A number of variables are critical to the success of GCIP and were designated as Principal Research Areas for GCIP. Each of these are described in this Section.

6.1. Precipitation

GOAL: To achieve better understanding and estimation of space-time precipitation structure over the Mississippi River Basin, including improvements in atmospheric model representation of precipitation to support improved coupled modeling.

The hypothesis behind GCIP precipitation research is that improved high-resolution precipitation prediction from atmospheric models is expected to result (via coupled land- atmosphere modeling) in better predictions of other hydrologic variables (e.g., soil moisture and runoff) at the small basin scale and over storm to daily to seasonal time scales. Such improved predictions will be useful for water management decisions and basin interpretations of climatic changes.

6.1.1. Space-time Structure of Precipitation Fields

Objective: Study the statistical structure of precipitation variability at a range of space-time scales and develop subgrid scale precipitation downscaling algorithms (from large to small scales) to be used in atmospheric models.

Activities to support this objective are:

6.1.2. Atmospheric Precipitation Processes

Objective: Understand the physics of precipitating clouds and their relation to the storm environment and the produced precipitation fields.

Activities to support this objective are:

6.1.3. Precipitation Predictability

Objective: Assess the limits of predictability of atmospheric model precipitation as a function of scale.

Activities to support this objective are:

6.1.4. Data for GCIP Precipitation Research

Objective: Improve the availability and quality of data that are neeeded to support the research activities descibed above.

Activities to support this objective are:

6.1.5 Snow

Objective: Produce gridded snow water equivalent data sets for the upper Mississippi River basin by integrating ground-based, airborne and satellite snow cover data sets.

Activities to support this objective are:

6.2 Soil moisture

Overall Objective: Improve understanding and estimation of the space-time structure of soil moisture, the relationship between model estimates of soil moisture and observations of soil moisture, and to produce soil moisture fields for the GCIP area to be used as diagnostic and input data for modeling.

6.2.1 Soil Moisture Fields

Objective: Produce the best possible estimates of soil moisture at four depths over the entire GCIP study area with the initial emphasis over the LSA-SW.

Activities that are needed or support this objective are:

A validated soil moisture product is needed for the Mississippi River Basin at a spatial scale of about 40 km and a daily temporal scale for four depths corresponding to the Eta and MAPS model output. This assimilated product must be produced from a variety of data sources, including output from hydrologic models driven by measured meteorological data, instu soil moisture observations, and remote sensing. The challenge will be to combine the various data forms to produce the "best" possible gridded product and to develop a way to validate the product with in-situ data, preferably data not used in the assimilation process.

Initially, a subset of the soil moisture product is to be developed for the LSA- SW because this is the area where the most in situ data are available and the region where remote sensing can provide the best information because of the relativly less dense vegetation cover.

A second subset of the soil moisture product needs to be developed for the LSA-NC. Here the Illinois in situ soil moisture data set can be used for validation and or assimilating the data set. The issue of cold season hydrology and frozen soils need to be addressed with this data set.

6.2.2 Model Estimates of Soil Moisture

Objective: Assess the role of soil moisture in hydrological models and develop understanding of the relationship between model soil moisture state variables and observation-based values of soil moisture, i.e., is the model produced value of soil moisture anything like that what we can measure?

Activities that are needed or support this objective are:

6.2.3 Local Variability of Soil Moisture

Objective: Use a combination of in situ, remotely sensed measurements, and physically based models to develop procedures for scaling up of soil moisture from point to hillslope to grid cell and to characterize the uncertainties associated with the data at all scales.

Activities that are needed or support his objective are:

6.2.4 Remote Sensing of Soil Moisture

Objective: Develop improved remote sensing techniques for areal estimation of soil moisture.

Activities that are needed or support this objective are:

6.3 Land Surface Characteristics

Overall Objective: Improve the quantitative understanding of the relationships between model parameterizations of land processes and land surface characteristics; and facilitate the development, test, evaluation, and validation of multiresolution land surface characteristics data and information required by GCIP researchers for developing, parameterizing, initializing, and validating atmospheric and hydrological models.

6.3.1 Land Surface Characteristics Research

The strategy for this land surface characterization research is twofold. In the near term, the primary emphasis is on facilitating the adaptation, tailoring, test and evaluation, and validation of existing land surface characteristics databases that will meet the immediate requirements of GCIP's Principal Research Areas. The first priority is to supplement the GCIP Implementation plan by further documenting the specific multiresolution land surface data requirements of GCIP researchers, with provisions for updating the land surface characterization research plan based on regular feedback from GCIP modelers, as well as research results concerning land process modeling activities of PILPS and ISLSCP. The near- term strategy also includes adapting and testing promising remote sensing algorithms that are available in the literature, for example published results from ISLSCP's remote sensing science activities involving FIFE, Boreal Ecosystem Atmosphere Study (BOREAS), or the GEWEX/ISLSCP global one-degree latitude-longitude land datasets recently published on compact disk, read-only memory (CD-ROM). Many GCIP modelers will conduct land characterization research as an integral part of their efforts to develop land surface process models and parameterizations. Therefore, facilitating the cross-disciplinary flow and sharing of land characterization results and information within the GCIP research community are also key factors in this near-term strategy. GCIP's longer-term strategy for land surface characterization research will focus on the SSAs and ISAs to develop high-resolution land data sets; to collect field data that are necessary to develop, adapt, test, and validate promising remote sensing algorithms for land cover characterization and model parameterizations; to conduct advanced remote sensing research, for example, canopy reflectance modeling; and to investigate landscape heterogeneity as related to land process parameterizations.

Multiresolution land characterization research in the near-term will be directed toward meeting the minimum requirements of GCIP Principal Research Areas for land cover, soils, and topographic data, including associated characteristics and properties of each, at four regional scales. The initial project regions and their associated gridding intervals include the CSA and LSA-SW (40-km grids), ARM/CART as the initial ISA (10-km grid), and Little Washita as the initial SSA (4-km grid). The primary land surface data sets that are currently available throughout the conterminous United States to potentially meet some of GCIP's immediate requirements for land data within these four regions include various 1-km and coarser spatial resolution, advanced very-high-resolution radiometer (AVHRR) data products from NOAA's polar-orbiting satellites; the 1:250,000-scale USDA/Natural Resources Conservation Service State Soil Geographic Database (STATSGO); and DEMs of 0.5-km and approximately 100-m grid cell resolutions, respectively, available from the USGS. Land characterization research will focus on the adaptation and use of these primary data sets as the basis to develop, test, and evaluate key derivative land surface characteristics data sets for use by GCIP modelers.

As GCIP evolves, land characterization research will focus on meeting changing land data requirements, developing data sets for new regions, and facilitating the use of geographic information systems (GIS) technology and other appropriate tools for land surface data analysis. For example, model sensitivity analysis from GCIP and PILPS investigators will help to more clearly identify requirements for specific types of land surface characteristics including accuracy specifications. Detailed analysis of multiresolution satellite data for the ARM/CART region will lead to remote sensing algorithms that can be applied within the LSA- and CSA-scale regions. GCIP would also significantly benefit from remote sensing algorithms developed and tested under the proposed ISLSCP Initiative No. 2 project.

Additional regions will be defined and higher resolution land data sources will be investigated and developed, as required. Candidate regions include the Upper Walnut River watershed located within the ARM/CART as related to the proposed Cooperative Atmosphere-Surface Exchange Study (CASES); the LSA-NC; and SSAs within the LSA EAST, for example, River subbasins within the Tennessee River Drainage basin and the Goodwin Creek watershed (part of the Yazoo River basin) a USDA/ARS experimental watershed located in north central Mississippi. Organization of the LSA-NC and LSA-East is underway.

Some of the key secondary land data sources will include various types of 30-m LANDSAT thematic mapper (TM) data products for land cover characterization within the ISA- and SSA-scale regions, selected county-level digital USDA/Natural Resources Conservation Service Soil Survey Geographic Database (SSURGO) (as available), USGS digital 60-m DEMs for the ARM/CART, and USGS 30-m DEMs available in a 7.5-minute quad format for selected locations within the GCIP domain. The land data sets developed for the Upper Mississippi region by the Scientific Assessment and Strategy Team (SAST) concerning flood plain management following the 1993 floods potentially represent a significant contribution to the land surface characterization requirements for the LSA-NC (see the World Wide Web at the URL address: http://edcwww.cr.usgs.gov/sast-home.html).

Additionally, the identification and facilitation of the use of appropriate data analysis tools, such as GISs and digital image processing systems, will be needed to tailor land surface characteristics from primary data sets and to integrate and analyze disparate data sets of interest to land process modelers. Both standard and new image processing techniques will be necessary for analysis of multitemporal land cover characteristics data, frequently available from satellite remote sensing systems with different spatial resolutions. Moreover, the application of appropriate geostatistical techniques, such as measures of dispersion or aggregation of landscape patterns, will be investigated to assist in understanding the spatial linkages extant between land surface characteristics and the hydrometeorological conditions within the GCIP study area.

This land surface characterization research strategy will be accomplished through objectives and associated research activities involving land cover characteristics, soils and geology, and topographic information. The research activities under each objective are listed according to priority for accomplishment. To meet the requirements of GCIP's other Principal Research Areas, the highest priority activities within this land surface characterization plan was initiated during 1996. Tailoring available multiresolution AVHHR land cover characteristics data for multiresolution model test and evaluation, preparing preliminary soils data sets from STATSGO for the Mississippi River basin, and evaluating high resolution land cover classifications and obtaining Landsat TM images for the ARM/CART and Little Washita regions are the top priorities for 1996 and early-1997.

Land surface characterization research is a highly interdisciplinary effort. Therefore, an equally important high-priority task is to develop Federal agency participation and resource support for beginning and working cooperatively on the accomplishment of these land surface characteristics research objectives and activities. Some of the potential Federal agency participants for conducting and supporting this land surface characterization research include NOAA (NWS and NESDIS), the USGS (National Mapping Division and Water Resources Division), NASA [Marshall Space Flight Center (MSFC) and GSFC] and the USDA [ARS, Natural Resources Conservation Service, and National Agricultural Statistics Service (NASS)]. In many cases, the results of this interdisciplinary land surface characterization research will directly benefit agency missions, such as those concerning land data set development, remote sensing science, operational programs involving atmospheric and hydrological modeling, natural resource assessment, and agricultural monitoring and forecasting. Furthermore, activities such as SAST, involving flood disaster management, can contribute to GCIP both in terms of a supplier of land data and as a key user of GCIP atmospheric, hydrologic, and water resource products for policy decision making. The efforts of such Federal agencies would complement contributions made by GCIP's research community including expertise at universities such as Penn State University, Colorado State University and Texas A&M University. The coordination of this research with potential contributions by GEWEX/ISLSCP presents an outstanding opportunity, especially for biophysical remote sensing algorithm development, operational database development, and scaling research.

6.3.2 Land Cover Characteristics

The biophysical remote sensing and land-atmosphere interactions modeling communities are currently addressing many of the research questions and related data development issues concerning the potential role of landcover characteristics as determinants of land surface processes. This research by atmospheric and hydrological modelers is concerned with understanding and parameterizing the effects of land cover characteristics in their models and parameterizations (i.e., land cover and vegetation type, land use, the physical and biophysical properties of vegetation including the temporal dynamics, and more recently the spatial heterogeneity of the landscape). In many cases, these two communities also share common interests in developing the experimental remote sensing algorithms that are needed to estimate or derive various types of land cover characteristics from satellite data over large areas. Examples range from the use of multitemporal satellite-derived vegetation greenness indexes for land cover classification and estimating leaf area index (LAI) to more advanced canopy reflectance modeling for estimating biophysical parameters and processes. Facilitating the adaptation and use of published research results and biophysical remote sensing algorithms within GCIP is a key requirement.

Some of the sources for vegetation/land cover characteristics data include the global one- degree latitude-longitude modeling data sets recently published on CD-ROM by NASA/GSFC under GEWEX/ISLSCP Initiative No. 1 and various AVHRR data sets produced by NOAA/NESDIS and USGS. For example, NASA's ISLSCP CD-ROM includes monthly one- degree by one-degree calibrated, continental NDVI data (1982 to 1990); enhanced NDVI fields; Fraction of Absorbed Photosynthetically Active Radiation (FPAR) fields derived from enhanced-NDVI data; LAI and canopy greenness resistance fraction calculated from the derived FPAR fields; surface albedo and roughness length fields derived from land process models; and canopy photosynthesis and canopy conductance fields estimated by inverting the Simple Biosphere (SiB) Model 2 land surface parameterization (LSP) with FPAR as the key model input. The CD-ROM also includes a one-degree global land cover data set.

Although these ISLSCP Initiative No. 1 CD-ROM data are of direct interest to GCM and coarse grid cell resolution mesoscale modeling, the remote sensing algorithms and approaches for inverting an LSP to derive the land cover characteristics will guide efforts to similar use of higher resolution AVHRR and LANDSAT TM data. NASA/GSFC is currently planning ISLSCP Initiative No. 2 which would focus on enhanced global land cover characteristics data sets at a 1/2-degree latitude-longitude grid. The ISLSCP No. 2 data are planned for release during late 1997.

The NOAA/NESDIS has developed AVHRR global vegetation index (GVI) data sets. These data sets include weekly satellite image composites consisting of five AVHRR channels, solar zenith and azimuth angles, and the GVI for 1985 to the present. These data are calibrated for sensor drift and intersensor variability, and are available in a 1/6-degree resolution latitude-longitude product. Recently, NOAA/NESDIS produced a five-year climatology of the GVI data, and is now working to derive vegetation fraction from the GVI. The NOAA/NESDIS is also working with NASA/GSFC on the AVHRR Global Area Coverage (GAC) Pathfinder project to develop calibrated 8-km AVHRR data with a period of record beginning in 1981.

The USGS EROS Data Center (EDC) has developed 1-km AVHRR databases for the conterminous United States and is now processing global 1-km AVHRR data for land areas. The databases for the conterminous United States include biweekly AVHRR time-series image composites on CD-ROM (1990-1995) and a prototype land cover characteristics database for 1990 on CD-ROM. This 1990 land cover characteristics database is currently undergoing validation based on field survey data. Ongoing USGS activities also include the preliminary development of experimental, temporally smoothed 1-km seasonal NDVI greenness statistics for test and evaluation. These statistics consist of 12 seasonal characteristics that are associated with each 1-km NDVI seasonal profile for each year during the period 1989 to 1993, as well as the five-year means throughout the conterminous United States. Under the auspices of the International Geosphere Biosphere Project (IGBP)-led 1-km AVHRR global landcover database development activity, the USGS is currently processing global, 10-day AVHRR image composites for land areas. A proto-type 1-km AVHRR land cover data for the North American continent was recently made available for test and evaluation. These land cover data (for example, the BATS, Sib2, IGBP, and other land cover classification schemes), ten-day global AVHRR data, and a 1-km digital elevation model (DEM) for North America can be obtained via ftp through the EDC Distributed Active Archive (DAAC) Homepage (http://edcwww.cr.usgs.gov/landdaac/). Several global climate change research modelers, including some GCIP investigators, are currently testing and evaluating these USGS data sets.

In mid-1998, the Earth Observing System (EOS) AM1 platform is scheduled for launch as part of NASA's Mission to Planet Earth (MTPE). A wide variety of land cover characteristics data will be produced from data collected by the MODIS, MISR, ASTER, and CERES sensors on board the AM1 Platform. These data will be subsequently available for GCIP research through the EOS Data and Information System. For example, atmospherically- corrected reflectance and vegetation index data will be potentially available. In addition, current NASA plans also call for the 1998-launch of Landsat 7, which will be in near- synchronous orbit with the AM1 package. Land surface research will benefit from concurrent overlapping Landsat 7 and EOS AM1 products.

Objective: Improve the quantitative understanding of the relationships between land cover characteristics and the land surface parameterizations and land process components of atmospheric and hydrological models, and meet the requirements of the GCIP modeling and research activities for multiresolution land cover characteristics data.

Activities to support this objective in order of priority follow:

a. Definition of the requirements of GCIP modelers for multiresolution land cover characteristics data, documentation of available data sources, and assessment of the adequacy of available data for GCIP Principal Research Areas.

As a first priority, the requirements of GCIP's scientific investigators for multiresolution land cover characteristics data must be determined, specifically, requirements for land cover classes, agricultural crop and land use categories, and seasonally variable biophysical properties (i.e., vegetation attributes or characteristics). The potential sources for land cover and land use data will be identified and documented. This effort includes published data on vegetation characteristics and biophysical attributes such as those prescribed for Biosphere-Atmosphere Transfer Scheme (BATS), Simple Biosphere Model 2 (SiB2), Land-Ecosystem-Atmosphere Feedback (LEAF), and other land cover classification schemes. The adequacy of available data sources for GCIP modeling, especially in terms of detailed agricultural land use classes and attributes, will be assessed.

Ongoing feedback is needed from GCIP, PILPS, and ISLCSP activities concerning requirements for land cover characteristics data and the results of model sensitivity analysis concerning land cover characteristics. (Note: The requirements for multiresolution land surface characteristics data on land cover, soil, and topography identified under this and subsequent objectives will be used to prepare a matrix gridding plan showing regions versus land surface characteristics data requirements.)

b. Tests and evaluation of AVHRR-derived land cover data in GCIP models and assessment of data accuracy limitations.

Available 1-km and coarser resolution AVHRR-derived land cover data sets (i.e., the BATS, SiB2, USGS Anderson Level II-modified, Olson, and similar vegetation and land use classifications) will be tailored for test and evaluation in the land surface parameterization component of atmospheric GCMs, nested mesoscale meteorological models, and multiscale watershed hydrological models. The sensitivity of GCIP models to potential data accuracy limitations will be assessed.

c. Facilitation of the use of GCIP model output and data assimilation products by remote sensing data centers to improve remote sensing processing techniques, especially approaches for making atmospheric corrections to satellite reflectance data for atmospheric water vapor content and aerosol concentrations.

d. Facilitation of the adaptation, development, and use of biophysical remote sensing algorithms to estimate seasonally variable land cover characteristics data needed to parameterize, initialize, and validate GCIP's models; validation of the remote sensing algorithms and tests to evaluate the biophysical data in GCIP's atmospheric and hydrological models.

This effort is focused on facilitating the use of multitemporal AVHRR channel reflectance, NDVI greenness, and GVI data to develop and evaluate seasonally variable, time-dependent biophysical land cover characteristics data that are required by GCIP investigators for development, initialization, test and evaluation, and validation of their land process models and parameterizations. For example, NOAA/NESDIS has developed 5-year climatologies of the GVI including derived estimates of vegetation fraction from the GVI. Another potential data resource is the experimental 1-km resolution vegetation seasonality characteristics data set now under development by the USGS from biweekly, 1-km AVHRR NDVI greenness temporal profile data (1989-1994). Following the application of temporal smoothing algorithms, calculated seasonality data for each 1-km pixel of each year and the five-year averages include the NDVI Julian dates and associated numerical values for onset, peak, and end of greenness; rates of NDVI change; duration of greenness season; greenness curve modality; and seasonally integrated total NDVI.

Some of the biophysical characteristics to be potentially developed and tested from these types of seasonality data include estimated LAI, ratio of vegetation to bare soil (i.e., vegetation fraction), greenleaf fraction, vegetation height, and FPAR. Research on the interannual variability of satellite vegetation indexes, for example, due to the effects of the atmospheric water vapor and aerosols or due to year-to-year weather variations or other factors is a key requirement to ensure the proper use of seasonality data. These atmospheric corrections are also essential to the use of channel reflectance data to estimate land surface albedo and other derived parameters. This biophysical remote sensing research is impeded by the lack of operational algorithms to correct satellite reflectance data for the effects of atmospheric water vapor and aerosols.

e. Development of high-resolution land cover classifications (i.e., vegetation type and land use) including preliminary land cover characteristics data sets for the ARM/CART site and selected SSAs, for example, based on 30-m resolution LANDSAT TM data.

Facilitating the use of relatively inexpensive LANDSAT TM data by Federal agencies and state university remote sensing centers to develop digital land cover classification maps is one possible strategy to meet this need. Comparisons with AVHRR-derived land cover characteristics data are needed.

The DOE ARM program has identified available high-resolution land cover and land use data sets for the ARM/CART, while the USDA/ARS is presently completing a GIS for the Little Washita including recent Landsat TM-derived land cover classifications, historical Landsat MSS products back to 1972, and other land surface characteristics data sets.

f. Exploration of multiresolution relationships among land cover/vegetation, soil, and topographic characteristics data, as well as relationships with microclimatic and hydrometeorological data, ranging from the landscape and watershed scales up to the ISA and LSA regions.

One prime reason for this research is to ensure that the land surface characteristics data sets on land cover, vegetation attributes, soil properties, and topography are appropriately and consistently tailored within model grid cells or watershed polygons for model applications. In addition to model sensitivity studies concerning accuracy issues for individual data layers, error propagation analysis will also be conducted to assess the net impact of effectively "overlaying" land cover, soil, and topographic data sets in the model, where these data are characterized by differing levels of accuracies, precision, uncertainties, and other data limitations.

g. Conductance of advanced land cover characterization research within SSAs and ISAs to meet GCIP's requirements for more detailed land cover characteristics data and information.

Several types of advanced land cover characterization research activities are needed. For example, a strategy is needed for collecting essential ground-based data and field observations that will be used in combination with published research results to develop, adapt, test, and/or validate remote sensing algorithms at the ISA/SSA-scales. In addition to basic remote sensing algorithms for making atmospheric corrections or other image processing, applications could also involve remote sensing algorithms for making regional extrapolations of land surface processes such as seasonal evapotranspiration or net primary productivity, or the validation of satellite-derived land cover characteristics data such as LAI. Field-based data sets are also needed to develop land surface parameterizations or to properly use satellite remote sensing data, such as multitemporal NDVI greenness data, as part of special studies to investigate how vegetation controls evapotranspiration.

Research on state-of-the-art canopy reflectance modeling is needed, especially as related to the estimation of canopy characteristics for agricultural crops. Model-based approaches for estimating key canopy parameters need investigation, for example the inversion of a land surface parameterization such as was done by NASA/GSFC with SiB2 as part of the ISLSCP Initiative No. 1 CD-ROM development. Advanced remote sensing research is also needed to investigate the potential use of other remote sensing data in GCIP, for example, the use of Airborne Visible Infrared Imaging Spectrometer (AVIRIS), Thermal Infrared Multispectral Scanner (TIMS), ERS-1, or RADARSAT data.

Research is needed to investigate how the spatial heterogeneity of vegetation (i.e., landscape patchiness) affects model parameterization, especially as related to spatial aggregation of data within model grid cells and polygons, or scaling parameterizations. This land surface characterization research emphasizes the spatial component within the landscape, for example, concerning the arrangement, pattern, distribution, and composition of various land cover types within a region that influence or potentially affect land- atmosphere interactions and hydrometeorological relationships.

Elements of land characterization research will focus on the use of remote sensing algorithms and geostatistical analysis tools to investigate the estimation and analysis of land surface energy fluxes and scaling issues, especially as related to comparisons with tower flux site, SSA, ISA ARM/CART observations, and GCIP model outputs. Scaling research issues involve remote sensing, for example, concerning the use of the NDVI or the simple ratio in canopy conductance modeling based on LANDSAT TM and AVHRR data inputs. The use of satellite-based remote sensing technology to directly or indirectly estimate surface energy fluxes is still an open research topic. This land surface characterization research will also contribute to efforts by GCIP researchers to investigate how landscape spatial heterogeneity contributes to surface flux distribution and parameterizations.

These aspects of land characterization research will use digital image processing, GIS, and various geostatistical tools for spatial and temporal analysis. Examples of geostatistical tools include autocorrelation analysis, kriging, variograms, and potentially other types of spatial analysis which may be useful in characterizing the impact of landscape pattern, arrangement, type, and distribution as a forcing function in hydrometeorological processes within the Mississippi River basin.

h. Organization of an annual joint GCIP/ISLSCP workshop on the development, test and evaluation, and validation of remote sensing algorithms for land cover characterization and regional estimation of biophysical processes.

6.3.3 Soils and Geology

Information on the nature of soils and geology is needed to support the parameterization of land surface processes in atmospheric and hydrological models. Soil is an important coupling mechanism between the land surface and the atmosphere. The pore space between the various constituent elements of the soil (sand-silt-clay particles, rock fragments, plant roots, etc.) forms the "reservoir" of water available for meeting the evaporation and transpiration demands at the land surface-atmosphere interface, in addition to being the recharge source for ground water. An accurate description of soil and soil-water relationships is a prerequisite for improving the simulation of water movement in the subsurface and, ultimately, the water and energy exchange at the land surface-atmosphere interface. Beneath the soil, the geologic structure and properties control the saturated zone (ground water) component of the hydrological cycle. A complete portrayal of the hydrological cycle requires an understanding of the physical and hydraulic properties of both the soil and geology beneath the land surface.

The land-atmosphere interactions modeling community is interested in the movement of water within the soil, as well as the influence of vegetation in linking soil water with the atmosphere. Modeling approaches are typically based on the Richards equation which describes the flow of water through the soil as a function of soil water content and its vertical gradient. The texture and structure of the soil medium are the primary controls on water movement. These physical properties determine the hydraulic nature (water-holding capacity and conductivity) of the soil. Due to the extremely difficult and tedious nature of the procedures required to measure the water content and hydraulic conductivity of soils, research since the early 1950s has focused on developing empirical relationships between traditionally observed soil physical properties and hydraulic characteristics. This work has been referenced by the land- atmosphere interactions modeling community in an effort to parameterize soil moisture conditions over the typically large domains encountered in mesoscale modeling. Unfortunately, the lack of a soil database corresponding to these regional scales has confounded efforts to improve this portion of the parameterization dilemma. Clearly, the community of modelers working in this area requires reliable, quantitative information on soil physical properties and, where feasible, direct observations of the hydraulic nature of the soil for use in quantification and validation of the empirical approaches used over large areas to estimate these properties. A range of soil survey products and databases will be required by GCIP researchers for use in land surface parameterizations.

The USDA-Natural Resources Conservation Service, through the National Cooperative Soil Survey (NCSS), is developing soil geographic databases at three scales. The familiar county-level soil survey is being converted to a digital database for use primarily in local-level planning. This database is SSURGO. At the regional level, the State Soil Geographic Database (STATSGO) has just been developed for river basin, multistate, state, and multicounty resource planning. The compiled soil maps were created with the USGS 1:250,000-scale topographic quadrangles as base maps and comply with national map accuracy guidelines. The third soil map product being created is the National Soil Geographic Database (NATSGO). This product is being compiled at a scale of 1:7,500,000 and is not yet available.

The STATSGO database provides the most useful resource for characterizing the role of soil in mesoscale atmospheric and hydrological models. This database was developed by generalizing soil-survey maps, including published and unpublished detailed soil surveys, county general soil maps, state general soil maps, state major land resource area maps, and, where no soil survey information was available, LANDSAT imagery. Map-unit composition is determined by transects or sampling areas on the detailed soil surveys that are then used to develop a statistical basis for map-unit characterization. The STATSGO map units developed in this manner are a combination of associated phases of soil series.

The STATSGO database will be useful for regional-scale analysis; however, GCIP researchers will require, on a selective basis, SSURGO data for detailed watershed studies and intense field observation programs. Although this database will not be complete for the entire United States or even the GCIP study area for many years, selected watersheds within the Mississippi basin should have this, or similar coverage, within the EOP. The SSURGO and STATSGO databases are linked through their mutual connection to the NCSS Soil Interpretation Record (Soil-5) and Map Unit Use File (Soil-6). A further description of soil characteristics data set derived from the STATSGO data base is given in Section 10.

A geologic map of surficial geology for the upper Mississippi River Basin was developed by Dr. David Soller of the U.S. Geological Survey in Reston, VA.

Objective: Develop methods for using soil physical property data for GCIP atmospheric and hydrological modeling.

Activities to support this objective in order of priority follow:

a. Definitions of the requirements of GCIP modelers and scientific investigators for multiresolution soil physical and derived hydraulic properties data.

Ongoing feedback is needed from GCIP, PILPS, and ISLCSP activities concerning data requirements for soil properties and the results of model sensitivity analysis to these properties.

b. Use of the STATSGO soils database to develop multiresolution gridded soil physical and hydraulic properties data for the entire GCIP domain to include soil texture, available water-holding capacity, vegetation rooting depth, and other soil physical and hydraulic properties that help to determine the soil thermal and moisture conditions.

These requirements will need to come from the GCIP modeling community. Conceivably a broad range of models ranging from detailed, distributed parameter, physically based models to lumped parameter and stochastic models will be used in GCIP activities. Each may require a unique level of detail of soils information. The modeling and database development (soil science) communities must consult on the nature of these needs.

c. Facilitated development of SSURGO databases for selected watersheds within the GCIP domain. This information will be vital for support of intense field observations and campaigns during the EOP.

d. Tests and evaluations of the STATSGO and SSURGO data in GCIP modeling activities.

e. Improve quantitative understanding of STATSGO and SSURGO data limitations for developing gridded soil physical and hydraulic properties. Specifically, GCIP researchers need quantitative estimates of the uncertainties inherent in the aggregation and disaggregation of soil properties based on sparse soil field measurements and of the limitations of traditional methods for estimating soil hydraulic characteristics (e.g., hydraulic conductivity/matrix potential) from soil physical properties.

This activity also entails research to determine the acceptable minimum resolution for gridding SSURGO and STATSGO data according to soil property and location within the GCIP domain. Research is required to investigate various approaches for generating soils information for models. Sensitivity analyses must be conducted.

f. Explore need for and availability of geologic databases on local and regional scales for use in defining the impact of ground water on land surface- atmosphere interactions.

The impact of ground water on land surface-atmosphere interactions must be further explored. Typically, the upper 2 to 4 m or less of soil profile has been the focus of concern for the parameterizations of these processes. Locally, however, the link to ground water may be significant. GCIP should support further research on this topic by studies of selected data as geologic properties, structure, and knowledge of their relationship to ground water characteristics are known.

6.3.4 Topographic Information

Topographic information includes surface elevation data and various derived characteristics such as aspect, slope, stream networks, and drainage basin boundaries. In general, the requirements of atmospheric modelers for topographic data (i.e., spatial and vertical resolution and accuracies) are much less demanding than the requirements for hydrological modeling. For example, available DEMs for the conterminous United States (0.5 km and approximately 100-m resolution) are generally adequate for most atmospheric modeling. A 60-m DEM derived by USGS from 2-arc second elevation contours is available for the entire ARM/CART region and other selected quads.

The 100-m DEM is generally appropriate for hydrological modeling in large basins (e.g., greater than 1,000 km2 in area). However, topographic data for small basins down to watersheds are needed at two general hydrological scales: hillslope and stream network. The hillslope scale is the scale at which water moves laterally to the stream network. Available USGS 60 m DEMs derived from 2-arcsecond contour data are generally available for the ARM/CART region.

Hillslope flow distances vary and may be as great as 500 m to 1 km. Definition of hillslope flow paths and the statistics of hillslope characteristics require surface elevation data at about 30 m spatial resolution. Such data have been digitized by the USGS from 1:24,000 scale map sheets for part, but not all of the Mississippi River basin. Also, stream locations (but not drainage boundaries) are available in vector form for these map sheets. Because 30- m resolution data are not available globally nor in some parts of the Mississippi basin, research is needed to see how well hillslope statistics, that are important to some hydrological models, can be estimated from topographic properties of lower resolution terrain data. Research is also needed to determine how important hillslope information is to hydrological response of the land surface. Because 1:24,000 scale maps are not available globally, research is needed on how best to use remote sensing techniques as part of a sampling strategy to develop regionalized hillslope statistics (which may be mapped at an appropriately large scale).

An important application of topographic information is to define the hydrological connectivity of basic hydrological computational elements of a model. These elements may be hydrological subbasins or grid elements. The model domain may be a river basin or a set of atmospheric model grid elements. In any case, a set of methods is needed to merge digital terrain, stream location, and existing basin boundary data to establish additional drainage boundaries relative to key locations in the stream channel network and to establish the hydrological connectivity of model elements. The research need is not so much to develop new methods but rather to organize some of the existing methods into a robust and user-friendly system to satisfy many of the needs for basin boundary locations and for hydrological connectivity. (The USGS/WRD and NOAA/NWS are developing a project to address some of these watershed basin and stream network delineation issues, especially standardization of algorithms and data).

The resolution at which stream network data are needed varies depending on the application. Digital stream locations data are available for the entire United States at several resolutions ranging from 1:250,000 to 1:24,000 scale.

Objective: Develop strategies to use available topographic information for model development and model parameter estimation, and investigate approaches suitable to obtain required multiresolution topographic data on a global basis.

Activities to support this objective follow:

a. Definitions of overall GCIP modeler and scientific investigator requirements for multiresolution topographic data including derivative topographic characteristics, documentation of available data sources, and assessment of data adequacy for GCIP Principal Research Areas.

b. Organization of existing topographic data analysis tools and algorithms into a user-friendly software package that will facilitate the generation of basin boundary locations and hydrological networks from existing topographic data resources, as well as hydrological modeling research.

c. Facilitation of hydrological modeling research that is focused on determining which topographic properties, including appropriate horizontal and vertical DEM resolution and accuracies, are essential for properly modeling the effects of hillslope processes on the surface water budget and on the timing of hillslope runoff.

d. Determination of the adequacy of available multiresolution topographic data sets to meet model requirements based on research results in the preceding activity.

e. Investigation of remote sensing technology as part of a sampling strategy to develop regionalized hillslope statistics that are suitable for global data set development, especially in other GEWEX project areas.

6.4 Streamflow and Runoff

Overall Objective: To improve the description of the space-time distribution of runoff over the GCIP study area and to develop mechanisms for incorporation of streamflow measurements in the validation and updating of coupled land/atmosphere models.

Streamflow is determined from measurements of stream stage at a stream-gauging station. Runoff is the spatially distributed supply of water to the stream network which cannot be measured directly. Both surface and sub-surface components are part of runoff. A delay is also inherent between runoff initiation and the time when the runoff reaches a stream-gauging station. This delay varies spatially depending on the distance to the gauge and on how much runoff is occurring.

This research area is concerned with relationships between runoff as computed by atmospheric models, which is distributed in space, and streamflow as measured at streamgauges. This area includes development of globally applicable routing methods to account for the time lags between occurrence of runoff and occurrence of streamflow. Such routing methods might be used in a model to translate runoff to streamflow or they may be used as part of an analysis system to infer runoff from streamflow. Streamflow data are needed to assist in model development, model parameter estimation, and model testing and validation. Although methods may already exist for making streamflow data useful for each of these purposes, additional studies are needed to improve these methods and make them more useful globally.

Two scales of time delay exist between the initiation of runoff and when the runoff reaches a downstream gauge. The first is the hillslope or landscape scale when runoff is moving above and below the surface into the stream channel network; the second is the stream network scale. Because the hydrological processes that occur at the hillslope scale influence both the amount and timing of runoff, this research area is also concerned with estimating both the amount and timing of runoff at the hillslope scale.

Streamflow data and runoff estimates are required both for the development and for the testing and verification of coupled atmospheric/hydrological models. Testing and verification may be approached in two complementary ways. First, runoff from the coupled models can be verified by routing the runoff from a number of grid points (10 or more) to a streamgauge and comparing the model discharge with the observed discharge on a designated basis. The gauges used for this purpose must be essentially unaffected by upstream regulation or diversion. In practice, most of the continental discharge gauges are influenced by regulation and diversion, and may not be good choices for verification (except perhaps on an annual or climatological basis). Therefore, a second complementary approach to compensate for these upstream effects is needed.

6.4.1 Relationships between Runoff and Streamflow

OBJECTIVE: Develop and apply improved techniques for the determination/estimation of runoff and streamflow appropriate to the scales of primary interest to GCIP.

Activities to support this objective follow:

6.4.2 Estimation of Runoff from Streamflow and Climate Data

OBJECTIVE: Apply sensitivity analysis to the error budgets in estimating runoff from streamflow and climate data.

Activities to support this objective follow:

The above activities will be supported by the following specific activities and outputs in 1997-1999.

6.5 Clouds And Radiation

Clouds and radiation are important for several GCIP studies. Cloud formation, in which water vapor condenses into water or ice phase droplets, is an important part of the hydrological cycle. Furthermore, clouds are the major modulator of the Earth's radiation budget. Radiative fluxes at the surface, in the atmosphere, and at the top of the atmosphere are critical factors in the land-atmosphere energy budget. The solar radiation that reaches the surface drives the diurnal and annual cycles of land-atmosphere interactions. Radiation absorbed in the atmosphere is also important for the diurnal cycle of some cloud systems (e.g., stratocumulus) and is always important for the annual cycle. Radiative forcings due to changes in aerosol and land use (surface albedo) have not been accurately quantified to date by the IRC. Satellite data, ground based measurements, and models will be integrated over the ARM/CART site to determine such forcings in GCIP.

Overall Objective: Improve the description and understanding of the radiative fluxes that drive land-atmosphere interactions and their parameterization in predictive models.

6.5.1 Satellite Product Development

Objective: Produce satellite products to define spatial and temporal variability of clouds and radiation over the Mississippi basin.

Activities to support this objective follow:

6.5.2 Validation of Satellite Algorithms to Retrieve the Surface and Atmospheric Radiation Budget

OBJECTIVE: Assess satellite retrieval algorithms and select a preferred algorithm for retrieving GCIP surface and atmospheric radiation budgets.

This objective meets one of the central goals of GCIP namely, the improvement of global systems for the observation of the energy cycle by means of intensive studies in well- instrumented areas. This GCIP activity will:

Recent advances in fast radiative transfer techniques (i.e., Fu and Liou, 1993), in satellite remote sensing, and in the deployment of surface instruments in the GCIP region permit the development of a more accurate and comprehensive description of the radiative fluxes in the atmospheric column. Previous efforts to obtain radiative fluxes by remote sensing have concentrated on the surface (SRB) and the top of the atmosphere (TOA). The full vertical profile of broadband fluxes, as well as the narrowband radiances observed by the satellites, can now readily be computed and compared with measurements at a number of sites. A more internally consistent description of atmospheric radiation is thereby produced. The resulting surface fluxes can be used to validate the operational retrievals described in the previous Section 1. They also serve to test the satellite-based retrievals of clouds, which are used for the calculations. The within-the-atmosphere flux profiles (SARB) can be used to test the fluxes produced by mesoscale and general circulation models. The SARB is the basic driver of the hydrological cycle, the general circulation, and global change.

Version 1 of the CERES/ARM/GEWEX Experiment (CAGEX) contains such a comprehensive radiative description of the atmosphere in the longwave (LW) and shortwave (SW). CAGEX (Charlock and Alberta, 1995) Version 1 provides, for 26 days in April 1994, a space-time grid with:

CAGEX is available by anonymous FTP (http://info.arm.gov/docs/data/CAGEX.html, with instructions). Version 0 was issued in February 1995 at NASA Langley, where it was used to test the Gupta LW algorithm for the next phase of the GEWEX SRB Project. CAGEX is used to test radiation codes at GKSS (Germany), McGill University (Canada), ECMWF, and other institutions. Version 1 also has SW fluxes and aerosol data. An expanded CAGEX run will span approximately one half of the GCIP region for six continuous months in 1996; this will be used to test Land Data Assimilation Systems (LDAS) and the within-the-atmosphere fluxes in the Eta model (Section 5.2).

One surprising result in CAGEX is the demonstration of a significant discrepeancy between measured and computed SW fluxes at the surface for clear skies; this has been confirmed by various ARM researchers in ARESE. In the NASA EOS, CAGEX serves as a window for community-wide access to preliminary retrievals of fluxes and cloud properties in the CERES program. CAGEX fluxes are determined with the Fu and Liou (1993) delta-4- stream radiative transfer code using the Minnis et al. (1993) cloud retrievals. Experiments with tuned fluxes, in which atmospheric constituents are adjusted to cause computed and observed fluxes to better match, are underway (Charlock et al., 1994). For limited time periods, within-the-atmosphere fluxes as measured by Unmanned Aerospace Vehicles (UAV) will be inserted in the data stream. Subsequent versions of CAGEX will be used to validate CERES determinations of atmospheric fluxes and similar exercises using ISCCP and ScaRab. Hence CAGEX will continue well after the launch of CERES on TRMM (August 1997; possible delay to January 1998) and EOS-AM (1998). The MODIS and CERES teams in EOS are now drafting plans for a concentrated validation effort over the ARM/CART site in September 1998.

The dense coverage of measurements over the ARM site are presently supplemented with the geographically dispersed SURFRAD (Section 10.4). When combined with comprehensive satellite-based retrievals and radiative transfer calculations, SURFRAD will provide a rigorous measure of the radiative forcing of climate at selected sites. For example, the present satellite-based record of the interannual variability (IAV) of snow cover lacks an exacting validation in terms of radiative flux; this poses a great uncertainty in monitoring a key climate feedback. There is a corresponding uncertainty in radiative forcing of aerosols; measurements of aerosols and measurements of fluxes have not been matched with calculations to satisfactory accuracy. The SURFRAD monitoring sites at Fort Peck, Montana (high seasonal snow cover and IAV) and Bondville, Illinois (large annual loading of atmospheric sulfur) are well-suited for diagnosing the impacts of snow and aerosols when combined with calculations such as CAGEX (above) or with the NOAA retrievals (Section 6.5.1), which are based on operational satellite data.

The procedures honed in these exercises will be used again with more advanced MODIS, MISR, ASTER, and CERES sensors after the launch of EOS-AM in 1998. In preparation for CERES, helicopter measurements of the SW bidirectional reflectance function (BDRF in 4 channels), the LW window directional radiance, and the broadband SW and LW fluxes (i.e., Purgold et al., 1994) will be made over the ARM site in 1996. The helicopter measurements are vital for improving the integration of space-based and surface-based data for two reasons. First, they are needed to determine the full angular dependence of surface radiation; a given satellite measurement covers only a single angle. Second, they are needed to determine the spatial distribution of radiation about the surface radiometer; the surface radiometer covers only a tiny area. It is hoped that resources will permit helicopter measurements over some SURFRAD and BSRN sites, too. Another supplement to routine surface measurement is enhancement with a spatial network of instruments. In conjunction with CERES preparations during the fall of 1995, NASA Langley deployed of a network of five additional radiometer sites to supplement CAGEX retrievals of surface fluxes in the ARM Enhanced Shortwave Experiment (ARESE). The enhanced spatial network will measure fluxes over a large area, as does a satellite pixel, permitting a more realistic validation of the satellite results.

The combination of (1) detailed radiative transfer calculations, (2) satellite-based retrievals, and (3) surface measurements as anticipated in GCIP will permit a significant advance in the description of atmospheric radiation and associated forcings and feedbacks. Supplements to the surface measurements are needed, however; only a single helicopter survey of ARM is definitely planned; deployment of photometers and cloud lidars at more surface sites is uncertain; the determination of aerosol optical properties is a step forward but not the answer; and snow sites especially should have a network of radiometers on towers.

6.5.3 Validation and Improvement of Operational GOES Shortwave Radiation Budget Products

The operational production of downwelling and upwelling shortwave (SW) and photosynthetically active radiation (PAR) for GCIP is done using the University of Maryland algorithm (Pinker and Laszlo, 1992), as modified for the GOES 8/9 imager. The model also allows estimation of top of the atmosphere shortwave radiative fluxes. The procedure uses clear sky and cloudy top of the atmosphere calibrated radiances in the visible band, the cloud fraction in the target, and information on the state of the atmosphere, as available in real-time from the Eta model, as input to the algorithm. Snow information is also appended, as available from the Eta model data base. Cloud detection is done with a two threshold method, from visible data only. The new GOES 8/9 procedures, namely, the algorithm, the cloud detection methods, the atmospheric input parameters, and changes in calibration, need to be evaluated. The need for incorporation of seasonal/monthly surface type models in the shortwave algorithm has also to be evaluated.

A process has been established whereby the University of Maryland accesses the GCIP insolation products as generated at NESDIS, as well as the input files used at NESDIS to generate the product. The input files are used to run the model off-line, compare with the product produced at NESDIS, and to test various options in the model configuration. Of particular interest are possibilities to optimize the models operation and/or introduce simplifications. The model output will be validated against ground observations,to include, in the near future, observations from SURFRAD, BSRN and ARM/CART. Ground with data for PAR are also needed for validation of this component of the SRB. This process is essential for achieving the best possible accuracy from satellite products.

In addition to the NOAA GOES-8/9 and POES operationally based retrievals in GCIP, the NASA CERES is sponsoring a more limited domain program of research retrievals of the SARB (Charlock et al., 1994). Satellite-based cloud retrievals, meteorological data, and radiative transfer calculations will be used to retrieve the SARB over the ARM/CART site in Oklahoma. Computed fluxes and radiances will be compared with ARM-observed surface and unmanned aerospace vehicles (UAV) fluxes, as well as with other satellite data. Tuning algorithms will subsequently adjust atmospheric and surface input parameters, bringing the calculated SARB to closer agreement with observations. Results of the SARB retrievals will be compared with those of other groups and with data. The aim is to develop accurate retrievals of the SARB based on satellite data and to foster the development of such retrievals in the atmospheric sciences community. The first research data set in this CERES/ARM/GEWEX activity covers the April 1994 IOP. In a 3 x 3 matrix with 0.3 increments, daylight cloud retrievals every 30 minutes are provided from GOES-7 with the Minnis et al. (1993) cloud retrievals for cloud albedo, cloud center height, cloud amount, cloud center temperature, cloud thickness, cloud infrared (IR) emissivity, cloud reflectance, cloud optical depth, cloud top height, cloud IR optical depth, cloud mean IR temperature, and cloud top temperature. In a subsequent ARM IOP, Dr. Charles Whitlock plans to employ a helicopter to measure the spectral bidirectional reflectance of the surface. This measurement will permit a detailed study of the clear as well as cloudy sky effects of the surface and aerosols on the profile of radiative fluxes.

The SARB drives the hydrological cycle, the general circulation, and the global climate change. The SARB computed by GCMs is not regarded to be sufficiently reliable for accurate climate prediction. The state of numerical weather prediction (NWP) model simulations of the SARB limits medium-range weather prediction, too. We lack an adequate observational record of the SARB either in clear or cloudy skies. Cloud feedback is generally considered vital to climate but remains uncertain. More fundamentally, forcing occurs, as well as feedback uncertainties because of the radiative effects due to atmospheric aerosols and the Earth's surface.

An observational SARB record is needed for the validation of GCMs and for diagnostic investigations of low-frequency variability and secular climate change. The development of an observational record of the SARB is one objective of the CERES activity (Wielicki and Barkstrom, 1991) in the EOS and GEWEX. The array of instruments deployed by ARM over the CART site presents a unique opportunity for developing and validating satellite-based retrievals of the SARB. The ARM is well suited to observing the profile of atmospheric water vapor, the vertical and horizontal structure of clouds, and aerosols; these parameters, as well as the ARM surface and UAV measurements of radiometric fluxes, are critical for testing satellite-based retrievals of the SARB. Activities to support this objective include:

6.5.4 Analyses of Clouds and Radiation

OBJECTIVE: Assess model estimates of clouds and radiation and develop improved parameterizations of clouds and radiation processes.

Activities to support this objective are:

6.5.5 Research relating to the GEWEX Cloud Systems Study

The goal of the GEWEX Cloud Systems Study (GCSS) is to improve the parameterization of cloud systems in climate and NWP models. This objective will be achieved through a better quantitative knowledge of the physical processes involved in cloud systems as well as a quantification of their large-scale effects (GCSS 1994). Key issues are described in Browning (1994). The investigation of continental cloud systems is part of the long-term objectives of the GCSS Working Group on Precipitating Convective Cloud Systems (Moncrieff et al. 1996).

One of the aims of GCIP is to improve the treatment of surface and hydrologic processes in NWP and climate models, but clouds have an important impact on these processes. GCSS involvement would contribute to the cloud component to GCIP, by way of cloud-resolving modeling and related activities. In turn, the GCIP data sets would be used to evaluate these models against observations.

Cloud Resolving Models

Cloud resolving models, identified by their ability to resolve cloud dynamics, are the approach of choice of the GCSS. These models derive from traditional nonhydrostatic cloud models but their scope is more ambitious. The effects of convection on the environment and the interaction among physical processes (boundary layer, surface layer, radiation, and microphysics) are the pacing issues, rather than individual processes per se. Since the time scales of some interactions (e.g., cloud--radiation) can be weeks, this is not only demanding on model design but also requires large computer resources.

When used to study precipitating convection (e.g., Grabowski et al. 1996a, b) or frontal cloud systems (Dudhia 1994) grid lengths of about 1km can be successfully employed to calculate bulk effects. Consequently, the domains of cloud resolving models span many NWP grid volumes. The time scales examined by 2D models is up to several weeks and these models are poised to address issues on intraseasonal time scales. An example is the effect of cloud-radiation interactions on the atmospheric and surface energy budgets (Wu et al. 1995b).

Cloud-resolving models also explicitly resolve convection-mean flow interactions that are impossible to accurately observe and since cloud-scale dynamics is explicitly simulated, one key uncertainty is minimized. Data sets from cloud resolving models can be used to evaluate single-column climate models - the testbeds for convective parameterization schemes. These data sets are also a key element in formulating new and more comprehensive approaches to parameterization.

Models need to be evaluated against atmospheric data sets. The GCIP region features several cloud system types, ranging from deep precipitating convection during the warm season, to frontal clouds dominated by ice processes in winter. GCIP will provide data sets for evaluating cloud resolving models, noting the relatively high density of routine observations over the U.S., not to say the special long-term observations available from the ARM CART site.

Two different types of evaluation are required. First, an evaluation of the physical parameterizations used in cloud-resolving models (e.g., microphysics, turbulence, surface processes and radiation) is needed. However, this requires detailed cloud-scale observations, as well as intensive observation periods involving airborne platforms. Neither is available from GCIP.

Second, the effect of clouds on the environment directly relates to convective parameterizations in GCMs and is, in principle, an area to which GCIP can contribute. It is, however, far from a simple matter to utilize data collected during the GCIP Enhanced Seasonal Observing Periods (ESOPs) to evaluate the models.

A basic issue is: what is the minimum observational detail required to evaluate cloud resolving models? An ultimate answer will involve data assimilation in both regional and global models to "fill in'' missing or data-void areas. However, present assimilation methods are neither a panacea nor even practicable on cloud resolving model grids. GCSS will therefore focus on basic problems such as the ensemble response of clouds (deep and shallow) to spatially-averaged, time-dependent forcing applied over scales comparable to or exceeding, climate model grid scales.


The GCSS has a cloud-resolving model intercomparison component. Modeling workshops have been conducted by the Working Group on Boundary Layer Clouds. Non-precipitating stratocumulus clouds in idealized environments were examined using Large Eddy Simulation models (Moeng et al. 1995).

The GCSS Working Group on Precipitating Convective Cloud Systems has an ongoing model intercomparison based on convection over the tropical western Pacific. The data set used in the model evaluation is from the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment (TOGA COARE). To identify scientific and numerical issues as well as to minimize the complications and difficulties of modeling precipitating cloud systems, prototype numerical experiments were conducted (e.g., Grabowski et al. 1995a). This working group intends to move on to continental cloud systems in due course. The GCIP ESOP in 1996, that focused on the GCIP Large Scale Area-South West (LSA-SW) during the warm season, is an opportunity to study organized precipitating systems. A prototype experiment relating to GCIP could start as soon as adequate resources are available and the ESOP data have been analyzed. (Note GCSS looks to the GCIP Data Management and Service System to provide data sets in the desired form).

GCSS/GCIP Projects

The following are candidate projects. Additional projects may arise; for example, noting that the 1997 GCIP ESOP will concentrate on wintertime processes, a GCSS initiative on frontal clouds is a possibility (Ron Stewart, private communication).