Atmospheric blocking in seasonal forecast: where are we now?

by paolo Davini (CNR, Torino)


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Proper simulation of atmospheric blocking is a tough challenge for numerical modelling, both from the weather and the climate point of view. We here investigate blocking representation in GCMs on a less studied time window, i.e. the seasonal timescale. We assess the simulation and prediction of the winter Northern Hemisphere atmospheric blocking in the ECMWF seasonal prediction systems: blocking statistics from the operational November-initialised seasonal hindcasts are evaluated in three generations of models - System3, System4 and System5 (SEAS5) - and in a series of complementary sensitivity experiments. 

Overall, we observe improvements in the climatological representation of blocking in the most recent model configurations, with reduced bias over North Pacific and Greenland. However, as seen in many GCMs, minor progress is recorded over the European sector.

Interannual variability is also underestimated and is found to be proportional to the climatological frequency, highlighting that a negative bias in blocking frequency implies an underestimation of the interannual variance. 

Predictive skill and signal-to-noise ratio remain low - also in SEAS5 - but interesting significant results are found over Western and Central Europe. Complementary experiments show that the statistics of blocking are improved following atmospheric and oceanic resolution increase. On the other hand, the implementation of stochastic parametrizations tends to displace blocking activity equatorward.

Finally, by comparing seasonal hindcasts with non-initialised climate runs (based on the same model) we highlight that the largest contributor to the chronic underestimation of blocking are persistent errors in the atmospheric model. It is also shown that the SST errors have a larger impact on blocking bias in climate runs than in seasonal runs, and that increased ocean model resolution contributes to improved blocking more effectively in climate runs.

We conclude that - although the predictive skill is still weak - seasonal forecasts can thus be considered a suitable test-bed for model development targeting blocking improvement in climate models.