Climate Console
2. Select a feature or set of features in the map
3. Explore results generated for the selected area.
(Results will appear here)
| Projected Change (Ensemble(Averages), Annual) | |
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The average max temperature in the selected area is projected to exceed the historical average by
1.8 °C over the first future time period2.9°C over the second future time period |
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The average min temperature in the selected area is projected to exceed the historical average by
1.8 °C over the first future time period2.9°C over the second future time period |
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The average precipitation in the selected area is projected to the historical average by
1.8 % over the first future time period2.9% over the second future time period (Note that precipitation projections vary widely among models) |
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The average potential evapotranspiration in the selected area is projected to the historical average by
% over the first future time period % over the second future time period
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The average vapor pressure in the selected area is projected to the historical average by
1.8 Pascals (Pa) over the first future time period2.9 Pascals (Pa) over the second future time period
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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.
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.
Historical Climate Data (PRISM)
Climate data used for the historical period (1981-2010) correspond to the LT71m PRISM (Parameter-elevation Relationships on Independent Slopes Model) 2.5 arc-minute spatial climate dataset for the Conterminous United States (Daly et al. 2008).
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.
Future Climate Projections (MACA)
For future climate projections, we selected 20 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).
| Model Name | Model Institution |
|---|---|
| BCC-CSM1.1 | Beijing Climate Center Climate System Model |
| BCC-CSM1.1m | Beijing Climate Center Climate System Model |
| BNUESM | College of Global Change and Earth System Science, Beijing Normal University |
| CanESM2 | Second Generation Canadian Earth System Model |
| CCSM4 | Community Climate System Model version 4 |
| CNRM.CM5 | Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5.1 |
| CSIRO-Mk3.6.0 | Commonwealth Scientific and Industrial Research Organisation in collaboration with the Queensland Climate Change Centre of Excellence |
| GFDL-ESM2G | Geophysical Fluid Dynamics Laboratory |
| GFDL-ESM2M | Geophysical Fluid Dynamics Laboratory |
| HadGEM2-CC3365 | Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais) |
| HadGEM2-ES3365 | Met Office Hadley Centre (additional HadGEM2-ES realizations contributed by Instituto Nacional de Pesquisas Espaciais) |
| INM-CM4 | Institute for Numerical Mathematics |
| IPSL-CM5A-LR | Institut Pierre-Simon Laplace |
| IPSL-CM5A-MR | Institut Pierre-Simon Laplace |
| IPSL-CM5B-LR | Institut Pierre-Simon Laplace |
| MIROC5 | Model for Interdisciplinary Research on Climate, version 5 |
| MIROC-ESM | Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies |
| MIROC-ESM-CHEM | Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies |
| MRI-CGCM3 | Meteorological Research Institute |
| NorESM1-M | Norwegian Climate Centre |
The 20 GCM/ESMs capture a wide range of projected changes in both average annual temperature and 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 chose ten ten-year periods to represent the projected futures. Each of the climate model projections were averaged over those periods.
The climate data used for the future projections were provided by the Multivariate Adaptive Constructed Analogs (MACA) project (Abatzoglou, Brown, 2011). MACA is a statistical downscaling method which utilizes a training dataset (i.e. a meteorological observation dataset) to remove historical biases and match spatial patterns in climate model output.
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.
Potential evapotranspiration (PET), defined as the amount of water transpired by a plant given an adequate amount of water in the soil profile (Penman 1948), was also calculated for historical and future periods using the Linacre method for estimating evaporation rates (Linacre 1977).
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, change was calculated as a percent change from the historical (((future-historical) / historical) * 100).
The mean dewpoint temp, defined as the temperature to which air must be cooled in order to reach saturation, was obtained from the MACA dataset and converted to saturation vapor pressure (vpr) using the August-Roche-Magnus approximation of the Clausius-Clapeyron equation (Lawrence 2005). VPR can be defined as the partial pressure of water vapor present in the air and determines how much water vapor can be held in the air. VPR is useful in determining relative humidity which in part determines how much ecological water can be lost to the atmosphere.
Representative Concentration Pathways (RCPs) describe future concentrations and emissions of greenhouse gases, air pollutants and land-use change, created by four different international teams using different sets of assumptions. The word ‘representative’ means that each RCP is only one of many possible scenarios found in the literature. The term ‘concentration’ is used because atmospheric concentrations of greenhouse gases are the primary simulation product rather than emissions. RCPs are tagged for their radiative forcing target level for year 2100. RCP2.6 is a stringent mitigation scenario that aims to keep global warming below 2°C above pre-industrial temperatures, with radiative forcing peaks near 3 W/m2 before 2100 and then declines. RCP4.5 and RCP6.0 are two intermediate stabilization scenarios in which radiative forcing is stabilized at approximately 4.5 W/m2 and 6.0 W/m2 after 2100. RCP8.5 (used here) is a scenario with high greenhouse gas emissions where radiative forcing reaches >8.5 W/m2 by 2100 and continues to rise for some time. (Van Vuuren et al. 2011; Stocker et al. 2013)
Post Processing
The zonal means for each climate dataset 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.
To learn about the differences between climate and weather, click on the play button below.
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).
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
Arora, V.K., Scinocca, J.F., Boer, G.J., Christian, J.R., Denman, K.L., Flato, G.M. et al. (2011). Carbon emission limits required to satisfy future representative concentration pathways of greenhouse gases. Geophys Res Lett, 38, L05805, doi: 10.1029/2010GL046270.
Barnston, A.G., He Y., Unger D. (2000) A forecast product that maximizes utility for state of the art seasonal climate prediction. Bull. Amer. Meteor. Soc. 81:1271-1279. Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P., Doggett, M.K., Taylor, G.H. et al. (2008). Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int J Climatol, 28, 2031-2064. Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140-158. ESRI (2014). ArcGIS Desktop: Release 10. Available at: http://help.arcgis.com/en/arcgisdesktop/10.0/help/index.html - /ESRI_ASCII_raster_format/009t0000000z000000/ Last accessed 3/25/15 2015. Feng, S. & Fu, Q. (2013). Expansion of global drylands under a warming climate. Atmos. Chem. Phys. Discuss., 13, 14637-14665. Lawrence MG 2005. The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bulletin of the American Meteorological Society, 86 225-233. Linacre, Edward T (1977). A simple formula for estimating evaporation rates in various climates, using temperature data alone. Agricultural Meteorology, 6, 409-424. Meinshausen, M., Smith, S.J., Calvin, K., Daniel, J.S., Kainuma, M.L.T., Lamarque, J.F. et al. (2011). The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change, 109, 213-241. Miller, D.A. & White, R.A. (1998). A conterminous United States multi-layer soil characteristic dataset for regional climate and hydrology modeling. Earth Interac, 2, 1-26. Penman, H.L. 1948. Natural evaporation from open water, bare soil and grass. Proceedings of the Royal Society of London, A193: 120-146. Rew, R.K., Davis, G.P., Emmerson, S. & Davies, H. (1997). NetCDF User's Guide for C, An Interface for Data Access, Version 3. Scoccimarro, E., Gualdi, S., Bellucci, A., Sanna, A., Fogli, P.G., Manzini, E. et al. (2011). Effects of Tropical Cyclones on Ocean Heat Transport in a High-Resolution Coupled General Circulation Model. J. Clim., 24, 4368-4384. Stocker, T. F., D. Qin, G. K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. M. Midgley. (2013) Climate change 2013: The physical science basis. Intergovernmental panel on climate change, working group I contribution to the IPCC fifth assessment report (AR5). (http://www.epa-pictaural.com/ctr/m/cc/transcript/stocker.pdf) Taylor, K.E., Stouffer, R.J. & Meehl, G.A. (2012). An Overview of CMIP5 and the Experiment Design. Bulletin of the American Meteorological Society, 93, 485-498. UNIDATA (2015). Network Common Data Form (NetCDF). Available at: http://www.unidata.ucar.edu/software/netcdf/ Last accessed 3/25/15. Van Vuuren, D.P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G.C., Kram, T., Krey, V., Lamarque, J.F. and T. Masui. (2011) The representative concentration pathways: an overview. Climatic change, 109: 5-31. Wischmeier, W.H. & Smith, D.D. (1978). Predicting rainfall erosion losses - A guide to conservation planning, United States Department of Agriculture, Science and Education Administration. No. 537. p. 85.
Climate Impacts data are not available for the selected reporting units and/or more than one feature has been selected in the map (the climate impacts tab currently only supports single feature selection)| Data Set | Size | Format | info | Download |
|---|---|---|---|---|
| Climate Data | 3.05GB | NetCDF |  |  |
| Climate Impacts | 2.05GB | NetCDF | | |
| Weather (temp) | 164KB | Text | | |
| Weather (precip) | 173KB | Text | | |
