Surface representation in atmospheric models A. Vukovic, Z. Janjic - PowerPoint PPT Presentation

Surface representation in atmospheric models A. Vukovic, Z. Janjic , A. Krzic & B. Rajkovic Faculty of Agriculture, University of Belgrade, Serbia NCEP 5200 Auth Rd., Camp Springs, MD 20746, USA Republic Hydro-meteorological Service of Serbia Faculty of Physics, University of Belgrade, Serbia

• General remarks and short history • The LISS • The future LISS + improved vegetation + very high resolution considerations • Dynamic vegetation

A quote • “Much improved understanding of land ‐ atmosphere interaction and far better measurements of land ‐ surface properties, especially soil moisture , would constitute a major intellectual advancement and may hold the key to dramatic improvements in a number of forecasting problems, including the location and timing of deep convection over land, quantitative precipitation forecasting in general, and seasonal climate prediction.” • US National Research Council, 1996

• Modelling it Parameterization instead of direct implementation of physics ( heat and water movements through soil ) mainly due to the complexity of the horizontal and vertical structure of the soil and partly due to the complexity of the processes especialy when we wanta to include vegetation into conseiderations.

• Single layer + one bucket for the hydrology Concerning the water movement one bucket model (Manabe 1969) assumes exchange only through top surfece • w – tot. water stored in the column, • p – precip. , • e – evap. , • r – sfc runoff and drainage. But that would be to svere asumption for the heat flux • Solutions : 1. The force-rstore approach originally proposed by Bhumralkar (1975) and Blackadar (1976) and later developed by Deardorff (1978), Lin (1980) and Dickinson (1988) 2. The other one is Nickerson-Smiley (1975) approach where bottom flux is proportional to the net radiation.

Schematic presentation of a soil column with different soil types and root structure

The L I S S Land Ice/snow Sea Surface

Roll of the Land Surface Models (LSM) in Numerical Weather Prediction Models (NWPM) in mathematical sense : Lower boundary condition for partial differential equations that describe processes in the atmosphere in physical sense: LSM makes connection between atmosphere and land surface in exchanging energy, mass and momentum processes at the ground are smaller scale than spatial NWPM resolution → parameterization of the processes must be performed complexity of the LSM: Compromise between needed and possible must be well made! Good forecast of the energy division between important! latent (moistening) and sensible (warming) heat The Bowen ratio

Vertical structure of LISS No snow 1 l. of snow 2 l. of snow T 1 = T T , dgt dsno / 2 1 2 2 = T 2 , dgt dsno = T , dgt dsno / 2 2 3 3 = T ( T ), dgt 0 1 s 1 T 2 , W T 3 , dgt T 4 , dgt 1 3 4 dgt 2 , dgw 1 γ K * , K * , * t 2 w 1 w 1 T 3 , W T 4 , dgt T 5 , dgt 2 4 5 dgt 3 , dgw 2 γ K * , K * , * t 3 w 2 w 2 T 4 , W T 5 , dgt T 6 . dgt 3 5 6 dgt 4 , dgw 3 γ K * , K * , * t 4 w 3 w 3 T 5 , W T 6 . dgt T 7 , dgt 4 6 7 dgt 5 , dgw 4 = γ K * , K * 0 , * t 5 w 4 w 4

LISS LISS (Land, Ice, Sea Surface) Model • part of the NWPM : NMM-B • 1D model, with vertical coordinate • multilayer in vertical, single layer for vegetation • tested offline for two sites: - Caumont (France) and Bondville (USA) - bare soil + soya - results compared with: - measurements (soil temperature and moisture, surface fluxes) - results obtained with NOAH-LSM (model in operational use in most of the NWPM)

LISS descriptio LISS description • prognostic equations for: soil temperature, soil moisture, melted snow amount, amount of water in the interception reservoir • important dijagnostic vvariables: surface temperature, surface fluxes • upper boundary condition: atmospheric forcing (from the atmospheric part of the NWPM) atm. forcing atmosphere ground soil temp. / fluxes

Soil temperature forecast 1. Soil surface temperature (skin temperature) calculation from the ( ) = 1 − S alb S surface energy balance equation w w inc linearization 4 = εσ L T ρ T q ws s lm a lm − T T = ρ H c K lm s a pa hs ∆ z ∆ z − q q ( T ) = β ρ E L K lm sat s T + − + + = S L L H E G a v hs ∆ z s w wa ws ∆ − z ( T T ) s = s G K T t ∆ z s Liner equation from which skin temperature can be explicitly calculated: T = ... s

T 2. Temperature of the soil layers ∂ ∂ ∂   T T T ( ρ = c )  K  Fourier law of diffusion: t ∂ ∂ ∂ t z  z  T upper boundary condition : skin temperature z • flux from last layer = 0 (if the last layer is deep enough) lower boundary condition : • temperature below last layer = const water phase change influence: latent heat → termic conductivity, transpiration = W f ( T ) W ∂ ∂ ∂ ∂   W T T i f ρ ) = + ρ ( c  K  L i swi t f w ∂ ∂ ∂ ∂ t z  z  t f 0 ≤ ≤ 1 f ∂   f ∂ ∂ ∂   T T f ρ ) − ρ = ( c L W  K    swi f w t ∂ ∂ ∂ ∂ T t z z     ρ ( c )

Snow • model has ability to divide snow in multi layers, when height of the snow cover exceed some prescribed value • temperature of the surface, snow layers and soil layers: same as in case without snow except that in the snow layers values ρ K for and are calculated for snow from its properties ( c ) t ° 0 C skin temperature = • when snow melt exists: amount of melted snow: from surface energy balance equation with term for latent heat of melting phase change S ρ = + − + + − L melt S L L H E G w f w wa ws ∆ t

β parameter evaporation parameterization = + + E E E E total latent heat flux is divided into : in et soil 1. evaporation from interception reservoir 2. evapo-transpiration 3. evaporation from bare soil = β + β + β E ( ) E idea for parameterization: in et soil p final expression for β parameter: ( ) ( )( ) 1 1 β = + − + − − C 1 C C 1 C 1 C liq liq veg liq veg + ∆ + ∆ 1 r K / z 1 r K / z c hs soil hs 1 2 3

upper boundary precipitation Soil moisture forecast condition evaporation interc. equation for volumetric liquid soil water content: surface runoff ∂ ∂ W F snow melt ρ = − + ρ l w R W w w ex ∂ ∂ t z l ∂ ψ = − ρ γ F according to Darcy law: w w w ∂ z W l flux gradient root extraction W ∂ ∂ ∂   W W l = + γ + l l K R   w w ex ∂ ∂ ∂ t z z   lower boundary condition water drainage diffusion (baseflow) baseflow

LISS verification : LISS verification : experiment 1 – experiment 1 – Caumont aumont site site (43 ◦ 41'N and 0 ◦ 06'W, altitude 113m) precipitation air temperature time (month) time (month) ● soil type - loam ; vegetation type - soya (cropland) ● 1. – 120. day bare soil ; 121 - 273 vegetation ; 273 - 365 bare soil ● measurements: HAPEX-MOBILHY atmospheric forcing on 30min, surface fluxes on 30min (147-182 days, IOP), soil moisture on 10cm, to 1.6m depth, on 7 days

LISS vs. NOAH-LSM and measurements: soil moisture LISS NOAH-LSM measured 5cm 25cm soil moisture 70cm 1.5m total for 1.6m depth one year

LISS vs. NOAH-LSM : water budget t ( ) dt ∫ − = − − W ( t ) W ( 0 ) precipitat ion evaporatio n runoff 0

LISS vs. NOAH-LSM and measurements : surface fluxes RMSE H E LISS / NOAH 64.6 / 61.4 119.6 / 124.1

LISS verification : LISS verification : experiment 2 – experiment 2 – Bondville ondville site ite (40.01N i 88.37W, altitude 219m) precipitation air temperature time (month) time (month) ● soil type – silty clay loam vegetation fraction vegetation – soya (cropland) ● measurements: on 30min, for one year - atmospheric forcing - soil temperature - surface fluxes

LISS vs. NOAH-LSM and measurements: mean annual diurnal change of the skin and near surface temperature

LISS vs. NOAH-LSM and measurements: skin temperature RMSE annual time (h)

LISS vs. NOAH-LSM and measurements: snow snow appeared at the end of the year, in the last 3 days