This dataset includes processed climate change datasets related to climatology, hydrology, and water operations. The climatological data provided are change factors for precipitation and reference evapotranspiration gridded over the entire State. The hydrological data provided are projected stream inflows for major streams in the Central Valley, and streamflow change factors for areas outside of the Central Valley and smaller ungaged watersheds within the Central Valley. The water operations data provided are Central Valley reservoir outflows, diversions, and State Water Project (SWP) and Central Valley Project (CVP) water deliveries and select streamflow data. Most of the Central Valley inflows and all of the water operations data were simulated using the CalSim II model and produced for all projections. These data were originally developed for the California Water Commission’s Water Storage Investment Program (WSIP). The WSIP data used as the basis for these climate change resources along with the technical reference document are located here: https://data.cnra.ca.gov/dataset/climate-change-projections-wsip-2030-2070. Additional processing steps were performed to improve user experience, ease of use for GSP development, and for Sustainable Groundwater Management Act (SGMA) implementation. Furthermore, the data, tools, and guidance may be useful for purposes other than sustainable groundwater management under SGMA. Data are provided for projected climate conditions centered around 2030 and 2070. The climate projections are provided for these two future climate periods, and include one scenario for 2030 and three scenarios for 2070: a 2030 central tendency, a 2070 central tendency, and two 2070 extreme scenarios (i.e., one drier with extreme warming and one wetter with moderate warming). The climate scenario development process represents a climate period analysis where historical interannual variability from January 1915 through December 2011 is preserved while the magnitude of events may be increased or decreased based on projected changes in precipitation and air temperature from general circulation models. 2070 Extreme Scenarios Update, September 2020 DWR has collaborated with Lawrence Berkeley National Laboratory to improve the quality of the 2070 extreme scenarios. The 2070 extreme scenario update utilizes an improved climate period analysis method known as "quantile delta mapping" to better capture the GCM-projected change in temperature and precipitation. A technical note on the background and results of this process is provided here: https://data.cnra.ca.gov/dataset/extreme-climate-change-scenarios-for-water-supply-planning/resource/f2e1c61a-4946-4863-825f-e6d516b433ed. Note: the original version of the 2070 extreme scenarios can be accessed in the archive posted here: https://data.cnra.ca.gov/dataset/sgma-climate-change-resources/resource/51b6ee27-4f78-4226-8429-86c3a85046f4

Regional boundaries for use by CA Nature to support activities related to Executive Order N-82-20. These include California's 30x30 effort, Climate Smart Land Strategies, and equitable access to open space. This layer is derived from the 4th California Climate Assessment regions, and enhanced using the California County Boundaries dataset (version 19.1) maintained by the California Department of Forestry and Fire Protection's Fire Resource Assessment Program, and the 3 Nautical Mile marine boundary for California sourced from the California Department of Fish and Wildlife.

This folder, titled "Data," contains the MATLAB code, final products, tables, and figures used in Parker, L.E., Zhang, N., Abatzoglou, J.T. et al. A variety-specific analysis of climate change effects on California winegrapes. Int J Biometeorol 68, 1559–1571 (2024). https://doi.org/10.1007/s00484-024-02684-8

Data Collection: Climatological data (daily maximum and minimum temperatures, precipitation, and reference evapotranspiration) were obtained from the gridMET dataset for the contemporary period (1991-2020) and from 20 global climate models (GCMs) for the mid-21st century (2040-2069) under RCP 4.5.Phenology Modeling: Variety-specific phenology models were developed using published climatic thresholds to assess chill accumulation, budburst, flowering, veraison, and maturity stages for the six winegrape varieties.Agroclimatic Metrics: Fourteen viticulturally important agroclimatic metrics were calculated, including Growing Degree Days (GDD), Cold Hardiness, Chilling Degree Days (CDD), Frost Damage Days (FDD), and others.Analysis Tools: MATLAB was used for data processing, analysis, and visualization. The MATLAB code provided in this dataset includes scripts for analyzing climate data, running phenology models, and generating visualizations.MATLAB Code: Scripts and functions used for data analysis and modeling.Processed Data: Results from phenology and agroclimatic analyses, including the projected changes in phenological stages and climate metrics for the selected varieties and AVAs.Tables: Detailed results of phenological changes and climate metrics, presented in a clear and structured format.Figures: Visual representations of the data and results, including charts and maps illustrating the impacts of climate change on winegrape development stages and agroclimatic conditions.

Research Description: This study investigates the impacts of climate change on the phenology and agroclimatic metrics of six winegrape varieties (Cabernet Sauvignon, Chardonnay, Pinot Noir, Zinfandel, Pinot Gris, Sauvignon Blanc) across multiple California American Viticultural Areas (AVAs). Using climatological data and phenology models, the research quantifies changes in key development stages and viticulturally important climate metrics for the mid-21st century.

Ontario-focused high resolution climate information and its application. The data contains the 50th percentile high resolution probabilistic projections of annual averaged temperature and precipitation over the province. It covers the: * 1970s * 2030s * 2050s * 2080s This data is provided in partnership with the University of Regina. More user-friendly visualizations and downloads of this data are also available at: * The Ontario Climate Change Data Portal (OCCDP) by the University of Regina * The Ontario Climate Change Projections (OCCP) page by York University This dataset's technical documentation contains a metadata record of projects funded by the Ministry of the Environment, Conservation and Parks alongside final reports and associated data.

This dataset consists of modeled projections of land use and land cover for the State of California for the period 2001-2101. The Land Use and Carbon Scenario Simulator (LUCAS) model was initialized in 2001 and run forward on an annual time step to 2100. In total 9 simulations were run with 10 Monte Carlo replications of each simulation. Two base scenarios were selected from Sleeter et al., 2017 (http://onlinelibrary.wiley.com/doi/10.1002/2017EF000560/full) for analysis, including a "business-as-usual" (BAU) land use scenario and a scenario based on "medium" population projections. For each base scenario we ran three alternative conservation scenarios where we simulated conversion of lands into conservation easements. The three conservation easement scenarios simulated conversion of 1) 120 km2/yr for 15 years, 2) 120 km2/yr for 30 years, and 3) 240 km2/yr for 30 years. All easement conversions were set to begin in 2020 and extend for their stated duration. In addition to the conservation easement scenarios, we also ran a variant of the BAU land use scenario where current Williamson Act lands were removed from the simulation of future conditions.

Tabular data output from a series of groundwater modeling simulations for five counties along the Central Coast of California, USA. We used a spatially explicit state-and-transition simulation model with stocks and flows that integrates climate, land-use change, human water use, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand at 270-m resolution from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future using output from the Basin Characterization Model, a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual pro ...

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

An introduction to the 2022 Indicators of Climate Change in California Report. The Indicators of Climate Change in California report documents observed changes in the state’s climate and its impacts in the state. Indicators are scientific measurements that track trends and conditions relating to climate change. Collectively, the indicators portray a statewide picture of how climate change has been impacting the environment and people of California. Through these indicators, the report tells the state’s climate change story, starting with the human influences on climate, or “drivers,” followed by the changes in climate Californians have been experiencing, and then their consequences on the physical environment, on plant and animal species, and on human health. https://oehha.ca.gov/climate-change/epic-2022

Climate change and climate variability impacts such as rising sea levels have the potential to exacerbate seawater intrusion and the strain on coastal freshwater resources in already stressed groundwater basins such as those in the Pajaro Valley groundwater basin, California. The U.S. Geological Survey (USGS) developed the Pajaro Valley Hydrologic model (PVHM) to quantitatively assess aquifer-system responses to climatic variation, surface-water deliveries, and seawater intrusion.The PVHM historical model (1963-2018) was updated, extended, and re-calibrated using a combination of manual adjustments to parameters and an assisted parameter estimation PEST++ software (White et al., 2020) to minimize differences between simulated values and historical observations of streamflow, groundwater levels, and agricultural pumping. Three future climate scenarios (2013-2100) were developed for the PVHM, using three climate projections from global general circulation models (GCMs) representing hot and dry conditions, average conditions, and cool and wet conditions. The climate ensemble was used to conduct a first-order second moment uncertainty analysis of groundwater level and seawater intrusion forecasts using PEST++ software (White et al., 2020). Understanding the reliability and uncertainty of forecasts is important for developing climate adaptation strategies such as developing protective thresholds, particularly at the basin scale where the impacts are felt, and adaptation is implemented. This USGS data release contains all of the input and output files for the historical updated PVHM and the three future climate scenarios described in the associated journal article.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This dataset contains a 30-year average of annual average minimum and maximum temperatures across all ten models and two greenhouse gas (RCP) scenarios in the ten model ensemble. Three named time periods are included “Historic Baseline (1961-1990)”, “Mid-Century (2035-2064)”, and “End of Century (2070-2099).” The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

This data series provides tabular output from a series of modeling simulations for the State of California. The methods and results of this research are described in detail in Sleeter et al. (2019). We used the LUCAS model to project changes in ecosystem carbon balance resulting from land use and land use change, climate change, and ecosystem disturbances such as wildfire and drought. The model was run at a 1-km spatial resolution on an annual timestep. We simulated 32 unique scenarios, consisting of 4 land-use scenarios and 2 radiative forcing scenarios as simulated by 4 global climate models. For each scenario, we ran 100 Monte Carlo realizations of the model. Additional details describing the modeling effort can be found in the Global Change Biology paper. Results presented here have been aggregated from the individual cell level to either ecoregion or state-wide summaries.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Annual and monthly averages for precipitation, dew point, dew point depression (DPD), sea level pressure (SLP), average temperature, max temperature (tmax), minimum temperature (tmin), visibility, wind speed, and NOx concentration. Data looks at fog season averages for variables, eg November-March. 'FogData' files includes number of fog days per year (days where visibility < .25 miles for 1 hour) and fog hours per year (hours where visibility < .25 miles). Daily averages were derived from hourly measurements. An daily measurements for all continuous variables were created by averaging measurements taken every 3 hours (0:00, 3:00, 6:00, 9:00, 12:00, 15:00, 18:00, 21:00). 7 of 8 hourly observations were required to calculate daily average. Each variable also includes a percent of measurements available for the month and year in a separate file. 65% of daily averages needed to be available in a month to determine a monthly average. All 5 months reported a monthly average in order to create a yearly average. Fog days and fog hours are episodic, thus 90% of hourly observations needed to be available to create an average. Sources:https://www7.ncdc.noaa.gov/CDO/cdoselect.cmdhttps://www.ncdc.noaa.gov/cdo-web/datatools/lcd.https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data https://www.arb.ca.gov/adam/

This data release includes data containing projections of unimpaired hydrology, reservoir storage, and downstream managed flows in the Sacramento River/San Joaquin River watershed, California for scenarios of future climate change generated for the CASCaDE2 project (Computational Assessments of Scenarios of Change for the Delta Ecosystem, phase 2). Code used to produce the data is also included. The dataset is produced using a multiple-model approach. First, downscaled global climate model outputs are used to drive an existing Variable Infiltration Capacity/Variable Infiltration Capacity Routing (VIC/RVIC) model of Sacramento/San Joaquin hydrology, resulting in projections of daily, unimpaired flows throughout the watershed. A management model, CASCaDE2-modified CalSim (C2-CalSim), uses these projections as inputs and produces monthly estimates of reservoir and other infrastructure operations and resulting downstream managed flows. The CASCaDE2 resampling algorithm (CRESPI), also uses the projected daily unimpaired flows, along with historical managed flows, to estimate the daily variability in managed flows throughout the watershed. The monthly and daily managed-flow estimates are combined in a way that preserves the multi-decadal variability and century-scale trends produced by the C2-CalSim model and the day-to-day variability produced by the CRESPI algorithm. The resulting data are analyzed and processed to produce tables, figures, and text for the associated publications. To reduce the data release's size, data from a given step in the analysis that are not used in a subsequent step have not been included in this data release. All code generated by the USGS to produce the data in this data release is also included. This includes all code to download and preprocess external data; to set up and control the RVIC model runs; to modify, set up, and control runs of the CalSim 2 model; to implement and run the CRESPI algorithm; to postprocess and analyze model outputs; and to produce published figures, tables and text that includes calculated values. A detailed README file is included with instructions for running the code, including how to obtain the external RVIC and CalSim 2 models.

This dataset contains a 30-year rolling average of annual average minimum and maximum temperatures from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099.The year identified is the mid-point of the 30-year average. eg. The year 2050 includes the values from 2036 to 2065. The downscaling and selection of models for inclusion in ten and four model ensembles is described in Pierce et al. 2018, but summarized here. Thirty two global climate models (GCMs) were identified to meet the modeling requirements. From those, ten that closely simulate California’s climate were selected for additional analysis (Table 1, Pierce et al. 2018) and to form a ten model ensemble. From the ten model ensemble, four models, forming a four model ensemble, were identified to provide coverage of the range of potential climate outcomes in California. The models in the four model ensemble and their general climate projection for California are: HadGEM2-ES (warm/dry),CanESM2 (average), CNRM-CM5 (cooler/wetter), and MIROC5 the model least like the others to improve coverage of the range of outcomes. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

These CLIM-MET stations are meteorological/geological stations that is designed to function in remote areas for long periods of time without human intervention. These stations measure meteorological and wind-erosion parameters under varying climatic and land-use conditions to detect and describe ongoing landscape changes. These data represent multiple years of local detailed landscape and environmental change observations. These data were collected at several discrete locations within southeastern California and in Mojave National Preserve, California, from 31 July 2016 to 23 March 2022. These data were collected by U.S. Geological Survey researchers utilizing site visits and automated data collection data loggers. These data can be used to inform studies of local and regional landscape change as well as to provide input into regional climatic models.

and MIROC5 the model least like the others to improve coverage of the range of outcomes. These data were downloaded from Cal-Adapt and prepared for use within CA Nature by California Natural Resource Agency and ESRI staff. Cal-Adapt. (2018). LOCA Derived Data [GeoTIFF]. Data derived from LOCA Downscaled CMIP5 Climate Projections. Cal-Adapt website developed by University of California at Berkeley’s Geospatial Innovation Facility under contract with the California Energy Commission. Retrieved from https://cal-adapt.org/ Pierce, D. W., J. F. Kalansky, and D. R. Cayan, (Scripps Institution of Oceanography). 2018. Climate, Drought, and Sea Level Rise Scenarios for the Fourth California Climate Assessment. California’s Fourth Climate Change Assessment, California Energy Commission. Publication Number: CNRA-CEC-2018-006.

Resurveys of historical collecting localities have revealed range shifts, primarily leading edge expansions, which have been attributed to global warming. However, there have been few spatially replicated community-scale resurveys testing whether species' responses are spatially consistent. Here we repeated early twentieth century surveys of small mammals along elevational gradients in northern, central and southern regions of montane California. Of the 34 species we analysed, 25 shifted their ranges upslope or downslope in at least one region. However, two-thirds of ranges in the three regions remained stable at one or both elevational limits and none of the 22 species found in all three regions shifted both their upper and lower limits in the same direction in all regions. When shifts occurred, high-elevation species typically contracted their lower limits upslope, whereas low-elevation species had heterogeneous responses. For high-elevation species, site-specific change in temperature better predicted the direction of shifts than change in precipitation, whereas the direction of shifts by low-elevation species was unpredictable by temperature or precipitation. While our results support previous findings of primarily upslope shifts in montane species, they also highlight the degree to which the responses of individual species vary across geographically replicated landscapes.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and the CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. A total of 31 climate indices have been calculated using the CanDCS-U6 and CanDCS-M6 datasets. The climate indices include 27 Climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the Climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the index. Monthly indices are also available in seasonal and annual versions. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.