Comparison of CMIP6 and CMIP5 models in simulating climate extremes

Future projections of temperature-related indices in Prince Edward Island using ensemble average of three CMIP6 models

Prince Edward Island (PEI) is an agricultural province heavily relying on rainfed agriculture. The island has already experienced significant impacts from climate change. Accurate projections of PEI temperature extreme indices are required to mitigate and adapt to the changing climate conditions. This study aims to develop ensemble projections using Coupled Model Intercomparison Project Phase 6 (CMIP6) global circulation models (GCMs) to analyze temperature extremes on PEI. In this study, the ECMWF ERA5 reanalysis dataset was chosen for stepwise cluster analysis (SCA) due to its high accuracy. Three CMIP6 (NorESM2-MM, MPI-ESM1.2-HR, and CanESM5) GCMs, along with their ensemble average, were utilized in the SCA model to project future changes in daily maximum temperature (Tmax) and minimum temperature (Tmin) at four meteorological stations on PEI (East Point, Charlottetown, Summerside, and North Cape) under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). These GCMs were selected based on their low, medium, and high Equilibrium Climate Sensitivity. The bias-corrected results for the future period of Tmax and Tmin showed that the GCM-specific changes in the ECS also impact the regional scale. Additionally, several temperature extreme indices, including the daily temperature range (DTR), summer days (SU), growing degree days (GDD), growing season length (GSL), ice days (ID), and frost days (FD), were analyzed for two future periods: FP1(202-2050) and FP2 (2051-2075). The results indicate that DTR, SU, GDD, and GSL are expected to increase, while ID and FD are projected to decrease during FP1 and FP2 under both scenarios. The future projected mean monthly changes in Tmax, Tmin, and the selected temperature extreme indices highlight warmer future periods and an increase in agriculture-related indices such as GDD and GSL. Specifically, July, August, and September are expected to experience even higher temperatures in the future. As the climate becomes warmer, cold extreme events are projected to be shorter in duration but more intense in terms of their impact. The largest increments/decrements for Tmax, Tmin, and their relevant indices were observed during FP2 under SSP5-8.5. The outcomes of this study provide valuable insights for agricultural development, water resource management, and the formulation of effective mitigation strategies to address the impacts of climate change on PEI.

Projections of the start of the airborne pollen season in Barcelona (NE Iberian Peninsula) over the 21st century

The effects of global warming are numerous and recent studies reveal that they can affect the timing of pollination. Temperature is the meteorological variable that presents a clearer relationship with the start of the pollination season of most of the observed airborne pollen taxa. In Catalonia, in the last fifty years, the average annual air temperature has increased by +0.23 °C/decade, and the local warming has been slightly higher than the one on a global scale. Projections point to an increase in temperature in the coming decades, which would be more marked towards the middle of the century. To analyse the effect of the increase in temperature due to global warming on the starting date of pollen season in Barcelona, a forecasting model has been applied to a set of projected future temperatures estimated by the European RESCCUE project. This model, largely used in the literature, is based on determining the thermal needs of the plant for the pollen season to begin. The model calibration to obtain the initial parameters has been made by using 20 years of pollen data (2000-2019), and the model effectiveness has subsequently been tested through an internal evaluation over the period of the calibration and an external evaluation on 4 years not included in the calibration (2020-2023). The mean bias error in the internal calibration ranged between -0.4 and - 0.6 days, and between +0.5 and - 8.3 in the external one, depending on the taxon. The results of the application of the model to the temperature projections over the 21st century point to a progressive advancement in the pollination dates of several pollen types abundant in the city, allergenic most of them. These advances ranged, at the end of the century, between 15 and 27 days, depending on the climate model, for the scenario of the highest concentrations (RCP8.5) and between 7 and 12 days for the emissions stabilization scenario (RCP4.5).

Recent and projected changes in climate patterns in the Middle East and North Africa (MENA) region

Observational and reanalysis datasets reveal a northward shift of the convective regions over northern Africa in summer and an eastward shift in winter in the last four decades, with the changes in the location and intensity of the thermal lows and subtropical highs also modulating the dust loading and cloud cover over the Middle East and North Africa region. A multi-model ensemble from ten models of the Coupled Model Intercomparison Project-sixth phase gives skillful simulations when compared to in-situ measurements and generally captures the trends in the ERA-5 data over the historical period. For the most extreme climate change scenario and towards the end of the twenty-first century, the subtropical highs are projected to migrate poleward by 1.5°, consistent with the projected expansion of the Hadley Cells, with a weakening of the tropical easterly jet in the summer by up to a third and a strengthening of the subtropical jet in winter typically by 10% except over the eastern Mediterranean where the storm track is projected to shift polewards. The length of the seasons is projected to remain about the same, suggesting the warming is likely to be felt uniformly throughout the year.

Climate change projection using statistical downscaling model over southern coastal Iran

Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021-2045), middle 21st century (2046-2071), and late 21st century (2071-2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021-2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071-2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (-1.94 °C) in Ch station in 2046-2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071-2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (-25.8 mm) in Bandar Abbas station in 2046-2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.

Global pattern of forest disturbances and its shift under climate change

Forests are continuously altered by disturbances. Yet, knowledge of global pattern of forest disturbance agents, its drivers, and shifts under changing climate remain scarce. Here we present a meta-analysis of current and projected (+2° and + 4 °C) distribution of forest disturbance agents causing immediate tree mortality (i.e., fire, pest outbreak, hydro-geomorphic, and wind) at country, continental, biome, and global scales. The model including combination of climatic (precipitation of driest quarter, actual evapotranspiration, and minimum temperature), geographical (distance to coast and topography complexity), and forest characteristics (tree density) performs better than any other model in explaining the distribution of disturbance agents (R2 = 0.74). We provide global maps (0.5° × 0.5°) of current and potential future distribution of forest disturbance agents. Globally, the most frequent disturbance agent was fire (46.09 %), followed by pest outbreak (23.27 %), hydro-geomorphic disturbances (18.97 %), and wind (11.67 %). Our projections indicate spatially contrasting shifts in disturbance agents, with fire and wind risk increase between ~50°S and ~ 40°N under warming climate. In particular, the substantial increase in fire risk, exceeding 31 % in the most affected areas, is projected over Mediterranean, the western and southeast USA, African, Oceanian, and South American forests. On the other hand, pest outbreak and hydro-geomorphic disturbances are projected to increase in more southern (> ~ 50°S) and northern (> ~ 40°N) latitudes. Our findings are critical for understanding ongoing changes and developing mitigation strategies to maintain the ecological integrity and ecosystem services under shifts in forest disturbances. We suggest that projected shifts in the global distribution of forest disturbance agents needs to be considered to future models of vegetation or carbon sink dynamics under projected climate change.

Projections of meteorological drought severity-duration variations based on CMIP6

This research utilized the outputs from three models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), specifically CanESM5, GFDL-ESM4, and IPSL-CM6A-LR. These models were used under the SSP1-2.6 and SSP5-8.5 scenarios, along with the SPI and SPEI, to assess the impacts of climate change on drought in Iran. The results indicated that the average annual precipitation will increase under some scenarios and decrease under others in the near future (2022-2050). In the distant future (2051-2100), the average annual precipitation will increase in all states by 8-115 mm. The average minimum and maximum temperature will increase by up to 4.85 ℃ and 4.9 ℃, respectively in all states except for G2S1. The results suggest that severe droughts are anticipated across Iran, with Cluster 5 expected to experience the longest and most severe drought, lasting 6 years with a severity index of 85 according to the SPI index. Climate change is projected to amplify drought severity, particularly in central and eastern Iran. The SPEI analysis confirms that drought conditions will worsen in the future, with southeastern Iran projected to face the most severe drought lasting 20 years. Climate change is expected to extend drought durations and increase severity, posing significant challenges to water management in Iran.

Recent decreasing precipitation and snowmelt reduce the floods around the Chinese Tianshan Mountains

Understanding and managing mountain floods has become increasingly urgent, with global climate change and human activities exacerbating flood risk. However, flood research in Tianshan Mountains, a typical flood-prone mountainous region in China, is still insufficient. Here, we customized a set of flood research methods based on rainstorms and extreme snowmelt events, including a new flood counting method that comprehensively considered the frequency and magnitude of floods and the methods of flood classification and change attribution. We found that floods around the Chinese Tianshan Mountains (CTM) increased from 2014 to 2016 but decreased rapidly from 2016 to 2021, with storm floods, snowmelt floods, and mixed floods accounting for 38.3 %, 26.5 %, and 34.6 % of total flood events, respectively. The variation of floods was most significantly correlated with the average and extreme precipitation, followed by the temperature-driven average snowmelt change. Furthermore, atmospheric circulation anomalies and water vapor input from the western boundary of CTM caused decreasing precipitation and storm floods. Meanwhile, the warming hiatus also greatly impacted declining flood frequency. Notably, flood frequency is projected to rebound soon because of the rising precipitation and temperature, infrastructure aging, and reservoir abandonment, implying the present flood decline unsustainable. Our research develops a strategy to investigate short-term flood anomalies under climate oscillations around the CTM, providing insights into flood research and prevention in global mountainous regions.

Future transition in climate extremes over Western Ghats of India based on CMIP6 models

The effect of climate change on the tropical river catchments in the Western Ghats of India is studied using the Coupled Model Intercomparison Project-6 data (CMIP-6). Multi-model ensembles of rainfall and temperature are constructed using the Random Forest ensemble technique for bias-corrected GCMs in the near future (2014-2050) and far future (2051-2100) horizons. For the two catchments each in the southern, central, and northern Ghats, the trend in minimum and maximum temperatures, precipitation, and other indices are calculated. By 2100, dry sub-humid and humid catchments will see a higher increase in mean annual temperature than per-humid central catchments. In future decades, the warm days and nights increase by 45-50% and 40-70%, respectively, with twofold warming in the winter season. Under a climate change scenario, annual rainfall increases in Vamanapuram, Ulhas, and Purna, while Chaliyar, Netravati, and Aghanashini catchments experience a decrease in rainfall in the far future with an increase in pre-monsoon rainfall. The southern catchments are anticipated to have contrasting variations in the rainfall extremes; northern catchments face a substantial increase in very wet to extremely wet days and medium to heavy rainfall. In all catchments (excluding Vamanapuram), cumulative wet days increase with a decrease in cumulative dry days. After the mid-twenty-first century, humid to per-humid catchments encompass an increase in cool nights, whereas it disappears in dry sub-humid catchments of the Ghat. Interestingly, warming tendencies begin to slow down after 2050. This investigation can assist in comprehending the regional climate extremes in the Western Ghats to formulate better climate risk planning and adaptation strategies.

Evolution and prediction of drought-flood abrupt alternation events in Huang-Huai-Hai River Basin, China

Drought-flood abrupt alternation (DFAA) as a compound natural disaster can cause severe socioeconomic loss and environmental destruction. Under climate change, the Huang-Huai-Hai River Basin has experienced evident increases in temperature and variability of precipitation. However, the study of the evolution characteristics of DFAA in the Huang-Huai-Hai River Basin is limited and the risk of exposure to DFAA events under future climatic conditions should be comprehensively assessed. In this study, the DFAA events including drought to flood (DTF) and flood to drought (FTD) events in the Yellow River Basin (YRB), Huai River Basin (HuRB), and Hai River Basin (HaRB) are identified by the long-cycle drought-flood abrupt alternation index (LDFAI) and the temporal variation and spatial distribution of the number and intensity of DFAA events from 1961 to 2020 are examined. The 24 climate model simulations of Coupled Model Intercomparison Project Phase 6 (CMIP6) are used to evaluate the variation of DFAA events based on the bias-corrected method. The results show that both DTF and FTD events occurred >10 times in most areas of the Huang-Huai-Hai River Basin from 1961 to 2020, and severe DFAA events occurred more frequently in the HaRB. The occurrence of DTF events decreased and FTD events continuously increased in the YRB, while they showed opposite trends in the HuRB and HaRB. In the future, the Huang-Huai-Hai River Basin is projected to experience more DTF events under the SSP1-2.6 and SSP2-4.5 scenarios, while more FTD events under the SSP3-7.0 and SSP5-8.5 scenarios. Most areas in the Huang-Huai-Hai River Basin are projected to be at medium or high risk of the frequency and intensity of DFAA events under different future scenarios, especially in the central part of the YRB. These findings can provide scientific reference to the formulation of management policies and mitigation strategies.

Projected mid-century rainfall erosivity under climate change over the southeastern United States

Recent observations and climate change projections indicate that changes in rainfall energy, intensity, duration, and frequency, which determine the erosive power of rainfall, will amplify erosion rates around the world. However, the magnitude and scope of these future changes in erosive power of rainfall remain largely unknown, particularly at finer-resolutions and local scales. Due to a lack of available projected future sub-hourly climate data, previous studies relied on aggregates (hourly, daily) rainfall data. The erosivity for the southeastern United States in this study was calculated using the RUSLE2 erosivity calculation method without data limitation and a recently published 15-min precipitation dataset. This precipitation data was derived from five NA-CORDEX climate models' precipitation products under the Representative Concentration Pathway (RCP) 8.5 scenario. In this dataset, hourly climate projections of precipitation were bias-corrected and temporally downscaled to 15-min resolution for 187 locations with collocated 15-min precipitation observations. Precipitation, erosivity (R-factor), and erosivity density (ED) estimations were provided for historical (1970-1999) and future (2030-2059) time periods. Ensemble results for projected values (as compared to historical values) showed increase in precipitation, erosivity, and erosivity density by 14 %, 47 %, and 29 %, respectively. The future ensemble model showed an average annual R-factor of 11,237±1299 MJ mm ha^-1h^-1yr^-1. These findings suggest that changes in rainfall intensity, rather than precipitation amount, may be driving the change in erosivity. However, the bias correction and downscaling limitations inherent in the original precipitation dataset and this study's analyses obscured this particular result. In general, coastal and mountainous regions are expected to experience the greatest absolute increase in erosivity, while other inland areas are expected to experience the greatest relative change. This study offers a novel examination of projected future precipitation characteristics in terms of erosivity and potential future erosion.

Future temperature extremes threaten land vertebrates

The frequency, duration, and intensity of extreme thermal events are increasing and are projected to further increase by the end of the century^1,2. Despite the considerable consequences of temperature extremes on biological systems^3-8, we do not know which species and locations are most exposed worldwide. Here we provide a global assessment of land vertebrates' exposures to future extreme thermal events. We use daily maximum temperature data from 1950 to 2099 to quantify future exposure to high frequency, duration, and intensity of extreme thermal events to land vertebrates. Under a high greenhouse gas emission scenario (Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5); 4.4 °C warmer world), 41.0% of all land vertebrates (31.1% mammals, 25.8% birds, 55.5% amphibians and 51.0% reptiles) will be exposed to extreme thermal events beyond their historical levels in at least half their distribution by 2099. Under intermediate-high (SSP3-7.0; 3.6 °C warmer world) and intermediate (SSP2-4.5; 2.7 °C warmer world) emission scenarios, estimates for all vertebrates are 28.8% and 15.1%, respectively. Importantly, a low-emission future (SSP1-2.6, 1.8 °C warmer world) will greatly reduce the overall exposure of vertebrates (6.1% of species) and can fully prevent exposure in many species assemblages. Mid-latitude assemblages (desert, shrubland, and grassland biomes), rather than tropics^9,10, will face the most severe exposure to future extreme thermal events. By 2099, under SSP5-8.5, on average 3,773 species of land vertebrates (11.2%) will face extreme thermal events for more than half a year period. Overall, future extreme thermal events will force many species and assemblages into constant severe thermal stress. Deep greenhouse gas emissions cuts are urgently needed to limit species' exposure to thermal extremes.

High emissions could increase the future risk of maize drought in China by 60-70

Drought events have considerable direct and indirect economic, environmental, and social impacts, but few studies have analyzed and assessed future changes in drought disasters from a risk perspective to guide responses and adaptations thoroughly. Studying the potential climate-related impacts on future crop yield is therefore urgently needed. Intercomparison of the three Shared Socio-economic Pathway (SSP) scenarios based drought risks and yield loss of China was carried out using the climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), and the hotspots of high drought risk regions were identified. This study found that the areas affected by severe maize drought (loss ratio larger than 0.2) accounted for 16.13 %, 20.79 %, and 18.87 % of the total national corn areas under three low, medium-to-high and high emission scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5) respectively. The northwest China maize region, the ecotone between agriculture and animal husbandry, and the western central northern China maize region have relatively high loss risk. Compared with SSP1-2.6, the yield loss rates increased with 70.73 % and 61.52 % of national corn areas for SSP3-7.0 and SSP5-8.5, respectively. There is a decrease in the areas with low-risk and a significant increase in the areas with high-risk for SSP3-7.0 and SSP5-8.5 compared to the SSP1-2.6. These results may provide theoretical support for agricultural drought risk reduction and adaptation planning to ensure food security under climate change.

The trend and spatial spread of multisectoral climate extremes in CMIP6 models

Climate change could exacerbate extreme climate events. This study investigated the global and continental representations of fourteen multisectoral climate indices during the historical (1979-2014), near future (2025-2060) and far future (2065-2100) periods under two emission scenarios, in eleven Coupled Model Intercomparison Project (CMIP) General Circulation Models (GCM). We ranked the GCMs based on five metrics centred on their temporal and spatial performances. Most models followed the reference pattern during the historical period. MPI-ESM ranked best in replicating the daily precipitation intensity (DPI) in Africa, while CANESM5 GCM ranked first in heatwave index (HI), maximum consecutive dry days (MCCD). Across the different continents, MPI-LR GCM performed best in replicating the DPI, except in Africa. The model ranks could provide valuable information when selecting appropriate GCM ensembles when focusing on climate extremes. A global evaluation of the multi-index causal effects for the various indices shows that the dry spell total length (DSTL) was the most crucial index modulating the MCCD for all continents. Also, most indices exhibited a positive climate change signal from the historical to the future. Therefore, it is crucial to design appropriate strategies to strengthen resilience to extreme climatic events while mitigating greenhouse gas emissions.

Mapping the Distribution and Dispersal Risks of the Alien Invasive Plant Ageratina adenophora in China

Identifying the distribution dynamics of invasive alien species can help in the early detection of and rapid response to these invasive species in newly invaded sites. Ageratina adenophora, a worldwide invasive plant, has spread rapidly since its invasion in China in the 1940s, causing serious damage to the local socioeconomic and ecological environment. To better control the spread of this invasive plant, we used the MaxEnt model and ArcGIS based on field survey data and online databases to simulate and predict the spatial and temporal distribution patterns and risk areas for the spread of this species in China, and thus examined the key factors responsible for this weed’s spread. The results showed that the risk areas for the invasion of A. adenophora in the current period were 18.394° N–33.653° N and 91.099° E–121.756° E, mainly in the tropical and subtropical regions of China, and densely distributed along rivers and well-developed roads. The high-risk areas are mainly located in the basins of the Lancang, Jinsha, Yalong, and Anning Rivers. With global climate change, the trend of continued invasion of A. adenophora is more evident, with further expansion of the dispersal zone towards the northeast and coastal areas in all climatic scenarios, and a slight contraction in the Yunnan–Guizhou plateau. Temperature, precipitation, altitude, and human activity are key factors in shaping the distribution pattern of A. adenophora. This weed prefers to grow in warm and precipitation-rich environments such as plains, hills, and mountains; in addition, increasing human activities provide more opportunities for its invasion, and well-developed water systems and roads can facilitate its spread. Measures should be taken to prevent its spread into these risk areas.

Climate Change Impact on Yield and Water Use of Rice-Wheat Rotation System in the Huang-Huai-Hai Plain, China

Global climate change has had a significant impact on crop production and agricultural water use. Investigating different future climate scenarios and their possible impacts on crop production and water consumption is critical for proposing effective responses to climate change. In this study, based on daily downscaled climate data from 22 Global Climate Models (GCMs) provided by Coupled Model Intercomparison Project Phase 6 (CMIP6), we applied the well-validated Agricultural Production Systems sIMulator (APSIM) to simulate crop phenology, yield, and water use of the rice-wheat rotation at four representative stations (including Hefei and Shouxian stations in Anhui province and Kunshan and Xuzhou stations in Jiangsu province) across the Huang-Huai-Hai Plain, China during the 2041-2070 period (2050s) under four Shared Socioeconomic Pathways (i.e., SSP126, SSP245, SSP370, and SSP585). The results showed a significant increase in annual mean temperature (Temp) and solar radiation (Rad), and annual total precipitation (Prec) at four investigated stations, except Rad under SSP370. Climate change mainly leads to a consistent advance in wheat phenology, but inconsistent trends in rice phenology across four stations. Moreover, the reproductive growth period (RGP) of wheat was prolonged while that of rice was shorted at three of four stations. Both rice and wheat yields were negatively correlated with Temp, but positively correlated with Rad, Prec, and CO₂ concentration ([CO2]). However, crop ET was positively correlated with Rad, but negatively correlated with [CO2], as elevated [CO2] decreased stomatal conductance. Moreover, the water use efficiency (WUE) of rice and wheat was negatively correlated with Temp, but positively correlated with [CO2]. Overall, our study indicated that the change in Temp, Rad, Prec, and [CO2] have different impacts on different crops and at different stations. Therefore, in the impact assessment for climate change, it is necessary to explore and analyze different crops in different regions. Additionally, our study helps to improve understanding of the impacts of climate change on crop production and water consumption and provides data support for the sustainable development of agriculture.

Persistence of increasing vegetation gross primary production under the interactions of climate change and land use changes in Northwest China

Substantial evidence suggests a widespread increase in global vegetation gross primary production (GPP) since the 1980s. If the increasing trend of GPP remains unchanged in the future, it is considered to be the persistence of increasing GPP. However, it is still unknown whether the vegetation increasing GPP is persistent under the interactive effects of climate change and land use changes in Northwest China. Using the Mann-Kendall and boosted regression tree models, we constructed the relationship between the increasing GPP and environmental variables, and further explored its persistence under the interactions between climate change and land use changes under SSP245 and SSP585 scenarios. The results indicated that: (1) Land use change (8.01%) was the most important variable for the increasing GPP. The surface net solar radiation (6.79%), and maximum temperature of the warmest month (6.78%) were also very important. Moreover, mean temperature of the warmest quarter had strong interactions with mean precipitation of the warmest quarter (9.82%) and land use change (8.24%). (2) Under the SSP245 scenario, the persistence of increasing GPP accounted for 65.06% of the area in 2100, mainly located in Qinghai, Ningxia, and Shaanxi, while it only accounted for 19.50% under the SSP585 scenario. (3) The SSP245 scenario moderate warming leads to a slight ecosystem benefit, with more areas developing an increase in GPP due to climate and land use change factors. On the other hand, under SSP585 scenario, there are widespread losses of increasing GPP, driven largely by climate change, while ecological engineering is conducive to the persistence of increasing GPP in southern Qinghai. The results highlight the importance of the interactive effects of climate change and land use changes for predicting the persistence of vegetation change.

High-resolution mapping of the global silicate weathering carbon sink and its long-term changes

Climatic and non-climatic factors affect the chemical weathering of silicate rocks, which in turn affects the CO₂ concentration in the atmosphere on a long-term scale. However, the coupling effects of these factors prevent us from clearly understanding of the global weathering carbon sink of silicate rocks. Here, using the improved first-order model with correlated factors and non-parametric methods, we produced spatiotemporal data sets (0.25° × 0.25°) of the global silicate weathering carbon-sink flux (SCSF_α ) under different scenarios (SSPs) in present (1950-2014) and future (2015-2100) periods based on the Global River Chemistry Database and CMIP6 data sets. Then, we analyzed and identified the key regions in space where climatic and non-climatic factors affect the SCSF_α . We found that the total SCSF_α was 155.80 ± 90 Tg C yr^-1 in present period, which was expected to increase by 18.90 ± 11 Tg C yr^-1 (12.13%) by the end of this century. Although the SCSF_α in more than half of the world was showing an upward trend, about 43% of the regions were still showing a clear downward trend, especially under the SSP2-4.5 scenario. Among the main factors related to this, the relative contribution rate of runoff to the global SCSF_α was close to 1/3 (32.11%), and the main control regions of runoff and precipitation factors in space accounted for about 49% of the area. There was a significant negative partial correlation between leaf area index and silicate weathering carbon sink flux due to the difference between the vegetation types. We have emphasized quantitative analysis the sensitivity of SCSF_α to critical factors on a spatial grid scale, which is valuable for understanding the role of silicate chemical weathering in the global carbon cycle.

Comparison of Projections of Precipitation over Yangtze River Basin of China by Different Climate Models

Based on the observational dataset CN05.1 and the Coupled Model Intercomparison Project (CMIP), this study assesses the performance of CMIP5 and CMIP6 projects in projecting mean precipitation at annual and seasonal timescales in the Yangtze River Basin of China over the period 2015–2020 under medium emission scenarios (RCP4.5/SSP2-4.5). Results indicate that the multi-model ensemble (MME) of CMIP6 overall has lower relative bias and root-mean square error of both annual and seasonal mean than that of CMIP5, except for winter, but both of the two ensembles show the best projected accuracy in winter. Generally, CMIP6 outperformed CMIP5 in capturing spatial and temporal pattern over the YRB, especially in the midstream and downstream areas, which have high precipitation. Further analyses suggest that the CMIP6 GCMs have lower median normalized root-mean square error than CMIP5 GCMs. Based on the Taylor skill (TS) score, both CMIP6 and CMIP5 GCMs are ranked to evaluate relative model performance. CMIP6 GCMs have higher ranks than CMIP5 GCMs, with an average TS score of 0.68 (0.55) for CMIP6 (CMIP5), and three out of the five highest scored GCMs are CMIP6 GCMs. However, the CMIP6 precipitation projections are still quite uncertain, thus requiring further assessment and correction.

Simulation and Projection of Climate Extremes in China by a Set of Statistical Downscaled Data

This study assesses present-day extreme climate changes over China by using a set of phase 6 of the Coupled Model Intercomparison Project (CMIP6) statistical downscaled data and raw models outputs. The downscaled data is produced by the adapted spatial disaggregation and equal distance cumulative distribution function (EDCDF) method at the resolution of 0.25° × 0.25° for the present day (1961–2014) and the future period (2015–2100) under the Shared Socioeconomic Path-way (SSP) 2-4.5 than SSP5-8.5 emission scenario. The results show that the downscaling method improves the spatial distributions of extreme climate events in China with higher spatial pattern correlations, Taylor Skill Scores and closer magnitudes no matter single model or multi model ensemble (MME). In the future projections, large inter-model variability between the downscaled models still exists, particular for maximum consecutive 5-day precipitation (RX5). The downscaled MME projects that total precipitation (PTOT) and RX5, will increase with time, especially for the northwest China. The projected heavy precipitation days (R20) also increase in the future. The region of significant increase in R20 locates in the south of river Yangtze. Maxi-mum annual temperature (TXX) and percentage of warm days (TX90p) are projected to increase across the whole country with larger magnitude over the west China. Projected changes of minimum annual temperature (TNN) over the northeastern China is the most significant area. The higher of the emission scenario, the more significant of extreme climates. This reveals that the spatial distribution of extreme climate events will become more uneven in the future.

Climate-associated major food crops production change under multi-scenario in China

To inform targeted adaptation measures, comprehensive assessments of climate change impacts on agricultural systems are urgently needed. The current study analyzed the production (including phenology, yield, ET, and WUE) of major crops in the near future (2011-2040) through probabilistic assessment. The Crop-Environment Resource Synthesis (CERES)-Wheat/Maize model was driven by ensemble climate projections from five global climate models (GCMs) under three emission scenarios (RCP2.6, RCP4.5, RCP8.5). Results showed that: (1) Compared with the base period, the probability of advanced maturity for wheat and maize was 90.36-91.18% and 62.96-64.50%, respectively. The probability of yield reduction for wheat and maize was 64.12-68.93% and 40.44-41.41%, respectively. The probability of water use efficiency (WUE) reduction for wheat and maize was 51.09-53.94% and 35.86-37.93%, respectively. (2) In the absence of adaptation measures, substantial yield loss was found in major crop-producing areas, including the northern winter wheat planting area and Huang-Huai Plain spring-summer maize zone. The spatial overlap of the vulnerable area will exacerbate food insecurity. (3) The decrease in wheat yield and WUE were both greater than that of maize. Replacing highly sensitive crops with heat-tolerant varieties and dietary diversity should be advocated to cope with future climate change. The results will contribute to adaptive decision-making in China.

Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6

This paper presents projections of climate extremes over China under global warming of 1.5, 2, and 3 °C above pre-industrial (1861-1900), based on the latest Coupled Model Intercomparison Project phase 6 (CMIP6) simulations. Results are compared with what produced by the precedent phase of the project, CMIP5. Model evaluation for the reference period (1985-2005) indicates that CMIP6 models outperform their predecessors in CMIP5, especially in simulating precipitation extremes. Areal averages for changes of most indices are found larger in CMIP6 than in CMIP5. The emblematic annual mean temperature, when averaged over the whole of China in CMIP6, increases by 1.49, 2.21, and 3.53 °C (relative to 1985-2005) for 1.5, 2, and 3 °C above-preindustrial global warming levels, while the counterpart in CMIP5 is 1.20, 1.93 and 3.39 °C respectively. Similarly, total precipitation increases by 5.3%, 8.6%, and 16.3% in CMIP6 and by 4.4%, 7.0% and 12.8% in CMIP5, respectively. The spatial distribution of changes for extreme indices is generally consistent in both CMIP5 and CMIP6, but with significantly higher increases in CMIP6 over Northeast and Northwest China for the hottest day temperature, and South China for the coldest night temperature. In the south bank of the Yangtze River, and most regions around 40°N, CMIP6 shows higher increases for both total precipitation and heavy precipitation. The projected difference between CMIP6 and CMIP5 is mainly attributable to the physical upgrading of climate models and largely independent from their emission scenarios.

Impact of Model Resolution on the Simulation of Precipitation Extremes over China

Climate models tend to overestimate light precipitation and underestimate heavy precipitation due to low model resolution. This work investigated the impact of model resolution on simulating the precipitation extremes over China during 1995–2014, based on five models from Coupled Model Intercomparison Project 6 (CMIP6), each having low- and high-resolution versions. Six extreme indices were employed: simple daily intensity index (SDII), wet days (WD), total precipitation (PRCPTOT), extreme precipitation amount (R95p), heavy precipitation days (R20mm), and consecutive dry days (CDD). Models with high resolution demonstrated better performance in reproducing the pattern of climatological precipitation extremes over China, especially in the western Sichuan Basin along the eastern side of the Tibetan Plateau (D1), South China (D2), and the Yangtze-Yellow River basins (D3). Decreased biases of precipitation exist in all high-resolution models over D1, with the largest decease in root mean square error (RMSE) being 48.4% in CNRM-CM6. The improvement could be attributed to fewer weak precipitation events (0 mm/day–10 mm/day) in high-resolution models in comparison with their counterparts with low resolutions. In addition, high-resolution models also show smaller biases over D2, which is associated with better capturing of the distribution of daily precipitation frequency and improvement of the simulation of the vertical distribution of moisture content.

Past variations and future projection of runoff in typical basins in 10 water zones, China

Understanding the historical and future changing characteristics of key climatic variables and runoff in 10 major river zones in China is essential for water resources evaluation and management. To this end, the historical and future changing trends of key hydrometeorological variables, including precipitation, potential evapotranspiration, and runoff were analyzed in detail for each water zone across China. The climate elasticity method was also established to quantify the impacts of climate change and human activities on historical runoff variations. The results indicate that the characteristics and causes of runoff variations in China were generally spatially heterogeneous. The runoff in water-scarce river basins of northern China decreased significantly during the period of 1961-2018, variations of which were more sensitive to human activities. For southern water zones in China, the runoff showed no significant trend and climate change was the main influencing factor. On basis of 9 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model ensemble simulations under three different shared socioeconomic pathways (ssp126, ssp245 and ssp585), the future runoff in 10 typical basins of the water zones were projected and the results suggested an increasing trend of runoff over China, thanks to increasing precipitation in the rest 21 century. While under ssp585, the rising air temperature tends to evaporate more water and offset the effect of precipitation increase to some extent, resulting in that the increments of runoff under ssp585 are not necessarily greater than those under ssp245 and ssp126. Overall, our study could be used as a basis to support climate adaptation strategies and policies to cope with future water resources conditions.

Projected Meteorological Drought over Asian Drylands under Different CMIP6 Scenarios

Asia currently has the world’s largest arid and semi-arid zones, so a timely assessment of future droughts in the Asian drylands is prudent, particularly in the context of recent frequent sandstorms. This paper assesses the duration, frequency, and intensity of drought events in the Asian drylands based on nine climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results show that a high percentage of land area is experiencing significant drought intensification of 65.1%, 89.9%, and 99.8% under Shared Socioeconomic Pathways (SSP)126, SSP245, and SSP585 scenarios, respectively. Furthermore, the data indicate that future droughts will become less frequent but longer in duration and more intense, with even more severe future droughts predicted for northwest China and western parts of Uzbekistan and Kazakhstan. Drought durations of 10.8 months and 13.4 months are anticipated for the future periods of 2021–2060 and 2061–2100, respectively, compared to the duration of 6.6 months for the historical period (1960–2000). Meanwhile, drought intensity is expected to reach 1.37 and 1.66, respectively, for future events compared to 0.97 for the historical period. However, drought severity under SSP245 will be weaker than that under SSP126 due to the mitigating effect of precipitation. The results of this study can provide a basis for the development of adaptation measures in Asian dryland nations.

Urban Resilience of Shenzhen City under Climate Change

The Chinese government attaches great importance to climate change adaptation and has issued relevant strategies and policies. Overall, China’s action to adapt to climate change remains in its infancy, and relevant research needs to be further deepened. In this paper, we study the future adaptive countermeasures of Shenzhen city in the Pearl River Delta in terms of climate change, especially urban flood risk resilience. Based on the background investigation of urban flood risk in Shenzhen, this paper calculates the annual precipitation frequency of Shenzhen from 1953 to 2020, and uses the extreme precipitation index as a quantitative indicator to analyze the changes in historical precipitation and the impact of major flood disasters in Shenzhen city in previous decades. Based on the six kinds of model data of the scenario Model Inter-comparison Project (MIP) in the sixth phase of the Coupled Model Inter-comparison Project (CMIP6), uses the Taylor diagram and MR comprehensive evaluation method to evaluate the ability of different climate models to simulate extreme precipitation in Shenzhen, and the selected models are aggregated and averaged to predict the climate change trend of Shenzhen from 2020 to 2100. The prediction results show that Shenzhen will face more severe threats from rainstorms and floods in the future. Therefore, this paper proposes a resilience strategy for the city to cope with the threat of flood in the future, including constructing a smart water management system and promoting the development of a sponge city. Moreover, to a certain extent, it is necessary to realize risk transfer by promoting a flood insurance system.

Means and Extremes: Evaluation of a CMIP6 Multi-Model Ensemble in Reproducing Historical Climate Characteristics across Alberta, Canada

This study evaluates General Circulation Models (GCMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) for their ability in simulating historical means and extremes of daily precipitation (P), and daily maximum (Tmax), and minimum temperature (Tmin). Models are evaluated against hybrid observations at 2255 sub-basins across Alberta, Canada using established statistical metrics for the 1983–2014 period. Three extreme indices including consecutive wet days (CWD), summer days (SD), and warm nights (WN) are defined based on the peak over the threshold approach and characterized by duration and frequency. The tail behaviour of extremes is evaluated using the Generalized Pareto Distribution. Regional evaluations are also conducted for four climate sub-regions across the study area. For both mean annual precipitation and mean annual daily temperature, most GCMs more accurately reproduce the observations in northern Alberta and follow a gradient toward the south having the poorest representation in the western mountainous area. Model simulations show statistically better performance in reproducing mean annual daily Tmax than Tmin, and in reproducing annual mean duration compared to the frequency of extreme indices across the province. The Kernel density curves of duration and frequency as simulated by GCMs show closer agreement to that of observations in the case of CWD. However, it is slightly (completely) overestimated (underestimated) by GCMs for warm nights (summer days). The tail behaviour of extremes indicates that GCMs may not incorporate some local processes such as the convective parameterization scheme in the simulation of daily precipitation. Model performances in each of the four sub-regions are quite similar to their performances at the provincial scale. Bias-corrected and downscaled GCM simulations using a hybrid approach show that the downscaled GCM simulations better represent the means and extremes of P characteristics compared to Tmax and Tmin. There is no clear indication of an improved tail behaviour of GPD based on downscaled simulations.