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2,322 results for “precipitation”
Multi-thousand-year simulations of December-February precipitation and zonal upper-level wind
<p>This dataset contains multi-thousand-year ensemble simulations of wintertime (December-February) precipitation total and average zonal winds at 250 hPa and 850 hPa. It includes data in a present-day scenario (2006-2015) and two future scenarios within which the world would be 1.5°C and 2.0°C warmer than pre-industrial conditions in 1850-1900. The simulations were run through the global model of the atmosphere and land surface HadAM4 (Williams et al., 2003) with a horizontal resolution of 5/6°x5/9° (approximately 60km in middle latitudes) and 38 vertical levels and a large ensemble. Following the HAPPI experiment design described by Mitchell et al. (2017), simulations were driven by prescribed fields of sea ice concentration, sea surface temperature, and atmospheric gas concentrations. The prescribed fields are observations for the present-day scenario. For future simulations, the prescribed fields were modified based on changes derived from CMIP5 multi-model means. The different realisations of the large ensemble were obtained through perturbing the initial conditions of each ensemble member on November 1st. For more details, see the description in Watson et al. (2020), who present the dataset, and in Bevacqua et al. (2021) where the dataset was used to study the spatial footprint of wintertime precipitation extremes.</p> <p><strong>IMPORTANT</strong>: Note that a small fraction of the ensemble members is repeated in the dataset. Duplicates should be identified (for example, via the function duplicated() in the R software) and removed prior to any analysis. </p>
Trained convolutional neural network for the identification of long-duration mixed precipitation in Montréal (Canada)
<p>In this dataset the trained convolutional neural network is published that accompanies the paper "A deep learning approach for the identification of long-duration mixed precipitation in Montréal (Canada)" submitted to the special issue on "Machine-Learning Applications in the Atmospheric and Oceanic Sciences" by the journal Atmosphere&Ocean.</p> <p>The files were created using tensorflow in python. The trained network is available in .h5-format the history as numpy-file (npy).</p>
Bias-corrected monthly precipitation data over South Siberia for 1979-2019
<p>Bias-<strong>C</strong>orrected <strong>P</strong>recipitation data over <strong>S</strong>outh <strong>S</strong>iberia (<strong>CPSS 1.2</strong>) contains monthly precipitation data for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il’in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude. CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format.</p> <p>Copernicus Climate Change Service (C3S), 2017. <em>ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate.</em> Copernicus Climate Change Service Climate Data Store (CDS), Available at <a href="https://cds.climate.copernicus.eu/cdsapp#!/home"><em>https://cds.climate.copernicus.eu/cdsapp#!/home</em></a></p> <p>Il’yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. <em>Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges</em>. Available at <a href="http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov"><em>http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov</em></a></p>
Level A Pan Europe Precipitation, E-HYPE 2.5
Time-series for 1961 to 2001 describing the input forcing precipitation for each E-HYPE subbasin. The data describes daily values for each subbasin (mean area 215 km2). Unit of measurement: mm. The precipitation is derived from the WFD gridded data set which consists of the ERA-40 reanalysis data set daily precipitation output (Weedon et al. 2011)corrected for wet days mean monthly precipitation and undercatch (for example to CRU and GPCC). For each subbasin, the gridpoint nearest the subbasin centroid is used. Within E-HYPE a correction is made to increase precipitation above 700 m elevation. Original data source was WFD Forcing data and HYDE was used for repurposing. The data format is a zipped text file. On the first row there is a brief description and the rest of the file contains one row per time step, with the sub basin id as each column header on the second row. The first item on each line is the time step using the format YYYY-MM-DD, so 1961-01-01 for the first of January 1961.
Moisture-Precipitation Couplings for Mesoscale Convective Systems in Tracking Data and Idealized Simulations
<p>Morphological properties, collocated synoptic conditions, and collocated rainfall for mesoscale convective systems in 1) the ISCCP Convective Tracking (CT) dataset with coincident data from the ERA-Interim (ERA-I) reanalysis and the Multi-Source Weighted-Ensemble Precipitation (MSWEP) product and 2) long-channel radiative-convective equilibrium (RCE) simulations in the System for Atmospheric Modeling (SAM).</p> <p><strong>ISCCP_tracking_colloc.tar.gz </strong>- NetCDF files by year from 2000 to 2004 inclusive including ISCCP-CT morphological properties of MCSs, a series of collocated synoptic variables from ERA-5 (including specific humidity, temperature, vertical velocity, and cloud condensate profiles), and collocated precipitation intensity and accumulation from MSWEP.</p> <p><strong>RCE_colloc_execution1.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by extracting and averaging the variables over grid cells where the precipitation is greater than either its mean (RCE_COL_MEAN_*.nc) or its 99th percentile (RCE_COL_99_*.nc).</p> <p><strong>RCE_colloc_execution2.tar.gz</strong> - NetCDF files including RCE-SAM extent of MCSs (RCE_COL_cluster-sizes_*.nc), as well as collocated synoptic variables. Collocation of synoptic variables is performed for these files by taking either the mean (RCE_COL_MEAN_*.nc) or the 99th percentile (RCE_COL_99_*.nc) value over all grid cells within the MCS.<br><br>For the NetCDF files from RCE output, the numeric value in the file name is the corresponding sea surface temperature from 280 to 310 K.</p>
Indian Precipitation Ensemble Dataset (IPED)
<p>The<strong> Indian Precipitation Ensemble Dataset (IPED)</strong> is the first observation-based ensemble gridded precipitation dataset for India. It includes the mean and standard deviation of 30 ensembles daily from 1991 to 2023 at a resolution of 0.1°.</p> <p>The dataset contains two folders:</p> <ol> <li>IPED Ensemble's Mean</li> <li>IPED Ensemble's Standard Deviation</li> </ol> <p>For detailed information about this dataset and its development, please refer to the original research article published in the <em>Scientific Data:</em></p> <p><em>Peringiyil, A., Saharia, M., O. P., S. <em>et al.</em> A station-based 0.1-degree daily gridded ensemble precipitation dataset for India. <em>Sci Data</em> <strong>12</strong>, 333 (2025). <a href="https://doi.org/10.1038/s41597-025-04474-2">https://doi.org/10.1038/s41597-025-04474-2</a></em></p> <p><strong>Disclaimer</strong></p> <p>When using the IPED dataset, users must cite it along with the associated research article published in "Scientific Data". </p> <p><strong>To Be Cited:</strong></p> <ol> <li>Peringiyil, A., Saharia, M., O. P., S. <em>et al.</em> A station-based 0.1-degree daily gridded ensemble precipitation dataset for India. <em>Sci Data</em> <strong>12</strong>, 333 (2025). <a href="https://doi.org/10.1038/s41597-025-04474-2">https://doi.org/10.1038/s41597-025-04474-2</a></li> <li>Anagha P, & Manabendra Saharia. (2025). Indian Precipitation Ensemble Dataset (IPED) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.8199138">https://doi.org/10.5281/zenodo.8199138</a></li> </ol>
seNorge/RR: daily total precipitation amounts over Norway
<p>seNorge_2018 is a collection of observational gridded datasets for several near surface variables. This is the dataset of daily total precipitation amount (RR and RRa) for the 66-year period 1957-2022. RR is the daily total amount of precipitation (precipitation day definition: yesterday at 06 UTC / today at 06 UTC). RRa is the daily total amount of precipitation without adjustment for the wind undercatch. The grid spacing is 1 km. The data sources are: the Norwegian Meteorological Institute Climate Database, the Swedish Meteorological and Hydrological Institute Open Data API, the Finnish Meteorological Institute open data API and the European Climate Assessment & Dataset (www.ecad.eu). See also: https://github.com/metno/seNorge_docs/wiki/seNorge_2018</p>
Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in Dec 2023.
<p>Dataset and plot generation script for article "Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits" by Francois Bouttier and Hugo Marchal, submitted in NHESS journal in Dec 2023.</p> <p>For further technical details read the file READMEdata in the zipfile. The script MAKEFIG remakes all the figures from the data.</p> <p>For scientific details read the associated article preprint on the NHESS egusphere website.</p>
108 Basins containing a minimum of 50 years of daily discharge and precipitation observations
<p>Contained within this dataset are the (1) list of USGS basins used in the Livneh et al. 2024 (in-review) analysis of the intermittency-flooding relationship, (2) shapefile of the basins, and (3) the script used to generate the list of basins from the GAGES II dataset. If using these basins or the script to generate a list of basins that meet a certain set of criteria, please cite this dataset accordingly. </p>
Streamflow, precipitation, soil moisture, and ephemeral stream nitrogen data for St. Croix, USVI
<p>These datasets were collected from two ephemeral stream sites within the Salt River watershed on St. Croix, USVI using high frequency (15-minute) sensors. The stream nitrogen data were collected via grab samples and were analyzed with a benchtop spectrophotometer. The data were collected to better understand the influence of precipitation and soil moisture conditions on stream nitrogen concentrations. </p>
Elevated increase in compound extreme heat-precipitation events over China
<p>This file contains the fractions (in percentage) of the compound extreme precipitation events that are preceded by an extreme heat event in China during 1961-2017. The compound events are identified based on the CN05.1 dataset at 0.5x0.5 resolution. Please contact us with any questions or concerns (email: luo.ming@hotmail.com).</p>
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
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>
A convection-permitting and limited-area model hindcast driven by ERA5 data: MOLOCH precipitation monthly data for the period 1979-2019
<p>This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";</p> <p>Grid spacing = "2.5 km";</p> <p>Grid = "506x626"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: BOLAM precipitation monthly data for the period 1979-2019
<p>This dataset represents a hindcast of monthly total precipitation for the period 1979-2019. Data were obtained using the BOLAM model fed by ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: -26 to 53.121 by 0.089 degrees_east; latitude: 25.035 to 58.705 by 0.07 degrees_north (the Mediterranean Sea and nearby areas)";</p> <p>Grid spacing = "7 km";</p> <p>Grid = "890x482"</p>
A convection-permitting and limited-area model hindcast driven by ERA5 data: MOLOCH precipitation daily data for the period 1979-2019
<p>This dataset represents a hindcast of daily total precipitation for the period 1979-2019. Data were obtained using the convection-permitting MOLOCH model fed by BOLAM and ERA5 data as initial and boundary conditions. For additional details, see the reference below.</p> <p>Citation = "Capecchi V, et al 'A convection-permitting and limited-area model hindcast driven by ERA5 data: precipitation performances in Italy.' Climate Dynamics 61.3 (2023): 1411-1437";</p> <p>Creator_name = "Valerio Capecchi";</p> <p>Contact = "capecchi@lamma.toscana.it";</p> <p>Institute = "LaMMA - Laboratorio di Meteorologia e Modellistica Ambientale per lo sviluppo sostenibile";</p> <p>Geospatial bounds = "longitude: 2.4 to 19.873; latitude: 34.21235 to 49.64985 (Italy and nearby areas)";</p> <p>Grid spacing = "2.5 km";</p> <p>Grid = "506x626"</p>
ARISE-SAI_1.5 : CESM2 Extreme Precipitation and Temperature Indices
<p>Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection (ARISE-SAI) is a set of simulations carried out with the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model, version 6 (CESM2(WACCM6)) that aims at simulating a plausible deployment of solar climate intervention of stratospheric aerosol injection to enable community assessment of responses of the Earth system.</p> <p>This dataset uses the first set of simulations, called ARISE-SAI-1.5, that utilized the middle-of-the-road SSP2-4.5 emission scenario, and targetted a global mean surface air temperature near 1.5°C above the pre-industrial value. ARISE-SAI-1.5 is described in Richter et al. (2022). Selected data are available at Richter & Visioni (2022a,b).</p> <p>The files contained here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022).</p> <p><strong>Precipitation Indices</strong></p> <p>PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT</p> <p><strong>Temperature Indices</strong></p> <p>TNN, TNX, FD, TR, TN90, TN10, TN90p, TN10p, TXX, TXN, ID, SU, TX90, TX10, TX10p, TX90p, WSDI</p> <p>Where T?10 is the number of days below an annual 10th percentile threshold and T?90 is the number of days above an annual 90th percentile threshold (i.e. around 30 days per year).</p> <p>T?10p as defined by ETCCDI is the frequency of days below the rolling 5-day average climatological day of year 10th percentile. This threshold is also used for the cold spell duration index (CSDI), or consecutive days that are cool for the season.</p> <p>T?90p as defined by ETCCDI is the frequency of days above the rolling 5-day average climatological day of year 90th percentile. This threshold is also used for the warm spell duration index (WSDI), or consecutive days that are warm for the season.</p>
GeoERA RESOURCE CHAKA data set which contains time series of precipitation and discharge of springs in the CHAKA pilot areas (D5.5)
<p>Dataset which contains time series of precipitation and discharge of springs in the pilot areas of the CHAKA work package of the GeoERA RESOURCE project. The file contains precipitation and spring discharge data of 16 pilot areas in the Karst & Chalk work package. A description of the application of the dataset for the characterisation of the typology of karst systems in given in the D5.3 deliverable of GeoERA RESOURCE of which the pdf is provided. Further information about the CHAKA results can be assessed though the webservices of the European Geological Data Infrastructure (EGDI). </p>
Data for Publication "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1"
<p>The data are made available as part of the paper "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1", submitted to Geoscientific Model Development. This data set incorporates selected postprocessed files needed to reproduce the results presented in the paper. </p> <p>The files including the monthly means of precipitation for domain 2 (5 km) are named following the same structure:</p> <p> RR-D02-EXPERIMENTNAME-25km3Doms-YYYY_monthly.nc</p> <p>These are the options available in each case:</p> <ul> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1 <br> from the paper.</li> <li>YYYY: 2008 or 2012. This is only applicable to precipitation.</li> </ul> <p>The field means for the northeastern slopes follow this structure:</p> <p> VARIABLE-D02-ERA5-Peru-25-Present-EXPERIMENTNAME-2008.EastLow.fldmean.nc</p> <ul> <li>VARIABLE: CLOUDFRA, PW, RH2, RR, SMOIS or T2. This abbreviations represent the following variable from the model<br> respectively: cloud fraction, precipitable water, relative humidity at 2 meters, total precipitation, soil moisture<br> and temperature at 2 meters.</li> <li>EXPERIMENTNAME: Europe, SouthAmerica, Kenya, Micro13 or NoCumulus. These are the names included in Table 1<br> from the paper.</li> </ul> <p>These files contained hourly values so to obtain the monthly means or sums the user must perform the following command:</p> <p> cdo monmean/monsum in.nc out.nc</p> <p>Two .txt files including the information about the stations considered for the validation of the WRF experiments for year 2008 or 2012 are also included. The data is separated with white spaces, and the structure of the columns is the following one:</p> <p> STATION LATITUDE LONGITUDE ELEVATION COUNTRY PROVIDER REGION</p> <p>Finally, two .zip files are provided. Scripts.zip includes all the scripts used to read, process and plot the data from the model, and Namelist_files.zip includes all the namelist files used to run the WRF simulations.</p> <p> </p>
Data for publication "Statistical characteristics of extreme daily precipitation during 1501 BCE - 1849 CE in the Community Earth System Model".
<p>Here, the data used in Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). "Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model" in <em>Climate of the Past </em>(<a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-6</a>) are provided.</p> <p>Two simulations covering the period 1501 BCE - 2008 CE are performed with CESM 1.2.2: the orbital-only and the full-forcing simulations. The full-forcing transient simulation includes the new long record of volcanic eruptions (<a href="https://doi.org/10.1594/PANGAEA.928646">https://doi.org/10.1594/PANGAEA.928646</a>) that covers the last 3500 years. The output from the simulations is used to examine the long-term variability and characteristics of daily extreme precipitation during 1501BCE-1849 CE.</p> <p>The following files are provided:</p> <ul> <li> <strong>CESM122.transient.PRECT.anom.above99th.1501BCE-1849CE_I and II</strong>: Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the full forcing simulation. The file is split into two parts, with the first file containing the first 50% of extremes (I) and the second file containing the rest 50% (II).</li> <li><strong>CESM122.orbital.PRECT.anom.above99th.1501BCE-1849CE I and II:</strong> Daily precipitation anomalies above the 99th percentiles relative to 1501BCE-1849CE from the orbital-only simulation.</li> <li> <strong>CESM122.transient.variables.mon.1979-2008CE:</strong> monthly precipitation, temperature, and geopotential height at 500 hPa for 1979-2008CE from the full-forcing simulation.</li> <li><strong>CESM122.trans.variable_names.years:</strong> Monthly variables from the full-forcing simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE. The variables are solar insolation (SOLIN), clear-sky net surface shortwave radiation (FSNSC), geopotential height at 500hPa (Z500), and surface temperature (TS).</li> <li><strong>CESM122.orbital.variable_names.years:</strong> Monthly variables from the orbital-only simulation. The simulation starts from the model year 1, which corresponds to the actual year 1501BCE.</li> <li><strong>CESM122.*.log-likelihood-GPDmodel-ExtForcing</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for external forcings.</li> <li> <strong>CESM122.*.log-likelihood-GPDmodel-ModesVar</strong>: Negative log-likelihood for the stationary and non-stationary Generalized Pareto Distribution models for modes of variability.</li> <li><strong>Evolk_EVA_distribution_1501BCE-2015CE</strong>: Distribution of volcanic aerosol for CAM5, produced based on Kim et al. (2021).</li> </ul> <p>If you use this dataset, please cite:</p> <p><em>Kim, W. M., Blender, R., Sigl, M., Messmer, M., & Raible, C. C. (2021). Statistical characteristics of extreme daily precipitation during 1501 BCE–1849 CE in the Community Earth System Model. Climate of the Past Discussions, 1-38. <a href="https://doi.org/10.5194/cp-2021-61">https://doi.org/10.5194/cp-2021-61</a></em></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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