The impact of global climate change will be manifest at the local scale and thus quantification of risks, vulnerability, opportunities and identification of appropriate adaptation measures require spatially disaggregated descriptions of the climate system derived using downscaling tools. Variability and change in near-surface wind speeds have particular importance for climate change impacts on society via their impact on, for example, the feasibility of harnessing renewable energy resources and risks to critical infrastructure. Using products from the North American Regional Climate Change Assessment Project and state-of-the-art global climate model simulations, Professors Sara Pryor from the Atmospheric Science Program at Indiana University and Justin Schoof from the Geography Department at Southern Illinois University at Carbondale will identify and quantify sources of wind climate variability on time scales from months to decades, and generate uniquely detailed projections of wind climates across the contiguous US under global climate change scenarios using both dynamical and empirical downscaling tools. This research will contribute to improved understanding of spatio-temporal variability in wind speeds, dynamic causes of variability in wind climates, mechanisms responsible for extreme wind speeds, and evaluation of the latest generation of Global and Regional Climate Model simulations. Given the key importance of proper assessment of sources of uncertainty and sensitivity in climate change projections, the relative sensitivity of wind climate projections (defined as spread in projections developed from different scenarios) to key sources of uncertainty will also be developed.
The project is motivated by the need for climate change mitigation and adaptation actions in key economic sectors including renewable energy and critical infrastructure. For example, changing wind climates may have implications for the national goal of obtaining 20% of US electricity supply from wind turbines by 2030. The project will generate better understanding of natural/internal variability of regional wind climates and possible influences of global climate change on regional wind climates and operation of existing wind power plants (to 2035) and quantification of possible longer-term shifts in wind climates, including extreme conditions, over the entire 21st century.
The goals of this collaborative (two-institution) project were to quantify sources of variability in regional winds and generate detailed projections of changes in wind climates across the United States. The research was also designed to contribute to assessment of coupled climate models (atmosphere-ocean general circulation models, or AOGCMs) that are used to investigate natural climate variability as well as climate system sensitivity to external forcing. The work conducted at SIU primarily sought to quantify the relationship between winds at multiple atmospheric levels and the major modes of climate variability in the Northern Hemisphere: El Niño-Southern Oscillation (ENSO), the Arctic Oscillation (AO) and the Pacific North American (PNA) mode. For each of these modes, spatial patterns were derived and daily and monthly indices were developed using historical data. We then investigated wind components (both zonal and meridional) associated with the positive and negative values relative to the neutral phase. We found that each phase of each mode was associated with significant changes in wind speeds in the study region. The cold phase of ENSO (La Niña) is associated with higher wind speeds, while the warm phase (El Niño) is associated with lower wind speeds. Both the positive and negative phases of the AO and PNA are associated with higher wind speeds (relative to ‘neutral’ conditions) as a result of differential changes in the zonal and meridional wind components. We also investigated the fidelity of climate models in representing both the modes and their associations with US winds. AOGCMs from the CMIP5 database produce AO- and PNA-like spatial patterns which exhibit good accord with those from historical observations. The AOGCM-derived indices also exhibit general agreement with observationally-derived indices in terms of the frequencies associated with highest variance, although the agreement is better for AO and PNA than it is for ENSO. Similarly, teleconnections between the AO and PNA and 500-hPa winds over the contiguous US exhibit good agreement with those in reanalysis data. However, for ENSO, AOGCMs fail to consistently capture the observed relationship between La Niña and near-surface to mid-troposphere winds. The analyses conducted for this project shed light on the interactions between climate system processes operating at different spatial scales and have important implications for development of regional climate projections using AOGCMs. The work is relevant for those seeking to understand the links between climate system processes occurring at different scales as well as those attempting to contextualize projections of possible future climates.
