The presence of water as solid, liquid, and gas is a feature that makes Earth unique in the solar system, and that makes possible life as we know it. The transport of water and the energy exchanged as it is converted from one state to another are important drivers in our weather and climate. One of the key missions of the GHCC is to develop a better understanding of the global water cycle at a variety of scales so that we can improve model forecasts of climate trends, predictions of short-term and regional weather events, and even their impacts on society's regional and global activities.
Although
water vapor is the most important greenhouse gas, we haven't adequately
sampled its four-dimensional variability--across space and time--over the
globe. Recent analyses of global satellite data have produced many new
observational data sets that describe the vertical and horizontal
structure of moisture over the last 10 to 20 years. These data sets are
being analyzed and validated to document the satellite measurements'
capability to describe accurately various components of the hydrologic
cycle. In some cases, the trends observed by satellites are consistent
with predictions by computer models, while in others, there are
significant discrepancies. Understanding when and why the models are right
or wrong and the nature and limitations of satellite data is key to their
intelligent use in climate studies.
Year-to-year variations in rainfall and temperature dominate our sense of
climate variability. We have only to look at the 1997-98 El Niņo event to
reaffirm the societal costs in terms of human suffering and economic
losses. A major challenge to climate researchers is to determine the
degree of predictability associated with these and other events. While El
Niņo is the best known of these phenomena, there are other modes of
variability that may or may not be predictable.
GHCC scientists are engaged in this research in a number of ways. One major task is the development of consistent descriptions of how changes in the ocean and land surface temperatures alter the atmospheric winds, temperatures, and moisture that cause regional droughts or excessive rainfall on continental scales. However, these diverse data sets are far from error free. Recently we developed a method of blending satellite observations of radiation, precipitation, and water vapor to correct our models of poorly measured winds that are important in circulating moisture and heat energy over the globe. By synthesizing descriptions of atmospheric processes and comparing them to climate model projections, we have been able to study how well (or how poorly) climate models perform--and why. As a result, several efforts are now under way to improve how clouds are represented in climate models.
GHCC research scientists are developing a water budget diagnostic model
designed to estimate the three-dimensional fields of vapor, cloud
condensate, and precipitation around the globe as a function of time. The
model uses wind, temperature, and initial moisture fields from a global
four-dimensional data set, then marches forward in time. To constrain the
evolution of the simulated water vapor field (and the resulting cloud
fields and precipitation), data from the satellite-borne Special
Sensor/Microwave Imaging precipitable water (total water vapor in a column
of air) measurements over the oceans, for example, are used to constrain
the evolving water vapor in the model. The local moisture profiles are
rescaled to give values of precipitable water equal to those observed by
the satellite, when satellite data are available.
While climate anomalies frequently assume continental or oceanic proportion, it is ultimately the local or "human" scale that is of importance to users. Regional scale modeling of hydrologic processes over domains the size of the southeastern U.S. are an important part of our research strategy. Research funded under NASA's Earth Science Enterprise and other Federal programs is aimed at understanding warm season precipitation and hydrology. Assimilating satellite-derived land surface temperature data into regional models is helping improve predictions of low-level temperature, moisture, clouds, and the resulting precipitation.
Information provided by: http://wwwghcc.msfc.nasa.gov