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<front>
<journal-meta>
<journal-id journal-id-type="publisher">NPGD</journal-id>
<journal-title-group>
<journal-title>Nonlinear Processes in Geophysics Discussions</journal-title>
<abbrev-journal-title abbrev-type="publisher">NPGD</abbrev-journal-title>
<abbrev-journal-title abbrev-type="nlm-ta">Nonlin. Processes Geophys. Discuss.</abbrev-journal-title>
</journal-title-group>
<issn pub-type="epub">2198-5634</issn>
<publisher><publisher-name></publisher-name>
<publisher-loc>Göttingen, Germany</publisher-loc>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5194/npg-2020-27</article-id>
<title-group>
<article-title>Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method</article-title>
</title-group>
<contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Mu</surname>
<given-names>Bin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Li</surname>
<given-names>Jing</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yuan</surname>
<given-names>Shijin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Luo</surname>
<given-names>Xiaodan</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
</contrib>
<contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Dai</surname>
<given-names>Guokun</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
</contrib-group><aff id="aff1">
<label>1</label>
<addr-line>Department of Software Engineering, Tongji University, Shanghai, China</addr-line>
</aff>
<aff id="aff2">
<label>2</label>
<addr-line>Department of Atmospheric and Oceanic Sciences &amp; Institute of Atmospheric Sciences, Fudan University, Shanghai, China</addr-line>
</aff>
<funding-group>
<award-group id="gs1">
<funding-source>National Natural Science Foundation of China</funding-source>
<award-id>41405097</award-id>
</award-group>
</funding-group>
<pub-date pub-type="epub">
<day>01</day>
<month>07</month>
<year>2020</year>
</pub-date>
<volume>2020</volume>
<fpage>1</fpage>
<lpage>29</lpage>
<permissions>
<copyright-statement>Copyright: &#x000a9; 2020 Bin Mu et al.</copyright-statement>
<copyright-year>2020</copyright-year>
<license license-type="open-access">
<license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri"  xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p>
</license>
</permissions>
<self-uri xlink:href="https://npg.copernicus.org/preprints/npg-2020-27/">This article is available from https://npg.copernicus.org/preprints/npg-2020-27/</self-uri>
<self-uri xlink:href="https://npg.copernicus.org/preprints/npg-2020-27/npg-2020-27.pdf">The full text article is available as a PDF file from https://npg.copernicus.org/preprints/npg-2020-27/npg-2020-27.pdf</self-uri>
<abstract>
<p>&lt;p&gt;The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in the North Hemisphere. It has a profound influence on the westerly wind strength and storm tracks in North Atlantic, which further affect the winter climate in Northern Hemisphere. Therefore, the identification for optimal precursor (OPR) of the NAO event is of research value and practical significance. In this paper, the conditional nonlinear optimal perturbation (CNOP) method, which has been widely used in research on the OPR of climatic event, is adopted to explore which kind of initial perturbation is most likely to trigger the NAO anomaly pattern in the Community Earth System Model (CESM). Since the adjoint model of CESM has yet to be developed, this kind of problem cannot be solved using traditional strategies based on gradient information provided by the adjoint model. We utilize an adjoint-free algorithm to solve CNOP in such a high dimensional numerical model, and OPRs of the NAO can be successfully identified. The results reveal that OPRs obtained by CNOP can cause the basic state to develop into typical dipole mode, and the nonlinear process plays an important role in the last stage of the prediction period. The algorithm adopted in this work can avoid falling into a local optimum and is accelerated with multiple parallel frameworks to enhance performance. The solution scheme can also be generalized to the OPR research of other climate events or other complex numerical models.&lt;/p&gt;</p>
</abstract>
<counts><page-count count="29"/></counts>
</article-meta>
</front>
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