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<article language="en">
	<journal>
		<journal_title>Climate of the Past Discussions</journal_title>
		<journal_url>www.clim-past-discuss.net</journal_url>
		<issn>1814-9340</issn>
		<eissn>1814-9359</eissn>
		<volume_number>5</volume_number>
		<issue_number>2</issue_number>
		<publication_year>2009</publication_year>
	</journal>
	<doi>10.5194/cpd-5-911-2009</doi>
	<article_url>http://www.clim-past-discuss.net/5/911/2009/</article_url>
	<abstract_html>http://www.clim-past-discuss.net/5/911/2009/cpd-5-911-2009.html</abstract_html>
	<fulltext_pdf>http://www.clim-past-discuss.net/5/911/2009/cpd-5-911-2009.pdf</fulltext_pdf>
	<start_page>911</start_page>
	<end_page>936</end_page>
	<publication_date>2009-03-12</publication_date>
	<article_title content_type="html">Changes in atmospheric variability in a glacial climate and the impacts on  proxy data: a model intercomparison</article_title>
	<authors>
		<author numeration="1" affiliations="1,2">
			<name>F. S. R. Pausata</name>
			<email>francesco.pausata@bjerknes.uib.no</email>
		</author>
		<author numeration="2" affiliations="1,3">
			<name>C. Li</name>
		</author>
		<author numeration="3" affiliations="1">
			<name>J. J. Wettstein</name>
		</author>
		<author numeration="4" affiliations="1">
			<name>K. H. Nisancioglu</name>
		</author>
		<author numeration="5" affiliations="2,4">
			<name>D. S. Battisti</name>
		</author>
	</authors>
	<affiliations>
		<affiliation numeration="1" content_type="html">Bjerknes Centre for Climate Research, Allegaten 55, 5007 Bergen, Norway</affiliation>
		<affiliation numeration="2" content_type="html">Geophysical Institute, University of Bergen, Allegaten 70, 5007 Bergen,  Norway</affiliation>
		<affiliation numeration="3" content_type="html">Department of Earth Science, University of Bergen, Allegaten 41, 5007 Bergen,  Norway</affiliation>
		<affiliation numeration="4" content_type="html">Department Atmospheric Sciences, University of Washington,  Seattle, WA 98195, USA</affiliation>
	</affiliations>
	<abstract content_type="html">We investigate sea level pressure variability in the extratropical
      North Atlantic in the preindustrial climate (1750 A.D.) and at the
      Last Glacial Maximum (LGM, 21 kyr before present) using four
      climate models. In general, the models exhibit a significant reduction
      in interannual variance of sea level pressure during the LGM compared
      to pre-industrial simulations and this reduction is concentrated in
      winter.
&lt;br&gt;&lt;br&gt;
      For the preindustrial climate, all the models feature a similar
      leading mode (EOF) of sea level pressure variability that is also
      similar to the leading mode of variability in the instrumental record:
      the North Atlantic Oscillation (NAO). In contrast, the leading mode of
      sea level pressure variability during the LGM is model dependent, but
      in each model different from that in the preindustrial climate.  In
      each model, the leading (NAO-like) mode of variability explains
      a smaller fraction of the variance and also less absolute variance in
      the LGM than in the preindustrial. The leading (NAO-like) mode of sea
      level pressure variability is shifted southward in the LGM simulations
      relative to the preindustrial simulations.
&lt;br&gt;&lt;br&gt;
      Finally, we correlate the leading mode of sea level pressure
      variability with surface temperature and precipitation within each
      model and for the two time periods. In the preindustrial climate, the
      leading mode of sea level pressure variability is similar from model
      to model and the temperature and precipitation correlation patterns
      are also similar.  In contrast, since the models find different
      dominant modes of sea level pressure variability for the LGM climate,
      they also disagree on the associated patterns of temperature and
      precipitation variability.  Assuming stationarity of the relationship
      between surface climate and the leading mode of sea level pressure
      variability could lead to a misinterpretation of signals recorded in
      proxy data.</abstract>
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</article>

