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Figure 1-4 data for "Anaconda-shaped Spiral Multi-layered Triboelectric Nanogenerators with Ultra-High Space Efficiency for Wave Energy Harvesting" Figure 1-4 data for "Anaconda-shaped Spiral Multi-layered Triboelectric Nanogenerators with Ultra-High Space Efficiency for Wave Energy Harvesting"

This data set is no longer current – The most current data and all historical data sets can be found at https://data.nrel.gov/submissions/244 This database represents a list of community solar projects identified through various sources as of Dec 2021. The list has been reviewed but errors may exist and the list may not be comprehensive. Errors in the sources e.g. press releases may be duplicated in the list. Blank spaces represent missing information. NREL invites input to improve the database including to - correct erroneous information - add missing projects - fill in missing information - remove inactive projects. Updated information can be submitted to the contact(s) located on the current data set page linked at the top.

Consists of a web crawl of unique URLs tweeted with the #YMMFire hashtag. #YMMFire collection took place from May 1-June 25, 2016. Unique URLs were extracted from the dataset, and harvested with Heritrix on August 31-September 2, 2017. This research was supported by a research grant -- 435-2015-0011 -- issued by Social Sciences and Humanities Research Council.

This README file was generated on 2024-03-04 by Adriana Parra. ## GENERAL INFORMATION 1\. Title of Dataset: **Climate-limited vegetation change in the conterminous United States of America** 2\. Author Information A. First Author Contact Information Name: Adriana Parra Institution: University of Nevada, Reno Address: Reno, NV USA Email: adrianaparra@unr.edu B. Co-author Contact Information Name: Jonathan Greenberg Institution: University of Nevada, Reno Address: Reno, NV USA Email: jgreenberg@unr.edu 3\. Coverage period of the dataset: 1986-2018 4\. Geographic location of dataset: Conterminous United States 5\. Description: This dataset contains the input and the resulting rasters for the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal. The dataset includes a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor. Additionally, the dataset includes a legend file for the identified climatic limiting factor rasters. ## SHARING/ACCESS INFORMATION 1\. Links to publications that cite or use the data: **Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204)** 2\. Links to other publicly accessible locations of the data: None 3\. Links/relationships to ancillary data sets: None 4\. Was data derived from another source? Yes A. If yes, list source(s): "Vegetative Lifeform Cover from Landsat SR for CONUS" product publicly available in the ORNL DAAC (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) TerraClimate data catalog publicly available at the website https://www.climatologylab.org/terraclimate.html 5\. Recommended citation for this dataset: Parra, A., & Greenberg, J. (2024). Climate-limited vegetation change in the conterminous United States of America. Global Change Biology, 30, e17204. [https://doi.org/10.1111/gcb.17204](https://doi.org/10.1111/gcb.17204) ## DATA & FILE OVERVIEW This dataset contains 16 geotiff files, and one csv file. There are 4 geotiff files per each of the lifeform classes evaluated in this study: herbaceous, tree, shrub, and non-vegetation. The files corresponding to each lifeform class are indicated by the first two letters in the file name, HC indicates herbaceous cover, TC indicates tree cover, SC indicates shrub cover, and NC indicates non-vegetation cover. 1\. File List: a) Observed change: Trends of vegetation change between 1986 and 2018. b) Potential predict: Predicted rates of vegetation change form the climate limiting factor analysis. c) Potential observed difference: Difference between the potential and the observed vegetation rates of change. d) Limiting variable: Climate variable identified as the limiting factor for each pixel the conterminous United States. e) Legend of the Limiting variable raster All the geotiff files are stored as Float 32 type, and in CONUS Albers Equal Area coordinate system (EPSG:5070) The csv file included in the dataset is the legend for the limiting variable geotiff files. This file includes the name of the climate variable corresponding to each number in the limiting variable files, as well as information on the variable type and the corresponding time lag. 2\. Relationship between files, if important: None 3\. Additional related data collected that was not included in the current data package: None 4\. Are there multiple versions of the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was the file updated? NA ii. When was the file updated? NA Input data We use the available data from the “Vegetative Lifeform Cover from Landsat SR for CONUS” product (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1809) to evaluate the changes in vegetation fractional cover. The information for the climate factors was derived from the TerraClimate data catalog (https://www.climatologylab.org/terraclimate.html). We downloaded data from this catalog for the period 1971 to 2018 for the following variables: minimum temperature (TMIN), precipitation (PPT), actual evapotranspiration (AET), potential evapotranspiration (PET), and climatic water deficit (DEF). Preprocessing of vegetation fractional cover data We resampled and aligned the maps of fractional cover using pixel averaging to the extent and resolution of the TerraClimate dataset (~ 4 km). Then, we calculated rates of lifeform cover change per pixel using the Theil-Sen slope analysis (Sen, 1968; Theil, 1992). Preprocessing of climate variables data To process the climate data, we defined a year time step as the months from July of one year to July of the next. Following this definition, we constructed annual maps of each climate variable for the years 1971 to 2018. The annual maps of each climate variable were further summarized per pixel, into mean and slope (calculated as the Theil-Sen slope) across one, two, three, four, five, ten-, and 15-year lags. Estimation of climate potential We constructed a final multilayer dataset of response and predictor variables for the CONUS including the resulting maps of fractional cover rate of change (four response variables), the mean and slope maps for the climate variables for all the time-lags (70 predictor variables), and the initial percent cover for each lifeform in the year 1986 (four predictor variables). We evaluated for each pixel in the CONUS which of the predictor variables produced the minimum potential rate of change in fractional cover for each lifeform class. To do that, we first calculated the 100% quantile hull of the distribution of each predictor variable against each response variable. To calculate the 100% quantile of the predictor variables’ distribution we divided the total range of each predictor variable into equal-sized bins. The size and number of bins were set specifically per variable due to differences in their data distribution. For each of the bins, we calculated the maximum value of the vegetation rate of change, which resulted in a lookup table with the lower and upper boundaries of each bin, and the associated maximum rate of change. We constructed a total of 296 lookup tables, one per lifeform class and predictor variable combination. The resulting lookup tables were used to construct spatially explicit maps of maximum vegetation rate of change from each of the predictor variable input rasters, and the final climate potential maps were constructed by stacking all the resulting maps per lifeform class and selecting for each pixel the minimum predicted rate of change and the predictor variable that produced that rate. Identifying climate-limited areas We defined climate-limited areas as the parts of the CONUS with little or no differences between the estimated climate potential and the observed rates of change in fractional cover. To identify these areas, we subtracted the raster of observed rates of change from the raster of climate potential for each lifeform class. In the study “CLIMATE-LIMITED VEGETATION CHANGE IN THE CONTERMINOUS UNITED STATES OF AMERICA”, published in the Global Change Biology journal, we evaluated the effects of climate conditions on vegetation composition and distribution in the conterminous United States (CONUS). To disentangle the direct effects of climate change from different non-climate factors, we applied "Liebig's law of the minimum" in a geospatial context, and determined the climate-limited potential for tree, shrub, herbaceous, and non-vegetation fractional cover change. We then compared these potential rates against observed change rates for the period 1986 to 2018 to identify areas of the CONUS where vegetation change is likely being limited by climatic conditions. This dataset contains the input and the resulting rasters for the study which include a) the observed rates of vegetation change, b) the climate derived potential vegetation rates of change, c) the difference between potential and observed values and d) the identified climatic limiting factor.

This archive includes a minimal dataset needed to reproduce the analysis as well as a table (CSV) and spatial polygons (ESRI shapefile) of the resulting output from the publication: Hoecker, T.J., S. A. Parks, M. Krosby & S. Z. Dobrowski. 2023. Widespread exposure to altered fire regimes under 2°C warming is projected to transform conifer forests of the Western United States. Communications Earth and Environment. Publication abstract: Changes in wildfire frequency and severity are altering conifer forests and pose threats to biodiversity and natural climate solutions. Where and when feedbacks between vegetation and fire could mediate forest transformation are unresolved. Here, for the western U.S., we used climate analogs to measure exposure to fire-regime change; quantified the direction and spatial distribution of changes in burn severity; and intersected exposure with fire-resistance trait data. We measured exposure as multivariate dissimilarities between contemporary distributions of fire frequency, burn severity, and vegetation productivity and distributions supported by a 2 °C-warmer climate. We project exposure to fire-regime change across 65% of western US conifer forests and mean burn severity to ultimately decline across 63% because of feedbacks with forest productivity and fire frequency. We find that forests occupying disparate portions of climate space are vulnerable to projected fire-regime changes. Forests may adapt to future disturbance regimes, but trajectories remain uncertain.

Drawing upon climate change damage functions previously proposed in the literature that we have calibrated to a common level of damages at 2.5 C, we examine the effect upon the social cost of carbon (SCC) of varying the specification of damages in a DICE-like integrated assessment model. In the absence of risk aversion, all of the SCC estimates but one agree within a factor of two. The effect of varying calibration damages is mildly sublinear. With a moderate level of risk aversion included, however, the differences among estimates grow greatly. By combining elements of different damage specifications and roughly taking into account uncertainty in calibration, we have constructed a composite damage function that attempts to approximate the range of uncertainty in climate change damages. In the absence of risk aversion, SCC values calculated with this function are in agreement with the standard quadratic DICE damage function; with a coefficient of relative risk aversion of 1.4, this damage function yields SCC values more than triple those of the standard function.

Lakes and rivers covered by seasonal ice are extensively used by humans. Although ice cover duration has been declining over the past 150 years for Northern Hemisphere lakes and rivers, we still know relatively little about how inland ice loss directly affects humans. Here we provide empirical examples that give quantitative evidence for a winter warming effect on a wide range of cultural ecosystem services. We show that in recent decades, warmer temperatures delayed the opening date of the James Bay winter ice road in northern Ontario, Canada and led to cancellations of religious celebrations (Lake Suwa, Japan and Lake Constance, Germany/Switzerland/Austria), an ice skating race on Lake M��laren, Sweden, and winter ice fishing tournaments in Central and Northern Minnesota. The years with no ice cover on Lake Suwa, Japan from 1443 - 2017. The years with ice cover on Lake Constance, Germany/Switzerland/Austria from 875 - 2018. The years with a normal Vikingar��nnet ice skating race, a modified route, or no race on Lake M��laren, Sweden from 1999 - 2017 as well as associated average winter air temperature. The years with canceled ice fishing tournaments in central and northern Minnesota, USA as well as associated average winter air temperature from 2005 - 2017. Road date openings of the James Bay winter ice road in northern Ontario, Canada and associated freezing degree days from 2005 - 2018.

The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community. The Arctic is one of Earth’s regions most susceptible to climate change. However, the in-situ long-term observations used for climate research are relatively sparse in the Arctic Ocean, and the simulations from current climate models exhibit remarkable biases in the Arctic. Here we present an Arctic Ocean dynamical downscaling dataset based on a high-resolution ice-ocean coupled model FESOM and a climate model FIO-ESM. The dataset includes 115-year (1900–2014) historical simulations and two 86-year future scenario simulations (2015–2100) under scenarios SSP245 and SSP585. The historical results demonstrate that the root mean square errors of temperature and salinity in the dynamical downscaling dataset are much smaller than those from CMIP6 (the Coupled Model Intercomparison Project phase 6) climate models. The common biases, such as the too deep and too thick Atlantic layer in climate models, are reduced significantly by dynamical downscaling. This dataset serves as a crucial long-term data source for climate change assessments and scientific research in the Arctic Ocean, providing valuable information for the scientific community.