No abstract

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-Obukhov functional dependencies.The forecasting system is evaluated against analyses and observations using high-resolution medium-range deterministic and ensemble forecasts, monthly and seasonal integrations, and 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 extratropical mesoscale, synoptic and planetary perturbations. Importantly, with the higher variability and reduced bias not only the probabilistic scores are improved, but also the midlatitude deterministic scores in the short and medium ranges. Furthermore, for the first time the model is able to represent a realistic spectrum of convectively coupled equatorial Kelvin and Rossby waves, and maintains a realistic amplitude of the Madden-Julian oscillation (MJO) during monthly forecasts. However, the propagation speed of the MJO is slower than observed. The higher tropical tropospheric wave activity also results 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 the amplitude of the initial perturbations in the ensemble prediction system to decrease by 30%.

A relationship between busted European forecasts, a Rockies trough, and storms over eastern North America suggests the importance of improving quality and use of observations, model depiction of convective systems, and representation of uncertainties.

ABSTRACT:Estimates of climate change remain uncertain -hampering strategic decision making in many sectors. In large part this uncertainty arises from uncertainty in the computational representation of known physical processes. This model component of climate change uncertainty is increasingly being assessed using perturbed model experiments. Some such model perturbations have, for example, led to headline global warming estimates of as much as 12°C. These experiments consider many differently perturbed versions of a given base model and assess the likelihood of each perturbed model's climate prediction based on how well it simulates present-day climate. In these experiments, the computational cost of the model assessment is extremely high unless one assumes that the climate anomalies associated with different model perturbations can be combined linearly. Here we demonstrate a different method, which harnesses the power of the data assimilation system to assess directly the perturbed physics of a model. Data assimilation involves the incorporation of daily observations to produce initial conditions (analyses) for numerical weather prediction (NWP). The method used here quantifies systematic initial tendencies in the first few time steps of a model forecast. After suitable temporal averaging, these initial tendencies imply systematic imbalances in the physical processes associated with model error. We show how these tendencies can be used to produce probability weightings for each model that could be used in the construction of probability distribution functions of climate change. The approach typically costs 5% of the cost of a 100-year coupled model simulation that might otherwise be used to assess the simulation of present-day climate. Importantly, since the approach is amenable to linear analysis, it could further reduce the cost of model assessment by several orders of magnitude: making the exercise computationally feasible. The initial tendency approach can only assess 'fast physics' perturbations, i.e. perturbations that have an impact on weather forecasts as well as climate. However, recent publications suggest that most of the present model parameter uncertainty is associated with fast physics. If such a test were adopted, assessment of the ability to simulate present-day climate would then only be required for models that 'pass' the fast physics test. The study highlights the advantages of a more seamless approach to forecasting that combines NWP, climate forecasting, and all scales in-between.

In a seamless prediction system, the reliability of coupled climate model forecasts made on seasonal time scales can provide useful quantitative constraints for improving the trustworthiness of regional climate change projections.Climate impacts