A Sea-Ice Lead Detection Algorithm for Use With High-Resolution Airborne Visible Imagery

2013 ◽  
Vol 51 (1) ◽  
pp. 38-56 ◽  
Author(s):  
Vincent-De-Paul Onana ◽  
Nathan T. Kurtz ◽  
Sinead Louise Farrell ◽  
Lora S. Koenig ◽  
Michael Studinger ◽  
...  
2017 ◽  
Vol 11 (1) ◽  
pp. 343-362 ◽  
Author(s):  
Sentia Goursaud ◽  
Valérie Masson-Delmotte ◽  
Vincent Favier ◽  
Susanne Preunkert ◽  
Michel Fily ◽  
...  

Abstract. A 22.4 m-long shallow firn core was extracted during the 2006/2007 field season from coastal Adélie Land. Annual layer counting based on subannual analyses of δ18O and major chemical components was combined with 5 reference years associated with nuclear tests and non-retreat of summer sea ice to build the initial ice-core chronology (1946–2006), stressing uncertain counting for 8 years. We focus here on the resulting δ18O and accumulation records. With an average value of 21.8 ± 6.9 cm w.e. yr−1, local accumulation shows multi-decadal variations peaking in the 1980s, but no long-term trend. Similar results are obtained for δ18O, also characterised by a remarkably low and variable amplitude of the seasonal cycle. The ice-core records are compared with regional records of temperature, stake area accumulation measurements and variations in sea-ice extent, and outputs from two models nudged to ERA (European Reanalysis) atmospheric reanalyses: the high-resolution atmospheric general circulation model (AGCM), including stable water isotopes ECHAM5-wiso (European Centre Hamburg model), and the regional atmospheric model Modèle Atmosphérique Régional (AR). A significant linear correlation is identified between decadal variations in δ18O and regional temperature. No significant relationship appears with regional sea-ice extent. A weak and significant correlation appears with Dumont d'Urville wind speed, increasing after 1979. The model-data comparison highlights the inadequacy of ECHAM5-wiso simulations prior to 1979, possibly due to the lack of data assimilation to constrain atmospheric reanalyses. Systematic biases are identified in the ECHAM5-wiso simulation, such as an overestimation of the mean accumulation rate and its interannual variability, a strong cold bias and an underestimation of the mean δ18O value and its interannual variability. As a result, relationships between simulated δ18O and temperature are weaker than observed. Such systematic precipitation and temperature biases are not displayed by MAR, suggesting that the model resolution plays a key role along the Antarctic ice sheet coastal topography. Interannual variations in ECHAM5-wiso temperature and precipitation accurately capture signals from meteorological data and stake observations and are used to refine the initial ice-core chronology within 2 years. After this adjustment, remarkable positive (negative) δ18O anomalies are identified in the ice-core record and the ECHAM5-wiso simulation in 1986 and 2002 (1998–1999), respectively. Despite uncertainties associated with post-deposition processes and signal-to-noise issues, in one single coastal ice-core record, we conclude that the S1C1 core can correctly capture major annual anomalies in δ18O as well as multi-decadal variations. These findings highlight the importance of improving the network of coastal high-resolution ice-core records, and stress the skills and limitations of atmospheric models for accumulation and δ18O in coastal Antarctic areas. This is particularly important for the overall East Antarctic ice sheet mass balance.


2018 ◽  
Vol 38 (10) ◽  
pp. 1028002 ◽  
Author(s):  
王权 Wang Quan ◽  
孙林 Sun Lin ◽  
韦晶 Wei Jing ◽  
周雪莹 Zhou Xueying ◽  
陈婷婷 Chen Tingting ◽  
...  

2019 ◽  
Author(s):  
Allison C. Michaelis ◽  
Gary M. Lackmann ◽  
Walter A. Robinson

Abstract. We present multi-seasonal simulations representative of present-day and future thermodynamic environments using the global Model for Prediction Across Scales-Atmosphere (MPAS) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select ten simulation years with varying phases of El Niño-Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analysed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations. The present-day simulations provide a reasonable reproduction of large-scale atmospheric features in the Northern Hemisphere such as the wintertime midlatitude storm tracks, upper-tropospheric jets, and maritime sea-level pressure features as well as annual precipitation patterns across the tropics. The simulations also adequately represent tropical cyclone (TC) characteristics such as strength, spatial distribution, and seasonal cycles for most of Northern Hemispheric basins. These results demonstrate the applicability of these model simulations for future studies examining climate change effects on various Northern Hemispheric phenomena, and, more generally, the utility of MPAS for studying climate change at spatial scales generally unachievable in GCMs.


2019 ◽  
Vol 36 (8) ◽  
pp. 1643-1656
Author(s):  
Li Yi ◽  
King-Fai Li ◽  
Xianyao Chen ◽  
Ka-Kit Tung

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.


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