{"id":23011,"date":"2020-03-09T12:31:25","date_gmt":"2020-03-09T17:31:25","guid":{"rendered":"http:\/\/www.realclimate.org\/?p=23011"},"modified":"2020-03-09T14:15:15","modified_gmt":"2020-03-09T19:15:15","slug":"why-not-use-a-clever-mathematical-trick","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2020\/03\/why-not-use-a-clever-mathematical-trick\/","title":{"rendered":"Why not use a clever mathematical trick?"},"content":{"rendered":"<div class=\"kcite-section\" kcite-section-id=\"23011\">\n\n<pre class=\"wp-block-preformatted\"><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">There is a clever mathematical trick for comparing different data sets, but it does not seem to be widely used. It is based on so-called empirical orthogonal functions (EOFs), which Edward Lorenz described in a Massachusetts Institute of Technology (MIT) <\/span><a href=\"https:\/\/eapsweb.mit.edu\/sites\/default\/files\/Empirical_Orthogonal_Functions_1956.pdf\"><span style=\"font-size: 11pt; font-family: Arial; color: #1155cc; text-decoration-line: underline; vertical-align: baseline; white-space: pre-wrap;\">scientific report<\/span><\/a><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\"> from 1956. The EOFs are similar to principal component analysis (PCA). \n\n<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">The EOFs and PCAs provide patterns of spatio-temporal covariance structure. Usually these techniques are applied to datasets with many parallel variables to show coherent patterns of variability. Myles Allen used to lecture on EOFs at Oxford University about twenty years ago and convinced me about their value. Many scientists do indeed use EOFs to analyse their data. \n\n<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">It is not that there is little use of EOFs (they are widely used), but the question is <\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; font-style: italic; vertical-align: baseline; white-space: pre-wrap;\">how<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\"> the EOFs are used and <\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; font-style: italic; vertical-align: baseline; white-space: pre-wrap;\">how<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\"> the results are interpreted. I learned that EOFs can be used in many different ways from Doug Nychka, when I visited University Corporation for Atmospheric Research (UCAR) in 2011.\n\n<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">The clever trick is to apply these techniques to data compiled from more than one source of data. When used this way, the technique is labelled \u201ccommon EOFs\u201d or \u201ccommon PCA\u201d. There are some scientific studies that have made use of common EOFs or common PCA, such as <a href=\"https:\/\/dl.acm.org\/doi\/book\/10.5555\/74760\">Flurry (1988)<\/a>, <a href=\"https:\/\/journals.ametsoc.org\/doi\/full\/10.1175\/1520-0442%281999%29012%3C0511%3ACONSAT%3E2.0.CO%3B2\">Barnett (1999)<\/a>, <\/span><a href=\"https:\/\/inis.iaea.org\/collection\/NCLCollectionStore\/_Public\/25\/002\/25002860.pdf\"><span style=\"font-size: 11pt; font-family: Arial; color: #1155cc; text-decoration-line: underline; vertical-align: baseline; white-space: pre-wrap;\">Sengupta &amp; Boyle (1993)<\/span><\/a><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">, <span id=\"cite_ITEM-23011-0\" name=\"citation\"><a href=\"#ITEM-23011-0\">Benestad (2001)<\/a><\/span>, and <span id=\"cite_ITEM-23011-1\" name=\"citation\"><a href=\"#ITEM-23011-1\">Gilett et al (2002)<\/a><\/span>. \n\n<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">Nevertheless, a Scholar Google recent search with \u201ccommon EOFs\u201d only gave 101 hits (2020-03-05). I find this low interest for this technique a bit puzzling, since it in many ways has lots in common to machine learning and artificial intelligence (AI), both which are hot topics these days. \n\n<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">Common EOFs are also particularly useful for quantifying local effects of global warming through a process known as empirical-statistical downscaling (ESD). It's pity that common EOFs aren't even mentioned in the recent textbook on ESD by <a href=\"https:\/\/www.amazon.com\/Statistical-Downscaling-Correction-Climate-Research\/dp\/1107066050\">Maraun and Widmann (2019)<\/a>  (they are discussed in <a href=\"https:\/\/www.worldscientific.com\/worldscibooks\/10.1142\/6908\">Benestad et al. (2008)<\/a><\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">). <\/span><\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" data-src=\"https:\/\/lh4.googleusercontent.com\/QQK_6P6gJVb9DSEx6Bzvha3vEryIJ0a035upfCmL-41kq4fjx5KWxbxn_PNaxoZaolT4tmQQLTSwyl03qPKgt0iYmnPkQ4Ndky72aw-ik-q4lUJO6jy7mG7IrV2Iwp0LI5SgzEtm\" alt=\"\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" \/><\/figure>\n\n\n\n<p><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\"><\/span><\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" data-src=\"https:\/\/lh5.googleusercontent.com\/QNdriSaJ5CzeN5RqC8rRGFjukNGCLuOr3drvQH0fbGgwTrxPs-WQ41v2P_4XhZAnTj3Mlqp87zO_4v9ghz3cgo0kdLVeqU5hF0YLwUSB2yJVXI3BjPNos3qXccPL4TXpOFDwDec8\" alt=\"\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" \/><\/figure>\n\n\n\n<p><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\"><\/span><em><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">Figure. Examples showing how common EOFs can be used to compare the annual cycle in T(2m) in the upper set of panels and precipitation (lower panels) simulated by global climate models from the CMIP5 experiment (<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #cc0000; font-weight: bold; vertical-align: baseline; white-space: pre-wrap;\">red<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">) and compared with the ERAINT reanalysis (<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; font-weight: bold; vertical-align: baseline; white-space: pre-wrap;\">black<\/span><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">).<\/span><\/em><\/p>\n\n\n\n<p><span style=\"font-size: 11pt; font-family: Arial; color: #000000; vertical-align: baseline; white-space: pre-wrap;\">&nbsp;<\/span><\/p>\n\n\n\n<p>The take-home message from these common EOFs, eigenvalues and principal components, is that the models do reproduce the large-scale patterns in the mean annual cycle. For those interested, common EOFs can easily be calculated with the R-based tool:  <\/p>\n\n\n\n<pre class=\"wp-block-preformatted\"><a href=\"https:\/\/github.com\/metno\/esd\">github.com\/metno\/esd<\/a>.<\/pre>\n<h2>References<\/h2>\n    <ol>\n    <li><a name='ITEM-23011-0'><\/a>\nR.E. Benestad, \"A comparison between two empirical downscaling strategies\", <i>International Journal of Climatology<\/i>, vol. 21, pp. 1645-1668, 2001. <a href=\"http:\/\/dx.doi.org\/10.1002\/joc.703\">http:\/\/dx.doi.org\/10.1002\/joc.703<\/a>\n\n\n<\/li>\n<li><a name='ITEM-23011-1'><\/a>\nN.P. Gillett, F.W. Zwiers, A.J. Weaver, G.C. Hegerl, M.R. Allen, and P.A. Stott, \"Detecting anthropogenic influence with a multi\u2010model ensemble\", <i>Geophysical Research Letters<\/i>, vol. 29, 2002. <a href=\"http:\/\/dx.doi.org\/10.1029\/2002GL015836\">http:\/\/dx.doi.org\/10.1029\/2002GL015836<\/a>\n\n\n<\/li>\n<\/ol>\n\n<\/div> <!-- kcite-section 23011 -->","protected":false},"excerpt":{"rendered":"<p>There is a clever mathematical trick for comparing different data sets, but it does not seem to be widely used. It is based on so-called empirical orthogonal functions (EOFs), which Edward Lorenz described in a Massachusetts Institute of Technology (MIT) scientific report from 1956. The EOFs are similar to principal component analysis (PCA). The EOFs [&hellip;]<\/p>\n","protected":false},"author":11,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-23011","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-climate-science","7":"entry"},"aioseo_notices":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/23011","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/comments?post=23011"}],"version-history":[{"count":18,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/23011\/revisions"}],"predecessor-version":[{"id":23030,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/23011\/revisions\/23030"}],"wp:attachment":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/media?parent=23011"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/categories?post=23011"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/tags?post=23011"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}