Skip to main content
Home
Department Of Physics text logo
  • Research
    • Our research
    • Our research groups
    • Our research in action
    • Research funding support
    • Summer internships for undergraduates
  • Study
    • Undergraduates
    • Postgraduates
  • Engage
    • For alumni
    • For business
    • For schools
    • For the public
Menu
Juno Jupiter image

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
  • Teaching
  • Talks and Media
  • DPhil applicants
  • Publications

Euro-Atlantic weather Regimes in the PRIMAVERA coupled climate simulations: impact of resolution and mean state biases on model performance

Climate Dynamics Springer Science and Business Media LLC 54:11-12 (2020) 5031-5048

Authors:

F Fabiano, Hm Christensen, K Strommen, P Athanasiadis, A Baker, R Schiemann, S Corti
More details from the publisher
Details from ORA
More details

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz’96 Model

Journal of Advances in Modeling Earth Systems American Geophysical Union 12:3 (2020) e2019MS001896

Authors:

David John Gagne, Hannah M Christensen, Aneesh C Subramanian, Adam H Monahan

Abstract:

Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and subgrid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate time scales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both time scales, and the networks closely reproduce the spatiotemporal correlations and regimes of the Lorenz '96 system. We also find that, in general, those models which produce skillful forecasts are also associated with the best climate simulations.
More details from the publisher
Details from ORA
More details

Constraining stochastic parametrisation schemes using high-resolution simulations

Quarterly Journal of the Royal Meteorological Society Wiley (2019)
More details from the publisher
Details from ORA
More details

Machine learning and artificial intelligence to aid climate change research and preparedness

Environmental Research Letters IOP Publishing 14 (2019) 12

Authors:

C Huntingford, ES Jeffers, Michael Bonsall, H Christensen, T Lees, H Yang
More details from the publisher
Details from ORA
More details

Does ENSO regularity increase in a warming climate?

Journal of Climate American Meteorological Society (2019) JCLI-D-19-0545.1

Authors:

Judith Berner, Hannah M Christensen, Prashant D Sardeshmukh

Abstract:

The impact of a warming climate on El Nino-Southern Oscillation (ENSO) is investigated in large ensemble simulations of the Community Earth System Model (CESM1). These simulations are forced by historical emissions for the past and the RCP8.5-scenario emissions for future projections. The simulated variance of the Nino-3.4 ENSO index increases from 1.4◦C2 in 1921-1980 to 1.9◦C2 in 1981-2040 and 2.2◦C2 in 2041-2100. The autocorrelation timescale of the index also increases, consistent with a narrowing of its spectral peak in the 3- to 7-yr ENSO band, raising the possibility of greater seasonal to interannual predictability in the future. Low-order linear inverse models (LIMs) fitted separately to the three 60-yr periods capture the CESM1 increase in ENSO variance and regularity. Remarkably, most of the increase can be attributed to the increase in the 23-month damping timescale of a single damped oscillatoryENSO eigenmode of these LIMs by 5 months in 1981-2040 and 6 months in 2041-2100. These apparently robust projected increases may however be compromised by CESM1 biases in ENSO amplitude and damping timescale. A LIM fitted to the 1921-1980 observations has an ENSO eigenmode with a much shorter 8-month damping timescale, similar to that of several other eigenmodes. When the mode’s damping timescale is increased by 5 and 6 months in this observational LIM, a much smaller increase of ENSO variance is obtained than in the CESM1 projections. This may be because ENSO is not as dominated by a single ENSO eigenmode in reality as it is in the CESM1.
More details from the publisher
Details from ORA
More details

Pagination

  • First page First
  • Previous page Prev
  • …
  • Page 4
  • Page 5
  • Page 6
  • Page 7
  • Current page 8
  • Page 9
  • Page 10
  • Page 11
  • Page 12
  • …
  • Next page Next
  • Last page Last

Footer Menu

  • Contact us
  • Giving to the Dept of Physics
  • Work with us
  • Media

User account menu

  • Log in

Follow us

FIND US

Clarendon Laboratory,

Parks Road,

Oxford,

OX1 3PU

CONTACT US

Tel: +44(0)1865272200

University of Oxfrod logo Department Of Physics text logo
IOP Juno Champion logo Athena Swan Silver Award logo

© University of Oxford - Department of Physics

Cookies | Privacy policy | Accessibility statement

Built by: Versantus

  • Home
  • Research
  • Study
  • Engage
  • Our people
  • News & Comment
  • Events
  • Our facilities & services
  • About us
  • Current students
  • Staff intranet