The NASA NEX-DCP30 dataset is comprised of downscaled climate scenarios for the conterminous United States that are derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5, see Taylor et al. 2012) and across the four greenhouse gas emissions scenarios known as …

ERA5 is the fifth generation ECMWF atmospheric reanalysis of the global climate. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 replaces its predecessor, the ERA-Interim reanalysis. ERA5 MONTHLY provides aggregated values for each month for seven ERA5 climate reanalysis parameters: 2m air temperature, 2m dewpoint temperature, total precipitation, mean sea level pressure, surface pressure, 10m u-component of wind and 10m v-component of wind. Additionally, monthly minimum and maximum air temperature at 2m has been calculated based on the hourly 2m air temperature data. Monthly total precipitation values are given as monthly sums. All other parameters are provided as monthly averages. ERA5 data is available from 1940 to three months from real-time, the version in the EE Data Catalog is available from 1979. More information and more ERA5 atmospheric parameters can be found at the Copernicus Climate Data Store. Provider's Note: Monthly aggregates have been calculated based on the ERA5 hourly values of each parameter.

Climate reanalysis and climate projection datasets offer the potential for researchers, students and instructors to access physically informed, global scale, temporally and spatially continuous climate data from the latter half of the 20th century to present, and explore different potential future climates. While these data are of significant use to research and teaching within biological, environmental and social sciences, potential users often face barriers to processing and accessing the data that cannot be overcome without specialist knowledge, facilities or assistance. Consequently, climate reanalysis and projection data are currently substantially under-utilised within research and education communities. To address this issue, we present two simple “point-and-click” graphical user interfaces: the Google Earth Engine Climate Tool (GEEClimT), providing access to climate reanalysis data products; and Google Earth Engine CMIP6 Explorer (GEECE), allowing processing and extraction of CMIP6 projection data, including the ability to create custom model ensembles. Together GEEClimT and GEECE provide easy access to over 387 terabytes of data that can be output in commonly used spreadsheet (CSV) or raster (GeoTIFF) formats to aid subsequent offline analysis. Data included in the two tools include: 20 atmospheric, terrestrial and oceanic reanalysis data products; a new dataset of annual resolution climate variables (comparable to WorldClim) calculated from ERA5-Land data for 1950-2022; and CMIP6 climate projection output for 34 model simulations for historical, SSP2-4.5 and SSP5-8.5 scenarios. New data products can also be easily added to the tools as they become available within the Google Earth Engine Data Catalog. Five case studies that use data from both tools are also provided. These show that GEEClimT and GEECE are easily expandable tools that remove multiple barriers to entry that will open use of climate reanalysis and projection data to a new and wider range of users.

Link to the Google Earth Engine (GEE) code: https://code.earthengine.google.com/cc3ea6593574e321acd7b68c975a9608

You can analyze and visualize the following spatial layers by accessing the GEE link:

Daytime summer land surface temperature (raster data, 30 m horizontal resolution, from Landsat-8 remote sensing data, years 2017-2022)

The surface thermal hot-spot pattern (raster data,30 m horizontal resolution) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS tool.

Here attached the .txt file from the GEE code.

E-mail

Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it

Marco Morabito, CNR-IBE, marco.morabito@cnr.it

Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it

PERSIANN-CDR is a daily quasi-global precipitation product that spans the period from 1983-01-01 to present. The data is produced quarterly, with a typical lag of three months. The product is developed by the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (UC-IRVINE/CHRS) using Gridded Satellite (GridSat-B1) IR data that are derived from merging ISCCP B1 IR data, along with GPCP version 2.2.

The layers included in the code were from the study conducted by the research group of CNR-IBE (Institute of BioEconomy of the National Research Council of Italy) and ISPRA (Italian National Institute for Environmental Protection and Research), published by the Sustainability journal (https://doi.org/10.3390/su14148412).

Link to the Google Earth Engine (GEE) code (link: https://code.earthengine.google.com/715aa44e13b3640b5f6370165edd3002)

You can analyze and visualize the following spatial layers by accessing the GEE link:

Daytime summer land surface temperature (raster data, horizontal resolution 30 m, from Landsat-8 remote sensing data, years 2015-2019)

Surface thermal hot-spot (raster data, horizontal resolution 30 m) was obtained by using a statistical-spatial method based on the Getis-Ord Gi* approach through the ArcGIS Pro tool.

Surface albedo (raster data, horizontal resolution 10 m, Sentinel-2A remote sensing data, year 2017)

Impervious area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)

Tree cover (raster data, horizontal resolution 10 m, ISPRA data, year 2018)

Grassland area (raster data, horizontal resolution 10 m, ISPRA data, year 2017)

Water bodies (raster data, horizontal resolution 2 m, Geoscopio Platform of Tuscany, year 2016)

Sky View Factor (raster data, horizontal resolution 1 m, lidar data from the OpenData platform of Florence, year 2016)

Buildings' units of Florence (shapefile from the OpenData platform of Florence) include data on the residential real estate value from the Real Estate Market Observatory (OMI) of the National Revenue Agency of Italy (source: https://www1.agenziaentrate.gov.it/servizi/Consultazione/ricerca.htm, accessed on 14 July 2022). Data on the characterization of the buffer area (50 m) surrounding the buildings are included in this shapefile [the names of table attributes are reported in the square brackets]: averaged values of the daytime summer land surface temperature [LST_media], thermal hot-spot pattern [Thermal_cl], mean values of sky view factor [SVF_medio], surface albedo [alb_medio], and average percentage areas of imperviousness [ImperArea%], tree cover [TreeArea%], grassland [GrassArea%] and water bodies [WaterArea%].

Here attached the .txt file of the GEE code.

E-mail

Giulia Guerri, CNR-IBE, giulia.guerri@ibe.cnr.it

Marco Morabito, CNR-IBE, marco.morabito@cnr.it

Alfonso Crisci, CNR-IBE, alfonso.crisci@ibe.cnr.it

WeatherNext Graph is an experimental dataset of global medium-range weather forecasts produced by an operational version of Google DeepMind's graphical neural network weather model. The experimental dataset includes real-time and historic data. Real-time data is any data that relates to a time that is no more than 48 hours in the past ("Real-Time Experimental Data"), while historic data is any data that relates to a time that is more than 48 hours ago ("Historic Experimental Data"). This dataset includes major surface fields including temperature, wind, precipitation, humidity, geopotential, vertical velocity, and pressure. The spatial resolution is 0.25 degrees. Forecast init times have 6 hour resolution (00z, 06z, 12z, 18z). Forecast lead times have 6 hour resolution up to a max lead time of 10 days. If you are interested in accessing the experimental dataset, please fill out this WeatherNext Data Request form. More information on the model is in "Learning skillful medium-range global weather forecasting". The model used to produce this experimental dataset is an operational version derived from that research model (formerly known as GraphCast). Please note that the accuracy of this operational model may not directly correspond to the accuracy reported for the research model, and that additional variables may be included in this forecast dataset. If you have any questions on using this experimental dataset, or would like to use it for purposes not currently permitted under the terms of use set out below, please contact weathernext@google.com. Accessing Raw Data (.zarr) A bucket containing the raw .zarr files for the historical dataset ("Historic Experimental Data") is available at gs://weathernext/59572747_4_0/zarr. To gain access to the bucket, please request access through the same WeatherNext Data Request form, and note your interest in receiving access to the raw .zarr files. Acknowledgements The experimental data was generated by models which communicate with and/or reference the following separate libraries and packages: Data and products of the European Centre for Medium-range Weather Forecasts (ECMWF), as modified by Google. Modified Copernicus Climate Change Service information 2023. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. ECMWF HRES datasets Copyright statement: Copyright "© 2023 European Centre for Medium-Range Weather Forecasts (ECMWF)". Source: www.ecmwf.int License Statement: ECMWF open data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). https://creativecommons.org/licenses/by/4.0/ Disclaimer: ECMWF does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.

A European Local Climate Zone map at a 100 m spatial resolution, derived from multiple earth observation datasets and expert LCZ class labels. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represent a valuable database of urban properties.

We apply a research approach that can inform riparian restoration planning by developing products that show recent trends in vegetation conditions identifying areas potentially more at risk for degradation and the associated relationship between riparian vegetation dynamics and climate conditions. The full suite of data products and a link to the associated publication addressing this analysis can be found on the Parent data release. To characterize the climate conditions across the study period, we use the Standardized Precipitation Evapotranspiration Index (SPEI). The SPEI is a water balance index which includes both precipitation and evapotranspiration in its calculation. Conditions from the prior n months, generally ranging from 1 to 60, are compared to the same respective period over the prior years to identify the index value (Vicente-Serrano et al., 2010). Values generally range from -3 to 3, where values less than 0 suggest drought conditions while values greater than 0 suggest wetter than normal conditions. For this study, we are using the 12-month, or 1-year, SPEI to compare annual conditions within the larger Upper Gila River watershed. The SPEI data was extracted into a CSV spreadsheet using data from the Gridded Surface Meteorological (GRIDMET) dataset, which provides a spatially explicit SPEI product in Google Earth Engine (GEE) at a 5-day interval and a spatial resolution of 4-km (Abatzoglou, 2013). In GEE, we quantify overall mean values of SPEI across each 5-day period for the watershed from January 1980 to December 2021. Using R software, we reduced the 5-day values to represent monthly mean values and constrained the analysis to water year 1980 (i.e., October 1980) through water year 2021 (i.e., October 2021). Using the monthly timeseries, we completed the breakpoint analysis in R to identify breaks within the SPEI time series. The algorithm identifies a seasonal pattern within the timeseries. When the seasonal pattern deviates, a breakpoint is then detected. These breaks can be used to pinpoint unique climate periods in the time series. This is a Child Item for the Parent data release, Mapping Riparian Vegetation Response to Climate Change on the San Carlos Apache Reservation and Upper Gila River Watershed to Inform Restoration Priorities: 1935 to Present - Database of Trends in Vegetation Properties and Climate Adaptation Variables. The spreadsheet attached to this Child Item consists of 5 columns, including the (i) month from January 1985 through October 2021, (ii) the 1-year SPEI monthly time series, (iii) the dates identified as breaks within the breakpoint algorithm, (iv) the breakpoint trend identified within the breakpoint algorithm, and (v) the dates that were used as the climate period breaks in this study. The climate periods identified in this spreadsheet using the SPEI data were used as the climate periods in our riparian study.

GEE-TED: A tsetse ecological distribution model for Google Earth Engine Associated publication forthcoming: Fox, L., Peter, B. G., Frake, A. N., and Messina, J. P. (Forthcoming). A Bayesian maximum entropy model for predicting tsetse ecological distributions. Journal TBD. Description GEE-TED is a Google Earth Engine (GEE; Gorelick et al. 2017) adaptation of a tsetse ecological distribution (TED) model developed by DeVisser et al. (2010), which was designed for use in ESRI's ArcGIS. TED uses time-series climate and land-use/land-cover (LULC) data to predict the probability of tsetse presence across space based on species habitat preferences (in this case Glossina Morsitans). Model parameterization includes (1) day and night temperatures (MODIS Land Surface Temperature; MOD11A2), (2) available moisture/humidity using a vegetation index as a proxry (MODIS NDVI; MOD13Q1), (3) LULC (MODIS Land Cover Type 1; MCD12Q1), (4) year selections, and (5) fly movement rate (meters/16-days). TED has also been used as a basis for the development of an agent-based model by Lin et al. (2015) and in a cost-benefit analysis of tsetse control in Tanzania by Yang et al. (2017). Parameterization in Fox et al. (Forthcoming): Suitable LULC types and climate thresholds used here are specific to Glossina Morsitans in Kenya and are based on the parameterization selections in DeVisser et al. (2010) and DeVisser and Messina (2009). Suitable temperatures range from 17–40°C during the day and 10–40°C at night and available moisture is characterized as NDVI > 0.39. Suitable LULC comprises predominantly woody vegetation; a complete list of suitable categories is available in DeVisser and Messina (2009). In the Fox et al. (Forthcoming) publication, two versions of MCD12Q1 were used to assess suitable LULC types: Versions 051 and 006. The GeoTIFF supplied in this dataset entry (GEE-TED_Kenya_2016-2017.tif) uses the aforementioned parameters to show the probable tsetse distribution across Kenya for the years 2016-2017. A static graphic of this GEE-TED output is shown below and an interactive version can be viewed at: https://cartoscience.users.earthengine.app/view/gee-ted. Figure associated with Fox et al. (Forthcoming) GEE code The code supplied below is generalizable across geographies and species; however, it is highly recommended that parameterization is given considerable attention to produce reliable results. Note that output visualization on-the-fly will take some time and it is recommended that results be exported as an asset within GEE or exported as a GeoTIFF. Note: Since completing the Fox et al. (Forthcoming) manuscript, GEE has removed Version 051 per NASA's deprecation of the product. The current release of GEE-TED now uses only MCD12Q1 Version 006; however, alternative LULC data selections can be used with minimal modification to the code. // Input options var tempMin = 10 // Temperature thresholds in degrees Celsius var tempMax = 40 var ndviMin = 0.39 // NDVI thresholds; proxy for available moisture/humidity var ndviMax = 1 var movement = 500 // Fly movement rate in meters/16-days var startYear = 2008 // The first 2 years will be used for model initialization var endYear = 2019 // Computed probability is based on startYear+2 to endYear var country = 'KE' // Country codes - https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var crs = 'EPSG:32737' // See https://epsg.io/ for appropriate country UTM zone var rescale = 250 // Output spatial resolution var labelSuffix = '02052020' // For file export labeling only //[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17] MODIS/006/MCD12Q1 var lulcOptions006 = [1,1,1,1,1,1,1,1,1, 0, 1, 0, 0, 0, 0, 0, 0] // 1 = suitable 0 = unsuitable // No more input required ------------------------------ // var region = ee.FeatureCollection("USDOS/LSIB_SIMPLE/2017") .filterMetadata('country_co', 'equals', country) // Input parameter modifications var tempMinMod = (tempMin+273.15)/0.02 var tempMaxMod = (tempMax+273.15)/0.02 var ndviMinMod = ndviMin*10000 var ndviMaxMod = ndviMax*10000 var ndviResolution = 250 var movementRate = movement+(ndviResolution/2) // Loading image collections var lst = ee.ImageCollection('MODIS/006/MOD11A2').select('LST_Day_1km', 'LST_Night_1km') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select('NDVI') .filter(ee.Filter.calendarRange(startYear,endYear,'year')) var lulc006 = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1') // Lulc mode and boolean reclassification var lulcMask = lulc006.mode().remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17],lulcOptions006) .eq(1).rename('remapped').clip(region) // Merge NDVI and LST image collections var combined = ndvi.combine(lst, true) var combinedList = combined.toList(10000) // Boolean reclassifications (suitable/unsuitable) for day/night temperatures and ndvi var con = function(image) { var ndviExp = image.expression("(b('NDVI') > "+ndviMaxMod+") ? 0" + ":(b('NDVI') > "+ndviMinMod+") ? 1" + ":(b('NDVI') < "+ndviMinMod+") ? 0" + ":1").rename('ndvi_suit') var lstDayExp = image.expression("(b('LST_Day_1km') > "+tempMaxMod+") ? 0" + ":(b('LST_Day_1km') > "+tempMinMod+") ? 1" + ":(b('LST_Day_1km') < "+tempMinMod+") ? 0" + ":1").rename('day_suit') var lstNightExp = image.expression("(b('LST_Night_1km') > "+tempMaxMod+") ? 0" + ":(b('LST_Night_1km') > "+tempMinMod+") ? 1" + ":(b('LST_Night_1km') < "+tempMinMod+") ? 0" + ":1").rename('night_suit') var add = image.addBands(ndviExp).addBands(lstDayExp).addBands(lstNightExp) var multiply = add.select('ndvi_suit').multiply(add.select('day_suit')).multiply(add.select('night_suit')) return add.addBands(multiply.rename('suit')).select('suit') } var conList = combined.map(con).toList(10000) var finish = conList.size().subtract(1).getInfo() // Fly movement rate model var iterateList = conList var kernel = ee.Kernel.square({radius: movementRate, units: 'meters'}) var fill = ee.Image(conList.get(0)).multiply(0) var expansion = fill.add(1) for (var range = 0; range <= finish; range = range + 1) { var img = ee.Image(iterateList.get(range)).select('suit').gt(0).clip(region) var multi = img.multiply(expansion).set('num',range) var mask = multi.eq(1) var masked = multi.updateMask(mask) var expand = masked.focal_max({kernel: kernel}) var expansion = fill.where(expand,1) var updateList = iterateList.add(multi) var iterateList = updateList } // Filter out first two years and compute tsetse probability var filtered = ee.ImageCollection(updateList).sort('num',false).limit(updateList.size().divide(2).subtract(46)) var filteredProb = filtered.sum().divide(filtered.size()).multiply(100).updateMask(lulcMask) var probMask = filteredProb.neq(0) var filteredProbMasked = filteredProb.updateMask(probMask).rename('probability') var startMod = startYear+2 var naming = 'GEE-TED_'+country+'-'+startMod+'-'+endYear+'_'+labelSuffix Map.addLayer(lulcMask, {min:0, max:1}, 'MCD12Q1 LULC mask', false) Map.addLayer(filteredProbMasked, {palette: ['FCFAE1','F6E497','BD8D46','B9121B','4C1B1B'], opacity: 0.8, min: 0, max: 100}, naming, false) Map.setOptions('HYBRID') Map.centerObject(region) // Export image to drive Export.image.toDrive({ image: filteredProbMasked, description: naming, fileNamePrefix: naming, maxPixels: 1e13, scale: rescale, crs: crs, region: region }) References • DeVisser, M.H. and Messina, J.P., 2009. Optimum land cover products for use in a Glossina-morsitans habitat model of Kenya. International Journal of Health Geographics, 8(1), pp.1-20. • DeVisser, M.H., Messina, J.P., Moore, N.J., Lusch, D.P. and Maitima, J., 2010. A dynamic species distribution model of Glossina subgenus Morsitans: The identification of tsetse reservoirs and refugia. Ecosphere, 1(1), pp.1-21. • Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D. and Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, pp.18-27. • Lin, S., DeVisser, M.H. and Messina, J.P., 2015. An agent-based model to simulate tsetse fly distribution and control techniques: a case study in Nguruman, Kenya. Ecological Modelling, 314, pp.80-89. • Yang, A., Messina, J.P., Grady, S.C. and White, R.A., 2017. Cost–Benefit Analysis of Tsetse Fly Control in Tanzania. Papers in Applied Geography, 3(2), pp.182-195.

Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE).

The 500m global surface blue-sky daily albedo climatology dataset is available at .... After reprojection and aggregation, the global Climate Modeling Grid (CMG) albedo climatology datasets at 0.05° and 0.5° are available here. All of the published datasets include historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached in the CMG files. The International Geosphere-Biosphere Programme (IGBP) and PFT classification results of MCD12Q1 since 2001 were reprojected and aggregated to 0.05° and 0.5° by find mode in each aggregation group. In order to check the heterogeneity of the land cover climatology, the percentage of the dominant type in each aggregation group was also calculated.

This file contains the main data used in the manuscript Microbial photosynthesis mitigates carbon loss from northern peatlands under climate warming.

R codes and GEE codes used to make the main figures are also provided.

Please read the READ_ME file for details.

Please see "Drivers and Projections of Global Surface Temperature Anomalies at the Local Scale" by Benz et al for details.

Climate change impacts manifest differently worldwide, with many African countries, including Senegal, being particularly vulnerable. The decline in ground observations and limited access to these observations continue to impede research efforts to understand, plan, and mitigate the current and future impacts of climate change. This occurs at a time of rapid growth in Earth observations (EO) data, methodologies, and computational capabilities, which could potentially augment studies in data-scarce regions. In this study, we utilized satellite remote sensing data leveraging historical EO data using Google Earth Engine to investigate spatio-temporal rainfall and temperature patterns in Senegal from 1981 to 2020. We combined CHIRPS precipitation data and ERA5-Land reanalysis datasets for remote sensing analysis and used the Mann–Kendall and Sen's Slope statistical tests for trend detection. Our results indicate that annual temperatures and precipitation increased by 0.73°C and 18 mm in Senegal from 1981 to 2020. All six of Senegal's agroecological zones showed statistically significant upward precipitation trends. However, the Casamance, Ferlo, Eastern Senegal, Groundnut Basin, and Senegal River Valley regions exhibited statistically significant upward trends in temperature. In the south, the approach to climate change would be centered on the effects of increased rainfall, such as flooding and soil erosion. Conversely, in the drier northern areas such as Podo and Saint Louis, the focus would be on addressing water scarcity and drought conditions. High temperatures in key crop-producing regions, such as Saraya, Goudiry, and Tambacounda in the Eastern Senegal area also threaten crop yields, especially maize, sorghum, millet, and peanuts. By acknowledging and addressing the unique impacts of climate change on various agroecological zones, policymakers and stakeholders can develop and implement customized adaptation strategies that are more successful in fostering resilience and ensuring sustainable agricultural production in the face of a changing climate.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world …

Climate data estimated using satellite images using Google Earth Engine (GEE) and Amazon Sagemaker with Geospatial Capabilities.

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.

If you use this dataset please cite the accompanying paper (Lea et al., 2024)

Maps of key (bio-)climatic variables derived from all currently available ERA5-Land reanalysis data (Muñoz Sabater et al., 2019). These have been calculated for:

1. All possible World Meteorological Organisation (WMO) 30 year climate baseline periods, including: 1951 to 1980; 1961 to 1990; 1971 to 2000; 1981 to 2010; and 1991 to 2020 (this dataset).

2. Annual timescales from 1951-2022 (see here).

Annual timescale data are calculated using monthly statistics using calendar months that account for leap years. WMO baseline maps are calculated by taking the mean of all annual timescale ERALClim maps that fall within the time periods stated above (inclusive). Image bands are named to map onto equivalent BioClim variables (Fick and Hijmans, 2017).

Global data are provided here in GeoTIFF format as multiband images (where each band represents a different year/variable depending on the data downloaded) at a spatial scale of 0.1 degrees within a WGS84 grid (EPSG:4326). If users require data from point locations and/or subset regions for a specific time range or for a custom range of variables, these can be easily accessed using the Google Earth Engine Climate Tool (GEEClimT). Access to this tool requires a Google Earth Engine account, and is free to use for academic research and education purposes, and users who access data through the tool should cite Lea et al., 2024.

Descriptions of each band within the dataset are listed below:

bio1 - Mean 2 m air temperature derived from hourly data (units: degrees C).

bio2 - Annual mean of monthly mean diurnal 2 m air temperature ranges (units: degrees C).

bio3 - Isothermality (100 * bio2 / bio7) (no units).

bio4 - Standard deviation of monthly mean 2 m air temperatures (units: degrees C).

bio5 - Mean of maximum 2 m air temperature for the warmest month (units: degrees C).

bio6 - Mean of minimum 2 m air temperature for the coldest month (units: degrees C).

bio7 - Annual range of 2 m air temperature (bio5 - bio6) (units: degrees C).

bio8 - Mean 2 m air temperature of wettest 3 month period (units: degrees C).

bio9 - Mean 2 m air temperature of driest 3 month period (units: degrees C).

bio10 - Mean 2 m air temperature of warmest 3 month period (units: degrees C).

bio11 - Mean 2 m air temperature of coldest 3 month period (units: degrees C).

bio12 - Total annual precipitation (units: mm).

bio13 - Total precipitation of wettest month (units: mm).

bio14 - Total precipitation of driest month (units: mm).

bio15 - Precipitation Seasonality (Coefficient of Variation, based on monthly total precipitation data) (no units).

bio16 - Total precipitation in wettest 3 month period (units: mm).

bio17 - Total precipitation in driest 3 month period (units: mm).

bio18 - Total precipitation in warmest 3 month period (units: mm).

bio19 - Total precipitation in coldest 3 month period (units: mm).

Climate change impacts manifest differently worldwide, with many African countries, including Senegal, being particularly vulnerable. The decline in ground observations and limited access to these observations continue to impede research efforts to understand, plan, and mitigate the current and future impacts of climate change. This occurs at a time of rapid growth in Earth observations (EO) data, methodologies, and computational capabilities, which could potentially augment studies in data-scarce regions. In this study, we utilized satellite remote sensing data leveraging historical EO data using Google Earth Engine to investigate spatio-temporal rainfall and temperature patterns in Senegal from 1981 to 2020. We combined CHIRPS precipitation data and ERA5-Land reanalysis datasets for remote sensing analysis and used the Mann–Kendall and Sen's Slope statistical tests for trend detection. Our results indicate that annual temperatures and precipitation increased by 0.73°C and 18 mm in Senegal from 1981 to 2020. All six of Senegal's agroecological zones showed statistically significant upward precipitation trends. However, the Casamance, Ferlo, Eastern Senegal, Groundnut Basin, and Senegal River Valley regions exhibited statistically significant upward trends in temperature. In the south, the approach to climate change would be centered on the effects of increased rainfall, such as flooding and soil erosion. Conversely, in the drier northern areas such as Podo and Saint Louis, the focus would be on addressing water scarcity and drought conditions. High temperatures in key crop-producing regions, such as Saraya, Goudiry, and Tambacounda in the Eastern Senegal area also threaten crop yields, especially maize, sorghum, millet, and peanuts. By acknowledging and addressing the unique impacts of climate change on various agroecological zones, policymakers and stakeholders can develop and implement customized adaptation strategies that are more successful in fostering resilience and ensuring sustainable agricultural production in the face of a changing climate.

Freshwater resources present in the Upper Indus Basin (UIB) supply water to Pakistan’s irrigation. Half of the annual water discharge in the UIB is contributed from the glacier and snow-fed basins in the Hindu Kush, Karakoram, and Himalaya (HKH) region, and it is currently under the threat of climate change. Therefore, it is very necessary to study and monitor the spatiotemporal changes in the snow cover area (SCA) and its response to climate variables to efficiently manage water reservoirs. Thus, keeping this problem in mind, we conducted this study using the Google Earth Engine Application Programming Interface (GEE API) for Astore and Shigar. We used mean annual Landsat data between 1991 and 2021 to derive the SCA using the normalized difference snow index (NDSI). ASTER GDEM data was used to extract the elevation and analyze the distribution of SCA on different elevation zones. Eventually, we used the climate research unit (CRU) data (rainfall and temperature) to analyze them with SCA. The results revealed that SCA in Astore and Shigar has an increasing trend with a rate of 11.16 km2/year and 4.27 km2/year, respectively. Mean annual precipitation and temperature also confirmed the increasing trend of SCA because mean annual precipitation is increasing and temperature is decreasing in both regions. SCA and elevation analysis revealed that SCA is decreasing on foothills while increasing at the valley top. This is because temperature is increasing and precipitation is decreasing from zone 1 to zone 3 and vice versa for zone 4. It is found that Astore and Shigar in UIB have an increasing trend of SCA and are not affected by global warming. Therefore, it is necessary to conduct studies on large scale to efficiently evaluate the impact of climate change on SCA.