There are a few substantive ways in which climate change affects the power plan. First, long-term trends in temperature will alter the magnitude and seasonal pattern of electricity demand and affect precipitation, river flows and availability of hydroelectric generation. Additionally, state policies and laws enacted to reduce greenhouse gases will affect existing resource operation and limit future resource choices. Also, how those policies, temperature impacts, and other factors like wind speed impact the availability and impact of new resources, including energy efficiency, demand response, and wind generation. These effects must be accounted for to ensure that the Council’s resource strategy will result in an economic and adequate future power supply. This section focuses on how climate change data are incorporated into the Council’s analytical tools.      

Climate Change Policies

Some state climate change policies and clean air laws set carbon dioxide emission targets for the electricity sector, thereby limiting the dispatch of existing fossil fuel burning resources or expediating their retirement. They also exclude such resources from being considered in resource expansion plans. Other clean air policies and laws include renewable portfolio standards (RPS), which set renewable resource development targets.

Climate Change Data

In 1988 the United Nations Environment Programme (UNEP) and the World Meteorological Organization (WMO) established the Intergovernmental Panel on Climate Change (IPCC), which was subsequently endorsed by the United Nations General Assembly.[1] The IPCC was established to assess available scientific, technical and socio-economic information concerning climate change, its potential effects, and options for adaptation and mitigation. The IPCC’s purpose is to collect and review the most recent scientific information produced worldwide relevant to the understanding of climate change. It does not conduct any research on its own nor does it monitor climate-related data. It is open to all member countries of the United Nations and currently has 195 participating countries that review all scientific material to ensure an objective and complete assessment of current information.

In November of 2014, the IPCC completed its Fifth Assessment Report (AR5).[2] Most participating organizations use complex computer models, commonly known as “general circulation models” or GCMs, to forecast long-term changes in the Earth’s climate. These models primarily focus on the effects of greenhouse gases on temperature and precipitation. They take into consideration the interaction of the atmosphere, oceans and land surfaces.[3] Each of these models has been “calibrated” to some degree and crosschecked against other such models to provide greater confidence in their forecasting ability.

There are at least 20 different general circulation models that are used to project future changes in temperature and precipitation. Every one of these models, to varying degrees, forecasts a warming trend for the Earth. Each uses sophisticated mathematical techniques to simulate changes in temperature as a function of atmospheric and other conditions. Like all fields of scientific study, however, there are uncertainties associated with GCM analyses, such as the effects of weather and ocean conditions, that are not as well-understood as scientists would like. Nonetheless, scientists are generally confident about GCM projections for large-scale global areas. 

Unfortunately, forecasts on a large spatial scale are of little use to planners in the Pacific Northwest. Therefore, methods to downscale output from global models to a more granular scale were developed by the River Management Joint Operating Committee (RMJOC), which is comprised of three federal agencies (Bonneville Power Administration, Corps of Engineers and the Bureau of Reclamation) and the University of Washington and Oregon State University.[4] 

Incorporating Climate Change Policies and Data into Analytical Tools

All existing clean air policies and laws are incorporated, to the extent possible, into the analytical models used to develop the power plan. For example, the AURORA model, used to estimate electricity market prices and potential West Coast resource expansion, includes renewable portfolio standards and other clean air policy requirements. The Council’s Regional Portfolio Model (RPM), which develops resource expansion strategies, enforces state renewable portfolio standards and clear air laws (for states with such policies and laws). In addition, the RPM includes a carbon damage cost that applies the social cost of carbon to emitting resources. Resources are included in the expansion strategy only if they are 1) economic, 2) needed to meet RPS targets, 3) needed for adequacy or 4) needed to reduce carbon emissions. 

Downscaled climate change data include projected future daily temperatures and precipitation for roughly every four-by-four square mile area within the Pacific Northwest region. Forecasted temperatures and precipitation are used with hydrological models to develop a range of natural (unregulated) river flows, modified for irrigation withdrawals and returns. Projected temperatures are also used in the Council’s load forecasting models to estimate future electricity demand. The forecasted demand is then used to provide a starting point for not only the broader regional power system analysis but also potentially available energy efficiency  and demand response [5]. .[6]  

Climate change forecasted wind speed and wind direction are produced by the Climatology Lab at the University of California at Merced. Projected wind speeds and directions are used to assess future hourly wind generation patterns. Regarding solar resources, the Council has found no substantive data to indicate that future hourly solar generation patterns would be affected by GCM projected temperatures and precipitation. Thus, the hourly capacity factors used to create solar generation patterns used for this plan have not been adjusted for climate change.    

Climate change projected natural (modified) river flows, forecast demand and wind generation patterns, among other data, are used in the Council’s adequacy model (GENESYS) to assess the adequacy reserve margins (ARM) and the associated system capacity contributions (ASCC), which are input to the Council’s Regional Portfolio Model (RPM). The RPM also uses climate change forecasted demand and wind generation patterns along with projected aggregate quarterly hydroelectric generation from the GENESYS model and other relevant resource data to develop 20-year resource strategies for the power plan.   

The following sections provide a more comprehensive description of existing greenhouse gas policies and laws, sources of climate change data, methods to adapt that temperature, precipitation and wind data for the power plan and finally, ways in which climate change policies and data are incorporated into the Council’s analytical tools. 

Below are additional details on how the climate data are incorporated into the Council’s models.

The RMJOC made available 19 climate scenarios (see RMJOC Report I and Summary of Climate Change Scenarios) with sufficient data to perform hydro-regulation studies in some of the Council’s models. Out of the 19 scenarios, 3 that could approximately represent the full range of the 19 were selected for the Power Plan (see Climate Change Scenario Selection Process). Furthermore, because the Council’s load forecast model requires hourly temperature data, the daily climate scenario temperature data were transformed into hourly data by using hourly shapes from one historical year (see Transforming Climate Change Daily to Hourly Temperatures). These two steps limit the variations in climate data and impacts that the models could explore, but were undertaken because of the limit in time, staff, and computational resources available for this Power Plan.

Using the GENESYS model as an example, for the three climate scenarios in the Power Plan, it required 387 studies to calculate the Associated System Capacity Contributions (ASCCs) for various combinations of generating resources. For each new climate scenario added, another 129 studies would be needed. Thus, using all 19 RMJOC scenarios would require over 2,000 additional studies. Furthermore, given the current structure of the load forecasting model, applying just one more historical hourly shape to the forecasted daily climate scenario temperatures to provide more variation in the hourly load shape would double the number of required studies.  Thus, for the GENESYS model, the number of ASCC studies would quickly add up if more climate scenarios or historical hourly temperature shapes were included in the Power Plan. Moreover, additional runs on other models would also be required.

Another consideration for incorporating climate data into the Council’s models is the sampling method for temperature years and water years. Because temperature and precipitation (which leads to streamflow) in the general circulation models (GCMs) are not independent, the temperature year and water year selected for each simulation must be for the same climate year.  Mixing climate years for temperature and precipitation could lead to unlikely occurrences.

For a rather simplified example, at a specific location in the region, if temperatures were mostly above freezing during fall and winter for a particular climate year, then most of the precipitation during that period would fall as rain, resulting in higher streamflow but depositing a smaller snowpack in the mountains. Then during spring and early summer, streamflow would be lower due to contribution from a smaller snowpack. 

Alternatively, if fall and winter temperatures were mostly below freezing, then precipitation would mostly fall as snow, resulting in lower streamflow but creating a larger snowpack.  Then spring and early summer of that climate year should see higher streamflow due to contribution from the larger snowpack.

These two simple examples illustrate that even with the same amount of seasonal precipitation, temperature can have a significant influence on the timing and magnitude of streamflow. Thus, pairing temperature and streamflow patterns from two different climate years could lead to unlikely situations such as during winter having freezing temperatures but with precipitation falling as rain that leads to higher streamflows and smaller snowpack.

[1]See “IPCC Factsheet: Timeline – highlights of IPCC history” at    

[2] See link at


[4] Wood, A.W., Leung, L. R., Sridhar, V., Lettenmaier, Dennis P., no date: “Hydrologic implications of dynamical and statistical approaches to downscaling climate model surface temperature and precipitation fields.”

[5] The Council also used these projected temperatures to estimate the savings potential for specific weather sensitive efficiency measures, such as heating measures, to reflect how those opportunities will save under future temperatures.

[6] Reference chapter on EE, which should describe how CC data is incorporated in EE models.