Publications by Hannah Christensen

The impact of stochastic physics on the El Niño Southern Oscillation in the EC-Earth coupled model

Climate Dynamics (2019)

C Yang, HM Christensen, S Corti, J von Hardenberg, P Davini

© 2019, Springer-Verlag GmbH Germany, part of Springer Nature. The impact of stochastic physics on El Niño Southern Oscillation (ENSO) is investigated in the EC-Earth coupled climate model. By comparing an ensemble of three members of control historical simulations with three ensemble members that include stochastics physics in the atmosphere, we find that in EC-Earth the implementation of stochastic physics improves the excessively weak representation of ENSO. Specifically, the amplitude of both El Niño and, to a lesser extent, La Niña increases. Stochastic physics also ameliorates the temporal variability of ENSO at interannual time scales, demonstrated by the emergence of peaks in the power spectrum with periods of 5–7 years and 3–4 years. Based on the analogy with the behaviour of an idealized delayed oscillator model (DO) with stochastic noise, we find that when the atmosphere–ocean coupling is small (large) the amplitude of ENSO increases (decreases) following an amplification of the noise amplitude. The underestimated ENSO variability in the EC-Earth control runs and the associated amplification due to stochastic physics could be therefore consistent with an excessively weak atmosphere–ocean coupling. The activation of stochastic physics in the atmosphere increases westerly wind burst (WWB) occurrences (i.e. amplification of noise amplitude) that could trigger more and stronger El Niño events (i.e. increase of ENSO oscillation) in the coupled EC-Earth model. Further analysis of the mean state bias of EC-Earth suggests that a cold sea surface temperature (SST) and dry precipitation bias in the central tropical Pacific together with a warm SST and wet precipitation bias in the western tropical Pacific are responsible for the coupled feedback bias (weak coupling) in the tropical Pacific that is related to the weak ENSO simulation. The same analysis of the ENSO behaviour is carried out in a future scenario experiment (RCP8.5 forcing), highlighting that in a coupled model with an extreme warm SST, characterized by a strong coupling, the effect of stochastic physics on the ENSO representation is opposite. This corroborates the hypothesis that the mean state bias of the tropical Pacific region is the main reason for the ENSO representation deficiency in EC-Earth.

From reliable weather forecasts to skilful climate response: A dynamical systems approach


HM Christensen, J Berner

Stochastic Parameterization of Subgrid-Scale Velocity Enhancement of Sea Surface Fluxes

MONTHLY WEATHER REVIEW 147 (2019) 1447-1469

J Bessac, AH Monahan, HM Christensen, N Weitzel

The benefits of global high-resolution for climate simulation: process-understanding and the enabling of stakeholder decisions at the regional scale.

Bulletin of the American Meteorological Society (2018)

MJ Roberts, PL Vidale, C Senior, HT Hewitt, C Bates, S Berthou, P Chang, HM Christensen, S Danilov, M-E Demory, SM Griffies, R Haarsma, T Jung, G Martin, S Minobe, T Ringler, M Satoh, R Schiemann, E Scoccimarro, G Stephens, MF Wehner

Systematic Errors in Weather and Climate Models: Nature, Origins, and Way Forward

Bulletin of the American Meteorological Society (2017)

A Zadra, K Williams, A Frassoni, M Rixen, Á Adames, J Berner, F Bouyssel, B Casati, HM Christensen, MB Ek, G Flato, Y Huang, F Judt, H Lin, E Maloney, W Merryfield, A van Niekerk, T Rackow, K Saito, N Wedi, P Yadav

The impact of stochastic parametrisations on the representation of the Asian summer monsoon

CLIMATE DYNAMICS 50 (2018) 2269-2282

K Strommen, HM Christensen, J Berner, TN Palmer

Forcing Single-Column Models Using High-Resolution Model Simulations.

Journal of advances in modeling earth systems 10 (2018) 1833-1857

HM Christensen, A Dawson, CE Holloway

To use single-column models (SCMs) as a research tool for parameterization development and process studies, the SCM must be supplied with realistic initial profiles, forcing fields, and boundary conditions. We propose a new technique for deriving these required profiles, motivated by the increase in number and scale of high-resolution convection-permitting simulations. We suggest that these high-resolution simulations be coarse grained to the required resolution of an SCM, and thereby be used as a proxy for the true atmosphere. This paper describes the implementation of such a technique. We test the proposed methodology using high-resolution data from the UK Met Office's Unified Model, with a resolution of 4 km, covering a large tropical domain. These data are coarse grained and used to drive the European Centre for Medium-Range Weather Forecast's Integrated Forecasting System (IFS) SCM. The proposed method is evaluated by deriving IFS SCM forcing profiles from a consistent T639 IFS simulation. The SCM simulations track the global model, indicating a consistency between the estimated forcing fields and the true dynamical forcing in the global model. We demonstrate the benefits of selecting SCM forcing profiles from across a large domain, namely, robust statistics, and the ability to test the SCM over a range of boundary conditions. We also compare driving the SCM with the coarse-grained data set to driving it using the European Centre for Medium-Range Weather Forecast operational analysis. We conclude by highlighting the importance of understanding biases in the high-resolution data set and suggest that our approach be used in combination with observationally derived forcing data sets.

On the Dynamical Mechanisms Governing El Nino-Southern Oscillation Irregularity

JOURNAL OF CLIMATE 31 (2018) 8401-8419

J Berner, PD Sardeshmukh, HM Christensen

Stochastic representations of model uncertainties at ECMWF: state of the art and future vision


M Leutbecher, S-J Lock, P Ollinaho, STK Lang, G Balsamo, P Bechtold, M Bonavita, HM Christensen, M Diamantakis, E Dutra, S English, M Fisher, RM Forbes, J Goddard, T Haiden, RJ Hogan, S Juricke, H Lawrence, D MacLeod, L Magnusson, S Malardel, S Massart, I Sandu, PK Smolarkiewicz, A Subramanian, F Vitart, N Wedi, A Weisheimer

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model


P Davini, J von Hardenberg, S Corti, HM Christensen, S Juricke, A Subramanian, PAG Watson, A Weisheimer, TN Palmer

Stochastic Parameterization and El Nino-Southern Oscillation

JOURNAL OF CLIMATE 30 (2017) 17-38

HM Christensen, J Berner, DRB Coleman, TN Palmer

Introducing independent patterns into the Stochastically Perturbed Parametrization Tendencies (SPPT) scheme


HM Christensen, S-J Lock, IM Moroz, TN Palmer

STOCHASTIC PARAMETERIZATION Toward a New View of Weather and Climate Models


J Berner, U Achatz, L Batte, L Bengtsson, A de la Camara, HM Christensen, M Colangeli, DRB Coleman, D Crommelin, SI Dolaptchiev, CLE Franzke, P Friederichs, P Imkeller, H Jarvinen, S Juricke, V Kitsios, F Lott, V Lucarini, S Mahajan, TN Palmer, C Penland, M Sakradzija, J-S von Storch, A Weisheimer, M Weniger, PD Williams, J-I Yano

Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model

CLIMATE DYNAMICS 44 (2015) 2195-2214

HM Christensen, IM Moroz, TN Palmer

Decomposition of a New Proper Score for Verification of Ensemble Forecasts

MONTHLY WEATHER REVIEW 143 (2015) 1517-1532

HM Christensen

Stochastic and Perturbed Parameter Representations of Model Uncertainty in Convection Parameterization*


HM Christensen, IM Moroz, TN Palmer

Evaluation of ensemble forecast uncertainty using a new proper score: Application to medium-range and seasonal forecasts


HM Christensen, IM Moroz, TN Palmer

Does the ECMWF IFS Convection Parameterization with Stochastic Physics Correctly Reproduce Relationships between Convection and the Large-Scale State?


PAG Watson, HM Christensen, TN Palmer


Significance 12 (2015) 2-7

H Christensen, B Tarran

Simulating weather regimes: impact of stochastic and perturbed parameter schemes in a simple atmospheric model

Climate Dynamics 44 (2015) 2195-2214

HM Christensen, IM Moroz, TN Palmer

© 2014, Springer-Verlag Berlin Heidelberg. Representing model uncertainty is important for both numerical weather and climate prediction. Stochastic parametrisation schemes are commonly used for this purpose in weather prediction, while perturbed parameter approaches are widely used in the climate community. The performance of these two representations of model uncertainty is considered in the context of the idealised Lorenz ’96 system, in terms of their ability to capture the observed regime behaviour of the system. These results are applicable to the atmosphere, where evidence points to the existence of persistent weather regimes, and where it is desirable that climate models capture this regime behaviour. The stochastic parametrisation schemes considerably improve the representation of regimes when compared to a deterministic model: both the structure and persistence of the regimes are found to improve. The stochastic parametrisation scheme represents the small scale variability present in the full system, which enables the system to explore a larger portion of the system’s attractor, improving the simulated regime behaviour. It is important that temporally correlated noise is used in the stochastic parametrisation—white noise schemes performed similarly to the deterministic model. In contrast, the perturbed parameter ensemble was unable to capture the regime structure of the attractor, with many individual members exploring only one regime. This poor performance was not evident in other climate diagnostics. Finally, a ‘climate change’ experiment was performed, where a change in external forcing resulted in changes to the regime structure of the attractor. The temporally correlated stochastic schemes captured these changes well.