Validation of SCIAMACHY HDO/H<sub>2</sub>O measurements using the TCCON and NDACC-MUSICA networks

Abstract

Abstract. Measurements of the atmospheric HDO/H2O ratio help us to better understand the hydrological cycle and improve models to correctly simulate tropospheric humidity and therefore climate change. We present an updated version of the column-averaged HDO/H2O ratio dataset from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The dataset is extended with two additional years, now covering 2003–2007, and is validated against co-located ground-based total column δD measurements from Fourier-Transform Spectrometers (FTS) of the Total Carbon Column Observing Network (TCCON) and the Network for the Detection of Atmospheric Composition Change (NDACC, produced within the framework of the MUSICA project). Even though the time overlap between the available data is not yet ideal, we determined a mean negative bias in SCIAMACHY δD of −35±30‰ compared to TCCON and −69±15‰ compared to MUSICA (the uncertainty indicating the station-to-station standard deviation). The bias shows a latitudinal dependency, being largest (∼ −60 to −80‰) at the highest latitudes and smallest (∼ −20 to −30‰) at the lowest latitudes. We have tested the impact of an offset correction to the SCIAMACHY HDO and H2O columns. This correction leads to a humidity and latitude dependent shift in δD and an improvement of the bias by 27‰, although it does not lead to an improved correlation with the FTS measurements nor to a strong reduction of the latitudinal dependency of the bias. The correction might be an improvement for dry, high-altitude areas, such as the Tibetan Plateau and the Andes region. For these areas, however, validation is currently impossible due to a lack of ground stations. The mean standard deviation of single-sounding SCIAMACHY–FTS differences is ∼ 115‰, which is reduced by a factor ∼ 2 when we consider monthly means. When we relax the strict matching of individual measurements and focus on the mean seasonalities using all available FTS data, we find that the correlation coefficients between SCIAMACHY and the FTS networks improve from 0.2 to 0.7–0.8. Certain ground stations show a clear asymmetry in δD during the transition from the dry to the wet season and back, which is also detected by SCIAMACHY. This asymmetry points to a transition in the source region temperature or location of the water vapor, and shows the added information that HDO/H2O measurements provide, if used in combination with variations in humidity.

Interannual variability of isotopic composition in water vapor over western Africa and its relationship to ENSO

“…IsoGSM simulates this spatial pattern qualitatively well. Although the average is negatively biased (about 20 ‰) (Yoshimura et al, 2011) and the latitudinal gradient is weaker in IsoGSM, bias and overestimated gradient are found in SCIAMACHY when compared with ground-based Fourier-transform spectrometers (Scheepmaker et al, 2014). Accordingly we cannot conclude whether such differences from the satellite are indeed problematic or not at this stage.…”

“…The bias in the mean field (Risi et al, 2010;Werner et al, 2011;Lee et al, 2012) and the underestimated seasonality (Risi et al, 2010) are also common in other GCMs. Again, the bias in SCIA-MACHY has also been indicated (Scheepmaker et al, 2014), and Risi et al (2010) pointed out the possibility that SCIA-MACHY may overestimate the variability by preferentially sampling high altitudes.…”

“…However, there is still debate regarding the relative importance of the various basins and mechanistic timescales involved (Nicholson, 2013). The Atlantic (Lamb, 1978;Joly and Voldoire, 2010;Mohino et al, 2011a), Pacific (Janicot et al, 2001;Mohino et al, 2011b), Indian Ocean , and Mediterranean (Rowell, 2003;Polo et al, 2008) have been linked to the variability of west African rainfall. In these oceans, the ENSO is thought to modulate the high-frequency component (interannual) of the variability (Ward, 1998;Joly et al, 2007).…”

“…Frankenberg et al (2009) measured column-averaged isotopologue ratio (δD) values in water vapor using the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) onboard the European research satellite ENVISAT. We used the updated and extended version of this data set from Scheepmaker et al (2014), covering the years [2003][2004][2005][2006][2007]. As measured δD is weighted by the H 2 O concentration at all heights, it is largely determined by the isotopic abundance in the lowest tropospheric layers, where most water vapor resides.…”

HDO and H<sub>2</sub>O total column retrievals from TROPOMI shortwave infrared measurements

“…This is directly related to improved remotesensing techniques that can accurately measure water vapour isotopologues from ground-based networks, such as the Total Carbon Column Observing Network (TCCON, Wunch et al, 2011) and the Network for Detection of Atmospheric Composition Change (NDACC, formerly the Network for Detection of Stratospheric Change, Kurylo and Solomon, 1990;Schneider et al, 2016), as well as global measurements from space with instruments such as the Interferometric Monitor for Greenhouse gases (IMG, Zakharov et al, 2004), the Thermal Emission Spectrometer (TES, Worden et al, 2007), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY, Frankenberg et al, 2009;Scheepmaker et al, 2015), the Infrared Atmospheric Sounding Interferometer (IASI, Herbin et al, 2009) and the Greenhouse gases Observing Satellite (GOSAT, Frankenberg et al, 2013;Boesch et al, 2013). These new techniques allow for more frequent and global measurements of the ratio HDO / H 2 O in water vapour and show the clear potential for furthering our understanding of the atmospheric hydrological cycle through comparisons with GCMs Yoshimura et al, 2011;Risi et al, 2012a, b).…”