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Lixin Lu and W. James Shuttleworth

Abstract

In this study, a climate version of the Regional Atmospheric Modeling System (ClimRAMS) was used to investigate the sensitivity of regional climate simulations to changes in vegetation distribution in the Great Plains and Rocky Mountain regions of the United States. The evolution of vegetation phenology was assimilated into the ClimRAMS in the form of estimates of the leaf area index (LAI) derived from the normalized difference vegetation index (NDVI). Initially, two model integrations were made. In the first, the NDVI-derived vegetation distribution was used, while the second integration used the model's “default” description of vegetation. The simulated near-surface climate was drastically altered by the introduction of NDVI-derived LAI, especially in the growing season, with the run in which observed LAI was assimilated producing, in general, a wetter and colder near-surface climate than the default run. A third model experiment was then carried out in which the (comparatively more homogeneous) spatial distribution of the LAI remained the same as in the “default” run, but the overall, domain-averaged magnitude of the LAI was reduced to be consistent with that of NDVI-derived LAI. This third run simulated a drier and warmer near-surface climate compared to the default run. Taken together, these results indicate that regional climates are indeed sensitive to seasonal changes in vegetation phenology, and that they are especially sensitive to the land surface heterogeneity associated with vegetation cover. The need to realistically represent both the spatial and temporal distribution of vegetation in regional climate models is thus highlighted, and the value of assimilating remotely sensed measures of vegetation vigor in Four-Dimensional Data Assimilation (4DDA) systems is demonstrated.

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W. James Shuttleworth and Ian R. Calder

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Long-term evaporation measurements are expressed in the Priestley-Taylor (1972) “Potential evaporation” framework for a spruce forest in Plynlimon, Wales, and a Scots Pine forest in Norfolk, England. The results are used to illustrate the possibility of significant variability in evaporation from forest vegetation in response to precipitation input, and so provide a warning against the indiscriminate use of the Priestley-Taylor formula. A tentative suggestion is made regarding a possible role for potential evaporation in the forest environment.

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Ismail Yucel, W. James Shuttleworth, James Washburne, and Fei Chen

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Data derived at the National Centers for Environmental Prediction via four-dimensional data assimilation using the Eta Model were evaluated against surface observations from two observational arrays, one located in the semihumid, continental climate of Oklahoma and Kansas and the second in the semiarid climate of southern Arizona. Comparison was made for the period of the Global Energy Water-cycle Experiment Continental-scale International Project’s “GIST” dataset in 1994 and their “ESOP-95” dataset in 1995, and for the months of March and May in 1996. Coding errors in the Eta Model’s postprocessor used to diagnose near-surface temperature and humidity are shown to have compromised the GIST and ESOP-95 near-surface data. A procedure was devised to correct the GIST and ESOP-95 near-surface fields by mimicking the corrected code used in the Eta Model since January 1996. Comparison with observations revealed that modeled surface solar radiation is significantly overestimated except in clear-sky conditions. This discrepancy in cloudy-sky solar radiation was altered little by the substantial January 1996 revisions to Eta Model physics, but the revisions are shown to have greatly improved the model’s ability to capture daily and seasonal variations in near-surface air temperature, specific humidity, and wind speed. The poorly modeled surface radiation complicates evaluation of modeled surface energy fluxes, but comparison with observations suggests that the modeled daytime Bowen ratio may be systematically high. This study clearly demonstrates the strong sensitivity of model-calculated, near-surface variables to the physics used to describe surface interactions in the data assimilation model. To mitigate against this and to aid intercomparisons between other data, it is recommended that model-derived data always include sufficient information to allow potential users to recalculate the extrapolation to the surface using a user-defined model of surface–atmosphere exchanges.

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Eleanor J. Burke, W. James Shuttleworth, and R. Chawn Harlow

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MICRO-SWEAT, a soil–vegetation–atmosphere transfer scheme (SWEAT) coupled with a microwave emission model, was used to predict the microwave brightness temperatures (T B) measured at El Reno, Oklahoma, during the Southern Great Plains 1997 (SGP97) field experiment. Comparison with soil-moisture time series measured at four intensively monitored sites revealed the need for a substantially greater soil-saturated hydraulic conductivity than that estimated from soil maps. After revision of the hydraulic conductivity, the modeled and measured time series of soil moisture and surface energy fluxes showed excellent agreement with observations at these 4 sites and with the measurements of the surface soil moisture and T B at the remaining 11 measurement sites. A two-dimensional array of calibrated MICRO-SWEAT models was implemented at 200-m resolution for the El Reno area. There were noticeable differences between the spatial distributions of modeled and measured T B. These differences likely result from imperfect knowledge of the spatial distributions of soil properties, precipitation, and the estimated optical depth of the vegetation used in MICRO-SWEAT. A statistical measure of the usefulness of assimilating the observed soil moisture was explored by assuming the estimation of the optical depth provided the main source of error in the relationship between soil moisture and microwave brightness temperature. Analyses indicated that there is merit in assimilating T B observations for significant portions of the modeled domain, but it is suggested that this would be enhanced if the optical depth of the vegetation were also directly remotely sensed, as proposed in the Soil Moisture and Ocean Salinity (SMOS) mission.

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David J. Gochis, W. James Shuttleworth, and Zong-Liang Yang

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This paper describes the second part of a study to document the sensitivity of the modeled regional moisture flux patterns and hydrometeorological response of the North American monsoon system (NAMS) to convective parameterization. Use of the convective parameterization schemes of Betts–Miller–Janjic, Kain–Fritsch, and Grell was investigated during the initial phase of the 1999 NAMS using version 3.4 of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) running in a pseudoclimate mode. Substantial differences in both the stationary and transient components of the moisture flux fields were found between the simulations, resulting in differences in moisture convergence patterns, precipitation, and surface evapotranspiration. Basin-average calculations of hydrologic variables indicate that, in most of the basins for which calculations were made, the magnitude of the evaporation-minus-precipitation moisture source/sink differs substantially between simulations and, in some cases, even the sign of the source/sink changed. There are substantial differences in rainfall–runoff processes because the basin-average rainfall intensities, proportion of rainfall from convective origin, and the runoff coefficients differ between simulations. The results indicate that, in regions of sustained, deep convection, the selection of the subgrid convective parameterization in a high-resolution atmospheric model can potentially have a hydrometeorological impact in regional analyses, which is at least as important as the effect of land surface forcing.

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Jaime Garatuza-Payan, Rachel T. Pinker, and W. James Shuttleworth

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The first stage in a program of research to develop a regional model capable of describing the hydrology of semiarid areas of northwest Mexico and southwest United States, using remotely sensed data, is described in this paper. Finescale information on cloud cover is required to provide the radiation forcing for making simple, near-real-time estimates of daytime evaporation in hydrologic models, and frequent satellite observations have the potential to document cloud variability at high spatial and temporal resolutions. In this study, the operational framework for obtaining information on cloud cover was developed and applied, using hourly sampled, 1-km resolution GOES-7 data as received in real time in Obregon, Mexico. These satellite data were collected and analyzed from 1 July 1993 to 31 July 1994 for an approximately 106 km2 rectangular area in northwest Mexico. An efficient method was devised to provide clear-sky radiance images for the study area, at 4 km × 4 km resolution, and updated at monthly intervals, by applying thresholds indexed to the locally appropriate clear-sky radiance, thereby allowing for spatial and temporal changes in surface conditions. Manual image inspection and comparison with ground-based measurements of cloud cover and surface solar radiation provided reassurance that the high-resolution cloud-screening algorithm gave satisfactory results.

This algorithm was applied to investigate the effects of temporal sampling frequency on estimates of daytime-average cloud cover and to document aspects of the cloud characteristics for the study area. The high-resolution algorithm proved to be efficient and reliable and bodes well for its future use in providing high-resolution estimates of surface solar radiation for use in a hydrologic model. Monthly clear-sky composite images were consistently generated, showing little evidence of contamination by persistent clouds, and tracked the seasonal evolution in surface radiance. Comparison with ground-based measurements gave confidence in the credibility of the satellite estimates and revealed weaknesses in the Campbell–Stokes solarimeter. The seasonal evolution of spatial patterns of cloud and its diurnal cycle were investigated. The average cloudiness for the study area is 0.25, with a substantial annual variation from 0.19 in April to 0.40 in December. Persistent cloudy conditions throughout the year were detected over the Pacific Ocean west of Baja California. The derived high-resolution cloud estimates, when compared with similar estimates from the International Satellite Cloud Climatology Project (ISCCP D1), were about half those obtained with the low-resolution data, indicating that, in this complex study area where land and water boundaries are in close proximity, low-resolution satellite observations of clouds may not be able to depict the true cloud cover.

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Omer L. Sen, W. James Shuttleworth, and Zong-Liang Yang

Abstract

Over the last decade, improved understanding of plant physiological processes has generated a significant change in the way stomatal functioning is described in advanced land surface schemes. New versions of two advanced and widely used land surface schemes, the Biosphere–Atmosphere Transfer Scheme (BATS) and the Simple Biosphere Model (SiB), reflect this change in understanding, although these two models make different assumptions regarding the response of stomata to atmospheric humidity deficit. The goal of this study was to evaluate the new, second version of BATS, here called BATS2, using Amazon field data from the Anglo–Brazilian Amazonian Climate Observational Study (ABRACOS) project, with an emphasis on comparison with the original version of BATS and the new, second version of SiB (SiB2). Evaluation of SiB2 using a 3-yr time series of ABRACOS data revealed that there is an unrealistic simulation of the yearly cycle in soil moisture status, with a resulting poor simulation of evaporation. Improved long-term simulation by SiB2 requires specification of a deeper rooting depth, and this requirement is general for all three models. In general, the original version of BATS with a revised root distribution and rooting depth gave good agreement with observations of the surface energy balance but occasionally showed excessive sensitivity to large atmospheric vapor pressure deficit. Evaluation of BATS2 revealed that changes are required in the parameters that determine stomatal behavior in the model for realistic simulation of transpiration, time-averaged respiration, and net carbon dioxide (CO2) uptake. When initiated with default values for carbon stores, BATS2 takes several hundred years to reach an equilibrium carbon balance. Aspects of the model’s representation of instantaneous carbon allocation and respiration processes indicate that BATS2 cannot be expected to provide a realistic simulation of hourly variations in CO2 exchanges. In general, all three models have weaknesses when describing the field data with default values of model parameters. If a few model parameters are modified in a plausible way, however, all three models can be made to give a good time-averaged simulation of measured exchanges. There is little evidence of sensitivity to the different forms assumed for the stomatal response to atmospheric humidity deficit, although this study suggests that assuming that leaf stress is related linearly to relative humidity is marginally preferred.

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David J. Gochis, W. James Shuttleworth, and Zong-Liang Yang

Abstract

This paper documents the sensitivity of the modeled evolution of the North American monsoon system (NAMS) to convective parameterization in terms of thermodynamic and circulation characteristics, stability profiles, and precipitation. The convective parameterization schemes (CPSs) of Betts–Miller–Janjic, Kain–Fritsch, and Grell were tested using version 3.4 of the PSU–NCAR fifth-generation Mesoscale Model (MM5) running in a pseudoclimate mode. Model results for the initial phase of the 1999 NAM are compared with surface climate station observations and seven radiosonde sites in Mexico and the southwestern United States. The results show substantial differences in modeled precipitation, surface climate, and atmospheric stability occuring between the different model simulations, which are attributable to the representation of convection in the model. Moreover, large intersimulation differences in the low-level circulation fields are found. While none of the CPSs tested gave perfect simulation of observations everywhere in the model domain, the Kain–Fritsch scheme generally gave significantly superior estimates of surface and upper air verification error statistics.

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Ismail Yucel, W. James Shuttleworth, X. Gao, and S. Sorooshian

Abstract

This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) provides an improved regional diagnosis of downward shortwave surface radiation fluxes and precipitation and enhances the model's ability to make short-range prediction. The high-resolution (4 km × 4 km) clear- and cloudy-sky radiances derived using a cloud-screening algorithm from visible band Geostationary Operational Environmental Satellite (GOES) data were used in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model to infer the vertically integrated cloud mass via cloud optical thickness. Three-dimensional cloud fields were created that took their horizontal distribution from the satellite image but derived their vertical distribution, in part, from the fields simulated by MM5 during the time step immediately prior to assimilation and, in part, from the observed cloud-top height derived from the infrared band of GOES. Linear interpolation was used to derive 1-min cloud images between 15-min GOES samples, and the resulting images were ingested every minute. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion substantially improved the ability of the MM5 model to capture temporal and spatial variations in surface fields associated with cloud cover. Experiments in which the model was operated in forecast mode suggest that cloud ingestion gave some limited enhancement in MM5 short-term prediction ability for up to 3 h. However, an analysis suggests that, in order to get additional forecasting capability, it will be necessary to modify the atmospheric dynamics and thermodynamics in the model to be consistent with the ingested cloud fields.

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Ismail Yucel, W. James Shuttleworth, R. T. Pinker, L. Lu, and S. Sorooshian

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This study investigates the extent to which assimilating high-resolution remotely sensed cloud cover into the Regional Atmospheric Modeling System (RAMS) provides an improved regional diagnosis of downward short- and longwave surface radiation fluxes and precipitation. An automatic procedure was developed to derive high-resolution (4 km × 4 km) fields of fractional cloud cover from visible band Geostationary Operational Environmental Satellite (GOES) data using a tracking procedure to determine the clear-sky composite image. Initial studies, in which RAMS surface shortwave radiation fluxes were replaced by estimates obtained by applying satellite-derived cloud cover in the University of Maryland Global Energy and Water Cycle Experiment's Surface Radiation Budget (UMD GEWEX/SRB) model, revealed problems associated with inconsistencies between the revised solar radiation fields and the RAMS-calculated incoming longwave radiation and precipitation fields. Consequently, in this study, the relationship between cloud albedo, optical depth, and water/ice content used in the UMD GEWEX/SRB model was applied instead to provide estimates of whole-column cloud water/ice that were ingested into RAMS. This potentially enhances the realism of the modeled short- and longwave radiation and precipitation. The ingested cloud image took the horizontal distribution of clouds from the satellite image but derives its vertical distribution from the fields simulated by RAMS in the time step immediately prior to assimilation. The resulting image was ingested every minute, with linear interpolation used to derive the 1-min cloud images between 15-min GOES samples. Comparisons were made between modeled and observed data taken from the Arizona Meteorological Network (AZMET) weather station network in southern Arizona for model runs with and without cloud ingestion. Cloud ingestion was found to substantially improve the ability of the RAMS model to capture temporal and spatial variations in surface fields associated with cloud cover. An initial test suggests that cloud ingestion enhanced RAMS short-term forecast ability.

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