A significant change in Arctic climate is the declining trend of September sea ice. However, large year-to-year variations are superimposed on this trend. Understanding this variability is important for understanding processes contributing to the long-term decline and for seasonal sea-ice predictions, which become increasingly important for a range of activities in an emerging ice-free Arctic summer. Previous studies suggested that the atmosphere plays a key role: transport of heat and moisture into the Arctic during spring enhances the incoming surface longwave radiation, thereby controling the initiation of the annual ice melt and setting the stage for the September ice minimum. Here we explore the atmospheric dynamics promoting advection of heat and moisture into the Arctic. We find that years with a low September sea-ice concentration (SIC) are characterized by periods of increased net surface longwave radiation (LWN) in spring, triggering an early melt onset. A set of atmospheric circulation patterns related to these episodes is identified that support transport of heat and moisture into the Arctic. The most dominant circulation patterns promote transport either from northern Russia and the Kara Sea or from the North Pacific; the latter resembles the so-called Arctic dipole anomaly. However, episodes of enhanced LWN also occur in years with high September SICs and are associated with similar atmospheric circulation patterns. Differences between years with low and high September SICs are not due to different spring processes resulting from different circulation patterns. Instead it is the duration and strength of these patterns that makes the difference. Years with low September SICs feature episodes that are consistently stronger and more persistent than years with high SICs.

The Arctic sea-ice cover plays an important role for the global climate system. Sea ice and the overlying snow cover reflect up to eight times more of the solar radiation than the underlying ocean. Hence, they are important for the global energy budget, and changes in the sea-ice cover can have a large impact on the Arctic climate and beyond. In the past 36 years the ice cover reduced significantly. The largest decline is observed in September, with a rate of more than 12% per decade. The negative trend is accompanied by large inter-annual sea-ice variability: in September the sea-ice extent varies by up to 27% between years. The processes controlling the large variability are not well understood. In this thesis the atmospheric contribution to the inter-annual sea-ice variability is explored. The focus is specifically on the thermodynamical effects: processes that are associated with a temperature change of the ice cover and sea-ice melt. Atmospheric reanalysis data are used to identify key processes, while experiments with a state-of-the-art climate model are conducted to understand their relevance throughout different seasons. It is found that in years with a very low September sea-ice extent more heat and moisture is transported in spring into the area that shows the largest ice variability. The increased transport is often associated with similar atmospheric circulation patterns. Increased heat and moisture over the Arctic result in positive anomalies of water vapor and clouds. These alter the amount of downward radiation at the surface: positive cloud anomalies allow for more longwave radiation and less shortwave radiation. In spring, when the solar inclination is small, positive cloud anomalies result in an increased surface warming and an earlier seasonal melt onset. This reduces the ice cover early in the season and allows for an increased absorption of solar radiation by the surface during summer, which further accelerates the ice melt. The modeling experiments indicate that cloud anomalies of similar magnitude during other seasons than spring would likely not result in below-average September sea ice. Based on these results a simple statistical sea-ice prediction model is designed, that only takes into account the downward longwave radiation anomalies or variables associated with it. Predictive skills are similar to those of more complex models, emphasizing the importance of the spring atmosphere for the annual sea-ice evolution.

Recent studies have shown that atmospheric processes in spring play an important role for the initiation of the summer ice melt and therefore may strongly influence the September sea ice concentration (SSIC). Here a simple statistical regression model based on only atmospheric spring parameters is applied in order to predict the SSIC over the major part of the Arctic Ocean. By using spring anomalies of downwelling longwave radiation or atmospheric water vapor as predictor variables, correlation coefficients between observed and predicted SSIC of up to 0.5 are found. These skills of seasonal SSIC predictions are similar to those obtained using more complex dynamical forecast systems, despite the fact that the simple model applied here takes neither information of the sea ice state, oceanic conditions nor feedback mechanisms during summer into account. The results indicate that a realistic representation of spring atmospheric conditions in the prediction system plays an important role for the predictive skills of a model system.

The summer sea-ice extent in the Arctic has decreased in recent decades, a feature that has become one of the most distinct signals of the continuing climate change. However, the interannual variability is large—the ice extent by the end of the summer varies by several million square kilometres from year to year. The underlying processes driving this year-to-year variability are not well understood. Here we demonstrate that the greenhouse effect associated with clouds and water vapour in spring is crucial for the development of the sea ice during the subsequent months. In years where the end-of-summer sea-ice extent is well below normal, a significantly enhanced transport of humid air is evident during spring into the region where the ice retreat is encountered. This enhanced transport of humid air leads to an anomalous convergence of humidity, and to an increase of the cloudiness. The increase of the cloudiness and humidity results in an enhancement of the greenhouse effect. As a result, downward long-wave radiation at the surface is larger than usual in spring, which enhances the ice melt. In addition, the increase of clouds causes an increase of the reflection of incoming solar radiation. This leads to the counterintuitive effect: for years with little sea ice in September, the downwelling short-wave radiation at the surface is smaller than usual. That is, the downwelling short-wave radiation is not responsible for the initiation of the ice anomaly but acts as an amplifying feedback once the melt is started.

The Arctic summer sea ice has diminished fast in recent decades. A strong year-to-year variability on top of this trend indicates that sea ice is sensitive to short-term climate fluctuations. Previous studies show that anomalous atmospheric conditions over the Arctic during spring and summer affect ice melt and the September sea-ice extent (SIE). These conditions are characterized by clouds, humidity and heat anomalies which all affect shortwave (SWD) and longwave (LWD) radiation to the surface. In general, positive LWD anomalies are associated with cloudy and humid conditions, whereas positive anomalies of SWD appear under clear-sky conditions. Here we investigate the effect of realistic anomalies of LWD and SWD on summer sea ice, by performing experiments with the Community Earth System Model. The SWD and LWD anomalies are studied separately and in combination for different seasons. It is found that positive LWD anomalies in spring and early summer have significant impact on the September SIE, whereas winter anomalies show only little effect. Positive anomalies in spring and early summer initiate an earlier melt onset, hereby triggering several feedback mechanisms that amplify melt during the succeeding months. Realistic positive SWD anomalies appear only important if they occur after the melt has started and the albedo is significantly reduced relative to winter conditions. Simulations where both positive LWD and negative SWD anomalies are implemented simultaneously, mimicking cloudy conditions, reveal that clouds during spring have a significant impact on summer sea ice while summer clouds have almost no effect.

This paper investigates the long-term variability of specific weather types that are associated with damaging hailstorms in Germany for past (1971-2000) and future (2011-2050) time periods. Forty large-scale weather types are determined by the objective weather type classification scheme of German Weather Service. This scheme is applied to both reanalyses (ERA-40) and eight different regional climate model (RCM) simulations. It is shown that the RCMs are able to approximately reproduce the distribution of weather type occurrences obtained from the reference of ERA-40. Using additional insurance loss data, the weather types are further identified as hail-related or hail-unrelated. Hailstorms are neither captured comprehensively by existing observation systems nor can they be modeled reliably and the large-scale weather types are here considered as proxies for hail occurrence. Four weather types that are most likely associated with damaging hailstorms show a slight increase both during the past and future period according to the RCM simulations. A novel statistical model is developed for the probabilistic prediction of the fraction of hail damage days conditional on the weather types. The model is Bayesian and uses a Markov Chain Monte Carlo approach. For the ERA-40 reanalysis the model prediction agrees well with fraction of hail damage days observed in the insurance data. For most of the RCM projections, the statistical model predicts a slight increase in the number of hail days in the future (2031-2045), with relative changes between 7 and 15% compared to the period 1971-2000.