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Land Surface Modeling

Land Surface Modeling . Helin Wei Acknowledge: Mike Ek provides most of slides here . Outline. • Role of Land Surface Models (LSMs) • Requirements and Noah LSM - atmospheric forcing, valid physics, land data sets/parameters, initial land states, cycled land states - Noah LSM

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Land Surface Modeling

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  1. Land Surface Modeling HelinWei Acknowledge: Mike Ek provides most of slides here

  2. Outline • Role of Land Surface Models (LSMs) • Requirements and Noah LSM - atmospheric forcing, valid physics, land data sets/parameters, initial land states, cycled land states - Noah LSM • Recent upgrades of Noah in the NCEP GFS • Validation • Future upgrade

  3. Role of Land Models • Traditionally, from a coupled (atmosphere-ocean-land-ice)Numerical Weather Prediction (NWP) and climate modeling perspective, a land-surface model provides quantities to a parent atmospheric model: - Surface sensible heat flux, - Surface latent heat flux (evapotranspiration) - Upward longwave radiation (or skin temperature and surface emissivity), • Upward (reflected) shortwave radiation (or surface albedo, including snow effects), • Surface momentum exchange.

  4. seasonal storage Atmospheric Energy Budget …to close the surface energy budget, & provide surface boundary conditions to NWP and climate models.

  5. Water Budget (Hydrological Cycle) • Land models close the surface water budget, and provide surface boundary conditions to models.

  6. LoCo: Local land-atmospheric modeling • NEAR-SURFACE LOCAL COUPLING METRIC: Evaporative fraction change with changing soil moisture for both bare soil & vegetated surface = function of near-surface turbulence, canopy control,soil hydraulics & soil thermodynamics, • Utilize GHP/CEOP reference site data sets (“clean” fluxnet), • Extend to coupling with atmos. boundary-layer and entrainment processes, • Link with approaches by Santanello et al. From “GEWEX Imperatives: Plans for 2013 and Beyond” 6

  7. Land-Atmosphere Interaction Betts et al (1996) Diurnal time-scales Seasonal Century

  8. History of Land Modeling (e.g. at NCEP) • 1960s (6-Layer PE model): land surface ignored • Aside from terrain height and surface friction effects • 1970s (LFM): land surface ignored • Late 1980s (NGM): first simple land model introduced (Tuccillo) • Single layer soil slab (“force-restore” model: Deardorff) • No explicit vegetation treatment • Temporally fixed surface wetness factor • Diurnal cycle is treated (as is PBL) with diurnal surface radiation • Surface albedo, skin temperature, surface energy balance • Snow cover (but not depth) • Early1990s (Global Model): OSU land model (Mahrt& Pan, 1984) • Multi-layer soil column (2-layers) • Explicit annual cycle of vegetation effects • Snow pack physics (snowdepth, SWE) • Mid-90s (Meso Model): OSU/Noah LSM replaces Force-Restore • Mid 2000s (Global Model): Noah replaces OSU (Ek et al 2003) • Mid 2000s (Meso Model: WRF): Unified Noah LSM with NCAR • 2000s-10s: Noah “MP” with explicit canopy, ground water,CO2

  9. Outline • Role of Land Surface Models (LSMs) • Requirements and Noah LSM - atmospheric forcing, valid physics, land data sets/parameters, initial land states, cycled land states - Noah LSM • Recent upgrades of Noah in the NCEP GFS • Validation • Future upgrade

  10. Land Model Requirements • • To provide proper boundary conditions, land model must have: • Necessary atmospheric forcing to drive the land model, • Appropriate physics to represent land-surface processes (for relevant time/spatial scales), • - Corresponding land data sets and associated parameters, e.g. land use/land cover (vegetation type), soil type, surface albedo, snow cover, surface roughness, etc., and • Proper initial land states, analogous to initial atmospheric conditions, though land states may carry more “memory” (e.g. especially in deep soil moisture), similar to ocean SSTs.

  11. Static vegetation, e.g. climatology or realtime observations Dynamic vegetation, e.g. plant growth Dynamic ecosystems, e.g. changing land cover Weather & Climate a “Seamless Suite” • Products and models are integrated & consistent throughout time & space, as well as across forecast application & domain. Land modeling example:

  12. Land Model Requirements • • To provide proper boundary conditions, land model must have: • Necessary atmospheric forcing to drive the land model, • Appropriate physics to represent land-surface processes (for relevant time/spatial scales), • - Corresponding land data sets and associated parameters, e.g. land use/land cover (vegetation type), soil type, surface albedo, snow cover, surface roughness, etc., and • Proper initial land states, analogous to initial atmospheric conditions, though land states may carry more “memory” (e.g. especially in deep soil moisture), similar to ocean SSTs.

  13. Unified NCEP-NCAR Noah land model • Four soil layers (10, 30, 60, 100 cm thick). • Linearized (non-iterative) surface energy budget; numerically efficient. • Soil hydraulics and parameters follow Cosby et al. • Jarvis-Stewart “big-leaf” canopy cond. • Direct soil evaporation. • Canopy interception. • Vegetation-reduced soil thermal conductivity. • Patchy/fractional snow cover effect on surface fluxes; coverage treated as function of snowdepth & veg type • Freeze/thaw soil physics. • Snowpack density and snow water equivalent. • Veg & soil classes parameters.

  14. Prognostic Equations Soil Moisture (Θ): • “Richard’s Equation”; DΘ (soil water diffusivity) and KΘ (hydraulic conductivity), are nonlinear functions of soil moisture and soil type (Cosby et al 1984); FΘ is a source/sink term for precipitation/evapotranspiration. Soil Temperature (T): • CT (thermal heat capacity) and KT (soil thermal conductivity; Johansen 1975), non-linear functions of soil/type; soil ice = fct(soil type/temp./moisture). Canopy water (Cw): • P (precipitation) increases Cw, while Ec (canopy water evaporation) decreases Cw.

  15. Surface Energy Budget

  16. Surface Water Budget S = change in land-surface water P = precipitation R = runoff E = evapotranspiration P-R = infiltration of moisture into the soil • S includes changes in soil moisture, snowpack (cold season), and canopy water (dewfall/frostfall and intercepted precipitation, which are small). • Evapotranspiration is a function of surface, soil and vegetation characteristics: canopy water, snow cover/ depth, vegetation type/cover/density & rooting depth/ density, soil type, soil water & ice, surface roughness. • Noah model provides: S, R and E.

  17. (Penman) open water surface Potential Evaporation  = slope of saturation vapor pressure curve Rn-G = available energy  = air density cp = specific heat Ch = surface-layer turbulent exchange coefficient U = wind speed e = atmos. vapor pressure deficit (humidity)  = psychrometric constant, fct(pressure)

  18. Surface Latent Heat Flux (Evapotranspiration) Canopy Water Evap. (LEc) Transpiration (LEt) Bare Soil Evaporation (LEd) canopy water canopy soil • LEc is a function of canopy water % saturation. • LEt uses Jarvis (1976)-Stewart (1988) “big-leaf” canopy conductance. • LEd is a function of near-surface soil % saturation. • LEc, LEt, and LEd are all a function of LEp.

  19. Surface Latent Heat Flux (cont.) Canopy Water Evaporation (LEc): • Cw, Cs are canopy water & canopy water saturation, respectively, a function of veg. type; nc is a coeff. Transpiration (LEt): • gc is canopy conductance, gcmax is maximum canopy conductance and gS, gT, ge, gΘ are solar, air temperature, humidity, and soil moisture availability factors, respectively, all functions of vegetation type. Bare Soil Evaporation (LEd): • Θd, Θs are dry (minimum) & saturated soil moisture contents, =fct(soil type); nd is a coefficient (nom.=2). s max

  20. Latent Heat Flux over Snow LE (shallow snow) < LE (deep snow) Sublimation (LEsnow) LEsnow = LEp LEsnow = LEp snowpack LEns < LEp LEns = 0 soil Shallow/Patchy SnowSnowcover<1 Deep snow Snowcover=1 • LEns = “non-snow” evaporation (evapotranspiration terms). • 100% snowcover a function of vegetation type, i.e. shallower for grass & crops, deeper for forests.

  21. (from canopy/soil snowpack surface) canopy bare soil snowpack soil Surface Sensible Heat Flux , cp = air density, specific heat Ch = surface-layer turbulent exchange coeff. U = wind speed Tsfc-Tair = surface-air temperature difference • “effective” Tsfc for canopy, bare soil, snowpack.

  22. (to canopy/soil/snowpack surface) canopy bare soil snowpack soil Ground Heat Flux KT = soil thermal conductivity (function of soil type: larger for moister soil, larger for clay soil; reduced through canopy, reduced through snowpack) z = upper soil layer thickness Tsfc-Tsoil = surface-upper soil layer temp. difference • “effective” Tsfc for canopy, bare soil, snowpack.

  23. Land Data Sets (e.g. GFS/CFS, GLDAS) Max.-Snow Albedo (1-deg, Robinson) Vegetation Type (1-deg, SIB) Soil Type (1-deg, Zobler) Jan July Jan July Green Vegetation Fraction (monthly, 1/8-deg, NESDIS/AVHRR) Snow-Free Albedo (seasonal, 1-deg, Matthews) 23

  24. 13-type SIB used in the OPS GFS (global 1-degree) 1: broadleaf-evergreen trees 2: broadleaf-deciduous trees 3: broadleaf and needleleaf trees 4: needleleaf-evergreen trees 5: needleleaf-deciduous trees (larch) 6: broadleaf trees with groundcover 7: groundcover only (perennial) 8: broadleaf shrubs with perennial groundcover 9: broadleaf shrubs with bare soil 10: dwarf trees and shrubs with groundcover (tundra) 11: bare soil 12: cultivations (the same parameters as for type 7) 13: glacial ice NEMS/GFS Modeling Summer School

  25. 9-type Zolber used in the OPS GFS (global 1-degree) 1: loamy sand 2: silty clay loam 3: light clay 4: sandy loam 5: sandy clay 6: clay loam 7: sandy clay loam 8: loam 9: loamy sand NEMS/GFS Modeling Summer School

  26. Land surface model physics parameters (examples) • Surface momentum roughness dependent on vegetation/land-use type. • Stomatal control dependent on vegetation type, direct effect on transpiration. • Depth of snow (snow water equivalent, or s.w.e.) for deep snow and assumption of maximum snow albedo is a function of vegetation type. • Heat transfer through vegetation and into the soil a function of green vegetation fraction (coverage) and leaf area index (density). • Soil thermal and hydraulic processes highly dependent on soil type (vary by orders of magnitude).

  27. Initial Land States • Valid land state initial conditions are necessary for NWP and climate models, & must be consistent with the land model used in a given weather or climate model, i.e. from same cycling land model. • Land states spun up in a given NWP or climate model cannot be used directly to initialize another model without a rescaling procedure because of differing land model soil moisture climatologies. May Soil Moisture Climatology from 30-year NCEP Climate Forecast System Reanalysis (CFSR), spun up from Noah land model coupled with CFS.

  28. Initial Land States (cont.) • In addition to soil moisture: the land model provides surface skin temperature, soil temperature, soil ice, canopy water, and snow depth & snow water equivalent. National Ice Center snow cover Air Force Weather Agency snow cover & depth

  29. Initial Land States (cont.) • In seasonal (and longer) climate simulations, land states are “cycled” so that there is an evolution in land states in response to atmospheric forcing and land model physics. • Land data set quantities may be observed and/or simulated, e.g. green vegetation fraction & leaf area index, and even land-use type (evolving ecosystems).

  30. NCEP-NCAR unified Uncoupled “NLDAS” (drought) Forecast NOAH Land Surface Model Noah Land Model Connections in NOAA’s NWS Model Production Suite Oceans HYCOM WaveWatch III Climate CFS 2-Way Coupled Hurricane GFDL HWRF MOM4 GLDAS 1.7B Obs/Day Satellites 99.9% Dispersion ARL/HYSPLIT Regional NAM WRF NMM (including NARR) Global Forecast System Global Data Assimilation Severe Weather Regional Data Assimilation WRF NMM/ARW Workstation WRF Short-Range Ensemble Forecast North American Ensemble Forecast System WRF: ARW, NMM ETA, RSM Air Quality GFS, Canadian Global Model NAM/CMAQ Rapid Update for Aviation (ARW-based)

  31. NCEP-NCAR unified Noah land model • Surface energy (linearized) & water budgets; 4 soil layers. • Forcing: downward radiation, precip., temp., humidity, pressure, wind. • Land states: Tsfc, Tsoil*, soil water* and soil ice*, canopy water*, snow depth/density*. *prognostic • Land data sets: green vegetation frac., veg. type, soil type, snow-free albedo & maximum snow albedo. • Noah coupled with NCEP models: North American Mesoscale model (NAM; short-range), Global Forecast System (GFS; medium-range), Climate Forecast System (CFS; seasonal), and uncoupled NCEP modeling systems, i.e. NLDAS & GLDAS.

  32. Outline • Role of Land Surface Models (LSMs) • Requirements and Noah LSM - atmospheric forcing, valid physics, land data sets/parameters, initial land states, cycled land states - Noah LSM • Recent upgrades of Noah in the NCEP GFS • Validation • Future upgrade

  33. Noah LSM (vegetation, snow, ice) 4 soil layers (10, 30, 60, 100 cm) Frozen soil physics included Add glacial ice treatment Two snowpack states (SWE, density) Surface fluxes weighted by snow cover fraction Improved seasonal cycle of vegetation Spatially varying root depth Runoff and infiltration account for sub-grid variability in precipitation & soil moisture Improved thermal conduction in soil/snow Higher canopy resistance Improved evaporation treatment over bare soil and snowpack OSU LSM 2 soil layers (10, 190 cm) No frozen soil physics Only one snowpack state (SWE) Surface fluxes not weighted by snow fraction Vegetation fraction never less than 50 percent Spatially constant root depth Runoff & infiltration do not account for subgrid variability of precipitation & soil moisture Poor soil and snow thermal conductivity, especially for thin snowpack GFS : Land Model UpgradeNoah LSM (new) versus OSU LSM (old): Noah LSM replaced OSU LSM in operational NCEP medium-range Global Forecast System (GFS) in late May 2005

  34. Mean GFS surface latent heat flux: 09-25 May 2005: Upgrade to Noah LSM significantly reduced the GFS surface latent heat flux (especially in non-arid regions) Pre-May 05 GFS: with OSU LSM Post-May 05 GFS: with new Noah LSM

  35. May 2011, the new thermal roughness scheme was implemented to deal with the daytime cold skin temp bias over semi-arid region during the warm season

  36. Outline • Role of Land Surface Models (LSMs) • Requirements and Noah LSM - atmospheric forcing, valid physics, land data sets/parameters, initial land states, cycled land states - Noah LSM • Recent upgrades of Noah in the NCEP GFS • Validation • Future upgrade

  37. grid2obs for 2m-T Rh, and 10-m wind from Fanglin Yanghttp://www.emc.ncep.noaa.gov/gmb/STATS_vsdb/g2o

  38. 2m-T Rh, and 10-m wind comparisons between GFS and NAMhttp://www.emc.ncep.noaa.gov/mmb/research/nearsfc/nearsfc.verf.html Red: OBS Blue: GFS Green: NAM April, 2013

  39. SURFX (Surface Flux) Network NOAA/ATDD, Tilden Meyers et al Compare monthly diurnal composites of model output versus observations from flux sites to assess systematic model biases.From Mike EK

  40. January Ft. Peck, Montana (grassland)

  41. July Ft. Peck, Montana (grassland)

  42. Downward Solar Upward Longwave Reflected Solar Downward Longwave Sensible Heat Flux Latent Heat Flux Ground Heat Flux Ft. Peck, Montana (grassland) SURFACE ENERGY BUDGET TERMS January 2008 monthly averages: model vs obs

  43. Outline • Role of Land Surface Models (LSMs) • Requirements and Noah LSM - atmospheric forcing, valid physics, land data sets/parameters, initial land states, cycled land states - Noah LSM • Recent upgrades of Noah in the NCEP GFS • Validation • Future upgrade

  44. Future improvements • More land surface data assimilation (soil moisture, snow, etc) • Replace land surface characteristic data by more recent and high-resolution one • Physics upgrades : -Noah MP: urbanization, dynamic vegetation, ground water, carbon cycle, multi-layer snow model) -River routing NEMS/GFS Modeling Summer School

  45. 13-type SIB used in the OPS GFS (global 1-degree) 1: broadleaf-evergreen trees! 2: broadleaf-deciduous trees 3: broadleaf and needleleaf trees! 4: needleleaf-evergreen trees 5: needleleaf-deciduous trees (larch) 6: broadleaf trees with groundcover 7: groundcover only (perennial) 8: broadleaf shrubs with perennial groundcover 9: broadleaf shrubs with bare soil 10: dwarf trees and shrubs with groundcover (tundra) 11: bare soil 12: cultivations (the same parameters as for type 7) 13: glacial ice 20-type IGBP used in the test (global 1-km) 1:Evergreen Needleleaf Forest 2:Evergreen Broadleaf Forest 3:Deciduous Needleleaf Forest 4:Deciduous Broadleaf Forest 5:Mixed Forests 6:Closed Shrublands 7:Open Shrublands 8:Woody Savannas 9:Savannas 10:Grasslands 11:Permanent wetlands 12:Croplands 13:Urban and Built-Up 14:Cropland/natural vegetation mosaic 15:Snow and Ice 16:Barren or Sparsely Vegetated 17:Water 18:Wooded Tundra 19:Mixed Tundra 20:Bare Ground Tundra

  46. 9-type Zolber used in the OPS GFS (global 1-degree) • loamy sand • silty clay loam • light clay • sandy loam • sandy clay • clay loam • sandy clay loam • loam • loamy sand 19-type STASGO used in the test (global 1-km) 1: sand 2: loamy sand 3: sandy loam 4: silt loam 5: silt 6:loam 7:sandy clay loam 8:silty clay loam 9:clay loam 10:sandy clay 11: silty clay 12: clay 13: organic material 14: water 15: bedrock 16: other (land-ice) 17: playa 18: lava 19: white sand

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