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High-accuracy reanalysis products are beneficial to the retrieval and modeling of zenith tropospheric delay (ZTD) in GNSS positioning and the study of GNSS Meteorology. The performance of meteorological parameters and ZTD derived from Copernicus Arctic Regional ReAnalysis (CARRA), ERA5 and ERA5-Land over the Greenland covering the year of 2020 are comprehensively compared and analyzed in this study. Firstly, the accuracies of the atmospheric pressure, temperature and water vapor pressure retrieved from three reanalysis data are compared with the observations obtained from 6 radiosondes and 30 automatic weather stations. The results show that the accuracy of temperature data from ERA5 pressure-level in the height range of 23–30 km is higher than that of CARRA pressure-level. The atmospheric pressure, temperature and water vapor pressure from CARRA single-level are superior to those of ERA5 single-level and ERA5-Land. Secondly, the ZTD derived from CARRA, ERA5 and ERA5-Land are compared with GNSS final ZTD data from 37 UNAVCO stations. The Root Mean Square error (RMS) values of ERA5 pressure-level ZTD is 6.7 mm, showing slightly better accuracy than CARRA pressure-level ZTD of 10.7 mm. The RMS values of ZTD derived from CARRA single-level, ERA5 single-level and ERA5-Land based on the Saastamoinen model are 11.9 mm, 13.4 mm and 22.7 mm, respectively, which indicates that CARRA single-level ZTD shows best consistency with GNSS ZTD among three single-level reanalysis data. Moreover, the reanalysis-derived ZTD show obviously poorer accuracy in warm-season than that in cold-season. [ABSTRACT FROM AUTHOR]

[Display omitted] • The calibrated reanalysis data with elevation differences shows significant improvement. • The surface freezing and thawing index changes with −7.16 °C·d/year and 4.49 °C·d/year, respectively. • The current Mongolian permafrost extent is 503.9 × 103 km2 and declined by 34.65 % from 1950 to 2022. Permafrost in Mongolia is located in the transition zone between high-latitude and high-altitude permafrost regions of the Northern Hemisphere, with large temperature differences and complex subsurface characteristics. In this study, the reliability of the skin temperature data from the ERA5-Land product covering Mongolia is assessed via site observations. The ERA5-Land skin temperature dataset shows a cold bias, which is more pronounced in the cold season. Following calibration based on elevation differences, significant improvements are observed at both the annual scale (92 % improvement in RMSE (root mean square error) and 98 % improvement in MBE (mean bias error)) and the seasonal scale (78 % improvement in RMSE and 82 % improvement in MBE). Additionally, the spatial variations in the surface freezing index (SFI) and surface thawing index (STI) are most pronounced in the central and northeastern Mongolia. The SFI exhibits a significant decreasing trend of 7.16 °C·d/year, while the STI shows a significant increasing trend of 4.49 °C·d/year. Furthermore, the permafrost extent in Mongolia is simulated from 1950 to 2022 using the frost number (Fn) model and the temperature on top of permafrost (TTOP) model. The validated results indicate that the accuracy of the Fn model is relatively high, with an overall accuracy of 0.9 and a Kappa coefficient of 0.47. The permafrost extent in Mongolia has declined from 734.7 × 103 km2 in the 1950 s to 480.1 × 103 km2 in the 2010 s, with a prominent decrease of 3.2 × 103 km2/decade after 1994. According to the variations in permafrost extent during past 72 years, the Hovsgol and Khentii Mountain ranges have experienced significant permafrost degradation. [ABSTRACT FROM AUTHOR]

• Heatwaves in CA are becoming earlier, more frequent, more intense, more persistent. • Heatwaves are more frequently observed in the west but less in the central CA. • Soil moisture depletion is the main factor in formulating heatwaves in CA. Heatwaves pose increasing threats on water resources for ecosystems and human consumption especially in arid region, such as Central Asia (CA). As the hinterland of Eurasia, water scarcity and land degradation in CA have increased its vulnerability to heatwaves. While the frequency of heatwaves has been examined, more heatwave metrics such as timing are less known, possible influences of soil moisture on heatwave changes in this region are not fully understood. Using reanalysis and observational datasets, here we examine the spatial–temporal changes of heatwaves in CA during 1981–2020 and the role of various associated factors including soil moisture. The results show that the extreme heatwaves occurred more frequently in northwestern Kazakhstan. Temporally, heatwaves have been intensifying with significant increases in frequency (0.10 events/year), intensity (0.81 °C/year), and duration (0.08 days/year) since the 1980 s, and these trends have accelerated since 1997. The first yearly heatwave event tended to occur earlier in more recent decades, indicating an advanced heatwave season. We also found that the heatwave occurrences were significantly correlated with hydro-meteorological factors. In northwestern of CA, extreme heatwaves are exacerbated by a rapid decrease in soil moisture resulting from reduced precipitation and increased solar net radiation in this area. Our findings provide a scientific basis for understanding the mechanisms and predictions of heatwave in arid regions from the climate change perspective. [ABSTRACT FROM AUTHOR]

• Conventional fixed KE–I relationships tend to overestimate KE values for high rainfall intensities in the UK, disregarding regional differences in rainfall microphysical characteristics. • Disdrometer-derived local KE–I relationship significantly enhance the correlation of the estimations, particularly when employing the fully fitting relationship which exhibits the highest correlation coefficient. • ERA5-land has higher correlation and lower error compared to IMERG for estimating rainfall energy, but generally underestimates rainfall intensity. Rainfall plays a critical role in soil erosion by detaching and transporting soil particles, making it a key factor in soil erosion models. Its potential effect on soil erosion is reflected by rainfall erosivity, which considers the interaction between raindrops and soil, primarily driven by the kinetic energy carried by raindrops. Traditional approaches rely on empirical relationships between rainfall rate and kinetic energy, but they often overlook regional differences in rainfall microphysical characteristics. This study aims to compare the performance of conventional fixed and disdrometer-derived local microphysics-based unit rainfall kinetic energy–rain rate (KE–I) relationships for estimating rainfall energy using the IMERG and ERA5-land precipitation datasets. The findings reveal that conventional method using the fixed KE–I relationship do not account for regional differences in rainfall microphysical characteristics and tend to overestimate KE values for high rainfall intensities in the UK. Disdrometer-derived microphysics-based KE–I relationships improve estimation correlations, with the fully fitting formula exhibiting the highest correlation coefficient. The findings highlight the need to consider local rainfall characteristics for improved estimation accuracy and demonstrate that ERA5-land exhibits higher correlation and lower error compared to IMERG for estimating rainfall energy, but it generally underestimates rainfall intensity. This study will provide valuable insights into the applicability of using gridded precipitation datasets for large-scale soil erosion estimation and emphasizes the significance of accounting for regional variations in rainfall characteristics. [ABSTRACT FROM AUTHOR]

• A robust NDVI estimation RF model was established by inputting SAR and auxiliary environmental data. • An improved feature importance measurement method based on RF was proposed to suggest model input parameters. • An ensemble-based uncertainty quantification method was proposed to optimize NDVI time series construction results. • Temperature-related data can effectively improve the accuracy and generalization ability of the SAR-NDVI relationship model. • The weighted least squares (WLS) approach outperformed the baseline method in NDVI time series construction. Satellite-based Normalized Difference Vegetation Index (NDVI) time series data are conducive for near-real-time (NRT) monitoring of crop progress, but they commonly suffer from data gaps due to cloudy weather conditions. Fortunately, Synthetic Aperture Radar (SAR) data, which are cloud-insensitive, has high potentials to fill these data gaps. However, the relationship between NDVI and SAR data is not generic and affected by various factors (e.g., crop canopy structure and phenology, soil exposure and humidity). Therefore, it is worth exploring to evaluate the relationships and develop a more robust SAR-NDVI fusion method for NDVI time series construction. In this study, we proposed and evaluated an ensemble-based data fusion framework that accounts for these factors, based on which dense NDVI time series for crop monitoring were constructed. The framework consisted of three steps: (1) NDVI was predicted from Sentinel-1 SAR data and auxiliary environmental factors using a random forest (RF) model; (2) an improved feature importance measurement method was proposed to reveal the different contributions of multisource data to the modeling, and suggestions for selecting optimal input parameters were presented; (3) the uncertainty of the predicted data was quantified by an ensemble-based method and incorporated into a Weighted Least Squares (WLS) method to construct dense NDVI time series. Results showed that the RF models were generally improved by the auxiliary data and achieved a satisfactory accuracy of NDVI estimation (R2 > 0.93, RMSE < 0.075) for both corn and soybean crops in Southwestern Ontario, Canada. The proposed method performed well in filling data gaps in both the vegetative and reproductive stages for corn and soybean, providing a practical and promising solution for continuous crop monitoring. [ABSTRACT FROM AUTHOR]

• An intercomparison of five RS SM products with in situ measurements was carried out. • Sixteen different European ecoregions were considered as test areas. • Product performances were evaluated in zones with diverse flora, fauna and climate. • SMAPL4 and ESA CCI provided the best performance as complementary products. • ASCAT products furnished the best results among the non-blended/modeled products. Several satellite microwave-based soil moisture (SM) products have become available in recent years, making possible to produce different datasets at continental and/or a global scale. Numerous variables, including geographic heterogeneity, weather and climate, and land cover, have an impact on these products' accuracy. In this study, we account for all these variables by using the ecoregions as the scale of analysis and conducting a thorough comparison of satellite products with ground-based SM measurements. The whole Europe has been selected as study area, considering it as the aggregation of several ecoregions, each of them classified as an homogeneous zones, in terms of climate, vegetation and potentially investigable soil cover. Selected SM satellite data includes: i) the National Aeronautics and Space Administration (NASA) SMAP L4 V5; ii) the European Space Agency (ESA) SMOS-IC V2.0; iii) the H115 and H116 SM products in time-series format generated by analyzing data collected by the Advanced Scatterometer (ASCAT) aboard MetOp satellites; iv) the CGLS SSM 1 km obtained by the radar onboard ESA's SENTINEL-1 platforms; and v) the European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) "COMBINED" ESA CCI SM v06.1 product, created by merging satellite-based passive/active microwave measurements. Such a variety, in terms of technologies and main features, of publicly available online SM products (in their up-to-date version), allowed for a comprehensive intercomparison against in situ measurements of the International Soil Moisture Network (ISMN), that spreads across all of Europe within the above-mentioned terrestrial ecoregion, using four different metrics, i.e., r, bias, ubRMSE and r/r ANOM. Overall the intercomparison has underlined how SMAP L4 and ESA CCI show the best performance regardless of the considered metric. ASCAT has achieved best performances among non-modeled/blended products, while SMOS-IC has showed slightly better performance than the CGLS SSM 1 km. Using ecoregions allowed to further characterize the differences of each satellite products and identify the areas where all these products are more reliable (e.g., group (1) made by ecoregions as the Cantabrian Mixed forests or the Balkan Mixed forests; or group 2 made by ecoregions which belong to Mediterranean area) or less performing (e.g., the Central European mixed forests and the Pontic steppe). Hence, these classes have been further analyzed exploiting the ERA5-Land data, in terms of the SM, air temperature, precipitation, evaporation and Leaf area index. The research has provided helpful insights into how the performance of particular SM satellite products changes based on both the characteristics of the product under consideration and the area under investigation (i.e., one ecoregion compared to another). [ABSTRACT FROM AUTHOR]

The continuous advances in numerical modeling of the atmosphere, computing power and data assimilation techniques entail frequent updates of numerical weather prediction (NWP) models that show improved forecast skill. This circumstance leads to the recurrent delivery of revised reanalysis databases that provide weather estimates for several decades back in time by combining the latest NWP models with observations. Since climate studies and agriculture water management applications require the availability of accurate and reliable weather data, assessing the performance of reanalysis products contributes to informed choices of potential weather proxy of ground-based agrometeorological data. CERRA (Copernicus European Regional ReAnalysis) dataset is the latest regional reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF), in August 2022. CERRA is forced by the global ERA5 reanalysis, and it provides weather data with resolution of 5.5 km for the pan-European territory from 1984. For the first time in literature, this study explores the performance of CERRA data at 38 ground-based weather stations located in Sicily, an Italian region with Mediterranean climate, during the irrigation seasons 2003–2022. The objective of the study lies in evaluating CERRA performance with respect to air temperature, actual vapor pressure, wind speed and solar radiation that are input variables for assessing reference evapotranspiration, ET O , which is a key variable for quantifying irrigation volumes needed for water resources studies. The accuracy of ET O estimates depends on those input variables through the equation provided by the Food and Agriculture Organization of the United Nations (FAO), i.e., the FAO Penman-Monteith equation. Here, it is also evaluated the performance of ET O estimates by using CERRA weather inputs in the FAO Penman-Monteith equation. The results show that the performances of CERRA weather data are excellent, especially for air temperature, and this determines that CERRA ET O estimates present high accuracy and reliability with a mean PBIAS and NRMSE equal to 5.6% and 13%, respectively, over the region. Those outcomes lead to the conclusion that CERRA dataset represents a valid alternative to ground-based agrometeorological measurements and their spatial interpolation for water resource regional studies. • Regional CERRA reanalysis weather data show excellent performance in Southern Italy. • CERRA data performance is comparable to interpolated ground data. • Accurate ET O estimates are obtained by using CERRA outputs as input weather data. [ABSTRACT FROM AUTHOR]

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 (∼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 ub RMSE (0.07(±0.02) m3/m3 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. • A newly developed 9 km global SMAP-IB SM product was evaluated. • SMAP-IB and SMAP-E SM had comparable accuracy with ERA5-Land over non-forest regions. • Difference in correlation between remote sensing and model SM decreased over time. [ABSTRACT FROM AUTHOR]

• Comparison of NASA POWER and ERA5-Land for tropical climate analysis. • Both products are able to capture the climatology patterns reasonably. • Both products underestimated no / tiny and heavy precipitation. • Both products underestimated peak precipitation during the 2014–2015 flood. • It is recommended to bias correct both products before extreme analysis. Long-term gridded climate data, NASA Prediction of Worldwide Energy Resources (NASA POWER) and the fifth generation of European ReAnalysis Land Component (ERA5-Land), are important alternatives to gauges, but their effectiveness in capturing tropical precipitation and temperature extremes has not been thoroughly studied. Therefore, this study aims to evaluate the performance of NASA POWER and ERA5-Land over the Kelantan River Basin from 1981 to 2020 with 19 gauges in the aspects of climatology characteristics, overall assessment, precipitation detection capability, probability distribution function (PDF), extreme indices calculation, and the 2014–2015 flood. The important findings are as follows: (1) NASA POWER and ERA5-Land are able to reasonably capture the climatology patterns of precipitation, maximum, and minimum temperatures, with the latter performing slightly better; (2) Both products have a better correlation with gauges over coastal regions than inland mountainous regions, but they tend to underestimate precipitation and maximum temperature and overestimate minimum temperature; (3) With high POD values and moderate FAR and CSI values, they are able to detect precipitation days more effectively than non-precipitation days; (4) The PDF assessment reveals that both products overestimate moderate precipitation (1–20 mm/day) and underestimate no/tiny (0–1 mm/day), heavy (20–50 mm/day), and violent (greater than50 mm/day) precipitation; (5) With the exception of CWD and R10mm, both products tend to significantly underestimate most of the precipitation extreme indices, while a better correlation with gauges can be found in the calculations of CDD, R10mm, R20mm, TXx, and TNn; (6) Both products have a higher correlation and a better precipitation detection ability during the 2014–2015 flood, but they significantly underestimate the amount of precipitation. The findings show that NASA POWER and ERA5-Land offer valuable climate information for locations without gauges, but it is recommended to apply bias correction prior to applying them in this tropical basin. [ABSTRACT FROM AUTHOR]

• ERA5-Land is evaluated and compared with IMERG over the Tibetan Plateau with a special focus on their hydrological utility. • ERA5-Land overestimates precipitation while IMERG-F provides best estimation. • The simulation performance of ERA5-Land is comparable to IMERG-F in ungauged catchment. • ERA5-Land is recommended for long-term hydrological simulation. In the context of global warming, precipitation information over the Tibetan Plateau (TP), known as the water tower of Asia, is crucial for both atmospheric and hydrological cycle research. As important alternatives of gauge observations, the satellite-based precipitation estimations and atmospheric reanalysis precipitation prodcuts have undergone rapid development over the past few decades. However, their performance and usabilities are not effectively investigated over the TP, particularly for the latestly released products. Hence, this study presents a comprehensive evaluation of a state-of-the-art reanalysis precipitation (ERA5-Land) and compares it with two IMERG products (i.e., IMERG-E and IMERG-L) from 2001 to 2018, with a special focus on their hydrological utility. Results indicate that ERA5-Land presents a severe overestimation of precipitation amount and precipitation frequency but well reflects their spatial–temporal patterns over the entire TP. The IMERG products, particularly for IMERG-F, outperform ERA5-Land in estimating precipitation amount. In terms of hydrological utility in the upper Yellow River basin, both ERA5-Land and IMERG-E show poor simulation performance using gauge-based model parameters, which is closely related to their overestimation/underestimation of precipitation amount. In contrast, IMERG-F forced simulation performs much better due to its low systematic error. When the hydrological model is recalibrated based on individual precipitation input, the simulation performance of ERA5-Land is comparable to IMERG-F, slightly inferior to gauge observation. Therefore, it is recommended that the selection of appropriate precipitation products need to accord their own research purposes. IMERG-F provides a more accurate estimation of precipitation amount, while, employing input-specific calibrated model parameters, ERA5-Land can provide long term hydrological simulations. [ABSTRACT FROM AUTHOR]

[Display omitted] • New empirical estimates are provided for monthly R-factors over Italy for 1981–2010. • R-factor is currently larger in autumn and summer, lower in spring and winter. • 29 EURO-CORDEX projections for 2021–2050 and 2051–2080 under three RCPs are used. • Future anomalies are critical in autumn (whole Italy), and in summer (North). • There is a large variability associated with the use of an ensemble of projections. The estimation of rainfall erosivity in the Revised Universal Soil Loss Equation (RUSLE) requires long series of sub-hourly rainfall observations. Due to the endemic unavailability of this information, at least with the required degree of detail and coverage, empirical models are frequently used for the estimation of rainfall erosivity basing on easily retrievable rainfall values at the monthly or annual scale. In this paper, an empirical model is calibrated for Italy to bridge the annual to monthly scale gap by means of 10-year series of monthly rainfall observations at 171 Italian rain gauges, and then applied using the ERA5-Land gridded rainfall dataset to obtain maps of monthly and seasonal rainfall erosivity covering the reference period 1981–2010. Successively, 29 EURO-CORDEX bias-corrected rainfall projections for two future horizons (2021–2050 and 2051–2080 under RCP 2.6, 4.5 and 8.5) are used to obtain expected anomalies of seasonal rainfall erosivity over Italy with respect to the reference period. Statistical and visual analysis of results shows that positive percentage anomalies are expected in large portions of the country, especially under RCP 2.6 but with significant increases expected also under RCPs 4.5 and 8.5, especially in autumn and, secondarily, in summer. This intra-annual rainfall erosivity anomaly pattern entails the existence of increasing trends in the erosion hazard that cannot be captured by empirical models only relying on annual rainfall. The position of those results in the related literature is deeply discussed, providing significant insights on uncertainty sources and possible future research developments. [ABSTRACT FROM AUTHOR]

The elevation-dependent surface warming (i.e., the surface temperature trend changes systematically with elevation, EDW) over the Tibetan Plateau (TP) largely affects the hydrological and ecological security in Asia. Although many physical mechanisms have been attributed to EDW, the relative importance of each mechanism remains unclear. Using land surface temperatures (LSTs) and other variables from the land component of the fifth generation of the European ReAnalysis product (ERA5-Land), we quantify the contributions of atmospheric and surface factors associated with the surface energy balance to LST changes at different elevations on the TP and its surrounding regions, and further explore the mechanisms of EDW. The results show that along with a positive EDW (enhanced high-elevation LST increases) up to approximately 4500 m, a negative EDW (reduced high-elevation LST increases) above 5000 m appeared in both daytime and nighttime and during all seasons. A cooling LST trend even occurred above 5200 m during daytime in summer. This negative EDW is mainly caused by the increased cloud cover fraction and evapotranspiration (heat storage) during daytime (nighttime) in summer, while in winter, it is mainly caused by the increased negative (decreased positive) contribution of heat storage during daytime (nighttime). Overall, EDW was controlled by atmospheric factors, while surface factors contributed variably and constrained or enhanced the LST changes depending on the season and time of day. In summer, cloud-radiation feedback and evapotranspiration processes are the main mechanisms controlling EDW, while in winter, snow/ice albedo feedback and the sensitivity of downward longwave radiation to the specific humidity are the main EDW mechanisms. Our results provide important insights into EDW over the TP. • The dominant mechanisms for EDW and reasons for the negative EDW at high elevations are identified. • In summer the negative EDW at high elevations is mainly caused by the increased cloud and ET (G) during daytime (nighttime). • In winter the negative EDW at high elevations is caused by the increased negative (decreased positive) G during daytime (nighttime). • In summer, cloud-radiation feedback and evapotranspiration processes are the main mechanisms controlling EDW. • In winter snow/ice albedo feedback and the sensitivity of DLR to specific humidity are the main mechanisms controlling EDW. [ABSTRACT FROM AUTHOR]

Global surface temperature has been setting new record highs in the recent decades, imposing increasing environmental challenges for societies and ecosystems worldwide. Global warming rates of the 20th century have been documented by a number of studies, nevertheless, the warming rates in the most recent decades in the 21st century are of particular interest for understanding the ongoing climate change. Analyzing temperature trends demands data with high spatial resolution and broad geographical coverage to allow for analyzing trends and changes on a regional scale. Land Surface Temperature data from NASA MODIS with global resolution of 0.05° and Skin Temperature data from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5-Land reanalysis with global resolution of 0.1° fulfill these demands. In this study, we analyze the remote-sensing-based MODIS data to estimate land surface temperature change rates over the period 2001–2020 in global, continental, and pixel-wise scales with statistical significance indicated. The model-based ERA5-Land data are also analyzed in parallel, extending the period of analysis back to 1981. These two independently-sourced datasets, one from satellites above the atmosphere and one from combining surface modeling and observations, are shown to produce highly consistent results. It is revealed that the trends in the shorter period 2001–2020 are spatially conforming to the trends in the longer period 1981–2020 despite the shorter time length. For the period 2001–2020, we show that the global average land surface temperature rate of change was 0.26 °C-0.34 °C per decade, with substantially different warming rates in different regions. The Arctic, Europe, and Russia show statistically significant warming in both datasets. The Arctic, in particular, warmed at a rate 2.5–2.8 times the global average, and data in the 40-year period 1981–2020 suggest that warming is accelerating in almost all the continents or large regions. Most noticeably, the two independent datasets both indicate that Arctic permafrost regions had the world's highest warming rate at the onset of the 21st century, reaching >2 °C per decade in some areas. • Decadal warming rates were analyzed in global, continental, and pixel scales. • Land temperature from MODIS and from ERA5-Land were analyzed and found consistent. • Continents were warming at substantially different rates in the past decades. • Arctic permafrost areas were the fastest warming land on Earth during 2001-2020. • ERA5-Land data reveal that warming was accelerating in the 40-yr period 1981-2020. [ABSTRACT FROM AUTHOR]

Precipitation data availability plays a crucial role in many climatic, hydrological and agricultural-related applications. In this study, the use of alternative data sources (i.e. interpolation methods and ERA5-Land reanalysis data) was combined for improving the spatially distributed precipitation estimates at the Simeto river basin, located in Eastern Sicily (Italy). A total of 51 rain gauges were used to generate a monthly precipitation dataset for the reference period 2002–2019. Among the 6 tested interpolation methods, Natural Neighbour was the method that predicted precipitation the best at monthly level with a Distance between Indices of Simulation and Observation (DISO) of 0.51. Radial Basis Functions and Inverse Distance Weighting provided the highest precipitation accuracies, respectively, for winter and autumn (with DISO values of 0.44 and 0.50, respectively), and for spring and summer seasons (with DISO values of 0.50 and 0.63, respectively). Underestimations on the ERA5-Land precipitation estimates were observed when compared to the most accurate interpolation methods both at monthly (25%) and seasonal temporal scales (21% in winter and summer, 36% in autumn), with the exception for spring. The performance was significantly improved when the interpolation estimates were corrected with local observations (with RMSD values ranging from 35.29 mm to 26.46 mm at monthly scale, and from 23.33–55.34 mm to 23.15–37.88 mm at seasonal level). The spatial distribution of the estimation errors associated to precipitation obtained from ERA5-Land reanalysis revealed a significant positive correlation (p value <0.05) with the altitude variation in each ERA5-Land cell within the basin under study. These results confirm the good performance on the combined use of alternative precipitation data sources, while adjustments are required to reduce site-specific uncertainties due to local microclimatic conditions occurring at the basin scale. • Monthly precipitation was estimated most accurately by deterministic interpolators. • ERA5-Land underestimated precipitation obtained by deterministic interpolators. • BIAS correction procedure improved the accuracy of the precipitation estimates. • Topography heterogeneity worsened the accuracy of ERA5-Land precipitation estimates. [ABSTRACT FROM AUTHOR]

Snow is a major element of the cryosphere with significant impact on the Earth's water cycle and global energy budget. Acquiring consistent and long time series data on the spatial extent of snow cover doubtlessly plays a key role in our understanding and modeling of the current and future environmental dynamics. Remote sensing offers a powerful tool for continuous retrieval of snow cover information by utilizing snow's contrasting reflectance characteristics at optical wavelengths. The pre-operational H35 covers the Northern Hemisphere, and it is the successor of the operational Pan-European H12 daily fractional snow-covered area (fSCA) product at ~1 km. Both products are developed through the Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) project of EUMETSAT by exploiting AVHRR channels. This study is focused on developing an alternative fully data-driven H35 product with improved accuracy using a machine learning (ML)-based approach. Multivariate adaptive regression splines (MARS) algorithm is trained by using AVHRR reflectance data as well as the well-known snow and vegetation indices (i.e., NDSI and NDVI) to generate the new version of H35 fSCA product. The reference fSCA maps required for the training of MARS models are obtained from the higher resolution Sentinel 2 multispectral imagery. The MARS-based fSCA models are validated against an initial test dataset composed of 15 Sentinel 2 scenes over European Alps, Tatra Mountain Range, and Turkey. The final MARS-H35 product is then rigorously assessed over the whole Northern Hemisphere within a temporal domain spanning from Nov 2018 to Nov 2019. The quantitative testing process involves the use of reference data in both continuous and dichotomous scales: i) Sentinel 2 derived reference fSCA maps, ii) ERA5-Land snow depth data, iii) MODIS MOD10A1 NDSI snow cover data, and finally iv) in-situ snow depth data. Additionally, qualitative assessment is also performed by visually comparing MARS-H35/MODIS false-color and MARS-H35/Sentinel 2-derived reference fSCA image pairs over various geographic regions. The overall results indicate that: i) the proposed MARS-H35 fSCA product overperforms the original H35, and ii) it has higher capability in detecting the fine variations in the extent of snow cover, especially across the fringes of the slopes in complex mountainous terrains. • Fully data-driven semi-global fSCA product (MARS-H35) from AVHRR data is developed. • Accuracy of H-SAF fSCA product H35 is improved by machine learning. • Validation of MARS-H35 using Sentinel 2, MODIS, ERA5-Land and in-situ data is done. • MARS-H35 exhibits good agreement with the reference data. • MARS-H35 performs better in catching the variations in the spatial extent of snow. [ABSTRACT FROM AUTHOR]

Long-term and high-quality surface soil moisture (SSM) and root-zone soil moisture (RZSM) data is crucial for understanding the land-atmosphere interactions of the Qinghai-Tibet Plateau (QTP). More than 40% of QTP is covered by permafrost, yet few studies have evaluated the accuracy of SSM and RZSM products derived from microwave satellite, land surface models (LSMs) and reanalysis over that region. This study tries to address this gap by evaluating a range of satellite and reanalysis estimates of SSM and RZSM in the thawed soil overlaying permafrost in the QTP, using in-situ measurements from sixteen stations. Here, seven SSM products were evaluated: Soil Moisture Active Passive L3 (SMAP-L3) and L4 (SMAP-L4), Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), European Space Agency Climate Change Initiative (ESA CCI), Advanced Scatterometer (ASCAT), and the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land). We also evaluated three RZSM products from SMAP-L4, ERA5-Land, and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah). The assessment was conducted using five statistical metrics, i.e. Pearson correlation coefficient (R), bias, slope, Root Mean Square Error (RMSE), and unbiased RMSE (ubRMSE) between SSM or RZSM products and in-situ measurements. Our results showed that the ESA CCI, SMAP-L4 and SMOS-IC SSM products outperformed the other SSM products, indicated by higher correlation coefficients (R) (with a median R value of 0.63, 0.44 and 0.57, respectively) and lower ubRMSE (with a median ubRMSE value of 0.05, 0.04 and 0.07 m3/m3, respectively). Yet, SSM overestimation was found for all SSM products. This could be partly attributed to ancillary data used in the retrieval (e.g. overestimation of land surface temperature for SMAP-L3) and to the fact that the products (e.g. LPRM) more easily overestimate the in-situ SSM when the soil is very dry. As expected, SMAP-L3 SSM performed better in areas with sparse vegetation than with dense vegetation covers. For RZSM products, SMAP-L4 and GLDAS-Noah (R = 0.66 and 0.44, ubRMSE = 0.03 and 0.02 m3/m3, respectively) performed better than ERA5-Land (R = 0.46; ubRMSE = 0.03 m3/m3). It is also found that all RZSM products were unable to capture the variations of in-situ RZSM during the freezing/thawing period over the permafrost regions of QTP, due to large deviation for the ice-water phase change simulation and the lack of consideration for unfrozen-water migration during freezing processes in the LSMs. • SSM and RZSM products are evaluated over the permafrost region of QTP • Uncertainties of SSM and RZSM products are analyzed • SSM products generally overestimate the in-situ measurements • Large errors were found in RZSM for both satellite-based and reanalysis products [ABSTRACT FROM AUTHOR]

• Machine learning was suitable to predict the occurrence of B. oleaee infestation. • Predicting B. oleae infestation is mandatory to adopt preventive IPM strategies. • Long-term data on infestation were related to geographic and bioclimatic indices. • Infestation of B. oleae gradually decreased with increasing distance from the sea. Predicting the occurrence of B. oleae infestation is needed in sustainable olive tree growing, for the application of preventive control strategies. In this study, machine learning (ML) algorithms were tested to predict the occurrence of B. oleae infestation. Regional-based and long-term (2002–2019) dataset on infestation collected by trained technicians in the sampling network (Tuscany, central Italy) was related to geographic variables, bioclimatic indices and the frequency of infestation in the previous year. Five ML algorithms were compared. Data collected from 2002 to 2017 were divided with an 80/20 proportion in "training" and "test 20%", considering the group "farm ID × year". In addition, data collected in 2018 and 2019 ("test 18–19") were also tested. The algorithm C5.0 was selected as the most suitable algorithm (AUC = 0.9) and used to predict the occurrence of B. oleae infestation. The following results were obtained: (i) a negative effect on the probability of infestation of the distance from the sea of the olive groves and of the frequency of infestation of the previous year and (ii) a positive effect of doy (day of the year), cumulative degree days and mean air temperature of the previous winter. The occurrence of infestation was predicted with an accuracy of 0.8 on "test 20%" and of 0.7 on "test 18–19". The potential of this methodology to predict the occurrence of B. oleae infestation in a short-time step, e.g., one week, needs to be tested in a real application, running the model weekly. [ABSTRACT FROM AUTHOR]