Comparison of land surface models

1. Comparison of land surface models CLIM 714 Randy Koster Paul Dirmeyer

2. GSWP LAND SURFACE MODEL INTERCOMPARISONS 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 single encompassing structure: GLASS (Global Land-Atmosphere System Study) GCM inter-comparisons Global gridded model analyses Single column model analyses LoCo Land-surface model intercom-parisons (in situ)

3. Offline local scale PILPS PIs: Ann Henderson-Sellers, Andy Pitman Project for the Intercomparison of Land-Surface Parameterization Schemes (http://cic.mq.edu.au/pilps-rice) Text from a recent PILPS report: • 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?

4. Slide courtesy of Adam Schlosser

5. PILPS has four levels of experimentation... Updates to table: PILPS2e was instead a study of land surface behavior in high northern latitudes (Torne-Kalix river basin in Sweden). PILPS - San Pedro (a study focusing on an arid region) begins in 2004. PILPS is also now doing an intercom- parison study focusing on carbon assimilation. Potential new studies involve land effects on: (1) water isotope distributions and (2) tropical cyclones.

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14. PILPS 2e: Simulation of Arctic hydrology Torne-Kalix River Basin, Sweden Results show, e.g., a strong sensitivity of annual runoff to aerodynamic resistance formulation and its associated impact on snow sublimation.

15. LSS Clusters in All Climates SiBlings Sfc. Energy Balances normalised by ensemble of 3 re-analyses – 3 clusters SiBlings Buckets Scaled sensible heat W m-2 SiBlings Buckets Buckets Scaled latent heat W m-2 Slide courtesy of Ann Henderson-Sellers

16. 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

17. www.iges.org/gswp/ gswp@cola.iges.org Global Soil Wetness Project • Overview • 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. • Motivation • The 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.

18. Global Soil Wetness Project GSWP-1 Data Issues

19. GSWP anomalies Free Soil moisture Relaxed Soil moisture From Douville and Chauvin, Climate Dyn. 2000 Sensitivity of JJAS climate to soil moisture relaxation toward GSWP: impact on soil moisture (kg/m²)

20. Runoff and gauge density • Spread is widest when gauge density is low • Errors are generally negative (too little runoff) Courtesy Taikan Oki

21. Courtesy Taikan Oki

22. Global Soil Wetness Project GSWP-1 Validation Issues

23. Global Soil Wetness Project GSWP-1 Model Sensitivity Studies I

24. Global Soil Wetness Project GSWP-1 Model Sensitivity Studies II

25. Global Soil Wetness Project http://www.cnrm.meteo.fr/mc2/projects/rhoneagg/ Rhône-AGG – A study of the effects of spatial aggregation • 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 (▲).

26. Global Soil Wetness Project http://www.cnrm.meteo.fr/mc2/projects/rhoneagg/ Rhône-AGG – Results I 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.

27. Global Soil Wetness Project http://www.cnrm.meteo.fr/mc2/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.

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

29. nd Global Soil Wetness Project • 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. www.iges.org/gswp/ gswp@cola.iges.org

30. MODEL Institute Bucket 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 Participants • Operational centers (COLA and IIS) • Land surface modelers • Validation group • Remote sensing application group • End users (hydrologists, civil engineers, biogeochemists, ecologists, etc.)

31. 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.

32. ISLSCP Initiative II • Objectives: • 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

33. Precipitation Topography Clear-Sky Albedo Cloud Amount II Soil Carbon LW Radiation Fossil Fuel Emissions Vegetation Biophysics (fPAR)

34. 2002 1999 Data Workshops Synthesis Workshops Reqts Workshops Final Data Collection Uniform Grid Uniform Documentation • Science Questions • Analysis Framework • Data Requirements Identify Potential Data Providers Data Providers Generate Data Sets Users Data Review Validation Peer Review Information System Design Documentation ISLSCP Data Collection Development Science Working Group (Monthly Telecons)

35. 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

36. 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

37. Annual Runoff Error by Latitude Figure 15 of Oki et al., 1999. Courtesy Taikan Oki

38. 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

39. Sevurk and Klemm, 1989 There are many kinds of gauges in the World !! Courtesy Taikan Oki

40. 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

41. WMO Intercomparisons Project Standard Gauge (DFIR) Courtesy Taikan Oki

42. Gauge Catch Ratio Versus Wind Speed Courtesy Taikan Oki

43. Total Compensation Ratio 100 % Courtesy Taikan Oki

44. Comparison of Annual Runoff Courtesy Taikan Oki

45. 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 Examples of data layers from the ISLSCP data project.

46. 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 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. 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).

47. Validation I • There will be three main modes of in situ validation of participating LSSs: • 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-termmonitoring – There are also long-term monitoring networks of soil moisture, radiative and turbulent fluxes that can provide local or regional validation for LSSs.

48. 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.

49. Global Soil Moisture Data Bankhttp://climate.envsci.rutgers.edu/soil_moisture/ • 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… • 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)

50. SCAN (Soil Climate Analysis Network) Sites • 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) Lind, WA Latitude: 47º 00‘ Longitude: 118º 34‘ Elevation: 1640 feet Fort Assiniboine,MT Latitude: 48º 29' Longitude: 109º 48' Elevation: 2710 feet Torrington, WY Latitude: 42º 04' Longitude: 104º 08' Elevation: 4280 feet Nunn, CO Latitude: 40º 52' Longitude: 104º 44' Elevation: 5900 feet Adams Ranch, NM Latitude: 34º 15' Longitude: 105º 25' Elevation: 6175 feet Geneva, NY Latitude: 42º 53' Longitude: 77º 02' Elevation: 725 feet Rogers Farm, NE Latitude: 40º 51' Longitude: 96º 28' Elevation: 1215 feet Bushland, TX Latitude: 35º 10' Longitude: 102º 06' Elevation: 3820 feet Prairie View, TX Latitude: 30º 05' Longitude: 95º 59' Elevation: 270 feet Crescent Lake, MN Latitude: 45º 25' Longitude: 93º 57' Elevation: 980 feet Mandan, ND Latitude: 46º 46' Longitude: 100º 55' Elevation: 1930 feet Princeton, KY Latitude: 37º 06' Longitude: 87º 50' Elevation: 615 feet Newton, MS Latitude: 32º 20' Longitude: 89º 05' Elevation: 300 feet Mason, IL Latitude: 40º 19' Longitude: 89º 54' Elevation: 570 feet Wabeno, WI Latitude: 45º 28' Longitude: 88º 35' Elevation: 1580 feet Ellicott, MD Latitude: 39º 15' Longitude: 76º 55' Elevation: 460 feet Molly Caren, OH Latitude: 39º 57' Longitude: 83º 27' Elevation: 1060 feet Sellers Lake, FL Latitude: 29º 06' Longitude: 81º 38' Elevation: 75 feet Wakula, FL Latitude: 30º 18' Longitude: 84º 25' Elevation: 150 feet Sites overlapping GSWP2 period