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270 results for “Climatology”
High resolution solar irradiance variability climatology dataset part 1: direct, diffuse, and global irradiance
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a>.</p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance observations from the Baseline Surface Radiation Network (BSRN) of Cabauw, the Netherlands. This dataset spans 10 years, <em>from 2011-02 until 2020-12-31</em>.</p> <p>This dataset is the preprocessed 1 Hz observational record of direct, diffuse, and global horizontal irradiance, which is the basis for the official BSRN 1-minute dataset published at <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">PANGAEA</a>. Please refer to the official dataset for detailed metadata, instrument information, quality control flags, the full radiation balance and more (1 minute resolution). More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>, and more general information about BSRN is <a href="https://essd.copernicus.org/articles/10/1491/2018/">published on ESSD</a>.</p> <p><strong>Part 1 of 2</strong></p> <p>This dataset is the basis for an analysis of surface solar irradiance variability, derived variables, supplementary meteorological data, and quicklooks, available in part 2 here: <a href="https://doi.org/10.5281/zenodo.7092058">https://doi.org/10.5281/zenodo.7092058</a></p> <p><strong>Usage disclaimer</strong></p> <p>While resampling this 1-Hz data to 1-minute, with the official BSRN quality flags, should reproduce an identical dataset to the official 1-minute BSRN dataset, this has not yet been validated for this version. When you require the most reliable version of the radiation measurements, where variability at a higher resolution than 1 minute is not of concern, please refer to the official BSRN dataset at PANGAEA.</p>
A 10-Year Thundersnow Climatology over China
<p>The data for the article "<strong>A 10-Year Thundersnow Climatology over China</strong>". It contains the monthly thundersnow events number and AO index from 2008-2017. It also contains the ADTD (cloud-to-ground flash) and WWLLN flash data of thundersnow events in China from 2008 to 2017. </p>
Datasets used in "A 23-Year Severe Hail Climatology Using GridRad MESH Observations"
<p>These datasets are those used in the Murillo et al. (2021) publication in the AMS journal of Monthly Weather Review. For any questions, please email Dr. Murillo at emurillo@ucar.edu.</p>
Southern Ocean Monthly Climatology
<p><strong>Outline</strong></p> <p>This is the Southern Ocean Monthly Climatology of Yamazaki et al. "Unlocking Southern Ocean Under-ice Seasonality with a New Monthly Climatology". The interpolation method follows Barth et al. (2014) available via DIVAnd Julia package (https://github.com/gher-uliege/DIVAnd.jl). CTD data sourced from Argo, MEOP, and World Ocean Database (including low resolution ocean station data).</p> <p>The dataset covers south of 40S and above 2000 dbar (above 1000 dbar for "_minimal"). The horizontal grid is 1/4 and 1/2 degs in latitude and longitude, and the vertical grid is the 66 WOA layers. Mixed layer depth, temperature, salinity, crudely derived from max("Δσθ_10m=0.03kg/m3", "Holte&Talley"), are also provided in "_MLD". </p> <p>The following variables are included (* are excluded in "_minimal"):</p> <p>In-situ temperature (°C) in ITS-90</p> <p>Practical salinity (psu)</p> <p>*Standard deviation of temperature (°C), inferred by the spread of observations</p> <p>*Standard deviation of practical salinity (psu), inferred by the spread of observations</p> <p>*Interpolation error of temperature (°C), inferred by the sparsity of observations</p> <p>*Interpolation error of practical salinity (psu), inferred by the sparsity of observations</p> <p>*Cabbeling correction for temperature (°C)</p> <p>*Cabbeling correction for practical salinity (psu)</p> <p>*Density stabilization factor for temperature (°C)</p> <p>*Density stabilization factor for practical salinity (psu)</p> <p> </p> <p><strong>Project Description</strong></p> <p>The advent of under-ice profiling float and biologging techniques has enabled year-round observation of the Southern Ocean and its Antarctic margin. These under-ice data are often overlooked in widely used oceanographic datasets, despite their importance in understanding seasonality and its role in sea ice changes, water mass formation, and glacial melt. We develop a monthly climatology of the Southern using Data Interpolating Variational Analysis, which excels in multi-dimensional interpolation and consistent handling of topography and horizontal advection. The dataset will be instrumental in investigating the seasonality and improving ocean models, thereby making valuable under-ice observations more accessible.</p> <p> </p> <p><strong>Methodology</strong></p> <p>A diverse set of observational data was collected from multiple platforms, including Argo profiling floats, biologging instruments attached to marine mammals, and Conductivity-Temperature-Depth (CTD) profiles obtained from ship-based measurements. This dataset spans more than a century, with a heavier reliance on recent data collected after 2005 by Argo float and biologging. The gridding covers the Southern Ocean from 75°S to 40°S with horizontal grid intervals of 1/2° longitude and 1/4° latitude, resulting in an approximate 25 km resolution at 60°S. The vertical grid consists of 66 WOA layers, ranging from 5 to 2,000 meters in depth, with monthly temporal resolution.</p> <p>The initial climatology field was constructed using Data Interpolating Variational Analysis (DIVA), a sophisticated method designed to handle multidimensional data interpolation. This method efficiently integrates residual data from an existing climatology (WOA23) and applies topographic and advection constraints to improve the spatial correlation of the data, particularly in regions with complex topography, such as the Antarctic margin. </p> <p>The DIVA model underwent a cross-validation process to optimize interpolation parameters, including spatial and temporal correlation scales and the signal-to-noise ratio. Additionally, calibration tests of advection weighting and correlation time scales was carried out to further refine the model's physical consistency. Post-processing involved generating error maps to assess the accuracy of the interpolated fields. Corrections for non-linearities in seawater properties (cabbeling) were applied, along with density stabilization techniques to ensure that the final data output remained physically consistent. <strong><br></strong></p> <p> </p> <p><strong>How to cite</strong></p> <p>Yamazaki, K., Bindoff, N. L., Phillips, H. E., Nikurashin, M., Herraiz-Borreguero, L., & Spence, P. (2025). Unlocking southern ocean under-ice seasonality with a new monthly climatology. <em>Journal of Geophysical Research: Oceans</em>, 130, e2024JC020920. <a href="https://doi.org/10.1029/2024JC020920">https://doi.org/10.1029/2024JC020920</a></p>
The Response of the Southern Ocean to Climatological Iceberg Freshwater Forcing
<p><strong>Data Availability Statement:</strong></p> <p>The data presented here are the model outputs for the study titled <em>"The Response of the Southern Ocean to Climatological Iceberg Freshwater Forcing."</em> Due to size limitations on data uploads, a coarser resolution dataset is provided. For access to the full-resolution dataset, please contact the corresponding author at <strong><a rel="noopener">jingwei.zhang@utas.edu.au</a></strong>.</p>
ACE AMIP climatology
<p>This dataset contains the seasonaly climatology from an AMIP run using ACE and PCMDI SST and sea ice forcings. </p>
Mean and standard deviation climatologies for T & S (for use in e.g. MITprof & ECCO)
<p>All variables are gridded on the 50 levels and lat-lon-cap (LLC90) grid defined in in the ECCO Version 4 paper (https://doi.org/10.5194/gmd-8-3071-2015)</p> <p>1. Monthly climatologies of potential temperature and salinity, blended as described in https://doi.org/10.5194/gmd-8-3071-2015 :</p> <p>- T_OWPv1_M_eccollc_90x50.bin<br> - S_OWPv1_M_eccollc_90x50.bin</p> <p>2. Ocean basin masks : basin_masks_eccollc_90x50.bin</p> <p>3. Non-seasonal standard deviation climatologies, which is used in MITprof as an estimate of representation error for data sets that do not resolve the mess-scale and below (see https://doi.org/10.1175/JPO3072.1 ) e.g. in ECCO v4 for the MITprof insitu data constraints (see https://doi.org/10.5194/gmd-8-3071-2015). </p> <p>These global, time-mean, maps are updated versions of the Forget and Wunsch estimate (https://doi.org/10.1175/JPO3072.1). The latest ones are based on median absolute deviations computed at different resolutions and rescaled according to theory for normal distributions.</p> <p>Non-seasonal temperature standard deviation :</p> <p>- sigma_T_eccollc.bin<br> - sigma_T_mad_feb2013.bin<br> - sigma_T_nov2015.bin (latest, used in MITprof))</p> <p>Non-seasonal salinity standard deviation :</p> <p>- sigma_S_eccollc.bin<br> - sigma_S_mad_feb2013.bin<br> - sigma_S_nov2015.bin (latest, used in MITprof)</p> <p> </p>
Data used in the GRL paper titled "Climatology of Tropical Cyclone Seed Frequency and Survival Rate in Tropical Cyclones"
<p>Data used in the GRL paper titled "Climatology of Tropical Cyclone Seed Frequency and Survival Rate in Tropical Cyclones" by Ikehata and Satoh</p> <p>Please download and decompress the zip file. We describe details of this dataset in "readme.txt".</p>
High resolution solar irradiance variability climatology dataset part 2: classifications, supplementary data, and statistics
<p><strong>Dataset paper</strong></p> <p>See the official dataset description paper (preprint) over at <a href="https://essd.copernicus.org/preprints/essd-2022-456/">Earth System Science Data</a></p> <p><strong>Dataset description</strong></p> <p>High resolution surface solar irradiance series classification, cloud shadow and enhancement statistics, and satellite observations for studying intra-day surface solar irradiance variability.</p> <p><strong>Part 2 of 2</strong></p> <p>This dataset is the derived from the <a href="https://doi.org/10.5281/zenodo.7093164">1 Hz observational record of direct, diffuse, and global horizontal irradiance</a> measured by the Baseline Surface Radiation Network station at Cabauw, the Netherlands. More information about the observational site Cabauw can be found at the <a href="https://ruisdael-observatory.nl/cabauw/">Ruisdael Observatory website</a>.</p> <p><strong>Methodology</strong></p> <p>An extensive dataset description is currently being written for Earth System Science Data. In the mean time, a more condensed description is available in preprint at <a href="https://arxiv.org/abs/2209.10284">Arxiv</a>.</p> <p>Processing scripts are published at this <a href="https://zenodo.org/record/7099491">Zenodo release</a>.</p> <p><strong>Dataset contents</strong></p> <p>This dataset contains daily time series with the following data, from 2011-02 until 2020-12-31:</p> <ol> <li>Cloud shadow and cloud enhancement time series classifications (see methodology)</li> <li>Overcast, clear-sky and variable time series classifications (see methodology)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> for clear-sky global horizontal irradiance (version 3.5)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-mcclear/">CAMS McClear</a> atmospheric composition input (aerosols, ozone, and total column water vapour)</li> <li>Solar elevation and azimuth angles (calculated using <a href="https://github.com/pingswept/pysolar/releases/tag/0.10">PySolar</a>)</li> <li>Quality flags: non-official 1 Hz and official 1-minute (from <a href="https://doi.pangaea.de/10.1594/PANGAEA.940531">BSRN at PANGAEA</a>)</li> <li>Cabauw observatory<a href="https://dataplatform.knmi.nl/dataset/cesar-tower-meteo-lb1-t10-v1-2"> tower wind speed and direction</a></li> </ol> <p>Additional satellite data time series from 2014-01 until 2016-12:</p> <ol> <li>MSGCPP satellite data for an area over central Netherlands (<a href="https://essd.copernicus.org/articles/9/415/2017/">CLAAS2 source</a>)</li> <li>Post processed timeseries of cloud types over Cabauw derived from this MSGCPP satellite data</li> <li>A nubiscope + satellite derived validation dataset for overcast and clear-sky classifications</li> </ol> <p>Statistics files:</p> <ol> <li>Cloud shadow and cloud enhancement event detection and event statistics based on the time series for 2011-2020</li> <li>Daily radiation statistics for 2011-2020</li> </ol> <p>And finally, for all days there are quicklooks available that visualize the irradiance time series, classification, and if available satellite data.</p> <p><strong>Version History</strong></p> <p><em>New in v1.1</em><strong> </strong></p> <ul> <li>CAMS McClear updated from v3.1 to v3.5 (2011-2020)</li> <li>Fix incorrect dominant cloud type in CLAAS2 timeseries (`claas2_processed.zip`, 2014-2016)</li> <li>Update timeseries statistics and quicklooks with new clear-sky data (2011-2020)</li> <li>Added official quality flags (2011-2020)</li> <li>Added preprocessed validation dataset (2014-2016)</li> <li>Added nubiscope to quicklooks (2014-2016)</li> </ul>
A Standardized Precipitation Index (SPI) Climatology for the Continental U.S.
<p>The standardized precipitation index SPI (McKee et al. 1993) was designed to standardize precipitation timeseries across an observational record in order to compute precipitation anomalies in both time and space. The SPI is a widely used drought index that represents the number of standard deviations that observed precipitation deviates from the climatological average precipitation measured at the same location and time period. We calculated the SPI using monthly 4 kilometer resolution precipitation data from the Parameter-elevation with independent slopes model (PRISM; Daly et al. 1994) for the conterminous United States. Monthly PRISM data from 1948-2022 were aggregated to warm season (May-September) and annual (calendar year) totals. SPI anomalies were calculated for each seasonal time window relative to the 1948-2022 timeperiod.</p> <p>Each timeseries of annual precipitation totals was fit to a Gamma probability distribution based on the L-moments of the data. We computed the cumulative distribution function (CDF) associated with the observations using the parameters from the aforementioned Gamma distribution. The CDF values were then evaluated within an inverse Gaussian function with a mean of zero and a standard deviation of one to obtain the final SPI value</p> <p>Coordinate Reference System (CRS) = EPSG:4326</p> <p>McKee, T.B., N.J. Doesken and J. Kleist, 1993: The relationship of drought frequency and duration to time scale. In: Proceedings of the Eighth Conference on Applied Climatology, Anaheim, California,17–22 January 1993. Boston, American Meteorological Society, 179–184.</p> <p>Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158.</p>
Model simulation data used in "A global climatology of ice-nucleating particles under cirrus conditions derived from model simulations with EMAC-MADE3" (Beer et al., Atmos. Chem. Phys., 2022)
<p>This dataset contains the output and the namelist setup of the EMAC-MADE3 global model simulations analysed and discussed in Beer et al. ("A global climatology of ice nucleating particles at cirrus conditions derived from model simulations with EMAC-MADE3", <em>Atmos. Chem. Phys.</em>, 2022).</p>
Climatology of Sundowner Winds in Coastal Santa Barbara, California, Based on 30 yr High Resolution WRF Downscaling
Open the record for dataset details and reuse information.
Supporting Data for Hahn et al. GRL: Antarctic elevation drives hemispheric asymmetry in polar lapse rate climatology and feedback
<p>This dataset includes CESM model output used to create figures in Hahn et al.: “Antarctic elevation drives hemispheric asymmetry in polar lapse rate climatology and feedback” in Geophysical Research Letters. Here we provide monthly climatologies averaged over the last thirty years of each experiment, for flat and control Antarctica with preindustrial and doubled CO<sub>2</sub> forcing. The variables hyam, hybm, and P0, useful for interpolating to pressure levels, are included in the ControlSOM1850.Q.nc file.</p>
Data from: Extending the climatological concept of 'Detection and Attribution' to global change ecology in the Anthropocene
<ol> <li>Research into global change ecology is motivated by the need to understand the role of humans in changing biotic systems. Mechanistic understanding of ecological responses requires the separation of different climatic parameters and processes that often operate on diverse spatiotemporal scales. Yet most environmental studies do not distinguish the effects of internal climate variability from those caused by external, natural (e.g. volcanic, solar, orbital) or anthropogenic (e.g. greenhouse gases, ozone, aerosols, land-use) forcing factors.</li> <li>We suggest extending the climatological concept of 'Detection and Attribution' (DA) to unravel abiotic drivers of ecological dynamics in the Anthropocene. We therefore apply DA to quantify the relative roles of natural versus industrial temperature change on elevational shifts in the outbreak epicentres of the larch budmoth (LBM; <i>Zeiraphera diniana</i> or <i>griseana</i> Gn.); the classic example of a cyclic forest defoliating insect.</li> <li>Our case study shows that anthropogenic warming shifts the epicentre of travelling LBM waves upward, which disrupts the intensity of population outbreaks that occurred regularly over the past millennium in the European Alps. Our findings demonstrate the ability of DA to detect ecological responses beyond internal system variability, to attribute them to specific external climate forcing factors, and to identify climate-induced ecological tipping points.</li> <li>In order to implement the climatological concept of 'Detection and Attribution' successfully into modern global change ecology, future studies should combine high-resolution paleoenvironmental reconstructions and state-of-the-art climate model simulations to inform inference-based ecosystem models.</li> </ol>
Data from: Climatologies at high resolution for the earth's land surface areas
High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.
LMDZ6iso output - Antarctic water stable isotopes in the global atmospheric model LMDZ6: from climatology to boundary layer processes
<h3><strong>Update of the Simulation</strong></h3> <p dir="ltr">This archive contains the output of a revised simulation, addressing issues identified in the original simulation described in the following article:</p> <p><em>Dutrievoz, N., Agosta, C., Risi, C., Vignon, É., Nguyen, S., Landais, A., ... & Prié, F. (2025). Antarctic water stable isotopes in the global atmospheric model LMDZ6: From climatology to boundary layer processes. Journal of Geophysical Research: Atmospheres, 130(5), e2024JD042073.</em></p> <p>All information is available here:<strong> <a href="https://docs.google.com/document/d/1x4FSGtNiJGqsMoks38yATzTQJ7T8sO8iXqeWagTU5yE/edit?usp=sharing">README</a></strong></p> <p>Researchers and colleagues are welcome to contact me at <strong>niels.dutrievoz@lsce.ipsl</strong>.<strong>fr</strong> for any further information regarding this updated simulation or potential collaborations. Please feel free to reach out if you are interested in daily or hourly output data from this simulation.</p> <p dir="ltr">I look forward to any exchanges that might arise from this work.</p> <p> </p>
MOPITT CO monthly climatology 2002-2021
<p>Monthly CO daytime measurements from the satellite-based instrument MOPITT, averaged between 2002 and 2021. We use level 3, version 9 joint NIR/TIR retrievals (doi: 10.5067/TERRA/MOPITT/MOP03JM.009). These data have been compiled for the NCAR ADF model evaluation package (https://github.com/NCAR/ADF).</p> <p>Note: August and September averages are missing 2009 data due to instrument downtime.</p>
Global monthly climatologies of thermal contrast
<p>Thermal contrast (TC) is a key parameter for the sensitivity of (nadir-viewing) infrared sounders. This global dataset includes a 18/01/2021 to 18/01/2022 climatology of TC with respect to three different altitudes (the standard meteorological height of 2 meters, half the height of the boundary layer (HBL), and the height corresponding to the maximum temperature of the lower troposphere (within a maximum of 3 km)): <br>- A gridded dataset as a function of latitude (641 bins), longitude (1280 bins), month (monthly) and local mean solar time (hourly)<br>- A gridded dataset as a function of land cover type (7 classes), latitudinal zone (3 zones), month (monthly) and local mean solar time (hourly)</p><p>This dataset is based on land surface temperature from the Copernicus Global Land Services dataset with air temperatures from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis. A detailed description can be found in T. Di Gioacchino et al. (submitted).</p>
Global climatology of light-absorbing particle deposition on snow
<p><strong>Description</strong></p> <p>Light-absorbing particles (LAPs) deposited at the snow surface significantly reduce its albedo and strongly affect the snow melt dynamics. To better quantify the spatial variability of LAP deposition on snow, Gaillard et al. (2024) have generated a global climatology of LAP deposition on snow. It combines a climatological dataset of LAP deposition rates (Black Carbon (BC) and dust) over the period 1979-2015 (Zhao et al., 2018) with a global dataset of snow cover (Romanov, 2017) to obtain a climatology of LAP deposition rates on snow. The climatology is accompanied by the 25th and 75th percentiles of LAP deposition rates on snow to quantify the inter-annual variability of LAP deposition on snow. </p> <p>The data are distributed in a NetCDF file (<em>climato_tot_publish_v2.nc</em>). More details about the dataset and the file format are given in the file <em>readme_climatology_v2.pdf</em>. </p> <p> </p>
HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)
<p>Codes and data made available as part of the data paper "HighResClimNevada: a high-resolution climatological dataset for a high-altitude region in Southern Spain (Sierra Nevada)" publication. This work presents the HighResClimNevada database, a climatic database for Sierra Nevada (southern Spain) based on data modeled with the Weather Research and Forecasting model. The data used as a reference for the evaluation of HighResClimNevada are freely available online at the websites of the different institutions that develop it, so they are not available here.</p> <p>This research was financially supported by the project "Plan Complementario de I+D+i en el área de Biodiversidad (PCBIO)" funded by the European Union within the framework of the Recovery, Transformation and Resilience Plan - NextGenerationEU and by the Regional Government of Andalucia, the project PID2021-126401OB-I00, funded by MICIU/AEI/10.13039/501100011033 and by FEDER, UE; and LifeWatch-2019-10-UGR-01 co-funded by the Ministry of Science and Innovation through the FEDER funds from the Spanish Pluriregional Operational Program 2014–2020 (POPE) LifeWatch-ERIC action line; and P20_00035 funded by FEDER/Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.