High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest the possibility for significant changes in both large-scale aspects of circulation, as well as improvements in small-scale processes and extremes. However, such high resolution global simulations at climate time scales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centers and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other MIPs. Increases in High Performance Computing (HPC) resources, as well as the revised experimental design for CMIP6, now enables a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility to extend to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulation. HighResMIP thereby focuses on one of the CMIP6 broad questions: “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges

Spatial and Temporal Association of Outbreaks of H5N1 Influenza Virus Infection in Wild Birds with the 0°C Isotherm

Wild bird movements and aggregations following spells of cold weather may have resulted in the spread of highly pathogenic avian influenza virus (HPAIV) H5N1 in Europe during the winter of 2005–2006. Waterbirds are constrained in winter to areas where bodies of water remain unfrozen in order to feed. On the one hand, waterbirds may choose to winter as close as possible to their breeding grounds in order to conserve energy for subsequent reproduction, and may be displaced by cold fronts. On the other hand, waterbirds may choose to winter in regions where adverse weather conditions are rare, and may be slowed by cold fronts upon their journey back to the breeding grounds, which typically starts before the end of winter. Waterbirds will thus tend to aggregate along cold fronts close to the 0°C isotherm during winter, creating conditions that favour HPAIV H5N1 transmission and spread. We determined that the occurrence of outbreaks of HPAIV H5N1 infection in waterbirds in Europe during the winter of 2005–2006 was associated with temperatures close to 0°C. The analysis suggests a significant spatial and temporal association of outbreaks caused by HPAIV H5N1 in wild birds with maximum surface air temperatures of 0°C–2°C on the day of the outbreaks and the two preceding days. At locations where waterbird census data have been collected since 1990, maximum mallard counts occurred when average and maximum surface air temperatures were 0°C and 3°C, respectively. Overall, the abundance of mallards (Anas platyrhynchos) and common pochards (Aythya ferina) was highest when surface air temperatures were lower than the mean temperatures of the region investigated. The analysis implies that waterbird movements associated with cold weather, and congregation of waterbirds along the 0°C isotherm likely contributed to the spread and geographical distribution of outbreaks of HPAIV H5N1 infection in wild birds in Europe during the winter of 2005–2006

On high precipitation in Mozambique, Zimbabwe and Zambia in February 2018

This multi-method study of high precipitation over parts of Mozambique, Zimbabwe, and parts of Zambia in February 2018 indicates decreased likelihood of such events due to climate change, but with substantial uncertainty based on the used observations and model

Using EC-Earth for climate prediction research

Climate prediction at the subseasonal to interannual time range is now performed routinely and operationally by an increasing number of institutions. The feasibility of climate prediction largely depends on the existence of slow and predictable variations in the ocean surface temperature, sea ice, soil moisture and snow cover, and on our ability to model the atmosphere’s interactions with those variables. Climate prediction is typically performed with statisticalempirical or process-based models. The two methods are complementary. Although forecasting systems using global climate models (GCMs) have made substantial progress in the last few decades (Doblas-Reyes et al., 2013), systematic errors and misrepresentations of key processes still limit the value of dynamical prediction in certain areas of the globe. At the same time, model initialisation, ensemble generation, understanding the processes at the origin of predictability, forecasting extremes, bias adjustment and model evaluation are all challenging aspects of the climate prediction problem. Addressing them requires both a large base of researchers with expertise in physics, mathematics, statistics, high-performance computing and data analysis interested in climate prediction issues and a tool for them to work with. This article illustrates how one of these tools, the EC-Earth climate model (Box A), has been used to train scientists in climate prediction and to address scientific challenges in this field. The use of model components from ECMWF’s Integrated Forecasting System (IFS) in EC-Earth means that some of the results obtained with EC-Earth can feed back into ECMWF’s activities. EC-Earth has been run extensively on ECMWF’s high performance computing facility (HPCF), among a range of HPCFs across Europe and North America. The availability of ECMWF’s HPCF to EC-Earth partners, including the use of the successful ECMWF Special Project programme, means that a substantial amount of EC-Earth’s collaborative work, both within the consortium and with ECMWF, takes place on this platform.JRC.D.5-Food Securit

An assessment of ten ocean reanalyses in the polar regions

Global and regional ocean and sea ice reanalysis products (ORAs) are increasingly used in polar research, but their quality remains to be systematically assessed. To address this, the Polar ORA Intercomparison Project (Polar ORA-IP) has been established following on from the ORA-IP project. Several aspects of ten selected ORAs in the Arctic and Antarctic were addressed by concentrating on comparing their mean states in terms of snow, sea ice, ocean transports and hydrography. Most polar diagnostics were carried out for the first time in such an extensive set of ORAs. For the multi-ORA mean state, we found that deviations from observations were typically smaller than individual ORA anomalies, often attributed to offsetting biases of individual ORAs. The ORA ensemble mean therefore appears to be a useful product and while knowing its main deficiencies and recognising its restrictions, it can be used to gain useful information on the physical state of the polar marine environment