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%. Copyright © 2008 Royal Meteorological Society

Polar amplification – the phenomenon where external radiative forcing produces a larger change in surface temperature at high latitudes than the global average – is a key aspect of anthropogenic climate change, but its causes and consequences are not fully understood. The Polar Amplification Model Intercomparison Project (PAMIP) contribution to the sixth Coupled Model Intercomparison Project (CMIP6; Eyring et al., 2016) seeks to improve our understanding of this phenomenon through a coordinated set of numerical model experiments documented here. In particular, PAMIP will address the following primary questions: (1) what are the relative roles of local sea ice and remote sea surface temperature changes in driving polar amplification? (2) How does the global climate system respond to changes in Arctic and Antarctic sea ice? These issues will be addressed with multi-model simulations that are forced with different combinations of sea ice and/or sea surface temperatures representing present-day, pre-industrial and future conditions. The use of three time periods allows the signals of interest to be diagnosed in multiple ways. Lower-priority tier experiments are proposed to investigate additional aspects and provide further understanding of the physical processes. These experiments will address the following specific questions: what role does ocean–atmosphere coupling play in the response to sea ice? How and why does the atmospheric response to Arctic sea ice depend on the pattern of sea ice forcing? How and why does the atmospheric response to Arctic sea ice depend on the model background state? What have been the roles of local sea ice and remote sea surface temperature in polar amplification, and the response to sea ice, over the recent period since 1979? How does the response to sea ice evolve on decadal and longer timescales? A key goal of PAMIP is to determine the real-world situation using imperfect climate models. Although the experiments proposed here form a coordinated set, we anticipate a large spread across models. However, this spread will be exploited by seeking “emergent constraints” in which model uncertainty may be reduced by using an observable quantity that physically explains the intermodel spread. In summary, PAMIP will improve our understanding of the physical processes that drive polar amplification and its global climate impacts, thereby reducing the uncertainties in future projections and predictions of climate change and variability.

▪ Abstract Weather and climate predictions are uncertain, because both forecast initial conditions and the computational representation of the known equations of motion are uncertain. Ensemble prediction systems provide the means to estimate the flow-dependent growth of uncertainty during a forecast. Sources of uncertainty must therefore be represented in such systems. In this paper, methods used to represent model uncertainty are discussed. It is argued that multimodel and related ensembles are vastly superior to corresponding single-model ensembles, but do not provide a comprehensive representation of model uncertainty. A relatively new paradigm is discussed, whereby unresolved processes are represented by computationally efficient stochastic-dynamic schemes.

The Finite Element Sea Ice-Ocean Model (FESOM) is the first global ocean general circulation model based on unstructured-mesh methods that has been developed for the purpose of climate research. The advantage of unstructured-mesh models is their flexible multi-resolution modelling functionality. In this study, an overview of the main features of FESOM will be given; based on sensitivity experiments a number of specific parameter choices will be explained; and directions of future developments will be outlined. It is argued that FESOM is sufficiently mature to explore the benefits of multi-resolution climate modelling and that its applications will provide information useful for the advancement of climate modelling on unstructured meshes.