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The Last Interglacial (Eemian, MIS 5e) can be considered a test-bed for climate dynamics under a warmer-than-present climate. In this study we present a chironomid record from the high latitude Sokli site (N Finland), where a long continuous sediment sequence from the last interglacial has been preserved from glacial erosion. The chironomid-analysis shows a diverse fauna, with dominance of warm-water indicators and shifts in assemblage composition that can be attributed to temperature, lake depth, productivity and habitat availability. Quantitative mean July paleotemperature estimates based on the chironomid data indicate overall mean July air temperatures up to 1 degrees C warmer than present. Two cooling events can be discerned, the Tunturi event, dated to about 127.5kaBP, in the lower part of the sequence, and the Varrio event, dated to about 119kaBP, associated with the beginning of a cooling trend in the upper part of the record. Warm conditions already at the onset of the interglacial contrast with a recent chironomid-based last interglacial temperature reconstruction from Denmark, which suggests a late onset of Eemian warming. The relatively small increase in inferred temperatures compared to present day temperatures at Sokli differs from other high latitude Eemian sites, and likely reflects the influence of the Atlantic Meridional Overturning Circulation in maintaining already elevated temperatures in Fennoscandia during interglacials.
The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.
With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a modelbased analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by winddominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport “pathways”. General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.