanui) grown during six consecutive years, from 2012-2013 to 2017-2018, at two farms located in Leeston and Wakanui in Canterbury, New Zealand. The simulations were carried out in the framework of the Agricultural Model Intercomparison and Improvement Project for wheat (AgMIP-Wheat). Data include local daily weather data, soil profile characteristics and initial conditions, crop measurements at maturity (grain, stem, chaff and leaf dry weight, ear number and grain number, grain unit dry weight), and at stem elongation and anthesis (total above ground dry biomass, leaf number per stem and leaf area index). Several in-season measurements of the normalized difference vegetation index (NDVI) and the fraction of intercepted photosynthetically active radiation (FIPAR) are also available. The crop model simulations include both daily in-season and end-of-season results from twenty-nine wheat models.

Strain variation and anomalous climate synergistically influence cholera pandemics.

Explanations for the genesis and propagation of cholera pandemics since 1817 have remained elusive. Evolutionary pathogen change is presumed to have been a dominant factor behind the 7th "El Tor" pandemic, but little is known to support this hypothesis for preceding pandemics. The role of anomalous climate in facilitating strain replacements has never been assessed. The question is of relevance to guide the understanding of infectious disease emergence today and in the context of climate change. We investigate the roles of climate and putative strain variation for the 6th cholera pandemic (1899-1923) using newly assembled historical records for climate variables and cholera deaths in provinces of former British India. We compare this historical pandemic with the 7th (El Tor) one and with the temporary emergence of the O139 strain in Bangladesh and globally. With statistical methods for nonlinear time series analysis, we examine the regional synchrony of outbreaks and associations of the disease with regional temperature and rainfall, and with the El Niño Southern Oscillation (ENSO). To establish future expectations and evaluate climate anomalies accompanying historical strain replacements, climate projections are generated with multi-model climate simulations for different 50-year periods. The 6th cholera pandemic featured the striking synchronisation of cholera outbreaks over Bengal during the El Niño event of 1904-07, following the invasion of the Bombay Presidency with a delay of a few years. Accompanying anomalous weather conditions are similar to those related to ENSO during strain replacements and pandemic expansions into Africa and South America in the late 20th century. Rainfall anomalies of 1904-05 at the beginning of the large cholera anomaly fall in the 99th percentile of simulated changes for the regional climate. Evolutionary pathogen change can act synergistically with climatic conditions in the emergence and propagation of cholera strains. Increased climate variability and extremes under global warming provide windows of opportunity for emerging pathogens.

Can climate change signals be detected from the terrestrial water storage at daily timescale?

The global terrestrial water storage (TWS), the most accessible component in the hydrological cycle, is a general indicator of freshwater availability on Earth. The global TWS trend caused by climate change is harder to detect than global mean temperature due to the highly uneven hydrological responses across the globe, the brevity of global freshwater observations, and large noises of internal climate variability. To overcome the climate noise and small sample size of observations, we leverage the vast amount of observed and simulated meteorological fields at daily scales to project global TWS through its fingerprints in weather patterns. The novel method identifies the relationship between annual global mean TWS and daily surface air temperature and humidity fields using multi-model hydrological simulations. We found that globally, approximately 50% of days for most years since 2016 have climate change signals emerged above the noise of internal variability. Climate change signals in global mean TWS have been consistently increasing over the last few decades, and in the future, are expected to emerge from the natural climate variability. Our research indicates the urgency to limit carbon emission to not only avoid risks associated with warming but also sustain water security in the future.

Attribution of the unprecedented summer 2022 compound marine and terrestrial heatwave in the Northwest Pacific

In boreal summer (July–August) 2022, an unprecedented marine heatwave (MHW) occurred in the northwest Pacific Ocean (NWP), while a record-breaking terrestrial heatwave (THW) hit the Yangtze River Basin (YRB). The temperature anomalies caused by this compound MHW-THW event exceeded climatology by 2.5 standard deviations (SDs), breaking the historical record for nearly 100 years, with severe impacts on the ecosystems and social economy. To investigate the underlying causes, we explored the potential roles of anthropogenic forcing, atmospheric circulation, and ‘triple-dip’ La Niña on this compound event using the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations. Results indicate that the 2022-like compound MHW-THW event was extremely unlikely to happen without anthropogenic warming, and that such extreme heatwaves were governed by the climatic mean temperature rather than changes in temperature variability. Notably, the anticyclone circulation patterns and associated high-pressure systems (i.e. western North Pacific subtropical high (WNPSH) and South Asian high (SAH)) increase the probability of a 2022-like MHW-THW event by 3.7 times. However, the La Niña phase has no significant effect on the occurrence probability of such events. We further estimate that the 2022-like MHW-THW event will become 7.5 and 11.4 times more likely under the SSP3-7.0 scenario by the middle and end of the 21st century, respectively. This study demonstrates the contribution of anthropogenic climate change and natural variability to the occurrence of compound MHW-THW events and highlights the urgent need to build mitigation strategies for compound MHW-THW events.

Fast reduction of Atlantic SST threatens Europe-wide gross primary productivity under positive and negative CO2 emissions

Climate change mitigation through negative CO2 emissions has been recognized as a crucial strategy to combat global warming. However, its potential effects on terrestrial productivity and agricultural activities remain uncertain. In this study, we utilized large ensemble simulations with an Earth system model of full complexity to investigate the response of Gross Primary Production (GPP) to CO2 forcings. Our findings reveal a significant asymmetry in the GPP response to CO2 ramp-up and symmetric ramp-down model experiments, especially in Europe, suggesting that GPP declines rapidly as CO2 levels decrease. Remarkably, during the CO2 removal period, the North Atlantic Sea surface temperature experienced cooling due to a delayed recovery of the Atlantic Meridional Overturning Circulation (AMOC). This cooling led to precipitation and soil moisture deficits, resulting in a rapid reduction in GPP. This asymmetry in GPP response holds consistent across multi-model simulations. These results underscore the potential implications of delayed recovery in ocean circulation, which could unexpectedly accelerate terrestrial GPP reduction. These insights are crucial for policymakers, aiding them in projecting agricultural activity and formulating targeted GPP control policies specific to the European region.

Warm Arctic–Cold Eurasia (WACE) Trend: Inference from Large Ensemble Model Simulations

Abstract Concurrent with global warming, the Eurasian continent has experienced frequent cold winters in recent decades. Although still debatable, such Eurasian winter cooling, which was particularly pronounced in the late twentieth century, has been attributed to internal climate variability, the process of which remains elusive. By examining multimodel large ensemble simulations, this study explores the potential sources of internal climate variability responsible for the Eurasian winter cooling trend over 1987–2006. Model simulations show a large ensemble spread in the Eurasian winter temperature trend with an ensemble mean close to zero. A comparison of the ensemble members shows that a circulation pattern favorable for the Eurasian cooling is characterized by the anticyclonic and cyclonic enhancements of sea level pressure (SLP) trend in the subarctic and Aleutian regions, respectively. This dipolar SLP trend is closely related to the deep Arctic warming, the change in midlatitude snow cover, and the enhancement of atmospheric convection over the tropical western Pacific. This result suggests that the Eurasian winter cooling is likely associated not only to the changes in mid- to high-latitude conditions but also to the changes in tropical convection. The possible mechanism of the tropically induced Aleutian low deepening is also discussed.

Evaluation of soil temperature in CMIP6 multimodel simulations

Soil temperature plays a crucial role in understanding land-atmosphere interactions. The Sixth Phase of Coupled Model Intercomparison Project (CMIP6) provides valuable information on soil temperature, however, the performance of these models in capturing soil temperature variations remains unclear. To comprehensively assess the performance of CMIP6 in simulating soil temperature, we employed a set of in-situ observation data, observation-derived gridded data (TS-GCB) and reanalysis data (ERA5L) to build various evaluating metrics for the surface (0–5 cm) and subsurface (5–15 cm) soils. At the global scale, the multimodel ensemble mean (MME) of CMIP6 generally captured the spatial, annual and seasonal variations of soil temperature, but overestimated measured soil temperature by 1.86 °C (vs. TS-GCB, surface) and 2.16 °C (vs. TS-GCB, subsurface), indicating more heat accumulated in soils than the reality. Surface soil temperature was better represented by MME compared to the subsurface soil layer (vs. TS-GCB). In addition, we found the largest simulation bias in the tropical zone, and both positive and negative bias in the arid regions. The large model spread of the individual models in representing soil temperatures in cold regions or periods highlights the needs of improved understanding of how snow and freeze-thaw affect soil thermal dynamics. Overall, the performance of MME is superior to that of the majority of individual models (vs. TS-GCB). Locally, the large discrepancy among observation-derived data, reanalysis data and CMIP6 simulations suggested that it is imperative to acquire more ground-truth soil temperature to better inform model simulation and forecasting.

Evaluation of 22 CMIP6 model-derived global soil moisture products of different shared socioeconomic pathways

The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides ample soil moisture (SM) products during 2015–2100, which are widely used in investigating future hydrological pattern. However, there remains limited understanding regarding the accuracy of multi-model CMIP6 SM future simulations following different Shared Socioeconomic Pathways (SSP). This study assesses the spatial–temporal accuracy of 22 CMIP6 model-derived SM products using 670 ground observation stations globally during 2015–2023. Each CMIP6 model encompasses SM estimations from SSP1-2.6 (The sustainable pathway), SSP2-4.5 (The medium pathway), and SSP5-8.5 (Fossil-fueled development). The latest Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM products serve as reference benchmarks. The SM among three SSPs within the same model exhibits similar performance in in situ validation, the extended triple collocation assessment, and the classical seasonal decomposition trend analysis. This similarity may suggest a subtle variation in the influence of different SSPs on SM in the early stages of the future scenario. The majority of CMIP6 model-derived SM could reasonably capture the spatial distribution pattern and temporal evolution trend of the truth value. Meanwhile, noticeable discrepancies in accuracy, particularly regarding goodness-of-fit, are evident when compared to SMAP and ERA5-Land SM products. The CMIP6 models could provide better SM estimation in temperate and tropical zones than cold zones. In terms of differences between the land cover types, CMIP6 model-derived SM products perform well over various vegetated regions except for barren, where there are sequential overestimation and relatively low fitting degree. Extensive accuracy discrepancies exist among the 22 CMIP6 model-derived SM products due to variations in model parameterization methods and atmospheric variables.

A Comparison of the Performance of the Weighted Ensembles Means in CORDEX-SEA Precipitation Simulations

Numerous studies stated that the performance of ensemble mean derived from multiple climate models generally surpassed the individual member model, and applying weighting factors potentially increase the ensemble mean of performance. This study aims to assess the performance of unweighted and weighted ensemble means of 9-modelled precipitation datasets in the CORDEX-SEA multi-model simulations for 1981-2005. The 9 datasets included: CNRM_a, ECE_b, GFDL_b, IPSL_b, HadGEM2_a, HadGEM2_c, HadGEM2_d, MPI_c, and NorESM1_d. The weighting factors were derived from the models' skill scores measured using five statistical-based metrics, namely Taylor, Pierce (SS), Tian skill score (Tian), Climate prediction index (CPI), and Performance and Independence (PI). The ERA5 and GPCP precipitation datasets were used as the references for comparison. Then, reliable metrics will be used to determine the weighting factor. The results found that three metrics namely Taylor, SS, and Tian were more reliable than the other two metrics (CPI and PI). Spatially, the weighted ensemble mean based on a random method was superior to other ensemble mean methods and individual models. We found that the CNRM_a and GFDL_b models were spatially performed best. In contrast, most the ensemble means was temporally less performed compared to the individual model. Our findings suggested that by removal of low performance models will significantly influence on the overall ensemble model performance. Further, the research may provide valuable considerations of climate models selection for climate projection assessments, especially in the Southeast Asia region.

Aerosol forcing regulating recent decadal change of summer water vapor budget over the Tibetan Plateau

The Tibetan Plateau (TP), known as the Asian water tower, has been getting wetter since the 1970s. However, the primary drivers behind this phenomenon are still highly controversial. Here, we isolate the impacts of greenhouse gases (GHG), aerosols, natural forcings and internal climate variability on the decadal change of summer water vapor budget (WVB) over the TP using multi-model ensemble simulations. We show that an anomalous Rossby wave train in the upper troposphere travelling eastward from central Europe and equatorward temperature gradient in eastern China due to the inhomogeneous aerosol forcing in Eurasia jointly contribute to anomalous easterly winds over the eastern TP. Such anomalous easterly winds result in a significant decrease in water vapor export from the eastern boundary of the TP and dominate the enhanced summer WVB over the TP during 1979-2014. Our results highlight that spatial variation of aerosol forcing can be used as an important indicator to project future WVB over the TP.

Detection of long-term land use and ecosystem services dynamics in the Loess Hilly-Gully region based on artificial intelligence and multiple models

Balancing intensive development with biodiversity conservation is critical for ecological sustainability. In the Loess Hilly-Gully region, which has experienced significant environmental changes from 1980 to 2020, land use changes are vital for ecosystem service maintenance. However, these processes are susceptible to environmental stressors such as climate change, land degradation, and inadequate land management. Despite recognizing these stressors, their combined impact on land use and ecosystem services is not well understood. This study addresses this gap by utilizing multi-model land use simulations combined with artificial intelligence to analyze changes over the four-decade period. We focused on identifying ecological nodes essential for landscape connectivity, finding that these nodes significantly enhance regional ecosystem services. We also examined the relationship between land use changes and ecosystem services under various environmental stressors within the specified timeframe. Our analysis indicates that land use changes are a strong indicator of ecosystem evolution in these areas. We observed that under natural conditions, land use and ecosystem services interactions are governed by both stochastic and deterministic factors. Interestingly, multiple stressors intensified the stochastic nature of land use changes and the deterministic aspects of ecosystem services. Specifically, climate change, coupled with suboptimal land management, significantly affects this relationship, with climate change being the most influential factor on land use patterns during the studied period. This research offers important insights for improved environmental management and reducing impacts on aquatic ecosystems amid escalating climate change threats.

Cenozoic Indo-Pacific warm pool controlled by both atmospheric CO2 and paleogeography

The Indo-Pacific warm pool (IPWP) is crucial for regional and global climates. However, the development of the IPWP and its effect on the regional climate during the Cenozoic remain unclear. Here, using a compilation of sea surface temperature (SST) records (mainly since the middle Miocene) and multimodel paleoclimate simulations, our results indicated that the extent, intensity and warmest temperature position of the IPWP changed markedly during the Cenozoic. Specifically, its extent decreased, its intensity weakened, and its warmest temperature position shifted from the Indian to western Pacific Ocean over time. The atmospheric CO2 dominated its extent and intensity, while paleogeography, by restricting the distribution of the Indian Ocean and the width of the tropical seaways, controlled the shift in its warmest temperature position. In particular, the eastward shift to the western Pacific Ocean from the middle to late Miocene inferred from compiled SST records likely resulted from the constriction of tropical seaways. Furthermore, by changing the atmospheric thermal structure and atmospheric circulation, the reduced extent and intensity of the IPWP decreased the annual precipitation in the western Indian Ocean, eastern Asia and Australia, while the shift in the warmest temperature position from the Indian to western Pacific Ocean promoted aridification in Australia. Qualitative model-data agreements are obtained for both the IPWP SST and regional climate. From the perspective of past warm climates with high concentrations of atmospheric CO2, the expansion and strengthening of the IPWP will occur in a warmer future and favor excessive precipitation in eastern Asia and Australia.

Climate change impacts on regional fire weather in heterogeneous landscapes of central Europe

Abstract. Wildfires have reached an unprecedented scale in the Northern Hemisphere. The summers of 2022 and 2023 demonstrated the destructive power of wildfires, especially in North America and southern Europe. Global warming leads to changes in fire danger. Specifically, fire seasons are assumed to become more extreme and will extend to more temperate regions in northern latitudes in the future. However, the extent to which the seasonality and severity of fire danger in regions of central Europe will change in the future remains to be investigated. Multiple studies claim that natural variability and model uncertainty hide the trend of increasing fire danger in multi-model climate simulations for future potentially fire-prone areas. Such a trend might be isolated with single-model initial-condition large ensembles (SMILEs), which help scientists to distinguish the forced response from natural variability. So far, the SMILE framework has only been applied for fire danger estimation on a global scale. To date, only a few dynamically downscaled regional SMILEs exist, although they enhance the spatial representation of climatic patterns on a regional or local scale. In this study, we use a regional SMILE of the Canadian Regional Climate Model version 5 Large Ensemble (CRCM5-LE) over a region in central Europe under the RCP8.5 (Representative Concentration Pathway) scenario from 1980 to 2099 to analyze changes in fire danger in an area that is currently not fire prone. We use the Canadian Forest Fire Weather Index (FWI) as a fire danger indicator. The study area covers four heterogeneous landscapes, namely the Alps, the Alpine Foreland, the lowlands of the South German Escarpment, and the Eastern Mountain Ranges of the Bavarian Forest. We demonstrate that the CRCM5-LE is a dataset suitable for disentangling climate trends from natural variability in a multi-variate fire danger metric. Our results show the strongest increases in the median (50th) and extreme (90th) quantiles of the FWI in the northern parts (South German Escarpment and Eastern Mountain Ranges) of the study area in the summer months of July and August. There, high fire danger becomes the median condition by the end of the century, and levels of high fire danger occur earlier in the fire season. The southern parts (Alps and Alpine Foreland) are less strongly affected by changes in fire danger than the northern parts. However, these regions reach their time of emergence (TOE) in the early 2040s because of very low current fire danger. In the northern parts, the climate change trend exceeds natural variability only in the late 2040s. We find that today's 100-year FWI event will occur every 30 years by 2050 and every 10 years by the end of the century. Our results highlight the potential for severe future fire events in central Europe, which is currently not very fire prone, and demonstrate the need for fire management even in regions with a temperate climate.

Future trends in the vertical structure of Arctic warming and moistening in different emission scenarios

Arctic warming is a pressing global concern, and understanding its future vertical structure is crucial for Arctic-mid-latitude connections. In this study, we employed the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-model simulations to investigate the vertical structure of Arctic warming concerning its current evolving (1980–2030) and future change (2050–2100). In addition to the dry atmosphere described only by temperature, this study analyzed humidity and moist static energy (MSE) for the moist (with the effect of moisture explicitly presented) atmosphere. Under the high-emission scenario, Arctic warming is projected to accelerate, while maintaining its existing bottom-heavy structure. Amplified humidity increases are anticipated in both the tropics and the Arctic, with the Arctic exhibiting greater moisture increases in percentage. Furthermore, we find that MSE, serving as a comprehensive metric for moist atmospheric warming, is projected to accelerate in the Arctic under the high-emission scenario. As a result, the warming of both the dry and moist atmospheres will intensify in the future, with the bottom-heavy vertical structure persisting due to enhanced warming and moistening in the lower troposphere. The accelerated Arctic moistening in the future is due to meridional atmospheric moisture transport in the summer and local moisture source in the winter. In contrast, under the intermediate-emission scenario, Arctic warming and moistening are not projected to accelerate. Despite the inter-model discrepancy, our findings underscore the reliability of projections derived from the high-emission scenario. In summary, our findings highlight that the bottom-heavy vertical structure of Arctic warming will not change and that enhanced upper-level Arctic warming will not occur in the future.

Decadal modulation of temperature pattern over East Asia by Pacific Decadal Oscillation

The decadal surface air temperature (SAT) variation over East Asia (EA) is characterized by an opposite pattern, warming over the northern EA along with cooling over the southern EA, and vice versa, fluctuating on decadal time scale. We found that Pacific Decadal Oscillation (PDO) has been largely responsible for the opposite decadal SAT variation. Positive (negative) phase of the PDO is related to the northern warming (cooling) and southern cooling (warming) over EA, and the decadal variation of the opposite SAT pattern is highly correlated with the PDO decadal variability, implying a decadal modulation of SAT by PDO. We investigated the influence of PDO on the opposite decadal SAT pattern by employing a dynamical adjustment method based on constructed circulation analogues. The results showed that the PDO associated variability in the large-scale atmospheric circulation over Eurasia, characterized by a dipole structure with cyclonic circulation occupying the northern Eurasia but anticyclonic circulation over the southern Eurasia when in positive PDO phase, is responsible for the opposite decadal SAT pattern. Further analysis on the origin of the anomalous atmospheric circulation was performed by combining observations and climate model simulations. Based on observations and the multi-model simulations of Atmospheric Model Intercomparison Project (AMIP) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we found a contribution of intrinsic atmospheric dynamics, which was further suggested by the results using the Community Atmosphere Model simulations.

Evaluation of CORDEX Africa regional climate models performance in simulating climatology of Zarima sub-basin northwestern Ethiopia

Climate models are basic tools to obtain reliable estimates of future climate change and its effects on the water resources and agriculture in given basin. However, all climate models are not equally valuable for all areas. Therefore, determining the most appropriate climate model for a specific study area is essential. This study examines the performance of 10 CORDEX-AFRICA-220 Regional Climate Models (RCMs), three downscaling institutional based ensembles mean (Reg ensemble, CCLM ensemble and REMOO ensemble) and the multi-model ensemble mean. The models were evaluated based on their ability in replicating the seasonal and annual rainfall, minimum and maximum temperature and inter-annual variability for the period of 1986–2005 using statistical metrics such as BIAS, Root Mean Square Error (RMSE), Pearson correlation coefficient (r), coefficient of variation (CV), Kling Gupta Efficiency (KGE) and Taylor diagram. The findings indicated that HadREMOO, MPI-Reg4-7, HadReg4-7, Reg ensemble, and multi-model ensemble mean performed relatively better in representing the mean annual observed rainfall at the Adiramets, Debarik Ketema, Niguse Maystebri, and Zarima stations, respectively. Whereas, NorESM-CCLM, MPI-CCLM, NorESM-Reg4-7, and NorESM-REMOO exhibited a weak performance in reproducing the observed mean annual rainfall at the Adiramets, Debarik Ketema Niguse, Maystebri, and Zarima stations, respectively. Similarly, RCMs generally capture the mean annual maximum temperature of climatic stationsof Zarima subbasin well. Specifically, the MPI-Reg4-7 simulation performs well in representing the mean annual observed maximum temperature at Adiramets and Maytsebri stations, while the Debarik and Ketema Niguse stations exhibit superior performance in the HadReg4-7 simulation and the Zarima station shows better representation in the CCLM ensemble simulations. The majority of the model simulations exhibit good representation of mean annual minimum temperature at Adiramets, Debarik, and Zarima stations. Specifically, CanESM-RCM, HadReg4-7, REMOOensemble, multi-model ensemble, and Regensemble simulations perform better at Adiramets, Debarik, Ketema niguse, Maystebri and Zarima stations respectively. This suggests that these models may have biases or shortcomings in capturing the temperature values in the subbasin. Furthermore, NorESM-CCLM at Adiramets, Ketema niguse, and Zarima stations, NorESM-REMOO at Debarik station, and HadReg4-7 at Maystebri station demonstrate poor performance in representing the observed mean minimum temprature. Majority of the RCMs, all institutional based ensemble means and the multi-model ensemble mean simulations overestimate the observed mean annual rainfall of the Zarima subbasin with minimum bias of 0.02 mm at Ketema niguse HadReg4-7and maximum bias of 2.81 mm at Maytsebri MPI-CCLM simulation. Similarly, HadReg4-7 simulation of Ketama Niguse MPI-CCLM showed a minimum 0.02 mm and Maytsebri simulation kiremit season mean rainfall showed a maximum bias of and 2.99 mm. Regarding mean annual and kiremit season maximum and minimum temperature of the Zarima subbasin were overestimated by majority of the simulation and the ensemble means. The correlation (r) of observed and model simulated mean annual and kiremit season rainfall was strong (0.60–0.79) and very strong (0.80–0.99) in the majority of the simulations except Ketema niguse station mean annual and kiremit season rainfall simulations of MPI-REMOO, NorESM-Reg4-7; Debarik station kiremit season rainfall of NorESM-CCLM and NorESM-REMOO, MPI-Reg4-7 and MPI-REMOO, which showed moderate correlation. The performance of the RCMs, institutional based ensemble means and multi-model ensemble mean were different in statistical metrics (BIAS, RMSE, r, CV and KGE) and Taylor diagram. Among the simulations and ensemble means, the multi-model ensemble mean was superiors in two or more of statistical metrics at each station of the Zarima subbasin except Maytsebri station kiremit season rainfall, where the CCLM ensemble was better. Consistently, the Taylor diagram showed that the multi-model ensemble was better in the replication of the areal annual and kiremit season rainfall, maximum and minimum temperature of the subbasin. This finding evidenced that selecting the best RCMs and ensemble mean is necessary for climate projection and climate change impact assessment study.

Seasonally distinct contributions of greenhouse gases and anthropogenic aerosols to historical changes in Arctic moisture budget

Arctic hydrological cycle has important global implications through modifying deep ocean circulation via fresh water fluxes. However, attribution of the observed changes in Arctic moisture budget remains challenging due to the lack of reliable observations. Here, as a first step, past changes in hydrological cycle over the Arctic were evaluated using CMIP6 historical simulations. To examine possible influences of individual external forcings, CMIP6 multi-model simulations performed under natural-plus-anthropogenic (ALL), greenhouse gas (GHG), natural (NAT), and aerosol (AER) forcings were compared. Results indicate that Arctic precipitation and evaporation increase in ALL and GHG but decrease in AER. In ALL and GHG, Arctic precipitation increases in summer mainly due to enhanced poleward moisture transport whereas Arctic moistening during winter is affected more by increased surface evaporation over sea-ice retreat areas. In AER, Arctic precipitation tends to decrease due to reduced evaporation over sea-ice advance areas during cold months. Poleward meridional moisture flux (MMF) across 70°N is the strongest in summer due to the highest moisture. GHG has stronger MMF than other forcing simulations due to larger increase in moisture in line with stronger warming. The MMF response is found to be largely determined by variations in transient eddies with distinct seasonal and regional contributions.

Evaluation and Projection of Gale Events in North China

As an important weather extreme, gales greatly impact the air quality, agriculture, aviation, and renewable energy in North China. However, the spatial–temporal changes in gale events remain unknown in North China. In this study, using the ERA5 reanalysis with high spatiotemporal resolution and multi-model simulations from the Coupled Model Intercomparison Project Phase Six (CMIP6), we investigate the temporal changes, including daily, seasonal and decadal variations in gale events in North China between 1980 and 2021, and we project the changes in gale events in the mid and late 21st century under two shared socio-economic pathways (SSPs). The gale events show large spatial heterogeneity in frequency, with a high frequency of >25 days/year in central Inner Mongolia province, northern Hebei province, and northwestern Beijing. Over the past four decades, the gale events in North China have shown a decadal reversal, with a decrease between 1980 and 2006 and an increase between 2007 and 2014. Furthermore, the gale events show strong temporal variations in North China. For seasonal variation, the gale events exhibit double peaks, with the largest peak in April and the smallest peak in October. For daily variation, the gale events show a single peak, with the maximum from 10:00 to 16:00 local time in North China. Multi-model simulations from CMIP6 reveal a continuous decreasing trend of gale events in North China by the end of the 21st century under both SSP1-2.6 and SSP2-4.5 scenarios relative to the historical period. Our results provide comprehensive support for planning aviation, renewable energy, and agriculture in the future.