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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]

This paper describes the extension of the previously CMIP5 based high-resolution climate projections with additional ones based on the more recent climate projections from the CMIP6 experiment. The downscaling method and data processing are the same but the reference dataset is now the ERA5-Land reanalysis (compared to ERA5 previously) allowing to increase the resolution of the new downscaled projections from 0.25° x 0.25° to 0.1°x 0.1°. The extension comprises 5 climate models and includes 2 surface variables at daily resolution: air temperature and precipitation. Three greenhouse gas emissions scenarios are available: Shared Socioeconomic Pathways with mitigation policy (SSP1-2.6), an intermediate one (SSP2-4.5), and one without mitigation (SSP5-8.5)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that have or could be perceived to have influenced the work reported in this article., (© 2022 The Author(s).)

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]

Nowadays, the Mediterranean region is generally recognized as a climate change hot spot given its strong response to global warming, with relevant impacts on rainfall amount and distribution. Within this context, in this work the temporal variability of rainfall at annual, seasonal and monthly scale was analyzed in Italy using rainfall data extracted from the reanalysis dataset ERA5-Land during the period 1950–2020. In particular, rainfall trend magnitude and significance have been estimated by means of non-parametric tests applied to 3215 grid points falling within the Italian territory. The main results of this analysis evidenced only a few relevant trends at the annual scale, mostly involving northern Italy (positive trend) and the Sardinia region (negative trend). At seasonal scale, the results showed a marked negative trend in winter, characterizing almost all the Italian territory, while in the other seasons a positive trend was identified in the majority of grid points, especially in the Alps. Finally, at the monthly scale, September was identified as the month of the year with the highest percentage of grid points with positive trends mainly located in central, southern, and north-eastern Italy. [ABSTRACT FROM AUTHOR]

Temperature is one of the most important meteorological variables for global climate change and human sustainable development. It plays an important role in agroclimatic regionalization and crop production. To date, temperature data have come from a wide range of sources. A detailed understanding of the reliability and applicability of these data will help us to better carry out research in crop modelling, agricultural ecology and irrigation. In this study, temperature reanalysis products produced by the China Meteorological Administration Land Data Assimilation System (CLDAS), the U.S. Global Land Data Assimilation System (GLDAS) and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version5 (ERA5)-Land are verified against hourly observations collected from 2265 national automatic weather stations (NAWS) in China for the period 2017–2019. The above three reanalysis systems are advanced and widely used multi-source data fusion and re-analysis systems at present. The station observations have gone through data Quality Control (QC) and are taken as "true values" in the present study. The three reanalysis temperature datasets were spatial interpolated using the bi-linear interpolation method to station locations at each time. By calculating the statistical metrics, the accuracy of the gridded datasets can be evaluated. The conclusions are as follows. (1) Based on the evaluation of temporal variability and spatial distribution as well as correlation and bias analysis, all the three reanalysis products are reasonable in China. (2) Statistically, the CLDAS product has the highest accuracy with the root mean square error (RMSE) of 0.83 °C. The RMSEs of the other two reanalysis datasets produced by ERA5-Land and GLDAS are 2.72 °C and 2.91 °C, respectively. This result indicates that the CLDAS performs better than ERA5-Land and GLDAS, while ERA5-Land performs better than GLDAS. (3) The accuracy of the data decreases with increasing elevation, which is common for all of the three products. This implies that more caution is needed when using the three reanalysis temperature data in mountainous regions with complex terrain. The major conclusion of this study is that the CLDAS product demonstrates a relatively high reliability, which is of great significance for the study of climate change and forcing crop models. [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]

It is important to accurately assess agricultural drought because of its harmful impacts on the ecosystem and economy. Soil moisture reanalysis datasets provide an important way to assess agricultural drought. In this study, the ERA5-Land surface and subsurface soil moisture was used to estimate the soil water deficit index (SWDI) in four southern provinces of China. The ERA5-Land dataset was evaluated with in situ soil moisture observations from agrometeorological stations. Agricultural drought was assessed for three climate zones at a weekly scale from 2017 to 2019 and was compared with the atmospheric water deficit (AWD). It was found that both ERA5-Land soil moisture and the derived SWDI have relatively high accuracy, and the wet bias in the ERA5-Land dataset can be reduced by the calculation of the SWDI. The subsurface layer has better performance than the surface layer in drought monitoring, though they are highly correlated. Different climate zones demonstrate different drought periods and drought severity, and the temperate climate zone with no dry season has less droughts. The most severe droughts with the largest spatial extent occurred in the early winter, especially in 2019. Differences in the SWDI and AWD are mainly shown in southwestern Yunnan. The results of this study have important reference values for drought risk management. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future. [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]

Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0. [ABSTRACT FROM AUTHOR]