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132 results for “climate, precipitation”
Data for "Impact of Precipitation Mass Sinks on Midlatitude Storms in Idealized GCM Simulations over a Wide Range of Climates"
<p>Code, simulation input files, and postprocessed simulation output data supporting "Impact of Precipitation Mass Sinks on Midlatitude Storms in Idealized GCM Simulations over a Wide Range of Climates", submitted to Weather and Climate Dynamics. Enclosed README file provides detailed descriptions of the archive contents.</p>
Model output for a storyline analysis of hurricane Irma's precipitation under various levels of climate warming
<p>Understanding how extreme weather, such as tropical cyclones, will change with future climate warming is an interesting computational challenge. Here, the hindcast approach is used to create different storylines of a particular tropical cyclone, Hurricane Irma (2017). Using the Community Atmosphere Model, we explore how Irma's precipitation would change under various levels of climate warming. Analysis is focused on a 48-hour period where the simulated hurricane tracks reasonably represent Irma's observed track. Under future scenarios of 2 K, 3 K, and 4 K global average surface temperature increase above pre-industrial levels, the mean 3-hourly rainfall rates in the simulated storms increase by 3-7%/K compared to present. This change increases in magnitude for the 95th and 99th percentile 3-hourly rates, which intensify by 10-13%/K and 17-21%/K, respectively. Over Florida, the simulated mean rainfall accumulations increase by 16-26%/K, with local maxima increasing by 18-43%/K. All percent changes increase monotonically with warming level.</p>
Climate characteristics and trends of extreme daily precipitation events associated with cold fronts in the metropolitan region of São Paulo, Brazil
<p>Data used in the paper "Climate characteristics and trends of extreme daily precipitation events associated with cold fronts in the metropolitan region of São Paulo, Brazil" from Theoretical and Applied Climatology</p>
Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China
<p>Data used in the manuscript "<strong>Object-based evaluation of precipitation systems in convection-permitting regional climate simulation over eastern China</strong>" which was submitted to Journal of Geophysical Research: Atmospheres. </p>
Data from: Climatic influences on winter precipitation use by trees in summer
<p>Trees in seasonal climates may use water originating from both winter and summer precipitation. However, the seasonal origins of water used by trees have not been systematically studied. We used stable isotopes of water to compare the seasonal origins of water found in three common tree species across 24 Swiss forest sites sampled in two different years. The data set provides information on the sites (e.g., latitude/longitude, site name), site characteristics (e.g., weather/climate), tree species studied (beech, spruce and oak), and corresponding observations of stable isotopes of hydrogen and oxygen in tree xylem water. </p>
Precipitation Efficiency Constraint on Climate Change
<p>Precipitation efficiency (PE) relates cloud condensation to precipitation and intrinsically binds atmospheric circulation to the hydrological cycle. Due to PE's inherent microphysical dependencies, definitions and estimates vary immensely. Consequently, PE's sensitivity to greenhouse warming and implications for climate change are poorly understood. Here, we quantify PE's role in climate change by defining a simple index as the ratio of surface precipitation to condensed water path. This macroscopic metric is reconcilable with microphysical PE measures and higher is associated with stronger mean Walker circulation. We further find that state-of-the-art climate models disagree on the sign and magnitude of future changes. This sign disagreement originates from models' convective parameterizations. Critically, models with increasing under greenhouse warming, in line with cloud-resolving simulations, show greater slowdown of the large-scale Hadley and Walker circulations and a two-fold greater increase in extreme rainfall than models with decreasing.</p>
Lagrangian Heavy Precipitation Events in convection-permitting Regional Climate Models over the Alps and in the Mediterranean
<p>The csv datafile contains a set of heavy precipitation events identified in cpRCMs.</p> <p>Each of the entries represents an event and is described with detailed properties:</p> <p>'Start Date [YYYYMMDD.HOUR/24]', 'Latitude [°]', 'Longitude [°]',<br> 'Duration [h]', 'Volume [km² h]', 'P99(pr) [mm h-1]',<br> 'P90(pr) [mm h-1]', 'P75(pr) [mm h-1]',<br> 'P50(pr) [mm h-1]', 'P25(pr) [mm h-1]',<br> 'P10(pr) [mm h-1]', 'Total Precipitation [m3]',<br> 'Maximum Precipitation [mm h-1$]',<br> 'Mean Precipitation [mm h-1$]', 'Direction [°]',<br> 'Distance Traveled [km]', 'Eccentricity [-]', 'Track Eccentricity [-]',<br> 'Mean Ellipsicity [-]', 'Track Ellipsicity [-]', 'Mean Major Angle [°]',<br> 'Track Major Angle [°]', 'Mean Major Axis [-]', 'Track Major Axis [-]',<br> 'My Orientation [°]', 'My Track Orientation [°]', 'max(Elevation) [m]',<br> 'min(Elevation) [m]', 'Start Year [YYYY]', 'Start Month [MM]',<br> 'LandFallSea [-]', 'Scenarios', 'Models', 'situations', 'Ensemble',<br> 'Speed [km h$^{-1}$]', 'Mean(Area) [km²]', 'orographic [-]',<br> 'Severity [-]', 'I/O OBS [-]', 'Region [-]', 'orographic1500 [-]',<br> 'orographic2000 [-]', 'orographic2500 [-]', 'orographic3000 [-]']</p>
How well does a convection-permitting climate model represent the reverse orographic effect of extreme hourly precipitation? - Observed precipitation data
<p>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:</p> <p>S.name: the identification name of the rain gauge station</p> <p>S.vals_mm: series of hourly rainfall in millimeter</p> <p>S.time_utc: time steps series, in UTC time</p> <p>S.elev_m: elevation of the station, in m a.s.l.</p> <p>S.xy_utm: station coordinates X and Y in meter in the Reference system WGS84/UTM zone 32N</p>
Early Mars EBM data for "CLIMATE SIMULATIONS OF EARLY MARS WITH ESTIMATED PRECIPITATION, RUNOFF, AND EROSION RATES"
<p>These are all the data and plots we used for the publication, CLIMATE SIMULATIONS OF EARLY MARS WITH ESTIMATED PRECIPITATION, 2 RUNOFF, AND EROSION RATES.</p>
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> <p>This dataset accompanies the research paper titled <strong>"On the Extrapolation of Generative Adversarial Networks for downscaling precipitation extremes in warmer climates"</strong>, 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>.</p> <p>The code can also be found as a ".zip" file: *On-the-Extrapolation-of-Generative-Adversarial-Networks-for-downscaling-precipitation-extremes-main. </p> <h2>Aims</h2> <p>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. </p> <h2>Geographic Focus</h2> <p>Our research focuses only on the New Zealand Region (165°E-184°W, 33°S-51°S).</p> <p> </p> <h2>Data Overview</h2> <h3>Training and Evaluation Data</h3> <p>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. </p> <ul> <li> <p><strong>Resolution:</strong> 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. </p> </li> <li> <p><strong>Period Coverage:</strong></p> <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> <p><strong>Models:</strong></p> <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> <p><strong>Training Data:</strong></p> <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> <p><strong>Evaluation Data:<br></strong>All other GCMs can be accessed in one single file, predictor and target variables have the dimensions (time, lat, lon, GCM).</p> <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> <p><strong>Regional Climate Model</strong>, 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>.</p> </li> <li> <p><strong>Predictor and Target Variables:</strong> 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.</p> </li> <li> <p><strong>Training Framework:</strong> 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></p> </li> </ul> <table> <tbody> <tr> <td> <p><strong>Algorithm</strong></p> </td> <td> <p><strong>Training Data</strong></p> </td> <td> <p><strong>Period</strong></p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Historical</p> </td> <td> <p>1960-2014 (~21,000 days)</p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Future (SSP370)</p> </td> <td> <p>2044-2099 (~21,000 days)</p> </td> </tr> <tr> <td> <p>Deterministic Baseline</p> </td> <td> <p>Historical and Future (SSP370)</p> </td> <td> <p>1960-2099 (~51,000 days)</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Historical</p> </td> <td> <p>1960-2014</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Future (SSP370)</p> </td> <td> <p>2044-2099</p> </td> </tr> <tr> <td> <p>Residual GAN</p> </td> <td> <p>Historical and Future (SSP370)</p> </td> <td> <p>1960-2099</p> </td> </tr> </tbody> </table> <p><strong>Table 1:</strong> The six RCM emulator experiments performed in this study.</p>
Data for the publication "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM"
<p>These data are a set of annual-mean values for 5yr simulations using the MIROC6-SPRINTARS global aerosol-climate model with different treatments of precipitation (i.e., diagnostic and prognostic). The outputs include diagnostics from the satellite simulator COSP2.<br>The data are used in the manuscript entitled "Evaluation of climatic impacts of ice nucleating particles through precipitation process with a GCM". All data used in this study are available from the corresponding author upon request.</p>
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.
Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020"
<p>Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020", submitted for publication in Climate Dynamics.</p> <p>Please see the file README for further details.</p> <p> </p>
Precipitation manipulation and terrestrial carbon cycle: the roles of treatment magnitude, experimental duration, and background climate
<p><b>Aim: </b><a name="_Hlk62140683">Precipitation manipulation experiments have shown diverse terrestrial carbon (C) cycling responses when the ecosystem is subjected to different magnitudes of altered precipitation, various experimental durations, or heterogeneity in local climates. However, how these factors combine to affect C cycle responses to changes in precipitation remains unclear.</a></p> <p><b>Location</b>: Global.</p> <p><b>Time period</b>: 1990–2019.</p> <p><b>Major taxa studied</b>: Terrestrial ecosystems.</p> <p><b>Methods</b>: Using observations from 230 published studies in which precipitation was manipulated and terrestrial C cycling variables were measured, we conducted a global meta-analysis to investigate responses of diverse C cycle processes to altered precipitation, including gross ecosystem productivity, ecosystem respiration, net ecosystem productivity, ecosystem carbon use efficiency, net primary productivity, aboveground and belowground net primary productivity, aboveground and belowground biomass, shoot:root ratio, soil respiration, and soil microbial biomass C.</p> <p><b>Results</b>: <a name="_Hlk50913646"></a><a name="_Hlk62140761">We found that C cycling responses correlated linearly and positively with the magnitude of precipitation treatments, in that C cycling variables increased under increased precipitation, and decreased under decreased precipitation. </a>We also detected that the responses of net primary productivity (NPP) and its aboveground component (ANPP) to altered precipitation weakened with experimental duration. Furthermore, gross ecosystem productivity, ecosystem respiration, and net ecosystem productivity had larger responses to precipitation treatments of greater magnitude over shorter time periods. The response of soil respiration, a key component of the C budget in most terrestrial ecosystems, particularly depended on the background climate. Local temperature and precipitation not only influenced the magnitude of the response of soil respiration to altered precipitation but also affected its sensitivity to the magnitude of the precipitation treatments, with higher sensitivities in the response of soil respiration to treatment magnitude at drier and colder sites.</p> <p><b>Main conclusions</b>: <a name="_Hlk62140806">Our findings highlight the importance of the interactions between the magnitude of precipitation treatments, their duration, and local climate in the response of ecosystem C cycling to precipitation, which is critical to better understanding and projecting ecosystem C processes and functioning under changing precipitation regimes.</a></p>
Data for the paper: Equilibrium climate sensitivity increases with aerosol concentration due to changes in precipitation efficiency
<p>This data-set contains the data requires for the paper "Equilibrium climate sensitivity increases with aerosol concentration due to changes in precipitation efficiency" by Guy Dagan</p> <p>The indexes (20/200/2000) in the variable name represent the aerosol concentration in the relevel simulation. The other index (1/2/4) represent the CO2 concentration (1 time, 2 times and 4 times the pre-industrial conditions). Other than that, the variables names are as they appear in the manuscript. </p>
Biasadjusted Regional Climate Model Data for Europe - Precipitation
<p>This repository contains the bias-adjusted precipitation data used in the production of numbers and figures contained in our research article entitled "Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change".</p> <p>Ferdini S., von Cossel M., Wulfmeyer V., Warrach-Sagi K. (2023) Climate-based identification of suitable cropping areas for giant reed and reed canary grass on marginal land in central and southern Europe under climate change. <em>Global Change Biology - Bioenergy.</em></p>
Data from: Indices of Extremes: Geographic patterns of change in extreme temperature and precipitation under climate intervention
<p>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</p> <p>Script is also included to process data from NCAR's HPC Campaign archive and produce figures 10 and 11.</p> <p>The full output from the GLENS simulation are available from from https://data.ucar.edu/dataset/stratospheric-aerosol-geoengineering-large-ensemble-project-glens</p> <p> </p> <p> </p>
Divergent responses of grassland productivity and plant diversity to intra-annual precipitation variability across climate regions: A global synthesis
<p><span>Global warming intensifies the hydrological cycle and may result in changes in the frequency and intensity of precipitation events. Although the effects of changes in precipitation amount and inter-annual precipitation variability on terrestrial plant productivity and carbon sequestration have been well studied, how intra-annual precipitation variability affects terrestrial ecosystem function remains unclear. </span><span>Here, we synthesized field manipulative experiments from 71 publications to quantify the effects of intra-annual precipitation variability increases (IPVI) on community biomass and plant diversity in grasslands worldwide. </span><span>At the global scale, we found that IPVI generally increased grassland community aboveground biomass (AGB) by 6%, and decreased grass biomass and soil ammonium nitrogen by 12% and 31%, respectively. IPVI stimulated AGB, belowground biomass, and plant species richness in arid regions, but not changed them in humid regions. Changes in AGB under IPVI were related to changes in the biomass of plant functional groups, species richness, and soil moisture. Structural equation modelling demonstrated that that climate conditions (mean annual temperature and mean annual precipitation) and background soil properties (soil sand content and soil organic carbon content) jointly regulated grassland AGB responses to IPVI across climate types.</span></p> <p><span>Synthesis: Overall, our study shows that grassland productivity and diversity may increase under IPVI in arid climates, and that humid grasslands may be highly resistant to the effects of IPVI. These findings have important implications for understanding ecosystem carbon cycling under global precipitation change scenarios.</span></p>
Data from: Climatic influences on winter precipitation use by trees in summer
Open the record for dataset details and reuse information.
Data from: Do precipitation extremes drive growth and migration timing of a Pacific salmonid fish in Mediterranean‐climate streams?
Open the record for dataset details and reuse information.
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