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Harshvardhan, David A. Randall, and Donald A. Dazlich

Abstract

Attempts to map the global longwave surface radiation budget from space have been thwarted by the presence of clouds. Unlike the shortwave, there is no physical relationship between the outgoing longwave and the surface longwave under cloudy skies. Therefore, there is no correlation between spatial and temporal averages of the outgoing longwave radiation and not longwave radiation at the surface. However, in regions where a particular cloud regime exists preferentially, a relationship between the mean longwave cloud radiative forcing (CRF) at the top of the atmosphere and at the surface can he shown to exist. Results from a general circulation model suggest that this relationship for monthly means is coherent over fairly large geographical areas. For example, in tropical convective areas, the longwave CRF at the top is very large, but at the surface it is quite small because of the high opacity of the lowest layers of the atmosphere. On the other hand, in areas of stratus over cool ocean surfaces, the longwave CRF at the top is very small but at the surface, it is quite substantial.

To the extent that the cloudiness simulated in the model mimics the real atmosphere, it may be possible to estimate the monthly mean longwave CRF at the surface from the Earth Radiation Budget Experiment cloud forcing at the top. The net longwave radiation at the surface can then be mapped if monthly means of the clear-sky fluxes are obtained by some independent technique.

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David A. Randall, Harshvardhan, and Donald A. Dazlich

Abstract

This paper presents an analysis of the diurnal and semidiurnal variability of precipitation, evaporation, precipitable water, horizontal moisture flux convergence, cloudiness, and cloud radiative forcing, as simulated by the Colorado State University General Circulation Model (GCM). In broad agreement with observations, the model produces an afternoon precipitation maximum over land in warm rainy regions, such as the tropics and the midlatitude summer continents, and an early morning maximum over the oceans far from land. The statistical significance of these model results is demonstrated using a chi-square test. The observed diurnal variation of temperature in the oceanic tropical middle troposphere is also realistically simulated.

Encouraged by these results, the model was used to investigate the causes of the diurnal cycle of precipitation over the oceans. For this purpose, experiments have been performed with an all-ocean global model. Results show that an oceanic diurnal cycle of precipitation occurs even in the absence of neighboring continents and tends to have a morning maximum. It is generally weaker than observed, however. When the radiative effects of clouds are omitted, the simulated diurnal cycle of precipitation is much weaker but still present, with essentially the same phase.

Several experiments have also been performed with a one-dimensional version of the GCM, in which time-dependent large-scale vertical motion can be prescribed. The results show that even in the absence of any systematic daily variation of the large-scale vertical motion, the model produces a diurnal cycle of precipitation with an amplitude of about 1 mm day−1, and a morning maximum.

Finally, previously published results have been followed up, which show that the diurnal cycle strongly affects the partitioning of precipitation between land and sea. The new analysis is based on comparison of three nondiurnal June-July integrations with three Julys from a multiyear diurnally forced seasonal simulation. The results show major changes in the time-averaged surface energy budget, and much more precipitation in “summer monsoon” regimes when the diurnal cycle is omitted.

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David A. Randall, Harshvardhan, Donald A. Dazlich, and Thomas G. Corsetti

Abstract

We have analyzed the effects of radiatively active clouds on the climate simulated by the UCLA/GLA GCM, with particular attention to the effects of the upper tropospheric stratiform clouds associated with deep cumulus convection, and the interactions of these clouds with convection and the large-scale circulation.

Several numerical experiments have been performed to investigate the mechanisms through which the clouds influence the large-scale circulation. In the “NODETLQ” experiment, no liquid water or ice was detrained from cumulus clouds into the environment; all of the condensate was rained out. Upper level supersaturation cloudiness was drastically reduced, the atmosphere dried, and tropical outgoing longwave radiation increased. In the “NOANVIL” experiment, the radiative effects of the optically thich upper-level cloud sheets associated with deep cumulus convection were neglected. The land surface received more solar radiation in regions of convection, leading to enhanced surface fluxes and a dramatic increase in precipitation. In the “NOCRF” experiment, the longwave atmospheric cloud radiative forcing (ACRF) was omitted, paralleling the recent experiment of Slingo and Slingo. The results suggest that the ACRF enhances deep penetrative convection and precipitation, while suppressing shallow convection. They also indicate that the ACRF warms and moistens the tropical troposphere. The results of this experiment are somewhat ambiguous, however; for example, the ACRF suppresses precipitation in some parts of the tropics, and enhances it in others.

To isolate the effects of the ACRF in a simpler setting, we have analyzed the climate of an ocean-covered Earth, which we call Seaworld. The key simplicities of Seaworld are the fixed boundary temperature with no land points, the lack of mountains, and the zonal uniformity of the boundary conditions. Results are presented from two Seaworld simulations. The first includes a full suite of physical parameterizations, while the second omits all radiative effects of the clouds. The differences between the two runs are, therefore, entirely due to the direct and indirect and indirect effects of the ACRF. Results show that the ACRF in the cloudy run accurately represents the radiative heating perturbation relative to the cloud-free run. The cloudy run is warmer in the middle troposphere, contains much more precipitable water, and has about 15% more globally averaged precipitation. There is a double tropical rain band in the cloud-free run, and a single, more intense tropical rain band in the cloudy run. The cloud-free run produces relatively weak but frequent cumulus convection, while the cloudy run produces relatively intense but infrequent convection. The mean meridional circulation transport nearly twice as much mass in the cloudy run. The increased tropical rising motion in the cloudy run leads to a deeper boundary layer and also to more moisture in the troposphere above the boundary layer. This accounts for the increased precipitable water content of the atmosphere. The clouds lead to an increase in the intensity of the tropical easterlies, and cause the midlatitude westerly jets to shift equatorward.

Taken together, our results show that upper tropospheric clouds associated with moist convection, whose importance has recently been emphasized in observational studies, play a very complex and powerful role in determining the model results. This points to a need to develop more realistic parameterizations of these clouds.

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Owen E. Thompson, Mitchell D. Goldberg, and Donald A. Dazlich

Abstract

Two Pattern recognition procedures are developed to provide improvements to first-guess fields for satellite temperature retrievals. The first is a technique whereby a radiometer measurement may be used to select one or more historical radiosonde temperature profiles as analog estimates of ambient thermal structure. The vertical scales of the analogs are those of radiosondes—the vertical resolving power of the satellite radiometer being relevant only to a decision process. The analog selection process is shown to be much more effective if implemented in an orthogonalized space of measurement information. The second procedure is one which partitions a priori dependent data into shape-coherent pattern libraries using structure information inherent in the data itself. This is an alternative to traditional partitioning schemes whereby proxy classifiers such as season, location and surface type are used.

These pattern recognition techniques are shown to be capable of reducing first-guess profile errors by nearly 50%, in an independent test of about 800 diverse retrievals. The impact of pattern recognition on temperature retrieval error is assessed using regression and physical-iterative retrieval algorithms. The influence of improved first-guess fields is markedly different on these two types of algorithms. Pattern recognition is shown to have a strong, positive impact on the physical-iterative method but little significant impact on regression when evaluated in an overall batch sense. A case study suggests that a small number of very poor retrievals may particularly mask the potential benefits of pattern recognition on both methods.

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Anna Harper, Ian T. Baker, A. Scott Denning, David A. Randall, Donald Dazlich, and Mark Branson

Abstract

Moisture recycling can be an important source of rainfall over the Amazon forest, but this process relies heavily upon the ability of plants to access soil moisture. Evapotranspiration (ET) in the Amazon is often maintained or even enhanced during the dry season, when net radiation is high. However, ecosystem models often over predict the dry season water stress. The authors removed unrealistic water stress in an ecosystem model [the Simple Biosphere Model, version 3 (SiB3)] and examined the impacts of enhanced ET on the dry season climate when coupled to a GCM. The “stressed” model experiences dry season water stress and limitations on ET, while the “unstressed” model has enhanced root water access and exhibits strong drought tolerance.

During the dry season in the southeastern Amazon, SiB3 unstressed has significantly higher latent heat flux (LH) and lower sensible heat flux (SH) than SiB3 stressed. There are two competing impacts on the climate in SiB3 unstressed: cooling resulting from lower SH and moistening resulting from higher LH. During the average dry season, the cooling plays a larger role and the atmosphere is more statically stable, resulting in less precipitation than in SiB3 stressed. During dry season droughts, significantly higher LH in SiB3 unstressed is a necessary but not sufficient condition for stronger precipitation. The moistening effect of LH dominates when the Bowen ratio (BR = SH/LH) is >1.0 in SiB3 stressed and precipitation is up to 26% higher in SiB3 unstressed. An implication of this analysis is that forest conservation could enable the Amazon to cope with drying conditions in the future.

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Piers J. Sellers, Compton J. Tucker, G. James Collatz, Sietse O. Los, Christopher O. Justice, Donald A. Dazlich, and David A. Randall

Abstract

The global parameter fields used in the revised Simple Biosphere Model (SiB2) of Sellers et al. are reviewed. The most important innovation over the earlier SiB1 parameter set of Dorman and Sellers is the use of satellite data to specify the time-varying phonological properties of FPAR, leaf area index. and canopy greenness fraction. This was done by processing a monthly 1° by 1° normalized difference vegetation index (NDVI) dataset obtained farm Advanced Very High Resolution Radiometer red and near-infrared data. Corrections were applied to the source NDVI dataset to account for (i) obvious anomalies in the data time series, (ii) the effect of variations in solar zenith angle, (iii) data dropouts in cold regions where a temperature threshold procedure designed to screen for clouds also eliminated cold land surface points, and (iv) persistent cloud cover in the Tropics. An outline of the procedures for calculating the land surface parameters from the corrected NDVI dataset is given, and a brief description is provided of source material, mainly derived from in situ observations, that was used in addition to the NDVI data. The datasets summarized in this paper should he superior to prescriptions currently used in most land surface parameterizations in that the spatial and temporal dynamics of key land surface parameters, in particular those related to vegetation, are obtained directly from a consistent set of global-scale observations instead of being inferred from a variety of survey-based land-cover classifications.

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