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66 results for “Precipitation changes”
Annual precipitation and photo-derived vegetation and litter cover (2013-2021) used for analysis in the manuscript “Growing grasses in the desert: Multi-scale Interactions and State Change Reversal in Drylands”
This dataset contains water year precipitation collected from meteorological stations, litter and vegetation cover values derived from overhead photos, and litter and soil accumulation in lateral photos in a long-term experiment (2013-2021) of cross-scale interactions (CSIs) at the Jornada Basin LTER site in southern New Mexico, U.S.A. Manipulations were initiated in 2013 at 15 experimental blocks, each with 4 treatment plots: plant-scale herbicide of mesquite shrubs, patch-scale connectivity modifiers (ConMods), herbicide + ConMods, control without manipulations. Litter, soil, and vegetation cover were estimated using repeat overhead photographs of microplots within treatment and control plots. Litter and soil accumulation were estimated from lateral photos of ConMods. This dataset utilized QuickBird imagery from 2011 to assess ground cover classes within the Jornada Basin, focusing on bare ground, herbaceous, and shrub cover. Daily precipitation data collected from 13 meteorological stations were used to calculate water year (1 October-30 September) precipitation from 2013 through 2021. This dataset provides supporting data for the manuscript "Growing grasses in the desert: Multi-scale Interactions and State Change Reversal in Drylands" by Peters et al.
Dataset and analyses for publication entitled: “Acclimation of the nitrogen cycle to changes in precipitation”
This dataset contains data and analysis code for the paper entitled “Acclimation of the nitrogen cycle to changes in precipitation" by Currier et al. As the frequency of precipitation extremes are expected to increase, especially in arid regions, we asked how prolonged shifts in water availability facilitate acclimation of the N cycle in a semiarid grassland. Using natural abundances of stable nitrogen isotopes for dominant plants and soils and rainfall manipulation experiments, we tested the hypothesis that N cycling will interact with water availability further amplifying the openness of the N cycle through time. For the dominant plant species, we found the relationship for N availability vs. ambient annual precipitation to be significantly positive, contrary to global spatial models. We also considered the temporal dynamics of our experiments, which imposed directional rainfall manipulations in duration ranging from 5 to 14 years. The slopes of these relationships decreased (became less positive) with more time since the onset of the directional precipitation extremes. These data and metadata supplement long-term foliar and soil isotope data from the Jornada LTER (Dataset ID: knb-lter-jrn.210586001) with a large spatial dataset from NEON data package DP1.10026.001 and Craine et al. 2018 (https://doi.org/10.5061/dryad.v2k2607).
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes
<p>Post-processed CPM simulation datasets used for the paper "Attribution of 2022 August Heavy Precipitation Event in South Korea Using High-resolution Pseudo Global Warming Simulations: Sensitivity to Vertical Temperature Changes".</p>
Dataset for: "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)"
<p>The version 1.0 contains the supporting data for the work (still under submission) "Last century changes in annual precipitation in a Mediterranean area and their spatial variability. Insights from northern Tuscany (Italy)".</p> <p>The following files are here available (all file are georeferenced in EPSG: 3003):</p> <p>- AVG_Rainfall_1990-2019.tif -> Raster map of the mean annual precipitation for the northern Tuscany, Italy. It encompasses the portion of the Tuscany region northern of the cities of Livorno - Florence. The interpolation was validated via a leave one out cross-validation procedure.</p> <p>- D3-1_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1921 to 1950 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- D3-2_Area2_ApuanAlps.tif -> Raster map of the differences in mean annual precipitation between the two 3-decades periods 1951 to 1980 and 1990 to 2019 for the Apuan Alps mountain ridge (Tuscany, Italy).</p> <p>- DeltaSHP_Points_AVG_Annual_Rainfall.zip -> Shape file of the raingauges locations with the mean annual precipitation values of the period 1990 to 2019.</p> <p>- RaingaugesSHP_Points_AVG_Annual_Rainfall_1990-2019.zip -> Shape file of the raingauges locations with the following information: differences in the mean annual precipitation values between the two 3-decades periods 1951 to 1980 and 1990 to 2019 (named D3-2); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1951 to 1980 and 1990 to 2019; difference in the mean annual precipitation values between the two 3-decades periods 1921 to 1950 and 1990 to 2019 (named D3-1); p values of the t-test for significance of the differences between the mean annual precipitation ofthe two 3-decades periods 1921 to 1950 and 1990 to 2019.</p>
Climate variability can outweigh the influence of climate mean changes for extreme precipitation under global warming
<p>Dataset used to analyize role of climate variability</p>
Data for: Long-term body size change in multiple landbird species, long-term change in temperature and precipitation as well as associations between temperature, precipitation, and morphological change in multiple landbird species, 2004 – 2019, 2021 - 2022.
<p>Six data sets used to look for long-term change in precipitation and temperature, body size change and possible environmental drivers of morphological change in birds captured during spring or fall migration in and around Lackawanna State Park, northeastern Pennsylvania, USA.</p> <p>The file labeled daily_temp_precip.csv contains daily precipitation and average daily temperature data from the Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA) and the file called daily_temp_precip_1400 contains daily precipitation and daily temperature data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude.</p> <p>The file called band_data_final.csv contains data collected from the first capture of individuals of multiple species during spring or fall migration, the file called all_hy_env_morph.csv contains temperature and precipitation anomaly data from Scranton/Wilkes Barre Airport (Avoca, Pennsylvania, USA), as well as morphological data from the first capture of all fall migrating young of the year.</p> <p>The file called all_hy_env_morph_1400.csv contains temperature and precipitation anomaly data from weather stations within 1,400 km of our study site location (41.6<sup>o</sup>N, 75.7<sup>o</sup>W), bounded by 80<sup>o</sup> W and 70<sup>o</sup>W longitude as well as morphological data from the first capture of all fall migrating young of the year while the file called local_hy_env_morph.csv contains temperature and precipitation anomaly data as well as first capture of local young of the year.</p>
Spatial patterns of extreme precipitation and their changes under ~2 °C global warming: A large-ensemble study of the western US: Data Release
<p>This dataset supports the analysis in Rupp et al. (2022). The dataset consists of 17,223 data files containing the water year (WY) maximum of the daily-averaged precipitation rate simulated with the HadRM3p regional climate model configured for the western United States. Each file contains the WY maxima across the model domain for a single WY, single model parameterization, and single set of initial conditions. Please refer to Hawkins et al. (2019) and Rupp et al. (2022) for a description of how the climate model data were generated.</p>
Supporting data for ``Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land'"
<p>Here we have the processed data used in the preprint ``'Summer-Winter Contrast in the Response of Precipitation Extremes to Climate Change over Northern Hemisphere Land''</p> <p>The README.md file includes explanations about the data in the repository.</p>
Dataset - Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau
<p>Materials and data results needed to reproduce the findings of the study published in (PNAS) Proceedings of the National Academy of Sciences of the United States of America:</p> <p>"<em>Warming-induced monsoon precipitation phase change intensifies glacier mass loss in the southeastern Tibetan Plateau"</em>. A. Jouberton, T. E. Shaw, E. Miles, M. McCarthy, S. Fugger, S. Ren, A. Dehecq, W. Yang and F. Pellicciotti</p> <p>It includes the meteorological forcing time-series, an exhaustive list of the model parameters, the outputs of TOPKAPI-ETH, and Matlab scripts allowing to reproduce the figures and compute the numbers given in the main manuscript as well as in the Supplementary Information.</p> <p>---------------</p> <p><strong>Contents</strong> :</p> <p>Folder : "Matlab_scripts"<br> '<strong>Climate_import.m</strong>' : Organizes meteorological forcing and generates Figure S9<br> <strong> 'TOPKAPI_result_import.m' :</strong> Imports TOPKAPI's reference run outputs and prepares them for analysis<br> <strong> 'Experiment_analysis.m' : </strong>Analyses the results of the forcing experiments, generates Figure 4 and Figure S25<br> <strong> 'Main_text_results.m' : </strong>Analyses the results of TOPKAPI's reference runs, generates Figure 1D, FIgure 2 and Figure 3<br> <strong> 'TOPKAPI_validation.m' : </strong>Compares TOPKAPI's reference run results with several validation datasets, generates the figures and performance metrics of the model calibration and validation procedure.<br> <strong> 'Parlung_albedo_regional_analysis.m'</strong>: Computes the mean glacier albedo per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the albedo of Parlung No.4 glacier.<br> <strong> 'Parlung_GMB_regional_analysis.m'</strong>: Computes the mean glacier mass balance per elevation band for each glacier within the Southeastern Tibean Plateau and compares it to the glacier mass balance of Parlung No.4 glacier.<br> <strong> 'Precipitation_phase_sensitivity_analysis.m'</strong>: Performs a sensitivity analysis on the simulated monsoon snowfall ratio per elevation band and on the attribution of glacier mass loss to precipitation<br> phase change using Monte Carlo simulations.<br> <strong> 'TOPKAPI_MODIS_validation.m'</strong>: Compares the snow cover at Parlung No.4 catchment simulated by TOPKAPI-ETH and observed by MODIS, generates Figure S19.</p> <p> </p> <p>Folder : "Remote_sensing" :</p> <p> Sub-Folder: 'Hugonnet' = Glacier mass balance averaged over 2000-2020 covering the Southeastern Tibetan Plateau, 100m resolution, derived from Hugonnet et al. 2021<br> Sub-Folder: 'MODIS' = contains the snow cover at Parlung No.4 derived from the daily product MOD10A1 version 61, for the period 2000-2018<br> Sub-Folder: 'Regional_glacier_albedo' = contains the annual glacier surface albedo from 2000 to 2020, covering the Southeastern Tibetan Plateau, 500m resolution.<br> Sub-Folder: 'Shapefiles' = contains the Parlung No.4 glacier outlines in 1974 and from the RGI 6.0<br> <strong>'ASTER_Nyainqentanglha_15m_utm.tif'</strong> = ASTER Digital elevation model at 15m resolution covering the Southeastern Tibetan Plateau<br> <strong> 'parlung_mask_1974.mat' </strong>= Parlung No.4 glacier mask as a matlab file<br> <strong> 'dh_ASTER_SRTM_30m.tif' </strong>= Mean elevation change rate from 2000 to 2016 at Parlung No.4 catchment.<br> <strong> 'Geodetic_map.mat'</strong> = Elevation change maps for the periods 1974-2000 and 1974-2014, as a matlab file<br> <strong> 'GMB_geodetic.mat' </strong>= Geodetic mass balance (glacier-wide mean and profile per elevation band) used in Figure S13<br> <strong> 'parlung_30m_catchment_mask.tif'</strong> = Parlung No.4 catchment mask<br> <strong>'parlung_1974_30m_dem.tif' </strong>= DEM of Parlung No.4 catchment, 30 m resolution<br> <strong> 'parlung_1974_30m_gla.tif'</strong> = Parlung No. 4 glacier mask, 30m resolution<br> <strong>'parlung_1974_30m_glah.tif' </strong>= Reconstructed ice thickness of 1975 for Parlung No.4 glacier<br> <strong>'parlung_1974_2000_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2000<br> <strong> 'parlung_1974_2014_diff_24m.tif' </strong>= Elevation change from DEM differencing at Parlung No.4 catchment for 1974-2014<br> <strong> 'Parlung_1974_bedrock_dem_30m.tif' </strong>= Bedrock surface digital elevation model of the catchment, 30m spatial resolution<br> <strong>'RGI_KangriKarpo_100m_utm_id.tif' </strong>= Glacier mask covering the Kangri Karpo mountain region, 100m resolution, with glacier IDs in the attribute table<br> <strong> 'RGI_Nyainqentanglha_100m_utm_id.tif' </strong>= = Glacier mask covering the Southeastern Tibetan Plateau, 100m resolution, with glacier IDs in the attribute table</p> <p> </p> <p>Folder : "TOPKAPI_forcing" :<br> <strong> CCT_AWS4600_extended.csv : </strong>Hourly cloud cover transmissivity from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Climate.mat : </strong>Organizes meteorological forcings, output from the matlab script '<strong>Climate_import.m</strong>'<br> <strong> LR_AWS4600_extended.csv :</strong> Hourly temperature lapse-rates from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Precipitation_AWS4600_extended.csv : </strong>Hourly precipitation from 1975 to 2018 reconstructed at AWSoff location <br> <strong> Ta_AWS4600_extended.csv :</strong> Hourly air temperature from 1975 to 2018 reconstructed at AWSoff location<br> Sub-Folder: 'National_meteorological_stations' = Contains the daily air temperature and precipitation measured at the national meteorological stations of Bomi, Zayu, Zuogong and Basu<br> Sub-Folder: 'Reference_run_inputs' = Contains the input files necessary to run TOPKAPI-ETH to obtain the outputs from which the results of this study are based on.</p> <p> </p> <p>Folder : "TOPKAPI_output":<br> <strong> </strong> Sub-Folder : "Forcing experiment" = organized TOPKAPI outputs from the forcing experiment<br> Sub-Folder :" Reference_run_outputs" = raw TOPKAPI outputs from the reference run (catchment average, spatial and grid cells)<br> Sub-Folder : "Reference_run_results" = organized TOPKAPI outputs from the reference run<br> Sub-Folder : "Snow_ice_cover" = contains TOPKAPI-ETH derived snow cover maps (daily map outputs)<br> Sub-Folder : "Regional_analysis" =<br> 'Alb'= Table containing the mean glacier albedo (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'GMB'= Table containing the mean glacier mass balance (2000-2020) per normalized elevation band, for each glacier in the SETP (RGI 6.0)<br> 'Hypso_xxm' = Table containing the percentage of glacier area per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> 'NormEl_100m' = Table containing the elevation per normalized elevation band, for each glacier in the SETP (RGI 6.0), resolution of 100/500m<br> Sub-Folder : "Semi_distributed_outputs" = Precipitation phase and amounts resulting from TOPKAPI-ETH simulation per elevation band, for the reference run and for the Monte Carlo sensitivity analysis</p> <p> </p> <p>Folder : "Validation_data"<br> <strong> 'topkapi.out_reference_discharge2016' </strong>= <strong> </strong>raw TOPKAPI outputs run in 2016 with AWSoff air temperature<br> <strong> </strong><strong> 'master_file_parlung.mat' </strong>=<strong> </strong>matlab structure containing AWS measurements, necessary for running <strong>'TOPKAPI_validation.m'</strong><br> <strong> 'Qdigit.mat' </strong>= Discharge measured at the Parlung No.4 glacier outlet, from Li et al., (2016)<br> <strong> 'Parlung_Q_1970.mat'</strong> = 'Discharge time-series used to run TOPKAPI-ETH (goes back to 1975, but filled with 0 when no measurements are available)</p> <p> </p> <p>In order to run the Matlab scripts, it is recommended to download all folders and gather them into the same folder. Any request about data or questions on how to run the Matlab scripts can be asked to the author of the paper (at achille.jouberton@wsl.ch).</p>
Risk Assessment of Extreme Precipitation on to Low- and Medium-Voltage Electrical Infrastructure Under the Influence of Climate Change
Open the record for dataset details and reuse information.
A precipitation gradient drives change in macroinvertebrate composition and interactions within bromeliads.
<p>Tank bromeliads accumulate water inside their leaf axils, providing habitat for communities of aquatic macro invertebrates. Here we sampled the macro invertebrate community in 100 bromeliads along the sand dunes of coastal Brazil in the states of Rio de Janeiro and São Paulo. We sampled ten sites, seven of which were within the Jurubatiba National Park in Rio de Janeiro state, Brazil. The other three sites were located in the sand dunes of Arraial do Cabo (Rio de Janeiro), Marica (Rio de Janeiro), and Ilha Bela (Sao Paulo).</p> <p>We sampled all macroinvertebrate communities between March and May 2015. In each site, we dissected ten bromeliads (totalling 100 bromeliads across all sites) to collect all the invertebrates in each plant. Macroinvertebrates were counted and identified to genus level whenever possible. For every bromeliad, we measured a suite of environmental variables to assess the amount and quality of habitat available to the invertebrates including: the height (cm) and diameter (cm, measured as the maximum distance between leaf tips) of the plant, maximum water volume (mL, calculated by emptying the plant and calculating how much water the plant could hold before it overflowed), actual water volume (mL), longest leaf length (cm), longest leaf width (cm), number of leaves, canopy cover (% of shaded pixels in photos taken looking directly up from the bromeliad), total detritus (g dry mass), pH, oxygen concentration (% saturation), salinity (ppt), temperature (oC), and turbidity (NTU). Water chemistry and temperature variables were measured using a portable multiparameter waterproof meter in the field as soon as the water was collected from the plant.</p> <p>The zip file contains two csv files. Environment contains all the environmental variables described above, and Species_presence contains the species ID and whether it is present in a given bromeliad. </p> <p> </p>
Asynchronous changes in precipitation and soil water content decelerate alpine vegetation greening
<p>data for "Asynchronous changes in precipitation and soil water content decelerate alpine vegetation greening".</p>
Changes in community-weighted trait mean, functional diversity, precipitation, temperature and surface area along an elevational gradient in Tenerife, Canary Islands
<p>This dataset comprises community-weighted trait means and functional diversity of leaf traits, precipitation, temperature and surface area of the elevational belt recorded in roadside (disturbed) and interior (less disturbed) plots, along an elevational gradient of 2,300 m in Tenerife, Canary Islands. The leaf traits measured were specific leaf area (SLA), nitrogen, carbon, phosphorous, nitrogen to carbon ratio, leaf dry matter content (LDMC), sodium, potassium and magnesium. The environmental variables measured are total precipitation of the growing season, mean temperature of the growing season and surface area of the elevation belt. This dataset has been used for the analysis presented in Ratier Backes et al. (in press). Mechanisms behind elevational plant species richness patterns revealed by a trait-based approach. <em>Journal of Vegetation Science</em>.</p>
Global transportation infrastructure exposure to the change of precipitation in a warmer world
<p>This repository provides the base data to perform a global transport asset exposure analysis for extreme precipitation under climate change.In this study, we comprehensively analyze the exposure of road and railway infrastructure assets to changes in precipitation return periods globally.</p> <p>For more details, please see:</p> <p>Liu, K., Wang, Q., Wang, M. <em>et al.</em> Global transportation infrastructure exposure to the change of precipitation in a warmer world. <em>Nat Commun</em> <strong>14</strong>, 2541 (2023). https://doi.org/10.1038/s41467-023-38203-3</p>
UKCP18 RCM precipitation and temperature bias corrected using ISIMIP3BA change-preserving quantile mapping.
<p>We present bias-corrected UK Climate Projections 2018 (UKCP18; Met Office Hadley Centre, 2018) regional datasets for temperature, precipitation, and potential evapotranspiration (1981-2080). All 12 members of the 12 km ensemble were corrected using quantile mapping and a change-preserving variant (Lange, 2019; Lange, 2020). Both methods effectively reduce biases in multiple statistics, while maintaining projected climatic changes. We provide guidance on using the bias-corrected datasets for climate change impact assessment. Please find a detailed description and evaluation in the metadata and accompanying data paper (Reyniers et al., 2025).</p> <p>---</p> <p>Met Office Hadley Centre (2018): UKCP18 Regional Projections on a 12km grid over the UK for 1980-2080. CEDA, <em>8 March 2022</em>. <a href="https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604">https://catalogue.ceda.ac.uk/uuid/589211abeb844070a95d061c8cc7f604</a></p> <p>Lange, S. (2019). Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1. 0). <em>GMD,</em> <em>12</em>(7), 3055-3070.</p> <p>Lange, S. (2020). ISIMIP3BASD (2.4.1). Zenodo. https://doi.org/10.5281/zenodo.3898426</p> <p>Reyniers, N., Zha, Q., Addor, N., Osborn, T. J., Forstenhäusler, N., & He, Y. (2025). Two sets of bias-corrected regional UK Climate Projections 2018 (UKCP18) of temperature, precipitation and potential evapotranspiration for Great Britain. <em>Earth System Science Data</em>, <em>2025, 17(5)</em>, 2113–2133.</p>
Changes in vegetation in northern Alaska under scenarios of climate change, 2003-2100: I - Trends in air temperature, precipitation, and cloudiness
These data contain estiamtes of changes in climate from northern AK based on a modeling study for the years 2003-2100. See Euskirchen et al., 2009 for more information. This file contains data for Figure 3.
Effect of changing precipitation in different periods on precipitation use efficiency in a semi-arid grassland
<p>Climate change intensifies global and regional water cycles, leading to changes in both the magnitude and timing of precipitation. Precipitation use efficiency (PUE) plays a crucial role in measuring the response of above-ground net primary productivity (ANPP) to precipitation changes. However, little is known about how changes in precipitation during different periods affect PUE. Using a manipulation precipitation experiment in a semi-arid steppe, we simulated a 60% increase and decrease in precipitation during the early (April-June), late (July-September), and entire (April-September) growing seasons across 2015-2021 to examine the effects of changes in precipitation timing on PUE. The results showed that: (1) decreased precipitation in the late growing season (DLP) and whole growing season (DWP) stimulated PUE by an average of 0.14 and 0.12 g m<sup>-2</sup> mm<sup>-1</sup> yr<sup>-1</sup>, respectively, whereas increased precipitation in the late growing season (ILP) and whole growing season (IWP) suppressed PUE by an average of 0.11 and 0.09g m<sup>-2</sup> mm<sup>-1</sup> yr<sup>-1</sup>, respectively. By contrast, neither decreased nor increased precipitation in the early growing season affected PUE; (2) the increased PUE under DLP was primarily attributed to the increase of PUE in grass (GR) and annuals and biennials (AB), whereas the elevation of PUE under DWP was mainly due to an increase of PUE in AB. By contrast, the reduction of PUE under ILP was mainly caused by a decline of PUE in GR; (3) changes in evapotranspiration and leaf dry matter content (LDMC) explained the variation of PUE in AB while changes of PUE in GR was mainly due to the alteration of soil water content and LDMC. These results suggest that precipitation during the late growing season has a crucial influence on PUE, highlighting the importance of evapotranspiration and leaf dry matter content in regulating ecosystem productivity in the semi-arid steppe.</p>
Context-dependent impact of changes in precipitation on the stability of grassland biomass
<p>Community stability plays a crucial role in ensuring the consistent provision of ecosystem services despite environmental changes, including alterations in precipitation patterns. Over the past decades, significant progress has been made in understanding the responses of the stability of grassland plant communities and underlying mechanisms, defined as the ratio of the temporal mean biomass to the standard deviation. However, a crucial knowledge gap remains regarding whether the impacts of precipitation on the stability of grassland biomass are contingent upon specific contextual factors.</p> <p>Here, we examined the stability of above- and below-ground biomass in adjacent grass- and shrub-dominated communities through a 7-year manipulation experiment involving seven precipitation levels: 20%, 40%, and 60% decreases, as well as 20%, 40%, and 60% increases in natural rainfall, in addition to ambient precipitation.</p> <p>We found that the stability of community biomass was influenced by three contextual factors including the magnitude and directionality of precipitation, above- and below-ground biomass, and the type of vegetation. In particular, higher and more intense precipitation resulted in higher stability of above-ground biomass in both grass- and shrub-dominated communities. Conversely, higher precipitation intensity led to decreased below-ground biomass stability in grass-dominated communities but increased below-ground biomass stability in shrub-dominated communities. Species stability and species asynchrony consistently played a positive role in explaining the stability of above-ground biomass in both grass- and shrub-dominated communities. However, species asynchrony negatively influenced below-ground biomass stability in grass-dominated communities without a comparable effect in shrub-dominated communities. The preeminent contribution to the total community biomass was identified in the stability of below-ground biomass, evident in both grass-dominated and shrub-dominated communities.</p> <p><em>Synthesis.</em> This study highlights that while the specific effects of changes in precipitation may vary depending on the context, the fundamental processes governing biomass stability are consistent. These findings elucidate the desert steppe ecosystems' adaptive response to precipitation variations and emphasize their pivotal role in maintaining ecosystem functions under climatic perturbations.</p>
The data for the article entitled "Nitrogen deposition drives response and recovery to precipitation change and its reversal in an arid ecosystem""
<p><strong>Title:</strong> Nitrogen deposition drives response and recovery to precipitation change and its reversal in an arid ecosystem</p> <p>Yu et al.</p> <p>Document 1: Data on plant community traits.</p> <p>Document 2: Data on soil features.</p>
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Annotated Behaviour and Observability Dataset (ABODe)
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