Currently Selected:

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Maximum Temperature Minimum Temperature Precipitation Aridity PET

Average Change

Annual Jan-Feb-Mar Apr-May-Jun Jul-Aug-Sep Oct-Nov-Dec

Units: °Celsius °Fahrenheit

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Scenario:RCP8.5/Resolution:30arc-secs. Learn more about the climate data

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 1.8% over the next 30 years 2.9% 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

Overview:

The Climate Console is a web mapping application designed for exploring climate projections, simulated impacts, and fuzzy logic (EEMS) model results for a specified area 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:

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 (definition borrowed from Pacific Institute for Climate Solutions).

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 (definition borrowed from Pacific Institute for Climate Solutions).

To learn more about the differences between climate and weather, click on the play button below.

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

Climate data used for the historical period (1971- 2000) correspond to the LT71m PRISM (Parameter-elevation Relationships on Independent Slopes Model) 30 arc-second spatial climate dataset for the Conterminous United States (Daly et al. 2008). For future climate projections, we selected four climate models, either General Circulation Models (GCMs) or Earth System Models (ESMs), (Table 1) from the 5th Coupled Model Intercomparison Project (CMIP5; Taylor et al. 2012). These models were chosen based on evaluations of their ability to simulate historical climate conditions globally and over the western United States (Rupp et al. 2013).

Model	Model Name	Model Institution
1	CanESM2	Second Generation Canadian Earth System Model
2	CCSM4	Community Climate System Model version 4
3	CNRM.CM5	Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5.1
4	HadGEM2-ES	Hadley Centre Global Environmental Model, version 2-Earth System

Click here for more information on the models listed above.

The four GCM/ESMs capture a wide 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). RCP8.5 is a highly energy-intensive scenario that results from high population growth and a moderate rate of technology development without establishment of climate change policies.

We used the downscaled climate projections from the NASA Earth Exchange (NEX) U.S. Downscaled Climate Projections (NEX US-DCP30) dataset (Thrasher et al. 2013) for the western United States. We chose two thirty-year periods, 2016-2045 and 2046-2075, to represent the projected futures. Each climate model projections were averaged over those periods and a multi-model ensemble mean of the ten model projections shown in the chart above was also calculated for each time period.

Calculation of Climate Variables

Climate variable values (tmax, tmin, and prec) were calculated as means of annual average temperatures and of annual total precipitation for each time period (1971-2000, 2016-2045, and 2046-2075) .

Two derived climate variables, potential evapotranspiration (PET) and aridity(the ratio of annual precipitation over PET), were also calculated for historical and future periods. PET was calculated using the 1985 version of the Hargreaves potential evaporation equation (Hargreaves and Allen 2003):

PET = 0.0023 x 0.408RA x (Tavg + 17.8) x TD 0.5        (1)

where RA is the extraterrestrial radiation expressed in (MJ m-2 d-1), Tavg (oC) is the average daily temperature, and TD (oC) is the temperature range. The constant 0.408 is the inverse of latent heat flux of vaporization at 20 oC. It is used to convert extraterrestrial radiation units from MJ m-2 d-1 to mm d-1. RA was calculated using the r.sun (Šúri & Hofierka 2004) routine in the GRASS geographic information system (GRASS 2015).

Aridity was defined as P/PET where P is annual precipitation. The percentage change in aridity was calculated by following Feng and Fu's method (2013):

Δ(P/PET)/(P/PET)≈ΔP/P−ΔPET/PET        (2)

All climate variables for historic and future projections (from four climate models and one ensemble mean) were calculated from the NetCDF files in the NCAR Command Language (NCL) software program (NCL 2014) and are listed in Table 2.

1971-2000				2016-2045				2046-2075
	Average for the Period	Seas- onal	Average for the Period	Seas- onal	Delta for Period	Delta for Seas- sons	Average for the Period	Seas- onal	Delta for Period	Delta for Seas- sons
Tmax	X	X	X	X	X	X	X	X	X	X
Tmin	X	X	X	X	X	X	X	X	X	X
Prec	X	X	X	X	X	X	X	X	X	X
PET	X	X	X	X			X	X
Aridity					X	X			X	X

The delta or change values between historic and future for minimum and maximum temperature were calculated by subtracting historical values from future values. However, for precipitation and aridity change was calculated as a percent change from the historical (((future-historical) / historical) * 100).

Description of Climate Model Datasets

The LT71m PRISM dataset is a gridded time series of monthly-modeled values for precipitation (rain + melted snow), maximum, minimum, and mean temperatures. It uses data from station networks that have at least some stations with ≥ 20 years of observed data. To create a grid with PRISM, Daly et al. (2008) use the climatologically-aided interpolation (CAI) method with 1971-2000 monthly climatologies.

The NEX US-DCP30 future climate dataset includes climate projections from 34 GCMs that have been statistically downscaled to 30 arc-second spatial resolution using the Bias-Correction Spatial Disaggregation (BCSD) method (Maurer and Hidalgo 2008). First the bias in temperature and precipitation projections is corrected by comparing GCM results with “observations” from the PRISM dataset. The projections are then downscaled to the finer 30 arc-second grid using the complex spatial interpolation method during the “spatial-disaggregation” step (Thrasher et al. 2013).

Climate Data Extraction

All PRISM data used for the historical period were converted from the ESRI ASCII raster format (ESRI 2014) to the Network Common Data Form (NetCDF; Rew et al. 1997, UNIDATA 2015). We discovered that when the NEX data were processed by Thrasher et al. (2013) the left lower corners of the PRISM data were erroneously used as the center coordinates for processing the NEX NetCDF files. Therefore, the origin of the NEX US-DCP30 data had to be altered to conform to the PRISM data. The NEX grids were adjusted by one-half grid cell (0.004166666667 decimal degrees) so that the two datasets would be spatially aligned and consistent.

Once all of the data were in the NetCDF format and spatially aligned, climate variables [maximum average monthly temperature (tmax), minimum average monthly temperature (tmin), and average monthly precipitation (prec)] were extracted just for the western United States. The multi-model ensemble was then created for each variable by taking the un-weighted mean of the projections from the ten climate models of interest.

Post Processing

Each climate dataset was projected to USA Contiguous Albers Equal Area Conic (USGS version) using a cubic convolution resampling method in ArcGIS 10.3. The zonal mean for both climate and impact projection datasets was 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 future climate projections and climate change impacts within one or more administrative units or ecological boundaries of interest.

Weather Forecast:

Weather is different from climate. "A weather forecast refers to a prediction about specific atmospheric conditions expected for a location in the short-term future (hours to days)." [definition from the USGCRP Climate Literacy Guide at http://bit.ly/2bVWDH4]

To learn about the differences between climate and weather, click on the play button below.

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:

Temperature
Precipitation

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 (Barnston et al. 2000).

Condition & Impact Models:

We used the Environmental Evaluation Modeling System (EEMS), a spatially explicit fuzzy logic modeling framework developed by Conservation Biology Institute (CBI) staff, to evaluate the potential impact of climate change throughout the study area. We defined potential impact as a combination of “site sensitivity” defined simply by soil characteristics and the nature and degree to which a site may experience a change 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 derives from 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 normalized by converting them to a common numeric domain or range (also referred to as fuzzy space) varying between, in this case, -1.0 (representing a "fully" false statement) and +1.0 (representing a "fully" true statement). Fuzzy values are assigned based on the relationship to a proposition. For instance, projected change in minimum temperature can be tested against the proposition, “will experience substantial departures from historical temperature” using fuzzy values varying between +1.0 (true statement) and -1.0 (false statement).

Normalized inputs are combined using logic operators to create a hierarchical, logic tree that produces an answer to a particular question (for instance “will this area experience considerable climate change?”). Each grid cell in the study area is evaluated independently, producing a map of fuzzy values answering the question for all the grid cells across the landscape.

We implemented the models using data in NetCDF format for the study area matching the domain of the input files.

To learn more about logic models, click on the play button below.

The Condition & Impacts tab shows results for several EEMS models averaged over the area selected by the user. The bar colors correspond to colors of the model inputs on the map. Clicking on the bar causes the entire dataset to display in the map.

In addition, when you click on a column, you’ll see the model diagram appear below the chart. The model diagram is an interactive graphical representation of the model used to create each of the EEMS results shown in the chart. The model flows from the bottom up — meaning that input nodes appear below the output nodes they create. The text in the gray boxes indicate the operation used to combine the input data (e.g., Fuzzy Union).

Clicking on any box makes the corresponding spatial dataset show up on the map on the left. By clicking on the color ramps on the right hand side of each box in the logic tree, the user can choose to display either of the two color scale (classified or stretched). When the user clicks on a box, the model diagram expands to show the underlying inputs used to create the box that was selected. Box colors indicate the number of inputs (see color scale below logic tree). The active box representing the map on display will be highlighted in green.

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.

Soil Data for Soil Sensitivity Calculation

Soil data for this analysis were obtained from the conterminous United States Multi-Layer Soil Characteristics data (Miller & White 1998) and the STATSGO soil database (Soil Survey Staff 2015). All variables used are listed in Table 3.

Variable	Acronym	Database	URL
Available Water Capacity	AWC	CONUS-SOIL	http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
K-Factor	Kffact	CONUS-SOIL	http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
pH	pH	CONUS-SOIL	http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
Depth to Bedrock	RD	CONUS-SOIL	http://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
Salinity	SAL	STATSGO	https://gdg.sc.egov.usda.gov/
Wind Erodibility Index	WEG	STATSGO	https://gdg.sc.egov.usda.gov/

Processing of Soil Data

All soil variables were downloaded for the conterminous United States and processed in ESRI ArcInfo workstation (ESRI 2014). Polygon data were converted to a raster dataset with a cell size of 0.0083333333 decimal degrees. The data were then clipped to the western United States and exported in NetCDF format.

Calculation of Water Erodibility Index

The index of susceptibility to water erosion (LSKf) was calculated from the Universal Soil Loss Equation (Wischmeier & Smith 1978) which is given as:

A = R * K * L * S * C * P          (3)

where A is predicted average annual soil loss, R is measured rainfall erosivity, K is soil erodibility, L is slope length factor, S is steepness of the slope, and C and P represent the respective erosion reduction effects of management (C) and erosion control practices (P).

Combining L and S represents the impact of topography on erosion and is calculated as (Hickey 2000):

LS= (As/22.13)^{^0.4}∙ (sinθ/0.09)^{^1.4}∙1.4

where As is the unit of contributing area (m), θ is the slope in radians. We then combined the K factor with LS to estimate the potential susceptibility of a soil to water erosion.

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, x_f is the mean of the variable in the future period, x_h is the mean of the variable in the historical period, and σ_xh 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:

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.

Human Modification

The human modification model measures the level of anthropogenic change across the landscape. It is calculated from stressors such as land use, land cover, and presence, use, and distance from roads (Theobald 2014). Click here to download the full research article.

The original values in the human modification dataset ranged from 0 to +1.0. A linear transformation (y=2x-1) was applied in order to scale the data to the range of fuzzy values allowed in the EEMS modeling framework (-1 to +1). Larger values indicate a higher degree of human disturbance on the landscape, whereas lower values indicate lower levels of human disturbance.

Climate Impacts:

The climate impacts data consists of decadal means (modes in the case of vtype_agg) of variables output by MC2 runs of the ConUS, at 2.5 arc-minute resolution, using MACA downscaled data for 20 GCMs. They use rcp 8.5, and values represent potential vegetation without fire-suppression.

Because the model runs used historical (PRISM) climate through 2014, the value for the first decade (2011-2020) is a combination of the final 4 years of the run using PRISM data, and the first 6 years of the run using MACA-downscaled data. Since the future runs end in 2099, the final decade is a mean (mode) of 9 years (2091-2099) rather than 10 as for the other decades. Time values for each step in the charts represent the beginning of each decade.

The fine-grained vegetation classes from MC2 were simplified before the 10-year modes were calculated. The vegetation classes are:

1: Tundra
2: Taiga tundra
3: Conifer forest
4: Cool mixed forest
5: Deciduous forest
6: Warm mixed forest
7: Tropical broadleaf forest
8: Woodland/Savanna
9: Shrubland/Woodland
10: Grassland
11: Arid land

Decadal means of the following MC2 variables are made available:

1. net biological production (NBP) (g C m-2 y-1) which corresponds to primary production minus soil respiration and minus harvest (agriculture or logging) and material lost through fire emission

2. total ecosystem carbon (g C m-2) including both herbaceous and woody plant material as well as soil carbon

3. forest carbon (g C m-2) including leaves, branches and boles, roots

4. dead aboveground carbon (g C m-2) corresponding to litter

5. biomass consumed by fire and emitted as gaseous emissions

6. stream flow including surface runoff and water that percolated through the soil profile without being evaporated or taken up by plant roots

7. climatic water deficit (CWD) which corresponds to the difference between potential and actual evapotranspiration.

The version of MC2 that was used here uses the Natural Resources Conservation Service (NRCS) runoff curve method ( http://bit.ly/2aawHpu)

Postprocessing of the MC2 data involved projecting each dataset to Albers Equal Area (USGS Contiguous US version) and calculating the zonal mean (for continuous variables) or area tabulation (for vegetation class) for a select set of reporting units using ArcGIS 10.3.

References:

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Conserving Nature's Stage (CNS) was a project completed by the Nature Conservancy (TNC) in 2015 for the purpose of identifying areas in the Pacific Northwest that collectively and individually best sustain native biodiversity, even as the changing climate alters current distribution patterns.

The Nature's Stage Climate Mapper is a web-based mapping application developed by the Conservation Biology Institute (CBI) in collaboration with the Nature Conservancy for the purpose of allowing users to explore the Climate Resilience data generated from the CNS project together with Climate Departure data produced by CBI.

From these two variables (climate resilience & climate departure), the tool was programmed to calculate a new variable termed Geoclimatic Stability, which is defined as a measure of a natural system's capacity to remain stable as the climate changes over time. The information provided by the tool is intended to help land managers prioritize conservation efforts and help guide future conservation investments.