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David Werth and Alfred Garrett

Abstract

One year’s worth of Global Forecast System (GFS) predictions of surface meteorological variables (wind speed, air temperature, dewpoint temperature, sea level pressure) are validated for land-based stations over the entire planet for forecasts extending from 0 h into the future (an analysis) to 7 days. Approximately 12 000 surface stations worldwide were included in this analysis. Root-mean-square errors (RMSEs) increased as the forecast period increased from 0 to 36 h, but the initial RMSEs were almost as large as the 36-h forecast RMSEs for all variables. Typical RMSEs were 3°C for air temperature, 2–3 mb for sea level pressure, 3.5°C for dewpoint temperature, and 2.5 m s−1 for wind speed.

An analysis of the biases at each station shows that the biggest errors are associated with mountain ranges and other areas of steep topography, with land–sea contrasts also playing a role. When the error is decomposed into the bias, variance, and correlation terms, the large initial RMSEs for the 0-h forecasts are seen to be due to a large forecast bias (which persisted into the longer forecasts) with errors in forecast correlation also making a large contribution.

A validation of two subdomains showed results similar to the global validation, but the dependence of the biases on the forecast time was clearer. Finally, the RMSE values climb as forecasts go out when validated out to a period of 7 days as the correlation error term grows.

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Roni Avissar and David Werth

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Past studies have indicated that deforestation of the Amazon basin would result in an important rainfall decrease in that region but that this process had no significant impact on the global temperature or precipitation and had only local implications. Here it is shown that deforestation of tropical regions significantly affects precipitation at mid- and high latitudes through hydrometeorological teleconnections. In particular, it is found that the deforestation of Amazonia and Central Africa severely reduces rainfall in the lower U.S. Midwest during the spring and summer seasons and in the upper U.S. Midwest during the winter and spring, respectively, when water is crucial for agricultural productivity in these regions. Deforestation of Southeast Asia affects China and the Balkan Peninsula most significantly. On the other hand, the elimination of any of these tropical forests considerably enhances summer rainfall in the southern tip of the Arabian Peninsula. The combined effect of deforestation of these three tropical regions causes a significant decrease in winter precipitation in California and seems to generate a cumulative enhancement of precipitation during the summer in the southern tip of the Arabian Peninsula.

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Roni Avissar and David Werth
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David Werth and Roni Avissar

Abstract

The annual cycle of evapotranspiration (ET) is an important component of the Amazon hydrological balance, which is of critical importance to the global water cycle. Understanding the changing water balance in this region is particularly important to estimate future global and regional hydroclimate change in response to projected deforestation of the rain forest in this region.

Several methods have been used to estimate the annual ET cycle in the Amazon basin. These different methods, which result in a spread of annual means, ranges, and phases of the ET cycle, are evaluated here. In an attempt to reconcile the differences between them, both the data and the assumptions upon which the methods are based are scrutinized. The differences seem to originate from the geographic site where radiation and ET are simulated and/or observed and, more significantly, from the way that vegetation controls ET in the different models being used.

While field campaigns conducted during the Large-Scale Biosphere Atmosphere (LBA) experiment in the Amazon have provided many new insights into the Amazon hydroclimate, additional observations of ET and precipitation in that region are needed to understand better the processes involved.

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Lance O’Steen and David Werth

Abstract

It is shown that a simple evolutionary algorithm can optimize a set of mesoscale atmospheric model parameters with respect to agreement between the mesoscale simulation and a limited set of synthetic observations. This is illustrated using the Regional Atmospheric Modeling System (RAMS). A set of 23 RAMS parameters is optimized by minimizing a cost function based on the root-mean-square (rms) error between the RAMS simulation and synthetic data (observations derived from a separate RAMS simulation). It is found that the optimization can be done with relatively modest computer resources; therefore, operational implementation is possible. The overall number of simulations needed to obtain a specific reduction of the cost function is found to depend strongly on the procedure used to perturb the “child” parameters relative to their “parents” within the evolutionary algorithm. In addition, the choice of meteorological variables that are included in the rms error and their relative weighting are also found to be important factors in the optimization.

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Natalia Hasler, David Werth, and Roni Avissar

Abstract

Two multimodel ensembles (MME) were produced with the GISS Model II (GM II), the GISS Atmosphere Model (AM), and the NCAR Community Climate System Model (CCSM) to evaluate the effects of tropical deforestation on the global hydroclimate. Each MME used the same 48-yr period but the two were differentiated by their land-cover types. In the “control” case, current vegetation was used, and in the “deforested” case, all tropical rain forests were converted to a mixture of shrubs and grassland. Globally, the control simulations produced with the three GCMs compared well to observations, both in the time mean and in the temporal variability, although various biases exist in the different tropical rain forests.

The local precipitation response to deforestation is very strong. The remote effect in the tropics (away from the deforested tropical areas) is strong as well, but the effects at midlatitudes are weaker. In the MME, the impacts tend to be attenuated relative to the individual models.

The significance of the geopotential and precipitation responses was evaluated with a bootstrap method, and results varied during the year. Tropical deforestation also produced anomalous fluxes in potential energy that were a direct response to the deforestation. These different analyses confirmed the existence of a teleconnection mechanism due to deforestation.

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Renato Ramos da Silva, David Werth, and Roni Avissar

Abstract

State-of-the-art socioeconomic scenarios of land-cover change in the Amazon basin for the years 2030 and 2050 are used together with the Regional Atmospheric Modeling System (RAMS) to simulate the hydrometeorological changes caused by deforestation in that region under diverse climatological conditions that include both El Niño and La Niña events. The basin-averaged rainfall progressively decreases with the increase of deforestation from 2000 to 2030, 2050, and so on, to total deforestation by the end of the twenty-first century. Furthermore, the spatial distribution of rainfall is significantly affected by both the land-cover type and topography. While the massively deforested region experiences an important decrease of precipitation, the areas at the edge of that region and at elevated regions receive more rainfall. Propagating squall lines over the massively deforested region dissipate before reaching the western part of the basin, causing a significant decrease of rainfall that could result in a catastrophic collapse of the ecosystem in that region. The basin experiences much stronger precipitation changes during El Niño events as deforestation increases. During these periods, deforestation in the western part of the basin induces a very significant decrease of precipitation. During wet years, however, deforestation has a minor overall impact on the basin climatology.

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David Werth, Grace Maze, Robert Buckley, and Steven Chiswell

Abstract

Airborne tracer simulations are typically performed using a dispersion model driven by a high-resolution meteorological model. Besides solving the dynamic equations of momentum, heat, and moisture on the resolved model grid, mesoscale models must account for subgrid-scale fluxes and other unresolved processes. These are estimated through parameterization schemes of eddy diffusion, convection, and surface interactions, and they make use of prescribed parameters set by the user. Such “free” model parameters are often poorly constrained, and a range of plausible values exists for each. Evolutionary programming (EP) is a process to improve the selection of the parameters. A population of simulations is first run with a different set of parameter values for each member, and the member judged most accurate is selected as the “parent” of a new “generation.” After a number of iterations, the simulations should approach a configuration that is best adapted to the atmospheric conditions. We apply the EP process to simulate the first release of the 1994 European Tracer Experiment (ETEX) project, which comprised two experiments in which a tracer was released in western France and sampled by an observing network. The EP process is used to improve a simulation of the RAMS mesoscale weather model, with weather data collected during ETEX being used to “score” the individual members according to how well each simulation matches the observations. The meteorological simulations from before and after application of the EP process are each used to force a dispersion model to create a simulation of the ETEX release, and substantial improvement is observed when these are validated against sampled tracer concentrations.

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Renato Ramos da Silva, Gil Bohrer, David Werth, Martin J. Otte, and Roni Avissar

Abstract

Meteorological observations and model simulations are used to show that the catastrophic ice storm of 4–5 December 2002 in the southeastern United States resulted from the combination of a classic winter storm and a warm sea surface temperature (SST) anomaly in the western Atlantic Ocean. At the time of the storm, observations show that the Atlantic SST near the southeastern U.S. coast was 1.0°–1.5°C warmer than its multiyear mean. The impact of this anomalous SST on the ice accumulation of the ice storm was evaluated with the Regional Atmospheric Modeling System. The model shows that a warmer ocean leads to the conversion of more snow into freezing rain while not significantly affecting the inland surface temperature. Conversely, a cooler ocean produces mostly snowfall and less freezing rain. A similar trend is obtained by statistically comparing observations of ice storms in the last decade with weekly mean Atlantic SSTs. The SST during an ice storm is significantly and positively correlated with a deeper and warmer melting layer.

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David Werth, Robert Kurzeja, Nelson Luís Dias, Gengsheng Zhang, Henrique Duarte, Marc Fischer, Matthew Parker, and Monique Leclerc

Abstract

A field project over the Atmospheric Radiation Measurement–Cloud and Radiation Test Bed (ARM–CART) site during a period of several nights in September 2007 was conducted to explore the evolution of the low-level jet (LLJ). Data were collected from in situ (a multilevel tower) and remote (sodar) sensors, and the observed LLJ activity during the project was found to agree well with data from earlier studies regarding jet speed, height, and direction. To study nocturnal boundary layer (NBL) behavior, the Regional Atmospheric Modeling System was used to simulate the ARM–CART NBL field experiment and was validated against the data collected from the site. This model was run at high resolution for calculating the interactions among the various motions within the boundary layer and their influence on the surface. The model faithfully simulated the formation and dissolution of the low-level nocturnal jet during a synoptic situation in which low pressure with warm southerly advection replaced high pressure. An additional simulation at 32.5-m resolution was performed for the most stable 5.5-h period, using a turbulence scheme adjusted to allow for greater resolved turbulent kinetic energy, and the model reproduced the turbulence statistics as determined by a power spectrum. The benefit of the high-resolution simulation is evident in the much more realistically resolved model turbulent kinetic energy and the fluxes of momentum, heat, and water vapor.

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