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218 results for “Global Warming”
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.
Supplementary dataset for "Global water gaps under future warming levels"
<p>The folder contains water gap data relative to the paper:<br>Rosa, L., Sangiorgio, M. Global water gaps under future warming levels. Nat Commun 16, 1192 (2025). https://doi.org/10.1038/s41467-025-56517-2</p> <p>All water gaps data are in km3/yr.</p> <p><br>Gridded data(NetCDF at 0.5°)<br> - baseline<br> water_gap_baseline.nc: Baseline water gap in the period 2001-2010<br> - 1.5°C warming (5 models + average)<br> water_gap_15C_average.nc: Water gap under 1.5°C warming (multi-model average)<br> water_gap_15C_h08_ipsl-cm6a-lr.nc: Water gap under 1.5°C warming (h08 + ipsl-cm6a-lr)<br> water_gap_15C_h08_mri-esm2-0.nc: Water gap under 1.5°C warming (h08 + mri-esm2-0)<br> water_gap_15C_h08_ukesm1-0-ll.nc: Water gap under 1.5°C warming (h08 + ukesm1-0-ll)<br> water_gap_15C_h08_mpi-esm1-2-hr.nc: Water gap under 1.5°C warming (h08 + mpi-esm1-2-hr)<br> water_gap_15C_h08_gfdl-esm4.nc: Water gap under 1.5°C warming (h08 + gfdl-esm4)<br> - 3°C warming (5 models + average)<br> water_gap_3C_average.nc: Water gap under 3°C warming (multi-model average)<br> water_gap_3C_h08_ipsl-cm6a-lr.nc: Water gap under 3°C warming (h08 + ipsl-cm6a-lr)<br> water_gap_3C_h08_mri-esm2-0.nc: Water gap under 3°C warming (h08 + mri-esm2-0)<br> water_gap_3C_h08_ukesm1-0-ll.nc: Water gap under 3°C warming (h08 + ukesm1-0-ll)<br> water_gap_3C_h08_mpi-esm1-2-hr.nc: Water gap under 3°C warming (h08 + mpi-esm1-2-hr)<br> water_gap_3C_h08_gfdl-esm4.nc: Water gap under 3°C warming (h08 + gfdl-esm4)</p> <p><br>Aggregated data (.xlsx)<br> - source_data.xlsx: Water gap aggregated by country and basin for all the considered scenarios (including multi-model average and agreement analysis)</p> <p>Note: the global water gap obtained by summing all the countries/basins is not completely equivalent to the sum of all the pixels because some pixels' center is outside the polygon of the corresponding country/basin (differences in the order of 1km/yr3, <0.3%). See sheet "Figure 3" A249:N251 (countries) and "Figure 5" A235:N237 (basins) for further details.</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>
Fast Surface Warming of Global Cities
<p>Using satellite land surface temperatures (2002 to 2021), this study investigates the surface warming trends over urban core, rural background and transitional land based on the time series decomposition algorithm. The individual contributions from background climate change, urbanization, and landscape greening to overall urban surface warming trends are then quantified using the statistical attribution approach.</p>
Code and data for "Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss"
<pre>This repository contains the following information: Datasets S1 to S4 can all be loaded, manipulated, and analysed in R using script provided in Data S5 to obtain the results of the paper, Reddin et al. 2022, "Global warming generates predictable extinctions of warm and cold-water marine benthic invertebrates via thermal habitat loss". Data S1. (separate file) The original downloaded PaleoDB dataset. Data S2. (separate file) The pre-prepared dataset of occurrences. Data S3. (separate file) The finished environmental dataset. Data S4. (separate file) Additional environmental dataset. Data S5. (separate file) The R-code for the main analysis. Data S6. (compressed directory) Output data and code from the simulations. Table S7 (separate file). List of data source publications for PaleoDB data used in our study. Listed are the data source author list (ref_author), year (ref_pubyr), and reference number as appears in the PaleoDB (reference_no). </pre>
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>
Data for: Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use
<p>This dataset contains the code and the data files needed to create the figures shown in the paper titled "Flexible emulation of the climate warming cooling feedback to globally assess the maladaptation implications of future air conditioning use".</p>
Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations
<p>Snapshot level data of TC extractions from the thermodynamical global warming runs described in "Changes in four decades of near-CONUS tropical cyclones in an ensemble of 12km thermodynamic global warming simulations."</p>
Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols
<p><span>This is the dataset to support our paper title of “Global warming effects of cropland expansion by reduction of biogenic secondary organic aerosols”. Cropland expansion has been the most significant global land use change since industrialization. However, evaluations of radiative forcing from land use changes have often neglected the radiative effects of secondary organic aerosols (SOA) linked to cropland expansion. Sensitivity experiments using an Earth system model that incorporates advanced SOA processes reveal approximately a 10% reduction in the global biogenic SOA burden due to cropland expansion since industrialization. This reduction weakens SOA</span><span>’</span><span>s role in scattering radiation and forming clouds, leading to a decline in its cooling effect by 146 mW m⁻², which is equivalent to 8% of the warming caused by CO₂ emissions since industrialization. This effect is expected to increase by nearly half under future climate warming and reduced emissions scenarios. Therefore, policies addressing food security and climate change must consider the radiative impacts of biogenic SOA associated with cropland expansion.</span></p> <p><span> </span></p> <p><span>The dataset consists of three zip files, which include model code and output from sensitivity simulations conducted with the Community Earth System Model (CESM) version 1.2.2, using the IMPACT aerosol module and an offline radiative model. The files are described as follows:</span></p> <p><span> </span></p> <p><strong><span>Model code.zip:</span></strong><span> Contains the source code for the IMPACT aerosol module, which was integrated as an additional aerosol module within CESM version 1.2.2, available from the NCAR repository.</span></p> <p><span> </span></p> <p><strong><span>PD_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in present-day cases. The Concentration folder includes 13 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 13 additional subfolders, which share the same names as those in the Concentration folder. These 13 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E20C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions and climate conditions; </span></p> <p><strong><span>20L20E20C</span></strong><span> represents simulations with present-day land use, emissions, and climate conditions.</span></p> <p><span>Eight subfolders for single vegetation type transition experiments include model output for cases where land use transitions from deciduous broadleaf forest to cropland (<strong>DBF2CRO</strong>), evergreen broadleaf forest to cropland (<strong>EBF2CRO</strong>), evergreen needleleaf forest to cropland (<strong>ENF2CRO</strong>), grassland to cropland (<strong>GRA2CRO</strong>), shrubland to cropland (<strong>SHR2CRO</strong>), deciduous broadleaf forest to grassland (<strong>DBF2GRA</strong>), evergreen broadleaf forest to grassland (<strong>EBF2GRA</strong>), and evergreen needleleaf forest to grassland (<strong>ENF2GRA</strong>).</span></p> <p><span>Three subfolders for latitude-specific experiments cover conversions for all vegetation types in tropical (20</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>N, <strong>LLAT</strong>), mid-latitude (50</span><span>°</span><span>S</span><span>–</span><span>20</span><span>°</span><span>S and 20</span><span>°</span><span>N</span><span>–</span><span>50</span><span>°</span><span>N, <strong>MLAT</strong>), and high-latitude (south of 50</span><span>°</span><span>S and north of 50</span><span>°</span><span>N, <strong>HLAT</strong>) regions.</span></p> <p><span> </span></p> <p><strong><span>FU_Cases.zip:</span></strong><span> This file contains two folders (<strong>Concentration and Radiation</strong>), which hold the simulation results for concentration and radiation in future cases. The Concentration folder includes 4 subfolders. The Radiation folder contains four subfolders representing different radiation simulation results. These include simulations with and without the direct radiative effects of SOA (labeled <strong>ADEwSOA</strong> and <strong>ADEwoSOA</strong> respectively) and simulations with and without the indirect radiative effects of SOA (labeled <strong>AIEwSOA</strong> and <strong>AIEwoSOA</strong> respectively). Each of these subfolders contains 4 additional subfolders, which share the same names as those in the Concentration folder. These 4 subfolders correspond to specific sensitivity experiments, the details of which are explained below. Within each subfolder, you will find the five-year averaged model output for all 12 months.</span></p> <p><strong><span>18L20E21C</span></strong><span> represents simulations with pre-industrial land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>18L21E21C</span></strong><span> represents simulations with pre-industrial land use, future emissions, and future climate conditions;</span></p> <p><strong><span>20L20E21C</span></strong><span> represents simulations with present-day land use, and present-day emissions, and future climate conditions; </span></p> <p><strong><span>20L21E21C</span></strong><span> represents simulations with present-day land use, future emissions, and future climate conditions.</span></p>
Forest resilience to global warming is strongly modulated by local-scale topographic, microclimatic and biotic conditions
<p>Resilience of endangered rear edge populations of cold-adapted forests in the Mediterranean basin is increasingly altered by extreme heatwave and drought pressures. It remains unknown, however, whether microclimatic variation in these isolated forests could ultimately result in large intra-population variability in the demographic responses, allowing the coexistence of contrasting declining and resilient trends across small topographic gradients. Multiple key drivers promoting spatial variability in the resilience of rear edge forests remain largely unassessed, including amplified and buffered thermal exposure induced by heat waves along topographic gradients, and increased herbivory pressure on tree saplings in defaunated areas lacking efficient apex predators. Here we analysed whether indicators of forest resilience to global warming are strongly modulated by local-scale topographic, microclimatic and biotic conditions.</p> <p>We studied a protected rear edge forest of sessile oak (<em>Q. petraea</em>), applying a suite of 20 indicators of resilience of tree secondary growth, including multidecadal and short-term indices. We also analysed sapling recruitment success, recruit/adult ratios and sapling thermal exposure across topographic gradients. We found large within population variation in secondary growth resilience, in recruitment success and in thermal exposure of tree saplings to heatwaves, and this variability was spatially structured along small-scale topographical gradients. Multidecadal resilience indices and curves provide useful descriptors of forest vulnerability to climate warming, complementing assessments based in the analysis of short-term resilience indicators. Species-specific associations of trees with microclimatic variability are reported.</p> <p>Biotic factors are key in determining long-term resilience in climatically-stressed rear edge forests, with strong limitation of sapling recruitment by increased roe deer and wild boar herbivory. Our results also support non-stationary effects of climate determining forest growth responses and resilience, showing increased negative effects of warming and drought over the last decades in declining stands.</p> <p>Our findings do not support scenarios predicting spatially homogeneous distributional shifts and limited resilience in rear-edge populations, and are more supportive of scenarios including spatially heterogeneous responses, characterised with contrasting intra-population trends of forest resilience. We conclude that forest resilience responses to climate warming are strongly modulated by local-scale microclimatic, topographic and biotic factors. Accurate predictions of forest responses to changes in climate would therefore largely benefit from the integration of local-scale abiotic and biotic factors.</p>
Increased risk of near term global warming due to a recent AMOC weakening
<p>This archive contains data from the IPSL-CM6A-LR ensemble of extended historical simulations dataset used in the main Figure of the publication: Bonnet et al., 2021, Nature Communication, "Increased risk of near term global warming due to a recent AMOC weakening".</p> <p>Corresponding authors: Rémy Bonnet, rbonnet@ipsl.fr</p> <p>Description of the archive:</p> <p>- Atlantic Meridional Overturning Circulation (AMOC in Sv) time series calculated as the maximum of the Atlantic meridional stream function at 20°N-50°N (<a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/AMOC_IPSL-EHS_1850-2059.nc?versionId=46314962-c295-4dfb-a226-1baecb465a25">AMOC_IPSL-EHS_1850-2059.nc </a>and <a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/AMOC_yr_IPSL-EHS_1900-2018.nc?versionId=df94c491-bfa9-4ec6-9a79-4ad9af989b59">AMOC_yr_IPSL-EHS_1900-2018.nc</a>)</p> <p>- Global near-Surface Air Temperature (GSAT in K) time series (<a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/GSAT_IPSL-EHS_1850-2059.nc?versionId=dfd7f22e-f143-4c7c-911e-8d991a929dfd">GSAT_IPSL-EHS_1850-2059.nc </a>and <a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/GSAT_yr_IPSL-EHS_1900-2018.nc?versionId=42b02149-0a08-40c1-b8d1-5f3cb6293878">GSAT_yr_IPSL-EHS_1900-2018.nc</a>)</p> <p>- Time series of the four AMOC fingerprints used:</p> <ul> <li>Sea Surface Temperature over the North Atlantic Subpolar Gyre (K): <a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/SST_SPG_NAtl_relative_GlobalSST_IPSL-EHS_1900-2018.nc?versionId=b912c68a-8092-413f-8664-1cd3137bd1dd">SST_SPG_NAtl_relative_GlobalSST_IPSL-EHS_1900-2018.nc</a></li> <li>The Atlantic Multidecadal Variability index (K): <a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/AMV_idx_historical_IPSL-CM6A-LR_1900-2018.nc">AMV_idx_historical_IPSL-CM6A-LR_1900-2018.nc </a></li> <li>A new index based on the (0-700 m) Ocean Heat Content in (10<sup>-9</sup> J.m<sup>-2</sup>): <a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/Delta_OHC_idx_IPSL-EHS_1940-2018.nc?versionId=50f1509e-2bbc-4acc-a16f-2a36a5f76bcd">Delta_OHC_idx_IPSL-EHS_1940-2018.nc</a></li> <li>The difference between the Northern and the Southern Hemisphere anomalies of near-surface air temperature (K): <a href="https://zenodo.org/api/files/ef334a8a-da6b-4d36-8c10-771b56f1d036/DITA_yr_IPSL-EHS_1900-2018.nc?versionId=c788c84b-a295-40e4-b5e6-82b386126bec">DITA_yr_IPSL-EHS_1900-2018.nc </a></li> </ul> <p>The netcdf files provided the time series described above for each of the 32 members of the IPSL ensemble ranked from the member 1 to the member 32. Note that the member 2 is not available over the 2030-2059 period. The AMOC and GSAT files covering the 1850-2059 period are therefore composed of 31 members instead of 32, with the second member corresponding to member 3 and so on. A description of the AMOC fingerprints can be found in the associated publication.</p> <p> </p> <p><br> </p>
Data for: Increasing hypoxia on global coral reefs under ocean warming
<p><span class="s1">Ocean deoxygenation is predicted to threaten marine ecosystems globally. However, current and future oxygen concentrations and the occurrence of hypoxic events on coral reefs remain underexplored. Here, using autonomous sensor data to explore oxygen variability and hypoxia exposure at 32 representative reef sites, we reveal that hypoxia is already pervasive on many reefs. 84% of reefs experienced weak to moderate (≤153 to ≤92 μmol O<sub>2</sub> kg<sup>-1</sup>) hypoxia and 13% experienced severe (≤61 μmol O<sub>2</sub> kg<sup>-1</sup>) hypoxia. Under different climate change scenarios based on 4 Shared Socioeconomic Pathways (SSPs), we show that projected ocean warming and deoxygenation will increase the duration, intensity, and severity of hypoxia, with more than 94% and 31% of reefs experiencing weak to moderate and severe hypoxia, respectively, by 2100 under SSP5-8.5. This projected oxygen loss could have negative consequences for coral reef taxa due to the key role of oxygen in organism functioning and fitness.</span></p>
Global warming leads to habitat loss and genetic erosion of alpine biodiversity
<p><span><strong>Aim</strong>:</span><span> Species living on steep environmental gradients are expected to be especially sensitive to global climate change. Here, we combined genetic, ecological niche modelling and climatic niche comparisons to investigate the influence of climate on the biogeography of three alpine species with overlapping ranges.</span></p> <p><span><strong>Location</strong>:</span><span> Te Waipounamu (South Island) Aotearoa</span>–<span>New Zealand.</span></p> <p><span><strong>Taxon</strong>:</span><span> Endemic alpine-adapted Cataontopinae grasshoppers.</span></p> <p><span><strong>Methods</strong>:</span><span> We used niche modelling to estimate and project the potential niche of three focal species under past and future climate scenarios.</span><span> Vulnerability assessments were</span><span> performed using </span><span>niche factor analyses. Demographic trends and phylogeographic structure were investigated using samples from 15 mountain tops to generate mitochondrial DNA haplotype networks and population genetic statistics.</span></p> <p><span><strong>Results</strong>:</span><span> Niche models and genetic data suggest suitable habitat for all three alpine species was more widespread and contiguous in the past than today. Demographic analyses indicate in situ survival rather than post-Pleistocene colonisation of current habitat. Population structuring and genetic divergence suggest that mountain uplift during the Pliocene and environmental barriers during Pleistocene glacial and interglacial stages shaped contemporary population structure of each species. Though geographically overlapping, niche analyses suggest these alpine species are not ecologically identical, and each shows similar but distinct responses to environmental change, but all will lose intraspecific diversity through population extinction.</span></p> <p><span><strong>Main</strong> <strong>conclusions</strong>:</span><span> Climatic, biological and geophysical factors controlled population structuring of three cold-adapted species during the Pleistocene with a legacy of spatially separate intraspecific lineages. Ecological niche models for each species emphasise distinct combinations of environmental proxies, but all are expected to experience severe habitat reduction during climate warming. Increased global temperatures drive available habitat to higher elevation resulting in population contractions, range shifts, habitat fragmentation, local extinctions, and genetic impoverishment. Despite alpine species not being ecologically identical, we predict all mountain biota will lose significant genetic diversity due to global warming.</span></p>
Kilometer-scale global warming simulations and active sensors reveal changes of tropical deep convection
<p>This zip file contains data and codes to reproduce the figures of a manuscript on X-SHiELD.</p> <p>Contact mbolot@princeton.edu for questions.</p>
All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal: high-resolution figures
<p>High resolution versions of Figure 2 and Figure S3 for the corrigendum of the paper "All options, not silver bullets, needed to limit global warming to 1.5 °C: a scenario appraisal" by Warszawski et al. (2021) published in Environmental Research Letters. </p> <p>Fig. 2: Spider plots for each of the 22 scenarios in the filtered ensemble (the corresponding model and scenario is printed above each plot), in order of increasing coverage, <em>V<sub>i</sub> </em>. Note that the AIM/CGE2.1 TERL_15D_LowCarbonTransportPolicy scenario has coverage of V<sub>i</sub>=1, despite E<sub>2050</sub> lying below themedium upper bound due to how the two energy-sector levers are combined to calculate the coverage (see Supplementary material). Each lever has been normalised to the high upper bound (the bold black inner circle on each plot; the absolute value of the upper bound is printed below the lever label). The centre of each spider plot corresponds to the minimum value across the entire ensemble of 50 scenarios for each lever. The medium upper bounds are shown as a dashed polygon. The absolute value of the lever for the given scenario is also printed on the plot. The top row contains the two scenarios singled out in figure <a href="https://iopscience.iop.org/article/10.1088/1748-9326/abfeec#erlabfeecf1">1</a>(c), which exceed the SR1.5 remaining carbon budget for staying below 1.5 °C with a 50% likelihood; these two scenarios also have the lowest coverage of all scenarios in the filtered ensemble. For a similar plot of the complete ensemble of 1.5 °C scenarios with no or low overshoot (50 scenarios), see the supplement.</p> <p>Fig. S3: Same as Fig. 2 in main text but for all 50 scenarios. Those scenarios shaded grey are categorised as ‘Below 1.5C’ in the SR1.5. All other scenarios fall into the ‘1.5C low overshoot’ category.</p>
GLOBAL WARMING: FEATURES AND IMPACT ON LIFE IN MEGACITIES
<p>The article considers one of the key factors affecting the climate - human activity. First of all, it is associated with emissions of carbon dioxide into the atmosphere, which creates a kind of greenhouse above the surface of the planet. The second factor is related to excess solar energy, which has accumulated over millions of years in oil, gas, coal, peat and other fossil hydrocarbons. Particular attention in the study is paid to those problems and methods of solution that can help or weaken global warming.</p>
CROVER model output data for paper "Future Warming Decreases the Benefits of Irrigation in Global Food Production"
<p>This data is output data simulated by the CROVER crop-river coupled model for the scientific paper entitled: “Future Warming Decreases the Benefits of Irrigation in Global Food Production”. Simulations are based on 120-year simulations (1981-2100) using 20 climate projections (four Representative Concentration Pathways (RCPs) (2.6, 4.5, 6.0, and 8.5) × five General Circulation Models (GCMs) (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M)). The dataset includes crop yield of the modeled five crops (maize, rice, soybean, spring wheat, and winter wheat) each for irrigated and rainfed with a spatial resolution of 1.125 degree.</p> <p>The file name consists of a series of identifiers, separated by underscore, according to the following pattern.</p> <p>crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.grd</p> <p>crover_<gcm>_<rcp>_yield_<crop>_<irrigated|rainfed>.ctl</p> <p>Format: grads binary (global, 1.125 degree)</p> <p>These files were compressed as a zip file for each GCM and RCP.</p> <p> </p>
Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals
<p><strong>Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals</strong></p> <p><em>Authors: Xi Guo; Puying Zhang; Yaojie Yue</em></p> <p>This is the outcome data of our research which is under submission.</p> <p>Though the impact of climate change on potential crop distributions has been extensively explored, there are few studies on potential wheat distributions at specific global warming levels (GWLs), e.g., 1.5 °C and 2 °C.</p> <p>Here, a grided (0.5 degree × 0.5 degree) dataset of global potential wheat distribution under the 1.5 °C and 2 °C GWLs is proposed. This dataset is produced using the MaxEnt model with support of multi-model data(GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M).</p> <p>The predictive accuracy of the proposed dataset was carefully validated between the predicted global wheat distribution and multiple known datasets. For more details of the approach used to predict the global wheat distribution please refer to: Yue, Y., Zhang, P., Shang, Y., 2019. <em>The potential global distribution and dynamics of wheat under multiple climate change scenarios</em>. <em>Sci Total Environ</em> 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.</p> <p>The results indicate the regional differences in the potential suitability of wheat cultivation under different GWLs. Eastern Europe, Pakistan, Northern India, Russia, and Canada witnessed a significant increase in wheat planting suitability. In contrast, Central Eastern Africa, Southeastern Australia, Southeastern China, Southern Brazil, France, Spain, and Italy demonstrated a significant decrease in wheat suitability. Compared with 1.5 °C GWLs, wheat planting suitability decreases more evidently in 2 °C GWLs in Central and Eastern Africa, Central and Southern India, Southeastern China, Australia, Mexico, Southern Brazil, and Argentina. Simultaneously, regions such as Russia, Pakistan, Canada, and the Great Lakes area of the United States observed further increases in wheat planting suitability. To ensure favorable conditions for the cultivation of wheat, it is crucial to limit the global average temperature increase to less than 2 °C.</p> <p>Our findings demonstrate the influence of different GWLs on potential global wheat distribution, highlighting the regional differences in the potential suitability of wheat cultivation under different GWLs.</p> <p>We argue that the potential global wheat distribution datasets under different GWLs are a valuable complement to currently available products. This potential global wheat distribution is one of the few products to take into account 1.5 °C and 2 °C GWLs based on multi-modal data. We believe that it can provide more valuable information for policymakers to make decisions for the warming world.</p> <p>The data of the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals is stored in a zip package, that is <strong>Global Planting Suitability of Wheat</strong><strong>.zip</strong>. This package consists of 1 folder, i.e., <strong>SR1.5&2.0</strong>.</p> <p>This subfolder contains GeoTIFF files for the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals. Correspondingly <strong>Wheat_SR15</strong><strong>.tif</strong> and <strong>Wheat_SR</strong><strong>20</strong><strong>.tif</strong>. The grid value of each file ranges from 0 to 1, indicating the possibility of wheat planting in each grid, and the higher the value, the higher the possibility that wheat exists.</p> <p>Reference:</p> <p>Yue, Y., Zhang, P., Shang, Y., 2019. <em>The potential global distribution and dynamics of wheat under multiple climate change scenarios</em>. <em>Sci Total Environ</em> 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.</p>
Figure data for Hansen et al. 2023, Global warming in the pipeline, OOCC
<p>Raw data used to produce the figures in Hansen et al. 2023, Global warming in the pipeline, published in Oxford Open Climate Change.</p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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