Global warming continues at an alarming rate, presenting unprecedented challenges to society that require urgent, science-led mitigation and adaptation. Earth system models (ESMs) are essential tools for projecting climate change, providing important information to decision makers. However, confidence in predicted climate change is undermined by a number of uncertainties; (i) ESMs disagree on how much the Earth will warm for a given increase in atmospheric carbon dioxide (CO2) (Earth’s equilibrium climate sensitivity); (ii) how much emitted CO2 will stay in the atmosphere to warm the planet (half the CO2 emitted by humans has been absorbed by the land and ocean) and (iii) how much excess heat in the Earth system will enter the ocean interior, delaying surface warming (~90% of the heat in the Earth system goes into the ocean). Central to these uncertainties are poorly understood, and poorly modelled, Earth system feedbacks, in particular cloud feedbacks, carbon cycle feedbacks and ocean heat uptake. Poor representation of these phenomena degrades the accuracy of ESM projections, with implications for anticipating future climate extremes and societal impacts. We aim to improve the representation of these feedbacks in ESMs, reducing uncertainty in global warming projections. We propose a multidisciplinary approach, focused on “learning” how to accurately describe processes underpinning these feedbacks, through a fusion of observations with advanced machine learning (ML) and artificial intelligence (AI). Such data and approaches, constrained by the laws of physics, will deliver a step change in the accuracy of Earth system models. AI4PEX will place Europe at the forefront of a revolution in Earth system modelling, leading to increased accuracy of climate change projections and superior support for implementation of the Paris Climate Agreement and the European Green Deal.
","AbstractOtherLang":"De opwarming van de aarde gaat in een alarmerend tempo door en brengt ongekende uitdagingen voor de samenleving met zich mee die dringende, door de wetenschap aangestuurde mitigatie en aanpassing vereisen. Aardsysteemmodellen (ESM's) zijn essentiële instrumenten voor het projecteren van klimaatverandering en verschaffen belangrijke informatie aan besluitvormers. Het vertrouwen in de voorspelde klimaatverandering wordt echter ondermijnd door een aantal onzekerheden; (i) ESM’s zijn het er niet over eens hoeveel de aarde zal opwarmen bij een bepaalde toename van kooldioxide (CO2) in de atmosfeer (de evenwichtsklimaatgevoeligheid van de aarde); (ii) hoeveel uitgestoten CO2 in de atmosfeer zal blijven om de planeet te verwarmen (de helft van de door mensen uitgestoten CO2 is geabsorbeerd door het land en de oceaan) en (iii) hoeveel overtollige warmte in het aardsysteem het binnenland van de oceaan zal binnendringen, het vertragen van de opwarming van het oppervlak (~90% van de warmte in het aardsysteem gaat de oceaan in). Centraal in deze onzekerheden staan slecht begrepen en slecht gemodelleerde terugkoppelingen van het aardsysteem, in het bijzonder terugkoppelingen naar de wolken, terugkoppelingen uit de koolstofcyclus en de opname van warmte door de oceaan. Een slechte weergave van deze verschijnselen verslechtert de nauwkeurigheid van de ESM-projecties, met gevolgen voor het anticiperen op toekomstige klimaatextremen en maatschappelijke gevolgen. Wij streven ernaar de representatie van deze feedbacks in ESM’s te verbeteren, waardoor de onzekerheid in de projecties van de opwarming van de aarde wordt verminderd. We stellen een multidisciplinaire aanpak voor, gericht op het ‘leren’ hoe processen nauwkeurig kunnen worden beschreven die ten grondslag liggen aan deze feedback, door een combinatie van observaties met geavanceerde machine learning (ML) en kunstmatige intelligentie (AI). Dergelijke gegevens en benaderingen, beperkt door de wetten van de natuurkunde, zullen een stapsgewijze verandering teweegbrengen in de nauwkeurigheid van de systeemmodellen van de aarde. AI4PEX zal Europa in de voorhoede plaatsen van een revolutie in de modellering van aardsystemen, wat zal leiden tot een grotere nauwkeurigheid van de voorspellingen van de klimaatverandering en superieure steun voor de implementatie van het Klimaatakkoord van Parijs en de Europese Green Deal.
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