Welcome to the Climate Console

The climate console is a web application designed for exploring climate change projections for a selected area of interest.

Getting Started

1. Select a reporting units layer .

2. Select a feature or set of features in the map .

3. Explore results generated for the selected area.
(Results will appear here)

Currently Selected:
Units: °Celsius °Fahrenheit
Projected Change (Model Averages, Annual)
The average max temperature in the selected area is projected to exceed the historical average by

1.8°C over the next 30 years
2.9°C over the 30 years after that

The average min temperature in the selected area is projected to exceed the historical average by

1.8°C over the next 30 years
2.9°C over the 30 years after that

The average precipitation in the selected area is projected to the historical average by

1.8mm over the next 30 years
2.9mm over the 30 years after that

(Note that precipitation projections vary widely among models)

The average aridity in the selected area is projected to the historical average by

% over the next 30 years
% over the 30 years after that

The average potential evapotranspiration in the selected area is projected to the historical average by

% over the next 30 years
% over the 30 years after that

▼ Scroll to see more ▼


The Climate Console is a web mapping application designed for exploring climate projections and fuzzy logic (EEMS) model results for a specified area of interest.

The zonal mean for several climate projection datasets is calculated for each of the reporting units and stored in a spatial database which can then be queried against using the tools provided on the left hand side of the map or by simply clicking on a feature of interest. This allows the user to examine the future climate projections and potential for climate change impact within one or more administrative units or ecological boundaries of interest.

Instructions for Use:

  1. Select a reporting units layer from the list provided in the upper left hand side of the map. Selecting "User Defined (1km)" will allow you to define an arbitrary area based on a 1km grid.
  2. Select a feature or set of features using the selection tools provided, or simply click on a feature of interest .
  3. The area weighted averages for the climate variables and EEMS model outputs for the selected area will appear in the charts on the right hand side of the screen. You can choose to plot a different climate variable by selecting the variable from the dropdown menu.
  4. Click a data point on the chart to display the corresponding dataset used to generate the plotted value. Click the point again to remove the dataset from the map.


Climate refers to the statistical properties of weather over periods ranging from months to decades or more, and includes average conditions, and the range of variability, as well as the frequency of extreme events.1

Climate is different from weather. A weather forecast refers to a prediction about the specific atmospheric conditions expected for a location in the short-term future (hours to days).2

A climate trend is a progressive change in the state of the climate based on weather statistics evaluated over long periods, typically of at least 30 years.1

For information on climate models click the following link to view a presentation created by the Pacific Institute for Climate Solutions: http://pics.uvic.ca/insights/module1_lesson4/player.html

The time series climate data used to represent the historical period (1971-2000) were obtained from the LT71m PRISM 30 arc-second spatial climate dataset for the Conterminous United States (Daly et al., 2008). We selected a subset of the 34 CMIP5 General Circulation Models (GCMs) that have been shown to reproduce several observed climate metrics and that captured the full range of projected change for both annual average temperature and annual precipitation under the representative concentration pathway 8.5 (RCP8.5; Meinshausen et al., 2011; van Vuuren et al., 2011). We then obtained downscaled time series climate projections for the selected GCMs from the NASA Earth Exchange (NEX) U.S. Downscaled Climate Projections (NEX US-DCP30) dataset (Thrasher et al., 2013) for the entire spatial extent of the study area and for the period 2016-2075 time. The multi-model ensemble mean of the four downscaled climate models was calculated for each of the climate variables.

Weather Forecast:

The near-term weather forecast data presented in the climate console streams directly from the following forecast distribution files generated by NOAA's Climate Prediction Center:


The data in these files represent probability of exceedance values. The field headers (98, 95, 90 ,80, 70, 60, 50, 40, 30, 20, 10, 5, 2) indicate the probability that the actual temperature or precipitation level during the three month period expressed by the LEAD time will be greater than the stated value within the specified climate division (CD). Click here for complete field descriptions and additional information on the forecast distribution files above.

The historical means and forecast means displayed in the climate console come directly from the values in the climatological mean field (C MEAN) and the forecast mean field (F MEAN), respectively. The forecast mean corresponds to the 50% probability of exceedance value. These data are automatically updated on the third Thursday of each month.

Reference: Barnston et al., 2000, Bull. Amer. Meteor. Soc. 81:1271-1279
Source: http://www.cpc.ncep.noaa.gov/

Condition & Impact Models

We used the Environmental Evaluation Modeling System (EEMS), a spatially explicit fuzzy logic modeling framework developed by Conservation Biology Institute (CBI), to evaluate the potential impact of climate change throughout the study area. We defined potential impact as a function of “site sensitivity” and the nature and degree to which a site may experience changes in climate, which we refer to as “climate exposure”. The EEMS logic model for each of the evaluated metrics, site sensitivity, climate exposure, and potential climate impacts are described in the sections below.

Overview of the EEMS Modeling Process

EEMS is based on the Ecosystem Management Decision Support System (EMDS; Reynolds, 2006). With fuzzy logic modeling (a quick introduction to fuzzy logic modeling can be found at: http://tinyurl.com/ox76lhb), input variables are first converted into a common numeric domain or range (also referred to as fuzzy space). Fuzzy values are assigned to the input variables based on the relationship to a true/false proposition. Values range between -1.0 (representing a fully false result) and +1.0 (representing a fully true result). For instance, input values of projected change in average minimum temperature can be tested against the proposition, “has high change from historical temperature” via a mathematical function with fuzzy values between +1.0 (the most true result) and -1.0 (the most false result), inclusive.

Fuzzy values are combined using logic operators in a hierarchical, tree-based logic structure to produce a result based on a single evaluation proposition (for instance “has a high degree of climate change”). Each cell in the study area is evaluated independently, and a map reflecting the value of each proposition across the landscape is produced.

Terrestrial Intactness:

Terrestrial intactness is an estimate of current condition based on the extent to which human impacts such as agriculture, urban development, natural resource extraction, and invasive species have disrupted the landscape across the DRECP study area. Terrestrial intactness values will be high in areas where these impacts are low.

This dataset provides an estimate of current terrestrial intactness, based on an EEMS fuzzy logic model that integrates multiple measures of landscape development and vegetation intactness.

This model integrates agriculture development (from LANDFIRE EVT v1.1), urban development (from LANDFIRE EVT v1.1 and NLCD Impervious Surfaces), linear development (from Tiger 2012 Roads, utility lines, and pipelines), OHV recreation areas, energy and mining development (from state mine and USGS national mines datasets as well as geothermal wells, oil/gas wells, wind turbines, and power plant footprints), vegetation departure (from LANDFIRE VDEP), invasive vegetation (multiple sources combined for invasives analyses), and measures of natural vegetation fragmentation calculated using FRAGSTATS. In this version, Maxent modeled Sahara Mustard was included in the Invasive's branch as well as in the Fragstats model run.

Caution is warranted in interpreting this dataset because it provides a single estimate of terrestrial intactness based on available data. The degree of terrestrial intactness likely varies for a particular species or conservation element, and may depend on additional factors or thresholds not included in this model. Instead, this model should be taken as a general measure of intactness that can serve as a template for evaluating across many species at the ecoregion scale, and provides a framework within which species-specific parameters can be incorporated for more detailed analyses.

View or Download this dataset on Data Basin

Site Sensitivity

The Site Sensitivity Model evaluates the study area for factors that make the landscape sensitive to climate change. These factors fall into two main branches of the model: soil sensitivity and water retention potential. As a final step in the model, we defined barren areas as having the lowest possible sensitivity since many of these areas will not be further degraded by climate change.

View or Download this dataset on Data Basin

Climate Exposure

The Climate Exposure Model is based on aridity and climate. Climate factors include maximum temperature, minimum temperature, and precipitation on a seasonal basis and an annual basis. Change was calculated for two future time periods, 2016-2045 and 2046-2075, compared to the historical period, 1971-2000. Projections for three climate futures were used along with the ensemble mean values from those models. Temperature and precipitation differences were normalized using the standard deviation over the historical period via the following formula:

where d is the difference, xf is the mean of the variable in the future period, xh is the mean of the variable in the historical period, and σxh is standard deviation of the variable in the historical period. Change in aridity was calculated as the percent change from the historical period. Projected future change is very high for temperatures and aridity. In order to capture both the differences across the region as well as the severity of change, nonlinear conversions were used to convert input data into fuzzy space:
Original value to fuzzy value conversion curves for a) climate variables and b) aridity.

View or Download this dataset on Data Basin (2016-2045)
View or Download this dataset on Data Basin (2046-2075)

Potential Climate Impact

EEMS model of potential climate impacts generated using data from STATSGO soils data and climate model results. Results from the Site Sensitivity and Climate Exposure models contribute equally to the results of the Potential Climate Impact model. As with the Climate Exposure Model, the Climate Impacts Model was run for each climate future (full results available on Data Basin). The results from the run with ensemble climate data are used in the Climate Console.

View or Download this dataset on Data Basin (2016-2045)
View or Download this dataset on Data Basin (2046-2075)


1 Pacific Institute for Climate Solutions

2 National Oceanic and Atmospheric Administration (NOAA), Climate Program Office, Climate Literacy

Three Month Forecast in Climate Division

Historical Mean
Show on Map
*Historical Period: 1971-2000
About the weather data

One Year Forecast at a Glance

Logic Model Results

Click on any column above to explore the model diagram