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A prognostic cloud scheme named the Microphysics of Clouds with the Relaxed Arakawa-Schubert Scheme (McRAS) and the Simple Biosphere Model have been implemented in a version of the Goddard Earth Observing System (GEOS) II GCM at a 4 degrees latitude x 5 degrees longitude x 20 sigma-layer resolution. The McRAS GCM was integrated for 50 months. The integration was initialized with the European Centre for Medium-Range Weather Forecasts analysis of observations for 1 January 1987 and was forced with the observed sea surface temperatures and sea-ice distribution: on land, the permanent ice and vegetation properties (biomes and soils) were climatological, while the soil moisture and snow cover were prognostic. The simulation shows that the McRAS GCM yields realistic structures of in-cloud water and ice, and cloud-radiative forcing (CRF) even though the cloudiness has some discernible systematic errors. The simulated intertropical convergence zone (ITCZ) has a realistic time mean structure and seasonal cycle. The simulated CRF is sensitive to vertical distribution of cloud water, which can be affected hugely with the choice of minimum in-cloud water for the onset of autoconversion or critical cloud water amount that regulates the autoconversion itself. The generation of prognostic cloud water is accompanied by reduced global precipitation and interactive CRE These feedbacks have a profound effect on the ITCZ. Even though somewhat weaker than observed, the McRAS GCM simulation produces robust 30-60-day oscillations in the 200-hPa velocity potential. Comparisons of CRFs and precipitation produced in a parallel simulation with the GEOS II GCM are included. Several seasonal simulations were performed with the McRAS-GEOS II GCM for the summer (June-July-August) and winter (December-January-February) periods to determine how the simulated clouds and CRFs would be affected by (i) advection of clouds, (ii) cloud-top entrainment instability, (iii) cloud water inhomogeneity correction, and (iv) cloud production and dissipation in different cloud processes. The results show that each of these processes contributes to the simulated cloud fraction and CRE Because inclusion of these processes helps to improve the simulated CRE it is inferred that they would be useful to include in other cloud microphysics schemes as well. Two ensembles of four summer (July-August-September) simulations, one each for 1987 and l988, were produced with the earlier 17-layer GEOS I GCM with McRAS. The differences show that the model simulates realistic and statistically significant precipitation differences over India, Central America, and tropical Africa. These findings were also confirmed in the new 20-layer GEOS II GCM with McRAS in the 1987 minus 1988 differences.

A prognostic cloud scheme named McRAS (Microphysics of Clouds with Relaxed Arakawa-Schubert Scheme) has been designed and developed with the aim of improving moist processes, microphysics of clouds, and cloud-radiation interactions in GCMs. McRAS distinguishes three types of clouds: convective, stratiform, and boundary layer. The convective clouds transform and merge into stratiform clouds on an hourly timescale, while the boundary layer clouds merge into the stratiform clouds instantly. The cloud condensate converts into precipitation following the autoconversion equations of Sundqvist that contain a parametric adaptation for the Bergeron-Findeisen process of ice crystal growth and collection of cloud condensate by precipitation. All clouds convect, advect, as well as diffuse both horizontally and vertically with a fully interactive cloud microphysics throughout the life cycle of the cloud, while the optical properties of clouds are derived from the statistical distribution of hydrometeors and idealized cloud geometry. An evaluation of McRAS in a single-column model (SCM) with the Global Atmospheric Research Program Atlantic Tropical Experiment (GATE) Phase III data has shown that, together with the rest of the model physics, McRAS can simulate the observed temperature, humidity, and precipitation without discernible systematic errors. The time history and time mean in-cloud water and ice distribution, fractional cloudiness, cloud optical thickness, origin of precipitation in the convective anvils and towers, and the convective updraft and downdraft velocities and mass fluxes all simulate a realistic behavior. Some of these diagnostics are not verifiable with data on hand. These SCM sensitivity tests show that (i) without clouds the simulated GATE-SCM atmosphere is cooler than observed; (ii) the model's convective scheme, RAS, is an important subparameterization of McRAS; and (iii) advection of cloud water substance is helpful in simulating better cloud distribution and cloud-radiation interaction. An evaluation of the performance of McRAS in the Goddard Earth Observing System II GCM is given in a companion paper (Part II).

Numerical simulation experiments were conducted to delineate the influence of in situ deforestation data on episodic rainfall by comparing two ensembles of five 5-day integrations performed with a recent version of the Goddard Laboratory for Atmospheres GCM that has a simple biosphere model (SiB). The first set, called control cases, used the standard SiB vegetation cover (comprising 12 biomes) and assumed a fully forested Amazonia, while the second set, called deforestation cases, distinguished the partially deforested regions of Amazonia as savanna. Except for this difference, all other initial and prescribed boundary conditions were kept identical in both sets of integrations. The differential analyses of these five cases show the following local effects of deforestation. 1) A discernible decrease in evapotranspiration of about 0.80 mm[d.sup.-1] (roughly 18%) that is quite robust in the averages for 1-, 2-, and 5-day forecasts. 2) A decrease in precipitation of about 1.18 mm [d.sup.-1](roughly 8%) that begins to emerge even in 1-2-day averages and exhibits complex evolution that extends downstream with the winds. A large r decrease in precipitation as compared to evapotranspiration produces some drying and warming. The precipitation differences are consistent with the decrease in atmospheric moisture flux convergence and are consistent with earlier simulation studies of local climate change due to large-scale deforestation. 3) A significant decrease in the surface drag force (as a consequence of reduced surface roughness of deforested regions) that, in turn, affects the dynamical structure of moisture convergence and circulation. The surface winds increase significantly during the first day, and thereafter the increase is well maintained even in the 2- and 5-day averages.

A new runoff routing model permits discharge hydrograph creation from regional- or continental-scale gridded runoff data. The horizontally coupled model is based on the principle that watershed upstream regions affect the downstream region. The overland flow network connectivity, residence time coefficients and land surface hydrology parameterization gridded runoff input are essential components of the model.

Previous estimates of land-atmosphere interaction (the impact of soil moisture on precipitation) have been limited by a lack of observational data and by the model dependence of computational estimates. To counter the second limitation, a dozen climate-modeling groups have recently performed the same highly controlled numerical experiment as part of a coordinated comparison project. This allows a multimodel estimation of the regions on Earth where precipitation is affected by soil moisture anomalies during Northern Hemisphere summer. Potential benefits of this estimation may include improved seasonal rainfall forecasts., Atmospheric chaos severely limits the predictability of precipitation on seasonal time scales. Weather forecasts, which rely heavily on atmospheric initialization, rarely demonstrate skill beyond about a week. Hope for accurate [...]

Past studies have suggested that the drought of the summer of 1988 over the midwestern United States may have been caused by sea surface temperature (SST) anomalies, an evolving stationary circulation, a soil-moisture feedback on circulation and rainfall, or even by remote forcings. The relative importance of various contributing factors is investigated in this paper through the use of Goddard Earth Observing System (GEOS) GCM simulations. Seven different experiments, each containing an ensemble of four simulations, were conducted with the GCM. For each experiment, the GCM was integrated through the summers of 1987 and 1988 starting from an analyzed atmosphere in early January of each yeah In the baseline case, only the SST anomalies and climatological vegetation parameters were prescribed, while everything else (such as soil moisture, snow cover, and clouds) was interactive. The precipitation differences (1988 minus 1987) show that the GCM was successful in simulating reduced precipitation in 1988, hut the accompanying low-level circulation anomalies in the Midwest were not well simulated. To isolate the influence of the model's climate drift, analyzed winds and analyzed soil moisture were prescribed globally as continuous updates (in isolation or jointly). The results show that remotely advected wind biases (emanating from potential errors in the model's dynamics and physics) are the primary cause of circulation biases over North America. Inclusion of soil moisture helps to improve the simulation as well as to reaffirm the strong feedback between soil moisture and precipitation. In a case with both updated winds and soil moisture, the model produces more realistic evapotranspiration and precipitation differences. An additional case also used soil moisture and winds updates, but only outside North America. Its simulation is very similar to that of the case with globally updated winds and soil moisture, which suggests that North American simulation errors originate largely outside the region. Two additional cases examining the influence of vegetation were built on this case using correct and opposite-year vegetation. The model did not produce a discernible improvement in response to vegetation for the drought year. One may conclude that the soil moisture governs the outcome of the land-atmosphere feedback interaction far more than the vegetation parameters. A primary inference of this study is that even though SSTs have some influence on the drought, model biases strongly influence the prediction errors. It must be emphasized that the results from this study are dependent upon the GEOS model's identified errors and biases, and that the conclusions do not necessarily apply to results from other models. [ABSTRACT FROM AUTHOR]