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Data Assimilation and Machine learning for online Parameter Estimation

CORDIS · observation · Publication date unknown

Data Assimilation and Machine learning for online Parameter Estimation Over the past four decades the Arctic sea ice cover has undergone dramatic changes as a result of anthropogenic CO2 emissions, with precipitous declines in both sea ice thickness and area. Such changes have direct consequences for global climate and human populations, through impacts Ono mid-latitude weather, large-scale ocean circulation patterns and high-latitude climate feedbacks with regulate global-mean temperature. Our ability to quantify the impacts of continued sea ice loss on society and the environment depends on the accuracy with which climate models simulate the coupled interactions between the atmosphere, ocean, and sea ice. However, structural errors associated with the calibration of model physics parameters lead to systematic biases and uncertainty in future projections. For sea ice, one major source of parameter uncertaint

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recordType

award

status

SIGNED

region

EU

value

260347.92

unit

EUR