Glossary

Because of the complexity of the climate system and limitation of computing power, Global Climate Models (GCMs) divide up the Earth into a series of boxes or "grid cells", with grid cells usually representing areas about 100km by 100km.

McSweeney, R., Hausfather, Z. (2020, July 30). How do climate models work? CarbonBrief.

To get more representative projections for California's complex geography, GCMs are downscaled. 32 GCMs were downscaled using the Localized Constructed Analogues (LOCA) statistical method. In these downscaled models, each grid cell represents climate conditions within a square area of 6km by 6km (3.7mi by 3.7mi).

When users select a point location on the map, data is retrieved for the LOCA grid cells from different downscaled GCMs that intersect that point. Climate Tools on Cal-Adapt offer users an option to spatially aggregate data over an area. When users select a county or census tract, data retrieved represents the mean of all the grid cells that intersect area of interest.

Users can select from several different boundary layers. This selection varies with the Climate Tool.

Annual average precipitation is the amount of total precipitation in the form of rain or snow over the course of a year.

The global climate models (GCMs) hosted on Cal-Adapt produced average precipitation rates for 24-hour periods. We converted daily average precipitation rates for each model into inches of precipitation per day. We then summed the daily precipitation amount across each year to determine annual average precipitation. Annual average precipitation values on Cal-Adapt are typically presented for calendar years (January 1 to December 31).

Annual average maximum temperature is the average of the hottest (maximum) temperatures for every day in a year.

We calculated these data for each model by selecting each day’s highest projected temperature and then averaging those daily highs across the entire year. Annual average maximum temperature is presented for calendar years (January 1 to December 31).

Annual average minimum temperature is the average of the coldest (minimum) temperatures for every day in a year.

We calculated these data for each model by selecting each day’s lowest projected temperature and then averaging those daily lows across the entire year. Annual average minimum temperature is presented for calendar years (January 1 to December 31).

An Application Programming Interface is a software intermediary that allows two applications to talk to each other. An API is like a cog that allows two systems to interact with each other, for example a web browser on your computer with a server.

The Cal-Adapt API is built using Django, Django REST framework, and Django-Spillway, an open source library developed at the GIF. The API follows an architectural style called REST (REpresentational State Transfer) which uses HTTP as the transport protocol for the message requests and responses.

Area projected to be at risk of burning in a given year. Note: Locations that are outside the combined fire state and federal protection responsibility areas were excluded from Wildfire Simulations produced for California’s Fourth Climate Change Assessment . These “No Data” areas are generally in landscapes that have been converted to intensive human use, including urban and agricultural areas.

Portion of the stream flow that is not from precipitation and results from seepage of water from the ground.

Baseflow projections on Cal-Adapt are produced using a land surface and hydrology model known as the Variable Infiltration Capacity (VIC) model.

A climate normal (also called a climatological normal) is a thirty-year average of a weather variable, such as daily maximum temperature or annual precipitation, for a given time of year. Climate normals are useful because they translate weather patterns into standardized descriptions of climate. Climate normals are used to compare climate across time and place and provide context for year-to-year variability in weather and climate.

Cal-Adapt recommends that you use climate normal time periods to assess both historical climate baselines and future climate conditions. Many Cal-Adapt tools pre-select time periods for you that align with frequently used climate normals:

• 1961-1990: historical baseline
• 2035-2064: mid-century
• 2070-2099: end-of-century

A climate projection is a simulation of Earth's climate in future decades, given assumptions about future greenhouse gas concentrations in the atmosphere and how earth systems interact and react to those greenhouse gas concentrations.

Climate projections are generated using global climate models (GCMs) and are sometimes downscaled to produce data at fine spatial scales. All climate projections on Cal-Adapt have been downscaled. Climate projections can help us plan adaptations to climate change impacts. Notably, climate projections are not weather projections; they cannot tell us what the weather will be like on any given day or in a year in the future. Instead, climate projections tell us what conditions in the future are going to be like on average over long time periods, often expressed as 30-year periods called climate normals.

Even though it is not known how greenhouse gas concentrations in the atmosphere will progress throughout the twenty-first century, climate projections for California generated using a suite of climate models all indicate with high certainty and agreement that temperatures will warm, sea level will rise, snowpack will decline, droughts will become more frequent and intense, extreme weather and precipitation events will become more frequent and variable, and wildfire risk will increase. For more information on California’s projected twenty-first century climate, explore Cal-Adapt and read the summary reports from California’s Fourth Climate Change Assessment.

CMIP5 stands for the Coupled Model Intercomparison Project, Phase 5. According to the World Research Climate Programme, the Coupled Model Intercomparison Project (CMIP) aims “to better understand past, present and future climate changes arising from natural, unforced variability or in response to changes in radiative forcing in a multi-model context. This understanding includes assessments of model performance during the historical period and quantifications of the causes of the spread in future projections.”

In other words, CMIP studies the extent to which climate models agree with one another and evaluates reasons for any disagreement. Performance is compared across models by running each model with a standard protocol and set of assumptions.

CMIP5, the fifth phase of CMIP, was conducted between 2010 and 2014. CMIP5 refined the scientific community’s understanding of climate system dynamics, modeling techniques, and model performance. CMIP5 also addressed questions raised during earlier CMIP phases. The global climate models (GCMs) employed for the climate projections for California’s Fourth Climate Change Assessment (and which are made available via Cal-Adapt) are from CMIP5.

CMIP6, the sixth phase of CMIP, is ongoing. The California Energy Commission is funding ongoing research that will downscale a new cohort of GCMs and projections for California from the CMIP6 archive.

A cooling degree day (CDD) is the number of degrees by which a daily average temperature exceeds a reference temperature and may therefore require additional energy for space cooling.

The reference temperature is typically 65 degrees Fahrenheit, although different utilities and planning entities sometimes use different reference temperatures. The reference temperature loosely represents an average daily temperature below which space cooling (e.g. air conditioning) is not needed.

The average temperature is the average of the minimum and maximum daily temperatures. CDDs can be summed over the entire year or over a portion of the year (e.g. the month of July) as a rough indicator of cooling energy used over that period.

The daily maximum temperature is the highest near-surface air temperature for a day. It usually occurs in the afternoon.

The daily maximum wind speed (in miles per hour).

The daily minimum temperature is the lowest near-surface air temperature for a day. It usually occurs in the early morning.

Daily precipitation in climate models represents all water that falls as rain or snow. In climate models, precipitation is modeled as a precipitation rate with units in kg/m2/s. 1 kg/m2/s is equivalent to 1 mm/s.

When you download datasets from the Cal-Adapt data server or the Data Download Tool, the data may be in the original units of kg/m2/s. To convert the values to mm/day multiply by 86,400 (24 hours/day x 60 minutes/hour x 60 seconds/minute). To convert values to inch/day multiply by 3,401.57.

Precipitation data used in Cal-Adapt climate tools are already converted into mm/day or inches/day, so any precipitation datasets you download from climate tools will list values in those units.

Probability of occurrence of 1 or more fires in a grid cell during the decade. This probability is estimated from Wildfire Simulations produced for California’s Fourth Climate Change Assessment using the methodology described in Dale, 2018.

Downscaling is a modeling method used to create finer-resolution outputs from coarser-resolution climate models. Commonly used downscaling methods include statistical downscaling and dynamical (regional simulation) downscaling. Much of the data hosted on Cal-Adapt has been statistically downscaled using locally constructed analogs (LOCA) so that data are available for a grid composed of roughly 6-kilometer by 6-kilometer cells. LOCA downscaling uses global climate model projections and observed historical patterns of the relationship between the coarse and the fine-scale to develop fine-scale model projections for the future. For more information on LOCA downscaling, visit the Get Started page.

Two drought scenarios were constructed for California’s Fourth Climate Change Assessment to investigate implications of an extended dry spell:

• Late 21st Century Drought: This scenario represents a late century dry spell from 2051–2070 identified from the HadGEM2-ES RCP 8.5 simulation. The extended drought scenario is based on the average annual precipitation over 20 years. This average value equates to 78% of historical median annual precipitation averaged over the North Coast and Sierra California Climate Tracker regions.

• Early 21st Century Drought: This scenario represents an early century dry spell from 2023–2042 derived from the late century drought scenario. The precipitation during this scenario is the same as in the late century scenario, however the temperature has been adjusted to take into account climate warming over the century.

For more details see:

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.

An emissions scenario is a representation of future greenhouse gas emissions and resulting atmospheric concentrations through time. An emissions scenario illustrates a plausible future so that climate projections for that emissions scenario can be generated, used to inform analysis and decision-making, and compared to other scenarios.

Changes in global and California temperatures depend on the accumulation of carbon dioxide and other heat-trapping gases emitted from human activities in the atmosphere. Future emissions and resulting accumulation of greenhouse gases (GHGs) could take a range of pathways depending on the success of international and local efforts to reduce GHG emissions and sequester GHGs.

Warming and other climatological changes experienced under different plausible future conditions are projected using representative concentration pathways (RCPs). RCPs are defined in terms of their total radiative forcing by 2100 (i.e., the net balance of radiation into and out of Earth’s surface due to human emissions of GHGs from all sources, measured in watts per square meter of Earth’s surface). In other words, each RCP represents a standardized set of assumptions about the human-influenced GHG trajectory in the coming years. RCPs do not represent a specific policy, demographic, or economic future.

California’s Fourth Climate Change Assessment uses two RCPs from the Fifth Intergovernmental Panel on Climate Change (IPCC) Assessment Report on Climate Change:

• RCP 4.5 (medium emissions scenario): a mitigation scenario where GHG emissions peak by 2040 and then decline. In California, annual average temperatures under this scenario are projected to increase 2°C - 4°C by the end of this century, depending on the location.

• RCP 8.5 (high emissions scenario): a no-mitigation scenario where global GHG emissions continue to rise throughout the 21st century. In California, annual average temperatures under this scenario are projected to increase 4°C - 7°C by the end of this century.

For more details see:

Bedsworth, Louise, Dan Cayan, Guido Franco, Leah Fisher, Sonya Ziaja. (California Governor’s Office of Planning and Research, Scripps Institution of Oceanography, California Energy Commission, California Public Utilities Commission). 2018. Statewide Summary Report. California’s Fourth Climate Change Assessment. Publication number: SUM-CCCA4-2018-013.

Water that is evaporated from the surfaces and transpired from plants.

Evapotranspiration projections on Cal-Adapt are produced using a land surface and hydrology model known as the Variable Infiltration Capacity (VIC) model.

An extreme heat day is a day in which the maximum temperature exceeds a specified threshold. That threshold is often referred to as the extreme heat threshold. Extreme heat days are concerning because they can increase fire danger, increase energy requirements, and produce public health impacts like heat stroke.

The extreme heat threshold is the maximum daily temperature used to identify an extreme heat day. Typically, extreme heat thresholds are identified based on a location’s historical climate, since ecosystems and built infrastructure is usually adapted to historical climate.

Cal-Adapt’s Extreme Heat Tool and Local Climate Change Snapshot Tool use a default extreme heat threshold temperature that is unique to every location. The default extreme heat threshold temperature is the 98th percentile of historical daily maximum temperatures for a place, computed using data from April through October for 1961 to 1990.

The extreme heat threshold temperature is set for every location in the Local Climate Change Snapshot Tool and cannot be changed. In the Extreme Heat Tool, users can either work with the default threshold temperature or input their own threshold temperature.

An extreme precipitation event is a weather event where precipitation (rain, snow, hail, etc.) is of a high (“extreme”) magnitude and greatly exceeds typical weather for a location and place.

There is no single definition of what constitutes the threshold above which a precipitation event is considered “extreme.” Precipitation events can be extreme in their duration, maximum precipitation rate, total volume of precipitation delivered, or a combination of these three factors. Often, extreme precipitation events are identified based on their return period, or the long-term average period of time between events of a specified magnitude. A precipitation event with a 100-year return interval is expected to occur once in every 100-year period, on average, over the long term (e.g., thousands of years). Accordingly, the chance of a 100-year precipitation event occurring in a given year is 1%. However, climate change is altering the return interval of storms of given magnitudes. Extreme events are projected to occur more frequently, such that the return period of a given storm type is likely to decrease.

GeoJSON is a file format used for encoding geospatial data and attributes. For technical information, visit the standard specification of the GeoJSON format.

A global climate model (GCM) is a mathematical model that represents the processes and interactions that drive the Earth’s climate. General circulation models incorporate the atmosphere, oceans, lands, and ice cover. Thirty-two global climate model simulations produced by institutions across the world served as a basis for California’s climate projections for California’s Fourth Climate Change Assessment, and thus for the data available on Cal-Adapt.

The California Department of Water Resources’s Climate Change Technical Advisory Group reduced the larger ensemble of 32 GCMs to a more manageable set of 10 GCMs as being most suitable for California water resource climate change studies. For some study teams and users of California’s Fourth Climate Change Assessment data, even the previously identified set of 10 GCMs was too much data. Accordingly, 4 of those 10 GCMs were identified whose project future climate can be described as producing:

• A “warmer/drier” simulation (HadGEM2-ES)
• An “average” simulation (CanESM2)
• A “cooler/wetter” simulation (CNRM-CM5)
• A “dissimilar” simulation that is most unlike the other three, to produce maximal coverage of possible future climate conditions (MIROC5)

Simulations produced with all climate models show substantial future warming; the “cooler/wetter” CNRM-CM5 simulation just shows less warming than the other models. The GCMs projections hosted on Cal-Adapt were generated for the periods 2006 to 2100 (future climate) and 1950 to 2005 (modeled historical climate).

To learn more about climate models, visit the Get Started page.

For more details on the downscaling and selection of climate models for impact studies, see:

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.

Climate Change Technical Advisory Group (California Department of Water Resources). 2015. Perspectives and Guidance for Climate Change Analysis.

Heat waves are periods of sustained, extreme heat. Heat waves can jeopardize infrastructure, ecosystems, and public health (including people’s lives).

There is no universal definition of a heat wave. On Cal-Adapt, a default heat wave event is defined as a period of four consecutive extreme heat days or warm nights when the daily maximum temperature is above the extreme heat threshold. Cal-Adapt’s tool counts every four-day heat wave as a unique event. So if extreme temperatures persist for 8 consecutive days and nights, that is considered two heat wave events. If you would like to define your own heat wave duration, you can do that in the Extreme Heat Tool.

A heating degree day (HDD) is the number of degrees by which a daily average temperature is below a reference temperature and may therefore require space heating.

The reference temperature is typically 65 degrees Fahrenheit, although different utilities and planning entities sometimes use different reference temperatures. The reference temperature loosely represents an average daily temperature above which space heating is not needed.

The average temperature is the average of the minimum and maximum daily temperatures. HDDs can be summed over the entire year or over a portion of the year (e.g. the month of January) as a rough indicator of heating energy over that period.

Localized Constructed Analogs (LOCA) is a statistical downscaling technique that uses past climate history to add fine-scale detail to coarse global climate model outputs.

LOCA data covering California are resolved to 6 square kilometers, meaning that unique climate projection values are available for each 6-kilometer by 6-kilometer grid cell. For more details, see:

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

NetCDF (Network Common Data Form) is a machine-independent data format that supports the creation, access, and sharing of array-oriented scientific data. For more information, visit the NetCDF page hosted by the University Corporation for Atmospheric Research.

A representative concentration pathway (RCP) is a representation of a plausible greenhouse gas emissions trajectory through time. See emissions scenario.

A return level is the estimated threshold amount or intensity of an extreme or rarely occurring event. For example, the return level of a 100-year storm is the estimated amount of precipitation that would be expected to be exceeded once every 100 years, on average. In Cal-Adapt tools like the Extreme Precipitation tool that explore extreme events, the return level is estimated from the statistical distribution of modeled future climate scenarios.

A return period or return interval is the average period of time between occurrences of an event of a specified magnitude. For example, suppose a storm that delivers 5 inches of rain in 24 hours has occurred in a location 5 times in the past 100 years. A storm of that magnitude therefore has a return interval of 20 years in that location. It’s important to note that return intervals are computed with long-term records to describe long-term average conditions; 20-year storms may occur in back-to-back years, but on average, they occur every 20 year. Return periods for many meteorological phenomena are changing under climate change.

Water that is discharged into the streams and largely results from precipitation and melting of snow.

Runoff projections on Cal-Adapt are produced using a land surface and hydrology model known as the Variable Infiltration Capacity (VIC) model.

Snow water equivalent (SWE) is the amount of liquid water contained in a snowpack. Snow can vary in its density and structure, so snow water equivalent estimates allow different snowpacks’ water contents to be compared.

SWE projections on Cal-Adapt are produced using a land surface and hydrology model known as the Variable Infiltration Capacity (VIC) model.

The California Adaptation Clearinghouse is the State of California’s consolidated, searchable database of resources for local, regional and statewide climate adaptation planning and decision-making. We encourage you to check out the many resources housed in the Adaptation Clearinghouse! For more information, visit the Adaptation Clearinghouse at https://resilientca.org/.

The daily average temperature for a day.

The Variable Infiltration Capacity (VIC) model is a large-scale hydrologic-land surface model that can be used to model the movement of water through the atmosphere and across a landscape. LOCA-downscaled precipitation and temperature data are used as inputs to the VIC model to produce estimates of evapotranspiration, runoff, soil moisture, snow water equivalent, relative humidity, and other precipitation-related variables. Details are described in:

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

A warm night is a night during which the minimum temperature does not fall below a defined threshold. Warm nights are concerning because buildings do not naturally cool down when overnight temperatures are warm, thereby potentially increasing overnight energy consumption for cooling and producing public health impacts such as heat stress and even excess mortality. Warm nights can also negatively impact ecosystems and water supplies, particularly snowpack.

The default warm night threshold temperature is the 98th percentile of historical overnight minimum temperatures for a place, computed using data from April through October for 1961 to 1990.

The warm night threshold temperature is set for every location in the Local Climate Change Snapshot Tool and cannot be changed. In the Extreme Heat Tool, users can either work with the default threshold temperature or input their own threshold temperature.