Speaker
Description
The hydrological cycle is a key component of the Earth system, constituting the largest movement of any substance on Earth. Water vapour, while only accounting for 0.001% of all water mass, is an important natural greenhouse gas influencing the radiative balance of the Earth as well as surface and soil moisture fluxes. The time moisture spends in the atmosphere (i.e. the time between evaporation and precipitation) is referred to as the Water Vapour Residency Time (WVRT) and is related to the rate of energy transformations and water mass turnover (sources and sinks) as well as providing essential insight into the time lags and linkages between processes (e.g. precipitation event and contributing evaporation sources). While WVRT is a key diagnostic for hydrological sensitivity, it is a variable we cannot directly observe. This study uses the long-established turnover time (TUT) method to estimate WVRT from observational, reanalysis, and climate model ensembles as part of a comparative analysis. We start by introducing the data ensembles used along with the TUT methodology and considerations needed for estimating WVRT. We will then present a global and large-scale regional analysis of TUT between 1988 and 2014 from these ensembles. We will contextualise our findings using the Precipitation Driver and Response Model Intercomparison Project (PDRMIP) results.
Additionally, we use tools from the GEWEX Water Vapor Assessment (G-VAP) to characterise these ensembles and provide trend estimates of total column water vapour (TCWV), precipitation, and TUT from recent rises in global temperatures. To better understand the impacts of future warming, we also present the results of changes to TUT due to potential future warming from the CMIP6 Shared Socioeconomic Pathways (SSP) scenarios and relate these changes to regional freshwater fluxes. Finally, we will introduce how improved capacity for the remote sensing of stable water vapour isotopologues can provide an observational constraint on atmospheric moisture pathways, reducing uncertainty around WVRT.
Acknowledgements: Daniel Watters1, Marc Schroeder2, Richard Allen3, Hartmut Boesch4, Matthias Schneider5, Farahnaz Khosrawi5, Amelie Röhling5, Christopher Diekmann6, Harald Sodemann7, and Iris Thurnherr8
(1) NASA, (2) DWD/CM SAF, (3) University of Reading, (4) University of Bremen, (5) Karlsruhe Institute of Technology, (6) EUMETSAT, (7) University of Bergen, (8) ETH Zurich
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