Climate Data

Water Isotopes from Satellites

Summary of 7 satellite-derived products providing a global snapshot of water isotope distribution in 3D, with good information on zonal gradients and seasonality.

Key Strengths:

  • 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.

Key Limitations:

  • The period of record is short for most of these data sets, and interannual variability and trends are poorly known
  • 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.

Expert User Guidance

#General Comments

Remote-sensing observations of the water vapor isotopic composition can be used as a complementary tool to evaluate the hydrological cycle in atmospheric models.

Collocation:

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.

Kernel convolution:

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.

Clear-sky sampling bias:

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.

Specific to TES:

Quality selection:

We select only retrievals for which the degree of freedom for signal is

higher than 0.5

Bias correction:

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.

Sensitivity:

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).

Kernel convolution:

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.

 

Specific to SCIAMACHY:

Quality selection:

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).

Kernel convolution:

No averaging kernel are available. We just need to calculate the total column average deltaD from the model outputs.

 

Specific to ACE:

Quality selection:

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).

Kernel convolution:

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).

Sampling:

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).

 

Specific to MIPAS:

Quality selection:

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).

Kernel convolution:

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).

Camille Risi, CNRS France, August, 2011###

Years of Record

2000/01 to 2012/09
temporal metadataID:

Formats

Timestep

Sub-daily | Daily

Domain

Ocean or Land

Ocean&Land

Missing Data Flag

missing data present

Vertical Levels

Input Data

satellite

Earth system components and main variables

Water Isotopes from Satellites

Summary of 7 satellite-derived products providing a global snapshot of water isotope distribution in 3D, with good information on zonal gradients and seasonality.

  1. 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.
  2. SCIMACHY: Frankenberg, C., Yoshimura, K., Warneke, T., Aben, I., Butz, A., Deutscher, N., Griffith, D., Hase, F., Notholt, J., Schneider, M., Schrijver, H., and Röckmann, T. (2009). Dynamic processes governing lower-tropospheric HDO/H2O ratios as observ
  3. TES: Lee, J., Worden, J., Noone, D., Bowman, K., Elering, A., LeGrande, A., Li, J.-L., Schmidt, G., and Sodemann, H. (2011). Relating tropical ocean clouds to moist processes using water vapor isotope measurements. Atmospheric Chemistry and Physics, 11:
  4. TES: Worden, J., Noone, D., and Bowman, K. (2007). Importance of rain revaporation and continental convection in the tropical water cycle. Nature, 445:528­532.
  5. TES: Worden, J., Noone, D., Galewsky, J., Bailey, A., Bowman, K., Brown, D., Hurley, J., Kulawik, S., Lee, J., and Strong, M. (2010). Estimate of bias in Aura TES HDO/H2O profiles from comparison of TES and in situ HDO/H2O measurements at the Mauna Loa Ob
  6. TES: Worden, J., Bowman, K., Noone, D., Beer, R., Clough, S., Eldering, A., Fisher, B., Goldman, A., Gunson, M., Herman, R., Kulawik, S. S., Lampel, M., Luo, M., Osterman, G., Rinsland, C., Rodgers, C., Sander, S., Shephard, M., and Worden, H. (2006). Tro
  7. MIPAS: Steinwagner, J., Fueglistaler, S., Stiller, G., von Clarmann, T., Kiefer, M., Borsboom, P.-P., van Delden, A., and Röckmann, T. (2010). Tropical dehydration processes constrained by the seasonality of stratospheric deuterated water. Nature Geosci
  8. 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.
  9. 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.
  10. IASI: H216O and HDO measurements with IASI/MetOp, H. Herbin, D. Hurtmans, C. Clerbaux, L. Clarisse, and P.-F. Coheur, Atmos. Chem. Phys., 9, 9433-9447, 2009, doi:10.5194/acp-9-9433-2009
  11. IASI: M. Schneider and F. Hase; Optimal estimation of tropospheric H2O and HDO with IASI/METOP, Atmos. Chem. Phys. Discuss., 11, 16107–16146, 2011, doi:10.5194/acpd-11-16107-2011

Key Figures

Click the thumbnails to view larger sizes

Thumbnails

Captions

NASA TES: Total column density of HDO (January 2008).
NASA TES: Minimum and Maximum total column density and the observation count (January 2008).

Cite this page

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.

Acknowledgement of any material taken from this page is appreciated. On behalf of experts who have contributed data, advice, and/or figures, please cite their work as well.