Significant Accomplishments
Completion of a 300-year simulation with the initial version of the Climate System Model (CSM), in which there are no significant drifts in the surface temperatures.
Completion of a 130-year simulation in which the atmospheric carbon dioxide (CO2) concentration increased 1% per year.
Analysis of Nimbus-7 earth radiation budget data has shown that there is a global signature for enhanced shortwave cloud absorption. This signature appears in the ratio of the visible to near infrared cloud albedo. Analysis of the Atmospheric Radiation Measurement (ARM) program data confirms the existence of enhanced shortwave cloud absorption.
A global sulfur model has been incorporated into the Community Climate Model version 3 (CCM3). This coupled climate predicts the distribution of sulfur species including the mass of sulfate aerosols. The direct radiative effect of the sulfate aerosol has been estimated from this new model.
A 1/10 degree North Atlantic simulation has been carried out using the Parallel Ocean Program (POP) model in collaboration with scientists from Los Alamos National Laboratory (LANL). It shows a very realistic mean path and variability of the Gulf Stream.
A one-year, high-resolution surface wind field has been produced by combining the National Centers for Environmental Prediction (NCEP) reanalysis fields with satellite scatterometer measurements. These winds will be used to force high-resolution ocean circulation models.
A natural radiocarbon simulation and an anthropogenic perturbation radiocarbon simulation have been performed in the global ocean component of the Climate System Model (CSM). These are contributing to the study of the ocean's role in the global carbon cycle.
Further work on reconciling the near-global monthly mean surface temperature anomalies with those of global Microwave Sounding Unit (MSU) channel 2R temperatures for 1979-1995 reveals that the chronic difficulty in obtaining reliable climate records from satellites through changes in instruments, platforms, equator-crossing times, and algorithms also applies to the MSU record. In particular, the MSU 2R satellite temperature record contains unreliable trends over a 17-year period (1979-95) because of transitions involving different satellites and complications arising from non-atmospheric signals associated with the surface.
The tropospheric biennial oscillation (TBO) has been diagnosed from observations, as well as global coupled model results. The TBO arises both as a consequence of air-sea interactions in the tropical Indian and Pacific Oceans and tropical-midlatitude interactions producing land-sea temperature contrast that exert a strong control over monsoon strength. South Asian snow cover can contribute to subsequent monsoon strength, but it is most likely symptomatic of large-scale midlatitude circulation changes that affect land temperatures and thus land-sea temperature contrast.
Analysis of the Climate System Model (CSM), the Community Climate Model version 3 (CCM3) forced by observed tropical monthly-mean sea-surface temperature (SST) variability (TOGA), and the CCM3 forced by observed global monthly-mean SST variability (GOGA) runs indicate that stochastic low-frequency variability in the atmosphere may be the primary mechanism behind observed midlatitude climate variability.
Analysis of operational National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction systems, along with the NCAR experimental prediction system, shows that at longer timescales, the dispersion of all ensembles were very similar, indicating the method of perturbation was not an important factor.
Analysis of five-year long, present day, and 2xCO2 regional model experiments were completed over two regions, one encompassing the Central Plains of the U.S. and one covering the Southeast U.S.
In cooperation with the Climate Modeling Section (CMS) and Oceanography Section (OS), a low-resolution version of the Climate System Model (CSM) (T31 for atmosphere and land models, x3 for ocean and sea-ice models) has been developed to meet the needs of the paleoclimate community. This model reproduces many features of present-day climate of the atmosphere, ocean, and sea ice. Details are on the Paleoclimate Model Development and Applications page.
A coupled climate model has been developed that can execute efficiently on new massively parallel processor (MPP) computers. It makes use of the Community Climate Model version 3 (CCM3), Parallel Ocean Program (POP) at 2/3 degree horizontal resolution, and a 27 km sea-ice model. Details are on the DOE/Climate Change Prediction Program Distributed Parallel Climate Model (PCM) page.
The development of a comprehensive 100-year climate history of the U.S. was completed. To understand how climate affects ecosystems, hydrology, and agriculture, long climate records with adequate spatial resolution to resolve regional and elevational climate variations are required. We developed a 100-year record, using geostatistical techniques to interpolate during periods of low station density and statistical techniques to infer the effects of elevation. The resulting data records both temperature and precipitation anomalies over time and with topography.
The development of the first practical radiative-transfer based algorithm for land surface remote sensing over wide areas was completed. Remote sensing is the only practical way to obtain comprehensive global data on vegetation for climate and biogeochemical studies. To date, remote sensing of vegetation has relied on empirical "vegetation index" techniques, techniques that are imprecise, techniques that use only a fraction of the information available, and techniques that are vulnerable to atmospheric and sun-sensor geometric artifacts. We have developed a simple but carefully calibrated radiative transfer model that is invertible and that yields much improved estimates of albedo, plant activity (light interception), and cover. The algorithm has been tested using Advanced Very High Resolution Radiometer (AVHRR) data and will be applied as a special product after the launch of the Earth Observing System's AM-1 spacecraft.
Satellite-derived wind estimates have high spatial resolution but are limited in global coverage; in contrast, operational analyses provided by the major weather centers provide complete wind fields but are of low spatial resolution. The goal is to blend these data in a manner that incorporates the space-time dynamics inherent in the surface wind field using Bayesian hierarchical models that preserve the spatial and temporal dependencies in the wind fields. This methodology provides a distinct advance over traditional interpolation/spatial methods, building in the physical constraints for atmospheric processes and being computationally feasible for massive data records.
Clouds play a fundamental role in controlling the amount of solar and infrared radiation available to the climate system. An accurate parameterization of cloud cover, based on first-order, time-lagged, nearest-neighbor models, has been developed. The key is to use neural network models to account for possibly highly nonlinear relationships between gridscale variables and subgrid scale cloud dynamics. Large-scale variables, such as relative humidity and available kinetic energy, have been used to classify the cloud cover according to different cloud types.