{"id":76,"date":"2026-10-17T11:30:48","date_gmt":"2026-10-17T11:30:48","guid":{"rendered":"https:\/\/therisk.global\/systems-modelling\/?page_id=76"},"modified":"2026-10-17T11:30:48","modified_gmt":"2026-10-17T11:30:48","slug":"model-intercomparison","status":"publish","type":"page","link":"https:\/\/therisk.global\/systems-modelling\/topics\/model-intercomparison\/","title":{"rendered":"Model intercomparison"},"content":{"rendered":"\n<!-- Nexus network page \u00b7 https:\/\/therisk.global\/systems-modelling\/topics\/model-intercomparison\/ \u00b7 source authored \u00b7 generated by integrations\/wordpress\/network\/build.mjs, never edit this file --><script type=\"text\/x-nexus\" class=\"_iub_cs_skip\" id=\"nxe-fig-engine\" 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auto;padding:0 16px;font-family:ui-sans-serif,system-ui,-apple-system,\"Segoe UI\",sans-serif;line-height:1.55;overflow-wrap:break-word}#nxe-n2j2e5yy.nxe :where(*,*::before,*::after){box-sizing:inherit}#nxe-n2j2e5yy.nxe :where(section,header,footer,nav,aside){display:block;margin:0 0 32px}#nxe-n2j2e5yy.nxe :where(h1,h2,h3,h4){line-height:1.2;margin:24px 0 12px;font-weight:600}#nxe-n2j2e5yy.nxe :where(p,ul,ol,dl,table,figure,details){margin:0 0 16px}#nxe-n2j2e5yy.nxe :where(ul,ol){padding-left:20px}#nxe-n2j2e5yy.nxe a{color:inherit;text-decoration:underline;text-underline-offset:2px}#nxe-n2j2e5yy.nxe table{border-collapse:collapse;width:100%;display:block;overflow-x:auto}#nxe-n2j2e5yy.nxe :where(th,td){padding:6px 8px;text-align:left;vertical-align:top;border-bottom:1px solid;border-color:inherit}#nxe-n2j2e5yy.nxe svg{display:none}#nxe-n2j2e5yy.nxe svg.nx-i{display:inline-block;width:18px;height:18px;vertical-align:-3px}#nxe-n2j2e5yy.nxe :where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n2j2e5yy.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-giu\" gradientUnits=\"userSpaceOnUse\" x1=\"0\" y1=\"0\" x2=\"800\" y2=\"0\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><linearGradient id=\"nx-gp\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gp-a\"\/><stop offset=\"1\" class=\"nx-gp-b\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero\" data-layout=\"dossier\" aria-labelledby=\"nx-title-1\"><div class=\"nx-container\"><div class=\"nx-theme-toggle\" role=\"group\" aria-label=\"Appearance of this page\" hidden><button type=\"button\" data-mode=\"light\" aria-pressed=\"false\">Light<\/button><button type=\"button\" data-mode=\"dark\" aria-pressed=\"false\">Dark<\/button><button type=\"button\" data-mode=\"auto\" aria-pressed=\"true\">Auto<\/button><\/div><div class=\"nx-folio\" aria-label=\"Document reference\"><strong>G-P19 \u00b7 TOPIC \u00b7 model-intercomparison<\/strong>GCRI dossier \u00b7 service level R0<\/div><div class=\"nx-hero-grid\"><div class=\"nx-hero-text\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">GCRI<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">Systems Science and Modelling<\/span><span class=\"nx-eyebrow\" aria-hidden=\"true\">\u00b7<\/span><span class=\"nx-eyebrow\">In development<\/span><\/div><div class=\"nx-accent-row\"><span class=\"nx-accent-rule\" aria-hidden=\"true\"><\/span><span>Topic dossier \u00b7 Systems Science and Modelling<\/span><\/div><h1 id=\"nx-title-1\" class=\"nx-hero-title\">Model intercomparison<\/h1><p class=\"nx-tagline\">No single model captures a complex system fully. Model intercomparison runs several models on the same experiment with the same inputs, so that their agreement and disagreement can be seen and explained. It is how climate, crop, hydrology and epidemic modelling communities learned what their results can and cannot support. This page sets out how intercomparisons are designed, what they reveal, and what evidence serves the institutions that rely on model ensembles.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/systems-modelling\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/systems-modelling\/topics\/\">Back to Topics<\/a><\/div><\/div><div class=\"nx-hero-panel\"><div class=\"nx-register\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Dossier<\/strong><\/span><span class=\"nx-eyebrow\">G-P19 \u00b7 topic<\/span><\/div><ol class=\"nx-register-rows\"><li><a href=\"#nx-sec-brief-answer\"><span class=\"nx-register-n\">01<\/span><span class=\"nx-register-k\">In brief<\/span><span class=\"nx-register-v\">Definition and scope<\/span><\/a><\/li><li><a href=\"#nx-sec-question\"><span class=\"nx-register-n\">02<\/span><span class=\"nx-register-k\">The question<\/span><span class=\"nx-register-v\">4 mechanisms<\/span><\/a><\/li><li><a href=\"#nx-sec-terms\"><span class=\"nx-register-n\">03<\/span><span class=\"nx-register-k\">Key terms<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-field\"><span class=\"nx-register-n\">04<\/span><span class=\"nx-register-k\">Field<\/span><span class=\"nx-register-v\">H3 field plate<\/span><\/a><\/li><li><a href=\"#nx-sec-indicators\"><span class=\"nx-register-n\">05<\/span><span class=\"nx-register-k\">Indicators<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">6<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-pipeline\"><span class=\"nx-register-n\">06<\/span><span class=\"nx-register-k\">Pipeline<\/span><span class=\"nx-register-v\">Run graph<\/span><\/a><\/li><li><a href=\"#nx-sec-sources\"><span class=\"nx-register-n\">07<\/span><span class=\"nx-register-k\">Sources<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">5<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-decisions\"><span class=\"nx-register-n\">08<\/span><span class=\"nx-register-k\">Decision holders<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">3<\/span><\/span><\/a><\/li><li><a href=\"#nx-sec-methods\"><span class=\"nx-register-n\">09<\/span><span class=\"nx-register-k\">Methods<\/span><span class=\"nx-register-v\"><span class=\"nx-register-num\">4<\/span><\/span><\/a><\/li><\/ol><p class=\"nx-register-foot\">GCRI \u00b7 In development \u00b7 service level R0<\/p><\/div><\/div><\/div><\/div><\/section> <nav class=\"nx-index-strip\" data-nx-spy=\"true\" aria-label=\"On this page\"><div class=\"nx-container\"><a href=\"#nx-sec-brief-answer\"><span>01<\/span>The short answer<\/a><a href=\"#nx-sec-field\"><span>02<\/span>Hazards and exposure in North America<\/a><a href=\"#nx-s-4-sec\"><span>03<\/span>Coupled model runner, in outline<\/a><a href=\"#nx-sec-question\"><span>04<\/span>Model intercomparison: how the risk\u2026<\/a><a href=\"#nx-sec-terms\"><span>05<\/span>Terms used on this page<\/a><a href=\"#nx-sec-indicators\"><span>06<\/span>What is measured, and how well<\/a><a href=\"#nx-sec-pipeline\"><span>07<\/span>From sources to decisions<\/a><a href=\"#nx-sec-sources\"><span>08<\/span>Where the evidence comes from<\/a><\/div><\/nav> <section id=\"nx-sec-brief-answer\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-2\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">01<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">In brief<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">The short answer.<\/h2><p class=\"nx-lede\">Model intercomparison is the coordinated running of several models on the same experiments with shared inputs, so that agreement and disagreement can be seen and explained. Common protocols isolate structural differences, spread measures structural uncertainty, and evaluation against observations shows which models capture key processes.<\/p><\/div><\/div><\/section><section id=\"nx-sec-field\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-3\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">02<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Geography<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Hazards and exposure in North America.<\/h2><p class=\"nx-lede\">For model intercomparison, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, flood water, rain and population density with relief in 3D and true colour from NASA GIBS, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-cmap-field\" data-nx-map=\"ctx-field\" data-cmap=\"north-america:risk\" data-ml-src=\"risk\" data-ml-plan=\"p-f894bc25\" data-ml-relief=\"1.3\"><div class=\"nx-wm\" data-nx-water=\"ctx-risk\" data-wm-domain=\"ctx-risk\" data-wm-tiles=\"on\" data-wm-flat=\"on\" data-wm-focus=\"-170,7,-52,72\" data-ws-live=\"off\" data-claim=\"INTEGRATED\" data-wm-panel=\"closed\" data-wm-mode=\"2d\"><div class=\"nx-wm-stage\" tabindex=\"0\" role=\"application\" aria-roledescription=\"interactive map\" aria-label=\"Hazards and exposure, North America: satellite and model layers from NASA on a 3D terrain of ASTER GDEM elevation. Arrow keys move; with Shift they turn and tilt in 3D; plus and minus zoom, 0 resets; I inspects, question mark lists the keys.\" aria-describedby=\"nx-wm-help\"><canvas class=\"nx-wm-canvas\" aria-hidden=\"true\"><\/canvas><p class=\"nx-wm-noscript\">The interactive map needs JavaScript; the layer list and sources are below.<\/p><div class=\"nx-wm-presets\" role=\"group\" aria-label=\"Quick views\"><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"flood,people||||\" aria-pressed=\"false\">People and floods<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"true||||\" aria-pressed=\"false\">True colour<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"flood||||\" aria-pressed=\"false\">Floods this week<\/button><button type=\"button\" class=\"nx-wm-preset\" data-wm-preset=\"imerg||||\" aria-pressed=\"false\">Rain now<\/button><\/div><div class=\"nx-wm-panel\" id=\"nx-wm-panel\" role=\"region\" aria-label=\"Map layers\"><div class=\"nx-wm-panel-head\"><p class=\"nx-wm-panel-title\">Layers <span class=\"nx-wm-count\">4<\/span><\/p><button type=\"button\" class=\"nx-wm-icon nx-wm-panel-close\" data-wm-act=\"panel\" aria-label=\"Close the layer list\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-panel-body\"><div class=\"nx-wm-group\"><p class=\"nx-wm-group-title\">Hazards and exposure, for this page<\/p><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"true\" aria-pressed=\"true\" data-wm-z=\"20\" data-wm-lag=\"1\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite image\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">True colour<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS NOAA-20 corrected reflectance \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"true\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"flood\" aria-pressed=\"true\" data-wm-z=\"21\" data-wm-lag=\"0\" data-wm-short=\"Flood water\" data-wm-prov=\"NASA LANCE\" data-wm-kind=\"Satellite detection; clouds leave gaps\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Flood water, 3-day composite<\/span><span class=\"nx-wm-layer-src\">NASA LANCE VIIRS Global Flood Product \u00b7 <span data-wm-date=\"flood\">14 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"flood\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"imerg\" aria-pressed=\"true\" data-wm-z=\"22\" data-wm-lag=\"0\" data-wm-short=\"Precipitation\" data-wm-prov=\"NASA GES DISC\" data-wm-kind=\"Satellite estimate, near real time\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Precipitation rate, daily mean<\/span><span class=\"nx-wm-layer-src\">NASA GES DISC GPM IMERG \u00b7 <span data-wm-date=\"imerg\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"imerg\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"people\" aria-pressed=\"true\" data-wm-z=\"23\" data-wm-lag=\"0\" data-wm-op=\"0.8\" data-wm-short=\"Population density\" data-wm-prov=\"NASA SEDAC (CIESIN, Columbia University)\" data-wm-kind=\"Census-based estimate, static (2020)\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Population density, 2020<\/span><span class=\"nx-wm-layer-src\">NASA SEDAC (CIESIN, Columbia University) Gridded Population of the World v4.11 \u00b7 <span data-wm-date=\"people\">static<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"people\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"cover\" aria-pressed=\"false\" data-wm-z=\"24\" data-wm-lag=\"0\" data-wm-time=\"latest\" data-wm-op=\"0.7\" data-wm-short=\"Land cover\" data-wm-prov=\"NASA LP DAAC\" data-wm-kind=\"Satellite classification; latest year GIBS publishes\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Land cover type (IGBP), yearly<\/span><span class=\"nx-wm-layer-src\">NASA LP DAAC MODIS Terra and Aqua land cover (MCD12Q1) \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"cover\" hidden><\/div><button type=\"button\" class=\"nx-wm-layer\" data-wm-toggle=\"ntl\" aria-pressed=\"false\" data-wm-z=\"25\" data-wm-lag=\"0\" data-wm-op=\"0.9\" data-wm-short=\"Night lights\" data-wm-prov=\"NASA LAADS DAAC\" data-wm-kind=\"Satellite radiance, corrected for moonlight and atmosphere\"><span class=\"nx-wm-check\" aria-hidden=\"true\"><\/span><span class=\"nx-wm-layer-text\"><span class=\"nx-wm-layer-name\">Night-time lights, daily<\/span><span class=\"nx-wm-layer-src\">NASA LAADS DAAC VIIRS Black Marble (VNP46A2) \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/span><\/span><\/button><div class=\"nx-wm-tools\" data-wm-tools=\"ntl\" hidden><\/div><\/div><\/div><\/div><div class=\"nx-wm-rail\" role=\"toolbar\" aria-orientation=\"vertical\" aria-label=\"Map controls\"><button type=\"button\" class=\"nx-wm-icon nx-wm-open\" data-wm-act=\"panel\" aria-controls=\"nx-wm-panel\" aria-expanded=\"false\" aria-label=\"Layers\" title=\"Layers\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 2.5l-6.5 3.5 6.5 3.5 6.5-3.5zm-6.5 6.5 6.5 3.5 6.5-3.5m-13 2.8 6.5 3.5 6.5-3.5\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"tools\" aria-controls=\"nx-wm-menu\" aria-expanded=\"false\" aria-label=\"Expert tools\" title=\"Expert tools\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3 4.5l10 0m-10 3.5 10 0m-10 3.5 10 0m-7.5-8.5 0 3m5 .5 0 3m-3.5.5 0 3\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><span class=\"nx-wm-sep\" aria-hidden=\"true\"><\/span><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"in\" aria-label=\"Zoom in\" title=\"Zoom in\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M8 3v10M3 8h10\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"out\" aria-label=\"Zoom out\" title=\"Zoom out\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3 8h10\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"reset\" aria-label=\"Reset the view\" title=\"Reset the view\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.2 8a4.8 4.8 0 1 0 1.5-3.5M3.2 2.6v2.6h2.6\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"full\" aria-label=\"Full screen\" title=\"Full screen\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M2.5 6l0-3.5 3.5 0m7.5 3.5 0-3.5-3.5 0m-7.5 7.5 0 3.5 3.5 0m7.5-3.5 0 3.5-3.5 0\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-menu\" id=\"nx-wm-menu\" role=\"menu\" aria-label=\"Expert tools\" hidden><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"inspect\">Inspect a point<kbd>I<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"measure\">Measure distance<kbd>M<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitemcheckbox\" aria-checked=\"false\" data-wm-tool=\"compare\">Compare two dates<kbd>C<\/kbd><\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"share\">Copy a link to this view<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"png\">Export as PNG with credits<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"xt\" data-wm-xt=\"6oPh6EhWj+gDMzpLLT8H\/wVfxJakWdyAB8snze2Nado=\">More expert tools<\/button><button type=\"button\" class=\"nx-wm-menu-item\" role=\"menuitem\" data-wm-tool=\"keys\">Keyboard shortcuts<kbd>?<\/kbd><\/button><\/div><div class=\"nx-wm-keys\" role=\"dialog\" aria-label=\"Keyboard shortcuts\" hidden><p class=\"nx-wm-keys-title\">Keyboard, with the map focused<\/p><dl class=\"nx-wm-keys-list\"><\/dl><button type=\"button\" class=\"nx-wm-icon nx-wm-keys-close\" data-wm-tool=\"keys\" aria-label=\"Close the shortcuts\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><p class=\"nx-wm-hint\" hidden>Hold Ctrl or \u2318 and scroll to zoom<\/p><p class=\"nx-wm-toolbar\" role=\"status\" hidden><\/p><div class=\"nx-wm-tip\" role=\"tooltip\" hidden><\/div><div class=\"nx-wm-pin\" hidden><div class=\"nx-wm-pin-head\"><p class=\"nx-wm-pin-title\"><\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-act=\"unpin\" aria-label=\"Close the card\"><svg viewBox=\"0 0 16 16\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M3.5 3.5l9 9M12.5 3.5l-9 9\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg><\/button><\/div><div class=\"nx-wm-pin-chart\"><\/div><div class=\"nx-wm-pin-body\"><\/div><\/div><div class=\"nx-wm-dock\"><div class=\"nx-wm-time\" role=\"group\" aria-label=\"Imagery date\"><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"prev\" aria-label=\"One day earlier\">\u2039<\/button><p class=\"nx-wm-time-label\">Latest available<\/p><button type=\"button\" class=\"nx-wm-icon\" data-wm-step=\"next\" aria-label=\"One day later\">\u203a<\/button><span class=\"nx-wm-track\" role=\"slider\" tabindex=\"0\" aria-label=\"Imagery date, up to 30 days back\"><\/span><button type=\"button\" class=\"nx-wm-latest\" data-wm-step=\"latest\" aria-pressed=\"true\">Latest<\/button><\/div><div class=\"nx-wm-chips\" role=\"group\" aria-label=\"Legends\"><\/div><p class=\"nx-wm-attrib\">NASA GIBS \/ EOSDIS \u00b7 ASTER GDEM (METI, NASA) \u00b7 Blue Marble (NASA) \u00b7 \u00a9 OpenStreetMap contributors (ODbL) \u00b7 Natural Earth<\/p><div class=\"nx-wm-legends\" aria-live=\"polite\" hidden><\/div><\/div><\/div><div class=\"nx-container nx-wm-below\"><p class=\"nx-wm-status\" role=\"status\">Loading the latest NASA imagery.<\/p><p id=\"nx-wm-help\" class=\"nx-wm-help\">Drag to move. Ctrl or \u2318 and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.<\/p><p class=\"nx-caption\"><span>For model intercomparison, in GCRI's spatial-intelligence reading, the map opens on true-colour imagery, flood water, rain and population density with relief in 3D and true colour from NASA GIBS, framed on North America; every other NASA layer, live feed and forecast waits in the layer list.<\/span><span data-honesty=\"true\">Satellite and model products as NASA publishes them, each with its own date; indicators, not assessments of any place; claim ceiling INTEGRATED.<\/span><\/p><details class=\"nx-wm-credits\"><summary><span class=\"nx-wm-credits-line\">Data: NASA GIBS \u00b7 ASTER GDEM \u00b7 Blue Marble \u00b7 \u00a9 OpenStreetMap contributors \u00b7 Natural Earth \u00b7 GDACS \u00b7 Copernicus Sentinel-2 L2A \u00b7 NASA HLS S30<\/span><span class=\"nx-wm-credits-more\">Sources and licences<\/span><\/summary><p class=\"nx-wm-credit\"><ul class=\"nx-wm-credit-list\"><li data-wm-credit=\"nasa.gibs.field-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.gibs.water-layers\">We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.lance.viirs-water\">VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.gpm.imerg\">GPM IMERG precipitation, NASA Precipitation Processing System; distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"sedac.gpw-v4\">Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\" rel=\"noopener\" target=\"_blank\">CC BY 4.0<\/a><\/li><li data-wm-credit=\"nasa.modis-viirs.land\">MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa.laads.black-marble\">VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"nasa-meti.aster-gdem-v3\">ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li data-wm-credit=\"naturalearth.all-scales\">Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. <a class=\"nx-link\" href=\"https:\/\/www.naturalearthdata.com\/about\/terms-of-use\/\" rel=\"noopener\" target=\"_blank\">Public domain<\/a><\/li><li data-wm-credit=\"nasa.gibs.reference-osm\">\u00a9 OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.openstreetmap.org\/copyright\" rel=\"noopener\" target=\"_blank\">ODbL<\/a><\/li><li data-wm-credit=\"nasa.earth-observatory.blue-marble\">Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into H3 cells where shown (modified). <a class=\"nx-link\" href=\"https:\/\/www.earthdata.nasa.gov\/engage\/open-data-services-software-policies\" rel=\"noopener\" target=\"_blank\">NASA open data<\/a><\/li><li>Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, \u00a9 OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.<\/li><li>Globe: MapLibre GL JS 6.9.1, BSD-3-Clause, \u00a9 MapLibre contributors.<\/li><li>Live: GDACS (EC JRC \/ UN OCHA) Events are indicative, not official warnings.<\/li><li>Observed (EO): Copernicus Sentinel-2 L2A (contains modified Copernicus Sentinel data 2026), Microsoft Planetary Computer<\/li><li>Observed (EO): NASA HLS S30 (contains modified Copernicus Sentinel data 2026). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).<\/li><li>Optional layers (35 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.<\/li><\/ul><p class=\"nx-wm-credit\"><strong>Layers and dates<\/strong><\/p><ul class=\"nx-wm-credit-list\"><li>True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE \u00b7 <span data-wm-date=\"true\">13 Oct 2026<\/span><\/li><li>Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE \u00b7 <span data-wm-date=\"flood\">14 Oct 2026<\/span><\/li><li>Precipitation rate, daily mean: GPM IMERG, NASA GES DISC \u00b7 <span data-wm-date=\"imerg\">12 Oct 2026<\/span><\/li><li>Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 \u00b7 <span data-wm-date=\"people\">static<\/span><\/li><li>Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC \u00b7 <span data-wm-date=\"cover\">2024<\/span><\/li><li>Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC \u00b7 <span data-wm-date=\"ntl\">12 Oct 2026<\/span><\/li><\/ul><\/p><\/details><\/div><\/div><div class=\"nx-live-slot nx-live-slot--map\" data-nxf-slot=\"map\" data-nxf-kind=\"map\" data-nxf-resource=\"nexus:\/\/gcri\/G-P19\/topics\/model-intercomparison\/#map\" data-nxf-title=\"Systems Science and Modelling live map\" data-nxf-height=\"560\" hidden><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-s-4-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-4\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">03<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Console<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">Coupled model runner, in outline.<\/h2><p class=\"nx-lede\">The working screen behind this subject: its structure, terms and states. Names are placeholders and values illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-systems-coupled\" data-cx=\"systems.coupled\" data-variant=\"section\" aria-label=\"Coupled model runner: illustrative console with placeholder names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Coupled model runner<\/h3><p class=\"nx-fk-sub\">Model intercomparison \u00b7 Coupled model A \u00b7 Scenario set 3<\/p><\/div><div class=\"nx-fk-actions\"><button type=\"button\" class=\"nx-fk-btn\" data-fk-act=\"menu\" aria-haspopup=\"true\" aria-expanded=\"false\" hidden><svg viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\"><path d=\"M12 4v11\"\/><path d=\"m7 10 5 5 5-5\"\/><path d=\"M5 20h14\"\/><\/svg>Export<\/button><ul class=\"nx-fk-menu\" data-fk-menu=\"true\" hidden><li><button type=\"button\" data-fk-act=\"export-png\">Image<small>PNG of the map or chart<\/small><\/button><\/li><li><button type=\"button\" data-fk-act=\"export-csv\">Data<small>CSV of the figure's values<\/small><\/button><\/li><\/ul><\/div><\/div><div class=\"nx-cx-status\"><span class=\"nx-fk-pill\" data-state=\"info\"><i aria-hidden=\"true\"><\/i>Ensemble running<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Systems Science and Modelling<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"map\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Model coupling graph<\/h4><p class=\"nx-fk-panel-sub\">nodes: models \u00b7 edges: exchanged variables<\/p><\/div><\/div><div class=\"nx-cx-map\"><svg class=\"nx-cx-svg\" viewBox=\"0 0 480 250\" role=\"img\" aria-label=\"Model coupling graph: schematic on placeholder geography. Illustrative.\" focusable=\"false\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Coupled components<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-systems-coupled-t1\"><table class=\"nx-fk-table\" data-fk-table=\"cx-systems-coupled-t1\" aria-label=\"Coupled components\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Component<\/button><\/th><th scope=\"col\" data-col=\"c1\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Time step<\/button><\/th><th scope=\"col\" data-col=\"c2\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Exchanges<\/button><\/th><th scope=\"col\" data-col=\"c3\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Validated<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-systems-coupled:r1-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Hydrology<\/td><td>1 day<\/td><td>runoff<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-systems-coupled:r1-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Power system<\/td><td>1 h<\/td><td>demand, outages<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><tr data-fk-key=\"cx-systems-coupled:r1-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Economy<\/td><td>1 quarter<\/td><td>output, prices<\/td><td><span class=\"nx-fk-pill\" data-state=\"near\"><i aria-hidden=\"true\"><\/i>Partial<\/span><\/td><\/tr><tr data-fk-key=\"cx-systems-coupled:r1-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Population<\/td><td>1 year<\/td><td>exposure<\/td><td><span class=\"nx-fk-pill\" data-state=\"met\"><i aria-hidden=\"true\"><\/i>Yes<\/span><\/td><\/tr><\/tbody><\/table><\/div><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Ensemble output<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Ensemble output\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-systems-coupled-c2\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Median|acc|l||0:45.81271631550044;1:49.599804025515915;2:47.264275825861844;3:48.83175166621804;4:51.03866457110271;5:54.94018412847072;6:52.108147668093444;7:53.30925300046802;8:56.184466839954254;9:58.35524802012369;10:60.3131158943288;11:59.97356223212555;12:60.328704679198566;13:62.15058767581358;14:64.30894925035537;15:60.777548731770366;16:64.35503084855154;17:64.3994992364198;18:63.9778757115826;19:67.85715275593103;20:70.85016172472388;21:72.80629187952727;22:72.48460160363466;23:73.82920011989772~Member spread, upper|s3|l||0:51.81271631550044;1:57.79980402551592;2:57.66427582586184;3:61.43175166621804;4:65.83866457110271;5:71.94018412847072;6:71.30814766809344;7:74.70925300046802;8:79.78446683995426;9:84.1552480201237;10:88.3131158943288;11:90.17356223212556;12:92.72870467919857;13:96.75058767581359;14:99;15:99;16:99;17:99;18:99;19:99;20:99;21:99;22:99;23:99~Member spread, lower|s3|l||0:39.81271631550044;1:41.599804025515915;2:37.264275825861844;3:36.83175166621804;4:37.03866457110271;5:38.94018412847072;6:34.108147668093444;7:33.30925300046802;8:34.184466839954254;9:34.35524802012369;10:34.3131158943288;11:31.973562232125552;12:30.328704679198566;13:30.150587675813583;14:30.30894925035537;15:24.777548731770366;16:26.355030848551536;17:24.3994992364198;18:21.9778757115826;19:23.857152755931025;20:24.850161724723876;21:24.806291879527265;22:22.48460160363466;23:21.82920011989772\" data-nx-z=\"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\"><\/svg><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Coupled model runner: the screen's structure, terms and states for Model intercomparison.<\/span><span data-honesty=\"true\">Placeholder names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-question\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-5\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">04<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">The question<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">Model intercomparison: how the risk develops.<\/h2><p class=\"nx-lede\">Comparing models of the same system, and what disagreement tells you. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cells\" data-cols=\"2\"><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"m12.83 2.18a2 2 0 0 0-1.66 0L2.6 6.08a1 1 0 0 0 0 1.83l8.58 3.91a2 2 0 0 0 1.66 0l8.58-3.9a1 1 0 0 0 0-1.83Z\"\/><path d=\"m22 17.65-9.17 4.16a2 2 0 0 1-1.66 0L2 17.65\"\/><path d=\"m22 12.65-9.17 4.16a2 2 0 0 1-1.66 0L2 12.65\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Common protocols isolate differences<\/h3><p class=\"nx-p nx-mt-1\">When models share inputs, scenarios and output formats, remaining differences point to model structure and parameters rather than set-up.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 3v18h18\"\/><path d=\"m19 9-5 5-4-4-3 3\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Spread shows structural uncertainty<\/h3><p class=\"nx-p nx-mt-1\">The range of results across credible models is a measure of how much the answer depends on modelling choices.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M16 21v-2a4 4 0 0 0-4-4H6a4 4 0 0 0-4 4v2\"\/><circle cx=\"9\" cy=\"7\" r=\"4\"\/><path d=\"M22 21v-2a4 4 0 0 0-3-3.87\"\/><path d=\"M16 3.13a4 4 0 0 1 0 7.75\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Ensembles are not independent<\/h3><p class=\"nx-p nx-mt-1\">Models share code, data and ideas, so an ensemble can agree for shared reasons and overstate confidence.<\/p><\/div><div><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\"><svg class=\"nx-i\" viewBox=\"0 0 24 24\" aria-hidden=\"true\" focusable=\"false\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3.85 8.62a4 4 0 0 1 4.78-4.77 4 4 0 0 1 6.74 0 4 4 0 0 1 4.78 4.78 4 4 0 0 1 0 6.74 4 4 0 0 1-4.77 4.78 4 4 0 0 1-6.75 0 4 4 0 0 1-4.78-4.77 4 4 0 0 1 0-6.76Z\"\/><path d=\"m9 12 2 2 4-4\"\/><\/svg><\/span><span class=\"nx-eyebrow\">Mechanism<\/span><\/div><h3 class=\"nx-h4 nx-mt-2\">Evaluation against observations weighs models<\/h3><p class=\"nx-p nx-mt-1\">Comparing models with observations shows which reproduce key processes, which can inform how ensemble results are interpreted.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-6\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">05<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Key terms<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">Terms used on this page.<\/h2><p class=\"nx-lede\">Each term as this page uses it. Where a standard-setting body defines the term, the wording follows that body.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Term<\/th><th scope=\"col\">Definition<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Term\">Ensemble<\/td><td data-label=\"Definition\">A set of model runs from different models or settings used to estimate a range of outcomes.<\/td><\/tr><tr><td data-label=\"Term\">Protocol<\/td><td data-label=\"Definition\">The agreed experiment design, inputs and output formats in an intercomparison.<\/td><\/tr><tr><td data-label=\"Term\">Model genealogy<\/td><td data-label=\"Definition\">The shared ancestry of models through common code and components.<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><\/section><section id=\"nx-sec-indicators\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-7\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">06<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Indicators<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">What is measured, and how well.<\/h2><p class=\"nx-lede\">Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Indicator<\/th><th scope=\"col\">Unit<\/th><th scope=\"col\">Cadence<\/th><th scope=\"col\">Source type<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Indicator\">Models participating in an intercomparison<\/td><td data-label=\"Unit\">count<\/td><td data-label=\"Cadence\">per round<\/td><td data-label=\"Source type\">Intercomparison records<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Ensemble spread<\/td><td data-label=\"Unit\">range or standard deviation across models<\/td><td data-label=\"Cadence\">per experiment<\/td><td data-label=\"Source type\">Intercomparison outputs<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Agreement on sign of change<\/td><td data-label=\"Unit\">percent of models<\/td><td data-label=\"Cadence\">per experiment<\/td><td data-label=\"Source type\">Intercomparison outputs<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Skill against observations<\/td><td data-label=\"Unit\">error metric by model<\/td><td data-label=\"Cadence\">per evaluation<\/td><td data-label=\"Source type\">Model evaluation<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Shared code lineage between models<\/td><td data-label=\"Unit\">count of related model families<\/td><td data-label=\"Cadence\">per assessment<\/td><td data-label=\"Source type\">Model genealogy studies<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Indicator\">Outputs published in standard formats<\/td><td data-label=\"Unit\">percent of outputs<\/td><td data-label=\"Cadence\">per round<\/td><td data-label=\"Source type\">Archive metadata<\/td><td data-label=\"Label\"><span class=\"nx-chip\">OBSERVED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"indicators\" data-nxf-kind=\"indicator\" data-nxf-resource=\"nexus:\/\/gcri\/G-P19\/topics\/model-intercomparison\/#indicators\" data-nxf-title=\"Indicators\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-pipeline\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-8\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">07<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Canvas<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">From sources to decisions.<\/h2><p class=\"nx-lede\">The topic as a computational graph: named sources feed the methods, an evidence review gates the outputs, and the decisions stay with the authorities that hold them.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-plate\" data-wide=\"true\"><div class=\"nx-plate-label\"><span class=\"nx-eyebrow\"><strong>Pipeline canvas<\/strong> \u00b7 Model intercomparison as a run graph<\/span><span class=\"nx-eyebrow\">Design, not a record<\/span><\/div><div class=\"nx-figure-frame\" data-scene=\"true\"><svg class=\"nx-figure nx-ui nx-dag\" viewBox=\"0 0 1200 564\" data-nx-dag=\"true\" data-w=\"1200\" data-h=\"564\" role=\"img\" aria-labelledby=\"nx-dag-g-p19-topics-model-intercomparison-system-t nx-dag-g-p19-topics-model-intercomparison-system-d\" data-nx-z=\"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\"><title id=\"nx-dag-g-p19-topics-model-intercomparison-system-t\">Model intercomparison: a cascade across interdependent systems<\/title><desc id=\"nx-dag-g-p19-topics-model-intercomparison-system-d\">A hazard hits one system; dependencies carry the failure into others; impact grows until buffers, redundancy or restoration priorities stop it; the analysis ranks the links to strengthen. Design of the process, not a record of a run.<\/desc><\/svg><\/div><\/div><p class=\"nx-caption\"><strong>Fig. 02<\/strong><span>Nodes carry their type, two metric rows and a state; connectors show what feeds what; dashed frames name the owners; the timeline is the cadence. Design of the pipeline, not a record of a run.<\/span><\/p><\/div><\/div><\/section><section id=\"nx-sec-sources\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" data-variant=\"split\" aria-labelledby=\"nx-s-9\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Sources<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Where the evidence comes from.<\/h2><p class=\"nx-lede\">Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Source<\/th><th scope=\"col\">What it gives<\/th><th scope=\"col\">Licence class<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Source\">CMIP6 archive via the Earth System Grid Federation<\/td><td data-label=\"What it gives\">Climate model outputs from common experiments<\/td><td data-label=\"Licence class\">Open, CC BY 4.0 for most models (to check per model)<\/td><\/tr><tr><td data-label=\"Source\">ISIMIP repository<\/td><td data-label=\"What it gives\">Impact model outputs for water, agriculture, health and other sectors<\/td><td data-label=\"Licence class\">Open, licence per dataset (to check)<\/td><\/tr><tr><td data-label=\"Source\">AgMIP crop model intercomparisons<\/td><td data-label=\"What it gives\">Crop model ensemble protocols and results<\/td><td data-label=\"Licence class\">Public, terms per project<\/td><\/tr><tr><td data-label=\"Source\">Scenario Modeling Hub publications for epidemics<\/td><td data-label=\"What it gives\">Multi-model epidemic scenario projections<\/td><td data-label=\"Licence class\">Public, terms per hub<\/td><\/tr><tr><td data-label=\"Source\">Literature on model genealogy and ensemble independence<\/td><td data-label=\"What it gives\">Analysis of shared model components<\/td><td data-label=\"Licence class\">Licence per publication<\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G-P19\/topics\/model-intercomparison\/#evidence\" data-nxf-title=\"Sources\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-decisions\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-10\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">09<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Decisions<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Who decides, and what the evidence serves.<\/h2><p class=\"nx-lede\">Systems Science and Modelling prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"ledger\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Who decides<\/th><th scope=\"col\">The question<\/th><th scope=\"col\">Evidence this page serves<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Who decides\">Modelling groups<\/td><td data-label=\"The question\">Which experiments and protocols to adopt<\/td><td data-label=\"Evidence this page serves\">Protocol designs; evaluation reports<\/td><\/tr><tr><td data-label=\"Who decides\">Institutions using model results<\/td><td data-label=\"The question\">How much confidence to place in ensemble findings<\/td><td data-label=\"Evidence this page serves\">Ensemble summaries with spread<\/td><\/tr><tr><td data-label=\"Who decides\">Research funders<\/td><td data-label=\"The question\">Whether to fund intercomparison infrastructure<\/td><td data-label=\"Evidence this page serves\">Evidence on value of intercomparison<\/td><\/tr><\/tbody><\/table><\/div><h3 class=\"nx-h4 nx-mt-4\">Outputs this topic produces<\/h3><ul class=\"nx-list\"><li>Intercomparison protocol designs for cross-sector models<\/li><li>Ensemble summaries with spread and agreement stated<\/li><li>Model evaluation reports against observations<\/li><li>Method notes in Nexus Reports Methods and Technical<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-core-questions\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" data-variant=\"inset\" aria-labelledby=\"nx-s-11\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Core questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">What experts ask about intercomparison.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\"><li>Do independently built models agree, and on which outputs?<\/li><li>Where they disagree, is it because of structure, parameters, inputs or errors?<\/li><li>Is the spread of an ensemble a meaningful measure of uncertainty?<\/li><li>Are the models truly independent, or do they share code, data and assumptions?<\/li><li>How should results be weighted when some models perform better against observations?<\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-mechanisms-and-drivers\" class=\"nx-section\" data-hue=\"project\" data-variant=\"split\" aria-labelledby=\"nx-s-12\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">11<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Mechanisms and drivers<\/span><\/div><h2 id=\"nx-s-12\" class=\"nx-section-title\">What disagreement tells you.<\/h2><\/div><div class=\"nx-section-body\"><p class=\"nx-p\">Every model embodies choices: which processes to include, how to represent them and how to calibrate them. Two well-built models can therefore produce different answers. When models agree, confidence increases, though not to certainty, since they may share the same flaw. When they disagree, the disagreement is information: it reveals structural uncertainty that no single model can show.<\/p><p class=\"nx-p\">Interpreting an ensemble is not straightforward. Models are rarely independent; many share components, data or lineage. An ensemble's spread may underestimate true uncertainty if the models share errors, or overstate it if poor models are included. Methods for weighting models by performance and independence are an active research area.<\/p><p class=\"nx-p\">Intercomparison also improves models. Diagnosing why one model differs often reveals errors or missing processes, and shared protocols raise standards across a community.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods-and-data\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-13\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">12<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods and data<\/span><\/div><h2 id=\"nx-s-13\" class=\"nx-section-title\">Methods and their limits.<\/h2><\/div><div class=\"nx-section-body\"><ol class=\"nx-rows\"><li><span class=\"nx-eyebrow nx-index\">01<\/span><div><h3 class=\"nx-h4\">Common protocols<\/h3><p class=\"nx-p nx-mt-1\">Shared inputs, scenarios, output variables and formats. Designing them takes time and agreement.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">02<\/span><div><h3 class=\"nx-h4\">Ensemble statistics<\/h3><p class=\"nx-p nx-mt-1\">Means, spreads and distributions across models, using <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\">probability and statistics<\/a>. Spread is not a probability unless justified.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">03<\/span><div><h3 class=\"nx-h4\">Performance and independence weighting<\/h3><p class=\"nx-p nx-mt-1\">Weighting models by skill and distinctness, using <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">uncertainty quantification<\/a>. Weights depend on chosen metrics.<\/p><\/div><\/li><li><span class=\"nx-eyebrow nx-index\">04<\/span><div><h3 class=\"nx-h4\">Process diagnostics<\/h3><p class=\"nx-p nx-mt-1\">Comparing internal variables to find sources of difference. Requires access to model internals.<\/p><\/div><\/li><\/ol><p class=\"nx-note nx-mt-3\">Agreement among models is not proof of correctness. Intercomparison results state which models took part and how they relate.<\/p><\/div><\/div><\/section><section id=\"nx-sec-methods\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-14\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">13<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Methods<\/span><\/div><h2 id=\"nx-s-14\" class=\"nx-section-title\">How it is studied, and what the methods cannot say.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-methods\" data-limits=\"true\"><ol class=\"nx-mledger\" aria-label=\"Methods\"><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/uncertainty-quantification\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">01<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Uncertainty quantification and model validation<\/span><span class=\"nx-mledger-role\">Uncertainty budgets \u00b7 Surrogate models for expensive simulations \u00b7 Credibility assessment<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\" aria-label=\"Uncertainty quantification and model validation: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/probability-statistics\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">02<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Probability and statistics<\/span><span class=\"nx-mledger-role\">Bayesian calibration \u00b7 Proper scoring of predictions \u00b7 Ensemble statistics<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/mathematics\/probability-statistics\/\" aria-label=\"Probability and statistics: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/computing\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">03<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">High-performance, cloud and edge computing<\/span><span class=\"nx-mledger-role\">Ensemble design within budgets \u00b7 Workflow orchestration \u00b7 Reproducible environments<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/technology\/computing\/\" aria-label=\"High-performance, cloud and edge computing: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><li><a class=\"nx-mledger-main\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/systems-engineering\/\"><span class=\"nx-mledger-n\" aria-hidden=\"true\">04<\/span><span class=\"nx-mledger-body\"><span class=\"nx-mledger-name\">Systems engineering<\/span><span class=\"nx-mledger-role\">Requirements for decision models \u00b7 Interface control for coupled models<\/span><\/span><span class=\"nx-mledger-go\"><span class=\"nx-mledger-go-label\">Applied here<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><div class=\"nx-mledger-aside\"><a class=\"nx-mledger-canon\" href=\"https:\/\/therisk.global\/stem\/engineering\/systems-engineering\/\" aria-label=\"Systems engineering: canonical page on the STEM Commons\">Canonical \u00b7 STEM Commons <span aria-hidden=\"true\">\u2197<\/span><\/a><\/div><\/li><\/ol><aside class=\"nx-limits\" aria-label=\"What the methods cannot say\"><span class=\"nx-eyebrow\">What the methods cannot say<\/span><p class=\"nx-limits-text\">Ensemble spread does not capture all uncertainty, because models share assumptions. Weighting models by skill is contested and can overfit to the past. Intercomparisons cover the models that take part, which may not be the best. Observations used for evaluation have their own errors. Systems Science and Modelling certifies no model; modelling groups and users decide.<\/p><\/aside><\/div><\/div><\/div><\/section><section id=\"nx-s-15-sec\" class=\"nx-section\" data-hue=\"risk\" data-tone=\"alt\" aria-labelledby=\"nx-s-15\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">14<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Questions<\/span><\/div><h2 id=\"nx-s-15\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What is a model intercomparison project?<\/summary><div class=\"nx-answer\">A coordinated effort in which several modelling groups run the same experiments with shared inputs and output formats, so that their results can be compared systematically.<\/div><\/details><details><summary>Does agreement between models mean they are right?<\/summary><div class=\"nx-answer\">Not necessarily. Models can share errors through common components and assumptions. Agreement increases confidence only when models are evaluated against observations.<\/div><\/details><details><summary>What is structural uncertainty?<\/summary><div class=\"nx-answer\">Uncertainty that comes from the way a model is built, such as which processes it includes and how, which shows up as differences between models.<\/div><\/details><details><summary>Does intercomparison find the best model?<\/summary><div class=\"nx-answer\">It can show which models perform better on chosen tests, but different models may be better for different purposes.<\/div><\/details><details><summary>Why not just average the models?<\/summary><div class=\"nx-answer\">Because models are not independent and not equally good. Simple averages can mislead without considering both.<\/div><\/details><details><summary>Can private models take part?<\/summary><div class=\"nx-answer\">Intercomparisons can be designed to include them, with rules on what is disclosed. Each protocol states its rules.<\/div><\/details><details><summary>Is the Nexus Universe exercise an intercomparison?<\/summary><div class=\"nx-answer\">The designed cross-system integration exercise uses intercomparison methods, described on the <a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/universe\/\">Universe page<\/a>.<\/div><\/details><\/div><\/div><\/div><\/section><section class=\"nx-section\" data-tone=\"alt\" aria-labelledby=\"nx-rel-16\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Related<\/span><h2 id=\"nx-rel-16\" class=\"nx-h3 nx-mt-2\">Around Systems Science and Modelling.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/uncertainty-and-validation\/\">Uncertainty and validation <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/coupled-systems-modelling\/\">Coupled systems modelling <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/networks-and-cascades\/\">Networks and cascades <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/agent-based-and-complexity\/\">Agent-based and complexity models <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/optimization-under-uncertainty\/\">Optimization under uncertainty <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/topics\/digital-twins-method\/\">Digital twins: method and limits <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/systems-modelling\/counterparts\/\">Counterparts across the Consortium <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/\">The Global Centre for Risk and Innovation home <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/therisk.global\/trust\/\">GCRI governance and trust <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-17\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Systems Science and Modelling<\/span><h2 id=\"nx-close-17\" class=\"nx-section-title\">Bring your expertise to this field.<\/h2><p class=\"nx-lede\">The current service level is R0: information and enquiries. An enquiry receives a reference and names the next step; applications, payments, compute allocations and registrations open only when their team, terms and review capacity exist.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"mailto:contact@therisk.global?subject=Systems%20Science%20and%20Modelling%3A%20Model%20intercomparison\">Write to the team <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/systems-modelling\/contact\/\">See all contact routes<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/therisk.global\/systems-modelling\/topics\/model-intercomparison\/#faq\",\"isPartOf\":{\"@id\":\"https:\/\/therisk.global\/systems-modelling\/topics\/model-intercomparison\/#webpage\"},\"inLanguage\":\"en\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is a model intercomparison project?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"A coordinated effort in which several modelling groups run the same experiments with shared inputs and output formats, so that their results can be compared systematically.\"}},{\"@type\":\"Question\",\"name\":\"Does agreement between models mean they are right?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Not necessarily. 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