ALBERT

Description: In polar ice sheets, the average grain size varies with depth. Ice grain size increases due to several factors including ice temperature and impurity content, which in turn varies with climate. The effect of impurities on grain growth is thought to be crucial but has never been observed experimentally. Using a methodology recently developed at Royal Holloway University of London, in situ chemical analysis of frozen ice at sub-ppm concentrations with unprecedented spatial resolution (~150 μm) is achievable using ultraviolet laser ablation inductively coupled plasma mass spectrometry (UV-LA-ICPMS) featuring a two-volume cryo-LA-cell. Following surface cleaning with a custom-built vice equipped with a ceramic blade, NGRIP ice slabs (~86 ka before AD 2000) have been analysed using a series of one-dimensional profiles and two-dimensional maps of laser spots at a resolution of 200–300 μm. Results demonstrate that cation impurities are not uniformly distributed in ice layers and show significant variations in concentration on a sub-millimetre scale. Furthermore, a different pattern of elemental distribution between clear ice and layers enriched in impurities (cloudy bands) has been identified: while concentration differences for cloudy bands are not resolvable between boundaries and inner grain domains, within clear ice, grain boundaries and junctions are significantly (up to 100 times) impurity-enriched relative to corresponding grain interiors.

Description: Predictability on seasonal time scales over the North Atlantic–Europe region is assessed using a seasonal prediction system based on an initialized version of the Max Planck Institute Earth System Model (MPI-ESM). For this region, two of the dominant predictors on seasonal time scales are El Niño–Southern Oscillation (ENSO) and sudden stratospheric warming (SSW) events. Multiple studies have shown a potential for improved North Atlantic predictability for either predictor. Their respective influences are however difficult to disentangle, since the stratosphere is itself impacted by ENSO. Both El Niño and SSW events correspond to a negative signature of the North Atlantic Oscillation (NAO), which has a major influence on European weather. This study explores the impact on Europe by separating the stratospheric pathway of the El Niño teleconnection. In the seasonal prediction system, the evolution of El Niño events is well captured for lead times of up to 6 months, and stratospheric variability is reproduced with a realistic frequency of SSW events. The model reproduces the El Niño teleconnection through the stratosphere, involving a deepened Aleutian low connected to a warm anomaly in the northern winter stratosphere. The stratospheric anomaly signal then propagates downward into the troposphere through the winter season. Predictability of 500-hPa geopotential height over Europe at lead times of up to 4 months is shown to be increased only for El Niño events that exhibit SSW events, and it is shown that the characteristic negative NAO signal is only obtained for winters also containing major SSW events for both the model and the reanalysis data.

Description: A German national project coordinates research on improving a global decadal climate prediction system for future operational use. MiKlip, an eight-year German national research project on decadal climate prediction, is organized around a global prediction system comprising the climate model MPI-ESM together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that targets future operational use. Three prediction-system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multi-year-mean European summer surface temperatures, extra-tropical cyclone tracks, the Quasi-Biennial Oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind-energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction-system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of two to three years.

Description: Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.