This dataset contains annual average precipitation from the four models and two greenhouse gas (RCP) scenarios included in the four model ensemble for the years 1950-2099.

The downscaling and selection of models for inclusion in ten and four model ensembles is described in 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>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 ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>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.

The North America climate data were derived from WorldClim, a set of global climate layers developed by the Museum of Vertebrate Zoology at the University of California, Berkeley, USA, in collaboration with The International Center for Tropical Agriculture and Rainforest CRC with support from NatureServe.The global climate data layers were generated through interpolation of average monthly climate data from weather stations across North America. The result is a 30-arc-second-resolution (1-Km) grid of mean temperature values. The North American data were clipped from the global data and reprojected to a Lambert Azimuthal Equal Area projection. Background information on the WorldClim database is available in: Very High-Resolution Interpolated Climate Surfaces for Global Land Areas; Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis; International Journal of Climatology 25: 1965-1978; 2005.Files Download

This dataset contains 30-year rolling average of annual average precipitation across all four models and two greenhouse gas (RCP) scenarios in the four model ensemble. The year identified for a 30 year rolling average 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 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>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 ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>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.

The Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group works on a range of projects, some of which support the development of spatial climate datasets. These PRISM datasets provide estimates of the basic climate element of precipitation (ppt), or the Daily total precipitation averaged over a month for both rain and melted snow. These datasets are modeled with PRISM using a digital elevation model (DEM) as the predictor grid and provide baselines describing average monthly precipitation between 1981 and 2000 to be used for display and/or analyses requiring spatially distributed monthly or annual precipitation. Grids were modeled on a monthly basis. Annual grids were produced by averaging (temperatures) or summing (precipitation) the monthly grids.

Water is an essential ingredient to life on Earth. In its three phases (solid, liquid, and gas), water continuously cycles within the Earth and atmosphere to create significant parts of our planet’s climate system, such as clouds, rivers, vegetation, oceans, and glaciers. Precipitation is a part of the water cycle, where water particles fall from clouds in the form of rain, sleet, snow, ice crystals, or hail. So how does precipitation form? As water on Earth’s surface evaporates it changes from liquid to gas and rises into the atmosphere. Because air cools as altitude increases, the vapor rises to a point in the atmosphere where it cools enough to condense into liquid water or freeze into ice, which forms a cloud. Water vapor continues to condense and stick to other water droplets in the cloud until the weight of the accumulated water becomes too heavy for the cloud to hold. If the air in the cloud is above freezing (0 degrees Celsius or 32 degrees Fahrenheit), the water falls to the Earth as rain. If the air in the cloud is below freezing, ice crystals form and it snows if the air between the cloud and the ground stays below 0 degrees Celsius (32 degrees Fahrenheit). If a snowflake falls through a warmer part of a cloud, it can get coated in water, then refrozen multiple times as it circulates around the cloud. This forms heavy pellets of ice, called hail, that can fall from the sky at speeds estimated between 14 and 116 kmph (9 and 72 mph) depending on its size. A hailstone can range from the size of a pea (approximately 0.6 cm or 0.25 inches) to a golf ball (approximately 4.5 cm or 1.75 inches), and sometimes even reach the size of a softball (approximately 10 cm or 4 inches).Precipitation doesn’t fall in the same amounts throughout the world. The presence of mountains, global winds, and the unequal distribution of land and sea cause some parts of the world to receive greater amounts of precipitation compared with others. Areas with rising moist air generally indicate regions with high precipitation. According to the Köppen Climate Classification System, tropical wet and tropical monsoon climates receive annual precipitation of 150 cm (59 inches) or greater. Tropical wet regions, where rain occurs year-round, are found near the equator in central Africa, the Amazon rainforest, and southern India. Monsoons are storms with large patterns of wind and heavy rain that can span over a continent. Tropical monsoon climates are located mainly in Southeast Asia and areas around the Pacific Ocean, where annual rainfall is equal to or greater than areas with a tropical wet climate. Here, intense monsoon rains fall during the three hottest months of the year, which are usually between June and October. Snow and ice, which are most common in high altitudes and latitudes, cover most of the Earth’s polar regions. High altitude regions of the Andes, Tibetan Plateau, and the Rocky Mountains maintain some amount of snow cover year-round.Over the next century, it is predicted warming global temperatures will increase the temperature of the ocean and increase the speed of the water cycle. With a quicker rate of evaporation, there will be more water in the atmosphere, allowing clouds to produce heavier precipitation and more intense storms. Although storms would be more intense in wetter regions, increased evaporation could also lead to extreme drought in drier areas of the world. This would greatly affect farmers who grow crops in dry locations like Southern California or Kansas.This map layer shows Earth's mean precipitation (measured in centimeters per month) averaged from 1981 to 2012 as calculated but the Copernicus Climate Change Service. The data was collected from the Copernicus satellite and validated with precipitation measurements from weather stations. Scientists averaged all of the amounts (originally collected in meters) occurring each month together, and they calculated the average of each month over 30 years to create this map.

This dataset contains a 30-year average of annual average precipitation 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.

These maps show, for emergency service managers in the San Francisco Bay region, the threshold rainfall that may be capable of triggering a level of debris-flow activity likely to threaten public safety. The maps are products of a continuing series of studies that began after a catastrophic storm on January 3-5, 1982 triggered 18,000 debris flows in the San Francisco Bay region, causing 25 deaths and $66 million in property damage. The threshold rainfall values were estimated by re-evaluating a previous empirical analysis of data from the 1982 storm, and other historical rainfall records, that normalized the rainfall intensity data by dividing by the mean annual precipitation (MAP) of the corresponding rain gage. The present analysis also takes into account the rainfall frequency, the mean annual number of days with non-zero rainfall (#RDs), thereby adjusting for the difference in rainfall frequency between windward-facing slopes where rainfall is orographically enhanced and leeward-facing slopes and valleys that lie within rain shadows where precipitation is reduced. The debris-flow threshold maps were created by digitally combining an existing regional map of mean annual precipitation, a newly compiled data set of #RDs from an analysis of long-term (20-40 years) records of daily rainfall for 33 rain gages in the region, and the re-normalized thresholds from the empirical analysis of historical storm data.

Contained within the 3rd Edition (1957) of the Atlas of Canada is a plate that shows two maps for the annual total precipitation. Annual precipitation is defined as the sum of rainfall and the assumed water equivalent of snowfall for a given year. A specific gravity of 0.1 for freshly fallen snow is used, which means that ten inches (25.4 cm) of freshly fallen snow is assumed to be equal to one inch (2.54 cm) of rain. The mean annual total precipitation and snowfall maps on this plate are primarily based on thirty-year data during the period 1921 to 1950 inclusive.

A simulation of projected changes in mean annual precipitation from the period 1975 to 1995 to the period 2040 to 2060, is shown on this map. On average, precipitation increases, but it is not evenly distributed geographically. There are marked regions of decreasing, as well as increasing precipitation, over both land and ocean. Annual average precipitation generally increases over northern continents, and particularly during the winter. Warmer surface temperature would speed up the hydrological cycle at least partially, resulting in faster evaporation and more precipitation. The results are based on climate change simulations made with the Coupled Global Climate Model developed by Environment Canada.

Percent of Average Precipitation represents the accumulation of precipitation for a location, divided by the long term average value. The long term average value is defined as the average amount over the 1981 – 2010 period. Products are produced for the following timeframes: Agricultural Year, Growing Season, Winter Season, as well as rolling products for 30, 60, 90, 180, 270, 365, 730, 1095, 1460 and 1825 days.

Contained within the 4th Edition (1974) of the Atlas of Canada is a set of two maps. One map shows the average precipitation for April to September. The second shows the average precipitation for October to March.

The Parameter-elevation Regressions on Independent Slopes Model (PRISM) Climate Group works on a range of projects, some of which support the development of spatial climate datasets. These PRISM datasets provide estimates of the basic climate element of precipitation (ppt), or the Daily total precipitation averaged over a month for both rain and melted snow. These datasets are modeled with PRISM using a digital elevation model (DEM) as the predictor grid and provide baselines describing average monthly precipitation between 1981 and 2000 to be used for display and/or analyses requiring spatially distributed monthly or annual precipitation. Grids were modeled on a monthly basis. Annual grids were produced by averaging (temperatures) or summing (precipitation) the monthly grids.

Total annual precipitation is shown along with elevation hillshade using the NAGI method. Hillshade is from Esri Elevation Service, and precipitation data is taken from WMO and FAO rain gages in addition to a number of national datasets. The annual and monthly averages for the period 1950-2000 was calculated and interpolated by WorldClim.org, a collaboration between the University of California, Berkeley, the International Cetner for Tropical Agrilculture, and the Cooperative Research Centre for Tropical Rainforest Ecology and Management.

Contained within the 4th Edition (1974) of the Atlas of Canada is a collection of six maps. Each map shows the average monthly precipitation for April, May, June, July, August and September.

This dataset contains a 30-year rolling average of annual average precipitation 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 'https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>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 ('https://www.energy.ca.gov/sites/default/files/2019-11/Projections_CCCA4-CEC-2018-006_ADA.pdf#page=11' rel='nofollow ugc'>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.

This non-interactive map displays change in annual average precipitation per century in British Columbia from 1900 to 2013. The map is a replication of visuals available through the Province of BC website found here. Full credit is given to the Pacific Climate Impacts Consortium, Environment Canada, and Province of BC for their involvement in the creation of visuals and data.British Columbia Ministry of Environment. (2015). Indicators of Climate Change for British Columbia: 2016 Update. Ministry of Environment, British Columbia, Canada.

Contained within the 1st Edition (1906) of the Atlas of Canada is a plate that shows 11 maps. Two maps at the top of this plate presenting isothermal lines for summer and for the entire year. The isotherms for summer display the great northern "loop" of the summer isotherm of 55 degrees Fahrenheit, which make cultivation of cereals possible. The annual isothermal lines follow an easterly and Westerly direction which would obscure the beneficial effect indicated by the summer isotherms. The next four maps show precipitation and snowfall for Eastern and Western Canada in inches. The remaining five maps show isobaric lines. One map shows the annual average, while the other four cover seasons (January-March, April-June, July-September, and October-December). Barometric pressure is measured in inches of mercury. In some of the maps, major railway systems are shown.

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

Contained within the 2nd Edition (1915) of the Atlas of Canada is a plate comprised of 11 maps. The two maps at the top of the plate show isothermal lines for summer and for the entire year, with temperature units measured in Fahrenheit. The annual isothermal lines follow an Easterly and Westerly direction which would obscure the beneficial effect indicated by the summer isotherms. The next four maps show precipitation and snowfall for Eastern and Western Canada in inches. The remaining five maps display isobaric lines (i.e. barometric pressure). One map shows the annual average, while the other four maps cover the seasons (January-March, April-June, July-September, and October-December). Barometric pressure is measured in inches of mercury. In some of the maps, major railway systems are shown.

This habitat model was developed to identify suitable habitat for the federally-endangered least Bell’s vireo (Vireo bellii pusillus) across its current and historic range in California. The vireo disappeared from most of its range by the 1980s, remaining only in small populations in southern California. Habitat protection and management since the mid-1980s has led to an increase in southern California vireo populations with small numbers of birds recently expanding into the historic range. Predictions from this model will be used to focus surveys in the historic range to determine where vireos are recolonizing and to track the status and distribution of populations over time. We used the Partitioned Mahalanobis D2 modeling technique to construct alternative models with different combinations of environmental variables. We developed calibration models for the current range in southern California using vireo locations recorded from 1990 to present. We selected spatially non-redundant observations reflecting average, below average and above average rainfall conditions. For each rainfall condition, we selected three to four years of spatially non-redundant location data from the period 1990-2013. We used this dataset to randomly select 70 percent of the observations for a calibration dataset and used the remaining 30 percent of observations as a validation dataset. We used supplementary validation datasets with observations from 2016, 2017 and 2018 representing average, above average, and below average rainfall conditions, respectively. We cross-walked and merged detailed digital vegetation maps for southern California and utilized the Fire Resource Assessment Program 2015 Vegetation Map as a base map for the rest of California. We used the Klausmeyer et al. (2016) Groundwater Dependent Ecosystems map to capture riparian areas not mapped with other source layers. We selected riparian vegetation types used by vireos to develop a grid of riparian points spaced 150m apart and buffered with 500m of adjacent non-riparian habitats. We calculated environmental variables at each grid point in the center of a 150m x 150m cell for the grid of points in this modeling landscape. Variables reflect various aspects of topography, climate, and land use (percent riparian vegetation and urbanization at 150m, 500m and 1km scales). We developed several Normalized Difference Vegetation Index (NDVI) variables based on means, maximums and percentages of pixels with a minimum specified value at the 150m and 500m spatial scales. We developed alternative calibration models with different combinations of environmental variables reflecting hypotheses about least Bell’s vireo habitat relationships. Due to spatial unevenness in vireo location data, we divided southern California into ten sampling regions and randomly subsampled 70 locations from each region. We repeated this process 1,000 times using a total of 2,270 spatially precise and non-redundant vireo locations in the calibration dataset. We model-averaged the results from sampling iterations to create a calibration model with partitions for each set of variables. We compared among these calibration model-partitions using the randomly selected validation dataset of 972 observations and the 2016, 2017 and 2018 validation datasets of 610, 1,066, and 882 observations, respectively. We created a presence and pseudo-absence dataset for evaluating each model-partition’s performance with the combined 3,530 observations in the validation datasets and 3,566 pseudo-absence points randomly selected from a grid of points encompassing the vireo’s current range in southern California. For every model-partition, we calculated Habitat Similarity Index (HSI) predictions for presence and pseudo-absence points ranging from Very High (0.75-1.00); High (0.50–0.74); Low to Moderate (0-0.49). Suitable habitat is identified as grid cells with HSI equal to or greater than 0.5. We calculated Area Under the Curve (AUC) values from a Receiver Operating Curve (ROC) to determine how well models distinguish between the combined presence and pseudo-absence points. We selected a set of best performing calibration model-partitions based upon median HSI calibration and validation values and AUC results. We then used these models to predict suitable habitat for the riparian grid across California, including the current and historic range. We qualitatively evaluated how well the model-partitions predicted suitable habitat in the historic range across California for historic and recent vireo records in the California Natural Diversity Database. We also used e-Bird observations to qualitatively assess how well the model predicted habitat at recently observed vireo locations in the historic range. Several top-performing model-partitions for southern California did not predict suitable habitat in the historic range. These models included climate variables, elevation, and NDVI variables, which vary widely between the current and historic ranges. Model 30 Partition 1 is the best model-partition for predicting habitat in both the current and historic ranges across California. This model-partition has an AUC of 0.98 and median calibration and random validation HSI values of 0.70. Supplementary validation datasets for 2016, 2017 and 2018 have median HSI values of 0.66, 0.64, and 0.63, respectively. This model includes the following variables: median slope, percent flat land, and percent riparian vegetation at the 150m-scale and distance from water (m). We mapped HSI predictions from this model for each cell in the 150m-scale grid across the California riparian study area to create the habitat suitability map.