Hannah Christensen (she/her): Publications

High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7

Authors:

Malcolm John Roberts, Kevin A Reed, Qing Bao, Joseph J Barsugli, Suzana J Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S Fučkar, Shabeh ul Hasson, Helene T Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A Ullrich, Pier Luigi Vidale, Michael F Wehner, Colin M Zarzycki, Bosong Zhang, Wei Zhang, Ming Zhao

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Interpretable Deep Learning for Probabilistic MJO Prediction

Authors:

Antoine Delaunay, Hannah Christensen

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Interpretable Deep Learning for Probabilistic MJO Prediction

Authors:

Antoine Delaunay, Hannah Christensen

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Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

Geoscientific Model Development European Geosciences Union

Authors:

Kristian Strommen, Hannah M Christensen, David MacLeod, Stephan Juricke, Tim N Palmer

Abstract:

<p><strong>Abstract.</strong> We introduce and study the impact of three stochastic schemes in the EC-Earth climate model, two atmospheric schemes and one stochastic land scheme. These form the basis for a probabilistic earth-system model in atmosphere-only mode. Stochastic parametrisations have become standard in several operational weather-forecasting models, in particular due to their beneficial impact on model spread. In recent years, stochastic schemes in the atmospheric component of a model have been shown to improve aspects important for the models long-term climate, such as ENSO, North Atlantic weather regimes and the Indian monsoon. Stochasticity in the land-component has been shown to improve variability of soil processes and improve the representation of heatwaves over Europe. However, the raw impact of such schemes on the model mean is less well studied, It is shown that the inclusion all three schemes notably change the model mean state. While many of the impacts are beneficial, some are too large in amplitude, leading to large changes in the model's energy budget. This implies that in order to keep the benefits of stochastic physics without shifting the mean state too far from observations, a full re-tuning of the model will typically be required.</p>

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Machine Learning for Stochastic Parametrisations

Environmental Data Science

Authors:

Hannah Christensen, Kouhen Salah, Miller Greta, Parthipan Raghul

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Hannah Christensen (she/her)

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High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7

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Interpretable Deep Learning for Probabilistic MJO Prediction

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Interpretable Deep Learning for Probabilistic MJO Prediction

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Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

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Abstract:

Machine Learning for Stochastic Parametrisations

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Hannah Christensen (she/her)

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High Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7

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Interpretable Deep Learning for Probabilistic MJO Prediction

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Interpretable Deep Learning for Probabilistic MJO Prediction

Authors:

Introducing the Probabilistic Earth-System Model: Examining The Impact of Stochasticity in EC-Earth v3.2

Authors:

Abstract:

Machine Learning for Stochastic Parametrisations

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