This paper discusses an important issue related to the implementation and interpretation of the analysis scheme in the ensemble Kalman filter. It is shown that the observations must be treated as random variables at the analysis steps. That is, one should add random perturbations with the correct statistics to the observations and generate an ensemble of observations that then is used in updating the ensemble of model states. Traditionally, this has not been done in previous applications of the ensemble Kalman filter and, as will be shown, this has resulted in an updated ensemble with a variance that is too low. This simple modification of the analysis scheme results in a completely consistent approach if the covariance of the ensemble of model states is interpreted as the prediction error covariance, and there are no further requirements on the ensemble Kalman filter method, except for the use of an ensemble of sufficient size. Thus, there is a unique correspondence between the error statistics from the ensemble Kalman filter and the standard Kalman filter approach.

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[1] In many regions the strength of El Niño-Southern Oscillation (ENSO) teleconnections has varied over the last century. It is an active area of research to investigate how such changes can be related to long-term climate variability or climate change. However, fluctuations due to the limited observational record and low signal-to-noise ratio also contribute to variations in the apparent strength of the teleconnections. These contributions are considered at 658 precipitation stations around the globe. For each station the probability is estimated that the observed decadal variations in the effect of ENSO on precipitation are explainable by random statistical fluctuations of a constant teleconnection. The number of stations with statistically significant decadal variations is much lower than the number with statistically significant ENSO teleconnections. It is close to the number expected from chance alone. The observed period is too short to reliably detect multiplicative decadal variability in ENSO precipitation teleconnections. Citation: van Oldenborgh, G.

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[1] Eastern Pacific sea surface temperature (SST) and mean equatorial Pacific thermocline depth are key variables in El Niño-Southern Oscillation (ENSO). A linear fit to observations leads to a remarkably simple picture: ENSO can be represented by a classical damped oscillator, with SST and thermocline depth playing the roles of momentum and position, respectively. An independent fit of observed relationships between western and eastern thermocline depth, central wind stress and eastern Pacific SST yields the same picture and supports a recharge oscillator interpretation. The oscillation arises from the interaction between the recharge time of the Warm Pool and the time delay between east and west Pacific. Both finite Kelvin wave speed and SST dynamics contribute to the time delay. Including seasonality in the description, we find two periods of relative instability: boreal spring, with a large phase progression, and autumn, with nearly stationary phase.

In the Essence project a 17‐member ensemble simulation of climate change in response to the SRES A1b scenario has been carried out using the ECHAM5/MPI‐OM climate model. The relatively large size of the ensemble makes it possible to accurately investigate changes in extreme values of climate variables. Here we focus on the annual‐maximum 2m‐temperature and fit a Generalized Extreme Value (GEV) distribution to the simulated values and investigate the development of the parameters of this distribution. Over most land areas both the location and the scale parameter increase. Consequently the 100‐year return values increase faster than the average temperatures. A comparison of simulated 100‐year return values for the present climate with observations (station data and reanalysis) shows that the ECHAM5/MPI‐OM model, as well as other models, overestimates extreme temperature values. After correcting for this bias, it still shows values in excess of 50°C in Australia, India, the Middle East, North Africa, the Sahel and equatorial and subtropical South America at the end of the century.