A new mathematical framework for atmospheric blocking events
We use a simple yet Earth-like hemispheric atmospheric model to propose a new framework for the mathematical properties of blocking events. Using finite-time Lyapunov exponents, we show that the occurrence of blockings is associated with conditions featuring anomalously high instability. Longer-live...
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Veröffentlicht in: | Climate dynamics 2020, Vol.54 (1-2), p.575-598 |
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description | We use a simple yet Earth-like hemispheric atmospheric model to propose a new framework for the mathematical properties of blocking events. Using finite-time Lyapunov exponents, we show that the occurrence of blockings is associated with conditions featuring anomalously high instability. Longer-lived blockings are very rare and have typically higher instability. In the case of Atlantic blockings, predictability is especially reduced at the onset and decay of the blocking event, while a relative increase of predictability is found in the mature phase. The opposite holds for Pacific blockings, for which predictability is lowest in the mature phase. Blockings are realised when the trajectory of the system is in the neighbourhood of a specific class of unstable periodic orbits (UPOs), natural modes of variability that cover the attractor the system. UPOs corresponding to blockings have, indeed, a higher degree of instability compared to UPOs associated with zonal flow. Our results provide a rigorous justification for the classical Markov chains-based analysis of transitions between weather regimes. The analysis of UPOs elucidates that the model features a very severe violation of hyperbolicity, due to the presence of a substantial variability in the number of unstable dimensions, which explains why atmospheric states can differ a lot in term of their predictability. Additionally, such a variability explains the need for performing data assimilation in a state space that includes not only the unstable and neutral subspaces, but also some stable modes. The lack of robustness associated with the violation of hyperbolicity might be a basic cause contributing to the difficulty in representing blockings in numerical models and in predicting how their statistics will change as a result of climate change. This corresponds to fundamental issues limiting our ability to construct very accurate numerical models of the atmosphere, in term of predictability of the both the first and of the second kind in the sense of Lorenz. |
doi_str_mv | 10.1007/s00382-019-05018-2 |
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Using finite-time Lyapunov exponents, we show that the occurrence of blockings is associated with conditions featuring anomalously high instability. Longer-lived blockings are very rare and have typically higher instability. In the case of Atlantic blockings, predictability is especially reduced at the onset and decay of the blocking event, while a relative increase of predictability is found in the mature phase. The opposite holds for Pacific blockings, for which predictability is lowest in the mature phase. Blockings are realised when the trajectory of the system is in the neighbourhood of a specific class of unstable periodic orbits (UPOs), natural modes of variability that cover the attractor the system. UPOs corresponding to blockings have, indeed, a higher degree of instability compared to UPOs associated with zonal flow. Our results provide a rigorous justification for the classical Markov chains-based analysis of transitions between weather regimes. The analysis of UPOs elucidates that the model features a very severe violation of hyperbolicity, due to the presence of a substantial variability in the number of unstable dimensions, which explains why atmospheric states can differ a lot in term of their predictability. Additionally, such a variability explains the need for performing data assimilation in a state space that includes not only the unstable and neutral subspaces, but also some stable modes. The lack of robustness associated with the violation of hyperbolicity might be a basic cause contributing to the difficulty in representing blockings in numerical models and in predicting how their statistics will change as a result of climate change. This corresponds to fundamental issues limiting our ability to construct very accurate numerical models of the atmosphere, in term of predictability of the both the first and of the second kind in the sense of Lorenz.</description><identifier>ISSN: 0930-7575</identifier><identifier>EISSN: 1432-0894</identifier><identifier>DOI: 10.1007/s00382-019-05018-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Atmospheric blocking ; Atmospheric circulation ; Atmospheric models ; Blocking events ; Climate change ; Climate models ; Climatology ; Data assimilation ; Data collection ; Dimensions ; Earth ; Earth and Environmental Science ; Earth Sciences ; Flow stability ; Geophysics/Geodesy ; Instability ; Liapunov exponents ; Markov chains ; Mathematical analysis ; Mathematical models ; Mathematical properties ; Natural history ; Numerical models ; Numerical prediction ; Oceanography ; Orbits ; Robustness (mathematics) ; Statistical analysis ; Statistical methods ; Subspaces ; Variability ; Weather ; Zonal flow ; Zonal flow (meteorology)</subject><ispartof>Climate dynamics, 2020, Vol.54 (1-2), p.575-598</ispartof><rights>The Author(s) 2019</rights><rights>COPYRIGHT 2020 Springer</rights><rights>Climate Dynamics is a copyright of Springer, (2019). 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Using finite-time Lyapunov exponents, we show that the occurrence of blockings is associated with conditions featuring anomalously high instability. Longer-lived blockings are very rare and have typically higher instability. In the case of Atlantic blockings, predictability is especially reduced at the onset and decay of the blocking event, while a relative increase of predictability is found in the mature phase. The opposite holds for Pacific blockings, for which predictability is lowest in the mature phase. Blockings are realised when the trajectory of the system is in the neighbourhood of a specific class of unstable periodic orbits (UPOs), natural modes of variability that cover the attractor the system. UPOs corresponding to blockings have, indeed, a higher degree of instability compared to UPOs associated with zonal flow. Our results provide a rigorous justification for the classical Markov chains-based analysis of transitions between weather regimes. The analysis of UPOs elucidates that the model features a very severe violation of hyperbolicity, due to the presence of a substantial variability in the number of unstable dimensions, which explains why atmospheric states can differ a lot in term of their predictability. Additionally, such a variability explains the need for performing data assimilation in a state space that includes not only the unstable and neutral subspaces, but also some stable modes. The lack of robustness associated with the violation of hyperbolicity might be a basic cause contributing to the difficulty in representing blockings in numerical models and in predicting how their statistics will change as a result of climate change. This corresponds to fundamental issues limiting our ability to construct very accurate numerical models of the atmosphere, in term of predictability of the both the first and of the second kind in the sense of Lorenz.</description><subject>Atmospheric blocking</subject><subject>Atmospheric circulation</subject><subject>Atmospheric models</subject><subject>Blocking events</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Data assimilation</subject><subject>Data collection</subject><subject>Dimensions</subject><subject>Earth</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Flow stability</subject><subject>Geophysics/Geodesy</subject><subject>Instability</subject><subject>Liapunov exponents</subject><subject>Markov chains</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Mathematical properties</subject><subject>Natural history</subject><subject>Numerical models</subject><subject>Numerical prediction</subject><subject>Oceanography</subject><subject>Orbits</subject><subject>Robustness (mathematics)</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Subspaces</subject><subject>Variability</subject><subject>Weather</subject><subject>Zonal flow</subject><subject>Zonal flow (meteorology)</subject><issn>0930-7575</issn><issn>1432-0894</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>C6C</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kdtKxDAQhoMouB5ewKuCIHhRzbFpwZtFPIEgeLgOaTrZrbbNmmRdfXujFXRvTCADw_fNEH6EDgg-IRjL04AxK2mOSZVjgUmZ0w00IZylVlnxTTTBFcO5FFJso50QnjEmvJB0gs6m2QCrrNdxDulpje4y63UPK-dfMut8pmPvwmIOvjVZ3Tnz0g6zDN5giGEPbVndBdj_qbvo6fLi8fw6v727ujmf3uYmLYm54LjhlImmpDKdsiaNKKjgvLbMCkuk5qYujKmtrKium8pqy0DUNQFZWV6wXXQ4zl1497qEENWzW_ohrVSUcSargkicqJORmukOVDtYF7026TbQt8YNYNvUnxaEMMlJQZNwvCYkJsJ7nOllCOrm4X6dPfrDzkF3cR5ct4ytG8I6SEfQeBeCB6sWvu21_1AEq6-s1JiVSlmp76zUl8RGKSR4mIH__eA_1idedpRs</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Lucarini, Valerio</creator><creator>Gritsun, Andrey</creator><general>Springer Berlin Heidelberg</general><general>Springer</general><general>Springer Nature B.V</general><scope>C6C</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>3V.</scope><scope>7TG</scope><scope>7TN</scope><scope>7UA</scope><scope>7XB</scope><scope>88F</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M1Q</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-9392-1471</orcidid></search><sort><creationdate>2020</creationdate><title>A new mathematical framework for atmospheric blocking events</title><author>Lucarini, Valerio ; Gritsun, Andrey</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c467t-540d4235d8277778b1d562544bf3f5f17a4cb6ccbf792abd9faf3e5bb1e79f463</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Atmospheric blocking</topic><topic>Atmospheric circulation</topic><topic>Atmospheric models</topic><topic>Blocking events</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climatology</topic><topic>Data assimilation</topic><topic>Data collection</topic><topic>Dimensions</topic><topic>Earth</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Flow stability</topic><topic>Geophysics/Geodesy</topic><topic>Instability</topic><topic>Liapunov exponents</topic><topic>Markov chains</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Mathematical properties</topic><topic>Natural history</topic><topic>Numerical models</topic><topic>Numerical prediction</topic><topic>Oceanography</topic><topic>Orbits</topic><topic>Robustness (mathematics)</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Subspaces</topic><topic>Variability</topic><topic>Weather</topic><topic>Zonal flow</topic><topic>Zonal flow (meteorology)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lucarini, Valerio</creatorcontrib><creatorcontrib>Gritsun, Andrey</creatorcontrib><collection>Springer Nature OA Free Journals</collection><collection>CrossRef</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Oceanic Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Military Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Military Database</collection><collection>ProQuest Science Journals</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Climate dynamics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lucarini, Valerio</au><au>Gritsun, Andrey</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new mathematical framework for atmospheric blocking events</atitle><jtitle>Climate dynamics</jtitle><stitle>Clim Dyn</stitle><date>2020</date><risdate>2020</risdate><volume>54</volume><issue>1-2</issue><spage>575</spage><epage>598</epage><pages>575-598</pages><issn>0930-7575</issn><eissn>1432-0894</eissn><abstract>We use a simple yet Earth-like hemispheric atmospheric model to propose a new framework for the mathematical properties of blocking events. Using finite-time Lyapunov exponents, we show that the occurrence of blockings is associated with conditions featuring anomalously high instability. Longer-lived blockings are very rare and have typically higher instability. In the case of Atlantic blockings, predictability is especially reduced at the onset and decay of the blocking event, while a relative increase of predictability is found in the mature phase. The opposite holds for Pacific blockings, for which predictability is lowest in the mature phase. Blockings are realised when the trajectory of the system is in the neighbourhood of a specific class of unstable periodic orbits (UPOs), natural modes of variability that cover the attractor the system. UPOs corresponding to blockings have, indeed, a higher degree of instability compared to UPOs associated with zonal flow. Our results provide a rigorous justification for the classical Markov chains-based analysis of transitions between weather regimes. The analysis of UPOs elucidates that the model features a very severe violation of hyperbolicity, due to the presence of a substantial variability in the number of unstable dimensions, which explains why atmospheric states can differ a lot in term of their predictability. Additionally, such a variability explains the need for performing data assimilation in a state space that includes not only the unstable and neutral subspaces, but also some stable modes. The lack of robustness associated with the violation of hyperbolicity might be a basic cause contributing to the difficulty in representing blockings in numerical models and in predicting how their statistics will change as a result of climate change. This corresponds to fundamental issues limiting our ability to construct very accurate numerical models of the atmosphere, in term of predictability of the both the first and of the second kind in the sense of Lorenz.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s00382-019-05018-2</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0001-9392-1471</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Atmospheric blocking Atmospheric circulation Atmospheric models Blocking events Climate change Climate models Climatology Data assimilation Data collection Dimensions Earth Earth and Environmental Science Earth Sciences Flow stability Geophysics/Geodesy Instability Liapunov exponents Markov chains Mathematical analysis Mathematical models Mathematical properties Natural history Numerical models Numerical prediction Oceanography Orbits Robustness (mathematics) Statistical analysis Statistical methods Subspaces Variability Weather Zonal flow Zonal flow (meteorology) |
title | A new mathematical framework for atmospheric blocking events |
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