Dynamical Downscaling Developing a Model Framework for WRF for Future GCM DownscalingJared H. BowdenTanya L. OtteJune 25, 20099th Annual Meteorological Users Meeting

OutlineEPAs mission and how it relates to dynamical downscalingDescribe regional climate modeling and differences with meteorological modelingPrevious related work Developing the model framework: testbed interests Background: some current limitations of RCMsPreliminary Results & Initial impressionsFuture work

The EPAs Interest in Dynamical DownscalingAn 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

Dynamical Downscaling (Regional Climate Modeling) Goal: Provide added value Reanalysis or GCM provideInitial 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 ProblemRegional climate modeling:Boundary Value Problem : as itforgets initial conditionsPrecipitation

Previous workClimate Impact on Regional Air Quality (CIRAQ)Regional downscaling of climate for U.S. via partnershipsIPCC SRES A1B greenhouse gas scenarioGlobal climate model: NASA GISS II Harvard Univ. (Mickley et al., Geophys. Res. Lett., 2004)Meteorological model for regional climate: NCARs MM5 PNNL (Leung and Gustafson, Geophys. Res. Lett., 2005)Regional future air quality simulations in-house in AMDFocus on impact of climate on ozone and fine particulate matterAir 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 quality10 years of regional climate, 5 years of air quality

Regional Climate model framework developmentThe impact of model physics choice on regional climate simulations

The EPAs 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.

Regional Climate Model framework development Downscaling approachs 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?

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.

North American Regional Climate Change Assessment Program(NARCCAP) Investment to improve regional climate simulationsProject 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.

Evaluating WRF in NARCCAPNorthwest USPrecipitationInter-annual VariabilityPrecipitationDaily meanTemperatureDaily meanSimulated by PNNL: Ruby Leung

Evaluating WRF in NARCCAPPlains USPrecipitationDaily meanPrecipitationInter-annual VariabilityTemperatureDaily meanSimulated by PNNL: Ruby Leung

Efforts are underway to developa 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 techniquesSensitivities using NCEP-NCAR reanalysis data; similar to NARCCAP providing a verifiable scenario. 108km-36km nestSensitivities using GCM to determine how well the method adapts with GCM boundary forcing. 108km resolutionNo PBL nudging vs. no nudging below a certain model level

Preliminary Results (Qualitative):Reanalysis driven downscaled simulationsJanuary

NARRWRF - NO FDDAWRF - AnalysisWRF - SpectralNARRWRF - NO FDDAWRF - AnalysisWRF - SpectralNO SPIN-UPPrecipitation (mm)With SPIN-UPPrecipitation (mm)

Preliminary Results (Qualitative):Reanalysis driven downscaled simulationsJanuaryNARRWRF - NO FDDAWRF - AnalysisWRF - SpectralNARRWRF - NO FDDAWRF - AnalysisWRF - SpectralNO SPIN-UP500mb Geopotential height (m)With SPIN-UP500mb Geopotential Height (m)

Preliminary Results (Qualitative):Reanalysis driven downscaled simulationsJuly

With SPIN-UPPrecipitation (mm)NO SPIN-UPPrecipitation (mm)NARRWRF - NO FDDAWRF - AnalysisWRF - SpectralNARRWRF - NO FDDAWRF - AnalysisWRF - Spectral

Preliminary Results (Qualitative):Reanalysis driven downscaled simulationsJuly

With SPIN-UP250mb windsNO SPIN-UP250mb windsNARRWRF - NO FDDAWRF - AnalysisWRF - SpectralNARRWRF - NO FDDAWRF - AnalysisWRF - Spectral

Preliminary Results (Qualitative):GCM driven downscaled simulations

JulyPrecipitationJanuaryPrecipitation

GCM - ModelEWRF - NO FDDAWRF - SpectralWRF - AnalysisGCM - ModelEWRF - NO FDDAWRF - AnalysisWRF - Spectral

Preliminary Results (Qualitative):GCM driven downscaled simulations

July500mb Geopotential HeightJanuary500mb Geopotential HeightGCM ModelEWRF - NO FDDAWRF - AnalysisWRF - SpectralGCM - ModelEWRF - NO FDDAWRF - AnalysisWRF - Spectral

Importance of the implementation of the nudging(Analysis nudging example)Model level nudging sensitivity

ModelEJanuary2m Temperature30.5 days intosimulation GCM driven

Initial ImpressionThe 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)

Future Work

Test sensitivity to nudging coefficients One year simulation using both nudging techniques vs. no nudging to test the seasonal cycleUnderstand 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.

About NASA-GISS ModelE Publically available model with e-mail exchange listRewrite of GISS Model II (used in CIRAQ)Includes better representation of stratosphere, tracer components, ocean component improvements Schmidt et al., J. Clim., 2006Used for IPCC AR4 and to be used for IPCC AR52 x 2.5 lat-lon with 40 hybrid s-p layers up to 0.1 hPa24 s layers below 150 hPa (interface of s-p)Collaboration with NASA GISS enables AMD to:Access IPCC AR5 fields before publically availableAccess improved, experimental science in ModelE that is not in public versionHave output fields (variables, levels, etc.) tailored for downscaling

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