Grantee Research Project Results
2010 Progress Report: Regional Air Quality Management Aspects of Global Change: Impact of Climate-ResponsiveControls and Forest Management Practices on Regional Air Quality and Associated Uncertainties
EPA Grant Number: R834281Title: Regional Air Quality Management Aspects of Global Change: Impact of Climate-ResponsiveControls and Forest Management Practices on Regional Air Quality and Associated Uncertainties
Investigators: Russell, Armistead G. , Bergin, Michelle S. , Wang, Y. T. , Nenes, Athanasios , Amar, Praveen
Current Investigators: Russell, Armistead G. , Bergin, Michelle S. , Wang, Y. T. , Tsimpidi, A.P. , Nenes, Athanasios , Tian, D. , Klieman, G. , Yang, H. , Rudokas, J. , Fahey, K. , Tsigaridis, K. , Trail, M. , Liu, P. , Amar, Praveen , Hu, Y.T.
Institution: Georgia Institute of Technology , NESCAUM
Current Institution: Georgia Environmental Protection Division , Georgia Institute of Technology , NASA Goddard Institute for Space Studies , NESCAUM
EPA Project Officer: Chung, Serena
Project Period: October 1, 2009 through September 30, 2012 (Extended to September 30, 2013)
Project Period Covered by this Report: October 1, 2009 through September 30,2010
Project Amount: $599,963
RFA: Adaptation for Future Air Quality Analysis and Decision Support Tools in Light of Global Change Impacts and Mitigation (2008) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Climate Change , Air
Objective:
- Assess and compare how climate-responsive control choices will impact air quality.
- Assess how forest management practices, including biomass fuel production, will impact future air quality.
- Quantify the sensitivities and uncertainties in results.
- Provide decision support analyses for use by air quality decision makers.
Progress Summary:
Year 1: Since air quality simulations will rely upon the meteorological fields developed from a global climate simulation generated by the Goddard Institute of Space Studies (GISS) model, one of the objectives during the first year is to improve and test the method of regional downscaling by the Weather Research and Forecasting (WRF) modeling system. Currently, most regional air quality studies use the lateral nudging approach, in which lateral meteorological boundary conditions in the regional model are nudged towards prospective global climate model (GCM) simulations. The large-scale features such as the shift of summertime storm tracks in the global model can be lost easily during regional downscaling because the impacts of boundary nudging diminish inside the model domain. Spectral nudging has been shown to be a promising way to improve the downscaling results.1,2 Therefore, 2.5*2.5 degrees NCEP/NCAR reanalysis data were used as the driving field to mimic the GCM output and were downscaled by WRF with grid and spectral nudging, respectively. Similarity was applied in order to evaluate the downscaling performance in different scales3 and the results also were compared with NARR data.4 It turned out that spectral nudging made a better balance in capturing large-scale features of the driving field, at the same time allowing the regional climate model to add enough variance at the smaller scales. Besides, sensitivity testing was conducted on spectral nudging, and appropriate parameters such as wave numbers were determined for the specific case of our project. In addition, one of the other issues that may affect the downscaling results is the spatial resolution difference between the driving and the nested data.5,6 We tested the case when a 2.5*2.5 degrees input field was directly downscaled to 36km*36km resolution and the case when the input field was first downscaled to 108km*108km resolution and then nested to 36km*36km resolution. No significant discrepancy was observed between these two cases.
Future Activities:
- Prepare meteorological fields for the current years (2008-2010) and future (2049-2051) by downscaling GISS Model E output using WRF with the downscaling configuration we have tested.
- Review strategies or technologies to mitigate climate change that also impact ozone and PM-related emission and process strategies using SMOKE.
- Develop future biomass burning scenarios.
References:
-
Miguez-Macho, G. (2004). Spectral nudging to eliminate the effects of domain position and geometry in regional climate model simulations. J. Geophys. Res.-Atmos., 109(D13).
-
Miguez-Macho,G. (2005). Regional climate simulations over North America: interaction of local processes with improved large-scale flow. Journal of Climate, 18(8):1227-1246.
-
von Storch, H., Langenberg, H. and Feser, F. (2000). A spectral nudging technique for dynamical downscaling purposes. Mon. Weather.Rev., 128:3664-3673.
-
Mesinger, F., et al. (2006). North American regional reanalysis. Bull. Am. Meteorol. Soc., 87:343-360.
-
Warner, T.T., Peterson, R. A., Treadon, R. E. (1997). A tutorial on lateral boundary conditions as a basic and potentially serious limitation to regional numerical weather prediction. Bull. Am. Meteorol. Soc., 78:2599-2617.
-
Giorgi, F., Mearns, L.O. (1999). Introduction to special section: regional climate modeling revisited. J. Geophys. Res., 104: 6335-6352.
Journal Articles:
No journal articles submitted with this report: View all 14 publications for this projectSupplemental Keywords:
air quality, climate policy, downscale, nudging, RFA, Scientific Discipline, Air, POLLUTION PREVENTION, Energy, climate change, Air Pollution Effects, Environmental Monitoring, Atmosphere, atmospheric nitrogen, particulate matter, decision making, energy efficiency, environmental policy, forests, deforestation, ecosystem sustainability, air quality, Global Climate ChangeProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.