In the context of the GCIP, a coupled atmospheric-hydrologic model is defined to be a model or combination of models which simultaneously represents both atmospheric and hydrological processes, which can operate in predictive mode without the need to specify variables or exchanges at the interface between the two model components, and which can benefit from the assimilation of data to specify that interface. This context provides the framework for the GCIP Objective: Develop and evaluate coupled hydrologic/atmospheric models at resolutions appropriate to large scale continental basins.

2.1 General Approach

The implementation of model development in GCIP has followed two paths as described in the GCIP Implementation Plan (IGPO 1993) and was shown in Figure 1-3. On the "research" path are the longer term modeling and analysis activities needed to achieve the GCIP coupled modeling Research Goal - To identify and understand the coupled processes that influence predictability at temporal time scales ranging from diurnal to seasonal and spatial scales relevant to water resource applications , and to develop a coupled model which can be validated (at these scales ) using data for the Mississippi River basin.

Research is focusing on determining , understanding and modeling those processes which are demonstrably important in coupling atmospheric and hydrological systems, rather than those processes which are separately important within these two systems. A GCIP Coupled Modeling Workshop (IGPO 1996b) resulted in a number of recommendations which are incorporated in this and other sections of the Major Activities Plan for 1998, 1999 and Outlook for 2000.

An "operational" path was started in 1993 during the GCIP Buildup Phase to develop and implement the improvements needed in the operational analysis and prediction schemes to produce the model assimilated and forecast output products for GCIP research, especially for energy and water budget studies. The regional mesoscale models also serve to test components of a regional climate model and can provide output for the evaluation of a coupled hydrologic/atmospheric model during the assimilation and early prediction time periods as a precursor to developing and testing a coupled hydrologic/atmospheric climate model. The output from three different regional mesoscale models is routinely compiled as part of the GCIP data set as described in Section 2.4 and Appendix B of this plan.

2.2 Coupled Modeling Research

The GCIP coupled modeling research is predicated on the hypothesis that the creation of regional-scale coupled models which simultaneously represent both relevant atmospheric and the land-surface processes, and the validation of these models against observations from GCIP, will improve our ability to:

In accordance with this hypothesis, GCIP is focusing on those research activities which create, calibrate, and apply coupled models of the atmospheric and hydrologic systems with priority given to research to improve climate prediction and to improve hydrological interpretation of meteorological predictions at the above time scales. The GCIP coupled modeling research is focusing on three program elements that address the three scientific questions and priority needs given in Table 2-1. These issues and planned research activities are described further in the following paragraphs.

Table 2-1: Scientific Agenda for the GCIP Coupled Modeling Research
1. "To what extent is meteorological prediction at daily to seasonal time scales sensitive to hydrologic-atmospheric coupling processes?" - the priority research issues to be addressed by GCIP are:
  • The Evidence for, and the Mechanisms Involved in, Seasonal Predictability
  • The Relative Importance of Hydrologic-Atmospheric Coupling over an annual cycle
  • The Need to Represent Diurnal Variations in Surface Energy Fluxes

2. "To what extent can meteorological predictions be given hydrological interpretation?" - the priority needs in GCIP are for:
  • Evaluation of Seasonal-to-Interannual Predictions
  • Definition of the Predictive Products Required by Hydrologists

3. "How can models of relevant hydrologic-atmospheric coupling processes be improved to enhance meteorological and hydrological prediction?"- the priority needs for GCIP are:
For Precipitation Processes
  • Improved Parameterization of Convective Precipitation in Atmospheric Models
  • Statistical Analyses of Subgrid Scale Precipitation
  • Research into Cold Season Precipitation Issues
  • Improved Understanding of Topographic Influences on Precipitation
For Soil Moisture Processes
  • Improved and Extended Soil Moisture Measurement
  • Coupled Modeling of the Effect of Soil Moisture Heterogeneity on the Atmosphere
  • Improved Parameterization of Hydrologic Submodels
For Snowcover and Other Cold Season Processes
  • Snow Cover
  • Subgrid Interactions
  • Hydrologic Interactions
For Biospheric Processes
  • Vegetation Influences on Hydrologic Cycle
4. "To what extent is model parameter estimation for the hydrologic part of coupled models basin dependent? - the priority needs for GCIP are:
  • Evaluate the Transferability of Existing Parameter Estimation Techniques
  • Improved and Extended Parameter Estimation Techniques

2.2.1 Atmospheric/Hydrologic Coupling Sensitivity

Progress in the representation of land-atmosphere interactions over the last two decades has been sufficient to motivate several operational modeling centers (for example, the National Center for Environmental Prediction, the European Centre for Medium Range Forecasting, and the Japanese Meteorological Center) to implement and benefit from modern-era, multi-layer soil-vegetation- atmosphere transfer schemes. Planetary, continental, and regional atmospheric circulation patterns in such assimilation systems are constrained near truth by the assimilation of atmospheric observations. Nonetheless, the implementation of improved representation of hydrologic-atmospheric interactions has undoubtedly improved the quality of the precipitation and low-level temperature analysis products provided by data assimilation systems. Evidence of and Mechanisms for Seasonal Predictability

GCIP provides an excellent rationale and data source for investigating the hypothesis, in the context of North America, that (globally determined) soil moisture anomalies at the beginning of the warm season influence the regional precipitation in the subsequent months. Atmospheric general circulation model runs with improved representation of interactive moist processes along with diagnostic studies are needed to test this hypothesis and determine the conditions and limitations of its applicability. These would involve comprehensive analyses to explore the lagged correlation, both locally and perhaps downwind, between all the relevant data (on rainfall, evaporation, temperature, clouds, radiation, vegetative state, etc.) now available within GCIP. In addition, it is now time to undertake experimental, free running seasonal to interannual simulations with coupled models of the land-atmosphere-ocean system to give global and regional forecasts. Realistically, early expectations of the skill of such forecasts should be limited to capturing modest indications of regional-scale monthly or seasonal anomalies of precipitation and temperature. Coupling importance in the annual cycle

The strength and influence of the hydrologic-atmospheric coupling varies between cool and warm seasons, which leads to seasonal differences in the importance of land-atmosphere coupling relative to other regional-scale and global-scale influences. An explicit understanding of the seasonal variation of relative coupling strength is necessary to define the relative prediction. Land-atmosphere coupling processes which are important in seasons when local controls are more important likely need more precise representation than those which are important in seasons when global-scale influences dominate.

The required studies will involve a combination of measurement and modeling activities. Observations would likely include atmospheric profiles of moisture, temperature, and wind during both warm and cool seasons and during the transition from cold to warm season, together with simultaneous measurements of the surface fluxes of water and energy. Modeling studies could include sensitivity studies using validated coupled models applied in different seasons and at different spatial scales. Significance of diurnal variations in surface energy fluxes

Based on the results from a coupled land-atmosphere model, Koster and Suarez (1995) suggested that large scale circulation is affected by short-term variability in the surface energy balance. Hence a land surface scheme that realistically reproduces the mean diurnal cycle of the surface energy balance may nonetheless be inadequate for coupled modeling purposes. The scheme might also need to reproduce the short-term variations in the balance of energy.

The extent to which short-term variations in surface energy balance require representation in predictive models when applied at seasonal-to-interannual time scales merits more detailed investigation. Modeling experiments are required to explore this limit on the complexity of the representation of hydrologic-atmospheric processes.

2.2.2 Hydrological interpretation of meteorological predictions

The nature of the meteorological predictions calculated by global-scale models of the ocean-atmosphere-land system is likely to be profoundly different from actual meteorological observations in terms of their spatial and temporal precision and accuracy, even when those predictions have been down-scaled through mesoscale, regional models. Existing hydrological models are designed to work from observations, and their form and function reflect the nature of these observations. Research is required to determine what type of hydrological prediction is possible from seasonal-to-interannual meteorological predictions and at what spatial and temporal scales hydrological interpretation can have worthwhile credibility and utility. Handling uncertainty in meteorological predictions is not a resolved issue in hydrological models, even for short-term forecasts, and reservoir management practice will always need to be incorporated into the hydrological interpretation for North American water resource issues.

There is opportunity to improve communication between atmospheric scientists and hydrologists on this issue, because neither of these two groups have hitherto had opportunity to fully appreciate the relevant capabilities of the other. Hydrologists do not yet appreciate what the nature and form of seasonal-to-interannual meteorological predictions might be, and there is some lack of clarity on this issue. Equally, meteorologists do not yet have an appreciation of what type of seasonal-to-interannual prediction might have practical value to hydrologists. At this time, therefore, the need is to provide better definition of these issues in order to establish a means of interaction between the two communities. Evaluation of seasonal-to-interannual predictions

As noted above, not only is GCIP in a strong position to foster experimental seasonal-to-interannual forecasts focused on the North American continent, it is also uniquely able to provide effective validation of such forecasts by virtue of the existing and new data that are being collected for the U.S. in general, and for the Mississippi River basin in particular. However, some redefinition of GCIP data products will be required. Specifically, once the form, nature, and spatial and temporal scale of seasonal-to-interannual prediction products are defined, it will be necessary to synthesize equivalent observational products from GCIP's precipitation and temperature measuring networks. Future westerly extension of the GCIP study area also seems essential if there is to be a better match between areas in the U.S., where seasonal-to-interannual prediction is most feasible, and areas in which data collection within GCIP has priority. Arguably, the single-most challenging technical problem will be providing a credible regional measurement of cold-season precipitation for the purposes of comparison with seasonal-to-interannual predictions. Definition of the predictive products required by hydrologists

Resource managers within the hydrological community might be able to make use of a range of predicted outputs from coupled land-atmosphere-ocean models, but hitherto they have tended to rely on traditional meteorological and hydrological measurements applied to conventional hydrological models for streamflow predictions. Although hydrologists have a good capability for using statistical forecast information, so far the coupled modeling community has not given priority to providing this type of information. However, research into the possible hydrological interpretation of these predictions cannot begin until the nature and form of such predictions are better defined.

There is a need to develop better understanding of the requirements of the hydrological community so that any predictive meteorological products provided at the seasonal-to-interannual time scale can be tailored more precisely and the opportunities for timely application of GCIP research within hydrology thus enhanced.

2.2.3 Improved coupling processes - issues and actions

Accepting the hypothesis that better representation of processes in coupled atmospheric-hydrologic models will yield improved meteorological prediction at all time scales, research is required to determine, understand, and model such coupling processes. The focus of research into several coupling processes might evolve in response to better specification. However, initially, research will address improved representation of precipitation, soil moisture and biospheric processes. Precipitation processes

Clouds and their associated precipitation are important in the global water and energy cycle and their accurate representation in atmospheric models is crucial. However, incorporating moisture processes is difficult because cloud and precipitation physics is poorly understood, and because the horizontal resolution of large-scale models is much larger than the scales at which clouds are formed --hence cloud-precipitation processes are subgrid-scale mechanisms which must be parameterized.

(1) Improved parameterization of convective precipitation in atmospheric models

Focused smaller-scale modeling studies are needed to investigate how to improve the parameterization of convective precipitation within regional-scale atmospheric models. To have credibility, such studies require experimental validation. Such experiments would involve simultaneous measurements in the atmosphere and at the surface, and would need to be framed in a proper regional context by specification of the atmospheric flow fields through the study area. GCIP has already begun planning the provision of some of the required observations, in the form of a Near-Surface Observation Data Set described in Section 10. GCIP is also fostering opportunities to validate regional models of precipitation within the Mississippi River basin through collaboration with other observational programs such as ARM, the US Weather Research Program, and the GEWEX Cloud System Study as described in Section 8.

(2) Statistical analyses of sub-grid scale precipitation

Studies are needed to characterize the true variability of precipitation in space and time and its relation with the state of the overlying atmosphere. Understanding the relationship between actual continental precipitation and that predicted by atmospheric models is a very high priority for GCIP. Such studies are especially important at hourly to daily time scales and at spatial scales up to the area covered by a few grid intervals in mesoscale and large-scale atmospheric models.

The accuracy with which precipitation can be measured (by gauges, radar, or both) is likely to be an issue in such studies. Recognizing this last point, the LSA-SW would be the appropriate initial focus for such studies since the stage 3, gauge-calibrated radar precipitation products provided by the Arkansas-Red Basin River Forecast Center (ABRFC) now have established value for comparison against modeled estimates using the Eta, MAPS and RFE regional NWP models.

(3) Research into cold season precipitation issues

Snow is an important component of precipitation, particularly so in the northern and western regions of the U.S., where it provides an important component of the available surface-water resource. Many of the basic atmospheric parameterization issues are similar for warm and cold season precipitation, though parameter values are likely to change between seasons. However, there are additional important research issues related to quantifying cold season precipitation and its partition into runoff or soil moisture which must be addressed. Such questions will be priority issues in the scientific agenda for GCIP studies in the LSA-NC.

The central question is how to develop precipitation volumes that give an accurate measure of the temporal and spatial distribution of snowfall. Associated with this question is the need to determine how representative are rain gauge measurements of snowfall and how to combine surface observations of snow depth and with remote-sensing estimates from aircraft and satellites. These questions of snowfall measurements are discussed further in Section 6.1.

(4) Improved understanding of topographic influences on precipitation

Water is a critical resource in the western U.S. It occurs mainly through the winter season and to a great extent depends on the total water vapor flux across the mountains and, hence, on large-scale circulation in the atmosphere in winter. However, it is strongly influenced by orography, and GCIP has the potential to make an important contribution to the improved seasonal-to-interannual prediction of water resources in the western U.S. by improving the predictability of orographic precipitation. Accurate forecasting of water resources requires better definition of the location of precipitation than is possible with current weather forecast models. The optimal spatial scale for these forecasts is around 2-3 km, but to achieve this would require a nested modeling approach as an extension of presently available systems. Exploratory research is required to evaluate the value of successively nested forecast models as a possible mechanism for applying seasonal-to-interannual forecasts to water resource issues. Soil Moisture Processes

Soil moisture possesses a memory during its seasonal evolution, and is determined as the residual between precipitation on the one hand and evaporation and surface and subsurface runoff on the other. Many of the modeling studies which have provided evidence that seasonal predictions show sensitivity to hydrologic-atmospheric coupling have in fact been framed in terms of sensitivity to modeled or prescribed soil moisture. There is, therefore, a clear understanding of the importance of soil moisture for climate prediction at the seasonal time scale.

Heterogeneity in the spatial distribution of soil moisture is an inevitable consequence of uneven precipitation, and this can be exacerbated by the subsequent flow of surface and subsurface water across uneven topography. Modeling investigations (e.g. Avissar and Liu 1996) indicate that naturally occurring soil moisture heterogeneity (acting through land-atmosphere coupling process) significantly influences the behavior of the overlying atmosphere. Progress in understanding the effect of area-average soil moisture, heterogeneity in soil moisture fields, and in validating models which describe the seasonal evolution of soil moisture in space and time have all been curtailed by the historic (and still current) lack of soil moisture measurements.

(1) Improved and extended soil moisture measurements

The growing deployment within GCIP of arrays of field systems capable of routine measurement of soil moisture and the prospect of future deployment of aircraft- and space-borne sensors capable of providing indirect measurements of near-surface soil wetness promise relief from observational limits on understanding for soil moisture processes in coupled models.

Installation of automated soil moisture sensors within the ARM-CART, Little Washita Watershed and the Oklahoma Mesonet are in place or underway, and plans are being made to extend deployment in the Oklahoma Mesonet to include all 114 sites and further extensions to similar distributed data collection networks elsewhere in the Mississippi River basin. GCIP is coordinating the collection of a set of soil moisture (and temperature) profile measurements along a north-south transect to make observations over the annual cycle, but with emphasis on documenting freezing and thawing episodes during the cold season. This transect from Plainview, TX (about 30N) to Bemidji, MN (about 47N) in the vicinity of the 96W longitude. In addition to these new data sources, the Illinois state water survey soil moisture data (Hollinger and Icard 1994) remain a valuable data resource for GCIP. The distribution of soil moisture data from these new arrays of soil moisture sensors to the GCIP coupled modeling community is a high priority, as is their synthesis into regional products for model initiation and calibration purposes. A more detailed description of the soil moisture measurement and analysis is given in Section 6.2.

The GCIP community strongly supports the proposal to provide routine remotely sensed measurements of soil moisture using a satellite L-band microwave radiometer. The community understands that such observations can only provide indirect estimates of near-surface soil wetness for certain vegetation covers, but also recognizes that these data are most reliable for short-rooted and sparse vegetation where soil moisture control is most important. Routinely provided soil wetness estimates from satellites could be exploited for coupled model initiation and validation using four-dimensional data assimilation techniques to improve the prospect of better seasonal climate predictions for North America. Moreover, GCIP provides a unique opportunity to validate and calibrate remote-sensing soil moisture data because of the richness of other data fields, such as WSR-88D and gauged rainfall, runoff, and modeled evaporation, from which alternative area-average soil wetness estimates can be made. Calibration of remotely sensed soil wetness data within the GCIP region could thus be the basis for their application elsewhere in the world.

The potential availability of new sources of soil moisture data gives rise to the need to determine how these data can best be used to initiate and validate coupled models. Research is required to investigate how to use sample data from arrays of surface measurements and exploratory remote-sensing data from airborne radiometers. Some modeling studies have been done, but with very limited field validation. Properly conceived combined field and modeling studies such as the recent CASES-97 should greatly illuminate this issue. The coupled modeling community is aware of and applauds the GCIP efforts during the last four years and the DOE, NSF, and NOAA sponsored CASES-97 and the NASA sponsored SGP97 field studies within the ARM-CART study area in the Mississippi River basin that fulfill some of these observational needs, and look forward to working with the data that will result .

(2) Coupled modeling of the effect of soil moisture on the atmosphere.

Investigations on the effect of soil moisture and its heterogeneity in a fully coupled 3-D, atmospheric-hydrological system are required. The opportunity exists to run fine-scale, nested grid microscale (large-eddy simulation) models that can resolve clouds and the resulting precipitation fields in the context of the upcoming observational studies just described. These model results (considered in a statistical sense) can be compared with the airborne sensor and ground soil moisture observations and with radar and gauged rainfall measurements to determine the quality of the model simulation.

An alternative approach to coupled modeling is to assume that precipitation and other atmosphere processes cannot be predicted deterministically and to conceive models that provide statistical representation of these processes. The challenge is then to develop complementary hydrological models that can be forced with statistical distributions of meteorological variables such as precipitation, solar radiation, etc., and to use these to calculate statistical estimates of the feedback to the atmosphere in the form of sensible-heat fluxes, etc. Statistical models of this type would also benefit from validation against the statistical distributions of precipitation and soil moisture observed in the upcoming observational studies discussed above.

A much greater understanding is needed on how coupled models represent and would utilize the soil moisture observations for testing and validation, both in a spatial and temporal context. Efforts are needed to bridge the gap between the disparate scales of the point measurement with the simulation model grid box. Further, basic research is needed to determine to what extent downscaling of remotely sensed soil moisture is required in order to be used in coupled models.

An important aspect of coupled modeling research concerns the possible importance of soil moisture on the formation and evolution of mesoscale convective complexes (MCCs) and mesoscale convective systems (MCSs). Such large mesoscale systems are often initiated over mountainous terrain and move eastward, and they produce a significant portion of warm season precipitation in the Mississippi River basin. Current studies in the western Mississippi River basin need to take account of these mesoscale systems because they play a major role in the warm season hydrological cycle in the southeastern Mississippi River basin. Fine-scale modeling studies are required to ensure adequate simulation of MCCs and to investigate their relation to the underlying soil moisture fields in the regional NWP models. Again these studies would be best linked to upcoming observational initiatives. After accurate trial simulation of MCCs is accomplished in these particular situations model tests of the effect of MCCs on the regional hydrology can be made under varying soil moisture conditions. A further description of this topic in connection with research relative to the GEWEX Cloud Systems Study is given in Section 8. Snowcover and Other Cold-Season Processes

(1) Snow cover

With its high albedo, low thermal conductivity, and considerable spatial and temporal variability, the seasonal snow cover overlying land plays a key role in governing the Earth's global radiation balance; this balance is the primary driver of the Earth's atmospheric circulation system and associated climate. Of the various surface radiation balance components, the location and duration of snow cover comprises one of the most important seasonal variables. In the northern hemisphere, the mean monthly land area covered by snow ranges from 7% to 40% during the annual cycle, making snow cover the most rapidly-varying surface-feature on Earth. In light of the role that snow plays in determining weather and climate, it is essential that regional and global models used to simulate weather and climate be capable of accurately describing the evolution of seasonal snow covers. In past years, significant strides have been made to better represent snow cover in climate models, but there are still indications that current representations of seasonal snow in these models are plagued by significant deviations from observed snow-related fields.

The timing of snow deposition and melt is one of the most important climatic and hydrologic influences affecting agriculture and water resources. A detailed understanding of snow pack processes, including thermal properties, meltwater percolation, density and albedo evolution (especially during the melt season) is necessary to understand the relationships between snow cover, atmospheric processes, and surface hydrology during 'normal' and 'anomalous' snow cover regimes. Lagged relationships between snow cover and other parameters are also expected to be important. A main goal of GCIP should be to improve our understanding of these processes, and to build better representations of snow cover in the global and regional climate models that are used for GCIP diagnostic and prediction studies. Accomplishing this will require studies which 1) evaluate sub-models using GCIP snow and meteorological field data; 2) develop parameterizations of snow-cover extent for atmospheric models; 3) evaluate coupled simulations using satellite data and other gridded data sets; and 4) identify snow-atmosphere feedback-processes operating within the GCIP regions.

(2) Subgrid Interactions

The interactions between wind, topography, vegetation, and snowfall produce snow covers of non-uniform snow-water-equivalent. During these blowing and drifting snow events, a largely unknown amount of moisture is returned to the atmosphere as the snow grains sublimate during transport. When the snow melts, the variable snow depth leads to a patchy mosaic of vegetation and snow that evolves as the snowmelt progresses. From the perspective of a surface energy balance, the interactions between land and atmosphere are particularly complex during this period. Subgrid-scale heterogeneity in land surface use/cover is likely to be an important factor in determining the time dependence of snow cover fraction and albedo during melt episodes. Field experiments need to be conducted to determine the magnitude and principal sources of subgrid-scale heterogeneity in albedo and snow cover for agricultural and mountainous landscapes typical of the Mississippi River Basin. This should be done using both ground-based and airborne measurements. These analyses should also focus on developing methods to incorporate heterogeneity effects into land-surface models using land-cover data. The estimation of snow-cover extent and winter surface albedo also needs to be addressed from the perspective of knowing the fractional snow-covered area within the model domain.

Because of importance of snow in simulating weather and climate, the level of snow-model complexity for use in these models needs to be addressed. Several features of process-level snowmelt models might be used to help capture subgrid-scale variability and improve snowmelt simulations. In particular, important tasks relevant to cold-season processes include: quantifying the feedbacks between exposed vegetation and the melt of adjacent snow covers, demonstrating the importance of fractional snow-covered area in surface-energy partitioning during snowmelt, and identifying significant interrelationships between non-uniform snow distributions, melt rates, and exposure of vegetation.

(3) Hydrologic interactions

Important cold-season hydrologic processes include frozen soil, the infiltration of frozen soil, frozen lakes, and relationships between snowmelt and stream discharge. Frozen soils have, until recently, been ignored by most land-surface macroscale models, but they can play an important role in increasing runoff during snowmelt events. In particular, the interactions between snowmelt, frozen soil, ponding, and infiltration need to be addressed. As part of studies focusing on the amount and timing of water resources and the soil moisture available for subsequent evaporation, it is also necessary to document, understand, and model how the water is partitioned into runoff and infiltration when snow and ice melts. Land-surface hydrology models developed for GCIP must include these cold-season-related interactions. Biospheric processes

Vegetation type and amount influence various aspects of the hydrologic cycle, from interception of rainfall to active control on the transpiration process through stomatal regulation. This has direct impact on the partitioning of available energy (net radiation) into sensible, latent, and ground heat fluxes. Development of a vegetated canopy also affects the absorption of momentum from the local wind and is manifested in the local friction velocity and corresponding surface roughness. Although the absorption of momentum by the canopy is determined in part by the total amount of leaf biomass, the energy partitioning is closely coupled to the amount of active or green leaf biomass. Land-surface models must be able to account for this difference not only during periods of drought or water stress, but also during the later stages of the yearly growth cycle when many plant species begin senescence. These various stages of the growth cycle are likely to contain a spectral signature that may be used not only to assess the total biomass and percent green leaf area for model evaluation but for future applications in near real-time model data assimilation mode for both short and long range forecasts. Much of this research will be addressed through the International Satellite Land Surface Climatology Project (ISLSCP) from joint NOAA/NASA sponsored observations and modelling studies. In addition to the NOAA sponsored long-term flux measurements, additional sites within the GCIP domain where observations of mass and energy fluxes and information on the planetary boundary layer are proposed. These measurements will be used to not only document the energy balance over the annual cycle, but to test many of the land-surface parameterizations currently used in the short, medium and long range forecasts. It is also anticipated that short-term field campaigns will be conducted in order to evaluate how the energy fluxes and forcing variables vary spatially. This will be accomplished with instrumented aircraft capable of similar energy flux and radiative measurements found on the surface flux systems.

Measurements of soil moisture will play an important role in assessing the role of water stress of plant response and biological processes and the resulting impacts on the surface energy balance. Water stress can play an important role in regulating the surface energy balance. Although an ample amount of precipitation can replenish the available soil water for plant processes, plant recovery is not immediate and may not always be entirely reversible. This "timescale" of recovery can be different depending on the plant species considered. Attempts will be made to evaluate these water related impacts and its impact on the local surface energy balance.

2.2.4 Model Parameter Estimation

A key step in applying land surface parameterization schemes is to estimate model parameters that vary spatially and are unique to each grid point. Local model parameters are estimated on the basis of information about vegetation, soils and geology, so gridded fields of these characteristics are needed at various scales to provide such estimates. It has been shown (e.g. In PILPS2c) that existing a priori parameter estimation techniques may produce large errors and biases in the water balance, especially in mean annual runoff. Improved methods for parameter estimation are needed. Parameter Estimation Techniques

Existing parameter estimation techniques generally assume that land surface model parameters are related to various land surface characteristics according to simple a priori relationships. These relationships are assumed to apply universally without regional or climatic variation. For example, the rooting depth of a given type of vegetation is assumed to be the same regardless of the climate where the vegetation is located. Existing parameter estimation schemes are largely untested. It remains to be seen if improved universal parameter estimation techniques can be found for at least some models or if regional relationships may be required.

There already is a wide range of models available. The Project for Intercomparison of Land surface Parameterization Schemes (PILPS) has shown that the available schemes can indeed produce a wide range of results given the same hydrometeorological forcing, land surface characteristics and common rules for parameter estimation. No doubt part of this is due to model structure differences, but much is due to the way model parameters are estimated One approach to developing improved parameter estimation techniques would be to use historical observations of runoff response to observed hydrometeorological forcing of many watersheds over a wide range of climatic and land surface conditions. The steps, which are illustrated in Figure 2-1 are:


Figure 2-1 Steps in Parameter Estimation for the Model Parameter Estimation Experiment (MOPEX).

An important step in achieving this goal is to assemble historical hydrometeorological data and river basin characteristics for about 200 intermediate scale river basins (500 - 10,000 km2) from a range of climates throughout the world. The data sets to be developed would not be model specific and would be appropriate for developing parameter estimation schemes for most, if not all, land surface parameterization schemes. A Model Parameter estimation EXperiment task (MOPEX) has been initiated by GCIP to begin to assemble the required data sets and to organize parameter estimation experiments among model developers and users. Potential Improvements for Model Parameterization

Some factors which need to be considered with regard to critical variables in land surface modeling are summarized below:

2.3 Improvements to regional mesoscale models

For the past four years there has been an extensive effort to acquire the model output from several operational/experimental centers from a range of operational models of varying resolution, physics and data assimilation systems. GCIP is concentrating on three regional mesoscale models (IGPO 1995):

The participation by the operational centers in providing regional model output for GCIP leads to a mutually beneficial relationship. The principal benefit to GCIP is to provide a measure of the inter-model variability of the outputs from the different regional models which can also be related to the global model output from the operational centers. GCIP can provide benefit to the operational centers by enabling them to make use of the enhanced data sets to calibrate and validate the model data assimilation and forecast systems.

The regional mesoscale models are supporting GCIP research in the following manner:

The regional models now running operationally (NCEP/Eta and CMC/GEM) will be upgraded with numerous improvements during the next several years. The GCIP investigators need to be aware of these plans for improvements and the schedule being followed to incorporate these improvements into the operational models. The experimental MAPS model will also be upgraded during the next several years. Projected improvements to each of the regional models is described in the remainder of this section.

2.3.1 The NCEP Mesoscale Eta Model and Eta Data Assimilation System (EDAS)

Since April 1, 1995, output from the NCEP Eta model (Black 1994) and its associated Eta-based 4-D Data Assimilation System known as EDAS (Rogers 1995) have been routinely archived for GCIP. In conjunction with this milestone, NCEP implemented for GCIP an extensive expansion of the routine ETA/EDAS output products, including a vast suite of surface and near-surface products that encompass all the surface energy and water fluxes, soil moisture and temperature, snowpack and snowmelt, and surface and subsurface runoff. These output products include a) 3-hourly analysis and 6-hourly forecast horizontal gridded fields (known in GCIP as MORDS) and b) hourly station time series output (known is GCIP as MOLTS) at nearly 300 sites. A number of GCIP investigators have completed and published assessments of the coupled ETA/EDAS land-surface and/or water budget performance, including Berbery et al. 1996; Yarosh et al. 1996; Betts et al. 1997; and Yucel et al. 1997. The assessment being done by Curtis Marshall at the University of Oklahoma is making use of the Oklahoma Mesonet, including the newly installed soil moisture profile sensors.

With GCIP support, and in collaboration with GCIP investigators including the NWS Office of Hydrology (OH), NCEP over the last four years has accelerated ETA/EDAS development and improvement in the following three key areas:

These three GCIP-supported NCEP development areas have resulted in the following GCIP-related improvements being implemented operationally into the ETA/EDAS system (including the GCIP model output data sets):

The steady march of major land-surface implementations in January 1996, February 1997, and January 1998 represent major GCIP coupled model milestones, resulting from the focused and concerted GCIP-funded efforts of both the GCIP Core Project (NCEP, OH, NESDIS) and external GCIP researchers. These operational milestones represent noteable success in the model development strategy laid out in Figure 1-3 of Section 1.4.1, which called for a clearly identifiable operational path to serve as a demonstration and implementation environment and for the research advancements in the research path.

In addition to the above ETA/EDAS model/assimilation changes, production of the following realtime products were initiated for GCIP:

The following is a list of ongoing GCIP-focused ETA/EDAS developments now underway with a projected implementation within the next 18 months:

The 3-D variational assimilation technique (3-D VAR) implemented in January 1997 represents a major data assimilation milestone. This advanced data assimilation technique allows easier incorporation of non-traditional data sources, such as direct use of satellite radiances, cloud cover, precipitation, radar radial winds, radar reflectivities, wind profilers, WVSS, ACARS, and ASOS.

The follow-on to 3-D VAR, namely 4-D variational assimilation (4-D VAR), employing the linear adjoint of the Eta model, is well advanced at NCEP/EMC and undergoing routine testing. The linear adjoint has been extended to include the new land-surface/hydrology physics, which provides an opportunity for the assimilation of land-surface related information such as streamflow and satellite-derived surface skin temperature. 4-D VAR is computationally expensive and its operational implementation awaits the next generation computer upgrade at NCEP.

2.3.2 Regional Model Upgrade at CMC

It is expected that the quality of the regional model outputs at CMC will improve significantly during the coming two or three years, especially in terms of the variables that are important for the water and energy budgets which are of prime interest to GCIP.

The developments during this period will stem from a major change that was made in February 1997 when the Regional Finite Element (RFE) was replaced by the Global Environmental Multiscale (GEM) model. This is the result of a major project which has been in progress at Recherche en prevision numerique (RPN) with the goal of developing a non-hydrostatic variable-resolution global model of the atmosphere. This model uses a finite-element based spatial discretization and has been designed for efficiency and flexibility to satisfy the requirements of operational weather forecasting on a wide range of time and space scales (see the description published earlier in the GCIP Major Activities Plan for 1995, 1996 and Outlook for 1997; IGPO 1994c). This model uses a global variable-resolution strategy, permitting the focusing of an arbitrarily-rotated high resolution latitude- longitude mesh on any geographical area of interest, be it tropical or extra-tropical, making it a more flexible strategy than the former operational RFE model which is limited to extra-tropical applications. The overhead associated with using a model of global extent for short-range forecasting, even at the meso-gamma scale, is relatively small: more than half of the total number of meshpoints are on the uniform-resolution area of interest, and the overhead of using variable resolution outside this area is consequently comparable to that of the sponge regions of one-way interacting models. To maintain the validity of the model at the mesoscale, the formulation uses the non-hydrostatic Euler equations, with a switch to revert to the hydrostatic primitive equations for larger scale applications where the hydrostatic assumption is valid. To this end a pressure-type hybrid vertical coordinate is adopted. Prototype tangent linear and adjoint versions have also been developed in preparation for a 4-dimensional variational (4DVAR) analysis system.

The first implementation of GEM was made with a configuration giving a uniform fine mesh grid over North America and the adjacent ocean areas at a resolution equivalent to the former 35 km RFE one. However, testing of an experimental version with a 15 km resolution over more limited regions of North America is already in progress. This experimental version uses the Fritsch-Chappell meso-scale convective parameterization as opposed to a Kuo-based scheme in the currently operational version. The recent installation of a 32 processor NEC SX-4 supercomputer, with other upgrades to come during the three-year period, should permit a continuing gradual increase in the horizontal resolution, with corresponding increases in the number of vertical levels. Progress with the 4DVAR analysis scheme should also lead to better assimilation of meso-scale data, especially related to moisture which is crucially important for GEWEX. Energy budget calculations are also expected to benefit from more sophisticated solar and radiation parameterizations.

A new concentrated activity on model coupling has been initiated at RPN, to do research and development on the coupling of atmospheric, land surface, hydrology, ocean, ice and wave models in order to construct a more comprehensive environmental prediction system. The construction of the baseline system is expected to take about a year and a half, but it is likely that a more interactive coupled data assimilation and prediction system will become available to CMC during the year 2000, with significant impact on the water and energy components in the analyses and forecasts.

2.3.3 Improvements to MAPS

The gridded output from the Mesoscale Analysis and Prediction System (MAPS) will improve over the next several years of the GCIP EOP in different areas including model physics, data assimilation, and spatial resolution.

Some improvements related to GCIP have already been implemented, including access to daily lake-surface temperatures (from NOAA's Great Lakes Environmental Research Laboratory), snow and ice cover (from NCEP and the US Air Force). MAPS is currently using monthly climatological sea-surface temperature, to be replaced by daily information from NCEP in the near future.

The most important of the GCIP-related changes has been the implementation of a multi-level soil/vegetation model. This model, currently running with 5 soil levels, is described by Smirnova et al. (1997). High-resolution data sets for fixed or seasonally varying surface characteristics (soil type, vegetation indices, albedo) made available by NCEP are being used now in MAPS.

Full atmospheric radiation has also been added to the MAPS experimental 40-km model, substantially improving lower troposphere temperature forecasts.

A number of changes implemented during WY'97 in the experimental 40-km MAPS, include:

Plans for WY98 -

Plans for WY99.

2.4 Model Assimilated And Forecast Data Sets

One of the principal functions of the regional mesoscale models, as was noted in Section 2.3 is to produce the model assimilated and forecast output products for GCIP research, especially for energy and water budget studies. The production of such data sets is designed to achieve the following objectives:

The activities relevant to the third objective above were described in the previous section. The activities relative to the first two objectives are summarized in the remainder of this section.

2.4.1 Regional Mesoscale Model Output

The list of model output fields needed by GCIP researchers was given in Table 3, Volume I of the GCIP Implementation Plan (IGPO 1993). From the beginning of GCIP, it has been the intent to acquire model output from several different models of varying resolution, physics and data assimilation systems. The large volume of data produced by the current generation of atmospheric models has forced a number of compromises in order to achieve a tractable data handling solution for model output data. The data volume is further enlarged by the GCIP need to enhance the traditional model output to include additional fields needed by researchers to perform meaningful studies of the water and energy cycles. The near-term GCIP needs for model output data will be met by concentrating on three regional mesoscale models:

The model output is divided into three types:

Each model output type is described in more detail in Appendix B.

2.4.2 Evaluation of Model Output

Objective: To produce a quantitative assessment of the accuracy and reliability of the model assimilated and forecast data products for applications to energy and water budgets.

All of the evaluations require a lengthy series of observed data for the variables considered critical to achieve the objective stated above. As a start on this evaluation effort , GCIP is compiling a composite data set for as many of the variables as reasonably available. In order to maximize the number of observed variables this composite is focused on the LSA-SW with the initial emphasis on the ARM/CART site as described in Section 10. Evaluations of the regional model output is also part of the budget studies described in Section 5.