{"id":100,"date":"2005-01-12T12:02:43","date_gmt":"2005-01-12T16:02:43","guid":{"rendered":"\/?p=100"},"modified":"2017-03-14T17:29:13","modified_gmt":"2017-03-14T22:29:13","slug":"is-climate-modelling-science","status":"publish","type":"post","link":"https:\/\/www.realclimate.org\/index.php\/archives\/2005\/01\/is-climate-modelling-science\/","title":{"rendered":"Is Climate Modelling Science? La modélisation climatique est-elle de la science?<\/lang_fr>"},"content":{"rendered":"
\n

At first glance this seems like a strange question. Isn’t science precisely the quantification of observations into a theory or model and then using that to make predictions? Yes. And are those predictions in different cases then tested against observations again and again to either validate those models or generate ideas for potential improvements? Yes, again. So the fact that climate modelling was recently singled out as being somehow non-scientific seems absurd.
\npar Gavin Schmidt (traduit par Gilles Delaygue)<\/small><\/p>\n

A premi\u00e8re vue, cela semble une question \u00e9trange. Est-ce-que la science n’est pas pr\u00e9cis\u00e9ment la quantification d’observations dans une th\u00e9orie ou un mod\u00e8le, et ensuite son utilisation pour faire des pr\u00e9dictions ? Oui. Et est-ce-que ces pr\u00e9dictions de diff\u00e9rents cas sont ensuite confront\u00e9es, maintes fois, aux observations, afin soit de valider ces mod\u00e8les ou bien de faire \u00e9merger des id\u00e9es d’am\u00e9lioration ? Oui, encore une fois. Ainsi la mise \u00e0 l’index r\u00e9cente de la mod\u00e9lisation climatique comme quelque chose de non scientifique semble absurde.<\/p>\n

(suite…<\/a>)<\/p>\n


\nGranted, the author of the statement in question has little idea of what climate modelling is, or how or why it’s done. However, that his statement can be quoted in a major US newspaper<\/a> says much about the level of public knowledge concerning climate change and the models used to try and understand it. So I will try here to demonstrate how the science of climate modelling works, and yes, why it is a little different from some other kinds of science (not that there’s anything wrong with that!).<\/p>\n

Climate is complex. Since climatologists don’t have access to hundreds of Earth’s to observe and experiment with, they need virtual laboratories that allow ideas to be tested in a controlled manner. The huge range of physical processes that are involved are encapsulated in what are called General Circulation Models<\/a> (or GCMs). These models consist of connected sub-modules that deal with radiative transfer, the circulation of the atmosphere and oceans, the physics of moist convection and cloud formation, sea ice, soil moisture and the like. They contain our best current understanding for how the physical processes interact (for instance, how evaporation depends on the wind and surface temperature, or how clouds depend on the humidity and vertical motion) while conserving basic quantities like energy, mass and momentum. These estimates are based on physical theories and empirical observations made around the world. However, some processes occur at scales too small to be captured at the grid-size available in these (necessarily global) models. These so-called ‘sub-gridscale’ processes therefore need to be ‘parameterised’.<\/p>\n

A good example is related to clouds. Obviously, in an actual cloud, the relative humidity is close to 100%, but at a grid box scale of 100’s of km, the mean humidity – even if there are quite a few clouds – will be substantially less. Thus a parameterisation is needed that relates the large scale mean values, to actual distribution of clouds in a grid box that one would expect. There are of course many different ways to do that, and the many modelling groups (in the US, Europe, Japan, Australia etc.) may each make different assumptions and come up with slightly different results.<\/p>\n

It’s important to note what these models are not good for. They aren’t any good for your local weather, or the temperature of the water at the nearest beach or for the wind in downtown Manhattan, because these are small scale features, affected by very local conditions. However, if you go up to the regional scale and beyond (i.e. Western Europe as a whole, the continental US) you start to expect better correlations.<\/p>\n

One of the most important features of complex systems is that most of their interesting behaviour is emergent.<\/em> It’s often found that the large scale behaviour is not a priori<\/em> predictable from the small scale interactions that make up the system. So it is with climate models. If a change is made to the cloud parameterisation, it is difficult to tell ahead of time what impact that will have on, for instance, the climate sensitivity. This is because the number of possible feedback pathways (both positive and negative) is literally uncountable. You just have to put it in, let it physics work itself out and see what the effect is.<\/p>\n

This means that validating these models is quite difficult. (NB. I use the term validating not in the sense of ‘proving true’ (an impossibility), but in the sense of ‘being good enough to be useful’). In essence, the validation must be done for the whole system if we are to have any confidence in the predictions about the whole system in the future. This validation is what most climate modellers spend almost all their time doing. First, we look at the mean climatology (i.e. are the large scale features of the climate reasonably modelled? Does it rain where it should, is there snow where there should be? are the ocean currents and winds going the right way?), then at the seasonal cycle (what does the sea ice advance and retreat look like? does the inter-tropical convergence zone move as it should?). Generally we find that the models actually do a reasonable job (see here<\/a> or here<\/a> for examples of different groups model validation papers) . There are of course problematic areas (such as eastern boundary regions of the oceans, circulation near large mountain ranges etc.) where important small scale processes may not be well understood or modelled, and these are the chief targets for further research by model developers and observationalists.<\/p>\n

Then we look at climate variability. This step is key, but it is also quite subtle. There are two forms of variability: intrinsic variability<\/em> (that occurs purely as a function of the internal chaotic dynamics of the system), and forced variability<\/em> (changes that occur because of some external change, such as solar forcing). Note that ‘natural’ variability includes both intrinsic and forced components due to ‘natural’ forcings, such as volcanoes, solar or orbital changes. A clean comparison relies on either being able to isolate just one reasonably known forcing, or having enough data to be able to average over many examples and thus isolate the patterns associated solely with that forcing, even though in any particular case, more than one thing might have been happening. (A more detailed discussion of these points is available here<\/a>).<\/p>\n

While there is good data over the last century, there were many different changes to planet’s radiation balance (greenhouse gases, aerosols, solar forcing, volcanoes, land use changes etc.), some of which are difficult to quantify (for instance the indirect aerosol effects) and whose history is not well known. Earlier periods, say 1850 going back to the 1500s or so, have reasonable coverage from paleo-proxy data, and only have solar and volcanic forcing. In my own group’s work, we have used the spatial patterns available from proxy reconstructions of this period to look at both solar<\/a> and volcanic<\/a> forcing in the pre-industrial period. In both cases, despite uncertainties (particularly in the magnitude of the solar forcing), the comparisons are encouraging.<\/p>\n

Recent volcanos as well have provided very good tests of the model’s water vapour feedbacks (Soden et al, 2002)<\/a>, dynamical feedbacks (Graf et al., 1994; Stenchikov et al., 2002), and overall global cooling (Hansen et al, 1992)<\/a>. In fact, the Hansen et al (1992) paper actually predicted the temperature impact of Pinatubo (around 0.5 deg C) prior to it being measured.<\/p>\n

The mid-Holocene<\/a> (6000 years ago) and Last Glacial Maximum (~20,000 years ago) are also attractive targets of model validation, and while some successes have been noted (i.e. Joussaume et al, 1999, Rind and Peteet, 1985) there is still some uncertainty in the forcings and response. Other periods such as the 8.2kyr event, or the Paleocene-Eocene Thermal Maximum are also useful, but clearly as one goes further back in time, the more uncertain the test becomes.<\/p>\n

The 20th Century though still provides the test that appears to be most convincing. That is to say, the models are run over the whole period, with our best guesses for what the forcings were, and the results compared to the observed record. If by leaving out the anthropogenic effects you fail to match the observed record, while if you include them, you do, you have a quick-and-dirty way to do ‘detection and attribution’. (There is a much bigger literature<\/a> that discusses more subtle and powerful ways to do D&A, so this isn’t the whole story by any means). The most quoted example of this is from the Stott et al. (2000) paper shown in the figure. Similar results can be found in simple models (Crowley, 2000) and in more up to date models (Meehl et al, 2004).<\/p>\n

It’s important to note that if the first attempt to validate the model fails (e.g. the signal is too weak (or too strong), or the spatial pattern is unrealistic), this leads to a re-examination of the physics of the model. This may then lead to additional changes, for example, the incorporation of ozone feedbacks to solar changes, or the calculation of vegetation feedbacks to orbital forcing – which in each case improved the match to the observations. Sometimes though it is the observations that turn out to be wrong. For instance, for the Last Glacial Maximum, model-data mis-matches highlighted by Rind and Peteet (1985)<\/a> for the tropical sea surface temperatures, have subsequently been more or less resolved in favour of the models.<\/p>\n

So, in summary, the model results are compared to data, and if there is a mismatch, both the data and the models are re-examined. Sometimes the models can be improved, sometimes the data was mis-interpreted. Every time this happens and we get improved matches between them, we have a little more confidence in their projections for the future, and we go out and look for better tests. That is in fact pretty close to the textbook definition of science.
\n<\/a>
\nJe vous l’accorde, l’auteur de la d\u00e9claration en question a tr\u00e8s peu id\u00e9e de ce qu’est la mod\u00e9lisation climatique, ou de comment et \u00e0 quoi elle sert. Pourtant, que sa d\u00e9claration puisse \u00eatre rapport\u00e9e dans un grand journal am\u00e9ricain<\/a> en dit beaucoup sur le niveau de connaissance du public du changement climatique et des mod\u00e8les utilis\u00e9s pour tenter de le comprendre. Ainsi je vais essayer ici de d\u00e9montrer comment fonctionne la science de la mod\u00e9lisation climatique, et, effectivement, pourquoi elle est un peu diff\u00e9rente d’autres types de sciences (sans qu’il y ait quoi que soit d’anormal \u00e0 cela !).<\/p>\n

Le climat est complexe. Comme les climatologues n’ont pas acc\u00e8s \u00e0 des centaines de Terre pour les observer et faire des exp\u00e9riences, ils ont besoin de laboratoires virtuels permettant de tester des id\u00e9es de fa\u00e7on control\u00e9e. L’immense palette de processus physiques impliqu\u00e9s est encapsul\u00e9e dans ce que l’on appelle les mod\u00e8les de circulation g\u00e9n\u00e9rale<\/a> (ou MCG). Ces mod\u00e8les sont constitu\u00e9s de modules connect\u00e9s traitant du transfert radiatif, de la circulation de l’atmosph\u00e8re et des oc\u00e9ans, de la physique de la convection humide et de la formation des nuages, de la glace de mer, humidit\u00e9 du sol et ainsi de suite. Ils contiennent notre meilleure compr\u00e9hension actuelle de l’interaction des processus physiques (par exemple, comment l’\u00e9vaporation d\u00e9pend du vent et de la temp\u00e9rature de surface, ou comment les nuages d\u00e9pendent de l’humidit\u00e9 et du mouvement vertical), en conservant les quantit\u00e9s de base telles que l’\u00e9nergie, la masse et le moment. Ces estimations sont bas\u00e9es sur des th\u00e9ories physiques ainsi que des observations empiriques r\u00e9alis\u00e9es partout dans le monde. N\u00e9anmoins, certains processus interviennent \u00e0 des \u00e9chelles trop petites pour \u00eatre d\u00e9crits avec la taille de grille disponible dans ces mod\u00e8les (n\u00e9cessairement globaux). Ces processus ‘sous-\u00e9chelles’ n\u00e9cessitent ainsi d’\u00eatre ‘param\u00e9tris\u00e9s’.<\/p>\n

Un bon exemple est li\u00e9 aux nuages. Evidemment, dans un vrai nuage, l’humidit\u00e9 relative est proche de 100%, mais \u00e0 l’\u00e9chelle d’une maille de centaines de kilom\u00e8tres, l’humidit\u00e9 moyenne \u2013m\u00eame s’il y a un certain nombre de nuages\u2013 sera nettement plus faible. Ainsi, une param\u00e9trisation est n\u00e9cessaire pour relier les valeurs moyennes \u00e0 grande \u00e9chelle avec la distribution r\u00e9elle de nuages \u00e0 laquelle on devrait s’attendre dans une maille. Il y a bien s\u00fbr de nombreuses fa\u00e7ons diff\u00e9rentes de faire \u00e7a, et les nombreux groupes de mod\u00e9lisation (aux Etats-Unis, en Europe, Japon, Australie, etc) pourront faire chacun des hypoth\u00e8ses diff\u00e9rentes et arriver \u00e0 des r\u00e9sultats l\u00e9g\u00e8rement diff\u00e9rents.<\/p>\n

Il est important de noter ce pour quoi ces mod\u00e8les ne sont pas bons. Ils ne sont bons en rien pour votre m\u00e9t\u00e9o locale, ou la temp\u00e9rature de l’eau de la plage du coin ou le vent \u00e0 Manhattan centre, parce que ceci correspond \u00e0 des caract\u00e9ristiques \u00e0 petite \u00e9chelle, affect\u00e9es par des conditions tr\u00e8s locales. Maintenant, si vous passez \u00e0 l’\u00e9chelle r\u00e9gionale ou au-dessus (par ex. l’Europe occidentale comme un tout, le continent US) on commence \u00e0 s’attendre \u00e0 de meilleures corr\u00e9lations.<\/p>\n

L’une des caract\u00e9ristiques les plus importantes de syst\u00e8mes complexes est que la plus grande part de leur comportement int\u00e9ressant est \u00e9mergente<\/i>. On s’aper\u00e7oit souvent que le comportement \u00e0 grande \u00e9chelle n’est pas pr\u00e9dictible a priori<\/i> \u00e0 partir des interactions \u00e0 petites \u00e9chelles qui composent le syst\u00e8me. Il en est ainsi avec les mod\u00e8les climatiques. Si on effectue un changement \u00e0 la param\u00e9trisation des nuages, il est difficile de dire \u00e0 l’avance quel va en \u00eatre l’impact sur, par exemple, la sensibilit\u00e9 climatique. C’est parce que le nombre de r\u00e9troactions possibles (positives et n\u00e9gatives) est litt\u00e9ralement incommensurable. Vous devez juste int\u00e9grer ce changement, laisser faire la physique, et voir quel en est l’effet.<\/p>\n

Ce qui veut dire que valider ces mod\u00e8les est tr\u00e8s difficile. (NB. J’utilise le terme valider non pas dans le sens de ‘prouver la justesse’ \u2013ce qui impossible\u2013, mais dans le sens de ‘suffisamment bon pour \u00eatre utile’). Par principe, la validation doit concerner le syst\u00e8me complet si l’on veut avoir un peu confiance dans les pr\u00e9dictions du syst\u00e8me complet dans le futur. Cette validation est ce sur quoi la majorit\u00e9 des mod\u00e9lisateurs du climat passe pratiquement tout leur temps. D’abord, nous regardons le climat moyen (c’est-\u00e0-dire, est-ce-que les grandes caract\u00e9ristiques sont raisonnablement mod\u00e9lis\u00e9es ? Est-ce-qu’il pleut l\u00e0 o\u00f9 il faut, est-ce-qu’il neige \u00e0 la bonne place ? Est-ce-que les courants oc\u00e9aniques et les vents vont dans la bonne direction ?), ensuite la saisonnalit\u00e9 (\u00e0 quoi ressemblent l’avanc\u00e9e et le recul de la glace de mer ? Est-ce-que la convergence intertropicale bouge comme il faut ?). De fa\u00e7on g\u00e9n\u00e9rale, nous trouvons que les mod\u00e8les font finalement du bon boulot (voir ici<\/a> ou ici<\/a> pour des exemples d’articles de validation de mod\u00e8le par diff\u00e9rents groupes). Il y a bien s\u00fbr des r\u00e9gions posant probl\u00e8mes (comme les r\u00e9gions de bord est des oc\u00e9ans, la circulation \u00e0 proximit\u00e9 des cha\u00eenes de montagne, etc) pour lesquelles des processus de petite \u00e9chelle peuvent \u00eatre mal compris ou mod\u00e9lis\u00e9s, et ce sont les buts principaux de recherche future des d\u00e9veloppeurs de mod\u00e8les et des observateurs.<\/p>\n

Ensuite, nous regardons la variabilit\u00e9. C’est une \u00e9tape cl\u00e9, mais aussi subtile. Il y a deux formes de variabilit\u00e9: la variabilit\u00e9 intrins\u00e8que<\/i> (qui provient uniquement de la dynamique chaotique interne au syst\u00e8me), et la variabilit\u00e9 forc\u00e9e<\/i> (changements provenant de changements externes, comme le for\u00e7age solaire). Notez que la variabilit\u00e9 ‘naturelle’ inclut \u00e0 la fois les composants intrins\u00e8que et forc\u00e9 dus aux for\u00e7ages ‘naturels’, comme les volcans, les changements solaires ou orbitaux. Une comparaison propre est garantie soit par la capacit\u00e9 \u00e0 isoler juste un for\u00e7age raisonnablement bien connu, ou d’avoir suffisamment de donn\u00e9es pour pouvoir moyenner sur plusieurs exemples et ainsi isoler la structure associ\u00e9e seulement avec ce for\u00e7age, m\u00eame si dans chaque cas, plusieurs origines sont possibles. (une discussion plus d\u00e9taill\u00e9e de ces points est accessible ici<\/a>.)<\/p>\n


\nAlors qu’il y a de bonnes donn\u00e9es sur le dernier si\u00e8cle, il y a eu plusieurs changements diff\u00e9rents de l’\u00e9quilibre radiatif de la plan\u00e8te (gaz \u00e0 effet de serre, a\u00e9rosols, for\u00e7age solaire, volcans, changements de l’utilisation du sol, etc), certains d’entre eux \u00e9tant difficiles \u00e0 quantifier (par exemple l’effet indirect des a\u00e9rosols) et dont l’histoire n’est pas bien connue. Les p\u00e9riodes plus anciennes, disons de 1850 aux ann\u00e9es 1500 \u00e0 peu pr\u00e8s, ont une couverture raisonnable par des donn\u00e9es li\u00e9s \u00e0 des pal\u00e9o-proxies, et ont seulement un for\u00e7age solaire et volcanique. Dans le travail de mon propre groupe, nous avons utilis\u00e9 les structures spatiales issues des reconstructions de cette p\u00e9riode bas\u00e9es sur des proxies, pour regarder \u00e0 la fois les for\u00e7ages solaire<\/a> et volcanique<\/a> de la p\u00e9riode pr\u00e9-industrielle. Dans les deux cas, malgr\u00e9 des incertitudes (particuli\u00e8rement sur l’amplitude du for\u00e7age solaire), les comparaisons sont encourageantes.<\/p>\n

Des \u00e9ruptions volcaniques r\u00e9centes, \u00e9galement, ont fourni de tr\u00e8s bons tests des r\u00e9troactions de la vapeur d’eau des mod\u00e8les (Soden et al, 2002)<\/a>, des r\u00e9troactions dynamiques (Graf et al., 1994; Stenchikov et al., 2002), et du refroidissement global complet (Hansen et al, 1992)<\/a>. En fait, l’article de Hansen et al. (1992) pr\u00e9disait vraiment l’impact en temp\u00e9rature du Pinatubo (environ 0,5 \u00baC) avant qu’il soit mesur\u00e9.<\/p>\n

L’Holoc\u00e8ne moyen<\/a> (il y a 6000 ans) et le dernier maximum glaciaire (il y a ~20\u00a0000 ans) sont aussi des objectifs attractifs de validation des mod\u00e8les, et tandis que certains succ\u00e8s sont \u00e0 noter (c’est\u2013\u00e0-dire Joussaume et al, 1999, Rind et Peteet, 1985) il y a encore des incertitudes sur les for\u00e7ages et les r\u00e9ponses. D’autres p\u00e9riodes comme l’\u00e9v\u00e9nement \u00e0 8200 ans, ou le maximum thermique Pal\u00e9oc\u00e8ne-Eoc\u00e8ne, sont aussi utiles, mais clairement plus on remonte loin dans le temps, plus le test devient incertain.<\/p>\n

Le 20ie si\u00e8cle, cependant, fournit toujours le test apparaissant comme le plus convaincant. C’est-\u00e0-dire que les mod\u00e8les tournent sur toute la p\u00e9riode, avec nos meilleures estimations des for\u00e7ages, et les r\u00e9sultats sont compar\u00e9s avec les enregistrements d’observations. Si en excluant les effets anthropog\u00e9niques vous n’arrivez pas \u00e0 reproduire les observations, tandis qu’en les incluant, vous y arrivez, vous avez un moyen simple et grossier de faire de la ‘d\u00e9tection et attribution’. (Il y a une beaucoup plus grosse litt\u00e9rature<\/a> discutant de meilleures et plus puissantes fa\u00e7ons de faire de la D&A, donc ce n’est pas du tout l’histoire compl\u00e8te.)
\nL’exemple le plus cit\u00e9 l\u00e0-dessus est dans l’article de Stott et al. (2000), montr\u00e9 sur la figure. Des r\u00e9sultats similaires peuvent \u00eatre trouv\u00e9s avec des mod\u00e8les simples (Crowley, 2000) et dans des mod\u00e8les plus \u00e0 jour (Meehl et al., 2004).<\/p>\n

Il est important de noter que si le premier essai de validation du mod\u00e8le \u00e9choue (par ex. le signal est trop faible \u2013ou trop fort\u2013, ou la structure spatiale n’est pas r\u00e9aliste), ceci am\u00e8ne \u00e0 un r\u00e9-examen de la physique du mod\u00e8le. Ceci peut alors amener \u00e0 quelques changements, par exemple l’incorporation de r\u00e9troactions entre ozone et insolation, ou le calcul de r\u00e9troactions entre for\u00e7age orbital et v\u00e9g\u00e9tation \u2013qui dans les deux cas am\u00e9liorent la reproduction des observations. Parfois, cependant, ce sont les observations qui s’av\u00e8rent fausses. Par exemple, pour le dernier maximum glaciaire, la diff\u00e9rence entre mod\u00e8les et donn\u00e9es soulign\u00e9e par Rind et Peteet (1985)<\/a> sur les temp\u00e9ratures de surface de l’oc\u00e9an tropical, a \u00e9t\u00e9 par la suite plus ou moins r\u00e9solue en faveur des mod\u00e8les.<\/p>\n

Donc, en r\u00e9sum\u00e9, les r\u00e9sultats de mod\u00e8les sont compar\u00e9s aux donn\u00e9es, et s’ils ne correspondent pas, \u00e0 la fois les donn\u00e9es et les mod\u00e8les sont r\u00e9-examin\u00e9s. Parfois les mod\u00e8les sont am\u00e9lior\u00e9s, parfois les donn\u00e9es ont \u00e9t\u00e9 mal interpr\u00e9t\u00e9es. Chaque fois que cela arrive et que nous obtenons une meilleure correspondance entre eux, nous avons un peu plus confiance dans les projections des mod\u00e8les pour le futur, et nous cherchons de meilleurs tests. C’est en fait plut\u00f4t proche de la d\u00e9finition classique de la science.<\/p>\n

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At first glance this seems like a strange question. Isn’t science precisely the quantification of observations into a theory or model and then using that to make predictions? Yes. And are those predictions in different cases then tested against observations again and again to either validate those models or generate ideas for potential improvements? Yes, […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","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":[5,1,13,2,4],"tags":[],"class_list":{"0":"post-100","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-climate-modelling","7":"category-climate-science","8":"category-faq","9":"category-paleoclimate","10":"category-sun-earth-connections","11":"entry"},"aioseo_notices":[],"post_mailing_queue_ids":[],"_links":{"self":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/100","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/comments?post=100"}],"version-history":[{"count":1,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/100\/revisions"}],"predecessor-version":[{"id":20306,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/posts\/100\/revisions\/20306"}],"wp:attachment":[{"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/media?parent=100"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/categories?post=100"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.realclimate.org\/index.php\/wp-json\/wp\/v2\/tags?post=100"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}