The Mediterranean region experiences the annual destruction of thousands of hectares due to climatic conditions. This study examines forest fires in Turkiye's Antalya region, a Mediterranean high-risk area, from 2000 to 2023, analyzing 26 fires that each damaged over 50 hectares. Fire danger maps created from fire weather indexes (FWI) indicated that 85.7% of the analyzed fire areas were categorized within the high to very extreme danger categories. The study evaluated fire danger maps from EFFIS FWI and ERA5 FWI, both derived from meteorological satellite data, for 14 forest fires between 2019 and 2023. With its better spatial resolution, it was found that EFFIS FWI had a higher correlation (0.98) with in situ FWIs. Since FWIs are calculated from temperature and fire moisture subcomponents, the correlations of satellite-based temperature (MODIS Land Surface Temperature-LST) and soil moisture (SMAP) data with FWIs were investigated. The in situ FWI demonstrated a positive correlation of 0.96 with MODIS LST, 0.92 with EFFIS FWI, and 0.93 with ERA5 FWI. The negative correlation between all FWIs and SMAP soil moisture highlighted a strong relationship, with the highest observed in in situ FWI (-0.93) and -0.90 and -0.87 for EFFIS FWI and ERA5 FWI, respectively.

A number of global surface soil moisture (SM) datasets have been retrieved from the L-band frequency Soil Moisture Active Passive (SMAP) and the Soil Moisture and Ocean Salinity (SMOS) missions to study the terrestrial water, energy, and carbon cycles. This paper presents the performance of the recently developed 9 km global SMAP product (hereafter SMAP-INRAE-BORDEAUX, SMAP-IB9). The product retrieves SM from the 9 km SMAP radiometric products using the forward model (L-MEB, L-band Microwave Emission of the Biosphere) of SMOS INRA-CESBIO (SMOS-IC) and SMOS L2 algorithms. We inter-compared SMAP-IB9 with two other products with a similar grid resolution (similar to 10 km): the SMAP Enhanced Level-3 SM dataset (SMAP-E) and the enhanced global dataset for the land component of the fifth generation of European reanalysis (ERA5-Land) with the main objective of assessing the discrepancy in accuracy between remotely sensed and model SM datasets. We found that ERA5-Land and SMAP-IB9 SM had the overall highest correlations (R = 0.62(+/- 0.15) for ERA5-Land vs. 0.60 (+/- 0.17) for SMAP-IB9 and 0.50(+/- 0.15) for SMAP-E) by comparing with the International Soil Moisture Network (ISMN) in-situ measurements from 22 networks. ERA5-Land showed better performances in the forest areas where SMAP-IB9 and SMAP-E still showed high potential in detecting the time variations of the observed SM, particularly in terms of median correlation values (0.62(+/- 0.18) for SMAP-IB9 vs. 0.66(+/- 0.16) for ERA5-and). The discrepancy in R between satellite and model SM products that were reported in some past studies has decreased to statistically insignificant levels over time. For instance, in the non-forest areas, we found that the latest versions of the SMAP SM products (SMAP-E and SMAP-IB9) had relatively comparable performances with ERA5-Land with regard to median ubRMSE (0.07(+/- 0.02) m(3)/m(3) for both SMAP-E and ERA5-Land) and R (0.59 (+/- 0.16) for SMAP-IB9 vs. 0.61(+/- 0.15) for ERA5-Land), respectively.

Many studies have focused on elevation-dependent warming (EDW) across high mountains, but few studies have examined both EDW and LDW (latitude-dependent warming) on Antarctic warming. This study analyzed the Antarctic amplification (AnA) with respect to EDW and LDW under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 from Coupled Model Intercomparison Project Phase 6 (CMIP6) during the period 2015-2100. The results show that the AnA appears under all socioeconomic scenarios, and the greatest signal appears in austral autumn. In the future, Antarctic warming is not only elevation-dependent, but also latitude-dependent. Generally, positive EDW of mean temperature (T-mean), maximum temperature (T-max) and minimum temperature (T-min) appear in the range of 1.0-4.5 km, and the corresponding altitudinal amplification trends are 0.012/0.012/0.011 (SSP1-2.6)- 0.064/0.065/0.053 (SSP5-8.5) degrees C decade(-1)center dot km(-1). Antarctic EDW demonstrates seasonal differences, and is strong in summer and autumn and weak in winter under SSP3-7.0 and SSP5-8.5. For T(mea)n, T-max and T-min, the feature of LDW is varies in different latitude ranges, and also shows seasonal differences. The strongest LDW signal appears in autumn, and the warming rate increases with increasing latitude at 64-79 degrees S under SSP1-2.6. The similar phenomenon can be observed at 68-87 degrees S in the other cases. Moreover, the latitude component contributes more to the warming of T-mean and T-max relative to the corresponding altitude component, which may relates to the much larger range of latitude (similar to 2600 km) than altitude (similar to 4.5 km) over Antarctica, while the EDW contributes more warming than LDW in the changes in T-min in austral summer. Moreover, surface downwelling longwave radiation, water vapor and latent heat flux are the potential factors influencing Antarctic EDW, and the variation in surface downwelling longwave radiation can also be considered as an important influencing factor for Antarctic LDW. Our results provide preliminary insights into EDW and LDW in Antarctica.

The Granger Causality (GC) statistical test explores the causal relationships between different time series variables. By employing the GC method, the underlying causal links between environmental drivers and global vegetation properties can be untangled, which opens possibilities to forecast the increasing strain on ecosystems by droughts, global warming, and climate change. This study aimed to quantify the spatial distribution of four distinct satellite vegetation products' (VPs) sensitivities to four environmental land variables (ELVs) at the global scale given the GC method. The GC analysis assessed the spatially explicit response of the VPs: (i) the fraction of absorbed photosynthetically active radiation (FAPAR), (ii) the leaf area index (LAI), (iii) solar-induced fluorescence (SIF), and, finally, (iv) the normalized difference vegetation index (NDVI) to the ELVs. These ELVs can be categorized as water availability assessing root zone soil moisture (SM) and accumulated precipitation (P), as well as, energy availability considering the effect of air temperature (T) and solar shortwave (R) radiation. The results indicate SM and P are key drivers, particularly causing changes in the LAI. SM alone accounts for 43%, while P accounts for 41%, of the explicitly caused areas over arid biomes. SM further significantly influences the LAI at northern latitudes, covering 44% of cold and 50% of polar biome areas. These areas exhibit a predominant response to R, which is a possible trigger for snowmelt, showing more than 40% caused by both cold and polar biomes for all VPs. Finally, T's causality is evenly distributed amongst all biomes with fractional covers between similar to 10 and 20%. By using the GC method, the analysis presents a novel way to monitor the planet's ecosystem, based on solely two years as input data, with four VPs acquired by the synergy of Sentinel-3 (S3) and 5P (S5P) satellite data streams. The findings indicated unique, biome-specific responses of vegetation to distinct environmental drivers.

In many high altitude river basins, the hydro-climatic regimes and the spatial and temporal distribution of precipitation are little known, complicating efforts to quantify current and future water availability. Scarce, or non-existent, gauged observations at high altitudes coupled with complex weather systems and orographic effects further prevent a realistic and comprehensive assessment of precipitation. Quantifying the contribution from seasonal snow and glacier melt to the river runoff for a high altitude, melt dependent region is especially difficult. Global scale precipitation products, in combination with precipitation-runoff modelling may provide insights to the hydro-climatic regimes for such data scarce regions. In this study two global precipitation products; the high resolution (0.1 degrees x 0.1 degrees), newly developed ERA5-Land, and a coarser resolution (0.55 degrees x 0.55 degrees) JRA-55, are used to simulate snow/glacier melts and runoff for the Gilgit Basin, a sub-basin of the Indus. A hydrological precipitation-runoff model, the Distance Distribution Dynamics (DDD), requires minimum input data and was developed for snow dominated catchments. The mean of total annual precipitation from 1995 to 2010 data was estimated at 888 mm and 951 mm by ERA5-Land and JRA-55, respectively. The daily runoff simulation obtained a Kling Gupta efficiency (KGE) of 0.78 and 0.72 with ERA5-Land and JRA-55 based simulations, respectively. The simulated snow cover area (SCA) was validated using MODIS SCA and the results are quite promising on daily, monthly and annual scales. Our result showed an overall contribution to the river flow as about 26% from rainfall, 37-38% from snow melt, 31% from glacier melt and 5% from soil moisture. These melt simulations are in good agreement with the overall hydro-climatic regimes and seasonality of the area. The proxy energy balance approach in the DDD model, used to estimate snow melt and evapotranspiration, showed robust behaviour and potential for being employed in data poor basins. (c) 2021 Published by Elsevier B.V.

Global warming increases the frequency and intensity of climate extremes, but the changes in climate extremes over the Antarctic Ice Sheet (AIS) during different periods are unknown. Changes in surface temperature extreme indices (TN10p, TX10p, TN90p, TX90p, CSDI, WSDI, TNn, TNx, TXn, TXx and DTR) are assessed during 2021-2050 and 2071-2100 under SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, based on the multi-model ensemble mean (MMEM) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The extreme indices, excluding TXn and DTR, illustrate the opposite trend in the two periods in SSP1-2.6 over the AIS. Generally, the changes in extreme indices reflect the continued warming over AIS in the future, and the warming is projected to intensify in SSP3-7.0 and SSP5-8.5. The variations in the extreme indices exhibit regional differences. The Antarctic Peninsula displays rapid changes in TNn, TXn and DTR. In SSP5-8.5, the magnitudes of all climate index tendencies are greater during 2071-2100 than 2021-2050. The variations in TX10p, TX90p, TN10p, TN90p, WSDI and CSDI are faster in the Antarctic inland than in the other regions over the AIS. However, the decrease in the DTR is concentrated along the AIS coast and extends to the interior region, whereas the increasing trend occurs in the Antarctic inland. In West AIS, TX90p and TN90p rapidly increase during 2021-2050, whereas the rapid changing signals disappear in this region in 2071-2100. The dramatic changes in TNn, TXn and DTR occur at the Ross Ice Shelf during 2071-2100, indicating an increased risk of collapse. For TNx and TXx, the degree of warming in the later part of the 21st century is divided by the transantarctic mountains, and greater changes appear on the eastern side. Generally, Antarctic amplification of TNn, TXn and DTR is observed except under SSP1-2.6. In addition, TNx and TXx amplifications occur in SSP3-7.0 and SSP5-8.5.

土壤冻融交替是陆地表层极其重要的物理过程,土壤冻融状态的频繁变化对地气能量交换、地表径流、植被生长、生态系统及土壤碳氮循环等均具有重要的影响。本文基于1981—2019年ERA5-LAND逐小时土壤温度数据,借助GIS空间分析功能,利用Python编程处理分析了中国东北地区近地表土壤冻融状态的时空变化特征。结果表明:从不同冻融状态起始日期的空间分布来看,近地表不同阶段的起始日期主要受纬度和地形的影响,具有明显的纬度地带性和垂直地带性。春季冻融过渡期和完全融化期的起始日期由东南向西北均呈逐渐推迟趋势,而秋季冻融过渡期与完全冻结期起始日期则由东南向西北随纬度升高越来越早。就不同冻融状态发生天数的空间分布而言,研究区南部春季冻融过渡期发生天数多于北部,西部多于东部,年均发生天数均在30 d以内;秋季发生冻融的天数空间差异不大,研究区一半以上的地区年均发生天数在10 d以内。完全融化期发生天数最多,从东南向西北呈逐渐减少趋势,年均发生天数主要介于150～240 d之间;完全冻结期发生天数则由南向北日益增多,其空间分布表现为一向南开口的簸箕形,各地年均发生天数集中于90～180 d之间。从时间变...

As the Water Tower of Asia and The Third Pole of the world, the Qinghai-Tibet Plateau (QTP) shows great sensitivity to global climate change, and the change in its terrestrial water storage has become a focus of attention globally. Differences in multi-source data and different calculation methods have caused great uncertainty in the accurate estimation of terrestrial water storage. In this study, the Yarlung Zangbo River Basin (YZRB), located in the southeast of the QTP, was selected as the study area, with the aim of investigating the spatio-temporal variation characteristics of terrestrial water storage change (TWSC). Gravity Recovery and Climate Experiment (GRACE) data from 2003 to 2017, combined with the fifth-generation reanalysis product of the European Centre for Medium-Range Weather Forecasts (ERA5) data and Global Land Data Assimilation System (GLDAS) data, were adopted for the performance evaluation of TWSC estimation. Based on ERA5 and GLDAS, the terrestrial water balance method (PER) and the summation method (SS) were used to estimate terrestrial water storage, obtaining four sets of TWSC, which were compared with TWSC derived from GRACE. The results show that the TWSC estimated by the SS method based on GLDAS is most consistent with the results of GRACE. The time-lag effect was identified in the TWSC estimated by the PER method based on ERA5 and GLDAS, respectively, with 2-month and 3-month lags. Therefore, based on the GLDAS, the SS method was used to further explore the long-term temporal and spatial evolution of TWSC in the YZRB. During the period of 1948-2017, TWSC showed a significantly increasing trend; however, an abrupt change in TWSC was detected around 2002. That is, TWSC showed a significantly increasing trend before 2002 (slope = 0.0236 mm/month, p < 0.01) but a significantly decreasing trend (slope = -0.397 mm/month, p < 0.01) after 2002. Additional attribution analysis on the abrupt change in TWSC before and after 2002 was conducted, indicating that, compared with the snow water equivalent, the soil moisture dominated the long-term variation of TWSC. In terms of spatial distribution, TWSC showed a large spatial heterogeneity, mainly in the middle reaches with a high intensity of human activities and the Parlung Zangbo River Basin, distributed with great glaciers. The results obtained in this study can provide reliable data support and technical means for exploring the spatio-temporal evolution mechanism of terrestrial water storage in data-scarce alpine regions.

The European Center for Medium-Range Weather Forecasts (ECMWF) released its latest reanalysis dataset named ERA5 in 2017. To assess the performance of ERA5 in Antarctica, we compare the near-surface temperature data from ERA5 and ERA-Interim with the measured data from 41 weather stations. ERA5 has a strong linear relationship with monthly observations, and the statistical significant correlation coefficients (p < 0.05) are higher than 0.95 at all stations selected. The performance of ERA5 shows regional differences, and the correlations are high in West Antarctica and low in East Antarctica. Compared with ERA5, ERA-Interim has a slightly higher linear relationship with observations in the Antarctic Peninsula. ERA5 agrees well with the temperature observations in austral spring, with significant correlation coefficients higher than 0.90 and bias lower than 0.70 degrees C. The temperature trend from ERA5 is consistent with that from observations, in which a cooling trend dominates East Antarctica and West Antarctica, while a warming trend exists in the Antarctic Peninsula except during austral summer. Generally, ERA5 can effectively represent the temperature changes in Antarctica and its three subregions. Although ERA5 has bias, ERA5 can play an important role as a powerful tool to explore the climate change in Antarctica with sparse in situ observations.

Soil moisture is a key variable in the process of land-atmosphere energy and water exchange. Currently, there are a large number of operational satellite-derived soil moisture products and reanalysis soil moisture products available. However, due to the lack of in situ soil moisture measurements over the Tibetan Plateau (TP), their accuracy and applicability are unclear. Based on the in situ measurements of the soil moisture observing networks established at Maqu, Naqu, Ali, and Shiquanhe (Sq) by the Institute of Tibetan Plateau Research, the Chinese Academy of Sciences, the Northwest Institute of Eco-Environmental Resources, the Chinese Academy of Sciences and the University of Twente over the TP, the accuracy and reliability of the European Space Agency Climate Change Initiative Soil Moisture version 4.4 (ESA CCI SM v4.4) soil moisture products and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5) soil moisture product were evaluated. The spatiotemporal distributions and interannual variations of the soil moisture were analyzed. Further, the climatological soil moisture changing trends across the TP were explored. The results show that with regard to the whole plateau, the combined product performs the best (unbiased root-mean-square error (ubRMSE) = 0.043 m(3)/m(3), R = 0.66), followed by the active product (ubRMSE = 0.048 m(3)/m(3), R = 0.62), the passive product (ubRMSE = 0.06 m(3)/m(3), R = 0.61), and the ERA5 soil moisture product (ubRMSE = 0.067 m(3)/m(3), R = 0.52). Considering the good spatiotemporal data continuity of the ERA5 soil moisture product, the ERA5 soil moisture data from 1979 to 2018 were used to analyze the climatological soil moisture changing trend for the entire TP surface. It was found that there was an increasing trend of soil moisture across the TP, which was consistent with the overall trends of increasing precipitation and decreasing evaporation. Moreover, the shrinkage of the cryosphere in conjunction with the background TP warming presumably contribute to soil moisture change.