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

Identifieur interne : 000013 ( Hal/Corpus ); précédent : 000012; suivant : 000014

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

Auteurs : R. D. Field ; Camille Risi ; G. A. Schmidt ; J. Worden ; A. Voulgarakis ; A. N. Legrande ; A. H. Sobel ; R. J. Healy

Source :

RBID : Hal:hal-01109269

Abstract

Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, "categorical" approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simulations were conducted using both the retrieval-based and categorical opera-tors. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the delta D fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30 parts per thousand over the ocean and decreases of up to 40 parts per thousand over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had delta D biases of -8 parts per thousand over ocean and -34 parts per thousand over land compared to TES delta D, which were less than the biases using raw model delta D fields.

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Hal:hal-01109269

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<idno type="halAuthorId">327780</idno>
<affiliation ref="#struct-408425"></affiliation>
</author>
<author role="aut">
<persName>
<forename type="first">J.</forename>
<surname>Worden</surname>
</persName>
<idno type="halAuthorId">212019</idno>
<affiliation ref="#struct-408446"></affiliation>
</author>
<author role="aut">
<persName>
<forename type="first">A.</forename>
<surname>Voulgarakis</surname>
</persName>
<idno type="halAuthorId">1120598</idno>
<affiliation ref="#struct-408425"></affiliation>
<affiliation ref="#struct-410965"></affiliation>
<affiliation ref="#struct-410966"></affiliation>
</author>
<author role="aut">
<persName>
<forename type="first">A. N.</forename>
<surname>LeGrande</surname>
</persName>
<idno type="halAuthorId">1120596</idno>
<affiliation ref="#struct-408425"></affiliation>
<affiliation ref="#struct-410965"></affiliation>
</author>
<author role="aut">
<persName>
<forename type="first">A. H.</forename>
<surname>Sobel</surname>
</persName>
<idno type="halAuthorId">1120599</idno>
<affiliation ref="#struct-410963"></affiliation>
<affiliation ref="#struct-410967"></affiliation>
<affiliation ref="#struct-410968"></affiliation>
</author>
<author role="aut">
<persName>
<forename type="first">R. J.</forename>
<surname>Healy</surname>
</persName>
<idno type="halAuthorId">1120597</idno>
<affiliation ref="#struct-410965"></affiliation>
</author>
</analytic>
<monogr>
<idno type="halJournalId" status="VALID">238</idno>
<idno type="issn">1680-7316</idno>
<idno type="eissn">1680-7324</idno>
<title level="j">Atmospheric Chemistry and Physics</title>
<imprint>
<publisher>European Geosciences Union</publisher>
<biblScope unit="volume">12</biblScope>
<biblScope unit="issue">21</biblScope>
<biblScope unit="pp">10485-10504</biblScope>
<date type="datePub">2012</date>
</imprint>
</monogr>
<idno type="doi">10.5194/acp-12-10485-2012</idno>
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<profileDesc>
<langUsage>
<language ident="en">English</language>
</langUsage>
<textClass>
<classCode scheme="halDomain" n="sdu.ocean">Sciences of the Universe [physics]/Ocean, Atmosphere</classCode>
<classCode scheme="halDomain" n="sdu.stu.cl">Sciences of the Universe [physics]/Earth Sciences/Climatology</classCode>
<classCode scheme="halTypology" n="ART">Journal articles</classCode>
</textClass>
<abstract xml:lang="en">Retrievals of the isotopic composition of water vapor from the Aura Tropospheric Emission Spectrometer (TES) have unique value in constraining moist processes in climate models. Accurate comparison between simulated and retrieved values requires that model profiles that would be poorly retrieved are excluded, and that an instrument operator be applied to the remaining profiles. Typically, this is done by sampling model output at satellite measurement points and using the quality flags and averaging kernels from individual retrievals at specific places and times. This approach is not reliable when the model meteorological conditions influencing retrieval sensitivity are different from those observed by the instrument at short time scales, which will be the case for free-running climate simulations. In this study, we describe an alternative, "categorical" approach to applying the instrument operator, implemented within the NASA GISS ModelE general circulation model. Retrieval quality and averaging kernel structure are predicted empirically from model conditions, rather than obtained from collocated satellite observations. This approach can be used for arbitrary model configurations, and requires no agreement between satellite-retrieved and model meteorology at short time scales. To test this approach, nudged simulations were conducted using both the retrieval-based and categorical opera-tors. Cloud cover, surface temperature and free-tropospheric moisture content were the most important predictors of retrieval quality and averaging kernel structure. There was good agreement between the delta D fields after applying the retrieval-based and more detailed categorical operators, with increases of up to 30 parts per thousand over the ocean and decreases of up to 40 parts per thousand over land relative to the raw model fields. The categorical operator performed better over the ocean than over land, and requires further refinement for use outside of the tropics. After applying the TES operator, ModelE had delta D biases of -8 parts per thousand over ocean and -34 parts per thousand over land compared to TES delta D, which were less than the biases using raw model delta D fields.</abstract>
</profileDesc>
</hal>
</record>

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