Comparison of land surface models
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Comparison of land surface models
CLIM 714 Randy KosterPaul Dirmeyer
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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
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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
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Slide courtesy of Adam Schlosser
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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.
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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Slide courtesy of Adam Schlosser
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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.
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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
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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).
Slide courtesy of Adam Schlosser
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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.
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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
Global Soil Wetness Global Soil Wetness ProjectProject
GSWP-1 GSWP-1
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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
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Runoff and gauge density
• Spread is widest when gauge density is low
• Errors are generally negative (too little runoff)
Courtesy Taikan Oki
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Courtesy Taikan Oki
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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)
Global Soil Wetness Global Soil Wetness ProjectProject
GSWP-1 GSWP-1
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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
Global Soil Wetness Global Soil Wetness ProjectProject
GSWP-1 GSWP-1
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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).
Global Soil Wetness Global Soil Wetness ProjectProject
GSWP-1 GSWP-1
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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 (▲).
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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/
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Rhône-AGG – Results II
Global Soil Wetness ProjectGlobal Soil Wetness Project
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.
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/
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Rhône-AGG – Results III
Global Soil Wetness ProjectGlobal Soil Wetness Project
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).
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/
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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.
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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.)
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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.
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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
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Precipitation
Cloud Amount
Fossil Fuel Emissions
Topography
LW Radiation
Clear-Sky Albedo
Vegetation Biophysics (fPAR)
Soil Carbon
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•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)
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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
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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
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Annual Runoff Error by Latitude
Figure 15 of Oki et al., 1999.Courtesy Taikan Oki
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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
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Sevurk and Klemm, 1989
There are many kinds of gauges in the World !!Courtesy Taikan Oki
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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
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WMO Intercomparisons Project
Standard Gauge
(DFIR)
Courtesy Taikan Oki
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Gauge Catch Ratio Versus Wind Speed
Courtesy Taikan Oki
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Total Compensation Ratio
100 %
Courtesy Taikan Oki
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Comparison of Annual Runoff
Courtesy Taikan Oki
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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.
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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.
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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.
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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.
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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…
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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
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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
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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.
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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
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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
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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
Courtesy Eleanor Burke
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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
Courtesy Eleanor Burke
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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
Courtesy Eleanor Burke
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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
Courtesy Eleanor Burke
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-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
horizontal polarization
L-band40 degrees
horizontal polarization
Soil water content profile information
10 K ~ 3 % volumetric soil water content
Courtesy Eleanor Burke
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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
Courtesy Eleanor Burke
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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
Courtesy Eleanor Burke
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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.
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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
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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
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Early Results
Soil Moisture Memory
Daily data – 30-day lagged autocorrelation (1-30 June) for 10 years.
Figure courtesy of Z. Guo
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Early Results
Validation: HAPEX
• Interesting grouping of models – is this attributable to types of parameterization?
Figure courtesy of T. D’Orgeval
OBS
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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.
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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.
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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
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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
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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
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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?
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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)”
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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
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Coupled large scale
Current experiment:GLACE, a study of land-atmosphere coupling strength in atmospheric GCMs. (Details next week…)