On the statistical contribution of cloud fraction cover to the summer sea-ice extent of 15 Arctic sub-regions, 1982-2015

Sea ice is one of the most important components of the polar climate system. The decline of the Arctic sea-ice extent (SIE), particularly during the melting season (Aug.-Oct.), is widely observed. Important roles in the melting process are played by the changes in thermodynamics and radiation forcing, in particular in relation to surface temperature and cloud cover, and also by the ocean and atmospheric circulation. Even if several studies already analysed the behaviour of SIE in the Arctic using standard linear and non-linear regression methods, this work aims to investigate the correlation between cloud fraction cover (CFC) and summer SIE in 15 Arctic sub-regions. CFC, together with surface temperature and u- and v- wind components, are also used as predictor variables in multiple regression equations for a statistical forecast of SIE for each one of the 15 sub-regions. The data used are: i) monthly SIE, obtained from the sea ice concentration (SIC) dataset over the Arctic as provided by the National Snow and Ice Data Center (NSIDC) and computed using the Nasa Team (NT) algorithm; ii) monthly CFC (for all, high, middle and low clouds) available from the CLARA-A2 dataset produced by the EUMETSAT Climate Monitoring Satellite Application Facility (CM SAF), the data being on a 25km x 25km regular grid; iii) monthly air temperatures and u- and v- wind components at sigma 0.995 collected from NCEP/NCAR R1. All data are given on a global regular lat-lon grid with resolution 2.5x2.5 and refer to the period 1982-2015; they were also seasonally and spatially averaged over each sub-region. As expected, the contribution of cloud fraction cover to the SIE variability is lower of that due to thermodynamic forcing through the ‘surface’ temperature and the ‘surface’ wind. However, for some sub-regions (e. g. Greenland Sea, Beaufort Sea) the cloud cover contribution to SIE became relevant. For most sub-regions, the largest contribution seems to come from the middle clouds (440hPa - 680 hPa)

Seasonal co-variability of surface downwelling longwave radiation for the 1982-2009 period in the Arctic

The decreasing of the sea ice cover observed in the Arctic represent a strong indicator of the ongoing climate change. Several physical processes are contributing to this one. The study of the co-variability of sea ice concentration (SIC) with other physical parameters may be useful to a better understanding of the strength and nature of the Arctic sea ice decline. This work concerns the investigation of the mutual variability between the seasonal fields of SIC and the downwelling surface shortwave radiation (SIS) in clear sky conditions, for the 1982-2009 period. SIC and SIS monthly data were collected from the National Snow and Ice Data Center (NSDIC) and from the Satellite Application Facility on Climate Monitoring (SAFCM), respectively. Then mainly analyzed through the method of maximum covariance analysis (MCA). Interesting results were found during the summer season, which is the relevant season since the sea ice melting: regions of maximum co-variability are located close to the Barents Sea and the Kara Sea. In addition, in these areas, expansion coefficients time series (of principal modes), show statistically significant (at 95%) correlations with climate oscillations such as the Northern Annular Mode (NAM), the North Atlantic Oscillation (NAO) and the Pacific North America (PNA) pattern

Sea ice extent annual extremes analysis in the Arctic regions

This work analyses the minimum and maximum annual values of Sea Ice Extent (SIE) in the entire Arctic region and in some of its sub-regions. The SIE was computed from the daily sea ice concentration (SIC) data provided by the National Snow and Ice Data Center (NSIDC). The analysis, which covers the 1979-2016 period, aims to answer the following questions: (1) Do annual SIE maxima and minima trends of the various Arctic sub-regions present substantial differences among them? (2) Is the time span between SIE maxima and minima changing over the 38 years of the analysed period? (3) Which maxima and minima extremes can be detected, according to some objective criteria? (4) How much effective are the cross-correlations between the annual SIE maxima and minima time series and between these and some important climatic oscillations indices (Southern Oscillation and Arctic Oscillation)? and (5) are there any relationships of these cross-correlations with the extremes eventually identified in point (3)? With regard to the first point, SIE minima show a substantial decreasing trend, more or less statistically significant; for some sub-regions it can be observed that, after 2007 \u2013 year of a strong summer melting\u2013 previous sea ice levels have never resumed, suggesting that, around 2007, a substantial reduction of multi-year ice occurred. On the contrary, SIE maxima, show a decreasing trend in some sub-regions and an increasing one in others, a result which may be explained by the relative geographic location. For the different sub-regions: i) the time span between maxima and minima does not change significantly; ii) extremes of SIE maxima and/or minima have been detected for several years; and iii) significant cross-correlations of SIE maxima and/or minima with SOI have been found

Seasonal co-variability of surface downwelling longwave radiation for the 1982–2009 period in the Arctic

Trends and variability of the Arctic sea ice extent depend on various physical processes, including those related to changes in radiative fluxes, which are associated with cloudiness and water vapour and, in turn, with the atmospheric moisture transport over the Arctic. Aim of this work was: (i) to extract seasonal spatial patterns of the co-variability between the sea ice concentration (SIC) and the surface downwelling longwave radiation (SDL) in the Arctic Ocean during the 1982–2009 period; and (ii) to estimate the correlation coefficients between these patterns and the indices associated to some climate oscillation modes (AO, NAO, PNA, PDO and AMO). Maximum Covariance Analysis (MCA) was the main technique used in this study. Among our results, we highlight two areas of maximum co-variability SIC/SDL centered over the Barents Sea in winter and over the Chukchi Sea in summer. In addition, some statistically significant correlations (at 95 %) between the spatial patterns of co-variability and climate oscillation indices were assessed, e.g. with PDO and AMO in November– January, with NAO and AMO in May–July, and with PNA in August–October

Liquid water path over Arctic and Antarctica between 1982 and 2015 during the summer season

This study investigates trend and variability of cloud liquid water path (LWP) over both the North Pole (NP) and the South Pole (SP). The period of analysis covers 34 years, from 1982 to 2015. Because of some main limitations of the LWP retrieval (availability of daylight and the problems with LWP retrieval over ice and snow) the study is focused on the summer seasons of the respective region that is June- July-August for the NP, and December-January-February for the SP). These summer seasons are those of sea ice melting whose trends have a signi cant importance in the study of climate change. Seasonal data were computed from monthly LWP, which is part of the cloud products of the CLARA-A2 archive, available from the EUMETSAT SAF on Climate Monitoring (CM SAF), derived from AVHRR observations of NOAA and EUMETSAT MetOp satellites. Observations for the Arctic and Antarctic regions are available on two equal-area polar grids at 25 km resolution and covers an area of 1000km 1000km. Linear trend in liquid water path during the analysed period sum- mer seasons are positive for both (about 1:0 kgm2=dec and 0:5 kgm2=dec for SP and NP, respectively), but the trend is statistical signi cant (at = 0:05) only for SP. At a ner spatial resolution, substantial differences are observed: i) in the Arctic, a signi cant increase over Greenland and a decrease over the Arctic Ocean; ii) in Antarctica, a signi cant increase over all the continent, apart a modest decrease around the pole, over the Ross Sea and near the western Antarctic coast. Spatial variability was tested by means of EOF technique. For both the Arctic and Antarctica, only the rst main eigenvector seems signi cant, in both cases explaining about the 20% of the variance. For the Arctic, the higher variability is noted near the pole and in the south-east of Greenland, while, for Antarctica, over the Ross Sea and its surrounding areas and near the opposite coastlines

Synthetic aperture radar analysis of floating ice at Terra Nova Bay-an application to ice eddy parameter extraction

In the framework of a study of ice formation in Antarctica, synthetic aperture radar (SAR) image acquisitions were planned over Terra Nova Bay (TNB). Thanks to the European Space Agency (ESA) Third Party Mission program, Cosmo-SkyMed and Radarsat-2 images over TNB were obtained for the period of February 20 to March 20, 2015; in addition, available Sentinel-1 images for the same period were retrieved from the ESA scientific data hub. The first inspection of the images revealed the presence of a prominent eddy, i.e., an ice vortex presumably caused by the wind blowing from the continent. The important parameters of an eddy are its area and lifetime. While the eddy lifetime was easily obtained from the image sequence, the area was measured using a specific processing scheme that consists of nonlinear filtering and Markov random field segmentation. The main goal of our study was to develop a segmentation scheme to detect and measure "objects" in SAR images. In addition, the connection between eddy area and wind field was investigated using parametric and nonparametric correlation functions; statistically significant correlation values were obtained in the analyzed period. After March 15, a powerful katabatic wind completely disrupted the surface eddy

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.</jats:p