CWRF  Downscaling  to  Improve  U.S.  Seasonal-­‐Interannual  Climate  Predic>on  

Xin-Zhong Liang1,2, Ligang Chen2, Shenjian Su2, Julian X.L. Wang3 1Department of Atmosphere & Ocean Science 2Earth System Science Interdisciplinary Center University of Maryland, College Park 3Air Resources Laboratory National Oceanic & Atmospheric Administration

2011 December 7

Regional Climate Modeling

Grants: NOAA NA110AR4310-194 & -195 & EPP COM/HU631017

D1

D2

D3

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

ICs

LBCs

SST

CFS

NARR CWRF Downscaling Seasonal Climate Prediction over the U.S.

CWRF UOM

CAM GFS

CFS SST ICs

NOAA 2008-2012

CSSP

CWRF Physics Options Diffusion

Upper Eddy

Const DIF L2.5 TKE L2 3D DEF

RadExt

Orbit Gases Aerosols

SfcExt

VEG SST OCN

PBL YSU UW ACM GFS MYJ MYNN QNSE BouLac ORO CAM

Microphysics

Kessler[2]

Zhao[2]

Thompson[7]

Tao[5]

Lin[6]

Morrison[10]

Hong[3]

Hong[7]

Hong[5]

Hong[8]

Hong[6]

Cumulus

BMJ NKF SAS GD G3

UW ZML CSU GFDL MIT ECP

Surface

Urban

UCM BEP

Land Ocean

SLAB RUC PX NOAH CSSP CROP SOM UOM

CAM AER GSFC CCCMA GFDL CAWCR MISC FLG

Cloud Aerosol Radiation LW + SW

http://cwrf.umd.edu

CWRF

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

Ø  Feng Zhang, Xin-Zhong Liang, and Shenjian Su: Evaluation of the cloud-aerosol-radiation ensemble modeling system.

A21H-05—Tue, 12/06, 8:00AM

Ø  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

Ø  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

Kanawha

Kentucky

Green

Tennessee

0

5

10

15

20

25

30

0 30 60 90 120 150 180 210 240 270 300 330 360Days Since Jan 1995

Stre

am F

low

(mm

/day

)

0

50

100

150

200

Prec

ipita

tion

(mm

/day

)

PrecipitationCLMCLM+CSSObserved

USGS Sta. 03320000Green River

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 .

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 Seasonal Climate Prediction

Observed (OBS), CFS-predicted, and CWRF-downscaled: a) number of rainy days, b) maximum dry spell length (day), c) daily rainfall 95th 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 Downscaling Seasonal Climate Prediction: Extreme Events

CWRF Seasonal-Interannual Climate Prediction

Nested with NOAA/IRI Operational

ECHAM

In collaboration with Dave DeWitt of IRI

CWRF  Downscaling  Improves  ECHAM  Extreme  Events  

No  of  Rainy  Days  Max  Dry  Spell  Daily  Pr  95th  Percen9le  

CWRF  

ECHAM  

OBS  

SON  

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