Water Vapor Transport

Focus

It is readily recognized that water vapor is the most important greenhouse gas in the atmosphere. Water vapor plays a critical role in climate processes at all scales by influencing the distribution of OLR and as a carrier of latent heat energy in the atmosphere. In a sense, one can think of atmospheric water vapor as a thermostat on global temperatures. Some scientists believe that increased amounts of atmospheric water vapor (a product of potential global warming) might lead to further warming due to water's absorption characteristics. On the other hand, the presence of more water vapor (and hence clouds) might limit the amount of incoming solar radiation thus cooling the global temperature. Part of the uncertainty to the role which water vapor plays in global climate changes is due to the lack of high spatial and temporal measurements (especially at upper levels). One of the ways to learn more about atmospheric water vapor and its variability is to monitor it from satellites. In this way, global and regional maps of atmospheric water vapor can be derived and studied to understand its distribution, sources and sinks, and its transport around the globe, all of which determine its variability. This research focuses on atmospheric water vapor in the upper troposphere (5-10 kilometers above the surface of the Earth). Measurements from particular channels on satellites provide information on the water vapor content in the atmosphere. This research is currently using the infrared water vapor channels on the GOES geostationary satellites to measure water vapor in the upper troposphere and to monitor its movement over the hemisphere.

Datasets Used

GOES (VAS) Pathfinder data from May 1987 through November 1988. A sequence of 3- hourly images centered about 1200 UTC are used for motions (winds) with the middle image used for mositure and height assignment. This dataset corresponds to the 1987-1988 ENSO period. 

Water vapor transport CD ROM now available!

GOES (8/9/10) from May 1997 through April 1999. A sequence of three images at 30-60 minute intervals are used. This period corresponds to the strong 1997-1998 ENSO event. See map for ENSO time series. 
These datasets cover the Americas and the eastern and central Pacific ocean (see map). 

Retrieval Methodology

Winds are derived from a time sequence of GOES imagery using the Marshall Automated Wind (MAW) algorithm. Input parameters to the tracking algorithm vary with satellite because of sensor spatial resolution and time separation of available data. 
The humidity fields are derived from the GOES brightness temperatures using a modified version of the Soden and Bretherton technique. The layer relative humidity is converted to speficic humidity required by the Water Vapor Transport Index formulation. The winds and humidity are gridded and then combined to form the various layer transport parameters used in the climate research. A height is assigned to the wind and humidity based on comparing the channel brightness temperature to a reference thermodynamic profile available from a global forecast model. Currently, only winds and humidity measurements from non-cloud environments are used in the analysis. Details of this approach can be found in Jedlovec et al. (1999). 

Results

A analysis of the daily and monthly transport of upper-level water vapor for the 19-month GOES Pathfinder period (May 1987 - November 1988) was examined preliminarily by Jedlovec et al. (1999) with the summer months of 1987 and 1988, and in more detail for the Ph.D. thesis of Dr. Jeff Lerner (Lerner 1999). The GOES-derived transport variables depicted the annual migration of dominant westerlies across the tropical and subtropical Americas and circulation features associated with El Nino and La Nina climate anomalies. More intense subtropical moisture transport occurred duriing the warm ENSO phase while stronger tropical moisture transport occurred during the cold phase. Spatial maps of seasonal meridional vapor transport revealed that the north-south flux of water vapor does not adhere to the classical Hadley cell circulation. On average, the meridional moisture transport favors the winter and spring hemispheres such that stronger southward (northward) transport is observed in the Southern (Northern) hemisphere during their respective winter and spring seasons. 
Pressure velocity was estimated from the satellite winds and pressure gradients using the adiabatic method to infer vertical circulations over the cloud free tropical oceans. The pressure velocites successfully portrayed ascending motions in the vacinity of the Eastern Tropical Pacific ITCZ with persistent subsidence over the south Pacific with stronger subsidence in JJA of 1988 rather than the preceeding months. 
Upper-level moisture transport compared favorably with a similar product derived from the NCEP and Goddard DAO reanalysis systems. The positioning of the vapor transport centers were in good agreement, however, the gradients and magnitudes of the transport values differed by a factor of two. The upper-troposphere of the models tended to be too moist and at times under-estimated the meridional component of the wind. 
 

• Example showing the Water Vapor Transport Index applied to 6 seasons of GOES-7 data
• Sample of the seasonal-mean meridional moisture transport
• Time evolution of the zonal-mean u-wind and upper tropospheric relative humidity during the 1987-88 El-Nino Southern Oscillation (ENSO)
• Applying water vapor transport estimates to regional water vapor budgets
• Comparison of satellite versus model estimates of water vapor transport

This work continues by focusing on the upper-level water vapor transport associated with the 1997-1999 ENSO event. Data from the GOES 8/9/10 spacecraft are being utilized to extend the data analysis region westward to 170 W longitude. Daily products will be produced for a 19-24 month period covering the ENSO event. 

A CD ROM of the data products similar to that produced for the 1987-1988 ENSO period will be available in late 2000. 

This work is currently funded by NOAA's Office of Global Programs under its Climate Change Data and Detection Program. 

Last updated on: January 17, 2002