Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The numbers used in the climate zone map don't have a title or legend. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.This is explained in the Title 24 energy efficiency standards glossary section:"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year.""The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone."Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:CZ 1: ArcataCZ 2: Santa RosaCZ 3: OaklandCZ 4: San Jose-ReidCZ 5: Santa MariaCZ 6: TorranceCZ 7: San Diego-LindberghCZ 8: FullertonCZ 9: Burbank-GlendaleCZ10: RiversideCZ11: Red BluffCZ12: SacramentoCZ13: FresnoCZ14: PalmdaleCZ15: Palm Spring-IntlCZ16: Blue Canyon

The Energy Commission has developed this app to quickly and accurately show addresses and locations to determine California’s climate regions. We invite builders and building officials to use this app to determine the climate zones applicable to building projects.Please note:Building Climates Zones of California Climate Zone Descriptions for New Buildings - California is divided into 16 climatic boundaries or climate zones, which is incorporated into the Energy Efficiency Standards (Energy Code). Each Climate zone has a unique climatic condition that dictates which minimum efficiency requirements are needed for that specific climate zone. The California climate zones shown in this map are not the same as what we commonly call climate areas such as "desert" or "alpine" climates. The climate zones are based on energy use, temperature, weather and other factors.This is explained in the Title 24 energy efficiency standards glossary section:"The Energy Commission established 16 climate zones that represent a geographic area for which an energy budget is established. These energy budgets are the basis for the standards...." "(An) energy budget is the maximum amount of energy that a building, or portion of a building...can be designed to consume per year.""The Energy Commission originally developed weather data for each climate zone by using unmodified (but error-screened) data for a representative city and weather year (representative months from various years). The Energy Commission analyzed weather data from weather stations selected for (1) reliability of data, (2) currency of data, (3) proximity to population centers, and (4) non-duplication of stations within a climate zone."Using this information, they created representative temperature data for each zone. The remainder of the weather data for each zone is still that of the representative city." The representative city for each climate zone (CZ) is:CZ 1: ArcataCZ 2: Santa RosaCZ 3: OaklandCZ 4: San Jose-ReidCZ 5: Santa MariaCZ 6: TorranceCZ 7: San Diego-LindberghCZ 8: FullertonCZ 9: Burbank-GlendaleCZ10: RiversideCZ11: Red BluffCZ12: SacramentoCZ13: FresnoCZ14: PalmdaleCZ15: Palm Spring-IntlCZ16: Blue CanyonThe original detailed survey definitions of the 16 Climate Zones are found in the 1995 publication, "California Climate Zone Descriptions for New Buildings."

CZ 7: San Diego-LindberghCZ 8: FullertonCZ 9: Burbank-GlendaleCZ10: Riverside

A plant's performance is governed by the total climate: length of growing season, timing and amount of rainfall, winter lows, summer highs, wind, and humidity.Sunset's climate zone maps take all these factors into account, unlike the familiar hardiness zone maps devised by the U.S. Department of Agriculture, which divides most of North America into zones based strictly on winter lows.ZONE 2A: Cold mountain and intermountain areasAnother snowy winter climate, Zone 2A covers several regions that are considered mild compared with surrounding climates. You’ll find this zone stretched over Colorado’s northeastern plains, a bit of it along the Western Slope and Front Range of the Rockies, as well as mild parts of river drainages like those of the Snake, Okanogan, and the Columbia. It also shows up in western Montana and Nevada and in mountain areas of the Southwest. This is the coldest zone in which sweet cherries and many apples grow. Winter temperatures here usually hover between 10 and 20°F (–12 to –7°C) at night, with drops between –20 and –30°F (–29 and –34°C) every few years. When temperatures drop below that, orchardists can lose even their trees. The growing season is 100 to 150 days.ZONE 3A: Mild areas of mountain and intermountain climatesEast of the Sierra and Cascade ranges, you can hardly find a better gardening climate than Zone 3a.Winter minimum temperatures average from 15 to 25°F (–9 to –4°C), with extremes between –8 and –18°F (–22 and –28°C). Its frost-free growing season runs from 150 to 186 days. The zone tends to occur at lower elevations in the northern states (eastern Oregon and Washington as well as Idaho), but at higher elevations as you move south crossing Utah’s Great Salt Lake and into northern New Mexico and Arizona. Fruits and vegetables that thrive in long, warm summers, such as melons, gourds, and corn, tend to do well here. This is another great zone for all kinds of deciduous fruit trees and ornamental trees and shrubs. Just keep them well watered.ZONE 18: Above and below the thermal belts in Southern CaliforniaZones 18 and 19 are classified as interior climates. This means that the major influence on climate is the continental air mass; the ocean determines the climate no more than 15 percent of the time. Many of the valley floors of Zone 18 were once regions where apricot, peach, apple, and walnut orchards flourished, but the orchards have now given way to homes. Although the climate supplies enough winter chill for some plants that need it, it is not too cold (with a little protection) for many of the hardier sub-tropicals like amaryllis. It is too hot, too cold, and too dry for fuchsias but cold enough for tree peonies and many apple varieties, and mild enough for a number of avocado varieties. Zone 18 never supplied much commercial citrus, but home gardeners who can tolerate occasional minor fruit loss can grow citrus here. Over a 20-year period, winter lows averaged from 22 to 17°F (–6 to –8°F).The all-time lows recorded by different weather stations in Zone 18 ranged from 22 to 7°F (–6 to –14°C).ZONE 19: Thermal belts around Southern California's interior valleysLike that of neighboring Zone 18, the climate in Zone 19 is little influenced by the ocean. Both zones, then, have very poor climates for such plants as fuchsias, rhododendrons, and tuberous begonias. Many sections of Zone 19 have always been prime citrus-growing country—especially for those kinds that need extra summer heat in order to grow sweet fruit. Likewise, macadamia nuts and most avocados can be grown here. The Western Plant Encyclopedia cites many ornamental plants that do well in Zone 19 but are not recommended for its neighbor because of the milder winters in Zone 19. Plants that grow well here, but not in much colder zones, include bougainvillea, bouvardia, calocephalus, Cape chestnut (Calodendrum), flame pea (Chorizema), several kinds of coral tree (Erythrina), livistona palms, Mexican blue and San Jose hesper palms (Brahea armata, B. brandegeei), giant Burmese honeysuckle (Lonicera hildebrandiana), myoporum, several of the more tender pittosporums, and lady palm (Rhapis excelsa). Extreme winter lows over a 20-year period ranged from 28 to 22°F (–2 to –6°C) and the all-time lows at different weather stations range from 23 to 17°F (–5 to –8°C). These are considerably higher than the temperatures in neighboring Zone 18.ZONE 20: Cool winters in Southern CaliforniaIn Zones 20 and 21, the same relative pattern prevails as in Zones 18 and 19. The even-numbered zone is the climate made up of cold-air basins and hilltops, and the odd-numbered one comprises thermal belts. The difference is that Zones 20 and 21 get weather influenced by both maritime air and interior air. In these transitional areas, climate boundaries often move 20 miles in 24 hours with the movements of these air masses. Because of the greater ocean influence, this climate supports a wide variety of plants. You can see the range of them at the Los Angeles County Arboretum in Arcadia. Typical winter lows are 37° to 43°F (3 to 6°C); extreme 20-year lows average from 25 to 22°F (–4 to –6°C).All-time record lows range from 21 to 14°F (–6 to –10°C).ZONE 21: Thermal belts in Southern CaliforniaThe combination of weather influences described for Zone 20 applies to Zone 21 as well. Your garden can be in ocean air or a high fog one day and in a mass of interior air (perhaps a drying Santa Ana wind from the desert) the next day. Because temperatures rarely drop very far below 30°F (–1°C), this is fine citrus growing country. At the same time, Zone 21 is also the mildest zone that gets sufficient winter chilling for most forms of lilacs and certain other chill-loving plants. Extreme lows—the kind you see once every 10 or 20 years—in Zone 21 average 28 to 25°F (–2 to –4°C).All-time record lows in the zone were 27 to 17°F (–3 to –8°C).ZONE 22: Cold-winter portions of Southern CaliforniaAreas falling in Zone 22 have a coastal climate (they are influenced by the ocean approximately 85 percent of the time).When temperatures drop in winter, these cold-air basins or hilltops above the air-drained slopes have lower winter temperatures than those in neighboring Zone 23. Actually, the winters are so mild here that lows seldom fall below freezing. Extreme winter lows (the coldest temperature you can expect in 20 years) average 28 to 25°F (–2 to –4°C). Gardeners who plant under overhangs or tree canopies can grow subtropical plants that would otherwise be burned by a rare frost. Such plants include bananas, tree ferns, and the like. The lack of a pronounced chilling period during the winter limits the use of such deciduous woody plants as flowering cherry and lilac. Many herbaceous perennials from colder regions fail here because the winters are too warm for them to go dormant.ZONE 23: Thermal belts of Southern CaliforniaOne of the most favored areas in North America for growing subtropical plants, Zone 23 has always been Southern California’s best zone for avocados. Frosts don’t amount to much here, because 85 percent of the time, Pacific Ocean weather dominates; interior air rules only 15 percent of the time. A notorious portion of this 15 percent consists of those days when hot, dry Santa Ana winds blow. Zone 23 lacks either the summer heat or the winter cold necessary to grow pears, most apples, and most peaches. But it enjoys considerably more heat than Zone 24—enough to put the sweetness in ‘Valencia’ oranges, for example—but not enough for ‘Washington’ naval oranges, which are grown farther inland. Temperatures are mild here, but severe winters descend at times. Average lows range from 43 to 48°F (6 to 9°C), while extreme lows average from 34 to 27°F (1 to –3°C).ZONE 24: Marine influence along the Southern California coastStretched along Southern California’s beaches, this climate zone is almost completely dominated by the ocean. Where the beach runs along high cliffs or palisades, Zone 24 extends only to that barrier. But where hills are low or nonexistent, it runs inland several miles.This zone has a mild marine climate (milder than Northern California’s maritime Zone 17) because south of Point Conception, the Pacific is comparatively warm. The winters are mild, the summers cool, and the air seldom really dry. On many days in spring and early summer, the sun doesn’t break through the high overcast until afternoon. Tender perennials like geraniums and impatiens rarely go out of bloom here; spathiphyllums and pothos become outdoor plants; and tender palms are safe from killing frosts. In this climate, gardens that include such plants as ornamental figs, rubber trees, and scheffleras can become jungles.Zone 24 is coldest at the mouths of canyons that channel cold air down from the mountains on clear winter nights. Several such canyons between Laguna Beach and San Clemente are visible on the map. Numerous others touch the coast between San Clemente and the Mexican border. Partly because of the unusually low temperatures created by this canyon action, there is a broad range of winter lows in Zone 24. Winter lows average from 42°F (5°C) in Santa Barbara to 48°F (9°C) in San Diego. Extreme cold averages from 35° to 28°F (2 to –2°C), with all-time lows in the coldest stations at about 20°F (–6°C).The all-time high temperatures aren’t greatly significant in terms of plant growth. The average all-time high of weather stations in Zone 24 is 105°F (41°C). Record heat usually comes in early October, carried to the coast by Santa Ana winds. The wind’s power and dryness usually causes more problems than the heat itself—but you can ameliorate scorching with frequent sprinkling.

This study used data from field plots in urban areas to describe forest structure (e.g., tree numbers, density, basal area, species composition) for six land use categories in six California climate zones: Southern California Coast, Inland Empire, Inland Valley, Southwest Desert, Northern, and Interior West. Two types of field plot data were utilized. The first set of data include 702 randomly sampled 0.04 hectare (ha) plots obtained from i-Tree Eco plot data for Los Angeles (in 2007-2008), Santa Barbara (2012) and the Sacramento area (2007). The second set of data (687 plots, in 2011) consisted of 0.067 ha (four 0.017 ha subplots) plots based on the Forest Service Forest Inventory and Analysis (FIA) plot design. The number of plots collected varied by climate zone and a total of 3,796 trees were sampled. Data collection included percentage of tree canopy cover over the plot, tree species, stem diameter at breast height (1.37 meters above ground, dbh), tree height, crown width, distance and azimuth to buildings that fit the requirements as specified in the i-Tree Eco and Urban FIA manuals.Plot data were used to assess forest structure and model energy effects, carbon storage, carbon sequestration, avoided emissions, rainfall interception, and property values.Original metadata date was 07/10/2017. On 12/11/2017 metadata were updated to include reference to a new publication related to these data. Minor metadata updates were made on 3/15/2021

Local climate zones have been developed in the climatology field to characterize the landscape surrounding climate monitoring stations, toward adjusting for local landscape influences on measured temperature trends. For example, a station surrounded by tall buildings may be influenced by the urban heat island effect compared to a station in an agricultural area. The local climate zone classification system was developed by Iain Stewart and Tim Oke at the University of British Columbia. The classification scheme has been adopted by the World Urban Database Access and Tools Portal (WUDAPT) project, which aims to produce local climate zone maps for the entire world at a scale of ~ 100m. Local climate zones take building and vegetation type and height into account, and therefore serve as indicators of urban form, from dense urban (high building with little vegetation) to industrial/commercial (large lowrise buildings with paved areas) and natural (dense trees, low plants, water). How local climate zones are related to human health is a new area of research.CANUE staff and students worked in collaboratation with WUDAPT researchers to map local climate zones for Canada, using scripts developed in Google Earth Engine and applied to LandSat imagery for key time periods. Each postal code has been assigned to one of 14 local climate zone classes. In adition, seven groups have been created by aggregating similar local climate zones, and the percentage of group in the neighbourhood (1km2) around each postal code has been calculated.

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.

The California State Energy Commission established climate zones that represent an area for which an energy budget is established. An energy budget is the maximum amount of energy that a building, or portion of a building can be designed to consume per year.

This data publication contains urban tree inventory data for 929,823 street trees that were collected from 2006 to 2013 in 49 California cities. Fifty six urban tree inventories were obtained from various sources for California cities across five climate zones. The five climate zones were based largely on aggregation of Sunset National Garden Book's 45 climate zones. Forty-nine of the inventories fit the required criteria of (1) included all publicly managed trees, (2) contained data for each tree on species and diameter at breast height (dbh) and (3) was conducted after 2005. Tree data were prepared for entry into i-Tree Streets by deleting unnecessary data, matching species to those in the i-Tree database, and establishing dbh size classes. Data included in this publication include tree location (city, street name and number), diameter at breast height, species name and/or species code, and tree type.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

The HOT2000 software contains monthly and annual climate data for 403 locations in Canada. Boundary lines for HOT2000 climate zones were defined through spatial interpolation of the annual Celsius heating degree-days for each weather station. In a number of instances, the positions of boundary lines may not be representative of the local climate conditions due to lack of appropriate climate data. Each HOT2000 climate zone contains one weather station to be used for all locations within the zone. Climate data represent 20-year averaged data from 1998 to 2017 for locations south of 58° latitude and 13-year averaged data from 2005 to 2017 for locations north of 58° latitude. Note that Whistler, BC uses 13 years of data. The following information is available in the climate map: o Location: the name of the weather station. o Region: the provincial or territorial location of the weather station. o Latitude: measured in degrees north of the equator. o Annual heating degree-days using a base of 18 °C. o Design heating dry bulb temperature (°C): the 2.5% January design temperature used to calculate the design heat loss for the house. o Design cooling dry bulb temperature (°C): the 2.5% July design temperature used to calculate the design cooling load for the house. o Design cooling wet bulb temperature (°C): the 2.5% July design temperature used to calculate the design cooling load for the house. The climate map is intended to be used by all users of the HOT2000 software under the EnerGuide Rating System, including energy advisors, service organizations, regulatory agencies, builders, utilities, and all levels of government. The weather locations and climate data are based on Environment and Climate Change Canada data, specifically the Canadian Weather Energy and Engineering Datasets (CWEEDS).

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 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.

Contained within 3rd Edition (1957) of the Atlas of Canada is a map that shows the division of Canada into climatic regions according to the classification of the climates of the world developed by W. Koppen. Koppen first divided the world into five major divisions to which he assigned the letters A, B, C, D, and E. The letters represent the range of divisions from tropical climate (A) to polar climate (E). There are no A climates in Canada. The descriptions of the four remaining major divisions are given in the map legend. Koppen then divided the large divisions into a number of climatic types in accordance with temperature differences and variations in the amounts and distribution of precipitation, on the basis of which he added certain letters to the initial letter denoting the major division. The definitions of the additional letters which apply in Canada are also given when they first appear in the map legend. Thus b is defined under Csb and the definition is, therefore, not repeated under Cfb, Dfb or Dsb. For this map, the temperature and precipitation criteria established by Koppen have been applied to Canadian data for a standard thirty year period (1921 to 1950 inclusive).

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.

🇺🇸 미국 English This dataset contains 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 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.

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.

Map showing 16 climate zones in California on a CA geology base map.

Mediterranean-climate region (MCR) shrublands have evolved a set of regeneration strategies in response to periodic, high-intensity wildfires: obligate seeding (OS), obligate resprouting (OR), and facultative seeding (FS) species. Spatial variation is seen in different regeneration strategies. In California, previous studies have found a higher abundance of OR species in mesic environments and OS species in xeric environments (Meentenmeyer et al. 2001). To date, however, data on their spatial distribution at a regional scale in California is limited and presents a significant information gap for resource managers of shrub-dominated landscapes. We developed a multinomial model using temporally dynamic and static variables to predict the distribution of the three shrub post-fire regeneration strategies, plus trees and herbs, in southern California. Cross-validation showed 50% of the predicted values for each of the five plant groups were within 8–24 percent of the actual value (Underwood et al. 2023). Spatial data for OS, OR, and FS provide an important contribution to resource management to help quantify carbon storage of shrublands and prioritize areas for post-fire restoration. Methods Our study area consists of shrublands within a 31,069 km2 (7,677,317 acres) footprint that encompasses all 36 Level IV ecoregions (Omernik and Griffith, 2014) that overlap with the Angeles, Cleveland, Los Padres, and San Bernardino National Forests in southern California, USA (Figure 1). Vegetation types in the study region are dominated by shrubland (62%), grassland (16%), broadleaf woodland (8%), and conifer and mixed conifer-broadleaf forests (8%). The predominant shrublands communities (following Wildlife Habitat Relations classification, Barbour et al., 2007; https://wildlife.ca.gov/Data/CWHR/Wildlife-Habitats [CWHR]) are: mixed chaparral (29%; dominated by scrub oak [Quercus berberidifolia], various species of Ceanothus and manzanita [Arctostaphylos], and other mostly resprouting shrub species); sage scrub (12%; dominated by California sagebrush [Artemisia californica], purple sage [Salvia leucophylla], black sage [Salvia mellifera], and California buckwheat [Eriogonum fasiculatum]); and chamise/redshank chaparral (6%; dominated by chamise [Adenostoma fasciculatum] and/or redshank [Adenostoma sparsifolium]). We analyzed 222 plots from the USDA Forest Service Forest Inventory and Analysis (FIA) program (Burkman, 2005) for which we estimated aboveground biomass using shrub species-specific allometric equations (McGinnis et al., 2010; Wakimoto, 1978) or a generalized shrub-herb biomass equation (Lutes et al., 2006, see Schrader-Patton and Underwood, 2021 for details). We assigned each species in the FIA plots to one of three lifeform categories: shrub, tree or herb (forbs and grasses). Shrub species were further categorized into one of three post-fire regeneration strategies: obligate seeder (OS), facultative seeder (FS), or obligate resprouter (OR) using descriptions of regeneration strategy and life history reported in primary literature and public databases (Gordon and White, 1994; Borchert et al., 2004; CNPS, 2021; FEIS, 2021). For each plot, we calculated the proportion of aboveground live biomass for each of these five plant groups: OS, FS, OR, tree, and herb, by dividing the estimated biomass of each by the summed total biomass across all groups. To predict the distribution of OS, FS, OR, trees and herbs, we used a multinomial regression model (mnet package and function multinom in R software, R Core Team, 2016; Ripley and Venables, 2021) using: average annual solar radiation, actual evapotranspiration (AET), climate water deficit (CWD), average annual precipitation, the normalized difference vegetation index (NDVI, using the maximum composite value from July to August each year), modeled aboveground biomass (a proxy for productivity), eastness (a measure of continentality and dryness), slope, flow accumulation, soil bulk density, soil clay content, and soil percent carbon (see Table S1). Finally, we included vegetation type from the CWHR classification system from the FVEG vegetation data (FRAP, 2015) and time since last fire from the Fire Return Interval Departure geodatabase (USDA, 2015). We omitted any variables that were strongly correlated (r > 0.55). To select the best model, we started with a full model and removed predictors sequentially using Akaike Information Criteria (AIC) to evaluate model fit. Predictor variables remained in the final model if they improved model fit by a minimum ΔAIC of –2 (Anderson and Burnham, 2004). For the final model, p-values were generated with a Wald-z test. Obligate seeder shrubs were selected as the baseline variable for the multinomial model (as we expected OS to differ most from FS and OR) against which the other four plant groups were calculated. To evaluate model performance, we performed leave-k-out cross-validation with k = 8 (Hastie et al., 2009) and examined the distribution of cross-validation errors to evaluate predictive accuracy: mean, standard deviation, kurtosis, skew, and interquartile range. We then used the raster surfaces corresponding to each predictor as model inputs into the ‘predict’ function in R software. We created a raster spatial layer (30 m resolution) with the proportion of biomass for each of the five plant groups by applying the model predictions of the proportion of each plant group to each pixel in our study area (see Underwood et al. 2023 for details).

Figure 1. The boundary of the study area in southern California within which we identify the distribution of obligate resprouter, obligate seeder, and facultative seeder species. CAVEATS In using these spatial data, users should note that the USFS FIA program that provided the input plots for the multinomial model is designed to measure forest conditions across the US, so FIA sampling is biased to upland interior, moister sites which contain trees. Consequently, OS species in coastal and desert scrub communities may not be well represented. In addition, studies (e.g., Keeley 2023) have shown some shrub species, such as Ceanothus leucodermis, vary temporarily and spatially in their post-fire regeneration strategy, changing from OR to OS with longer fire-fire intervals. REFERENCES

Anderson, D., and Burnham, K. (2004). Model Selection and Multi-Model Inference (2nd edition). New York, NY: Springer-Verlag. Barbour, M. G., Keeler-Wolf, T., and Schoenherr, A. A. (2007). Terrestrial Vegetation of California. Berkeley, CA: University of California Press. Borchert, M., Lopez, A., Bauer, C., and Knowd, T. (2004). Field guide to coastal sage scrub and chaparral alliances of Los Padres National Forest. USDA Forest Service Region 5, Ecological Field Guide. Burkman, B. (2005). Forest inventory and analysis sampling and plot design; FIA fact sheet Series. USDA Forest Service Forest Inventory and Analysis National Program. Washington DC, USA: USDA Forest Servicehttps://www.fia.fs.usda.gov/library/fact-sheets/data-collections/Sampling%20and%20Plot%20Design.pdf

CNPS (California Native Plant Society). (2021). A Manual of California Vegetation, online edition. Sacramento, CA: California Native Plant Society http://www.cnps.org/cnps/vegetation/

FEIS (Fire Effects Information System). https://www.feis-crs.org/feis/ FRAP (Fire and Resource Assessment Program). Data from: Fveg15_1 vegetation data. California Department of Forestry and Fire Protection’s CALFIRE Fire and Resource Assessment Program (FRAP). (2015) http://frap.fire.ca.gov/data/frapgisdata-sw-fveg_download

Gordon, H., and White, T. C. (1994). Ecological guide to southern California chaparral plant series. Transverse and Peninsular Ranges: Angeles, Cleveland, and San Bernardino National Forests. Report R5-ECOL-TF-005. Albany WA: USDA Forest Service Pacific Southwest Region. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction (2nd ed.). Springer. Lutes, D., Keane, R., Caratti, J., Key, C., Benson, N., Sutherland, S., and Gangi, L. (2006). FIREMON: Fire Effects Monitoring and Inventory System. Fort Collins, CO: Rocky Mountain Research Station. McGinnis, T. W., Shook, C. D., and Keeley, J. E. (2010). Estimating aboveground biomass for broadleaf woody plants and young conifers in Sierra Nevada, California, forests. West J App For 25, 203–209. Meentemeyer, R. K., Moody, A. and Franklin, J. (2001). Landscape-scale patterns of shrub-species abundance in California chaparral. Plant Ecol 156, 19–41. Omernik, J. M., and Griffith, G. E. (2014). Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework. Environ Manage 54, 1249–1266. https://doi.org/10.1007/s00267-014-0364-1. https://www.epa.gov/eco-research/ecoregions R Core Team. (2016). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Ripley. B., and Venables, W. (2021). nnet: feed-forward neural networks and multinomial log-linear models. R Package. Schrader-Patton, C. C., and Underwood, E. C. (2021). New biomass estimates for chaparral-dominated Southern California Landscapes. Remote Sens 13(8), 1581. https://doi.org/10.3390/rs13081581. Schrader-Patton, C. C., Underwood E. C., and Sorenson, Q. M. (2023). Annual biomass spatial data for southern California (2001–2021): Above- and belowground, standing dead, and litter. Ecology e4031. Underwood, E. C., Sorenson, Q. M., Schrader-Patton, C. C., Molinari N. A., and Safford, H. D. (2023). Assessing spatial and temporal variation in obligate resprouting, obligate seeding, and facultative seeding shrub species in California’s Mediterranean-type climate region. Front Ecol Evol https://doi.org/10.3389/fevo.2023.1158265 USDA [US Department of Agriculture]. Data from: Fire-Return Interval Departure (FRID) Geodatabase. (2015)

The United States Geological Survey has published "An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems" in Global Ecology and Conservation Journal. This work was produced by a team led by Roger Sayre, Ph.D., Senior Scientist for Ecosytems at the USGS Land Change Science Program with the support from The Nature Conservancy and Esri. We described this work using two introduction story maps, Introduction to World Ecosystems Map and Introduction to World Climate Regions Map. This story map is an introduction for World Climate Regions Map. You can have more information by accessing the published paper and you can access the dataset by downloading the pro package.

"Vegetation Zones of Canada: a Biogeoclimatic Perspective" maps Canadian geography in relation to gradients of regional climate, as expressed by potential vegetation on zonal sites. Compared to previous similar national-scale products, "Vegetation Zones of Canada" benefits from the work of provincial and territorial ecological classification programs over the last 30+ years, incorporating this regional knowledge of ecologically significant climatic gradients into a harmonized national map. This new map, reflecting vegetation and soils adapted to climates prior to approximately 1960, can serve as a broad-scale (approximately 1:5 M to 1:10 M) geospatial reference for monitoring and modeling effects of climate changes on Canadian ecosystems. "Vegetation Zones of Canada: a Biogeoclimatic Perspective" employs a two-level hierarchical legend. Level 1 vegetation zones reflect the global-scale latitudinal gradient of annual net radiation, as well as the effects of high elevation and west to east climatic and biogeographic variation across Canada. Within the level 1 vegetation zones, level 2 zones distinguish finer scale variation in zonal vegetation, especially in response to elevational and arctic climatic gradients, climate-related floristics and physiognomic diversity in the Great Plains, and maritime climatic influences on the east and west coasts. Thirty-three level 2 vegetation zones are recognized: High Arctic Sparse Tundra Mid-Arctic Dwarf Shrub Tundra Low Arctic Shrub Tundra Subarctic Alpine Tundra Western Boreal Alpine Tundra Cordilleran Alpine Tundra Pacific Alpine Tundra Eastern Alpine Tundra Subarctic Woodland-Tundra Northern Boreal Woodland Northwestern Boreal Forest West-Central Boreal Forest Eastern Boreal Forest Atlantic Maritime Heathland Pacific Maritime Rainforest Pacific Dry Forest Pacific Montane Forest Cordilleran Subboreal Forest Cordilleran Montane Forest Cordilleran Rainforest Cordilleran Dry Forest Eastern Temperate Mixed Forest Eastern Temperate Deciduous Forest Acadian Temperate Forest Rocky Mountains Foothills Parkland Great Plains Parkland Intermontane Shrub-Steppe Rocky Mountains Foothills Fescue Grassland Great Plains Fescue Grassland Great Plains Mixedgrass Grassland Central Tallgrass Grassland Cypress Hills Glaciers Please cite this dataset as: Baldwin, K.; Allen, L.; Basquill, S.; Chapman, K.; Downing, D.; Flynn, N.; MacKenzie, W.; Major, M.; Meades, W.; Meidinger, D.; Morneau, C.; Saucier, J-P.; Thorpe, J.; Uhlig, P. 2019. Vegetation Zones of Canada: a Biogeoclimatic Perspective. [Map] Scale 1:5,000,000. Natural Resources Canada, Canadian Forest Service. Great Lake Forestry Center, Sault Ste. Marie, ON, Canada.