|Title||Advances in simulating atmospheric variability with the ECMWF model: from synoptic to decadal time-scales|
|Publication Type||Technical memorandum|
|Secondary Title||ECMWF Technical Memorandum|
|Authors||Bechtold, P, Köhler, M, Jung, T, Doblas-Reyes, F, Leutbecher, M, Rodwell, M, Vitart, F, Balsamo, G|
Advances in simulating atmospheric variability with the ECMWF model are presented that stem from revisions of the convection and diffusion parametrizations. The revisions concern in particular the introduction of a variable convective adjustment time-scale, a convective entrainment rate proportional to the environmental relative humidity, as well as free tropospheric diffusion coefficients for heat and momentum based on Monin-Obukov functional dependencies. The forecasting system is evaluated against analyses and observations using high-resolution medium-range deterministic and ensemble forecasts, monthly and seasonal integrations as well as decadal integrations with coupled atmosphere-ocean models. The results show a significantly higher and more realistic level of model activity in terms of the amplitude of tropical and extra-tropical mesoscale, synoptic and planetary perturbations. Importantly, with the higher variability and reduced bias not only the probabilistic scores are improved, but also the mid-latitude deterministic scores in the short-range and medium-range. Furthermore, for the first time the model is able to represent a realistic spectrum of equatorial Kelvin and Rossby waves, and maintains a realistic amplitude of the Madden-Julian oscillation (MJO) during monthly forecasts. The propagation speed of the MJO, however is slower than observed. The higher tropical tropospheric wave activity results also in better stratospheric temperatures and winds through the deposition of momentum. The partitioning between convective and resolved precipitation is unaffected by the model changes with roughly 62% of the total global precipitation being of the convective type. Finally, the changes in convection and diffusion parametrizations resulted in a larger spread of the ensemble forecasts which allowed to decrease by 30% the amplitude of the initial perturbations in the ensemble prediction system.