4.2 Variability, trends and challenges in the long-term global satellite record of water vapor

Tuesday, 28 September 2010: 10:45 AM
Capitol C (Westin Annapolis)
Thomas H. Vonder Haar, Science and Technology Corporation, Hampton, VA; and J. M. Forsythe, J. L. Bytheway, and I. Wittmeyer

The depiction of global water vapor and its variability is challenged by the variety of observing systems used to measure it. Microwave satellite systems measure the total column amount over ocean, infrared measurements are useful over clear regions, and ground-based measurements are limited in time and space. Assembling these parts and leveraging their strengths to create a global record of water vapor is a challenge.

The NASA Water Vapor Project (NVAP) dataset covers 1988-2001 and is an early effort to assemble these differing observations into a coherent picture. In this paper, existing results from NVAP are examined with an eye towards future improved global water vapor analyses.

The early NVAP record of atmospheric water vapor shows a global, annual mean of 24.5 mm with an annual cycle of about 10% of the total value. Considerable interannual variability has also been observed. During 1988-2001, atmospheric water vapor responded to both the eruption of Mount Pinatubo and El Nino events in step both theoretically and physically with the record of tropospheric temperature. A study of daily water vapor variability shows that on a percentage basis water vapor content from 300 – 700 hPa in the midlatitudes shows the largest variability.

New results from the forthcoming NVAP-MEaSURES dataset, which will cover the period 1987 – 2010 and is currently in production, will also be discussed for their potential to improve the global time series of layered and total column water vapor.

Specific questions addressed are: What is the daily, interannual, and decadal variability of water vapor? What is the structure of its vertical distribution? Are there decadal trends in total column water vapor based on the NVAP dataset? What is the coupling between time series of water vapor and other atmospheric variables? Hovmöller diagrams of layered and total column water vapor will be presented as a tool to detect time-dependent biases.

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