Improvement of Land Surface Parameters and States: Diagnosing Forecast and Model Deficiencies Michael Barlage (NCAR) Xubin Zeng (UA), Patrick Broxton (UA), Fei Chen (NCAR) 1 12 th JCSDA Science Meeting – 22 May 2014

Introduction 2 Temperature biases in the Noah model can reduce the number of satellite observations that are assimilated In addition, snow melts too quickly and to correct for this, water is added during data assimilation, which results in too much melt Advances in model structure aim to improve surface temperature and snow simulation, increase atmospheric assimilation, and increase land surface assimilation This presentation documents deficiencies in the current forecast system and attributes part of them to model structural deficiencies

3 Noah LSM Deficiencies Flagstaff WRF/Noah v3.2 T 2m simulation (green) compared to METAR observations(black) Cold bias during the day results from capped surface temperature at freezing Bias recovers during the night When snow is gone, bias is low Challenges with Noah LSM Structure Days in Feb FEB1 FEB13 FEB20 FEB27 KFLG Forecast Bias (ºC) Forecast Hour (Initialized at 12Z daily)

Challenges with Noah LSM Structure May 2007 temperature time series for a single location in Arctic System Reanalysis (3D-Var, land assimilation of vegetation, snow and albedo) Observations in blue, analysis in red and model forecast in green Pre-snowmelt period cold bias exists, assimilation helps Significant cold bias exists during melt period (up to 15°C) Post-melt period performance is quite good

Challenges with Noah LSM Structure NCEP operational NAM model 24-hour forecasted snow minus analysis snow shows excessive melting during the entire month of March Simultaneously with low temperature biases, snow continuously gets assimilated during spring Due to model structure, this snow melts during the 24 hours until the next assimilation cycle This reinforces the cold bias and inserts more water into the system, potentially causing adverse effects to hydrology prediction

Snow Water Equivalent in GFS SWE (kg/m^2) (Forecast – Analysis) 6 *White areas: SWE <10 kg/m^2 in forecast and analysis For GFS, compare the forecast with a lead time of 4 days with the coincident analysis for each day in 2013

7 Noah LSM in NCEP Eta, MM5 and WRF Models (Pan and Mahrt 1987, Chen et al. 1996, Chen and Dudhia 2001, Ek et al., 2003) Noah-MP LSM in WRF and NCEP CFS (Yang et al., 2011; Niu et al., 2011) Snow (x,y) Reality T can (x,y,z) T snow (x,y,z) T bc (x,y,z) Challenges with Noah LSM Structure T g (x,y) Snow Noah Snow Noah-MP T can T snow (z) T bc TgTg T skin Single surface temperature Multiple surface temperatures and distinct canopy

Noah and Noah-MP LSM Structure Comparison Six-month simulations using coupled atmosphere-land model from March – June 2010 Compare only grids with 100% snow cover and evergreen needleleaf trees When temperatures are below freezing, Noah-MP is warmer but consistent with Noah When temperature approaches freezing, Noah temperature cannot get much above freezing

Noah and Noah-MP LSM Structure Comparison Compare to daily MODIS/Aqua land surface temperature at 13:30 overpass Noah peaks near-freezing Noah-MP is warmer than MODIS by 2-4K but distribution is much better than Noah

Snow Water Equivalent simulated by six LSMs Noah and Noah-MP LSM Structure Comparison Noah and Noah-MP can produce similar snow through a modified snow albedo formulation Chen, et al. 2014

Diurnal cycle of surface albedo: Niwot Ridge Jan, Mar, and Jul AmeriFlux Obs, MODIS, Noah, VIC, SAST, LEAF, CLM, Noah-MP Albedo: 0.66 (Cline, 1997) over snow, 0.34 (MODIS, Jin et al., 2002), Large variation among modeled winter albedo Noah: larger seasonal variations Noah-MP: drop during March spring melt Noah and Noah-MP LSM Structure Comparison Chen, et al. 2014

Monthly daytime min, max and mean absorbed SW and sensible heat (W/m 2 ) for Jan, Mar, May and Jul Comparison of observed (O), Noah (N), and Noah-MP (M). Noah has less absorbed solar radiation resulting in colder surface and lower (or negative) sensible heat flux Noah and Noah-MP LSM Structure Comparison O N M Jan 2007 O N M O N M O N M Mar 2007 Chen, et al. 2014

Noah-MP uses a two-stream radiative transfer treatment through the canopy based on Dickinson (1983) and Sellers (1985) Canopy parameters: – Canopy top and bottom – Crown radius, vertical and horizontal – Vegetation element density, i.e., trees/grass leaves per unit area – Leaf and stem area per unit area – Leaf orientation – Leaf reflectance and transmittance for direct/diffuse and visible/NIR radiation Multiple options for spatial distribution – Full grid coverage – Vegetation cover equals prescribed fractional vegetation – Random distribution with slant shading SW dn shaded fraction Advantages with Noah-MP LSM Structure

Over a Noah-MP grid, individual tree elements can be randomly distributed and have overlapping shadows Noah-MP albedo is calculated based on canopy parameters Noah prescribes snow-free and snow- covered albedo from satellite climatology SE Minnesota in Google Maps Advantages with Noah-MP LSM Structure

Using prescribed vegetation fraction varying from 5% to 100% as radiation fraction Increasing vegetation fraction increases snow, decreases albedo Advantages with Noah-MP LSM Structure

Using randomly distributed shadows as radiation-active fraction through use of sun angle and canopy morphology Complex interaction between vegetated and shadowed fraction and canopy/snow radiation absorption Advantages with Noah-MP LSM Structure

Fractional coverage of each land cover Type (%) - Alaska Type BU-IGBP+ tundra_1km MODIS km MODIS5.1 Fill by surrounding 0.5 degree forest type Evergreen Needleleaf Evergreen Broadleaf0.00 Deciduous Needleleaf Deciduous Broadleaf Mixed Forest Woody Savanna MODIS5.1 Fill by surrounding 0.5 degree forest type BU-IGBP+tundra_1km MODIS km Evergreen Needleleaf Deciduous Broadleaf Deciduous Needleleaf Deciduous BroadleafMixed ForestClosed ShrublandOpen ShrublandWoody SavannaSavannaGrasslandPermanent WetlandCroplandUrbanCropland/NaturalSnow/IceBarrenNot LandWooded TundraMixed TundraBare Ground Tundra Continue to Develop Satellite Land Datasets MODIS 500m land cover climatology delivered to WRF v3.6 (Broxton et al., 2014)

Marks － Individual value of year Original Pixel DataFinal Smooth Climatology Lines － black: median － yellow: tile climo (Savanna) 1.Remove suspect data 2.Fill missing data 3.Smooth Continue to Develop Satellite Land Datasets

WRF satellite-derived input datasets tend to produce too little vegetation outside of the tropics Using fraction of photosynthetic absorbed radiation as a vegetation proxy May Continue to Develop Satellite Land Datasets

Monthly mean WRF radiation June 2010 (Dudhia scheme) ERA- Interim monthly mean radiation June 2010 Up to 100 W/m 2 difference 20 – 40% too high 20 Can’t always blame the LSM

21 Domain 1 TEMPERATURE (°C) Mean errorRMSE U* fix VEGFRA Radiation OP D1 Domain 3 TEMPERATURE (°C) Mean errorRMSE U* fix VEGFRA Radiation OP D3 Continue to Develop Satellite Land Datasets

Noah model land data assimilation – Favorable to directly assimilate (use) “bulk” land surface properties Albedo Green vegetation fraction (via NDVI or EVI) Leaf Area Index (LAI) – Bulk surface treatment causes problems when heterogeneity is necessary (e.g., snow and vegetation) Noah-MP model land data assimilation – Increased prognostic states for assimilation LAI through dynamic vegetation model Albedo needs to be treated differently (parameter estimation) Vegetation fraction: what does it mean in the model? – More available states that can inform surface emissivity models Prognostic LAI, partition of canopy water into ice/liquid Both models use similar soil moisture treatment for soil moisture assimilation Relationship to Land Data Assimilation

Conclusions Temperatures: significant biases can occur in the current Noah model, many of these are due to structural limitations when heterogeneities exist at the surface. Snow: In Noah, generally there is too little snow during the spring. Assimilation replaces this snow, it immediately melts and gets added to the soil or surface runoff. Are we reaching a structural limit in the Noah model? Do we need to move toward a more process-based model to capture important states that can informed satellite assimilation? Before the eventual (hopefully) update of the operational LSM, can we exploit the benefits of a more complex model, e.g., in a system such as LIS? 23