The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series
Identifieur interne : 001B80 ( Istex/Corpus ); précédent : 001B79; suivant : 001B81The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series
Auteurs : J. Junk ; U. Feister ; A. Helbig ; K. Görgen ; E. Rozanov ; J. W. Krzy Cin ; L. HoffmannSource :
- Journal of Geophysical Research: Atmospheres [ 0148-0227 ] ; 2012-08-27.
Abstract
Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long‐term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.
Url:
DOI: 10.1029/2012JD017659
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<front><div type="abstract">Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long‐term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.</div>
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<surname>Junk</surname>
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<author xml:id="author-2"><persName><forename type="first">U.</forename>
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<author xml:id="author-3"><persName><forename type="first">A.</forename>
<surname>Helbig</surname>
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<affiliation>Department of Environmental Meteorology, University of Trier, Trier, Germany</affiliation>
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<author xml:id="author-4"><persName><forename type="first">K.</forename>
<surname>Görgen</surname>
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</author>
<author xml:id="author-5"><persName><forename type="first">E.</forename>
<surname>Rozanov</surname>
</persName>
<affiliation>Institute for Atmospheric and Climate Science, Zurich, Switzerland</affiliation>
<affiliation>Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center, Davos, Switzerland</affiliation>
</author>
<author xml:id="author-6"><persName><forename type="first">J. W.</forename>
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<abstract><p>Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long‐term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.</p>
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<abstract style="short"><p>Successful reconstruction of a 99‐year time series of daily erythemal UV totals Reduced uncertainties in the reconstructed time series due to additional ozone Significant trends in the reconstructed UV time series were detected</p>
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<affiliation xml:id="jgrd17969-aff-0002" countryCode="DE" type="organization"><unparsedAffiliation>German Meteorological Service (DWD), Meteorological Observatory Lindenberg–Richard-Aßmann-Observatory, Lindenberg, Germany</unparsedAffiliation>
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<affiliation xml:id="jgrd17969-aff-0003" countryCode="DE" type="organization"><unparsedAffiliation>Department of Environmental Meteorology, University of Trier, Trier, Germany</unparsedAffiliation>
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<keywordGroup type="author"><keyword xml:id="jgrd17969-kwd-0001">artificial neural network</keyword>
<keyword xml:id="jgrd17969-kwd-0002">column ozone</keyword>
<keyword xml:id="jgrd17969-kwd-0003">erythemal UV radiation</keyword>
<keyword xml:id="jgrd17969-kwd-0004">sunshine duration</keyword>
<keyword xml:id="jgrd17969-kwd-0005">time series reconstruction</keyword>
<keyword xml:id="jgrd17969-kwd-0006">trend analysis</keyword>
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<fundingInfo><fundingAgency>Minist�re de l'Enseignement sup�rieur et de la Recherche (MESR) of the Grand Duchy of Luxembourg</fundingAgency>
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<caption>Tab‐delimited Table 1.</caption>
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<caption>Tab‐delimited Table 2.</caption>
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<abstractGroup><abstract type="main"><p xml:id="jgrd17969-para-0001" label="1">Solar erythemal UV radiation (UV<sub>ER</sub>
) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long‐term UV<sub>ER</sub>
records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UV<sub>ER</sub>
time series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UV<sub>ER</sub>
at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UV<sub>ER</sub>
, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UV<sub>ER</sub>
for the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UV<sub>ER</sub>
data set are presented. The time series of monthly and annual totals of UV<sub>ER</sub>
provide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.</p>
</abstract>
<abstract type="short"><title type="main">Key Points</title>
<p xml:id="jgrd17969-para-0002"><list style="bulleted"><listItem>Successful reconstruction of a 99‐year time series of daily erythemal UV totals</listItem>
<listItem>Reduced uncertainties in the reconstructed time series due to additional ozone</listItem>
<listItem>Significant trends in the reconstructed UV time series were detected</listItem>
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<mods version="3.6"><titleInfo lang="en"><title>The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series</title>
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<titleInfo type="abbreviated" lang="en"><title>RECONSTRUCTION OF RADIATION TIME SERIES</title>
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<titleInfo type="alternative" contentType="CDATA" lang="en"><title>The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series</title>
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<name type="personal"><namePart type="given">J.</namePart>
<namePart type="family">Junk</namePart>
<affiliation>Centre de Recherche Public–Gabriel Lippmann, Department of Environment and Agro-Biotechnologies, Belvaux, Luxembourg</affiliation>
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<name type="personal"><namePart type="given">U.</namePart>
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<role><roleTerm type="text">author</roleTerm>
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<name type="personal"><namePart type="given">A.</namePart>
<namePart type="family">Helbig</namePart>
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<name type="personal"><namePart type="given">K.</namePart>
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<name type="personal"><namePart type="given">E.</namePart>
<namePart type="family">Rozanov</namePart>
<affiliation>Institute for Atmospheric and Climate Science, Zurich, Switzerland</affiliation>
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<name type="personal"><namePart type="given">J. W.</namePart>
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<affiliation>Institute of Geophysics, Polish Academy of Sciences, Warsaw, Poland</affiliation>
<role><roleTerm type="text">author</roleTerm>
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<name type="personal"><namePart type="given">L.</namePart>
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<edition>Junk, J., U. Feister, A. Helbig, K. Görgen, E. Rozanov, J. W. Krzyścin, and L. Hoffmann (2012), The benefit of modeled ozone data for the reconstruction of a 99‐year UV radiation time series, J. Geophys. Res., 117, D16102, doi:10.1029/2012JD017659.</edition>
<copyrightDate encoding="w3cdtf">2012</copyrightDate>
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<abstract>Solar erythemal UV radiation (UVER) is highly relevant for numerous biological processes that affect plants, animals, and human health. Nevertheless, long‐term UVER records are scarce. As significant declines in the column ozone concentration were observed in the past and a recovery of the stratospheric ozone layer is anticipated by the middle of the 21st century, there is a strong interest in the temporal variation of UVERtime series. Therefore, we combined ground‐based measurements of different meteorological variables with modeled ozone data sets to reconstruct time series of daily totals of UVER at the Meteorological Observatory, Potsdam, Germany. Artificial neural networks were trained with measured UVER, sunshine duration, the day of year, measured and modeled total column ozone, as well as the minimum solar zenith angle. This allows for the reconstruction of daily totals of UVERfor the period from 1901 to 1999. Additionally, analyses of the long‐term variations from 1901 until 1999 of the reconstructed, new UVER data set are presented. The time series of monthly and annual totals of UVERprovide a long‐term meteorological basis for epidemiological investigations in human health and occupational medicine for the region of Potsdam and Berlin. A strong benefit of our ANN‐approach is the fact that it can be easily adapted to different geographical locations, as successfully tested in the framework of the COSTAction 726.</abstract>
<abstract type="short">Successful reconstruction of a 99‐year time series of daily erythemal UV totals Reduced uncertainties in the reconstructed time series due to additional ozone Significant trends in the reconstructed UV time series were detected</abstract>
<note type="additional physical form">Tab‐delimited Table 1.Tab‐delimited Table 2.</note>
<note type="funding">Minist�re de l'Enseignement sup�rieur et de la Recherche (MESR) of the Grand Duchy of Luxembourg - No. AirQ; </note>
<subject><genre>keywords</genre>
<topic>artificial neural network</topic>
<topic>column ozone</topic>
<topic>erythemal UV radiation</topic>
<topic>sunshine duration</topic>
<topic>time series reconstruction</topic>
<topic>trend analysis</topic>
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<relatedItem type="host"><titleInfo><title>Journal of Geophysical Research: Atmospheres</title>
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<topic authorityURI="http://psi.agu.org/subset/ACL">Climate and Dynamics</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/1600">GLOBAL CHANGE</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/1616">Climate variability</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/1620">Climate dynamics</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3300">ATMOSPHERIC PROCESSES</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3309">Climatology</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3359">Radiative processes</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3394">Instruments and techniques</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3399">General or miscellaneous</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/3305">Climate change and variability</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/4200">OCEANOGRAPHY: GENERAL</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/4215">Climate and interannual variability</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/8400">VOLCANOLOGY</topic>
<topic authorityURI="http://psi.agu.org/taxonomy5/8408">Volcano/climate interactions</topic>
</subject>
<subject><genre>article-category</genre>
<topic>Climate and Dynamics</topic>
</subject>
<identifier type="ISSN">0148-0227</identifier>
<identifier type="eISSN">2156-2202</identifier>
<identifier type="DOI">10.1002/(ISSN)2156-2202d</identifier>
<identifier type="CODEN">JGREA2</identifier>
<identifier type="PublisherID">JGRD</identifier>
<part><date>2012</date>
<detail type="volume"><caption>vol.</caption>
<number>117</number>
</detail>
<detail type="issue"><caption>no.</caption>
<number>D16</number>
</detail>
<extent unit="pages"><start>n/a</start>
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<total>10</total>
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<identifier type="DOI">10.1029/2012JD017659</identifier>
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<accessCondition type="use and reproduction" contentType="copyright">©2012. American Geophysical Union. All Rights Reserved.</accessCondition>
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