{"id":5852,"date":"2021-01-16T09:00:30","date_gmt":"2021-01-16T08:00:30","guid":{"rendered":"https:\/\/nersc.no\/?p=5852"},"modified":"2023-11-22T09:31:23","modified_gmt":"2023-11-22T08:31:23","slug":"correcting-biases-in-norcpm-for-the-tropical-atlantic-with-an-innovative-approach","status":"publish","type":"post","link":"https:\/\/nersc.no\/en\/features\/correcting-biases-in-norcpm-for-the-tropical-atlantic-with-an-innovative-approach\/","title":{"rendered":"Correcting biases in NorCPM for the tropical Atlantic with an innovative approach"},"content":{"rendered":"

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Correcting biases in NorCPM for the tropical Atlantic with an innovative approach<\/h1><\/div>[vc_column_text]<\/p>\n

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Several of our Climate Dynamics and Prediction researchers just published a\u00a0new article<\/a>\u00a0in\u00a0Climate Dynamics<\/em>, under the lead of Fran\u00e7ois Counillon (Nansen Center). They showed how reducing model biases can improve seasonal prediction for the tropical Atlantic Ocean. <\/strong><\/h3>\n

Climate models are very useful and have been in use for some decades. They help us understand the past, current, and future climate. How well these models represent climate has improved vastly in the past years and using methods such as data assimilation is contributing to the advancements being made. Standard climate models couple ocean and atmospheric dynamics to be able to simulate phenomena like the El Ni\u00f1o Southern Oscillation. Such phenomena are behind the skill in seasonal predictions. However, all models, including coupled models, suffer from biases that decrease their ability to perfectly predict climate. Fran\u00e7ois Counillon, Noel Keenlyside, and Yiguo Wang (all NERSC), along with other Bjerknes Centre co-authors, set out to tackle biases in the tropical Atlantic Ocean region using an innovative modelling approach. Their article\u2019s title is \u201cRelating model bias and prediction skill in the equatorial Atlantic<\/a>\u201d.<\/p>\n

The tropical Atlantic Ocean is a region in which coupled Earth system models traditionally suffer from large biases: The\u00a0temperature of the sea surface<\/em>, the\u00a0amount of rain<\/em>, and\u00a0winds<\/em>\u00a0are not modelled correctly with standard models. These shortcomings lead to forecasts not being reliable enough, which is problematic in this region \u2013 where the societal impact of climate variability is large.<\/p>\n

To address the problem, Counillon and his colleagues made use of \u201canomaly coupling\u201d, an innovative technique developed in 2018<\/a> by two of the co-authors of this paper, Toniazzo and Koseki. This technique makes it possible to limit the known biases in the region effectively, and by doing so, the authors were able to assess how much better the model performed in predicting on a seasonal scale without the large biases.<\/p>\n

What Counillon and his co-authors did is a comparison between the performance of the standard version of the Norwegian Climate Prediction Model (NorCPM) and a version including the \u201canomaly coupling\u201d technique.<\/p>\n

Their findings indicate the following:<\/p>\n