These data sets provide the most complete global coverage of water isotope distribution available for comparison with isotope-enabled models.
Though no single dataset provides complete 3D coverage, the datasets can be used in combination to understand the 3D distribution of water isotopes from the surface to the tropopause.
Isotopic data hold promise for diagnosing several aspects of models' hydrological cycle.
To account for differeing sensitivites and sampling biases, model output must be processed to estimate what the instruments would see if they measured the model's atmosphere.
The derived δD values have generally high uncertainties. Absolute values are not in agreement among the datasets, though each dataset shows realistic zonal gradients and seasonal cycles.
Remote-sensing observations of the water vapor isotopic composition can be used as a complementary tool to evaluate the hydrological cycle in atmospheric models.
Whatever the dataset used to evaluate a model, to take into account the spatio-temporal sampling biases of the data, it is preferable to collocate the model output with each of the measurements at a scale daily or shorter. To ensure that the large-scale circulation in the model is consistent with that of the data on a day-to-day basis, it is preferable to use simulations whose winds are nudged by reanalyses.
In addition to the collocation, to take into account the sensitivity of the instruments, the model outputs need to be convolved with averaging kernels available for some of the datasets.
All datasets sample preferentially clear-sky scenes. Even when collocating model outputs from a nudged simulation, the model might simulate cloudy conditions when the data sees clear-sky and vice versa. Therefore, in the model the clear-sky sampling bias can be under-estimated compared to the data. This effect is difficult to take into account since the definition of a cloud varies between instruments and between models.
When using the TES data, a correction must be applied which decreases the deltaD by about 4 permil. This correction depends on the averaging kernels of individual measurements, as described in Lee et al 2011 and Risi et al submitted.
The sensitivity of the TES deltaD retrievals decreases as surface temperature decreases, as humidity decreases and as cloud cover increases. The sensitivity becomes very small poleward of 45°S and 45°N. When the sensitivity is small, the retrieved deltaD tends toward a constant a-priori profile which is relatively enriched. Therefore, the TES retrievals underestimate the deltaD latitudinal gradient (Risi et al submitted).
Model outputs need to be convolved with averaging kernels, as described in Worden et al 2006, Risi et al submitted, Yoshimura et al in press, kurita et al submitted. This is crucial for a fair model-data comparison.
To avoid potential isotopic biases related to the presence of clouds or sampling of an incomplete atmospheric column, we discard all retrievals associated with a cloud fraction higher than 10% or with a retrieved precipitable water differing from ECMWF reanalyses by more than 10% (Risi et al 2010, Risi et al submitted).
No averaging kernel are available. We just need to calculate the total column average deltaD from the model outputs.
We discarded measurements with errors in H2O and HDO higher than the retrieved values. However, this leads to a slight bias towards measurements when H2O. In addition, we apply a 3 median average deviation filter to remove outliers (Risi et al submitted).
ACE does not use optimal estimation, and averaging kernels are not computed. To take into account the vertical resolution of the data, we convolved the model outputs with a triangular kernel of base 3 km (Dupuy et al 2008).
The sampling is very sparse. In the upper troposphere, the spatial coverage is insufficient to plot maps. Only zonal averages at best can be analyzed (Risi et al submitted).
We discard data with the visibility flag equal to zero and with diagonal elements of the averaging kernels lower than 0.03 (Risi et al submitted).
Model outputs need to be convolved with averaging kernels and a-priori profiles for each measurement the model is being collocated with.
Alternatively, since averaging kernels depend mainly on the tropopause height, we can use pre-computed representative averaging kernels for different tropopause height (Risi et al submitted).
Risi, C. and 29 others (2011): Process evaluation of tropospheric humidity simulated by general circulation models using water vapor isotopologues. Part 1: Comparison between models and observations, J Geophys. Res., submitted.
TES: Worden, J., Noone, D., and Bowman, K. (2007). Importance of rain revaporation and continental convection in the tropical water cycle. Nature, 445:528­532.
MIPAS: Steinwagner, J., Milz, M., von Clarmann, T., Glatthor, N., Grabowski, U., Höpfner, M., Stiller, G. P., and Röckmann, T. (2007). HDO measurements with MIPAS. Atmos. Chem.Phys., 7:2601­2615.
ACE: Nassar, R., Bernath, P. F., Boone, C. D., Gettelman, A., McLeod, S. D., and Rinsland, C. P. (2007). Variability in HDO/H2O abundance ratio in the Tropical Tropopause Layer. J. Geo- phys. Res., 112 (D21):doi:10.1029/2007JD008417.
NASA TES: Total column density of HDO (January 2008).
NASA TES: Minimum and Maximum total column density and the observation count (January 2008).
Risi, Camille & National Center for Atmospheric Research Staff (Eds). Last modified 20 Aug 2013. "The Climate Data Guide: Water Isotopes from Satellites." Retrieved from https://climatedataguide.ucar.edu/climate-data/water-isotopes-satellites.
5.3x8.5 km, 600hPA 5.3x8.5 km, 3 independent tropospheric levels limb measurement, down to 500 hPA limb measurement, down to 400 hPA ?
Start of record 2003 2004 2004 2003 Sept., 2002 2009? ?
End of record 2005 2011 211 2008 March, 2004 (might be longer) ongoing? ?
Timestep (raw) 1x per day 2x per day, 2am & 2pm local sunset and sunrise 10a & 10p local time ?
Timestep (processed) as measured as measured as measured as measured ?
Data format(s) available ascii HDF netCDF ascii ascii ? ?