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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n23z37zv.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-scene nx-es-scene\" data-scene=\"text\" data-ground=\"none\" data-es-org=\"gcri\" aria-labelledby=\"nx-scene-title\"><div class=\"nx-scene-intro nx-es-intro\"><p class=\"nx-eyebrow\"><abbr title=\"The Global Centre for Risk and Innovation\">GCRI<\/abbr> \u00b7 Reports<\/p><span class=\"nx-scene-rule\" aria-hidden=\"true\"><\/span><h1 id=\"nx-scene-title\" class=\"nx-scene-title\">Uncertainty in published assessments<\/h1><p class=\"nx-scene-lede\">Uncertainty quantification and model validation decide how much a synthesis statement or a modelled result can bear. In publishing, this means calibrated language for confidence and likelihood, certainty ratings for bodies of evidence, and validation of the models behind scenario and test reports, so readers see the strength of evidence beside each claim.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/therisk.global\/nexus-reports\/write-to-us\/contact\/\">Make an enquiry <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/therisk.global\/nexus-reports\/stem\/\">Back to STEM foundation of Reports<\/a><\/div><\/div><\/section><section id=\"nx-s-1-sec\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-1\"><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\">Contents<\/span><\/div><h2 id=\"nx-s-1\" class=\"nx-section-title\">Uncertainty in published assessments in five parts.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-es-fig\" data-es-layout=\"protocol\"><div class=\"nx-hero-panel\"><div class=\"nx-lv-steps\"><div class=\"nx-register-head\"><span class=\"nx-eyebrow\"><strong>Protocol<\/strong><\/span><span class=\"nx-eyebrow\">5 steps<\/span><\/div><ol><li><a href=\"#nx-sec-application\"><span class=\"nx-lv-steps-n\">01<\/span><span class=\"nx-lv-steps-t\">Application<\/span><span class=\"nx-lv-steps-d\">What uncertainty quantification and model validation does here<\/span><\/a><\/li><li><a href=\"#nx-sec-data\"><span class=\"nx-lv-steps-n\">02<\/span><span class=\"nx-lv-steps-t\">Data<\/span><span class=\"nx-lv-steps-d\">What the method consumes<\/span><\/a><\/li><li><a href=\"#nx-sec-checks\"><span class=\"nx-lv-steps-n\">03<\/span><span class=\"nx-lv-steps-t\">Validation<\/span><span class=\"nx-lv-steps-d\">How results are checked<\/span><\/a><\/li><li><a href=\"#nx-sec-connections\"><span class=\"nx-lv-steps-n\">04<\/span><span class=\"nx-lv-steps-t\">Connections<\/span><span class=\"nx-lv-steps-d\">Topics that rely on this method<\/span><\/a><\/li><li><a href=\"#nx-sec-stewardship\"><span class=\"nx-lv-steps-n\">05<\/span><span class=\"nx-lv-steps-t\">Stewardship<\/span><span class=\"nx-lv-steps-d\">Who maintains the method<\/span><\/a><\/li><\/ol><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-application\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-2\"><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\">Application<\/span><\/div><h2 id=\"nx-s-2\" class=\"nx-section-title\">What uncertainty quantification and model validation does here.<\/h2><p class=\"nx-lede\">Uncertainty quantification and model validation characterise how uncertainty in inputs, parameters and model structure affects predictions. They establish how much confidence a model&#8217;s outputs deserve. In research and evidence, uncertainty quantification shows where risk estimates are robust and where they are fragile. It guides where better data or models would change decisions. The canonical description is on <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">the STEM Commons<\/a>.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-cols\"><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><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\"><circle cx=\"12\" cy=\"12\" r=\"10\"\/><polygon points=\"16.24 7.76 14.12 14.12 7.76 16.24 9.88 9.88 16.24 7.76\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Calibrated language in assessments<\/h3><p class=\"nx-p nx-mt-1\">Authors assess evidence and agreement for each key statement and express it with the IPCC confidence and likelihood terms, with a traceable account linking the term to the assessed literature.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><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><\/div><h3 class=\"nx-h3 nx-mt-2\">Certainty of evidence ratings<\/h3><p class=\"nx-p nx-mt-1\">Bodies of evidence are rated with GRADE for risk of bias, inconsistency, indirectness, imprecision and publication bias, and the rating is shown beside each summary finding.<\/p><\/article><article><div class=\"nx-glyph-row\"><span class=\"nx-glyph\" data-tone=\"accent\" data-size=\"lg\"><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\"><rect width=\"8\" height=\"8\" x=\"3\" y=\"3\" rx=\"2\"\/><path d=\"M7 11v4a2 2 0 0 0 2 2h4\"\/><rect width=\"8\" height=\"8\" x=\"13\" y=\"13\" rx=\"2\"\/><\/svg><\/span><\/div><h3 class=\"nx-h3 nx-mt-2\">Validation of models in method reports<\/h3><p class=\"nx-p nx-mt-1\">Models behind scenario and test reports are checked against held-out observations or benchmark cases, with <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 sensitivity-analysis\" data-tk=\"Nexus risk vocabulary\" data-td=\"Testing how much a model's outputs change when inputs or assumptions change, to identify which drive results.\" data-ts=\"NRS-C1\" data-tr=\"draft\">sensitivity analysis<\/a> showing which assumptions drive the result.<\/p><\/article><\/div><\/div><\/div><\/section><section id=\"nx-s-3-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-3\"><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\">Monitor<\/span><\/div><h2 id=\"nx-s-3\" class=\"nx-section-title\">Uncertainty analysis.<\/h2><p class=\"nx-lede\">The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.<\/p><\/div><div class=\"nx-section-body\"><figure class=\"nx-fk nx-cx\" data-fk=\"cx-reports-method-uq\" data-cx=\"reports.method-uq\" data-variant=\"section\" aria-label=\"Uncertainty analysis: illustrative console with example names and values\"><div class=\"nx-fk-head\"><div class=\"nx-fk-heading\"><h3 class=\"nx-fk-title\">Uncertainty analysis<\/h3><p class=\"nx-fk-sub\"><a class=\"nx-link\" href=\"https:\/\/docs.therisk.global\/organization\/programme\/reports\">Reports<\/a> \u00b7 Uncertainty in published \u00b7 Run R-9820<\/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&#8217;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>Sensitivity ranked<\/span><span class=\"nx-cx-prod\">GCRI \u00b7 Reports<\/span><\/div><div class=\"nx-cx-grid\"><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Total-order sensitivity<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 520 210\" width=\"520\" height=\"210\" role=\"img\" focusable=\"false\" preserveAspectRatio=\"xMidYMid meet\" aria-label=\"Total-order sensitivity\" data-fk-chart=\"cx-reports-method-uq-c0\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"3\" data-t=\"chart\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Output distribution<\/h4><\/div><\/div><svg class=\"nx-fk-svg\" viewBox=\"0 0 460 210\" width=\"460\" height=\"210\" role=\"img\" aria-label=\"Output distribution\" focusable=\"false\" tabindex=\"0\" preserveAspectRatio=\"xMidYMid meet\" data-fk-chart=\"cx-reports-method-uq-c1\" data-fk-xs=\"lin,0,23,36,446\" data-fk-ys=\"lin,0,100,180,20\" data-fk-series=\"Median|acc|l||0:43.11193686183542;1:43.24409166360274;2:47.612900513689965;3:45.7659846487455;4:48.69254039302468;5:47.84457436548546;6:53.7110490036197;7:53.371187046635896;8:53.982594845257694;9:55.45842987559735;10:57.39934602677822;11:58.37470031771809;12:56.636970998719335;13:57.451577606052155;14:62.82877857452259;15:60.339921195246276;16:62.886840579193084;17:61.625873448792845;18:65.40025243060663;19:67.93583397176117;20:67.37522355020046;21:69.83908817134798;22:67.41162653267384;23:72.60801410991698~Sample spread, upper|s3|l||0:49.11193686183542;1:51.44409166360274;2:58.012900513689964;3:58.3659846487455;4:63.49254039302468;5:64.84457436548546;6:72.9110490036197;7:74.7711870466359;8:77.58259484525769;9:81.25842987559736;10:85.39934602677822;11:88.57470031771808;12:89.03697099871934;13:92.05157760605215;14:99;15:99;16:99;17:99;18:99;19:99;20:99;21:99;22:99;23:99~Sample spread, lower|s3|l||0:37.11193686183542;1:35.24409166360274;2:37.612900513689965;3:33.7659846487455;4:34.69254039302468;5:31.844574365485457;6:35.7110490036197;7:33.371187046635896;8:31.982594845257694;9:31.458429875597353;10:31.39934602677822;11:30.374700317718087;12:26.636970998719335;13:25.451577606052155;14:28.828778574522588;15:24.339921195246276;16:24.886840579193084;17:21.625873448792845;18:23.400252430606628;19:23.93583397176117;20:21.37522355020046;21:21.83908817134798;22:17.411626532673836;23:20.60801410991698\" data-nx-z=\"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\"><\/svg><\/div><div class=\"nx-fk-panel nx-cx-p\" data-w=\"6\" data-t=\"table\"><div class=\"nx-fk-panel-head\"><div><h4 class=\"nx-fk-panel-title\">Inputs<\/h4><\/div><\/div><div class=\"nx-fk-tablewrap\" data-fk-view=\"table:cx-reports-method-uq-t2\"><table class=\"nx-fk-table\" data-fk-table=\"cx-reports-method-uq-t2\" aria-label=\"Inputs\"><thead><tr><th scope=\"col\" data-col=\"c0\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c0\">Input<\/button><\/th><th scope=\"col\" data-col=\"c1\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c1\">Distribution<\/button><\/th><th scope=\"col\" data-col=\"c2\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c2\">Range<\/button><\/th><th scope=\"col\" data-col=\"c3\" aria-sort=\"none\"><button type=\"button\" data-fk-sort=\"c3\">Source<\/button><\/th><\/tr><\/thead><tbody><tr data-fk-key=\"cx-reports-method-uq:r2-0\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Editions in production<\/td><td>Uniform<\/td><td>editions, stated<\/td><td>expert elicitation<\/td><\/tr><tr data-fk-key=\"cx-reports-method-uq:r2-1\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Values traced to sources<\/td><td>Uniform<\/td><td data-sv=\"0\"> of values, stated<\/td><td>expert elicitation<\/td><\/tr><tr data-fk-key=\"cx-reports-method-uq:r2-2\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Review rounds<\/td><td>Beta<\/td><td>rounds, stated<\/td><td>expert elicitation<\/td><\/tr><tr data-fk-key=\"cx-reports-method-uq:r2-3\" tabindex=\"0\" role=\"row\" aria-selected=\"false\"><td>Corrections issued<\/td><td>Beta<\/td><td>corrections, stated<\/td><td>expert elicitation<\/td><\/tr><\/tbody><\/table><\/div><\/div><\/div><div class=\"nx-fk-tip\" aria-hidden=\"true\" hidden><\/div><figcaption class=\"nx-fk-cap\"><span>Uncertainty analysis: the screen&#8217;s structure, terms and states for Reports.<\/span><span data-honesty=\"true\">Example names and illustrative values; a design, not a record or a live system.<\/span><\/figcaption><\/figure><\/div><\/div><\/section><section id=\"nx-sec-data\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-s-4\"><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\">Data<\/span><\/div><h2 id=\"nx-s-4\" class=\"nx-section-title\">What the method consumes.<\/h2><p class=\"nx-lede\">Datasets and records used by uncertainty quantification and model validation on this platform, with the role each plays and the label its values carry. Sources are listed in <a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/research\/\">the source register<\/a>.<\/p><\/div><div class=\"nx-section-body\" data-lv=\"register\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Data<\/th><th scope=\"col\">Role<\/th><th scope=\"col\">Label<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Data\">Assessed literature and traceable accounts<\/td><td data-label=\"Role\">Basis for confidence and likelihood terms<\/td><td data-label=\"Label\"><span class=\"nx-chip\">REPORTED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Evidence profiles and summary of findings tables<\/td><td data-label=\"Role\">Certainty ratings by outcome<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><tr><td data-label=\"Data\">Model ensembles and scenario databases<\/td><td data-label=\"Role\">Spread and sensitivity across assumptions<\/td><td data-label=\"Label\"><span class=\"nx-chip\">MODELLED<\/span><\/td><\/tr><tr><td data-label=\"Data\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 expert-elicitation\" data-tk=\"Nexus risk vocabulary\" data-td=\"A structured process for obtaining and combining judgements from experts about uncertain quantities, with attention to bias and calibration.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Expert elicitation<\/a> records<\/td><td data-label=\"Role\">Judgement where data are absent<\/td><td data-label=\"Label\"><span class=\"nx-chip\">ESTIMATED<\/span><\/td><\/tr><\/tbody><\/table><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"data\" data-nxf-kind=\"data\" data-nxf-resource=\"nexus:\/\/gcri\/G11\/stem\/uncertainty-quantification\/#data\" data-nxf-title=\"Method record\" data-nxf-height=\"520\" hidden><\/div><div class=\"nx-live-slot\" data-nxf-slot=\"evidence\" data-nxf-kind=\"evidence\" data-nxf-resource=\"nexus:\/\/gcri\/G11\/stem\/uncertainty-quantification\/#evidence\" data-nxf-title=\"Evidence\" data-nxf-height=\"520\" hidden><\/div><\/div><\/div><\/section><section id=\"nx-sec-audience\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-5\"><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\">Methods and evidence<\/span><\/div><h2 id=\"nx-s-5\" class=\"nx-section-title\">How uncertainty in published assessments is modelled, measured and tested.<\/h2><p class=\"nx-lede\">The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-au\" data-au-voice=\"science\" data-au-n=\"1\"><figure class=\"nx-au-c\" data-au-fig=\"uncertainty-band\"><h3 class=\"nx-h4\">Uncertainty bands<\/h3><p class=\"nx-p nx-au-what\">How far the outlook for uncertainty in published assessments could spread, shown as ranges rather than a single line.<\/p><div class=\"nx-au-fig\"><svg class=\"nx-au-svg\" viewBox=\"0 0 560 240\" role=\"img\" aria-label=\"Central estimate with likely and very likely ranges for Uncertainty in published assessments\" focusable=\"false\"><line class=\"nx-au-ax\" x1=\"46\" y1=\"206\" x2=\"544\" y2=\"206\"\/><line class=\"nx-au-ax\" x1=\"46\" y1=\"18\" x2=\"46\" y2=\"206\"\/><path class=\"nx-au-band nx-au-band-w\" d=\"M46 142.1l83-18.8 83-18.8 83-22.6 83-20.7 83-18.8 83-18.8 0 124.1-83 0-83 0-83 1.9-83 3.8-83 0-83 3.7z\"\/><path class=\"nx-au-band\" d=\"M46 145.8l83-15 83-13.2 83-16.9 83-15 83-13.2 83-13.1 0 52.6-83 5.6-83 5.7-83 7.5-83 9.4-83 5.6-83 7.6z\"\/><path class=\"nx-au-ac\" d=\"M46 149.6l83-11.3 83-9.4 83-13.1 83-11.3 83-9.4 83-9.4\"\/><text class=\"nx-au-t\" x=\"46\" y=\"222\" text-anchor=\"middle\">Now<\/text><text class=\"nx-au-t\" x=\"129\" y=\"222\" text-anchor=\"middle\">+5<\/text><text class=\"nx-au-t\" x=\"212\" y=\"222\" text-anchor=\"middle\">+10<\/text><text class=\"nx-au-t\" x=\"295\" y=\"222\" text-anchor=\"middle\">+15<\/text><text class=\"nx-au-t\" x=\"378\" y=\"222\" text-anchor=\"middle\">+20<\/text><text class=\"nx-au-t\" x=\"461\" y=\"222\" text-anchor=\"middle\">+25<\/text><text class=\"nx-au-t\" x=\"544\" y=\"222\" text-anchor=\"middle\">+30 y<\/text><text class=\"nx-au-t\" x=\"40\" y=\"36.8\" text-anchor=\"end\">high<\/text><text class=\"nx-au-t\" x=\"40\" y=\"187.2\" text-anchor=\"end\">low<\/text><text class=\"nx-au-t\" x=\"540\" y=\"17.64\" text-anchor=\"end\">very likely range<\/text><text class=\"nx-au-t\" x=\"540\" y=\"73.36\" text-anchor=\"end\">likely range<\/text><\/svg><\/div><dl class=\"nx-au-kv\"><div><dt>Central estimate<\/dt><dd>median of the ensemble<\/dd><\/div><div><dt>Ranges<\/dt><dd>likely and very likely, in IPCC calibrated language<\/dd><\/div><div><dt>Ensemble<\/dt><dd>members, scenarios and initial conditions stated<\/dd><\/div><div><dt>Read with<\/dt><dd>the sensitivity of the result to each input<\/dd><\/div><\/dl><figcaption class=\"nx-caption\"><span>Illustrative ensemble spread on an index scale; the form follows IPCC uncertainty guidance. Not a projection.<\/span><\/figcaption><\/figure><\/div><div class=\"nx-chips nx-au-fw\" aria-label=\"Frameworks cited\"><span class=\"nx-chip\">FAIR guiding principles \u00b7 GO FAIR and FORCE11<\/span><span class=\"nx-chip\">W3C PROV \u00b7 World Wide Web Consortium<\/span><span class=\"nx-chip\">Guidance on consistent treatment of uncertainties \u00b7 IPCC<\/span><span class=\"nx-chip\">CEOS Analysis Ready Data \u00b7 Committee on Earth Observation Satellites<\/span><\/div><\/div><\/div><\/section><section id=\"nx-sec-checks\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-6\"><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\">Validation<\/span><\/div><h2 id=\"nx-s-6\" class=\"nx-section-title\">How results are checked.<\/h2><p class=\"nx-lede\">Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.<\/p><\/div><div class=\"nx-section-body\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Check<\/th><th scope=\"col\">How it is done<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Check\">Term consistency audit<\/td><td data-label=\"How it is done\">Calibrated terms are checked for consistent use across chapters and between summary and full report<\/td><\/tr><tr><td data-label=\"Check\">Traceability check<\/td><td data-label=\"How it is done\">A sample of summary statements is traced to the literature and judgements behind them<\/td><\/tr><tr><td data-label=\"Check\">Out-of-sample validation<\/td><td data-label=\"How it is done\">Model performance is reported on data not used in calibration<\/td><\/tr><tr><td data-label=\"Check\">Sensitivity reporting<\/td><td data-label=\"How it is done\">The assumptions driving most of the output range are named in the text, not only in annexes<\/td><\/tr><\/tbody><\/table><\/div><p class=\"nx-note nx-mt-3\">Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.<\/p><\/div><\/div><\/section><section id=\"nx-s-7-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-7\"><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\">Process<\/span><\/div><h2 id=\"nx-s-7\" class=\"nx-section-title\">How the process runs.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-canvas-wrap\"><div class=\"nx-pe\" id=\"nx-pe-ipcc-risk\" data-nx-pe=\"nx-pe-ipcc-risk\" data-active=\"ipcc-risk\"><div class=\"nx-pe-shell\"><p class=\"nx-pe-eb\">Process<\/p><h2 class=\"nx-pe-title\">Uncertainty in published assessments: Hazard, exposure, vulnerability<\/h2><p class=\"nx-pe-cap\">In IPCC AR6 Working Group II, climate risk results from interactions between hazards and a system&#8217;s exposure and vulnerability, and responses can add risk.<\/p><ol class=\"nx-pe-steps\"><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">01 Drivers<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">01<\/span><span class=\"nx-pe-st\">Hazard<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The potential occurrence of a natural or human-induced physical event or trend that may harm people, property, services or ecosystems.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">02<\/span><span class=\"nx-pe-st\">Exposure<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The presence of people, livelihoods, species, ecosystems, infrastructure or assets in places and settings that could be adversely affected.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">03<\/span><span class=\"nx-pe-st\">Sensitivity<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The degree to which a system or species is affected, either adversely or beneficially, by <a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 climate-variability\" data-tk=\"Nexus risk vocabulary\" data-td=\"Fluctuations of the climate around its average state at all time scales beyond individual weather events, including modes that recur over seasons to decades.\" data-ts=\"NRS-C1\" data-tr=\"draft\">climate variability<\/a> or change.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">04<\/span><span class=\"nx-pe-st\">Capacity to cope and adapt<\/span><span class=\"nx-pe-k\">Dataset \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The ability of <a class=\"nx-link nx-term\" href=\"https:\/\/docs.therisk.global\/organization\/systems\" data-tc=\"Systems\" data-tk=\"Nexus Standards\" data-td=\"The twenty Nexus systems, NXS-CMD to NXS-LAB\">systems<\/a>, institutions, humans and other organisms to adjust to damage, take opportunities or respond to consequences.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">02 Assessment<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">05<\/span><span class=\"nx-pe-st\">Risk from responses<\/span><span class=\"nx-pe-k\">Component \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">Risks from climate responses arise when responses miss their objectives, or from trade-offs or negative side effects.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">06<\/span><span class=\"nx-pe-st\">Vulnerability<\/span><span class=\"nx-pe-k\">Component \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The propensity or predisposition to be adversely affected. It includes sensitivity to harm and lack of capacity to cope and adapt.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">07<\/span><span class=\"nx-pe-st\">Risk<\/span><span class=\"nx-pe-k\">Model \u00b7 Reports<\/span><span class=\"nx-pe-sm\">Risk results from dynamic interactions between hazards and the system&#8217;s exposure and vulnerability. Each may be uncertain and change.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">03 Response<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">08<\/span><span class=\"nx-pe-st\">Risk management and response<\/span><span class=\"nx-pe-k\">Step \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">Risk management is plans, actions, strategies or policies that reduce the likelihood or size of adverse consequences.<\/span><\/li><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">09<\/span><span class=\"nx-pe-st\"><a class=\"nx-link nx-term\" href=\"https:\/\/nexustandards.com\/standards\/nrs\/#nrs-c1\" data-tc=\"NRS-C1 residual-risk\" data-tk=\"Nexus risk vocabulary\" data-td=\"The risk that remains after controls, mitigation and transfer have been applied. It must be known, owned and either accepted or further treated.\" data-ts=\"NRS-C1\" data-tr=\"draft\">Residual risk<\/a><\/span><span class=\"nx-pe-k\">Record \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The climate impact risk that remains after adaptation and mitigation efforts. Adaptation can redistribute risk between areas and groups.<\/span><\/li><\/ol><\/li><li class=\"nx-pe-lane\"><h3 class=\"nx-pe-lane-t\">04 Decision<\/h3><ol><li class=\"nx-pe-step\"><span class=\"nx-pe-n\" translate=\"no\">10<\/span><span class=\"nx-pe-st\">Decision by the responsible party<\/span><span class=\"nx-pe-k\">Decision \u00b7 Series editor<\/span><span class=\"nx-pe-sm\">The framing names no single decision maker. Decisions on responses rest with the responsible party under its own rules.<\/span><\/li><\/ol><\/li><\/ol><p class=\"nx-pe-foot\"><span>Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.<\/span><\/p><\/div><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-connections\" 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\">08<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connections<\/span><\/div><h2 id=\"nx-s-8\" class=\"nx-section-title\">Topics that rely on this method.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-list\" data-nav=\"true\"><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/global-systems-series\/\"><span>Global systems series<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/country-and-regional-series\/\"><span>Country and regional series<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/sectoral-series\/\"><span>Sectoral series<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/frontier-technology-series\/\"><span>Frontier technology risk assessment<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/scenarios-and-methods\/\"><span>Scenarios and methods<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/labs-and-foundry-series\/\"><span>Labs and foundry series<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/universe-annual-synthesis\/\"><span>Universe annual synthesis<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><li><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-reports\/topics\/editorial-and-corrections\/\"><span>Editorial and corrections<\/span><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-sec-stewardship\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" data-variant=\"stack\" aria-labelledby=\"nx-s-9\"><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\">Stewardship<\/span><\/div><h2 id=\"nx-s-9\" class=\"nx-section-title\">Who maintains the method.<\/h2><p class=\"nx-lede\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/systems-modelling\/\">Systems Science and Modelling<\/a> stewards uncertainty quantification and model validation for the Consortium and reviews how it is applied across platforms.<\/p><\/div><div class=\"nx-section-body\"><p class=\"nx-p\"><a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">Canonical page: Uncertainty quantification and model validation<\/a><\/p><\/div><\/div><\/section><section id=\"nx-s-10-sec\" 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\">10<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Connected<\/span><\/div><h2 id=\"nx-s-10\" class=\"nx-section-title\">Where this page leads across the Consortium.<\/h2><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/systems-modelling\/stem\/uncertainty-quantification\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Steward platform<\/span><span class=\"nx-dest-host\">GCRI<\/span><\/span><span class=\"nx-dest-title\">Uncertainty quantification<\/span><span class=\"nx-dest-why\">Systems Science and Modelling stewards this method.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><\/ul><\/div><\/div><\/section><section id=\"nx-s-11-sec\" class=\"nx-section\" data-hue=\"project\" aria-labelledby=\"nx-s-11\"><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\">Questions<\/span><\/div><h2 id=\"nx-s-11\" class=\"nx-section-title\">Questions and answers.<\/h2><\/div><div class=\"nx-section-body\"><div class=\"nx-faq\"><details><summary>What are the limits of uncertainty quantification and model validation in this field?<\/summary><div class=\"nx-answer\">Structural uncertainty and unknown unknowns are difficult to represent. Computational cost limits exploration of high-dimensional models.<\/div><\/details><details><summary>How are results checked?<\/summary><div class=\"nx-answer\">Predictive distributions are tested for calibration and sharpness against held-out observations. Verification confirms that code solves the intended equations.<\/div><\/details><details><summary>Are the values on this page real?<\/summary><div class=\"nx-answer\">The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method&#8217;s use, not a run; no result is reported here.<\/div><\/details><details><summary>Where else is this method applied?<\/summary><div class=\"nx-answer\">The <a class=\"nx-link\" href=\"https:\/\/therisk.global\/stem\/mathematics\/uncertainty-quantification\/\">canonical method page<\/a> lists every platform that applies it.<\/div><\/details><\/div><\/div><\/div><\/section><section id=\"nx-sec-nr-gateway\" class=\"nx-section\" data-hue=\"project\" data-tone=\"alt\" aria-labelledby=\"nx-nr-gateway-h\"><div class=\"nx-container\"><div class=\"nx-section-head\"><div class=\"nx-eyebrow-row\"><span class=\"nx-eyebrow\">Reports<\/span><span class=\"nx-rule\" aria-hidden=\"true\"><\/span><span class=\"nx-eyebrow\">Contribute<\/span><\/div><h2 id=\"nx-nr-gateway-h\" class=\"nx-section-title\">Contribute evidence from this page.<\/h2><p class=\"nx-lede\">Reports is the Consortium&#8217;s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.<\/p><div class=\"nx-chips\"><span class=\"nx-chip\">GCRI<\/span><span class=\"nx-chip\">Reports<\/span><span class=\"nx-chip\">Global Systems Risk and Resilience<\/span><span class=\"nx-chip\">Country Systems Risk and Resilience<\/span><span class=\"nx-chip\">Country Quarterly<\/span><\/div><\/div><div class=\"nx-section-body\"><ul class=\"nx-dest\"><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-reports\/write-to-us\/contact\/\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Email<\/span><span class=\"nx-dest-host\">Gateway GW-GCRI<\/span><\/span><span class=\"nx-dest-title\">Send it from this page<\/span><span class=\"nx-dest-why\">Evidence, a correction, a case, a method, a review, a translation or profile input. It arrives tagged with this page, so the editor who owns it receives it.<\/span><span class=\"nx-dest-go\" aria-hidden=\"true\"><svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/span><\/a><\/li><li><a class=\"nx-dest-card\" href=\"https:\/\/therisk.global\/nexus-reports\/open-science\/#nx-sec-zenodo\"><span class=\"nx-dest-top\"><span class=\"nx-dest-rel\">Zenodo<\/span><span class=\"nx-dest-host\">Open deposit<\/span><\/span><span class=\"nx-dest-title\">Deposit it and submit to the community<\/span><span class=\"nx-dest-why\">Publish your own record on Zenodo with your ORCID iD, then submit it to the Reports community there. 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