1 Xiaoyan Jiang, Guo-Yue Niu and Zong-Liang Yang The Jackson School of Geosciences The University of Texas at Austin 03/20/2007 Feedback between the atmosphere, vegetation and groundwater represented in WRF/Noah  Offline validation of soil moisture with Illinois data  Coupled WRF/Noah simulations of rainfall in central U.S.

2 Offline validation of soil moisture with Illinois data (at two stations; daily from 1/1/1998 to 12/31/2002) Noah + DVGW produces a much wetter soil than the default Noah. DVGW reduces the amplitude of temporal variations.

3  Introduction  Objectives  Hypothesis  Land cover and hydrogeological characteristics over the Central U.S.  Model description  Experiment design  Simulation results and discussions  Conclusions The Impacts of Vegetation and Groundwater Dynamics on North American Warm Season Precipitation over the Central U.S.

4  Understand the role of vegetation growth and groundwater dynamics in land- atmosphere interaction.  Improve the prediction of warm season precipitation in a coupled land-atmosphere model.  Identify the high-impact locations (Local or regional?).  Account for the role of initialization in intra- seasonal forecasting through ensemble simulations. Objectives

5 Hypothesis Representing interactive canopy (or vegetation growth) and groundwater dynamics in a coupled land surface and atmospheric model improves seasonal precipitation.

6 Study domain

7 Land cover and hydrogeological characteristics over the Central U.S. Dominant land cover types over the Central U.S. Aquifer distribution from Atlas

8 Dickinson, R. E., M. Shaikh, R. Bryant, et al., 1998 Niu, G.-Y., Z.-L. Yang, R.E. Dickinson, L.E. Gulden, and H. Su, 2007 A Coupled Land-Atmosphere Model System

9 Model configurations  The version of the Weather Research and Forecasting model (WRF) with time-varying sea surface temperatures.  Physics options and input data: Lin et al. microphysics scheme; Kain-Fritsch cumulus parameterization scheme; Yonsei University Planetary boundary layer; A simple cloud interactive radiation scheme; Rapid Radiative Transfer Model longwave radiation scheme  A dynamic vegetation model of Dickinson et al. (1998) in Noah LSM.  A simple groundwater model (SIMGM) (Niu et al. 2006) in Noah LSM.  NCEP-NARR reanalysis data.  The model domain covers the whole continental U.S. and the grid spacing is 32 km

10 Ensemble experiments with WRF Cases Start from different dates to 8/31/2002 Experiment description DEFAULT Prescribed greenness fraction DV Predicted greenness fraction (or dynamic vegetation) DVGW Predicted greenness fraction and water table depth 05/31 00:00 05/31 06:00 05/31 12:00 05/31 18:00 06/01 00:00

11 Initial water table level from offline Noah LSM

12 Observed and simulated precipitation in June, July and August (JJA) (mm/day)

13 Simulated versus observed cumulative precipitation over the Central U.S. The performance of DVGW for precipitation is much closer to the observation; DV is also better than DEFAULT.

14 Simulated and observed monthly mean precipitation

15 Differences of surface temperature between the DV and DEFAULT, DVGW and DV DV-DEFAULT DVGW-DV JJA July

16 Latent heat flux Sensible heat flux DVGW and DV produce higher latent heat flux than DEFAULT over the Central U.S. DVGW and DV cause less sensible heat flux than DEFAULT over the Central U.S.

17 Differences of latent heat flux and precipitation DV-DEFAULT June July August June July August Latent heat flux Precipitation

18 Differences of latent heat flux and precipitation DVGW-DV Latent heat fluxPrecipitation

19 Differences of greenness fraction between DV and DEFAULT; DVGW and DV DV-DEFAULTDVGW-DV June August DV causes higher greenness fraction over most part of the Central U.S.; DVGW further increase the greenness fraction in this area.

20 MODIS NDVI-derived and model simulated greenness fraction over the Central U.S. (in August) Fg = (NDVIi - NDVImin) / (NDVImax - NDVImin) NDVImin= 0.04 and NDVImax= 0.52 (Gutman and Ignatov 1997)

21 Water balance over the Central U.S. in JJA, 2002 VariablesPrecipitation (mm/day) Evapotranspiration (mm/day) Moisture Flux Convergence (mm/day) NARR2.3642* DEFAULT DV DVGW GW Note: * using CPC observed gauged precipitation

22 Diurnal cycle of precipitation

23 Diurnal cycle of Surface Fluxes

24 Lifting condensation level as a function of soil moisture index

25 Conclusions  The WRF/Noah model with augmented vegetation and groundwater dynamics can improve the simulation of summertime precipitation over the Central U.S.  The increased precipitation (by 65%) corresponds to the increased latent heat flux (by 34%).  In summer, precipitation in the Central U.S. mostly comes from local evapotranspiration, showing strong land–atmosphere coupling.  The role of vegetation is significant (by 37%) in the grassland and cropland areas in summer.  Groundwater has impacts (by 16%) on summer precipitation in the transition zone.

26 Conclusions (Cont) Throughout the day, precipitation is increased (improved) when vegetation dynamics is included, and it is further increased (improved) when groundwater dynamics is added. These increases are consistent with higher (lower) latent (sensible) heat fluxes. The increased precipitation with the Noah enhancements are also consistent with reduced lifting condensation levels, suggesting a positive soil moisture-precipitation feedback (wetter soil, more evapotranspiration, lower lifting condensation levels, and higher rainfall).

27 Thanks for your attention! Questions and suggestions?