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M. Ek - Land Surface in Weather and Climate Models; "Surface scheme"
 

M. Ek - Land Surface in Weather and Climate Models; "Surface scheme"

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    M. Ek - Land Surface in Weather and Climate Models; "Surface scheme" M. Ek - Land Surface in Weather and Climate Models; "Surface scheme" Presentation Transcript

    • Land Surface in Weather and Climate Models “IV WorkEta: Esquema de superfície” Michael Ek (michael.ek@noaa.gov) Environmental Modeling Center (EMC)National Centers for Environmental Prediction (NCEP) NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 National Weather Service (NWS) National Oceanic and Atmospheric Administration (NOAA)IV WorkEta – Workshop em Modelagem Numérica de Tempo e Clima em Mesoescala utilizando o Modelo Eta: Aspectos Físicos e Numéricos CPTEC/INPE, Cachoeira Paulista, São Paulo, 3-8 de Março de 20131
    • NOAA Center for Weather and ClimatePrediction (NCWCP), College Park, Maryland, USA 2World Weather Building
    • www.noaa.gov www.nws.noaa.gov Development, upgrade, transition, maintain modelswww.ncep.noaa.gov www.emc.ncep.noaa.gov
    • www.wmo.int “www.wmo.int “WWRP” “…improve accuracy, lead time and utilization of weather prediction.” www.wcrp-climate.org “Determine the predictability of climateand effect of human activities on climate.” “Weather, Water, Climate” www.gewex.org
    • Outline• Role of Land Surface Models (LSMs)• Requirements:- atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 5
    • Outline• Role of Land Surface Models (LSMs)• Requirements:- atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 6
    • 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. 7
    • Weather & Climate a “Seamless Suite” • Products and models are integrated & consistent throughout time & space, as well as across forecast application & domain. Land Static vegetation, Dynamic Dynamic e.g. climatology vegetation, ecosystems,modeling or realtime e.g. plant e.g. changing 8example: observations growth land cover
    • Atmospheric Energy Budget…to close the surface energy budget, & provide surface boundary conditions to NWP and climate models. seasonal storage 9
    • Water Budget (Hydrological Cycle)• Land models close the surface water budget, andprovide surface boundary conditions to models. 10
    • 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
    • MODEL PREDICTIONS:PROCESSES CLOUDS INCOMING SOLAR AND LONGWAVE BOUNDARY-LAYER TURBULENT MIXING EVAPORATIONHEATING THE AIR& OUTGOING LONGWAVE SOIL HEATING
    • MODEL PREDICTIONS: STATESFREE ATMOSPHERE ATMOSPHERIC ATMOSPHERIC: BOUNDARY TEMPERATURE LAYER HUMIDITY WINDS SOIL: LAND-SURFACE TEMPERATURE MOISTURE
    • Land-Atmosphere Interaction•  Many land-surface/atmospheric processes.•  Interactions & feedbacks between the land andatmosphere must be properly represented.•  Global Energy and Water Exchanges (GEWEX)Program / Global Land/Atmosphere System Study(GLASS) project: Accurately understand,model, and predict the role of Local Land-Atmosphere Coupling “LoCo” in water andenergy cycles in weather and climate models.•  All land-atmosphere coupling is local……initially. 14
    • LoCo: Local land-atmospheric modeling• NEAR-SURFACELOCAL COUPLINGMETRIC: Evaporativefraction change withchanging soil moisturefor both bare soil &vegetated surface =function of near-surface turbulence,canopy control, soilhydraulics & soilthermodynamics,• Utilize GHP/CEOPreference site datasets (“clean” fluxnet),• Extend to couplingwith atmos. boundary-layer and entrainmentprocesses,• Link with approachesby Santanello et al. From “GEWEXImperatives: Plans for 2013 and Beyond” 15 15
    • Land-Atmosphere InteractionBetts et al (1996) Diurnal time-scales Seasonal Century 16
    • Outline• Role of Land Surface Models (LSMs)• Requirements: - atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 17
    • 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. 18
    • Atmospheric Forcing to Land Model Precipitation Incoming solar Wind speed Air temperature Incoming Longwave Specific humidity 19+ Atmospheric Pressure Example from 18 UTC, 12 Feb 2011
    • 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 •  Freeze/thaw soil physics. fluxes; coverage treated •  Snowpack density and snow water as function of equivalent. 20 snowdepth & veg type •  Veg & soil classes parameters.
    • Prognostic EquationsSoil Moisture (Θ):• “Richard’s Equation”; DΘ (soil water diffusivity) andKΘ (hydraulic conductivity), are nonlinear functions ofsoil moisture and soil type (Cosby et al 1984); FΘ is asource/sink term for precipitation/evapotranspiration.Soil Temperature (T):• CT (thermal heat capacity) and KT (soil thermalconductivity; Johansen 1975), non-linear functions ofsoil/type; soil ice = fct(soil type/temp./moisture).Canopy water (Cw):• P (precipitation) increases Cw, while Ec (canopywater evaporation) decreases Cw. 21
    • Surface Energy Budget Rn = Net radiation = Sê - Sé + Lê - Lé Sê = incoming shortwave (provided by atmos. model) Sé = reflected shortwave (snow-free albedo (α) provided by atmos. model; α modified by Noah model over snow) Lê = downward longwave (provided by atmos. model) Lé = emitted longwave = εσTs4 (ε=surface emissivity, σ=Stefan-Boltzmann const., Ts=surface skin temperature) H = sensible heat flux LE = latent heat flux (surface evapotranspiration) G = ground heat flux (subsurface soil heat flux)SPC = snow phase-change heat flux (melting snow)• Noah model provides: α, Lé, H, LE, G and SPC. 22
    • Surface Water Budget ΔS = change in land-surface water P = precipitation R = runoff E = evapotranspirationP-R = infiltration of moisture into the soil• ΔS includes changes in soil moisture, snowpack (coldseason), and canopy water (dewfall/frostfall andintercepted precipitation, which are small).• Evapotranspiration is a function of surface, soil andvegetation characteristics: canopy water, snow cover/depth, vegetation type/cover/density & rooting depth/density, soil type, soil water & ice, surface roughness.• Noah model provides: ΔS, R and E. 23
    • Potential Evaporation (Penman) open water surface Δ = slope of saturation vapor pressure curveRn-G = available energy" ρ = air density cp = specific heat Ch = surface-layer turbulent exchange coefficient U = wind speed δe = atmos. vapor pressure deficit (humidity) γ = psychrometric constant, fct(pressure)" 24
    • Surface Latent Heat Flux (Evapotranspiration) Canopy Water Evap. (LEc) Transpiration (LEt) Bare Soil canopy water Evaporation (LEd) 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. 25
    • 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): max sê• gc is canopy conductance, gcmax is maximum canopyconductance and gSê, gT, gδe, gΘ are solar, airtemperature, humidity, and soil moisture availabilityfactors, respectively, all functions of vegetation type.Bare Soil Evaporation (LEd):• Θd, Θs are dry (minimum) & saturated soil moisturecontents, =fct(soil type); nd is a coefficient (nom.=2). 26
    • 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 Snow Deep snow Snowcover<1 Snowcover=1• LEns = “non-snow” evaporation (evapotranspiration terms).• 100% snowcover a function of vegetation type, i.e.shallower for grass & crops, deeper for forests. 27
    • Surface Sensible Heat Flux (from canopy/soil snowpack surface) canopy bare soil snowpack soil ρ, 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. 28
    • Ground Heat Flux (to canopy/soil/snowpack surface) canopy bare soil snowpack soil KT = soil thermal conductivity (function of soil type: larger for moister soil, larger for clay soil; reduced through canopy, reduced through snowpack) Δz = upper soil layer thicknessTsfc-Tsoil = surface-upper soil layer temp. difference• “effective” Tsfc for canopy, bare soil, snowpack. 29
    • Land Data Sets (e.g. uncoupled Noah) Vegetation Type Soil Type Max.-Snow Albedo (1-km, USGS) (1-km, STATSGO-FAO) (1-deg, Robinson)Jan July winter summer Green Vegetation Fraction Snow-Free Albedo (monthly, 1/8-deg, NESDIS AVHRR) (seasonal, 1-deg, Matthews)• Fixed climatologies or (near real-time) observations.• Some quantities predicted/assimilated (e.g. soil moist., snow, vegetation). 30
    • Land Data Sets (e.g. meso NAM) Vegetation Type Soil Type Max.-Snow Albedo(1-km, IGBP-MODIS) (1-km, STATSGO-FAO) (1-km, UAz-MODIS)mid-Jan mid-July Jan July Green Vegetation Fraction Snow-Free Albedo (weekly, 1/8-deg, (monthly, 1-km, new NESDIS/AVHRR) BU-MODIS) 31
    • Land Data Sets (e.g. GFS/CFS, GLDAS) Vegetation Type Soil Type Max.-Snow Albedo (1-deg, UMD) (1-deg, Zobler) (1-deg, Robinson)Jan July Jan July Green Vegetation Fraction Snow-Free Albedo (monthly, 1/8-deg, NESDIS/ (seasonal, 1-deg, AVHRR) Matthews) 32
    • 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). 33
    • 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. 34
    • 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 Air Force Weather Agency Center snow cover snow cover & depth 35
    • 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). 36
    • Land Data Assimilation: Motivation• Better initial conditions of land statesfor numerical weather prediction (NWP) andseasonal climate model forecasts. • Assess model (physics) performanceand make improvements by assimilatingreal land data (sets).• Application to regional and globaldrought monitoring and seasonalhydrological prediction. 37
    • Land Data Assimilation (LDA)• Observations incorporated into weather, seasonalclimate and hydrological models in analysis cycles.• In each analysis cycle, observations of current(and possibly past) state of a system are combinedwith results from a weather/climate/hydro model.• Analysis step is considered “best” estimate of thecurrent state of the weather/climate/hydro system.• Analysis step balances uncertainty in data and inforecast. The model is then advanced in time & itsresults becomes the forecast in next analysis cycle. 38
    • Land Data Assimilation (LDA)• Minimization of a "cost function” has the effect ofmaking sure that the analysis does not drift too faraway from observations and forecasts that areknown to usually be reliable. 39
    • Developing Land Data Assimilation Capabilities Figure 1: Snow water equivalent (SWE) based on Terra/MODIS and Aqua/AMSR-E. Figure 2: Annual average precipitation from 1998 toFuture observations will be provided by JPSS/ 2009 based on TRMM satellite observations. Future observations will be provided by GPM. VIIRS and DWSS/MIS. Figure 5: Current lakes and reservoirs monitored byFigure 3: Daily soil moisture based on Aqua/ OSTM/Jason-2. Shown are current height variations AMSR-E. Future observations will be Figure 4: Changes in annual-average terrestrial relative to 10-year average levels. Future provided by SMAP. water storage (the sum of groundwater, soil water, observations will be provided by SWOT. surface water, snow, and ice, as an equivalent heightC. Peters-Lidard et al of water in cm) between 2009 and 2010, based on 40(NASA) GRACE satellite observations. Future observations will be provided by GRACE-II.
    • NASA Land Information System Inputs Physics Outputs Applications Topography, Surface Land Surface Models Energy Soils SAC-HT/SNOW17 Fluxes (Static) Water Noah, CLM, VIC, Catchment (Qh,Qle) Supply & Demand Land Cover, Leaf Area Index Soil Moisture Agriculture (MODIS, AMSR, Evaporation Hydro- TRMM, SRTM) Electric Power, Meteorology: WaterModeled & Observed Surface Quality (NLDAS, DMIP II, WaterGFS,GLDAS,TRMM Fluxes Improved GOES, Station) (e.g., Runoff) Short Term & Observed States Data Assimilation Modules Long Term (Snow Surface Predictions (DI, EKF, EnKF) States Soil Moisture Temperature) (Snowpack) 41 41 Christa Peters-Lidard et al., NASA/GSFC/HSB
    • Soil Moisture Data Assimilation Data Assimilated: •  AMSR-E LPRM soil moisture •  AMSR-E NASA soil moisture Variables Analyzed: •  Soil Moisture •  Evapotranspiration •  Steamflow Experimental Setup: •  Domain: CONUS, NLDAS •  Resolution: 0.125 deg. •  Period: 2002-01 to 2010-01 •  Forcing: NLDASII •  LSM: Noah 2.7.1,3.2Figure 3: Daily soil moisture based on Aqua/ AMSR-E. Future observations will be provided by SMAP. Peters-Lidard, C.D, S.V. Kumar, D.M. Mocko, Y. Tian, 2011: Estimating evapotranspiration with land data 42 assimilation systems, Hydrological Processes, 25(26), 3979--3992, DOI: 10.1002/hyp.8387
    • LDA: Ensemble Kalman Filter (EnKF) From Rolf Reichle (2008) Nonlinearly propagates ensemble of model trajectories. y Can account for wide range of k model errors (incl. non-additive). Approx.: Ensemble size. Linearized update. xki state vector (eg soil moisture) Pk state error covariance Rk observation error covariancePropagation tk-1 to tk: Update at tk: xki+ = xki- + Kk(yki - xki- ) xki+ = f(xk-1i-) + wki for each ensemble member i=1…N w = model error Kk = Pk (Pk + Rk)-1 with Pk computed from ensemble spread
    • Soil moisture Assimilation -> soil moistureALL available (Evaluation vs SCAN)stations (179) Anomaly OL NASA-DA LPRM-DA correlation Surface soil 0.55 +/- 0.49 +/- 0.56 +/- moisture 0.01 0.01 0.01 (10cm) Root zone soil 0.17 +/- 0.13 +/- 0.19 +/- moisture (1m) 0.01 0.01 0.01 (21) Stations Anomaly OL NASA-DA LPRM-DA listed in correlation Reichle et al. Surface soil 0.62 +/- 0.53 +/- 0.62 +/- (2007) moisture 0.05 0.05 0.05 (10cm) Root zone soil 0.16 +/- 0.13 +/- 0.19 +/- moisture (1m) 0.05 0.05 0.05 44
    • Latent Heat Flux (Qle) Estimates over US (“Observed” vs. “Modeled Open Loop” (OL)) DJF MAM JJA SON FLUXNET MOD16 GLDAS NLDAS (Noah 2.7.1) NLDAS (Noah 3.2)1 Peters-Lidard, C.D, S.V. Kumar, D.M. Mocko, Y. Tian, 2011: Estimating evapotranspiration with land data Pg. assimilation systems, Hydrological Processes, 25(26), 3979--3992, DOI: 10.1002/hyp.8387 45
    • Soil Moisture Assimilation -> Evapotranspiration (Qle) FLUXNET MOD16 RMSE Bias 1 Peters-Lidard, C.D, S.V. Kumar, D.M. Mocko, Y. Tian, 2011: Estimating evapotranspiration with land data Pg. assimilation systems, Hydrological Processes, 25(26), 3979--3992, DOI: 10.1002/hyp.8387 46
    • Where Does Soil Moisture Assimilation Help Improve Qle (i.e. Reduce RMSE) ? FLUXNET MOD16 FLUXNET MOD16 DJF DJF MAM MAM JJA JJA SON SON1 1 LPRM-DA NASA-DA Peters-Lidard, Christa D., Sujay V. Kumar, David M. Mocko and Yudong Tian, (2011), Estimating Pg. Evapotranspiration with Land Data Assimilation Systems, In press, Hyd. Proc. 47
    • Where Does Soil Moisture Assimilation Help Improve Qle (i.e. Reduce RMSE) ? Qle RMSE % Difference FLUXNET MOD16 (DA-OL) Landcover type NASA-DA LPRM-DA NASA-DA LPRM-DA (Wm-2) (Wm-2) (Wm-2) (Wm-2) Evergreen needleleaf forest 17.6 7.9 10.5 -3.6 Deciduous broadleaf forest 3.2 12.7 0.3 0.7 Mixed forest 1.8 8.0 -0.7 -0.9 Woodlands 16.4 18.9 11.5 -5.9 Wooded grassland 8.8 -0.5 9.6 0.3 Closed shrubland 7.3 3.4 2.5 8.9 Open shrubland 9.0 7.4 3.6 12.1 Grassland 23.9 7.1 32.9 46.4 Cropland 12.3 34.7 30.9 40.8 Bare soil -0.1 0.6 -0.8 1.4 Urban -0.1 -0.1 -0.2 -0.3 Pg. 48
    • Soil Moisture Assimilation -> StreamflowEvaluation vs. USGS gauges – by major basins 49
    • Soil Moisture Assimilation -> Streamflow (average seasonal cycles of RMSE– using Xia et al. (2011) stations) NEW ENGLAND SOUTH ATLANTIC MID ATLANTIC 80 50 32 OL OL OL 70 LPRM-DA 30 LPRM-DA 45 LPRM-DA 28 60 40 26 RMSE (m3/s) RMSE (m3/s) RMSE (m3/s) 50 35 24 40 30 22 30 20 25 18 20 20 16 10 15 14 0 10 12 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec GREAT LAKES OHIO TENNESSE 26 200 18 OL OL OL 24 LPRM-DA 180 LPRM-DA LPRM-DA 16 22 160 20 RMSE (m3/s) RMSE (m3/s) RMSE (m3/s) 140 14 18 16 120 12 14 100 10 12 80 10 60 8 8 6 40 6 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec UPPER MISSISSIPPI LOWER MISSISSIPPI SOURIS RED RAINY 120 40 40 OL OL OL 110 LPRM-DA 35 LPRM-DA 35 LPRM-DA 100 30 30RMSE (m3/s) RMSE (m3/s) RMSE (m3/s) 90 25 80 25 20 70 20 15 60 15 50 10 40 10 5 50 30 5 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
    • Soil Moisture Assimilation -> Streamflow (average seasonal cycles of RMSE– using Xia et al. (2011) stations) MISSOURI ARKANSAS-WHITE-RED 110 26 OL OL 100 LPRM-DA 24 LPRM-DA 90 22 80RMSE (m3/s) RMSE (m3/s) 70 20 60 18 50 16 40 14 30 20 12 10 10 0 8 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec UPPER COLORADO LOWER COLORADO 10 10 OL OL 9 LPRM-DA 9 LPRM-DA 8 8 7 RMSE (m3/s) RMSE (m3/s) 7 6 6 5 5 4 4 3 3 2 2 1 1 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec GREAT BASIN PACIFIC NORTHWEST CALIFORNIA 10 OL 100 80 9 LPRM-DA OL OL 90 LPRM-DA 70 LPRM-DA 8 80 60 7RMSE (m3/s) RMSE (m3/s) RMSE (m3/s) 6 70 50 5 60 40 4 50 30 3 40 2 30 20 1 20 10 51 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 10 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
    • SMOS (ESA satellite) soil moisture assimilation tests in the NCEP Global Forecast Model The simplified ensemble Kalman Filter (EnKF) was" embedded in the GFS latest version to assimilate soilmoisture observation"  Case: 00Z July 6, 2011. (GFS free forecast)"  Experiments: CTL: Control run EnKF: Sensitivity run 52 Weizhong Zheng and Xiwu Zhan (NESDIS/STAR)
    • Comparison of Soil Moisture between GFS & SMOS SMOS CTL EnKF 53
    • Comparison of Soil Moisture between GFS & SMOS SMOS CTL EnKF 54
    • Improved Precipitation in EnKF vs CTL CTL EnKF OBS EnKF- CTL 55
    • Snow Data Assimilation Snow Cover Fraction Comparison of snow cover fraction between the MODIS (blue circles), the open loop simulation (black line) and the assimilation simulation (green line). Snow Water Equivalent Comparison of snow water equivalent between the open loop simulation (green), the assimilation simulation (red) and the in-situ measurement (black) averaged over all SNOTEL sites in the study region. 56
    • Outline• Role of Land Surface Models (LSMs)• Requirements:- atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 57
    • APPLICATIONS: e.g. Noah land model in NCEP Weather and Climate models Climate led Oceans Coup CFS Hurricane 2 -Way HYCOM MOM GFDL HWRF WaveWatch III 1.7B Obs/Day Satellites 99.9% Dispersion Global Regional NAM WRF NMMGlobal Data Forecast (including NARR) ARL/HYSPLITAssimilation System Severe Weather Regional Data WRF NMM/ARW Assimilation Short-Range Workstation WRF Ensemble Forecast North American Ensemble Forecast System WRF: ARW, NMM Air Quality ETA, RSM GFS, Canadian Global Model NAM/CMAQ For eca st NLDAS Rapid Update (drought) for Aviation (ARW-based) 58 NOAH Land Surface Model
    • NCEP-NCAR unified Noah land model• Surface energy(linearized) & waterbudgets; 4 soil layers.• Forcing: downwardradiation, precip., temp.,humidity, pressure, wind.• Land states: Tsfc, Tsoil*,soil water* and soil ice,canopy water*, snow depthand snow density.*prognostic• Land data sets: veg. type,green vegetation fraction,soil type, snow-free albedo& maximum snow albedo.• Noah coupled with NCEP models: North American Mesoscalemodel (NAM; short-range), Global Forecast System (GFS;medium-range), Climate Forecast System (CFS; seasonal), anduncoupled NLDAS (drought) & GLDAS (climate & drought). 59
    • Global Land Data Assimilation System (GLDAS)• GLDAS (run Noah LSM under NASA/Land Information System)forced with CFSv2/GDAS atmos. data assimil. output & blendedprecip.• “Blended precipitation”: satellite (CMAP; heaviest weight intropics where gauges sparse), surface gauge (heaviest in middlelatitudes) and GDAS (modeled; high latitudes).• CFSv2/GLDAS ingested into CFSv2/GDAS every 24-hours (semi–coupled) with adjustment to soil states (e.g. soil moisture).• Snow cycled in CFSv2/GLDAS if model within 0.5x to 2.0xobserved value (IMS snow cover, and AFWA snow depthproducts), else adjusted to 0.5 or 2.0 of observed value. 60GDAS-CMAP precip Gauge locations IMS snow cover AFWA snow depth
    • Climate Forecast Syst. & 30-yr Reanalysis• Global Land Data Assimilation System (GLDAS): Daily update of land states via semi-coupled Noah model run in NASA Land Information System.•  Driven by CFS assimilation cycle atmos. forcing & “blended” precipitation obs (satellite, gauge, model).•  Reasonable land climatology: energy/water budgets.Precipitation Evaporation Runoff Soil Moisture SnowJanuary (top), July (bottom) Climatology from 30-year NCEP CFS Reanalysis (Precip, Evap, Runoff [mm/day]; Soil Moisture, Snow [mm]) 61
    • Land Modeling and Drought (NLDAS)•  North Amererican Land Data Assimilation System•  Noah (& 3 other land models) run in an uncoupled mode driven by obs. atmos. forcing to generate surface fluxes, land/soil states, runoff & streamflow.•  Land model runs provide 30-year climatology.•  Anomalies used for drought monitoring, and seasonal hydrological prediction (using climate model downscaled forcing); supports the National Integrated Drought Information System (NIDIS). July 30-year climatology July 1988 (drought year) July 1993 (flood year) 62 NLDAS four-model ensemble soil moisture monthly anomaly
    • • NLDAS is a multi-model land modeling & data assimil. system… • …run in uncoupled mode driven by atmospheric forcing (using surface meteorology data sets)… • …with “long-term” retrospective and near real-time output of land-surface water and energy budgets. NLDAS Configuration: Land models
    • NLDAS Data Sets and Setup
    • NLDAS websitewww.emc.ncep.noaa.gov/mmb/nldas ldas.gsfc.nasa.gov/nldas NLDAS NLDAS Drought Monitor Drought PredictionAnomaly and percentile for six variablesand three time scales:• Soil moisture, snow water, runoff, streamflow,evaporation, precipitation• Current, Weekly, Monthly
    • June 1988: Drought YearMonthly total soil moisture anonamlies forNLDAS land models and ensemble mean
    • June 1993: Flood YearMonthly total soil moisture anonamlies forNLDAS land models and ensemble mean
    • NLDAS Seasonal Hydrological Forecast System• System uses VIC land model and includes threeforecasting approaches for necessary downscaled/ensemble temperature and precipitation forcing datasets: (1) NCEP Climate Forecast System (CFS), (2)traditional ESP, and (3) CPC.• System jointly developed by Princeton University andUniversity of Washington.• Transitioned to NCEP/EMC local system in November2009 as an experimental seasonal forecast system.• Run at the beginning of each month with forecastproducts staged on NLDAS website by mid-month.• For CFS forcing, transitioning from use of CFSv1 tonow-operational CFSv2.
    • Weekly Drought Forecast System Using CFS forcingDrought Monitoring (top) & Corresponding Monthly Fcst (bottom) 03 May 2012 28 June 2012 02 August 2012 Courtesy Dr. L. Luo at Michigan State U.
    • Global Land Data Assimilation System (GLDAS) for droughtŸ  GLDAS running Noah LSM part of NCEP ClimateForecast System (CFSv2) Reanalysis (CFSR)1979-2010; similar to NLDAS.Ÿ  GLDAS/Noah uses atmospheric forcing from ClimateData Assimilation System (CDAS) from CFSR andobserved precipitation.Ÿ  GLDAS part of the now operational CFSv2 (January2011).Ÿ  GLDAS being developed for Global DroughtInformation System (GDIS); connections with WorldClimate Research Programme.
    • CFSR/GLDAS Soil Moisture Climatology 71 71
    • GLDAS CFSR Soil Moisture Anomaly May 1988 (US drought) May 1993 (US floods) 72 72
    • GLDAS CFSR Soil Moisture Anomaly July 2012 (US drought)
    • Outline• Role of Land Surface Models (LSMs)• Requirements:- atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 74
    • Land Model Validation• Land model validation uses (near-) surface observations, e.g. routine weather observations of air temperature, dew point and relative humidity, 10-meter wind, along with upper-air validation, precipitation scores, etc.• To more fully validate land models, surface fluxes and soil states (soil moisture, etc) are also used.• Comparing monthly diurnal composites useful to assess systematic model biases (averaging out transient atmospheric conditions).• Such validations suggest land model physics upgrades. 75
    • Near-surface verification regionsWestern US! Eastern US! 76
    • July 2008 diurnal monthly mean NAM (dashed line) vs obs (solid) 00-84hr, 00z cycle, 2-m temp & RHtemp 1C daytime warm bias slight 1C daytime warm bias! nighttime warm bias! Western US Eastern US daytime dry bias (increasing dry trend) slight daytime dry biasRH nighttime dry bias slight nighttime dry bias 77
    • Validation with flux site data (e.g. via Fluxnet , GEWEX/ CEOP reference sites). 78
    • Compare monthly diurnal composites of model! output versus observations from flux sites to! assess systematic model biases. ! SURFX (Surface Flux) Network! NOAA/ATDD, Tilden Meyers et al! 79
    • Ft. Peck,! Montana!(grassland)! January! 80
    • Ft. Peck,! Montana!(grassland)! July! 81
    • Audubon!Grassland,! Arizona! January! NOAA/ATDD Surface Flux Network! 82
    • Audubon!Grassland,! Arizona! July ! NOAA/ATDD Surface Flux Network! 83
    • Ozarks,! January! Missouri!(mixed forest)!NOAA/ATDD Surface Flux Network! 84
    • Ozarks,! July ! Missouri!(mixed forest)!NOAA/ATDD Surface Flux Network! 85
    • January! Walker Branch,! Tennessee! (deciduous forest)!NOAA/ATDD Surface Flux Network! 86
    • July ! Walker Branch,! Tennessee! (deciduous forest)!NOAA/ATDD Surface Flux Network! 87
    • Ft. Peck, Montana (grassland) SURFACE ENERGY BUDGET TERMSDownward Solar January 2008Upward Longwave monthly averages: model vs obs Reflected Solar Downward LongwaveSensible Heat Flux Latent Heat Flux Ground Heat Flux 88
    • Ft. Peck, Montana (grassland) Sensible heat flux monthly averages: model vs obsJanuary 2008 October 2008 better surface- layer physics in NAM vs GFS July 2007 April 2008 July 2008 89
    • Ft. Peck, MT Brookings, SD model (grassland) (grassland) high bias obs Walker Branch, TN (deciduous) low biasBlack Hills, SD Ozarks, MO(conifer) (deciduous)July 2005, Sensible heat flux monthly averages: NAM(North American Mesoscale) Bondville, IL model vs obs (cropland) 90
    • Land Model Benchmarking• Comprehensive evaluation of land models that goes beyondtraditional validation, with an established level of a prioriperformance for a number of metrics, e.g. modeled surfacefluxes within ±ε [W/m2] of the observations, PDFs of observed& modeled surface fluxes overlap by at least X%, an annualcarbon budget within Y%, etc.• Metrics depend on community, e.g. climate, weather, carbon,hydrology.• Empirical approaches: e.g. statistical, neural network, othermachine learning; relationship between training and testingdata sets manipulated to see how well a model utilizesavailable information.• Land model (physics) should be able to e.g. out-performsimple empirical models, persistence (for NWP models),climatology (for climate models). 91
    • Benchmarking vs Evaluation (Martin Best et al, GEWEX/GLASS) •  Evaluation Model A Ø  Run model Model B Ø  Compare output with observations and ask: Benchmark Error q  How good is the model? •  Benchmarking Ø  Decide how good model needs to be Ø  Run model and ask: Site / Variable ü  Does model reach the level required? PALS Land sUrface Model Benchmarking Evaluation pRoject (PLUMBER)© Crown copyright Met Office
    • Joint GEWEX/GLASS-GHP project: Land Model Benchmarking• GLASS tools, i.e. Protocol for the Analysis of Land Surface models (PALS): www.pals.unsw.edu.au.• GEWEX Hydroclimatology Panel (GHP) providesreference site/model output data sets for differentregions, seasons, &variables: evaluate energy, water& carbon budgets; coupled, uncoupled model output. GHP “CEOP” reference sites Fluxnet 93 93
    • Joint GEWEX/GLASS-GHP project: Land Model Benchmarking• PALS example: CABLE (BOM/Australia) land model, Bondville, IL, USA (crolandp), 1997-2006, average diurnal cycles. Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv 400 400 Total score: 1.1 Score: 0.91 Score: 2.9 Total score: 0.46 Score: 1.7 Score: 0.37 winter summer (NME) (NME) (NME) (NME) (NME) (NME) Sensible heat flux W/ m2 Sensible heat flux W/ m2 300 300 250 250 Latent heat flux W/ m2 Latent heat flux W/ m2 Observed Observed Modelled Modelled obs 200 200 150 150 100 100 model 0 50 0 50 0 0 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 DJF hour of day JJA hour of day DJF hour of day JJA hour of day Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv 400 400 Score: 0.51 Score: 0.55 Score: 0.57 Score: 0.28 spring autumn (NME) (NME) (NME) (NME) LE Sensible heat flux W/ m2 Sensible heat flux W/ m2 H 300 300 250 250 Latent heat flux W/ m2 Latent heat flux W/ m2 200 200 150 150 100 100 0 50 0 50 0 0 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 MAM hour of day SON hour of day MAM hour of day SON hour of day Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv 100 150 100 150 Total score: 0.3 Score: 0.59 Score: 0.37 Total score: 1.6 Score: 3.6 Score: 1.5 (NME) (NME) (NME) (NME) (NME) (NME) 600 600 Ground heat flux W/ m2 Ground heat flux W/ m2 Net radiation W/ m2 Net radiation W/ m2 Observed Observed Modelled Modelled 400 400 50 50 200 200 0 0 −50 −50 0 0 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 DJF hour of day JJA hour of day DJF hour of day JJA hour of day Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv Obs: BondvFluxnet.1.2 Model: Cab1.4bBondv 100 150 100 150 Score: 0.19 Score: 0.22 Score: 1.2 Score: 1.8 (NME) (NME) (NME) (NME) 600 600 Rn G Ground heat flux W/ m2 Ground heat flux W/ m2 Net radiation W/ m2 Net radiation W/ m2 400 400 50 50 200 200 0 0 −50 −50 0 0 94 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 0 6 12 18 23 MAM hour of day SON hour of day MAM hour of day SON hour of day
    • Outline• Role of Land Surface Models (LSMs)• Requirements:- atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 95
    • Land Model Improvement Surface flow Snowpack & Saturated Urban-canopy frozen soil subsurface flow model• Urban-canopy & land-use/land cover changes• Refined surface layer turbulence (esp. stable cond.)• Refined evapotranspiration (empirical Jarvis-Stewart)• Surface/subsurface lateral flow (hydrology)• Dynamic (growing) veg. with multi-layer canopy*• CO2-based photosynthesis* *Noah-MP upgrades• Groundwater/water table*• Multi-layer snowpack*, refined frozen soil processes 96
    • Land Model Improvement: TranspirationŸ Use surface fluxes (e.g. latent and sensible) toevaluate land-surface physics formulations andparameters, e.g. invert transpiration formulation toinfer canopy conductance. (Similarly for aero. cond.) 97
    • Noah-MP: a land component in Mesoscale Weather Research & Forecasting (WRF) model• Noah-MP developed by Niu et al (2011, JGR), Univ. Texas-Austin, NCAR, NCEP, Univ. Arizona• Explicit canopy/subcanopy layers, dynamic (growing) vegetation, and canopy conductance based on CO2- photosynthesis• Ground water (connection with hydrology models)• Multi-layer snowpack• Implicit solution of surface energy budget (SEB, for better SEB closure)• Recent NCAR Improvements to Noah-MP version 3.5 98
    • Canopy Absorbed Solar Radiation in Noah-MP SWdnWhat are the units of solar absorbedradiation in the canopy?-  W/m2grid : original two stream makes this assumption-  W/m2veg : Noah-MP calculates absorption for the grid shaded fraction but then applies it to the vegetated fractionOriginal Noah-MP uses both fractional vegetation cover (turbulent fluxes) andvegetation stem density (radiation fluxes). In the original code, these wereinconsistent.For Evergreen Needleleaf Forest, the prescribed stem density gives a constanthorizontal coverage of 72% while turbulent flux fractional vegetation can be any 99 valuefrom 0% to 100%
    • Noah-MP vs. Noah: Improved canopy radiation effect and surface-layer changes in coupled mesoscale WRF v3.5 Before (V3.4.1) After (Pre V3.5)• Reduced low-level (2-meter) air temperature bias 100  
    • Tiled or Separate Land Model GridŸ  A land model grid may comprise sub-atmospheric- grid-scale “tiles” of e.g. grass/crop/shrubland, forest, water, etc, of O(1-4km, or even less), with coarser-resolution atmospheric forcing, and an aggregate flux fed to the “parent” atmospheric. Aggregated ABL “blending” height surface • Boundary-layer issues, flux e.g. when blending height > BL depth? • Land-surface tiling run under NASA Land Information System (LIS); other LIS tools. • NASA/LIS to be part of 5-50km NOAA Environmental Modeling System surface tiles with different fluxes (NEMS). 101
    • Upgraded land/surface-layer physics: Improvement of Satellite Data Utilization overDesert & Arid Regions in NCEP Operational NWP Modeling and Data Assimilation Systems• Change to surface-layer turbulence physics (thermalroughness change) and surface microwave emissivityMW model…•…yields a reduction of large bias in calculatedbrightness temperatures was found for infrared andmicrowave satellite sensors for surface channels…•…so many more satellite measurements can beutilized in GSI data assimilation system. 102
    • LST [K] Verification with GOES 3-Day Mean: July 1-3, 2007 (a) and SURFRAD GFS-GOES: (b GFS-GOES: New ) CTR ZotLarge cold (c) (d bias LST ) Ch Improved significantly Aerodynamic 103103 during daytime! conductance: CTR vs Zot
    • Outline• Role of Land Surface Models (LSMs)• Requirements: - valid physics, land data sets/parameters, initial land states, atmospheric forcing, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 104
    • Expanding Role of Land ModelsŸ In more fully-coupled Earth System models, role is changing, with Weather & Climate connections to: Hydrology (soil moisture & ground water/water tables, irrigation and groundwater extraction, water quality, streamflow and river discharge to oceans, drought/flood, lakes, and reservoirs with management, etc), Biogeochemical cycles (application to ecosystems --terrestrial & marine, dynamic vegetation and biomass, carbon budgets, etc.) Air Quality (interaction with boundary-layer, biogenic emissions, VOC, dust/aerosols, etc.)Ÿ More constraints, i.e. we must close the energy and water budgets, and those related to air quality and biogeochemical cycles. 105
    • NCAR Common Land Model (CLM) 106
    • Hurricanes and Inland Flooding•  Physical-based Noah model included in (mesoscale) Hurricane Weather Research & Forecasting model, with little degradation in track & intensity & precip.•  Inland flood Hurricane Katrina forecasting (right) using GFS Soil Moisture Noah runoff Initial Condictions & streamflow USGS gauge station (IC; default) model. NAM•  Extend to IC global/climate models: river NLDAS discharge to observations IC ops oceans. NAM 107
    • Hydro-Coastal LinkageŸ  Impact of adding freshwater input to Gulf of Mexico(implemented RTOFS June 2007). Freshwater nearshore 108
    • Sea Nettle ForecastingSST Likelihood of Chrysaora NCEP, with NESDIS, NOS Habitat Model Salinity 109
    • Lake modeling•  Thousands of lakes in N. America on the scale of 4km (NAM target), not resolved by SST analysis.•  Influence of previously unresolved lakes may be felt on this scale and can no longer just be filled in . 110
    • Lake modeling•  Freshwater lake FLake model (Dmitrii Mironov, DWD).•  In use in regional COSMO, HIRLAM (European models), UKMO, ECMWF (global). -  two-layer -  temperature & energy budget -  mixed-layer & thermocline -  snow-ice module -  atmospheric forcing inputs -  specified depth & turbidity 111
    • Outline• Role of Land Surface Models (LSMs)• Requirements:- atmospheric forcing, valid physics, land data sets/ parameters, initial land states, cycled land states• Applications for Weather & Climate• Validation• Improving LSMs• Expanding role of Land Modeling as part of an integrated Earth System• Summary 112
    • Land Surface: Summary•  Provide surface boundary conditions for weather & climate models (e.g. NCEP models), proper representation of interactions with atmosphere.•  Valid physics, land data sets & parameters, initial land states, atmospheric forcing, cycled land states.•  Land model validation using (near-) surface observations, e.g. air temperature, relative humidity, wind, soil moisture, surface fluxes, etc … suggests model physics improvements.•  Expanding role of land models for weather & climate in more fully-coupled Earth System models with connections between Weather & Climate and Hydrology, Biogeochemical cycles (e.g. carbon), and Air Quality communities and models. 113
    • Land-Hydrology Partners/Projects•  Noah land model development: NCAR, U. Ariz., U. Texas/Austin, U. Wash., WRF land & PBL working groups, other national/internat’l partners; freshwater “FLake” community),•  NLDAS/GLDAS drought: Princeton, Univ. Wash., NASA, NWS Hydro., Climate Prediction Center),•  Remote sensing/land data sets/data assimil. of soil moisture, vegetation, surface temp, snow: JCSDA, NESDIS, NIC, NASA, AFWA,•  NOAA research: NLDAS, GLDAS support,•  WCRP/GEWEX: GLASS/land-hydrology, GABLS/boundary-layer, GHP/hydroclimate 114 projects, UKMO, ECMWF, Meteo-France, etc.
    • Thank you! Obrigado! ¡Gracias! 115
    • Michael Ek (michael.ek@noaa.gov) Environmental Modeling Center (EMC)National Centers for Environmental Prediction (NCEP) NOAA Center for Weather and Climate Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 National Weather Service (NWS) National Oceanic and Atmospheric Administration (NOAA) 116
    • Local Land-Atmosphere Interactions above-ABL above-ABL dryness cloud cover stability incoming precipitation downward solar 8 longwave 1 entrainment wind boundary- layer growth 7 1 turbulence 2 2 4 3 relative humidity temperature 5 7 3 4 5 emitted * + moisture longwave sensible reflected canopy flux surface heat flux solar conductance temperature albedo 8 6 6 soil moisture soil heat flux soil temperature+positive feedback for C3 & C4 plants, negative feedback for CAM plants positive feedback*negative feedback above optimal temperature 117 negative feedback land-surface processes surface layer & ABL radiation