Regional Climate- Weather Research and Forecas5ng (CWRF) Model Development & Applica5on 2011 / 09 / 29 Xin-Zhong Liang Department of Atmosphere & Ocean Science Earth System Science Interdisciplinary Center University of Maryland, College Park
EPA STAR 2003-2011 FOCUS Consolidate O 3 Elaborate PM Explore Hg EPA STAR 2009-2012 FOCUS Nutrients Pathogens Bacteria Sediments Agriculture Urban
RCM Better Resolves Extremes Northeast U.S. Assessment Anderson, B.T., K. Hayhoe, and X.-Z. Liang, 2009: Anthropogenic-induced changes in the 21st Century summertime hydroclimatology of the Northeastern US., Climate Change, doi.10.1007/s10584-009-9673-3.
Propagation of GCM Present Climate Biases into Future Change Projections: Temperature
Outline What is RCM the EaSM core? What are values added by RCM downscaling? What are CWRF advances over other RCMs? What are needed to make a credible RCM run? What challenges face RCM development? Ø Scale dependence Ø Physics configuration selection Ø Optimized physics ensemble Ø System uncertainty
NARR CWRF Downscaling Seasonal Climate Prediction over the U.S. LBCs ICs CWRF D2 D3 MLO CAM GFS SST ICs CFS SST D1 NOAA 2008-2011 CFS Urban and Built-up Dryland Crpland and Pasture Irrigated Cropland and Pasture Cropland/Grassland Mosaic Cropland/Woodland Mosaic Grassland Shrubland Mixed Shrubland/Grassland Savanna Deciduous Broadleaf Forest Evergreen Broadleaf Forest Evergreen Needleleaf Forest Mixed Forest Water Bodies Wooded Wetland Barren or Sparsely Vegetated Wooded Tundra Mixed Tundra
RCM Downscaling is Science + Art Do NOT take an RCM off the shelf (localization) Domain Design (integrating planetary forcing) Physics Configuration (regime & scale dependence) Verification or Evaluation (obs. data & added values) Ensemble Approach (prediction skill & uncertainty) Doing details is the key to success!
Ensemble Global Forecast System ICs, SSTs, LBCs NCEP ECMWF :::::::::::::: OP DASs ICs NOAA CFS NASA GEOS ::::::::::::::::::::::::: Bias corrections OP CGCMs SSTs NOAA GFS NCAR CAM IRI ECHAM :::::::::::::::::::: AGCMs LBCs
NCEP/AMIP II vs ECMWF-Interim Reanalysis
Recent Advances Comparing with Other RCMs Ability to reproduce observations All driven by the same reanalysis Result comparison on Ø Ø Ø Seasonal variations Interannual anomalies Extreme events
Summer 1993 U.S. Midwest Record Flood OBS WRF CWRF Rainfall (mm d -1 ) CWRF has made significant improvements. WRF T2m Bias ( C) CWRF
Surface Temperature Biases All driven by NCEP/DOE AMIP II Reanalysis
Surface SW d Biases All driven by NCEP/DOE AMIP II Reanalysis
Understanding Biases WRFG & CMM5: SWd are too large, while T2m biases are rela@vely small HRM3: SWd is quite realis@c, while T2m is substan@ally overes@mated CRCM: SWd is fairly realis@c, but T2m has notable cold biases RCM3: SWd is substan@ally underes@mated, yet T2m is reasonable CWRF: SWd and T2m both are quite realis@c Conclusion: SWd seems not the dominant factor that cause T2m biases; the lajer may largely result from deficiencies in the water cycle.
Interannual CORR over USA
Why Do RCM Results Differ? Domain: U.S. + Adjacent for CWRF & CMM5, Extended North America for NARCCAP Resolu@on: 30 km for CWRF & CMM5, 50 km for all other NARCCAP RCMs Forcing: linear- exp relaxa@on in buffer zones of 14 (CWRF, CMM5), 10 (WRFG) grids linear relaxa@on in 4 grids (MM5I, HRM3) domain spectral nudging (ECP2, CRCM) NARCCAP IA correla@ons differ largely due to the strength of forcing integrated Physics: CWRF is much bejer than CMM5, being iden@cal in all other se`ngs Different dynamics may also contribute
Physics Representation Evaluating Skill under Correct Forcing Conditions
Scale Dependence Model physics representation and predictive skill depend on spatial scale Challenging
CWRF Terrestrial Hydrology Kanawha Kentucky Green Tennessee Stream Flow (mm/day) 30 25 20 15 10 5 0 USGS Sta. 03320000 Green River Precipitation CLM CLM+CSS Observed 0 30 60 90 120 150 180 210 240 270 300 330 360 Days Since Jan 1995 0 50 Precipitation (mm/day) 100 150 200 Choi 2006; Choi et al. 2007, 2011; Choi and Liang 2010; Yuan and Liang 2010; Liang et al. 2010d
Illinois Soil Moisture Simulations Driven by NARR Yuan and Liang 2011 (J. Hydrometeorology)
CWRF Physics Options Upper Diffusion Eddy Cloud Aerosol Const DIF L2.5 TKE L2 3D DEF Radiation LW + SW RadExt MISC CAM AER GSFC CCC GFDL CSIRO F-L Orbit Gases Aerosols Surface SfcExt VEG SST OCN UCM Urban BEP Land Ocean SLAB RUC PX NOAH CSSP CROP SOM UOM PBL Cumulus YSU ACM GFS MYJ MYNN QNSE Boulac TEMF HIR CAM UW ORO Microphysics BMJ NKF SAS NSAS TDK GD G3 Kessler[2] Thompson[7] Lin[6] Hong[3] Hong[5] Hong[6] UW ZML CSU GFDL MIT GR ECP Zhao-Carr[2] Tao[5] Morrison[10] Hong[7] Hong[8] http://cwrf.umd.edu
cst ( 3) ccs ( 4) ccb ( 5) cci ( 2) bin ( 3) cover (360) Cloud- Aerosol- Radia5on Ensemble Model types (13) rel ( 6) rei ( 7) rer ( 1) liquid ( 7) ice (16) rain ( 3) graup ( 1) snow ( 1) liquid ( 4) ice (17) rain ( 2) graup (1) snow ( 1) emis ( 3) binding ( 3) overlap ( 3) (336) (408) mosaic (9) radius (42) water (3) optics (137088) geometry (9) Cloud Cloud ) (10 10 ) (10 6 ) 10 18 configurations Aerosol (10 6 ) vertical profiles (8) optics (1152) indirect effects (18) (72) bgd (1) sul (2) ssa (2) dst (2) cab (2) tot (3) bgd (1) sul (2) ssa (2) (16) dst (2) ccn (6) cloud radius (3) cab (2) observation in situ & satellite earth orbit radiative gases surface characteristics infrared (9) Radiation Radiation (10 2 ) 2 ) topographic effect solar (10) C DOE 2009-2011 gsfclxz cccma cam fuliou gfdl rrtmg csiro eta rrtmlw gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw swrad
CAR Ensemble Flux Frequency Distribu5on
Optimized Physics Ensemble Increasing predic5ve skill Quan5fying uncertainty
Optimized Physics-Ensemble Prediction MKF MGR OBS EOP
Optimized Physics Ensemble Prediction of Precipitation In summer 1993 The physics ensemble mean substantially increases the skill score over individual configurations, and there exists a large room to further enhance that skill through intelligent optimization. Spatial frequency distributions of correlations (top) and rms errors (bottom) between CWRF and observed daily mean rainfall variations in summer 1993. Each line depicts a specific configuration in group of the five key physical processes (color). The ensemble result (ENS) is the average of all runs with equal (Ave) or optimal (OPT) weights, shown as black solid or dashed line.
CWRF Seasonal-Interannual Climate Prediction Nested with NOAA Operational CFS Yuan, X., and X.-Z. Liang, 2011: Improving cold season precipitation prediction by the nested CWRF-CFS system. Geophys. Res. Lett., 38, L02706, doi:10.1029/2010gl046104.
CWRF Improves Seasonal Climate Prediction a) Spatial frequency distributions of root mean square errors (RMSE, mm/day) predicted by the CFS and downscaled by the CWRF and b) CWRF minus CFS differences in the equitable threat score (ETS) for seasonal mean precipitation interannual variations. The statistics are based on all land grids over the entire inner domain for DJF, JFM, FMA, and DJFMA from the 5 realizations during 1982-2008. From Yuan and Liang 2011 (GRL).
CWRF improves predic5ons at regional- local scales Ø CWRF includes advanced physics schemes crucial to climate Ø CWRF couples essen@al components directly linking to impacts Ø CWRF builds upon a super ensemble of alterna@ve physics schemes for skill op@miza@on and uncertainty quan@fica@on Ø CWRF has greater capability & bejer skill than CMM5, WRF Ø CWRF downscaling improves CFS precipita@on predic@ons
Faculty & Student Research ² ² ² ² Thesis ² ² ² Fun Develop, test, evaluate physics parameteriza5on Improve weather forecast, climate predic5on Project climate change and impacts Understand process, mechanism Develop new schemes Compare schemes Diagnose processes