When discussing changes on the water cycle, the IPCC Fifth Assessment on Long-term Climate Change stated, “It is virtually certain
that, in the long term, global precipitation will increase with increased global mean surface temperature. Global mean precipitation will increase at a rate per degree Celsius smaller than that of atmospheric water vapour. […] The precipitation sensitivity (about 1 to 3% °C–1
) is very different from the water vapour sensitivity (~7% °C–1
) as the main physical laws that drive these changes also differ. Water vapour increases are primarily a consequence of the Clausius–Clapeyron relationship associated with increasing temperatures in the lower troposphere (where most atmospheric water vapour resides)”. According to the 5th
assessment report, the main reasons for the inter-model spread of the precipitation sensitivity estimate among GCMs have not been fully understood. In contrast, the GCM model predictions of precipitable water vapor (PWV) have different mean states but very similar fractional trends over the next century. A series of papers by Roman et al. (2012, 2014, 2015) quantify the GCM predicted PWV trends and multi-model variability for both the mean and the extremes of the PWV distribution broken down by climate zone for the period 2000 to 2099. A recent JGR paper by Roman et al. (2016) provides a timely assessment of the systematic biases in PWV products derived routinely from hyperspectral infrared sensors on the NASA Aqua and the EUMETSAT MetOp satellites. The NASA Aqua is the research precursor to the operational JPSS satellites beginning with the Suomi-NPP bridge mission.
The current presentation presents the results of a recent study which addresses the issue of PWV trend detection when a multi-decadal time series record is composed of multiple satellite sensors with corresponding measurement uncertainty. The preliminary results suggest that the current state-of-the-art PWV products produced over land validation sites from a combination of microwave and hyperspectral infrared data is fairly unbiased (< 5%) for the mean of the PWV distribution. However, a wet bias is generally found for low water amounts and a dry bias for high water amounts. Requirements on measurement accuracy for detection of GCM predicted trends will be described and ideas for improving the global observing system discussed, especially regarding extreme high PWV.