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617 results for “Climate models”
Output files from SPEEDY v.42 ensembles described in the paper: "Multi-decadal pacemaker simulations with an intermediate-complexity climate model" by F. Molteni, F. Kucharski and R. Farneti (part 2 of 2)
<p>The monthly-mean output from SPEEDY v.42 ensembles (either driven by prescribed sea-surface temperature (SST) or coupled to the TOM3 model) consists of a series of IEEE little-endian binary files and metadata files in text format.<br> For each year of integration (indicated by a 4-digit number YYYY) and ensemble member (indicated by a 3-digit number NNN), two binary files are present, named:<br> • attmNNN_YYYY.grd, including data on the 120x60 grid-point atmospheric grid;<br> • sftmNNN_YYYY.grd, including data on the 360x180 grid-point surface grid.<br> The metadata for these files are contained in the text files <strong>attmEEE.ctl</strong> and <strong>sftmEEE.ctl</strong> respectively, where EEE is a 3-digit ensemble identifier (usually, but not necessarily, equal to one of the ensemble-member number NNN).</p> <p><br> This repository contains data from:</p> <ul> <li>(part 1) a 41-year 5-member ensemble (653) run with prescribed SST</li> <li>(part 2) a 70-year 5-member ensemble (104) run with the coupled SPEEDY-TOM3 model.</li> </ul> <p>Integration years are 1980 to 2020 for ensemble 653 and 1951 to 2020 for ensemble 104.</p> <p><br> The structure of the binary data and metadata files follows the conventions for gridded datasets set by the GrADS diagnostic and plotting package (developed by the Center for Ocean-Land-Atmosphere Studies of George Mason University), as described here:<br> http://cola.gmu.edu/grads/gadoc/aboutgriddeddata.html</p> <p><br> In addition to the COLA-GMU web site, free version of the GrADS package for different platforms can be downloaded from the OpenGrADS web site:<br> http://opengrads.org/</p> <p><br> Specifically, the SPEEDY v.42 output consists of sequential-access files where each record contains a two-dimensional field. Three-dimensional fields are stored as a sequence of consecutive records, one for each of the 8 pressure levels where model-level data are interpolated by the post-processing routines. For each month of the year:</p> <p><br> the <strong>attmNNN_YYYY.grd</strong> files contain a sequence of <strong>9 3-D variables and 26 2-D variables</strong>;<br> the <strong>sftmNNN_YYYY.grd</strong> files contain a sequence of <strong>21 2-D variables</strong>.</p> <p>Within each record, grid-point data are stored as a NLONxNLAT array with longitude varying from west to east and latitude varying from south to north. The list of variables and levels is specified in the <strong>attmEEE.ctl</strong> and s<strong>ftmEEE.ctl</strong> files. These files contain descriptors which allow the data of each ensemble to be accessed as a single dataset by the GrADS package.</p> <p>Although the metadata files are specific to the GrADS package, the binary data can be read by different types of code. As example of fortran90 instructions to read the content of the <strong>attmNNN_YYY.grd</strong> and <strong>sftmNNN_YYY.grd</strong> files for one year/ens.member is as follows:</p> <p>integer, parameter :: nlon=120<br> integer, parameter :: nlat=60<br> integer, parameter :: nlev=8<br> integer, parameter :: nlon0=360<br> integer, parameter :: nlat0=180</p> <p>integer :: jmonth, jvar3d, jvar2d, jlev<br> real :: fld3d(nlon,nlat,nlev)<br> real :: fld2d(nlon,nlat), fld0(nlon0,nlat0)</p> <p>open (unit=1, file=”attmNNN_YYY.grd”, form=”formatted”, access=”sequential”)<br> open (unit=2, file=”sftmNNN_YYY.grd”, form=”formatted”, access=”sequential”)</p> <p>do jmonth=1,12</p> <p> do jvar3d=1,9<br> do jlev=1,nlev<br> read (1) fld3d(:,:,jlev)<br> …………<br> enddo<br> enddo</p> <p> do jvar2d=1,26<br> read (1) fld2d(:,:)<br> ………<br> enddo</p> <p> do jvar2d=1,21<br> read (2) fld0(:,:)<br> ………<br> enddo</p> <p>enddo</p> <p>close (1)<br> close (2)</p>
WRF model configuration and data used for the NHESS manuscript "Heat wave characteristics: evaluation of regional climate model performances for Germany"
<p>The file contains:</p> <ul> <li>the namelist.input document with the description of the WRF model configuration used in Warscher et al. (2019)</li> <li>WRF simulation outputs from the reanalysis run: daily values of maximum temperature for the time period 1980-2009 from the innermost (5 km grid resolution) and second innermost (15 km) domain; from both domains the same section, relevant for the study, was taken; the data was bilineraily interpolated to 12.5 km horizontal grid resolution to match the EUR-11 CORDEX format</li> </ul>
Advancing and retreating fronts in a changing climate: a percolation model of range shifts
<p>Climate change causes considerable shifts in the geographic distribution of species worldwide. Most data on range movements, however, derive from relatively short periods, within which it is difficult to distinguish directional shifts from random fluctuations. For detecting a shift, it is indispensable to delineate the range precisely. We propose a new method for delineation based on percolation theory. We suggest marking the boundary between the connected and fragmented occurrence of the species (the hull). We demonstrate the advantages of this connectivity-based method on simulated examples in which a metapopulation is advancing vs. retreating along an environmental gradient with different velocities. The simulations show that the hull is a fractal and has the same dimension (7/4) even when the front is advancing or retreating relatively fast, compared to the generation time. It is particularly robust in the retreating (trailing) edge. Accordingly, we propose marking the range edge at the mean position of the hull, the 'connectivity limit' of the species. Theoretical considerations suggest that the position of the connectivity limit is statistically more reliable than those limits that are delineated according to the outermost occurrences, and the connectivity-based method is broadly applicable to real-life data.</p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
MAgPIE model runs csv for plotting: Climate change-driven global land-use system adaptation under CMIP6-based crop model projections
<p>This .zip file contains the data used to create the figures for the paper. It includes .csv files and .nc files for maps. This version includes additional files like the mapping between countries and MAgPIE're economic regions.</p>
Urban Heat: Forward-Looking Climate Modeling for Skopje, North Macedonia
<p>We produced actionable data on heat stress in cities to inform analysis and client dialogue on the part of World Bank teams. We applied an urban-scale climate modeling framework to generate datasets describing modeled heat stress exposure for present-day and future conditions under selected climate scenarios. The study domain focuses on Skopje, North Macedonia.</p> <p>More details about the dataset: </p> <ul> <li>The dataset includes calculations for each indicator across three scenarios (<strong>present, SSP1-1.9, SSP3-7.0</strong>) and three twenty-year periods (<strong>2001-2020, 2021-2040, and 2041-2060</strong>). The present period refers to 2001-2020, while the other two periods correspond to the two SSP scenarios.</li> <li>All indicators are available in both <strong>NetCDF</strong> and <strong>GeoTiff</strong> formats.</li> <li>The indicators are calculated at a resolution of <strong>100 m</strong>, consistent with the UrbClim and WBGT simulations. Additionally, downscaled versions of the indicators are provided at a resolution of <strong>30 m</strong>.</li> <li>The UrbClim and WBGT simulations, as well as the postprocessing, are conducted using the regional projection <strong>E</strong><strong>PSG 32634</strong>. The NetCDF and GeoTiff data also adopt this projection. Furthermore, a GeoTiff data file with <strong>EPSG 4326</strong> projection is included.</li> <li>All indicators are calculated as <strong>yearly averages</strong>. Some indicators also have additional calculations for <strong>seasonal averages</strong>, including Spring (MAM), Summer (JJA), Autumn (SON), and Winter (DJF).</li> <li>Ten representative locations within the study domain have been selected to retrieve the WBGT profile on a chosen date (2017-07-11). The results and the locations are stored in wbgt_profile.xlsx.</li> <li>Images for <strong>quick viewing</strong> <strong>in</strong> <strong>png</strong> format visualizing the results for each indicator. Present denotes the period 2001-2020; 2030 denotes the period 2021-2040; & 2050 denotes the period 2041-2060.</li> <li>The NetCDF and GeoTiff data can be found in the data.zip; The png files for quick viewing can be found in quickview.zip; more information about the dataset, including the methodology, all available data list, contact information, etc. can be found in the Technical_Annex_Skopje.docx</li> </ul>
Data for "Reducing Southern Ocean biases in the FOCI climate model"
<p>Jupyter notebooks and time-averaged data needed to reproduce all plots in "Reducing Southern Ocean biases in the FOCI climate model" submitted to JAMES.</p> <p>Source code modifications needed to compile and run the model is also included.</p> <p>See attached README for more information.</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 8
<p>Future projections of precipitation by the BM10 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 5
<p>Future projections of precipitation by the BM1 model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 3
<p>Future projections of 2-meter minimum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 2
<p>Future projections of 2-meter maximum temperature by the CNN models (BM1, BM10 and BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 11
<p>Future projections of 2-meter maximum, mean and minimum temperatures by the BMlinear model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 1
<p>Input data (ERA5 and the seven GCMs) used to train the CNN models (BMlinear, BM1, BM10 and BMdense) used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 9
<p>Historical projections of all predictands (2-meter maximum, mean and minimum temperatures, and precipitation) by all CNN models (BMlinear, BM1, BM10, BMdense) forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia". Each CNN model architecture is available in the file "model.json" and its optimized weights for each case are available in the file "model_weights.h5".</p>
High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia Part 7
<p>Future projections of precipitation by the BMdense model forced by the seven GCMs used in the GMD paper "High resolution downscaling of CMIP6 Earth System and Global Climate Models using deep learning for Iberia".</p>
Investigating the local temperature response to forest management with a regional climate model
<p>Data used in the manuscript "Investigating the local temperature response to forest management with a regional climate model" which will be submitted to Journal of Geophysical Research: Atmospheres.</p>
A global hybrid tropical cyclone risk model based upon statistical and coupled climate models - Supporting figures and data
<p><strong>Introduction</strong></p><p>This contribution consists of 1) supporting figures and 2) supporting data for the submitted manuscript "A Global Hybrid Tropical Cyclone Risk Model based upon Statistical and Coupled Climate Models." Supporting figures are presented in two interactive HTML documents. The supporting datasets contain tropical cyclone event sets, catalogs, and an example analysis that plots summaries of the simulated catalogs and compares them to historical observations. All files are provided for the 400 ensemble members (event sets) that represent climate model years 1981-2020 (40 years) from the first 10 CESM-LE members.</p><p><strong>Contents</strong></p><p>./Catalog/sim_650</p><p>Tropical cyclone annual catalog for each basin based on the CESM-LE distribution of ENSO phases. 650 simulations are provided for each of 400 event sets. Each file contains the catalog for a single basin and is written as catalog_tc_(BASIN)_sim650_my400_nbinom_condmeanensojma.csv., where BASIN can be NA, EP, WP, NI, SI, SP.</p><p>./Documentation</p><p>TCMODEL_UQAM_EXAMPLE.html: Analysis script showing example of use of the tropical cyclone catalogs and comparison to historical observations.</p><p>UQAM_TC_Model_Data_Supplement_Dictionary.xlsx: Data dictionary of all data supplement file contents.</p><p>./IBTRACS</p><p>Summary of IBTrACS data required in the analysis script.</p><p>./TrajectoryBanks</p><p>Contains subdirectories for each basin (EP, NAT, NI, SI, SP, WP)</p><p>Each subdirectory contains several files summarizing the event sets, or banks, of tropical cyclone trajectories.</p><p><strong>Versions</strong></p><p>Version 1.0.1: Updated TCMODEL_UQAM_SUPPORTING_FIGURES.zip for revisions to submitted manuscript.</p><p>Version 1.0.0: Original version.</p><p> </p>
Data from: Too much of a good thing? Supplementing current species observations with fossil data to assess climate change vulnerability via ecological niche models
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Coupled PPE model output - land parameter impacts on the mean climate state
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Genetic data and climate niche suitability models highlight the vulnerability of a functionally important plant species from south-eastern Australia
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