1. Dynamical Downscaling Developing a Model Framework for WRF for Future GCM Downscaling Jared H. Bowden Tanya L. Otte June 25, 2009 9 th Annual Meteorological Users’ Meeting

2. Outline EPA’s mission and how it relates to dynamical downscaling Describe regional climate modeling and differences with meteorological modeling Previous related work Developing the model framework: testbed interests Background: some current limitations of RCMs Preliminary Results & Initial impressions Future work

3. The EPA’s Interest in Dynamical Downscaling An interest from the Global Change Research Program at the EPA is to assess the impacts of global change on air and water quality, ecosystems, and human health. –Improve scientific basis for evaluating effects of global change –To help provide timely and useful information for decision- support tools for policy makers and resource manager to help them adapt to a changing climate. The 2009-2014 EPA strategic plan targets impacts of global climate change as an area of needed improvement. Development: dynamical downscaling of IPCC AR5 NASA-GISS ModelE GCM Create strong partnerships with external institutions that have established credible research programs in global climate and regional modeling

4. Dynamical Downscaling (Regional Climate Modeling) Goal: Provide added value Reanalysis or GCM provide Initial Conditions @ coarse resolution (soil moisture / temp., SST, sea ice) & Lateral boundary conditions (winds,pressure, temperature, humidity) every ~ 6 hours Regional Climate Model includes: - high resolution topography - land/water interfaces - land use/land cover -potential physics improvements Regional Climate Model run for a month to years * Provides high resolution output for: -Surface fields - Atmospheric fields - Radiation fields Numerical Weather Prediction: Initial Value Problem Regional climate modeling: Boundary Value Problem : as it forgets initial conditions Precipitation

5. Previous work Climate Impact on Regional Air Quality (CIRAQ) –Regional downscaling of climate for U.S. via partnerships IPCC SRES A1B greenhouse gas scenario Global climate model: NASA GISS II’ Harvard Univ. (Mickley et al., Geophys. Res. Lett., 2004) Meteorological model for regional climate: NCAR’s MM5 PNNL (Leung and Gustafson, Geophys. Res. Lett., 2005) –Regional future air quality simulations in-house in AMD Focus on impact of climate on ozone and fine particulate matter Air quality model: CMAQ Modeling System (Nolte et al., J. Geophys. Res., 2008) –Continental U.S. simulations of current and future (ca. 2050) simulations were developed for regional climate and air quality 10 years of regional climate, 5 years of air quality

6. Regional Climate model framework development The impact of model physics choice on regional climate simulations The EPA’s meteorological model framework physics options may or may not work for regional climate modeling. Understand the use of in-house developments such as Pleim-Xiu LSM and Asymmetric Convective PBL model for regional climate simulations. –Advantage: staying consistent with the community multi-scale air quality modeling system (CMAQ). –Technical difficulties for climate simulations; still in progress.

7. Regional Climate Model framework development Downscaling approach’s impact could be larger than the physics options (Lo et. al. 2008) There is a common problem typical problem in the mid-latitudes where the atmospheric state simulated by the regional climate model deviates from the driving model state at large-scales (von Storch et al. 2000). The problem arises from a distortion of the large scale circulation by way of interaction of the modeled flow with lateral boundaries. It is found that the domain size matters (Castro et. al. 2005; Rockel et. al. 2008). Large-scale variability is lost with larger domains. Sampling of the synoptic feature at the boundary, hence domain sensitive? Resolving this issue using some WRF Four Dimensional Data Assimilation options?

8. Regional Climate Model framework development Understand the potential implications of using WRF Four-Dimensional Data Assimilation techniques for regional climate simulations. Analysis nudging is common in meteorological air quality modeling to drive off-line models such as CMAQ. Limitations for high resolution RCM simulations via coarser grid analysis (i.e. GCMs)? Can a fine balance be established for the nudging coefficients such as that for moisture? Development of spectral analysis nudging in WRFv3.1; –Miguez-Macho (2004) suggest that this technique is necessary for RCM simulations larger than several thousand kilometers. –Castro et. al. (2005) and Rockel et al. (2008) test the sensitivity of spectral nudging and find that spectral nudging gives more added variability than grid nudging and is not model specific. –Castro et. al. (2005) and Rockel et al. (2008) results suggest that nudging has large implications on the surface fields where the regional model is likely to add more value because of the highly resolved surface forcing.

9. North American Regional Climate Change Assessment Program (NARCCAP) Investment to improve regional climate simulations Project to produce high resolution climate change simulations to investigate regional climate uncertainties. Increase confidence in downscaling methodology using present-day (verifiable) scenarios; Multiple RCMs driven with NCEP NCAR reanalysis II data. Help us evaluate areas of needed improvement in RCMs. However, how can we get around so many years of simulations? NARCCAP allows us to understand what shorter simulations mean in context of the climatology.

10. Evaluating WRF in NARCCAP Northwest US Precipitation Inter-annual Variability Precipitation Daily mean Temperature Daily mean Simulated by PNNL: Ruby Leung

11. Evaluating WRF in NARCCAP Plains US Precipitation Daily mean Precipitation Inter-annual Variability Temperature Daily mean Simulated by PNNL: Ruby Leung

12. Efforts are underway to develop a downscaling method based on state of the science Current focus, should we use nudging techniques to correct large-scale bias? –Sensitivities studies using various nudging techniques –Sensitivities using NCEP-NCAR reanalysis data; similar to NARCCAP providing a verifiable scenario. 108km-36km nest –Sensitivities using GCM to determine how well the method adapts with GCM boundary forcing. 108km resolution –No PBL nudging vs. no nudging below a certain model level

13. Preliminary Results (Qualitative): Reanalysis driven downscaled simulations January NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral NO SPIN-UP Precipitation (mm) With SPIN-UP Precipitation (mm)

14. Preliminary Results (Qualitative): Reanalysis driven downscaled simulations January NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral NO SPIN-UP 500mb Geopotential height (m) With SPIN-UP 500mb Geopotential Height (m)

15. Preliminary Results (Qualitative): Reanalysis driven downscaled simulations July With SPIN-UP Precipitation (mm) NO SPIN-UP Precipitation (mm) NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral

16. Preliminary Results (Qualitative): Reanalysis driven downscaled simulations July With SPIN-UP 250mb winds NO SPIN-UP 250mb winds NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral NARRWRF - NO FDDA WRF - AnalysisWRF - Spectral

17. Preliminary Results (Qualitative): GCM driven downscaled simulations July Precipitation January Precipitation GCM - ModelEWRF - NO FDDA WRF - SpectralWRF - Analysis GCM - ModelEWRF - NO FDDA WRF - AnalysisWRF - Spectral

18. Preliminary Results (Qualitative): GCM driven downscaled simulations July 500mb Geopotential Height January 500mb Geopotential Height GCM – ModelEWRF - NO FDDA WRF - AnalysisWRF - Spectral GCM - ModelE WRF - NO FDDA WRF - AnalysisWRF - Spectral

19. Importance of the implementation of the nudging (Analysis nudging example) Model level nudging sensitivity ModelE AN Reg Zfac17 AN Reg Zfac19 AN Reg Zfac21 January 2m Temperature 30.5 days into simulation GCM driven

20. Initial Impression –The NARCCAP WRF simulations indicates needed improvement in the mean and inter-annual variability of precipitation and 2m temperature (with particular focus east of the Rocky Mountains). –Without nudging the model solution deviates from reality in the NCEP-NCAR simulations. –The GCM driven model simulations also deviates from the large-scale. However, do we trust the GCM long wave patterns? –Spectral nudging and analysis nudging both correct the large-scale circulation bias; however, the surface fields are sensitive to the implementation of the nudging (e.g. model level and nudging coefficients)

21. Future Work –Test sensitivity to nudging coefficients –One year simulation using both nudging techniques vs. no nudging to test the seasonal cycle –Understand the influence of the two-way nesting vs. the one-way nest –Currently both nests are nudged; Simulate just nudging the outer domain which would provide better representation of the long waves for the interior domain. –For the long simulation with nudging - turn nudging off to see if the simulation degrades and if so how long? –These tests should be performed in parallel with the GCM to understand the potential of nudging for climate change simulations.

22. About NASA-GISS ModelE Publically available model with e-mail exchange list Rewrite of GISS Model II’ (used in CIRAQ) Includes better representation of stratosphere, tracer components, ocean component improvements –Schmidt et al., J. Clim., 2006 Used for IPCC AR4 and to be used for IPCC AR5 2° x 2.5° lat-lon with 40 hybrid  -p layers up to 0.1 hPa –24  layers below 150 hPa (interface of  -p) Collaboration with NASA GISS enables AMD to: –Access IPCC AR5 fields before publically available –Access improved, experimental science in ModelE that is not in public version –Have output fields (variables, levels, etc.) tailored for downscaling