Evaluation of the Community Noah Land Surface model against tower flux data in the Tier I region of NAME

Transcription

1 Evaluation of the Community Noah Land Surface model against tower flux data in the Tier I region of NAME D. Gochis,, NCAR E. Vivoni,, New Mexico Tech. S. Rajagopal,, U. Arizona P. Troch,, U. Arizona R. Scott, USDA-ARS ARS

2 Background and Motivation Mo et al (7) data impact study: Assimilation of precipitation yields significant differences in NAM thermodynamic structure Gochis et al. (8) satellite QPE analysis: Different QPE products yield markedly different land surface fluxes on the order of interannual variability Recent groundwater influence work: In many regions the influence of shallow groundwater can significantly alter land-atmosphere coupling (Niu & Yang, Fan & Miguez- Macho) Kelly and Mapes work on NAMAP-II LSMs: Coupled model partitioning of land-atmosphere surface fluxes is poorly constrained

3 Recent Research Highlights Land Surface Representation in NAMAP-2 Models Poor constraint in representation of landatmosphere exchanges in coupled models NAMAP-II LSM model analysis reveals fundamental differences in modeled fluxes LSMs contain very different EF-rainfall relationships Several ongoing efforts aimed at collecting data and characterizing landatmosphere exchanges in NAM region (5 tower sites in SW U.S. and Mex. Courtesy Kelly and Mapes

4 Research Questions 1) What is the warm season cycle of surface energy flux partitioning in the core NAM region? (including pre-/post-onset differences) 2) How well does a regional implementation of the Noah LSM represent land-atmosphere exchange? 3) What are the critical sensitivities in the Noah LSM and how can they best be constrained?

5 Noah model overview and recent Community Noah land surface model: a) Designed for use in numerical weather prediction b) Relatively simple, robust and efficient, emphasizing computational efficiency for operational forecasting c) Coupled to NCEP NAM, GFS and NCAR WRF enhancements

6 NAM Tower Flux Sites: R. San Pedro, AZ (26 Jun 3 Sep., 4): Riparian area, sacaton grass site, shallow ground water (Scott et al) Rayon/R. Sonora, Son. (23 Jul. 3 Sep. 4): Complex terrain, deciduous scrub, shallow impervious layer (Vivoni et al) Tesopaco, Son. (4): Deciduous scrub (Watts et al) San Pedro, AZ Rayon Tesopaco Est. Obispo Estacion Obispo, Sin. (4): Site located in agricultural region (NOAA-ETL/ESRL)

7

8 Forcing Data Issues: Generally: Non-standard measurement suites, tower sizes, canopies, site characteristics, energy balance closure issues Somewhat differing periods of record Specific sites: R. San Pedro, AZ: Several gaps in met. data filled with nearby wx. station *R. Sonora, Son.: Pressure data estimated from NARR *Tesopaco, Son.: Some issues with dates in met. data *Estacio Obispo, Sin.: Gaps in LW radiation data, filled in w/ constant (425 W/m2) derived from summertime mean, potentially contaminated by sea-breeze * Available through NAME/SMEX-4 websites

9 Results: Observed Sensible and Latent Heat Flux Partitioning Most sites indicate a mean dominance of sensible vs. latent heat fluxes Large degree of scatter at all sites San Pedro riparian site appears constrained in low LE events, likely due to groundwater inputs Sensible vs. Latent Heat Fluxes - R. San Pedro, AZ Sensible vs. Latent Heat Fluxes - Rayon/R. Sonora Sensible vs. Latent Heat Fluxes - Tesopaco Sensible vs. Latent Heat Fluxes - Obispo y =.648x R 2 =.717 San Pedro Obs. Noah San Pedro Linear (San Pedro Obs.) y =.2798x R 2 =.1526 Rayon Obs. Linear (Rayon Obs.) San Pedro Rayon Tesopaco Obispo y =.9664x R 2 =.3968 Tesopaco Obs. Linear (Tesopaco Obs.) LE (W/m^2) H (W/m^2) y =.9997x R 2 =.3955 Obispo Obs. Noah Linear (Obispo Obs.)

10 Results: Regional Implementation Identical model setup and parameters, different forcing data Shrubland, sandy loam soils, LAI=4., albedo =.25, max. rooting depth = 1m Noah model yields greater sensible heat flux partitioning at all sites Noah model also under-dispersed compared to tower flux estimates for both high LE and high H events Relative bias in mean partitioning smallest at Rayon/R. Sonora site, larger at Obsipo and San Pedro Flux correlations generally high at all sites due to predominance of diurnal cycle, except Obispo where model and obs. are essentially uncorrelated Sensible vs. Latent Heat Fluxes - R. San Pedro, AZ Sensible vs. Latent Heat Fluxes - Rayon/R. Sonora Sensible vs. Latent Heat Fluxes - Tesopaco Sensible vs. Latent Heat Fluxes - Obispo y =.648x R 2 = San Pedro Obs. Noah San Pedro Linear (San Pedro Obs.) Linear (Noah San Pedro) y =.2684x R 2 = y =.2798x R 2 =.1526 Rayon Obs. Noah Rayon Linear (Rayon Obs.) Linear (Noah Rayon) y =.3893x R 2 = y =.9664x R 2 =.3968 Tesopaco Obs. Noah Tesopaco Linear (Tesopaco y =.6439x R 2 =.8132 LE (W/m^2) 6 y =.9997x R 2 = Obispo Obs. Noah Linear (Obispo Obs.) Linear (Noah) y =.3725x R 2 = H (W/m^2) San Pedro Rayon Tesopaco Obispo

11 Results: Model Parameter Sensitivity Evaluation Site adjusted parameters (vegetation class, soils class, soil depth, impervious layers, LAI, albedo) where data is available Generally an improvement in H-LE partitioning w.r.t. observations compared to regional sim. Sensible vs. Latent Heat Fluxes - R. San Pedro, AZ Sensible vs. Latent Heat Fluxes - Rayon/R. Sonora Sensible vs. Latent Heat Fluxes - Tesopaco Sensible vs. Latent Heat Fluxes - Obispo y =.648x San RPedro 2 =.717 Obs. y =.2684x Noah R 2 San Pedro =.6883 Noah San Pedro (local params) Linear (San Pedro Obs.) Linear (Noah San Pedro) Linear (Noah San Pedro (local params)) y =.344x R 2 = y =.2798x R 2 =.1526 y =.3893x R 2 =.7775 Rayon Obs. Noah Rayon Noah Rayon (local params) Linear (Rayon Obs.) Linear (Noah Rayon) Linear (Noah Rayon (local params)) y =.4384x R 2 = y =.9664x R 2 =.3968 Tesopaco Obs. 4 3 Noah Tesopaco Noah Tesopaco (local params) y =.6439x R 2 =.8132 y =.7439x R 2 =.2718 LE (W/m^2) H (W/m^2) y =.9997x Obispo Obs. R 2 =.3955 Noah Noah Tesopaco (local params) Linear (Obispo Obs.) Linear (Noah) Linear (Noah Tesopaco (local params)) y =.3725x R 2 =.5383 y =.5651x R 2 =.438 San Pedro Savannah free drainage* Rayon shallow soil trop. decid. Tesopaco trop. decid. shallow soil Obispo cropland free drainage

12 Results: Tesopaco & Rayon Sensitivity Soil Depth Sensible vs. Latent Heat Fluxes - Tesopaco Sensible vs. Latent Heat Fluxes - Rayon/R. Sonora y =.9664x R 2 =.3968 y =.6439x R 2 =.8132 Tesopaco Obs. Noah Tesopaco Noah Tesopaco (local params) Noah Teso. (local-deep soil) Linear (Tesopaco Obs.) Linear (Noah Tesopaco) Linear (Noah Tesopaco (local params)) Linear (Noah Teso. (locald il)) y =.7439x R 2 =.2718 y =.941x R 2 = y =.2798x R 2 =.1526 y =.3893x R 2 =.7775 Rayon Obs. Noah Rayon Noah Rayon (local params) Noah Rayon (local params, deep soil) Linear (Rayon Obs.) Linear (Noah Rayon) Linear (Noah Rayon (local params)) Linear (Noah Rayon (local y =.4384x R 2 =.5676 y =.651x R 2 =.7899 Removal of shallow soil specification, results in under-dispersion of flux behavior

13 Results: R. San Pedro, AZ & groundwater influence Simple fixed water table specification Analytical capillary flux to active soil profile Generally positive ET bias introduced over 4 at shallow WT depths Obs. depth around m below ground surface Also currently lacking seasonal water table dynamics R. San Pedro

14 Summary & Other Issues: Unified set of forcing data generated for all 4 tower flux sites for 4 Regional implementation yields biases (high H, low LE) and under-dispersion, dispersion, local parameters offer significant improvements Characterization of shallow soils appears critical Groundwater influences are also important in some sites though this is likely a small fractional area over NAMS region Interesting contrast to Vivoni et al. who found that NARR- Noah yielded too much ET Lack of parameters sets in Noah to deal with shallow, rocky,, fractured-rock rock soils (Tesopaco( and much SMO) Lack of closure of obs yields significant uncertainty in true fluxes Lack high elevation flux data from the coniferous forests of the SMO and semi-tropical regions further south

15 End