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A Tropospheric Emission Spectrometer HDO/H2O retrieval simulator for climate models

by R. D. Field, C. Risi, G. A. Schmidt, J. Worden, A. Voulgarakis, A. N. Legrande, A. H. Sobel, R. J. Healy
Atmospheric Chemistry and Physics ()

Abstract

Retrievals of the isotopic composition of water vapor from the Aura\nTropospheric Emission Spectrometer (TES) have unique value in constraining\nmoist processes in climate models. Accurate comparison between simulated\nand retrieved values requires that model profiles that would be poorly\nretrieved are excluded, and that an instrument operator be applied\nto the remaining profiles. Typically, this is done by sampling model\noutput at satellite measurement points and using the quality flags\nand averaging kernels from individual retrievals at specific places\nand times. This approach is not reliable when the model meteorological\nconditions influencing retrieval sensitivity are different from those\nobserved by the instrument at short time scales, which will be the\ncase for free-running climate simulations. In this study, we describe\nan alternative, "categorical" approach to applying the instrument\noperator, implemented within the NASA GISS ModelE general circulation\nmodel. Retrieval quality and averaging kernel structure are predicted\nempirically from model conditions, rather than obtained from collocated\nsatellite observations. This approach can be used for arbitrary model\nconfigurations, and requires no agreement between satellite-retrieved\nand model meteorology at short time scales. To test this approach,\nnudged simulations were conducted using both the retrieval-based\nand categorical operators. Cloud cover, surface temperature and free-tropospheric\nmoisture content were the most important predictors of retrieval\nquality and averaging kernel structure. There was good agreement\nbetween the δD fields after applying the retrieval-based and more\ndetailed categorical operators, with increases of up to 30‰ over\nthe ocean and decreases of up to 40‰ over land relative to the raw\nmodel fields. The categorical operator performed better over the\nocean than over land, and requires further refinement for use outside\nof the tropics. After applying the TES operator, ModelE had δD biases\nof −8‰ over ocean and −34‰ over land compared to TES δD, which were\nless than the biases using raw model δD fields.

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