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"A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP": Python statistical codes of the study

Martin Renoult; James Douglas Annan


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    <dct:title>"A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP": Python statistical codes of the study</dct:title>
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    <dcat:keyword>Emergent constraints</dcat:keyword>
    <dcat:keyword>Climate sensitivity</dcat:keyword>
    <dcat:keyword>Bayesian statistics</dcat:keyword>
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    <dct:description>&lt;p&gt;These 4 codes can be ran to estimate climate sensitivity by using the tropical temperature of the models participating in the Paleoclimate Modelling Intercomparison Project (PMIP) and simulating either the Last Glacial Maximum or the mid-Pliocene Warm Period.&lt;/p&gt; &lt;p&gt;Any climate properties can replace the variables x or y, following the theory of emergent constraints.&lt;/p&gt; &lt;p&gt;A detailed description of each method can be found in: M. Renoult, J.D. Annan, J.C. Hargreaves, N. Sagoo, C. Flynn, M.-L. Kapsch, U. Mikolajewicz, R. Ohgaito, T. Mauritsen, In Review. A Bayesian framework for emergent constraints: case studies of climate sensitivity with PMIP. Climate of the Past.&lt;/p&gt; &lt;p&gt;The original repository of the codes can be found at&amp;nbsp;https://git.bolin.su.se/bolin/renoult-2020&lt;/p&gt;</dct:description>
    <dct:description>This result is part of a project that has received funding from the European Research Council (ERC) (Grant agreement No.770765) under the European Union's Horizon 2020 research and innovation program (Grant agreement No.820829).</dct:description>
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