CWRF Downscaling to Improve U.S. Seasonal- Interannual Climate Predic>on Xin-Zhong Liang 1,2, Ligang Chen 2, Shenjian Su 2, Julian X.L. Wang 3 1 Department of Atmosphere & Ocean Science 2011 December 7 Regional Climate Modeling 2 Earth System Science Interdisciplinary Center University of Maryland, College Park 3 Air Resources Laboratory National Oceanic & Atmospheric Administration Grants: NOAA NA110AR4310-194 & -195 & EPP COM/HU631017
NARR CWRF Downscaling Seasonal Climate Prediction over the U.S. LBCs ICs CWRF D3 CAM GFS CSSP D2 UOM SST ICs CFS SST D1 NOAA 2008-2012 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
CWRF Physics Options Upper Diffusion Eddy Cloud Aerosol Const DIF L2.5 TKE L2 3D DEF Radiation LW + SW RadExt MISC CAM AER GSFC CCCMA GFDL CAWCR FLG 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 CAM UW ORO Microphysics BMJ NKF SAS GD G3 Kessler[2] Thompson[7] Lin[6] Hong[3] Hong[5] Hong[6] UW ZML CSU GFDL MIT ECP Zhao[2] Tao[5] Morrison[10] Hong[7] Hong[8] http://cwrf.umd.edu
CWRF Ø Ø Ø Ø Min Xu, Xin-Zhong Liang, and Wei Gao: Regional climatic effects of crop growth modeled by the coupled CWRF-CROP system. B11B-0480 Mon, 12/05, 8:00AM Feng Zhang, Xin-Zhong Liang, and Shenjian Su: Evaluation of the cloudaerosol-radiation ensemble modeling system. A21H-05 Tue, 12/06, 8:00AM Fengxue Qiao and Xin-Zhong Liang: Effects of cumulus parameterization on the U.S. summer precipitation prediction by the CWRF. A31H Wed, 12/07, 9:45AM Shuyan Liu and Xin-Zhong Liang: Effects of planetary boundary layer parameterizations on CWRF regional climate simulation. A41A-0041 Thu, 12/08, 8:00AM
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)
Illinois Soil Moisture by Offline CSSP vs NOAH
Illinois Soil Moisture by CWRF/CSSP vs WRF/NOAH Liang et al. 2011 (Bull. Amer. Met. Soc.)
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 Downscaling Seasonal Climate Prediction: Extreme Events Observed (OBS), CFS-predicted, and CWRF-downscaled: a) number of rainy days, b) maximum dry spell length (day), c) daily rainfall 95 th percentile (mm/day), and d) difference in number of rainy days averaged between the El Niño (warm) and La Niña (cold) events for JFM during 1983-2008. From Yuan and Liang 2011 (GRL).
CWRF Seasonal-Interannual Climate Prediction Nested with NOAA/IRI Operational ECHAM In collaboration with Dave DeWitt of IRI
SON CWRF Downscaling Improves ECHAM Extreme Events No of Rainy Days Max Dry Spell Daily Pr 95 th Percen9le OBS ECHAM CWRF In collabora9on with Dave DeWi? of IRI
U.S. Land Seasonal Precipita9on Spa9al Pa?ern Correla9on CWRF downscaling is much more realis9c than ECHAM In collabora9on with Dave DeWi? of IRI
CWRF improves predic>ons at regional- local scales Ø CWRF includes advanced physics schemes crucial to climate Ø CWRF couples essen9al components directly linking to impacts Ø CWRF builds upon a super ensemble of alterna9ve physics schemes for skill op9miza9on and uncertainty quan9fica9on Ø CWRF has greater capability & be?er skill than CMM5, WRF Ø CWRF downscaling improves CFS precipita9on predic9ons