comparison of land surface models

Comparison of land surface models

CLIM 714 Randy KosterPaul Dirmeyer

In recognition of the importance of land surface processes in the climate system,the international community developed various programs to characterize and compare land surface model behavior. Recently, these crystallized into a singleencompassing structure: GLASS (Global Land-Atmosphere System Study)

LAND SURFACE MODEL INTERCOMPARISONS

GCM inter-comparisons

Single column model analyses

Land-surface model intercom-parisons (in situ)

Global griddedmodel analyses

PILPS

PILPS is a community-driven initiative which provides opportunities to compare land-surface schemes’ performance against others (past and present). Within this framework, PILPS serves the international modelling community in two different and complementary ways:

a) by providing open, quality controlled intercomparison opportunities as international benchmarks against which new and revised LSS can be validated; and

b) by providing the tools and intercomparison frameworks by means of which the behaviour of LSS can be more fully understood.

The first of these, open to all who wish to participate, demands high quality observational data, careful experimental design and agreed quality control procedures. The second involves a smaller group of researchers and land-surface schemes with the specific goal of enhanced understanding. The two questions being posed by these two complementary activities of PILPS are:

a) does your scheme perform well enough to be part of international modelling and prediction efforts? b) is it understood why your scheme performs the way it does?

Project for the Intercomparison of Land-Surface Parameterization Schemes(http://cic.mq.edu.au/pilps-rice)Text from a recent PILPS report:

PIs: Ann Henderson-Sellers,

Andy Pitman

Offlinelocal scale

Slide courtesy of Adam Schlosser

PILPS has four levels of experimentation...

Potential new studies involve land effects on: (1) water isotope distributions and (2) tropical cyclones.

PILPS2e was insteada study of land surface behaviorin high northern latitudes (Torne-Kalixriver basin in Sweden).

PILPS - San Pedro(a study focusing on an arid region) begins in 2004.

Updates to table:

PILPS is also nowdoing an intercom-parison study focusingon carbon assimilation.

PILPS 2e: Simulation of Arctic hydrology

Torne-Kalix River Basin,Sweden

Results show, e.g., a strong sensitivity ofannual runoff to aerodynamic resistanceformulation and its associated impacton snow sublimation.

LSS Clusters in All Climates

Scaled latent heat W m-2

Sca

led

sens

ible

hea

t W

m-2

Sfc. Energy Balances normalised by ensemble of 3 re-analyses –

SiBlings

Buckets

3 clustersSiBlings

Buckets

SiBlings

Buckets

Slide courtesy of Ann Henderson-Sellers

SnowMIP (Snow Model Intercomparison Project)(E. Martin and P. Etchevers at Meteo-France, CNRM)

• Models show a wide range in the evolution of season snow pack (forced by identical atmospheric conditions).


Global Soil Wetness Global Soil Wetness ProjectProject

www.iges.org/gswp/[email protected]@cola.iges.orgs.org

www.iges.org/gswp/[email protected]@cola.iges.orgs.orgOverview

The Global Soil Wetness Project (GSWP) is an ongoing modeling activity of the International Satellite Land-Surface Climatology Project (ISLSCP) and the Global Land-Atmosphere System Study (GLASS), both contributing projects of the Global Energy and Water Cycle Experiment (GEWEX). GSWP is charged with producing large-scale data sets of soil moisture, temperature, runoff, and surface fluxes by integrating one-way uncoupled land surface schemes (LSSs) using externally specified surface forcings and standardized soil and vegetation distributions.

MotivationThe motivation for GSWP stems from the paradox that soil wetness is an important component of the global energy and water balance, but it is unknown over most of the globe. Soil wetness is the reservoir for the land surface hydrologic cycle, it is a boundary condition for atmosphere, it controls the partitioning of land surface heat fluxes, affects the status of overlying vegetation, and modulates the thermal properties of the soil. Knowledge of the state of soil moisture is essential for climate predictability on seasonal-annual time scales. However, soil moisture is difficult to measure in situ, remote sensing techniques are only partially effective, and few long-term climatologies of any kind exist. The same problems exist for snow mass, soil heat content, and all of the vertical fluxes of water and heat between land and atmosphere.

Goals•Produce state-of-the-art global data sets of soil moisture, surface fluxes, and related hydrologic quantities.

•Develop and test large-scale validation techniques over land.

•Provide large-scale validation and quality check of the ISLSCP data sets. •Compare LSSs, and conduct sensitivity studies of specific parameterizations which should aid future model development.

http://www.iges.org/gswp/






Surface temperatureand humidity can beassimilated to adjustsoil moisture towardanalyzed values

But if analysis andobserved conditionsvary greatly, soilmoisture will notconverge to samevalues

GSWP soil moisture,when used as BC in

GCM, improvessimulation of rainfall.

Interannual variabilityof soil moisture is

important

Quality of the simulation ofrunoff is a strong function

of the densitiy of theraingauge network

At least 25 gauges permillion km² is necessary

Data Issues


GSWP-1 GSWP-1

Sensitivity of JJAS climate to soil moisture relaxation toward GSWP: impact on soil moisture

(kg/m²)

GSWPanomalies

FreeSoil

moisture

RelaxedSoil

moisture

From Douville and Chauvin, Climate Dyn. 2000

Runoff and gauge density

• Spread is widest when gauge density is low

• Errors are generally negative (too little runoff)

Courtesy Taikan Oki

Courtesy Taikan Oki

Validation Issues

Once mean soil wetness values for eachmodel are removed, they are found tobehave consistently with each other andobservations (phasing is very good, andvariability agrees within a factor of two)


GSWP-1 GSWP-1

Model Sensitivity Studies I

Changes to infiltration and sub-gridparameterization of precipitation in the ISBA schemelead to improved basin-scale runoff simulation

Installation of a redesigned snowmodel in SSiB greatly reduces the error

in the timing of spring snow melt


GSWP-1 GSWP-1

Model Sensitivity Studies II

Mis-phasing of downwardlongwave radiation impacts snowmelt and runoff at high latitudes

Including the effects of sub-gridvariability of soil properties cansignificantly increase simulated runoff(diffusivity and conductivity can varyby several orders of magnitude oversmall distances).


GSWP-1 GSWP-1

Rhône-AGG – A study of the effects of spatial aggregation

Global Soil Wetness ProjectGlobal Soil Wetness Project http://www.cnrm.meteo.fr/mc2/http://www.cnrm.meteo.fr/mc2/projects/rhoneagg/projects/rhoneagg/http://www.cnrm.meteo.fr/mc2/http://www.cnrm.meteo.fr/mc2/projects/rhoneagg/projects/rhoneagg/

Contacts: Aaron Boone, Florence Habets & Joel Noilhan Météo-France, CNRM, Toulouse, France

The Rhône-AGGregation (Rhône-AGG) project will examine how various LSSs are able to simulate the river discharge over several annual cycles when inserted into the Rhône modeling system, and to explore the impact of the various scaling or aggregation methods on the simulation of certain components of the hydrological cycle.

The Rhône basin size is on the order of that of a coarse-resolution Global atmospheric Climate Model (GCM), but the atmospheric forcing, the soil and vegetation parameters, and the observed river discharges are available at a significantly higher spatial resolution, on the order of 8 km.

The Rhône modeling system, which was developed in recent years by the French research community, is comprised of three distinct components:

•A distributed hydrological model

•An analysis system to determine the near-surface atmospheric forcing

•A SVAT model interface

Rhône Basin and examples of (clockwise from top) the 8km grid

for meteorological forcing data, topography, surface and sub-surface

hydrology, vegetation, and distribution of rain gauges (+) and

meteorological stations (▲).

Rhône-AGG – Results I

Global Soil Wetness ProjectGlobal Soil Wetness Project

The MODCOU hydrologic

scheme was used to route runoff for

validation. LSSs showed a wide

range of skill, particularly in terms of daily

discharge in small basins, where schemes with

sub-grid runoff formulations

fared best.

The MODCOU hydrologic

scheme was used to route runoff for

validation. LSSs showed a wide

range of skill, particularly in terms of daily

discharge in small basins, where schemes with

sub-grid runoff formulations

fared best.

http://www.cnrm.meteo.fr/mc2/http://www.cnrm.meteo.fr/mc2/projects/rhoneagg/projects/rhoneagg/http://www.cnrm.meteo.fr/mc2/http://www.cnrm.meteo.fr/mc2/projects/rhoneagg/projects/rhoneagg/

Rhône-AGG – Results II


Total runoff and ET were similar among LSSs, but

partitioning between components varied greatly, leading to

different equilibrium soil water states.

Total runoff and ET were similar among LSSs, but

partitioning between components varied greatly, leading to

different equilibrium soil water states.


Rhône-AGG – Results III


LSSs with more complex snow

schemes generally performed better. Explicit multi-layer

treatments of thermodynamic

properties and ripening led to better

simulations. The only scheme whose snow

simulation did not degrade at lower

resolutions was the scheme with sub-grid

altitude banding (VIC).

LSSs with more complex snow

schemes generally performed better. Explicit multi-layer

treatments of thermodynamic

properties and ripening led to better

simulations. The only scheme whose snow

simulation did not degrade at lower

resolutions was the scheme with sub-grid

altitude banding (VIC).


ndnd Global Soil Wetness Global Soil Wetness ProjectProject

www.iges.org/www.iges.org/gswp/gswp/[email protected]@cola.iges.org.org

www.iges.org/www.iges.org/gswp/gswp/[email protected]@cola.iges.org.org

This phase of the project will take advantage of:

• The10-year ISLSCP Initiative 2 data set

• The ALMA data standards developed in GLASS

• The infrastructure developed in the pilot phase of GSWP

GSWP-2 represents an evolution in multi-model large-scale land-surface modeling with the following goals:

• Produce state-of-the-art global data sets of soil moisture, surface fluxes, and related hydrologic quantities.

• Develop and test in situ and remote sensing validation, calibration, and assimilation techniques over land.

• Provide a large-scale validation and quality check of the ISLSCP data sets.

• Compare LSSs, and conduct sensitivity analyses of specific parameterizations.

ParticipantsMODEL InstituteBucket University of Tokyo

CLM-TOP University of Texas at Austin

CBM/CHASM Macquarie University, Australia

CLASS Meteorological Service of Canada

CLM NASA GSFC/HSB

COLA-SSiB COLA

ECMWF ECMWF

HY-SSiB NASA GSFC/CRB

ISBA MétéoFrance/CNRM

JMA-SiB Meteorological Research Institute, Japan Meteorological Agency

LaD USGS & NOAA/GFDL

MATSIRO Frontier RSGC

MECMWF KNMI (Dutch MetOffice), Netherlands

Mosaic NASA GSFC/HSB

MOSES-2 Met Office, UK

NOAH NOAA NCEP/EMC

NSIPP-Catchment NASA GSFC/NSIPP (GMAO)

ORCHIDEE LMD, France

SiBUC Kyoto University

Sland University of Maryland

SPONSOR Institute of Geography, Russian Academy of Sciences

SWAP Institute of Water Problems, Russian Academy of Sciences

VIC U. Arizona or Princeton U.

VISA University of Texas at Austin

• Operational centers (COLA and IIS)

• Land surface modelers

• Validation group

• Remote sensing application group

• End users (hydrologists, civil engineers, biogeochemists, ecologists, etc.)

Input Data Sets

•Land surface parameters for participating LSSs will be specified from the ISLSCP Initiative II data set.•Near-surface meteorological data at 3-hour time steps will come from the NCEP/DOE and ERA40 reanalyses. ISLSCP Initiative II is producing 1° global grids of the reanalysis data. GSWP will produce “hybrid” forcing data, combining the reanalysis model products with mean gridded observational data (precipitation, downward radiation, and temperature). The hybrid data preserves the observed time means, but uses reanalysis to provide variability on shorter (diurnal-synoptic) timescales.

The hybrid process as applied to reanalysis

precipitation, where rainfall

rates are scaled by the mean error

of the reanalysis compared to

gridded observed monthly means.

ISLSCP Initiative IIObjectives:

Revisit global change modeling data requirements and algorithm approaches.

Provide user services to target community.

Develop science driven, satellite-derived vegetation data sets for global change process studies.

Validate ISLSCP II data sets.

ISLSCP II expands upon the ISLSCP I collection:Spatial resolution is 1/4, 1/2, and 1 degree.

Temporal resolution covers 10-year period from 1986 to 1995.

New data sets added (e.g. Carbon modeling data sets).

ISLSCP II provides a comprehensive collection of high priority global data sets in a consistent data format and Earth projection.

http://islscp2.sesda.com

Precipitation

Cloud Amount

Fossil Fuel Emissions

Topography

LW Radiation

Clear-Sky Albedo

Vegetation Biophysics (fPAR)

Soil Carbon

•Science Questions

•Analysis Framework

•Data Requirements

Identify PotentialData Providers

Data ProvidersGenerate Data Sets

Documentation Peer ReviewInformation System

Design

Reqts Workshops Data Workshops Synthesis WorkshopsFinal Data Collection

Uniform GridUniform Documentation

Users Data ReviewValidation

1999 2002

ISLSCP Data Collection Development

Science Working Group (Monthly Telecons)

Data Process and Types

Parameter data (monthly, yearly, fixed)

ISLSCP-II GDT 1.2 ALMA standard 360 x 180 gathered format 1 x 1 degree land compress vector ASCII 90o N ~ 60o S NetCDF

Fig. 5 Example station density, for 23 Jul 98, of daily total precipitation reports for retrospective (left) and realtime (right) NLDAS data streams.

Courtesy Ken Mitchell

Annual Runoff Error by Latitude

Figure 15 of Oki et al., 1999.Courtesy Taikan Oki

What’s ‘Gauge Correction’?1. Precipitation gauge cannot catch

100% of particles under the strong wind condition.

2. Systematic error depends also on the gauge shape, attachment height (see Sevurk and Klemm, 1989), besides wind speed.

3. It becomes a big error especially in case of snowfall.

Courtesy Taikan Oki

Sevurk and Klemm, 1989

There are many kinds of gauges in the World !!Courtesy Taikan Oki

Activities for ‘Gauge Correction’

Pioneers: Sevruk(Switzerland), Goodison(Canada), and D. Yang(Alaska)

WMO Intercomparisons Project of Precipitation Gauges

Instruments and observing methods report (Sevruk and Hamon,1984;

Goodison et al., 1989)

Correspondence with various gauges and a WMO standard gauge (DFIR) was investigated.

Courtesy Taikan Oki

WMO Intercomparisons Project

Standard Gauge

(DFIR)

Courtesy Taikan Oki

Gauge Catch Ratio Versus Wind Speed

Courtesy Taikan Oki

Total Compensation Ratio

100 %

Courtesy Taikan Oki

Comparison of Annual Runoff

Courtesy Taikan Oki

ALMA Categories of Model Output Variables

Time-mean (fluxes)•General energy balance components •General water balance components •Evapotranspiration components•Streamflow

•Instantaneous•Surface state variables •Sub-surface state variables •Variables for validation with remote sensed data•Cold season processes

Time-mean (fluxes)•General energy balance components •General water balance components •Evapotranspiration components•Streamflow

•Instantaneous•Surface state variables •Sub-surface state variables •Variables for validation with remote sensed data•Cold season processes Examples of data layers from

the ISLSCP data project.

Multi-Model Analysis

A major product of GSWP2 will be a multi-model land surface analysis for the ISLSCP II period. This will be a land surface analog to the atmospheric reanalyses. There will be a climatological annual cycle data set, and a larger data set for the entire series. Compiling the results of multiple LSSs to produce a single analysis will provide a model-independent result. Of particular value, uncertainty estimates can be put on

Example of the multi-model mean (inset) and spread in evapotranspiration over North America during one decad from GSWP1. Over some regions the models are in good agreement (e.g., the mid-Atlantic coast), but in others (e.g., New England) the spread among models exceeds the mean of the models (color scale is the same for both plots).

all of the fields, based on inter-model spread. Additional uncertainties regarding forcing data can be quantified, based on the results of the sensitivity studies.

Validation I

There will be three main modes of in situ validation of participating LSSs:

1. Field campaigns – The ISLSCP2/GSWP2 period overlaps a number of relevant field campaigns, which can provide validation data. Comparison of measured meteorological variables with the reanalysis-based forcing data will also provide a validation of those products.

2. Observational networks and long-term monitoring – There are also long-term monitoring networks of soil moisture, radiative and turbulent fluxes that can provide local or regional validation for LSSs.

ARM/CART sites • Oklahoma Mesonet sites

The Great Plains has one of the world’s densest and most complete arrays of atmosphere, surface and sub-surface observations at the meso-scale.

Global Soil Moisture Data Bankhttp://climate.envsci.rutgers.edu/soil_moisture/

• Mongolia– 42 stations; 1986-93 several

through 1998.– 25 pasture vegetation and 17

wheat. – Gravimetric observations in10-

cm layers to a depth of 1 m, with the first layer divided into two 5-cm layers.

– Volumetric plant-available soil moisture. the 7th, 17th and 27th of the month from April through the end of October.

• China– 43 stations; 1986-91. – Gravimetric observations – 10-cm layers down to a depth of 1

m, with the first layer divided into two 5-cm layers.

– observed on the 8, 18 and 28th each month

– agriculture or grassland category.• India

– Data for the period 1987-95, 10 stations

• Iowa (Entin et al., 1999)– 1986-1994, from 2catchments,

~twice a month– 12 soil layers extending to a

depth of 2.6 m; gravimetric upper, neutron probe deeper layers

– 41.2°N, 95.6°W (southwestern part of the state)

• Illinois (Hollinger and Isard, 1994)– 19 stations; 1986 to 1995 – Neutron probe observations. – Top 10 cm of soil, and then for 20 cm layers to a depth of 2 m.– Vegetation at all stations is grass– Field capacity and wilting level data

• Russia– 102 Districts (~600 stations)– 1986-1995– Gravimetric observations– 20 cm and 1 m soil layers – Cereal crop vegetation. – Approx. every 10 days– Water-balance stations:

• Valdai (1986-1995)• And others…

SCAN (Soil Climate Analysis Network) Sites

Lind, WALatitude: 47º 00‘Longitude: 118º 34‘Elevation: 1640 feet

Fort Assiniboine,MTLatitude: 48º 29' Longitude: 109º 48' Elevation: 2710 feet

Torrington, WYLatitude: 42º 04'Longitude: 104º 08'Elevation: 4280 feet

Nunn, COLatitude: 40º 52'Longitude: 104º 44'Elevation: 5900 feet

Adams Ranch, NMLatitude: 34º 15'Longitude: 105º 25'Elevation: 6175 feet

Geneva, NYLatitude: 42º 53' Longitude: 77º 02' Elevation: 725 feet

Rogers Farm, NELatitude: 40º 51'Longitude: 96º 28'Elevation: 1215 feet

Bushland, TXLatitude: 35º 10'Longitude: 102º 06'Elevation: 3820 feet

Prairie View, TXLatitude: 30º 05'Longitude: 95º 59'Elevation: 270 feet

Crescent Lake, MNLatitude: 45º 25'Longitude: 93º 57'Elevation: 980 feet

Mandan, NDLatitude: 46º 46'Longitude: 100º 55'Elevation: 1930 feet

Princeton, KYLatitude: 37º 06'Longitude: 87º 50'Elevation: 615 feet

Newton, MSLatitude: 32º 20'Longitude: 89º 05'Elevation: 300 feet

Mason, ILLatitude: 40º 19'Longitude: 89º 54'Elevation: 570 feet

Wabeno, WILatitude: 45º 28'Longitude: 88º 35'Elevation: 1580 feet

Ellicott, MDLatitude: 39º 15'Longitude: 76º 55'Elevation: 460 feet

Molly Caren, OHLatitude: 39º 57'Longitude: 83º 27'Elevation: 1060 feet

- Supporting data sites for GSWP 2 (19 of them) typically start 10/1/1994- Data sampling at 6 hour intervals- Data content:

• Soil moisture and temperature profiles (2”, 4”, 8”, 20”, 40”)• Meteorological data • Some flux data (i.e. water, radiation and heat)

- Measurement sites are typically made within a grass plot• Representative of GSWP grid? (or at least one of tile outputs)

Sellers Lake, FLLatitude: 29º 06'Longitude: 81º 38'Elevation: 75 feet

Wakula, FLLatitude: 30º 18'Longitude: 84º 25'Elevation: 150 feet

Sites overlapping GSWP2 period

Watershed:Surface-Water RunoffRecording-Gauge PrecipitationLysimeter:RunoffLysimeter PrecipitationPercolationEvapotranspirationSoil:4-Inch Soil Temp (Bare)4-Inch Soil Temp (Grass)Soil MoistureSoil Temp at .5, 3, 6, 12 and 24”

North Appalachian Experimental Watershed (NAEW)Coshocton, OhioNorth Appalachian Experimental Watershed (NAEW)Coshocton, Ohio

• 1941 - NAEW in full operation

• “Large-scale” watershed hydrology research program

• Weighing lysimeter, a block of undisturbed soil, 8 ft. (2.4m) deep, with surface dimensions of 6 x 14 feet (2x4.3m) having concrete sidewalls and a steel bottom, and a total weight of 65 tons, equipped to measure surface runoff and percolating water as well as to compute evapotranspiration.

• 11 lysimeters were constructed and are still in operation.

Meteorological:Air TemperatureBarometric Pressure10-Meter Wind Speed10-Meter Wind Miles10-Meter Wind DirectionSolar RadiationA-Pan EvaporationAverage Dew Point

Validation II

There will be three main modes of in situ validation of participating LSSs:

3. Streamflow – Runoff fluxes from LSSs will be routed with a common river model to compare with streamflow measurements across a large portion of the globe. This analysis can also uncover problems in the forcing data at basin scales.

Validation data will be converted to the ALMA data convention to aid in comparison with LSS output, and to make it readily available for other uses within GLASS and the broader land surface modeling community.

Validation data will be converted to the ALMA data convention to aid in comparison with LSS output, and to make it readily available for other uses within GLASS and the broader land surface modeling community.

Remote Sensing Applications

One of the new thrusts for GSWP-2 is a stronger connection to applications in remote sensing. In addition to the classical attempts to validate the typical land-surface state variables using satellite retrievals, GSWP-2 also intends to expand the validation and assimilation capabilities of current LSSs. This is to be done by the development of algorithms by which LSSs can directly report brightness temperatures, like those sensed by instruments in orbit. •Infrared/skin temperature•Visible/surface albedo

The principal goal of the effort in remote sensing applications is to expand validation and calibration beyond those few areas where in situ data on land surface state variables are readily available. A secondary goal is to facilitate efforts to assimilate remotely sensed observations of the land surface into LSSs.

Example of validation of 0000UTC LSS predicted skin temperature

with TOVS retrievals for ±1 hour window.

•Microwave/soil wetness •Vegetation index/DVM

Satellite based passive microwave instruments

AMSR-E(also SMMR)

(C band, ~6 GHznon optimal for soil moisture)

HYDROS(L band, 1.4 GHz, optimal)

Reserve mission

SMOS (L band, 1.4 GHz, optimal)

Courtesy Eleanor Burke

Coupled Land Surface and Microwave Emission Models

Land Surface Model (LSM)

SolarRadiation

LatentHeat

SensibleHeat

Passive MicrowaveEmission Model

MicrowaveEmission (TB)

Modeledtemperature and

soil moisture profiles

roughnessvegetationtopographyatmosphere


An example of a coupled LSM & emission model

(MICRO-LDAS)

soil moisture at 2.8 cmCLM in LDAS

L-band TB

0° look angle½ ° resolution


Accuracy of optical depth

120

150

180

210

240

270

300

0 10 20 30 40 50

Volumetric water content (%)

Birg

htne

ss tem

pera

ture

(K

)

Error of 0.08 in optical depth

(~ 0.66 kg m-2)

L-band

Need to know accurate optical

depth

veg.water = 0.8 kg m-2

veg. water = 4.1 kg m-2


Soil water content () profile information

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.15 0.25 0.35 0.45

Volumetric water content (cm3cm-

3)

Depth

(m

)137.4 K, 0.228117.5 K, 0.335120.3 K, 0.317110.1 K, 0.353102.6 K, 0.342102.6 K, 0.336

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.15 0.2 0.25 0.3Volumetric water content (cm3cm-3)

Dep

th (

m)

138.6 K, 0.224133.3 K, 0.252131.5 K, 0.259145.9 K, 0.241158.7 K, 0.249158.9 K, 0.252

Profile 2

Profile 1

Depth integrated in top 1.6 m equal Same no. layers input to emission model

Different no. layers in LSM output

C-band55 degrees

horizontal polarization


-40

-20

0

20

40

0 5 10 15 20

No. of layers in LSM

Err

or in

TB (

K)

Profile 1

Profile 2

-40

-20

0

20

40

0 10 20

No. of layers in LSM

Err

or in

TB (

K)

Profile 1

Profile 2

C-band55 degrees


L-band40 degrees


Soil water content profile information

10 K ~ 3 % volumetric soil water content


Accounting for effects of vegetation

Emission from canopy

Emission from soil surface

attenuated by canopy

Emission from canopy reflected by

soil surface attenuated by

canopy

vegb Simplifying assumption:

b ~= 0.12

Scatteringnegligible


An example of a coupled LSM & emission model

DOY Bias (K)

180 -2.3

183 -0.21

184 -0.12

(MICRO-SWEAT)

DOY RMSE (K)

180 24.1

183 16.4

184 15.3


Implementation

The input data will be available via the Internet from one of several regional DODS servers, for easy access and subsetting. It may be possible for participants to run their LSSs without downloading the forcing data sets to their local disks. Data will also be made available on magnetic or optical media if necessary.

The model output will go to the Inter-Comparison Center (ICC) in Japan for consistency checks and preliminary analysis. Output data from base runs and sensitivity studies will also be available for further analysis and validation.

The model output will go to the Inter-Comparison Center (ICC) in Japan for consistency checks and preliminary analysis. Output data from base runs and sensitivity studies will also be available for further analysis and validation.

Sensitivity Experiments

• Computing and storage burdens are not trivial

• Four categories of sensitivity studies:– Precipitation

forcing

– Radiation forcing

– Meteorological forcing

– Vegetation BCs

Exp Description

B0 Baseline integration

P1 Hybrid ERA-40 precipitation (instead of NCEP/DOE)

P2 NCEP/DOE hybrid with GPCC corrected for gauge undercatch (no satellite data)

P3 NCEP/DOE hybrid with GPCC (no undercatch correction)

P4 NCEP/DOE precipitation (no observational data)

P5 NCEP/DOE hybrid with Xie daily gauge precipitation

R1 NCEP/DOE radiation

RS NCEP/DOE shortwave only

RL NCEP/DOE longwave only

R2 ERA-40 radiation

M1 All NCEP meteorological data (no hybridization with observational data)

M2 All ECMWF meteorological data (no hybridization with observational data)

V1 U.Maryland vegetation class data

I1 Climatological vegetation

Early Results

Soil Moisture

• GSWP-2 models (blue) typically outperform reanalyses (green), even NCEP2 (source of much of the forcing data).

• GSWP-2 models are comparable to specialty soil wetness products (red), but offer more information on other variables, model spread, etc.

Figure courtesy of Z. Guo

Early Results

Soil Moisture Memory

Daily data – 30-day lagged autocorrelation (1-30 June) for 10 years.

Figure courtesy of Z. Guo

Early Results

Validation: HAPEX

• Interesting grouping of models – is this attributable to types of parameterization?

Figure courtesy of T. D’Orgeval

OBS

EVALUATING SUCCESS OF OFFLINE LSM TESTS(A GSWP STUDY)

Prescribedatmospheric

forcing(P, Rsw, etc.)

Land Surface Model

LSM Products(E, H, Tc, etc.)

E

Observed Simulated

Offline forcing of land surface models is useful for model validation.

Question: How do we objectively judge this level of agreement?

Problem: Some degree of success is “guaranteed” by the prescribed atmospheric forcing, even if the land surface model used is poor. Examples:

High rainfall combined with high radiation will tend to produce high evaporation rates, even with a poor model. Low rainfall or low radiation forcing will tend to produce low evaporation rates.

Consider a simple function that relates evaporation to atmospheric forcing without consideration of any explicit land surface physics or properties: Eest = f ( P, Rsw, Rlw, Ta, qa, …)

Two situations are possible:

E

Observed Simulated Eest

1. The simple function does significantly worse than the LSM, implying that the modeled LSM physics do contribute to a realistic response.

E

Observed Simulated Eest

1. The simple function does about as well as the LSM, implying that the modeled LSM physics do not contribute to a realistic response.

Logical choice for a simple function: Budyko’s equation.

Compare:

Observations: Annual runoff rates for well-instrumented catchments around the world

Model results: Annual runoff rates in these catchments as produced by numerous LSMs, as part of GSWP.

Simple Function: Annual runoff rates computed with R = P - EBudyko

Use only catchments withadequate precipitation gauge density

The results show that on average,the Budyko function does aboutas well as the complicated LSMs,implying that the physics within theLSMs do not contribute to a moreaccurate annual energy balance.

Does this imply that current, state-of-the-artLSMs are useless? NO! 1. The error seen at the annual timescale may, in fact, be acceptably small. 2. Given the mechanics of surface moisture storage (as soil water, canopy water, and snow), only an LSM can provide realistic fluxes at shorter time scales (diurnal, synoptic, seasonal). Budyko’s equation would fail consistently at these scales.

Results forindividualcatchments

Average error over all catchments

obsmodels

Budyko

models Budyko

Are the results of PILPS or GSWP affected by the lack of land surface-atmosphere feedback?

Is the use of offline land surface models in LDAS making optimal use of the assimilated data?

We need experiments designed to quantify land – atmosphere feedback in land surface modelling and data assimilation.

This will take the next step in the complexity chain from offline land surface models to fully coupled GCM’s.

Focus on land – atmosphere coupling by means of turbulent exchange, but discarding the processes related to radiation and precipitation.

Report of the GLASS workshop on land – atmosphere interaction

Bart van den Hurk, Paul Houser and Jan Polcher19-20 April, 2002

TOA Radiation

Horizontal Flux Divergence

Single Column Model

Land Surface Model

(www.arm.gov)

A Single Column Model

Local coupled scale

The main scientific questions:

• When and where does land – atmosphere interaction play a significant role in the evolution of land-atmosphere fluxes and state variables?

• Does the absence of this coupling in PILPS-like calibration/evaluation experiments put a strong constraint on the general applicability of the results of these experiments?

• Is the solution of a land data assimilation experiment using an offline land surface model configuration different from a system that includes land – atmosphere feedback?

Strawman Proposal: LoCo• Phase 1: Synthetic column –extract

column boundary conditions from model

• Phase 2: Past Field Campaigns– PILPS sites: Cabauw, FIFE, Boreas,

others?– GABLS sites: which?– Others: SGP/ARM/IHOP, LBA, Wangara,

others?

• Phase 3: Future Field Campaigns– Upcoming Sites: HEAT (Houston, TX)-

Urban; others?– New Site(s): Design “local coupled

testbed(s)”

Design of the land data assimilation system (ELDAS)

precipitation

radiation

evaporation

Soil moisturecorrection scheme

Soil moisturecontent

(sub)surfacerunoff

Observations drivingsoil moisture correction

Synops data

METEOSAT/MSG

Land surfaceparameterizationscheme

Boundary layerscheme

AMSR (?)

Courtesy Pedro Viterbo

Coupled large scale

Current experiment:GLACE, a study of land-atmosphere coupling strength in atmospheric GCMs. (Details next week…)

comparison of land surface models

Documents

intercomparison of land

land surface model behavior

land effects

international community

international benchmarks

pilps san pedroa study

intercomparison frameworks

recent pilps report