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The representation of winter Northern Hemisphere atmospheric blocking in ECMWF seasonal prediction systems
Author(s) -
Davini Paolo,
Weisheimer Antje,
Balmaseda Magdalena,
Johnson Stephanie J.,
Molteni Franco,
Roberts Christopher D.,
Senan Retish,
Stockdale Timothy N.
Publication year - 2021
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3974
Subject(s) - blocking (statistics) , northern hemisphere , climatology , environmental science , climate model , sea surface temperature , seasonality , atmospheric sciences , meteorology , climate change , geology , geography , oceanography , mathematics , statistics
The simulation and prediction of winter Northern Hemisphere atmospheric blocking in the seasonal prediction systems from the European Centre for Medium‐Range Weather Forecasts (ECMWF) is analysed. Blocking statistics from the operational November‐initialised seasonal hindcasts are evaluated in three generations of models: System3, System4, and System5 (SEAS5). Improvements in the climatological representation of blocking are observed in the most recent model configurations, with reduced bias over North Pacific and Greenland. Minor progress is seen over the European sector, where SEAS5 still underestimates the observed blocking frequency. SEAS5 blocking interannual variability is underestimated too and is proportional to the climatological frequency, highlighting that a negative bias in the blocking frequency implies an underestimation of the interannual variance. SEAS5 predictive skill and signal‐to‐noise ratio remain low, but interesting positive results are found over Western and Central Europe. Improved forecasts with reduced ensemble spread are obtained during El Niño years, especially at low latitudes. Complementary experiments show that the statistics of blocking are improved following atmospheric and oceanic resolution increase. Conversely, they remain largely insensitive to coupled model sea‐surface temperature (SST) errors. On the other hand, the implementation of stochastic parameterisations tends to displace blocking activity equatorward. Finally, by comparing seasonal hindcasts with climate runs using the same model, we highlight that the largest contributors to the chronic underestimation of blocking are persistent errors in the atmospheric model. It is also shown that 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. Seasonal forecasts can thus be considered a suitable test‐bed for model development targeting blocking improvement in climate models.