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Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Future global temperature change will have significant effects on society and ecosystems. Earth system models (ESM) are the primary tools to explore future climate change. However, ESMs have great uncertainty and often run at a coarse spatial resolution (usually about 2°). Accurate high‐spatial‐resolution temperature dataset are needed to improve our understanding of temperature variations and for many other applications. We apply Super resolution (SR) in computer vision using deep learning (DL) to merge 31 ESMs data. The proposed method performs data merging, bias‐correction, and spatial downscaling simultaneously. The CRU TS (Climate Research Unit gridded Time Series) data is used as reference data in the model training process. To find a suitable DL method, we select five SR methodologies with different structures. We compare the performances of the methods based on mean square error (MSE), mean absolute error (MAE) and Pearson correlation coefficient (R). The best method is used to merge the projected monthly data (1850–1900), and monthly future scenarios data (2015–2100), at the high spatial resolution of 0.5°. Results show that the merged data have considerably improved performance compared with individual ESM data and their ensemble mean (EM), both spatially and temporally. The MAE shows significant improvement; the spatial distribution of the MAE widens along the latitudes in the Northern Hemisphere. The MAE of merged data is ranging from 0.60 to 1.50, the South American (SA) has the lowest error and the Europe has the highest error. The merged product has excellent performance when the observation data is smooth with few fluctuations in the time series. This work demonstrates the applicability and effectiveness of the DL methods in data merging, bias‐correction and spatial downscaling when enough training data are available. Data can be accessed at https://doi.org/10.5281/zenodo.5746632. [ABSTRACT FROM AUTHOR]

Abstract: This study investigated uncertainties in the modeling of hydrological impacts of climate change on projected flood frequencies of the Zhujiang River, South China. The hydrological model HBV-D was applied to simulate and project future stream flow based on a multi-model ensemble. As this implies high uncertainties, the magnitude of three uncertainty sources, i.e. emission scenarios, GCM structure, and downscaling techniques, were determined in relation to the observed and projected natural variability. The relative change in each uncertainty source and the overall dominance among the three sources were further analyzed. The changes in flood frequency are projected for five return periods (2, 5, 10, 20, and 50 years) and three future time periods (2020s, 2050s, and 2080s). The results suggest that in comparison to the natural variability of the multi-model ensemble, the uncertainty sources show much stronger variations. The range of their relative change and their dominance vary with the lead-time and return period. In most of the return periods, the dominant uncertainty can primarily be attributed to downscaling techniques and emission scenarios, while GCMs structure is minor in the 2020s. However, downscaling technique is the second dominant source behind GCM structure, while emission scenarios represent the lowest uncertainty ranges of the three sources for the projected flood frequency in the 2050s and 2080s. The uncertainty and projected impact of climate change differs also between the four applied GCMs, as compared to the natural variability MK3_5 shows higher ranges than CCSM3, MK3_5 and ECHAM5. The upper bounds (95% percentile) in uncertainty mostly show an increasing tendency with increasing return period, and partially with increasing lead-time. Hence, the more extreme the return period (higher flood frequency) the higher is the uncertainty of the model projections. It is therefore essential that climate change impact assessments consider a wide range of climate scenarios derived from different GCMs under multiple emission scenarios and including several downscaling techniques. The uncertainty due to natural variability should also be considered more intensely. The projection of flood frequency and the identification and quantification of the uncertainties in the modeling is important for the implementation of adaptation policies into water resource planning in the Zhujiang River basin, South China. This study will enrich the scientific research on the uncertainty from different sources of modeling results in river basin parameters, and help to obtain conclusive results on the importance of different sources of uncertainty. [Copyright &y& Elsevier]

Abstract: Significant quasi-cycles in precipitation time series of 132 climate stations spread over China from 1951 to 2002 have been detected by applying Autocorrelation Spectral Analysis (ASA). By the same method, significant quasi-cycles have also been identified in time series of the potential influencing factors: Southern Oscillation Index (SOI), Sea Surface Temperature (SST) and Sea Level Pressure (SLP). Similarities between some precipitation time series spectra and the spectrum of a potential driving force have been detected; e.g. several series from the Yangtze River''s middle reaches show 3–4-year cycles which are similar to the SOI and several SST signals. These time series have been further investigated by low-pass filtering with the Savitzky–Golay filter and by means of correlation analysis. It is proved that the SOI and the SSTs of the Bay of Bengal are in relatively stable anti-phase. However, no stable link between these signals and the precipitation series in the Yangtze River basin can be detected. Subsequent analyses of 850hPa winds lead to the outcome that certain wind patterns could either suppress or force the SST and SOI teleconnection signals. [Copyright &y& Elsevier]

Potential evapotranspiration (PET) plays a pivotal role in resource management and drought assessment. However, future PET estimates remain underexplored in the African region. This study employs twenty General Circulation Models (GCMs) to estimate past (1979–2014) and future PET changes across near-term (2021–2040), mid-term (2061–2080), and long-term (2081–2100) periods, considering four Shared Socioeconomic Pathways (SSPs) including SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5. The research assesses the impact of various climatic factors on PET across Africa and its sub-regions using the Penman-Monteith model. The analysis reveals that Penman-Monteith estimates for PET during 1979–2014 exhibit an increase of 0.68 mm per year (mm/a) across Africa. Notably, the Northern region (NAF), Sahara (SAH), Southern region (SAF), and Eastern region (EAF) experience higher PET changes of 1.78 mm/a, 1.75 mm/a, 1.09 mm/a, and 0.12 mm/a, respectively. Conversely, the Western region (WAF) and Central region (CAF) exhibit negative trends of −0.03 mm/a, and − 0.28 mm/a. Future PET in whole Africa is projected to increase by 0.05 mm/a in SSP1–2.6 and SSP2–4.5, and 0.07 mm/a in higher emissions for 2021–2040, by 0.02 mm/an under SSP1–2.6, 0.07 in SSP2–4.5, 0.09 mm/a, and 0.16 mm/a, in SSP3–7.0 and SSP5–8.5 for 2061–2080, and by −0.01 mm/a in SSP1–2.6, 0.05 mm/a SSP2–4.5, 0.10 mm/a SSP3–7.0, and 0.18 mm/a SSP5–8.5 for 2081–2100. Furthermore, higher emissions are anticipated to drive PET increases in various regions during 2081–2100, with NAF, SAH, and SAF projected to rise by 0.17 mm/a, 0.16 mm/a, and 0.23 mm/a, respectively. WAF, CAF, and EAF are expected to experience increases of 0.20 mm/a, 0.19 mm/a, and 0.15 mm/a, respectively. Contribution analysis indicates that solar radiation played a major factor in PET over Africa as well as in WAF, CAF, and EAF. Maximum temperatures were pivotal in NAF, SAH, and SAF. In future periods (2021-2040, 2061-2080, and 2081-2100), maximum temperatures take precedence to Africa's PET, and at varying percentages to different sub-regions. The findings underscore the significance of PET estimation, particularly in the context of drought evaluation locally and regionally. • PET estimates an increase of 0.68 mm/annum across Africa from 1979 to 2014. • PET is projected to increase under all scenarios, shows stronger in high forcing scenario. • Solar radiation contributed to PET in the past andmaximum temperatures in future but varies across periods and regions. • The study underscores the implications of PET estimations for drought evaluation. [ABSTRACT FROM AUTHOR]

Under climate change, land use has an essential effect on the carbon cycle in the climate change sensitive area of Qinghai-Tibet Plateau (QTP). This study about ambitions to check out the spatial and temporal patterns of carbon storage (CS) on the QTP under future land-use changes and their influencing factors. Based on historical and projected climate and land-use information and the compiled carbon-intensity dataset derived from 839 sampling points, we projected future changes in CS in the region. We projected changes of CS in the QTP in the near-term (2021–2040), mid-term (2041–2060), and long-term (2081–2100) periods, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. We found that the total carbon storage (TCS) from 1980 to 2020 was approximately equal to 247–259 × 108 t, where C soil had the greatest contribution (about 88% of the TCS), while C above , C below and C dead accounted for approximately 5%, 5% and 2% of the TCS, respectively. Furthermore, the TCS decreased from southeast to northwest, corresponding to decreasing forest coverage and increasing unused land area in the QTP. Compared to the baseline period (1995–2014, 259 × 108 t), the CS in the QTP under the Shared Socioeconomic Pathways (SSPs) is expected to increase by 7.3–13.5%, 6.2–14.7% and 5.4–14.3% in the three future periods, respectively. Spatially, the carbon increase will be concentrated in the southeastern and northern parts of the QTP, where the land-use types are mainly forest land and grassland. Increases in precipitation and temperature are predicted to take place for the duration of the QTP in the future, driving land-use changes that are projected to result in increased area of forest land, which would increase CS. The CS in QTP is high, being influenced by land use and climatic factors, and future climate change may enhance the carbon sink capacity of the QTP. [ABSTRACT FROM AUTHOR]

ABSTRACT In this article, the analysis of simulated and projected climatic changes in the Indus River Basin ( IRB) is presented. The performance of the regional climate model COSMO-CLM ( CCLM) driven by the global circulation model MPI-ESM-LR is evaluated on the ability of reproducing temperature, precipitation, and wind pattern for the period 1961-2005. There exist quantitative biases, especially in precipitation pattern, which are associated to the diverse reproduction of the atmospheric circulation by the global circulation model. The overall results show that CCLM is able to satisfyingly capture the dominant spatial characteristics in annual temperature and precipitation time series, including the warming trend, and the seasonal variations. The changes in precipitation and temperature over the IRB are projected for the mid- (2046-2065) and late-21st century (2081-2100). Relative to the baseline period (1986-2005), the average annual temperature will increase during the mid- and late-21st century over the whole basin. Extreme temperature events, i.e. heat waves, are more likely to occur. An increase in summer temperature is also projected especially in the upper IRB, where the persistent increase is likely to cause further melting of glaciers. The annual precipitation will decrease in both the mid- and late-21st century with significant spatial changes. A decrease in the monsoon precipitation is also projected, particularly in the central and southern plains of lower altitudes, which might be highly related to the reduction of wet air from the Indian Ocean and the increment in outflow to the east of the IRB. For winter and spring precipitation, except for the late-21st century under the representative climate pathway (RCP) 2.6, a decreasing trend is projected, which might be caused by the reduction of water vapour from the west. This will further weaken the availability of water resources in these seasons, especially in the upper IRB of higher altitudes. [ABSTRACT FROM AUTHOR]

The simulation results of CMIP5 (Coupled Model Inter-comparison Project phase 5) multi-model ensemble in the Indus River Basin (IRB) are compared with the CRU (Climatic Research Unit) and APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation) datasets. The systematic bias between simulations and observations is corrected by applying the equidistant Cumulative Distribution Functions matching method (EDCDFm) and high-resolution simulations are statistically downscaled. Then precipitation and temperature are projected for the IRB for the mid-21st century (2046–2065) and late 21st century (2081–2100). The results show that the CMIP5 ensemble captures the dominant features of annual and monthly mean temperature and precipitation in the IRB. Based on the downscaling results, it is projected that the annual mean temperature will increase over the entire basin, relative to the 1986–2005 reference period, with greatest changes in the Upper Indus Basin (UIB). Heat waves are more likely to occur. An increase in summer temperature is projected, particularly for regions of higher altitudes in the UIB. The persistent increase of summer temperature might accelerate the melting of glaciers, and has negative impact on the local freshwater availability. Projections under all RCP scenarios show an increase in monsoon precipitation, which will increase the possibility of flood disaster. A decreasing trend in winter and spring precipitation in the IRB is projected except for the RCP2.6 scenario which will cause a lower contribution of winter and spring precipitation to water resources in the mid and high altitude areas of the IRB. [ABSTRACT FROM AUTHOR]

Motivated by the Paris Agreement, this study aims to investigate the changes in precipitation extremes across South Asia and its five climatic zones for each 0.5 °C of warming above the pre-industrial level from 1.5 °C to 3.0 °C. In this regard, 20 global climate model outputs from the latest CMIP6 under four combinations of SSP-RCP scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) are used. Four widely used extreme precipitation indices such as number of consecutive wet days (CWD), number of heavy precipitation days (R10mm), maximum consecutive 5-day precipitation (RX5day), and the number of very wet days (R99pTOT) defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) are applied to detect the extreme events. To ensure reduced uncertainty, we used bias-corrected and multi-model ensemble outputs. Results indicate that as the degree of global warming increases, the changes in magnitudes of extreme events are projected to intensify (except for CWD) and the largest growth is found under 3.0 °C in the entire domain and its five climatic zones. The polar climatic zone is anticipated to experience the highest magnitude of changes for extreme events under all the warming scenarios. The largest percentage of area with significant increase is found under the highest warming level (3.0 °C), especially for R95pTOT (95.5% of the entire domain). For an additional 0.5 °C (2.0–1.5 °C) of warming, the anticipated precipitation-related extremes will increase by 3.5%, (RX5day), 3.6% (R10mm) and 6.6% (R99pTOT), as mitigation targets set out in the Paris Agreement. The projected intense sea surface temperature over the Arabian Sea, and large water vapor content over South Asian landmass are expected to influence precipitation-related extreme events. Notably, considering all aspects of our analysis, R99pTOT is expected to be the most predominant extreme precipitation index across the domain with continued warming. So, our findings strongly support the Paris Agreement target to limit global warming to 1.5 °C and provide a scientific basis. • Precipitation extremes over South Asia will intensify with continued warming. • The polar climatic zone will experienceprecipitation-related extremes largely. • Extra 0.5 °C (2.0–1.5 °C) will increase the extreme event by 3.5%, (RX5day), 3.6% (R10mm) and 6.6% (R99pTOT). • R99pTOT is expected to be the most predominant event for South Asia. [ABSTRACT FROM AUTHOR]

Abstract: Gridded climate data sets are widely used in the analysis, modeling and forecasting of the consequences of climate change. The objective of this study is to compare the impact of different climate datasets (station vs. gridded) on the parameterization of a hydrological model (developed using SWAT2005) of the River Xiangxi, the largest tributary of Yangtze River in the Hubei part of the Three Gorges Reservoir. Climate data used in this study derive from two sources: point daily observations from the Xingshan meteorological station (STN) and gridded (0.5°×0.5°) monthly observations of the CRU TS3.0 global dataset (CRU) downscaled to daily data using a weather generator. Data from 1970 to 1974 were applied for sensitivity analyses and autocalibration and subsequently validate hindcasts over the period 1976–1986. Despite there being only slight differences in mean annual precipitation (1003mm vs. 1052mm) between STN and CRU, the data differ more in their estimates of the number of rain days (136 vs. 112) and wet days standard deviation (11.75mm vs. 18.49mm). The mean, maximum and minimum temperatures from CRU are all lower than those from STN. SWAT parameter sensitivity analysis results show slight differences in the relative rank of the most sensitive parameters, with the differences mainly caused by the lower temperature and more intensive rainfall in CRU relative to STN. Autocalibrated parameters showed very similar values, except for the surface runoff lag coefficient which is higher for the CRU dataset compared to that derived from the STN dataset. Statistic results for discharge simulated based on CRU compared rather well with that based on STN CRU as evaluated using the standard statistics of the Nash–Sutcliffe efficiency, coefficient of determination, and percent error. The sensitivity analysis and autocalibration tool embedded in SWAT2005 is a powerful utility in hydrological modeling of the River Xiangxi, and the CRU dataset appears to be appropriate for application to hydrological modeling in this case, thus providing a good basis for climate change studies. [Copyright &y& Elsevier]

The realistic simulation and projection of the Amundsen Sea Low (ASL) are essential for understanding the Antarctic climate and global climate change. Using 14 models that participated in phase 6 of the Coupled Model Intercomparison Project (CMIP6), this study evaluates the climatological characteristics of ASL with comparison to the ERA5 reanalysis and their CMIP5 versions and assesses the future change of ASL under 1.5 °C–4 °C global warming. The climatological spatial distribution of ASL is captured reasonably but with underestimated intensity by CMIP6 multi-model ensemble (MME). Among the CMIP6 models, EC-Earth3 has most accurate representation of ASL according to the pattern correlation and biases. The seasonal variation of the ASL depth and location are found to be reasonably reproduced by the CMIP6 models. CMIP6 MME has higher skills in simulating the seasonal cycle of absolute depth and zonal migration of the ASL center. The relative central pressure of ASL is underestimated in all seasons and there is a 4-degree northward shift bias of the ASL center in austral winter, which were also evident in the CMIP5. The semiannual cycle of ASL absolute depth with two deepest pressure in April and October is also captured by CMIP6 MME. However, the observed peak of pressure between the two months occurs in June, while it delays one month and appears until July in CMIP6 MME. Compared with CMIP5, CMIP6 MME exhibit evident reduced uncertainties and overall improvement in simulating absolute depth and location of the ASL center, which might be attributed to models' capability of representing the location of Southern Hemisphere westerlies, while the biases in relative depth become even large in CMIP6 MME. In response to future warming from 1.5 °C to 4 °C above pre-industrial levels, the absolute depth of ASL will very likely deepen with larger amplitude in all seasons, while the relative depth might enhance only under high-level warmer world in austral autumn to winter. The CMIP6 MME also projects that the ASL will shift poleward constantly in austral summer and migrate southwestward during austral autumn with the rising global mean temperature. Among all the seasons, the most prominent future changes in intensity and location of ASL are found in autumn. The enhancement and poleward movement of ASL could also be identified during the Ross Sea ice advance season under 1.5 °C–4 °C global warming. The results reveal the potential of CMIP6 models in the ASL study and the impact of ASL on Antarctic climate under different global warming levels. • Latest CMIP6 models exhibit generally better representation and reduced uncertainties in reproducing Amundsen Sea Low (ASL) than CMIP5. • The improvement in ASL of CMIP6 models might be associated with more robust simulation of Southern Hemisphere westerlies. • ASL will very likely deepen and migrate poleward constantly under 1.5 °C to 4 °C global warming above pre-industrial levels. [ABSTRACT FROM AUTHOR]

In this paper, future drought characteristics (frequency, duration and intensity) over China are analysed by using four climate models from CMIP6 under the seven SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, SSP370, and SSP585) for three defined periods of 2021–2040 (near-term), 2041–2060 (mid-term) and 2081–2100 (long-term). The corresponding four climate models output from CMIP5 are also used to conduct a comparison analysis between CMIP5 and CMIP6 to address the improvements added to CMIP6 in terms of drought identification. The drought characteristics are identified by applying the standardized precipitation-evapotranspiration index (SPEI) at a 12-month timescale and run theory. The results show that CMIP6 has a robust capability to capture historical (1986–2005) drought characteristics. For the future period of 2021–2040, the decrease in precipitation and increase in potential evapotranspiration will lead to continuous dry conditions in the upper and middle Yangtze River basin and eastern Pearl River basin. Relative to the reference period, drought events will be more frequent and severe with longer durations in the Northwest River basins and middle Yangtze River basin. Furthermore, higher emissions signify a greater increase in drought frequency and intensity in the long-term period. Except for the SSP585 scenario, the lower emission scenario corresponds to the higher drought duration soon and in the mid-21st century (2021–2060). This finding is regarded as a "strange phenomenon", which cannot be detected by the previous CMIP5-based emission scenarios (RCP2.6, RCP4.5 and the unlikely pathway RCP8.5). Therefore, additional "possible future"-based scenarios (SSP119, SSP126, SSP434, SSP245, SSP460, and SSP370) should be included in extreme climate studies, especially for the near future and mid-21st century. Notably, compared with CMIP5, the reduced biases in drought characteristics are more likely associated with improvements in the representation of physical processes in climate models from CMIP6. The results of this study could provide a basis for the development of drought adaptation measures over China. • Latest CMIP6 climate models and seven SSP-RCP scenarios are used to analyze the possible changes in droughts over China. • More robust projections will be presented by CMIP6 than CMIP5, due to the better representations in physical processes. • The higher emission signifies shorter drought duration, lower precipitation and PET in mid-21st century over China. [ABSTRACT FROM AUTHOR]