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

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

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

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)

GPS coordinate estimates by a priori tropospheric delays from NWP using ultra-rapid orbits

High accuracy GPS positioning estimates using scientific GPS software through three different processing strategies were compared. The two Italian baselines in a time period of 5 months during 2004 made a calculus data set. For high accuracy GPS differential positioning the use of global tropospheric delay models can be replaced by the implementation of other techniques. The GPS coordinate can be repeated when the tropospheric delay is calculated in Near-Real Time (NRT) from a Numerical Weather Prediction (NWP) model. For the NRT approach IGS ultra-rapid orbits instead of precise orbits were used. Concerning coordinate repeatability, the NWP-based strategy with tropospheric error adjustment appeared more accurate (at the submillimetric level) than a standard GPS strategy. Furthermore, several hundreds km long baselines demonstrated the standard deviation at the level of millimeters (from 4.2 to 7.6 mm). Practically, the NWP-based strategy offers the advantage of tropospheric delay estimations closer to realistic meteorological values. The application of a more accurate meteorology leads to satisfactory coordinate estimations, and vice versa well-defined GPS estimations of coordinates may serve as the additional meteorological parameters source

Understanding slope behavior through microseismic monitoring

It is well known that microseismic activity originates as an elastic stress wave at locations where the material is mechanically unstable. Monitoring techniques focusing on this phenomenon have been studied for over seventy years and are now employed in a wide range of applications. As far as the study of unstable slope is concerned, microseismic monitoring can provide real-time information about fracture formation, propagation and coalescence and may be an appropriate solution to reduce the risk for human settlements when structural mitigation interventions (e.g., rock fall nets and ditches) cannot cope with large rock volumes and high kinetic energies. In this work we present the datasets collected in a 4-year period with a microseismic monitoring network deployed on an unstable rock face in Northern Italy. We mainly focus on the classification and the interpretation of collected signals with the final aim of identifying microseismic events related to the kinematic and dynamic behavior of the slope. We have analyzed signal parameters both in time and frequency domains, spectrograms, polarization of 3-component recordings supported by principal component analysis. Clustering methodologies have been tested in order to develop an automatic classification routine capable to isolate a cluster with most of the events related to slope behavior and to discard all disturbances. The network features both geophones and meteorological sensors so that we could also explore the correlation between microseismic events and meteorological datasets, although no significant relationships emerged. On the contrary, it was found that the majority of the events collected by the network are short-time high-frequency signals generated by electromagnetic activity caused by near and far thunderstorms. Finally, we attempted a preliminary localization of the most promising events according to an oversimplified homogeneous velocity model to get a rough indication about the regions of the monitored area that could be prone to collapse

Analysis of microseismic signals collected on an unstable rock face in the Italian Prealps

In this work we present the analysis of more than 9000 signals collected from February 2013 to January 2016 by a microseismic monitoring network installed on a 300 m high limestone cliff in the Italian Prealps. The investigated area was affected by a major rockfall in 1969 and several other minor events up to nowadays. The network features five three-component geophones and a weather station and can be remotely accessed thanks to a dedicated radio link. We first manually classified all the recorded signals and found out that 95 per cent of them are impulsive broad-band disturbances, while about 2 per cent may be related to rockfalls or fracture propagation. Signal parameters in the time and frequency domains were computed during the classification procedure with the aim of developing an automatic classification routine based on linear discriminant analysis. The algorithm proved to have a hit rate higher than 95 per cent and a tolerable false alarm rate and it is now running on the field PC of the acquisition board to autonomously discard useless events. Analysis of lightning data sets provided by the Italian Lightning Detection Network revealed that the large majority of broad-band signals are caused by electromagnetic activity during thunderstorms. Cross-correlation between microseismic signals and meteorological parameters suggests that rainfalls influence the hydrodynamic conditions of the rock mass and can trigger rockfalls and fracture propagation very quickly since the start of a rainfall event. On the other hand, temperature seems to have no influence on the stability conditions of the monitored cliff. The only sensor deployed on the rock pillar next to the 1969 rockfall scarp typically recorded events with higher amplitude as well as energy. We deem that this is due to seismic amplification phenomena and we performed ambient noise recording sessions to validate this hypothesis. Results confirm that seismic amplification occurs, although we were not able to identify any spectral peak with confidence because the sensors used are not suitable for this task. In addition, we found out that there is a preferential polarization of the wave field along the EW direction and this is in agreement with the geological analysis according to which the pillar is overhanging towards the 1969 rockfall scarp and may preferentially evolve in a wedge failure. Event location was not possible because of the lack of a velocity model of the rock mass. We tried to distinguish between near and far events by analysing the covariance matrix of the three-component recordings. Although the parameters and the outcomes of this analysis should be evaluated very carefully, it seems that about 90 per cent of the considered microseismic signals are related to the stability conditions of the monitored area

GPS Zenith Total Delays and precipitable water in comparison with special meteorological observations in Verona (Italy) during MAP-SOP

Continuous meteorological examination of the Pre-Alpine zones in Northern Italy (Po Valley) is important for determination of atmospheric water cycles connected kith floods and rainfalls. During a special meteorological observing period (MAP-SOP). radiosounding and other measurements were made in the site of Verona (Italy), This paper deals with Zenith Total Delay (ZTD) and Precipitable Water (PW) comparisons obtained by GPS, radiosounding and other meteorological measurements. PW and ZTD from ground-based GPS data in comparison with classical techniques (e.g.. WVR, radiosounding,) from recent literature present an accurate tool for use in meteorology applications (e.g., assimilation in Numerical Weather Prediction (NWP) models oil short-range precipitation forecasts). Comparison of such ZTD for MAP-SOP showed a standard deviation of 16.1 mm and PW comparison showed a standard deviation of 2.7 mm, confirming the accuracy of GPS measurements for meteorology applications. In addition, PW data and its time variation are also matched with time series of meteorological situations. Those results indicate that changes in PW values could be connected to changes in air masses, i.e. to passages of both cold and warm fronts. There is also a correlation between precipitation. forthcoming increase and the following decrease of PW. A good agreement between oscillation of PW and precipitation and strong cyclonic activities is found