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Sensitivity of the global agricultural sector to changes in climate policy - EU countries compared to the rest of the world
<p>The files contain data from the FAOSTAT database used in the article: DOI:10.2478/oszn-2023-0012</p> <p>File content:<br>Agricultural emissions data for the period 1961-2020<br>Population data for 1950-2020<br>Production value from agriculture for the period 1961-2020<br>Agricultural area for the period 1961-2020</p> <p>The layout of the tables and the description of the columns is the same as the FAOSTAT database methodology</p>
F I G U R E 7 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 7 Model prediction of the proportion of juvenile Atlantic salmon choosing to smolt as 1-year-olds (full saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), 2-year-olds (medium saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5), and 3-year-olds (low saturation, black = historical, green = SSP1-RCP2.6, orange = SSP3-RCP7.0, and red = SSP5-RCP8.5). The red line is the point of reaction norm calibration to Piggins and Mills (1985).
F I G U R E 5 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 5 Ensemble average daily water temperature by day of year for the future projections under the three shared socioeconomic pathways and representative concentration pathways (SSP1-RCP2.6 left, SSP5-RCP7.0 middle, and SSP5-RCP8.5 right). Each line represents the day of year average temperature for the climate forcing ensemble with colors transitioning from blue to red toward the end of the century (starting with 2020 and ending with 2100). The lower dashed line represents the lower growth threshold temperature of 7 C, and the upper dashed line represents the upper growth threshold temperature for 23 C (Elliott & Hurley, 1997).
F I G U R E 6 Projected change between 1960 and 2100 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 6 Projected change between 1960 and 2100 in length-at-smoltification decision (a, b, and c), length-at-smoltification as 1-year-olds (d, e, and f), and length-at-smoltification as 2-year-olds (g, h, and i) under the three shared socioeconomic pathways and representative concentration pathways: SSP1-RCP2.6 (green), SSP3-RCP7.0 (orange), and SSP5-RCP8.5 (red) for juvenile Atlantic salmon in the Burrishoole. The gray-shaded area represents the historical reference (2000 to 2020), and the red vertical line represents the historical average.
F I G U R E 4 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 4 Generalized linear model of body length (mm) as a function of cumulative growing degree days (CGDD, C day) for the 23 observed cohorts of juvenile Atlantic salmon in the Burrishoole watershed. The solid line represents the mean length, and the gray bands represent the 95% prediction interval. The outer lines represent the sample density.
F I G U R E 3 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 3 The residual error between observed and predicted water temperature (top panel), and the in-situ water temperature (black line) and long short-term memory neural network water temperature prediction (red crosses) for the training (1961–1994) and validation (1995–2019) dataset in the Mill Race (bottom panel). Years excluded due to accumulation of internal sate (green), prolonged periods of missing data (blue shaded), and measurement error (red shaded) are shown in the top panel, and the delineation of the training and validation period is shown by the vertical dashed line in both panels.
F I G U R E 2 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 2 The four-step model workflow for quantitatively estimating length-at-age and life history of juvenile Atlantic salmon in response to climate change. Step 1 describes the collation of necessary data and construction of the water temperature model. Step 2 details the data preparation and construction of the length-at-age model for juvenile Atlantic salmon. Step 3 shows the coupling of the ISIMIP phase 3B projections to the water temperature model, and the subsequent coupling with the length-at-age model. Step 4 shows the post-processing of length-at-age projections to estimate smoltification probability and proportion of 1-, 2- and 3-year-old smolts. Shapes are according to ISO 5807 standard.
F I G U R E 1 in Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar
F I G U R E 1 Location of electrofishing sites (green circles) and fish traps (red circles) in the Burrishoole catchment, Co. Mayo, Ireland.
Data for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"
<p>Data repository for "Archetypal flow regime change classes and their associations with anthropogenic drivers of global streamflow alterations"</p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)
<p><strong>Agricultural land resources – a global suitability evaluation (v3.0)</strong></p> <p>Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature. </p> <p><strong>Determining Agricultural Suitability</strong></p> <p>Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach.</p> <p><strong>Agricultural Suitability</strong></p> <p>The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km<sup>2</sup> at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops.</p> <table> <caption><strong>Food, feed, fiber and first-generation bioenergy crops</strong></caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> <p><strong>Second-generation bioenergy crops</strong></p> </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> <p><strong>Growing Season Adaptation</strong></p> <p>The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account.</p> <p><strong>Most Suitable Crop</strong></p> <p>The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability.</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publications:</p> <p>Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a></p> <p>Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a></p> <p>Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a></p> <p>Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a></p> <p>Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, <em>J. Adv. Model. Earth Syst.</em>, 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>.</p> <p><strong>Improvements in v3.0</strong></p> <p>Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) <em>uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.</em></p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</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>
Fig. 1. The potential distribution map for B in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 1. The potential distribution map for B. bombina under contemporary climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig. 4. The potential distribution map for B. bombina under projected 2050 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig. 4. The potential distribution map for B. bombina under projected 2050 climatic conditions. The colour gradient represents high (red) to low (green) habitat suitability for the species.
Fig 3 in Long-Term Bioclimatic Modelling The Distribution Of The Fire-Bellied Toad, Bombina Bombina (Anura, Bombinatoridae), Under The Influence Of Global Climate Change
Fig 3. Response curve showing how the logistic prediction changes as the environmental variable Bio2 (Mean diurnal temperature range, oC, X-axis) is varied, keeping all other environmental variables at their average sample value. The curve shows the mean response of the 10 replicate Maxent runs (red) and and the mean +/– one standard deviation (blue).
GLOBMAP SWF: a global annual surface water cover frequency dataset since 2000 for change analysis of inland water bodies
<p>The extent of surface water has been changing significantly due to climatic change and human activities. However, it is challenging to capture the interannual changes and trends of inland water bodies due to their high seasonal variation and abrupt change. We generated a global annual surface water cover frequency dataset (GLOBMAP SWF) from the MODIS land surface reflectance products to describe the seasonal and interannual dynamics of surface water. Surface water cover frequency (SWF) was proposed as the percentage of the time period when a pixel is covered by water in a year. Instead of determination of the water observations directly, the SWF was estimated indirectly by identifying land observations among annual clear-sky observations to reduce the influence of clouds and variability of water body and surface background characteristics, which helps to improve the applicability of the algorithm for different regions across the globe. Regional analysis demonstrates that our estimation results show reasonable performances on frozen water, saline lake, bright surface and cloud-frequent regions. This dataset can be used to analyze the interannual variation and change trend of highly dynamic inland water body extent with consideration of its seasonal variation.</p> <p>The GLOBMAP SWF dataset is provided in Version 1.0 (https://zenodo.org/record/6462883#.YxC16HZBw2w). Here we provide the number of MOD09A1 (MODIS 8-day composite land surface reflectance) clear-sky snow/ice-free observations (<em>N<sub>Clear</sub></em>) data as a quality dataset of GLOBMAP SWF product. The clear-sky observation refers to the valid MOD09A1 observation that not covered with clouds and snow/ice. The more available clear-sky observations, the more reliable the estimated SWF.</p> <p>The <em>N<sub>Clear </sub></em>dataset is provided by 296 1200 km × 1200 km tiles at annual temporal and 500 m spatial resolutions in the sinusoidal projection with Geotiff format for each year during 2000-2020. The file is named as "GLOBMAPClearCount. AYYYY001.hHHvVV.V01.tif", where “YYYY” refers to the year of the file, and “HH” and “VV” explains the number of tiles that are the same with MODIS standard tile. The valid range is 0-46, scale factor is 1.0. The <em>N<sub>Clear </sub></em>of permanent water (land obervation count of 46), permanent snow/ice and terrain shadows are set to 50.</p>
Dataset: Global range dynamics of the Bearded Vulture (Gypaetus barbatus) from the Last Glacial Maxima to climate change scenarios
<p>This dataset consists of Bearded Vulture <em>Gypaetus barbatus </em>occurrence points which were used to develop a distribution model to study its suitable habitat of this species. Using these data, we modelled the current distribution of Bearded Vulture throughout its entire range and projected the Last Glacial Maxima (LGM), Mid-Holocene (MH) and future distribution under 2070s climate change scenarios. We compiled these data from the entire distribution range in Asia, Europe and Africa using different sources: freely accessible online resources including, eBird and GBIF repositories, published reports and grey literature and occurrence data collected by the authors in the field, mostly in Nepal.</p> <p> </p> <p> </p>
CESM2 MDM data for "Historical changes in wind driven ocean circulation can accelerate global warming" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MD = mechanically decoupled model (referred to as MDM in paper), CESM2</li> <li>FC = fully coupled model (referred to as FCM in paper), CESM2</li> </ul> <p>Decoding file names:</p> <p>Variables that are a single value per time step (e.g. global means and globally integrated values) are given in dimensions of time by ensemble member. Variables that include values at every grid point at each point in time are provided with an ensemble mean trend and an ensemble standard deviation of the trend. </p> <ul> <li>ensmean refers to ensemble mean</li> <li>ensstd refers to ensemble standard deviation</li> <li>trend refers to linear trend over 1979-2014</li> <li>annual refers to annual mean anomalies, relative to reference period of 1941-1970</li> </ul> <p>Variables:</p> <ul> <li>aice = ice area</li> <li>AMOC = Atlantic meridional overturning circulation</li> <li>N_HEAT = northward heat transport </li> <li>BSF = barotropic streamfunction </li> <li>TREFHT = reference level air temperature </li> <li>Qnet = net surface heat flux (defined as FSNS - FLNS - LHFLX - SHFLX)</li> <li>TOA = top of atmosphere radiation </li> <li>TOAC = top of atmosphere radiation, clearsky </li> <li>FLNT = net longwave flux at top of model</li> <li>FLNTC = net longwave flux at top of model, clearsky</li> <li>FSUTOA = upwelling solar flux at top of atmosphere</li> <li>FSNTOA = net solar flux at top of atmosphere</li> <li>FSNTOAC = net solar flux at top of atmosphere, clearsky</li> </ul> <p> </p> <p> </p> <p> </p>
Dataset for paper "Solutions to global agricultural green water scarcity under climate change"
<p>These are datasets used to generate figures of paper "Solutions to global agricultural green water scarcity under climate change".</p> <p>(1) fig1_GWS_Baseline, fig1_GWS_1.5C and fig1_GWS_1.5C are NetCDF files reporting agricultural green water scarcity (GWS) under Baseline climate conditions (1996-2005 period), 1.5°C and 3°C warmer climates, respectively. </p> <p>(2) fig2_num_of_month_Baseline, fig2_num_of_month_1.5C and fig2_num_of_month_3C are NetCDF files reporting the number of months that each grid cell faces GWS (with threshold 0.2) under Baseline climate conditions (1996-2005 period), 1.5°C and 3°C warming climates, respectively. fig2_data_Baseline, fig2_data_1.5C and fig2_data_3C are csv files reporting the countries with the highest exposure to GWS and number of months under Baseline, 1.5 °C and 3 °C warmer climates, respectively. </p> <p>(3) fig3_data_01, fig3_data_02 and fig3_data_03 are csv files reporting the area of rain-fed croplands facing agricultural GWS in each month under GWS thresholds 0.1, 0.2 and 0.3, respectively. </p> <p>(4) fig4_data is the csv file reporting the number of people impacted by crop production loss induced by GWS under Baseline, 1.5 °C and 3 °C warmer climates with GWS thresholds of 0.1, 0.2 and 0.3.</p> <p>(5) fig5_Baseline, fig5_1.5C and fig5_3C are csv files reporting the reduction of area facing GWS and increased people fed due to green water management solutions with different evapotranspiration reduction and infiltration increase levels, under Baseline, 1.5 °C and 3 °C warmer climates, respectively.</p> <p>(6) fig6_data_area and fig6_data_population are csv files reporting reduced rain-fed croplands facing GWS and additional people fed from decreased GWS, respectively, with evapotranspiration reduction and infiltration increase levels as 0.2.</p> <p>(7) irrigation_fraction is the NetCDF file reporting the percent of irrigated cropland in each grid cell. We use it as a mask to exclude croplands with larger than 5% irrigation. It is calculated based on "Mehta, P. <em>et al.</em> Majority of 21st century global irrigation expansion has been in water stressed regions. (2022)."</p>
Data used for the article "Global evaluation of the dry gets drier and wet gets wetter paradigm from terrestrial water storage changes perspective".
<p>This is the data used in the HESS paper "Global evaluation of the dry gets drier and wet gets wetter paradigm from terrestrial water storage changes perspective".</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.