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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)


Click the play button below to view an instructional video

Currently Selected:
x
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

1.8% 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

▼ Scroll to see more ▼

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.

Instructional Video:

Click on the play button below to view an instructional video on the California Climate Console. The video will open in a popup.


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 10 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 10 models were chosen based on evaluations of their ability to simulate historical climate conditions globally and over the southwestern United States, for the specific needs of California water resource planning (O'Daly et al. 2015)

Table 1 List of models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) used for the analysis.

Model Model Name Model Institution
1 ACCESS1.0 Australian Community Climate and Earth-System Simulator, version 1.0
2 CanESM2 Second Generation Canadian Earth System Model
3 CCSM4 Community Climate System Model version 4
4 CESM1-BGC Community Earth System Model, version 1 Biogeochemistry
5 CMCC-CM Centro Euro-Mediterraneo sui Cambiamenti Climatici Climate Model
6 CNRM.CM5 Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5.1
7 GFDL-CM3 Geophysical Fluid Dynamics Laboratory Climate Model, version 3
8 HadGEM2-CC Hadley Centre Global Environmental Model, version 2- Carbon Cycle
9 HadGEM2-ES Hadley Centre Global Environmental Model, version 2-Earth System
10 MIROC5 Model for Interdisciplinary Research on Climate, version 5
Click here for more information on the models listed above.

The ten GCM/ESMs capture a wide range of projected changes in both average annual temperature and precipitation (Figure 1) 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.

Figure 1 (Click to Enlarge): Evaluation of the projected change in temperature and precipitation for nine models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the representative concentration pathway 8.5 (RCP8.5) for the time period 2046–2075 compared to a 1971-2000 base climatology. The models HadGEM-ES, CanESM2.1, CCSM4, and CNRM-CM5 represent the “hot-dry”, “hot-wet”, “warm-dry”, and “warm-wet” scenarios respectively.

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 entire state of California. 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 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 ten 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.

Table 2 Climate variables (maximum temperature – tmax, minimum temperature – tmin, precipitation – prec, and potential evapotranspiration – PET) used in the analysis. Seasons were defined as Winter (October – March) and Summer (April – September). Delta values for the future periods were calculated by subtracting historical values from future values.

1971-20002016-20452046-2075
Average for the PeriodSeas-
onal
Average for the PeriodSeas-
onal
Delta for PeriodDelta for Seas-
sons
Average for the PeriodSeas-
onal
Delta for PeriodDelta for Seas-
sons
TmaxXXXXXXXXXX
TminXXXXXXXXXX
PrecXXXXXXXXXX
PETXXXX  XX  
Aridity    XX  XX

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).

Representative Concentration Pathways (RCPs)

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)

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 state of California. 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 California Teale Albers (NAD83) 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.

View or Download this dataset on Data Basin

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.

Table 3 Soil variables used in the EEMS model, their acronym, the database, and URL where the data reside.

VariableAcronymDatabaseURL
Available Water CapacityAWCCONUS-SOILhttp://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
K-FactorKffactCONUS-SOILhttp://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
pHpHCONUS-SOILhttp://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
Depth to BedrockRDCONUS-SOILhttp://www.soilinfo.psu.edu/index.cgi?soil_data&conus&data_cov
SalinitySALSTATSGOhttps://gdg.sc.egov.usda.gov/
Wind Erodibility IndexWEGSTATSGOhttps://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 state of California boundaries 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, 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)

Climate Impacts:

Climate Impacts data were simulated with the Dynamic Global Vegetation Model MC2 (http://bit.ly/2a9TrKD) at 30 arc-secs. The model was run from 1895-2010 using PRISM historical data, and from 2011-2099 using the bias-corrected spatially downscaled (BCSD) climate futures (Thrasher et al. 2013) for 9 global climate models (because vapor pressure/relative humidity was missing for CMCC-CMS available results, it could not be used by MC2 which requires this input). All climate futures were run with the RCP8.5 emission scenario.

MC2 vegetation types were aggregated into broader categories listed below:

0:undefined
1:taiga/tundra
2:conifer forest
3:mixed forest
4:broadleaf forest
5:shrubland/woodland/savanna
6:grassland
7:arid land
8:annual agriculture
9:perennial agriculture
10:developed/mined

The mode of the vegetation cover was calculated for each decade starting in 2011 (2011-2020, 2021-2030, etc). The final decade (2091-2099) ends in 2099 since most climate models were missing data for 2100. 'Year' on the chart corresponds to the start of each decade.

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 data involved projecting to California Teale Albers (NAD83) and calculating the zonal mean (for continuous variables) or area tabulation (for vtype_agg) for a select set of reporting units using ArcGIS 10.3.

References:

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.

Bi, D., Dix, M., Marsland, S.J., Siobhan O’Farrell, S., Rashid, H,, Uotila, P. et al. (2011). ACCESS: The Australian Coupled Climate Model for IPCC AR5 and CMIP5. Extended Abstracts, 18th Annual AMOS National Conf., Sydney, Australia, Australian Meteorological and Oceanographic Society, 123.

Collins, W.J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T. et al. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geosci. Model Dev., 4, 1051-1075.

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.

Donner, L.J., Wyman, B.L., Hemler, R.S., Horowitz, L.W., Ming, Y., Zhao, M. et al. (2011). The Dynamical Core, Physical Parameterizations, and Basic Simulation Characteristics of the Atmospheric Component AM3 of the GFDL Global Coupled Model CM3. J. Clim., 24, 3484-3519.

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.

Gent, P.R., Danabasoglu, G., Donner, L.J., Holland, M.M., Hunke, E.C., Jayne, S.R. et al. (2011). The Community Climate System Model Version 4. J. Clim., 24, 4973-4991.

GRASS (2015). Geographic Resources Analysis Support System (GRASS) Software. Open Source Geospatial Foundation.

Hargreaves, G.H. & Allen, R.G. (2003). History and Evaluation of Hargreaves Evapotranspiration Equation. Journal of Irrigation and Drainage Engineering, 129, 53-63.

Hickey, R. (2000). Slope angle and slope length solutions for GIS. Cartography, 29, 1-8.

Maurer, E.P. & Hidalgo, H.G. (2008). Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods. Hydrol. Earth Syst. Sci., 12, 551-563.

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.

NCL (2014). The NCAR Command Language UCAR/NCAR/CISL/VETS Boulder, CO.

O'Daly, W., Keeley, F. & Woled, J. (2015). Perspectives and Guidance for Climate Change Analysis. California Department of Water Resources and Climate Change Technical Advisory Group, p. 142. Available at: http://www.water.ca.gov/climatechange/docs/2015/Perspectives_Guidance_Climate_Change_Analysis.pdf.

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.

Soil Survey Staff (2015). State Soil Geographic (STATSGO) Database. Natural Resources Conservation Service, United States Department of Agriculture. Available at:http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/index.html2015.

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)

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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.

Voldoire, A., Sanchez-Gomez, E., Salas y Mélia, D., Decharme, B., Cassou, C., Sénési, S. et al. (2012). The CNRM-CM5.1 global climate model: description and basic evaluation. Clim Dynam, 40, 2091-2121.

Watanabe, M., Suzuki, T., O’ishi, R., Komuro, Y., Watanabe, S., Emori, S. et al. (2010). Improved Climate Simulation by MIROC5: Mean States, Variability, and Climate Sensitivity. J. Clim., 23, 6312-6335.

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.

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


Currently Selected:
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i
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Climate Impacts data are not available for the selected reporting units.

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Vegetation Composition
Carbon, Fire, and Water
Values in the chart above represent the mean averages calculated across the selected reporting unit. About the climate impacts data