Do we understand the global water cycle? Applying novel EO methods to diagnose the processes controlling the global water cycle

Water vapour is the most important greenhouse gas, and an accurate representation of the water cycle and its associated feedback mechanisms is crucial for reliable climate model predictions. The water cycle is a complex system involving many different competing processes. Thus it is important not only that climate models manage to reproduce the tropospheric water vapour concentrations but also that the individual processes are correctly represented. The isotopic composition of water vapour between H216O and the heavier HDO (or H218O) changes during phase changes due to condensation and evaporation. In addition, kinetic and equilibrium fractionations yield different HDO/H2O ratios. Thus, the history of water vapour in an air parcel is imprinted in the ratio of HDO/H2O, and measurements of the HDO/H2O ratio can contribute to improving our understanding of the processes involved in the water cycle.
The HDO/H2O ratio is measured by several surface stations and aircraft but their coverage is very sparse. Recently, HDO datasets have become available from satellites such as IASI or TES for the upper troposphere or SCIAMACHY and GOSAT for the full tropospheric column. (Figure 1 and Boesch et al., 2013). These satellite instruments provide global observations of the distribution of water isotopes which promise an unique view on the processes that control the global water cycle which can represent a novel way of critically testing the representation of key processes such as the convection/cloud schemes in climate models not possible from other datasets.



Figure 1: Seasonal distribution of the water fractionation over land as observed from GOSAT (Boesch et al., 2013).

This project combines the use of state-of-the-art retrieval methods for satellite observations with exciting new EO missions to assess and help advancing our understanding of the processes that control the global water cycle.
We will apply and further improve an optimal estimation retrieval software to infer HDO from spectral measurments in the shortwave-infrared region as measured by GOSAT and Sentinel 5P (S5P). Careful charachterization of the HDO dataset will be obtained via comparisons to ground-based observations from networks such as TCCON (Total Column Carbon Observation Network), NDACC (Netrok for Detection of Atmospheric Composition Change) and the data from the Musica project (M. Schneider, KIT) and from comparions to free-tropospheric observations from the IASI satellite mission (M. Schneider, KIT).

GOSAT combined with S5P will allow to generate a global dataset from 2009 to present which will allow us to gain new insights into trends and year-to-year variations for key climatic regions and to diagnose the potential underlying drivers such as changes in sea surface temperature. One region that we will be focus on will be the Amazon which has experienced a major drought in 2010 followed by a wet year in 2011.

J. Tindall (University of Leeds) has implemented water isotopic fractionation into the Hadley Centre Coupled Model HadCM3 and we will evaluate calculations from this climate model for the time period of the satellite dataset. In particular, this will allow us to diagnose the impact of the convection and cloud parameterizations on the water cycle and to subsequently help improving these critical but uncertain elements of climate models.