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Hannah Christensen (she/her)

Associate Professor

Research theme

  • Climate physics

Sub department

  • Atmospheric, Oceanic and Planetary Physics

Research groups

  • Atmospheric processes
Hannah.Christensen@physics.ox.ac.uk
Telephone: 01865 (2)72908
Atmospheric Physics Clarendon Laboratory, room F52
  • About
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  • Publications

Interpretable Deep Learning for Probabilistic MJO Prediction

Authors:

Antoine Delaunay, Hannah Christensen
More details from the publisher

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>
More details from the publisher
Details from ORA

Parametrization in Weather and Climate Models

Oxford Research Encyclopedias, Climate Science

Authors:

Hannah Christensen, Laure Zanna
More details from the publisher

Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model

Authors:

Raghul Parthipan, Hannah M Christensen, J Scott Hosking, Damon J Wischik
More details from the publisher

Using reliability diagrams to interpret the ‘signal-to-noise paradox’ in seasonal forecasts of the winter North Atlantic Oscilation

Authors:

Kristian Strommen, Molly MacRae, Hannah Christensen
More details from the publisher

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