3.2.4. Model intercomparison
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3.2.4. Model intercomparison
Use the future to build the present
Model intercomparison
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1.1Advanced AI1.2QuantumRevolution1.3UnconventionalComputing1.4AugmentedReality1.5CollectiveIntelligence2.1CognitiveEnhancement2.2HumanApplicationsof GeneticEngineering2.3HealthspanExtension2.4ConsciousnessAugmentation2.5Organoids2.6FutureTherapeutics3.1Decarbonisation3.2EarthSystemsModelling3.3FutureFoodSystems3.4SpaceResources3.5OceanStewardship3.6SolarRadiationModification3.7InfectiousDiseases4.1Science-basedDiplomacy4.2Advancesin ScienceDiplomacy4.3Foresight,Prediction,and FuturesLiteracy4.4Democracy-affirmingTechnologies5.1ComplexSystemsScience5.2Futureof Education5.3Future Economics,Trade andGlobalisation5.4The Scienceof theOrigins of Life5.5SyntheticBiology
1.1Advanced AI1.2QuantumRevolution1.3UnconventionalComputing1.4AugmentedReality1.5CollectiveIntelligence2.1CognitiveEnhancement2.2HumanApplicationsof GeneticEngineering2.3HealthspanExtension2.4ConsciousnessAugmentation2.5Organoids2.6FutureTherapeutics3.1Decarbonisation3.2EarthSystemsModelling3.3FutureFoodSystems3.4SpaceResources3.5OceanStewardship3.6SolarRadiationModification3.7InfectiousDiseases4.1Science-basedDiplomacy4.2Advancesin ScienceDiplomacy4.3Foresight,Prediction,and FuturesLiteracy4.4Democracy-affirmingTechnologies5.1ComplexSystemsScience5.2Futureof Education5.3Future Economics,Trade andGlobalisation5.4The Scienceof theOrigins of Life5.5SyntheticBiology

Sub-Field:

3.2.4Model intercomparison

    There is no single “best” model of how Earth Systems interact to create our environment. All of the various models in existence have strengths and weaknesses in different areas. This is partly because of a lack of understanding of the details of many Earth system processes, and partly because of necessary compromises in resolution and complexity; running these models is already computationally expensive. Because the models are built using a range of strategies, their performances diverge in complex ways.

    An essential component of Earth system science is therefore systematic comparison of “ensembles” of models.17 Efforts such as the Program for Climate Model Diagnosis and Intercomparison (PCMDI) co-ordinated at Lawrence Livermore National Laboratory have made headway in systematising the comparison process.18 This has enabled the use of the models in the Intergovernmental Panel on Climate Change (IPCC) assessment reports. In the most recent round of intercomparisons, CMIP6, researchers found that it was necessary to weighting some models more strongly than others to give a more accurate ensemble than a simple average:19 optimising such weightings is a significant research problem.

    An ongoing challenge for Earth system modellers is to understand in which circumstances the Earth system is stable and/or resilient, and when it instead behaves chaotically or changes violently. Palaeoclimatologists have documented many sudden shifts in the climate: these include the 4.2ka BP event (a widespread east Mediterranean drought that may have lasted a century) and the rapid temperature shifts known as Dansgaard-Oeschger events that punctuated the last glacial period. Consequently, some climatologists have suggested the models are unrealistically stable, although a lack of suitably configured models means that these conclusions are premature. It remains uncertain just how much natural instability the system possesses and how to represent this in models.

    All these uncertainties would be reduced by improved gathering of observational data, better anchoring the models in reality.

    Future Horizons:

    ×××

    5-yearhorizon

    Leading models become more integrated

    Insights into what makes some climate models more accurate than others facilitates the creation of weighted ensembles of models that show marked improvement in performance.

    10-yearhorizon

    AI accelerates performance and reliability of models across Earth Systems

    Researchers converge on a constructed set of constrained models that work reasonably well across all Earth Systems, capturing uncertainty in a structured way. AI-based model improvements begin to accelerate performance, for example by improving parameterisation of processes too complex to be explicitly modelled. AI is also used to estimate outcomes of scenarios for the Earth system, without the need for full simulations.

    25-yearhorizon

    Model uncertainties are significantly reduced

    Growing alignment between models’ predictions gives a clear indication of climate instabilities and tipping elements.

    Model intercomparison - Anticipation Scores

    How the experts see this field in terms of the expected time to maturity, transformational effect across science and industries, current state of awareness among stakeholders and its possible impact on people, society and the planet. See methodology for more information.

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