Find research datasets worth reusing

opencc-by-4.0Sep 2022View details →

Atlas of Depth-Duration-Frequency Relationships for Extreme Precipitation in South America and Africa

A continental-scale parameterization of a stochastic weather generator (CLIGEN) was used to determine depth-duration-frequency relationships with coverage of Africa and South America.

opencc-by-4.0Oct 2022View details →

How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data

The dataset contains the rain gauge hourly rainfall series used in the paper "How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation?". Each rain gauge series is saved in one Matlab variable, organized as a structure S with five fields: S.name: the identification name of the rain gauge station S.vals_mm: series of hourly rainfall in millimeter S.time_utc: time steps series, in UTC time S.elev_m: elevation of the station, in m a.s.l. S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N

opencc-by-4.0Jun 2024View details →

Changes in productivity partitioning induced by precipitation extremes increase inaccuracy of grassland carbon estimation

The fraction of net primary productivity (NPP) allocated to belowground organs (fBNPP) in grasslands is a critical parameter in global carbon cycle models; moreover, understanding the effect of precipitation changes on this parameter is vital to accurately estimating carbon sequestration in grassland ecosystems. Here, by conducting both a precipitation gradient experiment (100–500 mm) and a long-term observational study (34 years) in the Inner Mongolia grassland, we showed that fBNPP decreased linearly along the precipitation gradient from extreme drought to extreme wetness due to stronger responses in aboveground NPP (ANPP) to drought and wet conditions than those of BNPP. The linearly decreasing response pattern in fBNPP should be incorporated into models that forecast carbon sequestration in grassland ecosystems; failure to do so will lead to underestimation of the carbon stock in drought years and overestimation of the carbon stock in wet years in grasslands.

opencc-by-4.0Sep 2024View details →

Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"

<h1>Code and Dataset for "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</h1> This dataset accompanies the research paper titled "On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates", currently under review for the AGU Journal GRL. The study introduces a novel Regional Climate Model (RCM) emulator focusing on high-resolution climate downscaling for the New Zealand region. For additional insights and access to the codebase utilized in this research, please refer to our <a href="https://github.com/nram812/On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes">Github Repository</a>. The code can also be found as a ".zip" file: *On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes-main.  <h2>Aims</h2> Our study focuses on two important gaps in the literature regarding the extrapolation of empirical downscaling algorithms. First, we examine how well relationships learned from a historical period extrapolate to future unobserved climates. We compare two widely used algorithms, a GAN and a deterministic CNN baseline, that use a similar architecture (i.e. convolutional layers) trained in a model-as-truth framework to downscale daily precipitation over New Zealand. We evaluate their accuracy in capturing climate change signals in mean and extreme precipitation. Second, we explore whether training on future vs. only historical periods combined with different-sized training datasets can improve extrapolation skill.  <h2>Geographic Focus</h2> Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).   <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> The training data used in this study (for our RCM emulator) spans the historical period and future period (SSP370) of simulation. It comprises daily accumulated precipitation as the primary target variable, alongside large-scale predictor variables.  <ul> <li> Resolution: The target variable is presented at a 12km resolution, reflecting the highest resolution face of RCM for the New Zealand region. Predictor variables are coarsened to a 1.5-degree resolution from original CCAM outputs using conservative interpolation.  </li> <li> Period Coverage: <ul> <li>Training Data: 1960-2100 (Depending on Experiment, see Table 1 for list of experiment configurations)</li> <li>Validation Data: 1985-2014 + 2070-2099 (to compute the climate change signal)</li> </ul> </li> <li> Models: <ul> <li>Training on: ACCESS-CM2</li> <li>Validated on: EC-Earth3, NorESM2-MM, CNRM-CM6-1, AWI-MR-1 </li> </ul> </li> </ul> <h3>File Structure</h3> <ul> <li> Training Data: <ul> <li>Target/Ground Truth (Y): <code>target_ACCESS-CM2_hist_ssp370_pr.nc</code></li> <li>Predictor (X): <code>predictor_ACCESS-CM2_hist_ssp370.nc</code></li> </ul> </li> <li> Evaluation Data: All other GCMs can be accessed in one single file, predictor and target variables have the dimensions (time, lat, lon, GCM). <ul> <li>Target/Ground Truth (Y): <code>Other_GCMs_hist_SSP370_target_fields_pr.nc</code></li> <li>Predictor (X): <code>Other_GCMs_hist_SSP370_predictor_fields.nc</code></li> </ul> </li> </ul> <h2>Methodological Insights</h2> <ul> <li> Regional Climate Model, Our Regional Climate Model training data is from the Conformal Cubic Atmospheric Model (CCAM) which is a global non-hydrostatic atmospheric model renowned for its variable-resolution cubic grid. . For more information about CCAM, please see the following <a href="https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2023JD038530">paper</a>. </li> <li> Predictor and Target Variables: Daily-averaged large-scale prognostic variables, including zonal wind, meridional wind, temperature, and specific humidity, are employed as predictors at the 500mb and 850mb pressure levels. These are normalized (see the GitHub repository for the mean and standard deviation fields). Precipitation is taken as is from CCAM and accumulated for each given day. Static predictors are also used in our model, which is stored in a GitHub repository. </li> <li> Training Framework: Our dataset benefits from the "perfect framework" training strategy, which uses CCAM-coarsened predictor variables. For more information about the perfect and imperfect training frameworks, see the following <a title="review" href="https://journals.ametsoc.org/view/journals/aies/3/2/AIES-D-23-0066.1.xml">review</a> </li> </ul> <table> <tbody> <tr> <td> Algorithm </td> <td> Training Data </td> <td> Period </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Historical </td> <td> 1960-2014 (~21,000 days) </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Future (SSP370) </td> <td> 2044-2099 (~21,000 days) </td> </tr> <tr> <td> Deterministic Baseline </td> <td> Historical and Future (SSP370) </td> <td> 1960-2099 (~51,000 days) </td> </tr> <tr> <td> Residual GAN </td> <td> Historical </td> <td> 1960-2014 </td> </tr> <tr> <td> Residual GAN </td> <td> Future (SSP370) </td> <td> 2044-2099 </td> </tr> <tr> <td> Residual GAN </td> <td> Historical and Future (SSP370) </td> <td> 1960-2099 </td> </tr> </tbody> </table> Table 1: The six RCM emulator experiments performed in this study.

opencc-zeroDec 2018View details →

Data from: Do precipitation extremes drive growth and migration timing of a Pacific salmonid fish in Mediterranean‐climate streams?

Climate change is expected to increase weather extremes and variability, including more frequent weather whiplashes or extreme swings between severe drought and extraordinarily wet years. Shifts in precipitation patterns will alter stream flow regimes, affecting critical life history stages of sensitive aquatic organisms. Understanding how threatened fish species, such as steelhead/rainbow trout (Oncorhynchus mykiss), are affected by stream flows in years with contrasting environmental conditions is important for their conservation. Here, we report how extreme wet and dry years, from 2015 to 2018, affected stream flow patterns in two tributaries to the South Fork Eel River, California, USA, and aspects of O. mykiss ecology, including over‐summer fish growth and body condition as well as spring out‐migration timing. We found that stream flow patterns differed across years in the timing and magnitude of large winter–spring flow events and in summer low‐flow levels. We were surprised to find that differences in stream flows did not impact growth, body condition, or timing of out‐migration of O. mykiss. Fish growth was limited in the late summer in these streams (average of 0.02 ± 0.05 mm/d), but was similar across dry and wet years, and so was end‐of‐summer body condition and pool‐specific biomass loss from the beginning to the end of the summer. Similarly, O. mykiss migrated out of tributaries during the last week of March/first week of April regardless of the timing of spring flow events. We suggest that the muted response to inter‐annual hydrologic variability is due to the high quality of habitat provided by these unimpaired, groundwater‐fed tributaries. Similar streams that are likely to maintain cool temperatures and sufficient base flows, even in the driest years, should be a high priority for conservation and restoration efforts.

opencc-by-4.0Jul 2021View details →

Comparing the Influence of Global Warming and Urban Anthropogenic Heat on Extreme Precipitation in Urbanized Pearl River Delta Area Based on WRF Dynamical Downscaling

The simulation outputs from the Weather Research and Forecasting (WRF) v3.8.1 coupled with single layer urban canopy model from three experiments (HIST_AH300, HIST_AH0, and RCP85_AH300). Variables include hourly precipitation, wind, specific humidity, relative humidity, convective available potential energy, convective inhibition, temperature, model height, land use land cover, and topography.

opencc-by-4.0Jan 2021View details →

Dataset for "A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods"

Dataset for "A novel method to identify sub-seasonal clustering episodes of extreme precipitation events and their contributions to large accumulation periods". Added file "Obs_metrics_all.RData" containing p-value for significance test of the clustering metric S_cl. Accepted version (August 2021).

opencc-by-4.0Jan 2023View details →

Data from: Indices of Extremes: Geographic patterns of change in extreme temperature and precipitation under climate intervention

This dataset comprises the python notebooks and associated data used to produce Figures 1-9, 12-14, and all supplemental figures in  Tye et al. 2022 "Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention" Earth System Dynamic, 13, 1233-1257. https://doi.org/10.5194/esd-13-1233-2022 Script is also included to process data from NCAR's HPC Campaign archive and produce figures 10 and 11. The full output from the GLENS simulation are available from from https://data.ucar.edu/dataset/stratospheric-aerosol-geoengineering-large-ensemble-project-glens

opencc-by-4.0Mar 2023View details →

Nonlinear response of productivity to precipitation extremes in the Inner Mongolia grassland

As precipitation (PPT) regimes are becoming more extreme worldwide, our understanding of how terrestrial ecosystems will respond to this aspect of climate change remains unclear. We addressed this research gap by a PPT gradient experiment that included PPT amounts simulating extremely dry and wet years in combination with a long-term (40 years) observational study in a grassland ecosystem. This study illustrates how a grassland responded to greater PPT variability and highlights the role of limiting resource shift in ecosystem responses to climate extremes. Thus, to accurately forecast future ecosystem feedbacks to climate change, we should take into account the negative asymmetric feature of ecosystem response to stronger climate variability.

opencc-by-4.0Apr 2023View details →

Sharing data and code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols"

The public data repository contains the data and plotting code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols". The dataset includes annual maximum one-day precipitation (Rx1day), its proxy computed by a physical scaling diagnostic (scaling), and the decomposed components of the scaling (i.e., thermodynamic response, dynamic response, and their interaction). Several reanalyses (including ERA5 and JRA55) and CMIP6 simulations under several scenarios (including ALL, GHG, AER, and piControl) are applied to compute the historical Rx1day, scaling, and the associated components following a GitHub Python repository (<a href="https://github.com/oliverangelil/precip_extremes_scaling">https://github.com/oliverangelil/precip_extremes_scaling</a>). Note that the decomposed components are calculated as anomalies.  The data results of the extreme precipitation decomposition in the NetCDF format are available in zip files “ERA5”, "JRA55", "ALL", "GHG", "AER", "NAT", and "piControl". Code for visualizations is available in the "Jupyter Notebooks" zip file.

opencc-by-4.0May 2023View details →

ARISE-SAI_1.5: UKESM1 Extreme Temperature and Precipitation Indices

The UKESM1 ARISE experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere. The injections occur at four different latitudes: 15 degrees N & S and 30 degrees N & S, at an altitude of approximately 20 km. The simulations are based on the medium-emissions CMIP6 scenario ssp245 and cover the years 2035 to 2070. They form a 5-member ensemble with initial conditions taken from the corresponding five members of the ssp245 simulations upon which they were based. Data for the UKESM1 ARISE-SAI-1.5 simulations is available at https://data.ceda.ac.uk/badc/deposited2022/arise and the data for the UKESM1 SSP2-4.5 simulations is available on the Earth System Grid Federation database. The files included 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). Precipitation Indices PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT Temperature Indices TNN, TNX, FD, TR, TN90, TN10, TXX, TXN, ID, SU, TX90, TX10

opencc-by-4.0Nov 2022View details →

North American Monsoon Extreme Precipitation Supporting Datasets

This dataset contains 1) shapefiles for the North American Monsoon domain and its subdomain derived from the global CPC precipitation dataset, and 2) moisture surges along the Gulf of California identified with the vertical-integrated vapor transportation from the ERA5 reanalysis dataset. 3) supporting figures for the NAM EPE analysis.

publicMar 2019View details →

Data from: Do precipitation extremes drive growth and migration timing of a Pacific salmonid fish in Mediterranean‐climate streams?

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publicAug 2024View details →

Global record-breaking recurrence rates indicates more widespread and intense surface air temperature and precipitation extremes

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publicOct 2025View details →

Data from: More intermittent mid-latitude precipitation accompanied extreme early Paleogene warmth

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dryad32/100

Data from: Extreme precipitation variability, forage quality and large herbivore diet selection in arid environments

Nutritional ecology forms the interface between environmental variability and large herbivore behaviour, life history characteristics, and population dynamics. Forage conditions in arid and semi-arid regions are driven by unpredictable spatial and temporal patterns in rainfall. Diet selection by herbivores should be directed towards overcoming the most pressing nutritional limitation (i.e. energy, protein [nitrogen, N], moisture) within the constraints imposed by temporal and spatial variability in forage conditions. We investigated the influence of precipitation-induced shifts in forage nutritional quality and subsequent large herbivore responses across widely varying precipitation conditions in an arid environment. Specifically, we assessed seasonal changes in diet breadth and forage selection of adult female desert bighorn sheep Ovis canadensis mexicana in relation to potential nutritional limitations in forage N, moisture and energy content (as proxied by dry matter digestibility, DMD). Succulents were consistently high in moisture but low in N and grasses were low in N and moisture until the wet period. Nitrogen and moisture content of shrubs and forbs varied among seasons and climatic periods, whereas trees had consistently high N and moderate moisture levels. Shrubs, trees and succulents composed most of the seasonal sheep diets but had little variation in DMD. Across all seasons during drought and during summer with average precipitation, forages selected by sheep were higher in N and moisture than that of available forage. Differences in DMD between sheep diets and available forage were minor. Diet breadth was lowest during drought and increased with precipitation, reflecting a reliance on few key forage species during drought. Overall, forage selection was more strongly associated with N and moisture content than energy content. Our study demonstrates that unlike north-temperate ungulates which are generally reported to be energy-limited, N and moisture may be more nutritionally limiting for desert ungulates than digestible energy.

opencc-zeroDec 2016View details →

opencc-by-4.0Dec 2023View details →

Northeast US Extreme Precipitation Trends in GFDL SPEAR_HI

Zenodo repository for: <ul><li>Jong, B.-T., H. Murakami, T. L. Delworth, and W. F. Cooke: Synoptic-scale contributions to extreme precipitation trends over the Northeast U.S. in 25-km GFDL SPEAR (to be submitted to Earth's Future)</li></ul>Please also cite and refer to:<ul><li>Jong, B.-T., Delworth, T. L., Cooke, W. F., Tseng, K.-C., & Murakami, H. (2023). Increases in extreme precipitation over the Northeast United States using high-resolution climate model simulations. Npj Climate and Atmospheric Science, 6(1), 18. <a href="https://doi.org/10.1038/s41612-023-00347-w">https://doi.org/10.1038/s41612-023-00347-w</a></li><li>Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J., Dunne, K. A., et al. (2020). SPEAR: The Next Generation GFDL Modeling System for Seasonal to Multidecadal Prediction and Projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS00189. <a href="https://doi.org/10.1029/2019ms001895">https://doi.org/10.1029/2019ms001895</a></li></ul>For more details about SPEAR_HI simulations, please see <a href="https://www.gfdl.noaa.gov/spear/">https://www.gfdl.noaa.gov/spear/</a>

opencc-by-4.0Dec 2023View details →

Data for the paper Constraints on regional projections of mean and extreme precipitation under warming

Data for the paper " Constraints on regional projections of mean and extreme precipitation under warming"

opencc-by-4.0Jan 2024View details →

Global variable-resolution simulations of extreme precipitation over Henan, China in 2021 with MPAS-Atmosphere v7.3

This repository encompasses data and software to "Global variable-resolution simulations of the 2021 extreme precipitation event in Henan, China with MPAS-Atmosphere v7.3". The data and model are integral to our study, which focuses on simulating a significant rainstorm event in Henan, China, in July 2021. Contained within this archive are the following components: MPAS-Atmosphere v7.3: This is the core model employed in our study. MPAS v7.3 is a versatile global variable-resolution model, adept at simulating extreme weather events at varying scales. MPAS mesh data: The global meshes generated for the experiments. CMA Observation Data: Ground-based observational data from the China Meteorological Administration, crucial for the validation of our simulation results. ERA5 Reanalysis Data: These datasets are used to further validate the outcomes of our simulations, providing a comprehensive set of atmospheric parameters. GFS Data: The Global Forecast System (GFS) data serve as the input fields for the MPAS model, providing essential initial conditions for our simulations. This collection is aimed at offering researchers a holistic package for studying, replicating, or extending our findings on extreme weather phenomena. The data and model provided here are not only crucial for reproducing the results presented in our study but also offer a valuable resource for further research in atmospheric sciences and climate modeling.