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A multisensor data fusion algorithm based on a multilayer neural network is presented for sea ice classification in the winter period. The algorithm uses European Remote Sensing (ERS), RADARSAT synthetic aperture radar (SAR), and low-resolution television camera images and image texture features. Based on a set of in situ observations made at the Kara Sea, a neural network is trained, and its structure is optimized using a pruning method. The performance of the algorithm with different combinations of input features (sensors) is assessed and compared with the performance of a linear discriminant analysis (LDA)-based algorithm. We show that for both algorithms a substantial improvement can be gained by fusion of the three different types of data (91.2% for the neural network) as compared with single-source ERS (66.0%) and RADARSAT (70.7%) SAR image classification. Incorporation of texture increases classification accuracy. This positive effect of texture becomes weaker with increasing number of sensors (from 8.4 to 6.4 percent points for the use of two and three sensors, respectively). In view of the short training time and smaller number of adjustable parameters, this result suggests that semiparametric classification methods can be considered as a good alternative to the neural networks and traditional parametric statistical classifiers applied for the sea ice classification. Index Terms--Data fusion, European Remote Sensing (ERS), neural network, RADARSAT, satellite remote sensing, sea ice classification, synthetic aperture radar (SAR).

To allow for climate impact studies on human and natural systems high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. This publication aims at addressing one of these regional gaps by presenting an evaluation study for two regional climate models (RCMs) (REMO and ALARO-0) at a horizontal resolution of 0.22° (25 km) over Central Asia. The output of the ERA-Interim driven RCMs is compared with different observational datasets over the 1980-2017 period. The choice of the observational dataset has an impact on the scores but in general one can conclude that both models reproduce reasonably well the spatial patterns for temperature and precipitation. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. More detailed studies of the annual cycle over subregions should be carried out to reveal whether this is due to an incorrect simulation in cloud cover, atmospheric circulation or heat and moisture fluxes. In general, the REMO model scores better for temperature whereas the ALARO-0 model prevails for precipitation. This publication demonstrates that the REMO and ALARO-0 RCMs can be used to perform climate projections over Central Asia and that the produced climate data can be applied in impact modelling. [ABSTRACT FROM AUTHOR]

The observed warming in the Arctic is more than double the global average, and this enhanced Arctic warming is projected to continue throughout the 21st century. This rapid warming has a wide range of impacts on polar and sub-polar marine ecosystems. One of the examples of such an impact on ecosystems is that of coccolithophores, particularly Emiliania huxleyi , which have expanded their range poleward during recent decades. The coccolithophore E. huxleyi plays an essential role in the global carbon cycle. Therefore, the assessment of future changes in coccolithophore blooms is very important. Currently, there are a large number of climate models that give projections for various oceanographic, meteorological, and biochemical variables in the Arctic. However, individual climate models can have large biases when compared to historical observations. The main goal of this research was to select an ensemble of climate models that most accurately reproduces the state of environmental variables that influence the coccolithophore E. huxleyi bloom over the historical period when compared to reanalysis data. We developed a novel approach for model selection to include a diverse set of measures of model skill including the spatial pattern of some variables, which had not previously been included in a model selection procedure. We applied this method to each of the Arctic and sub-Arctic seas in which E. huxleyi blooms have been observed. Once we have selected an optimal combination of climate models that most skilfully reproduce the factors which affect E. huxleyi , the projections of the future conditions in the Arctic from these models can be used to predict how E. huxleyi blooms will change in the future. Here, we present the validation of 34 CMIP5 (fifth phase of the Coupled Model Intercomparison Project) atmosphere–ocean general circulation models (GCMs) over the historical period 1979–2005. Furthermore, we propose a procedure of ranking and selecting these models based on the model's skill in reproducing 10 important oceanographic, meteorological, and biochemical variables in the Arctic and sub-Arctic seas. These factors include the concentration of nutrients (NO3 , PO4 , and SI), dissolved CO2 partial pressure (pCO2), pH, sea surface temperature (SST), salinity averaged over the top 30 m (SS 30m), 10 m wind speed (WS), ocean surface current speed (OCS), and surface downwelling shortwave radiation (SDSR). The validation of the GCMs' outputs against reanalysis data includes analysis of the interannual variability, seasonal cycle, spatial biases, and temporal trends of the simulated variables. In total, 60 combinations of models were selected for 10 variables over six study regions using the selection procedure we present here. The results show that there is neither a combination of models nor one model that has high skill in reproducing the regional climatic-relevant features of all combinations of the considered variables in target seas. Thereby, an individual subset of models was selected according to our model selection procedure for each combination of variable and Arctic or sub-Arctic sea. Following our selection procedure, the number of selected models in the individual subsets varied from 3 to 11. The paper presents a comparison of the selected model subsets and the full-model ensemble of all available CMIP5 models to reanalysis data. The selected subsets of models generally show a better performance than the full-model ensemble. Therefore, we conclude that within the task addressed in this study it is preferable to employ the model subsets determined through application of our procedure than the full-model ensemble. [ABSTRACT FROM AUTHOR]

Atmospheric studies document both a periodic variability in the winter temperatures in the Kara Sea region related to internal Arctic climate variability and a recent trend of winter warming—one of the strongest warming trends in the whole Arctic. This study aims to analyse side by side with other energy budget terms the contribution of horizontal heat and moisture advection to the observed recent warming. The atmospheric energy budget terms vertically integrated from the surface up to 500 hPa are estimated using the ERA‐Interim reanalysis for the period 1979–2014. The core of this study is to relate variability and changes in heat fluxes in the Kara Sea region to synoptic‐scale processes using the Russian weather‐type classification system. Singular spectrum analysis is applied to the heat flux data which were previously stratified into changes due to changes in frequency and due to changes in amplitude. We have shown that the multi‐decadal variations in the horizontal heat advection (now on a rising phase) are comparable to the positive trend in the turbulent heat fluxes due to sea ice reduction, although relatively weaker. We have also found that the changes in sensible heat flux (a constant increase in the first principal component since 1999) is a general regional feature and not related to any weather pattern, while the changes in horizontal heat advection are clearly related to the changes in frequency of the weather type A (characterized by a low‐pressure system in the central Arctic surrounded by high pressure in the continents, winds of S, S‐W direction and a positive horizontal heat advection and surface temperature anomaly over the Kara Sea) which are periodic in nature with a period of about 22 years. In recent years (at least until 2012) not only is warm air advection, associated with A type, more frequent, but it also brings much more energy from the continent to the Kara Sea compared to the reference period (1980–1990). This is most probably related to the increase in the radiatively forced surface air temperature at the source. There are number of sources of heat to the cold Arctic in winter period and neither of them is stable in time, currently leading to Arctic warming that is pronounced over the Kara Sea. The major source is turbulent heat transferred to the atmosphere in the areas of ice‐free water as shown in the image. We discuss here the second source too—the transport of heat to the Arctic from the middle latitudes by synoptic waves and how this process changes in time due to atmospheric circulation re‐configuration. [ABSTRACT FROM AUTHOR]