Regional Climate- Weather Research and Forecas5ng (CWRF) Model Development & Applica5on. Xin-Zhong Liang

Transcription

1 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

2 EPA STAR FOCUS Consolidate O 3 Elaborate PM Explore Hg EPA STAR FOCUS Nutrients Pathogens Bacteria Sediments Agriculture Urban

3 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 /s

4 Propagation of GCM Present Climate Biases into Future Change Projections: Temperature

5 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

6 NARR CWRF Downscaling Seasonal Climate Prediction over the U.S. LBCs ICs CWRF D2 D3 MLO CAM GFS SST ICs CFS SST D1 NOAA 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

7 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!

8 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

9 NCEP/AMIP II vs ECMWF-Interim Reanalysis

10 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

11 Summer 1993 U.S. Midwest Record Flood OBS WRF CWRF Rainfall (mm d -1 ) CWRF has made significant improvements. WRF T2m Bias ( C) CWRF

12 Surface Temperature Biases All driven by NCEP/DOE AMIP II Reanalysis

13 Surface SW d Biases All driven by NCEP/DOE AMIP II Reanalysis

14 Understanding Biases WRFG & CMM5: SWd are too large, while T2m biases are small HRM3: SWd is quite while T2m is CRCM: SWd is fairly but T2m has notable cold biases RCM3: SWd is yet T2m is reasonable CWRF: SWd and T2m both are quite Conclusion: SWd seems not the dominant factor that cause T2m biases; the lajer may largely result from deficiencies in the water cycle.

15 Interannual CORR over USA

16 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

17 Physics Representation Evaluating Skill under Correct Forcing Conditions

18 Scale Dependence Model physics representation and predictive skill depend on spatial scale Challenging

19 CWRF Terrestrial Hydrology Kanawha Kentucky Green Tennessee Stream Flow (mm/day) USGS Sta Green River Precipitation CLM CLM+CSS Observed Days Since Jan Precipitation (mm/day) Choi 2006; Choi et al. 2007, 2011; Choi and Liang 2010; Yuan and Liang 2010; Liang et al. 2010d

20 Illinois Soil Moisture Simulations Driven by NARR Yuan and Liang 2011 (J. Hydrometeorology)

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22 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]

23 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 ) 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 gsfclxz cccma cam fuliou gfdl rrtmg csiro eta rrtmlw gsfclxz cccma cam fuliou gfdl rrtmg csiro eta gsfcsw swrad

24 CAR Ensemble Flux Frequency Distribu5on

25 Optimized Physics Ensemble Increasing predic5ve skill Quan5fying uncertainty

26 Optimized Physics-Ensemble Prediction MKF MGR OBS EOP

27 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 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.

28 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: /2010gl

29 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 From Yuan and Liang 2011 (GRL).

30 CWRF improves predic5ons at regional- local scales Ø CWRF includes advanced physics schemes crucial to climate Ø CWRF couples 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

31 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