125 research outputs found

    Dramatic interannual changes of perennial Arctic sea ice linked to abnormal summer storm activity

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    Copyright © 2011 American Geophysical UnionThe perennial (September) Arctic sea ice cover exhibits large interannual variability, with changes of over a million square kilometers from one year to the next. Here we explore the role of changes in Arctic cyclone activity, and related factors, in driving these pronounced year-to-year changes in perennial sea ice cover. Strong relationships are revealed between the September sea ice changes and the number of cyclones in the preceding late spring and early summer. In particular, fewer cyclones over the central Arctic Ocean during the months of May, June, and July appear to favor a low sea ice area at the end of the melt season. Years with large losses of sea ice are characterized by abnormal cyclone distributions and tracks: they lack the normal maximum in cyclone activity over the central Arctic Ocean, and cyclones that track from Eurasia into the central Arctic are largely absent. Fewer storms are associated with above-average mean sea level pressure, strengthened anticyclonic winds, an intensification of the transpolar drift stream, and reduced cloud cover, all of which favor ice melt. It is also shown that a strengthening of the central Arctic cyclone maximum helps preserve the ice cover, although the association is weaker than that between low cyclone activity and reduced sea ice. The results suggest that changes in cyclone occurrence during late spring and early summer have preconditioning effects on the sea ice cover and exert a strong influence on the amount of sea ice that survives the melt season

    The atmospheric response to three decades of observed arctic sea ice loss

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    © Copyright 2013 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or [email protected] sea ice is declining at an increasing rate with potentially important repercussions. To understand better the atmospheric changes that may have occurred in response to Arctic sea ice loss, this study presents results from atmospheric general circulation model (AGCM) experiments in which the only time-varying forcings prescribed were observed variations in Arctic sea ice and accompanying changes in Arctic sea surface temperatures from 1979 to 2009. Two independent AGCMs are utilized in order to assess the robustness of the response across different models. The results suggest that the atmospheric impacts of Arctic sea ice loss have been manifested most strongly within the maritime and coastal Arctic and in the lowermost atmosphere. Sea ice loss has driven increased energy transfer from the ocean to the atmosphere, enhanced warming and moistening of the lower troposphere, decreased the strength of the surface temperature inversion, and increased lower-tropospheric thickness; all of these changes are most pronounced in autumn and early winter (September–December). The early winter (November–December) atmospheric circulation response resembles the negative phase of the North Atlantic Oscillation (NAO); however, the NAO-type response is quite weak and is often masked by intrinsic (unforced) atmospheric variability. Some evidence of a late winter (March–April) polar stratospheric cooling response to sea ice loss is also found, which may have important implications for polar stratospheric ozone concentrations. The attribution and quantification of other aspects of the possible atmospheric response are hindered by model sensitivities and large intrinsic variability. The potential remote responses to Arctic sea ice change are currently hard to confirm and remain uncertain

    Postresectional lung injury in thoracic surgery pre and intraoperative risk factors: a retrospective clinical study of a hundred forty-three cases

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    <p>Abstract</p> <p>Introduction</p> <p>Acute respiratory dysfunction syndrome (ARDS), defined as acute hypoxemia accompanied by radiographic pulmonary infiltrates without a clearly identifiable cause, is a major cause of morbidity and mortality after pulmonary resection. The aim of the study was to determine the pre and intraoperative factors associated with ARDS after pulmonary resection retrospectively.</p> <p>Methods</p> <p>Patients undergoing elective pulmonary resection at Adnan Menderes University Medical Faculty Thoracic Surgery Department from January 2005 to February 2010 were included in this retrospective study. The authors collected data on demographics, relevant co-morbidities, the American Society of Anesthesiologists (ASA) Physical Status classification score, pulmonary function tests, type of operation, duration of surgery and intraoperative fluid administration (fluid therapy and blood products). The primary outcome measure was postoperative ARDS, defined as the need for continuation of mechanical ventilation for greater than 48-hours postoperatively or the need for reinstitution of mechanical ventilation after extubation. Statistical analysis was performed with Fisher exact test for categorical variables and logistic regression analysis for continuous variables.</p> <p>Results</p> <p>Of one hundred forty-three pulmonary resection patients, 11 (7.5%) developed postoperative ARDS. Alcohol abuse (p = 0.01, OR = 39.6), ASA score (p = 0.001, OR: 1257.3), resection type (p = 0.032, OR = 28.6) and fresh frozen plasma (FFP)(p = 0.027, OR = 1.4) were the factors found to be statistically significant.</p> <p>Conclusion</p> <p>In the light of the current study, lung injury after lung resection has a high mortality. Preoperative and postoperative risk factor were significant predictors of postoperative lung injury.</p

    Consistency and discrepancy in the atmospheric response to Arctic sea-ice loss across climate models

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    This is the author accepted manuscript. The final version is available from Springer Nature via the DOI in this recordThe decline of Arctic sea ice is an integral part of anthropogenic climate change. Sea-ice loss is already having a significant impact on Arctic communities and ecosystems. Its role as a cause of climate changes outside of the Arctic has also attracted much scientific interest. Evidence is mounting that Arctic sea-ice loss can affect weather and climate throughout the Northern Hemisphere. The remote impacts of Arctic sea-ice loss can only be properly represented using models that simulate interactions among the ocean, sea ice, land and atmosphere. A synthesis of six such experiments with different models shows consistent hemispheric-wide atmospheric warming, strongest in the mid-to-high-latitude lower troposphere; an intensification of the wintertime Aleutian Low and, in most cases, the Siberian High; a weakening of the Icelandic Low; and a reduction in strength and southward shift of the mid-latitude westerly winds in winter. The atmospheric circulation response seems to be sensitive to the magnitude and geographic pattern of sea-ice loss and, in some cases, to the background climate state. However, it is unclear whether current-generation climate models respond too weakly to sea-ice change. We advocate for coordinated experiments that use different models and observational constraints to quantify the climate response to Arctic sea-ice loss.J.A.S. and R.B. were funded by the Natural Environment Research Council (NE/P006760/1). C.D. acknowledges the National Science Foundation (NSF), which sponsors the National Center for Atmospheric Research. D.M.S. was supported by the Met Office Hadley Centre Climate Programme (GA01101) and the APPLICATE project, which is funded by the European Union’s Horizon 2020 programme. X.Z. was supported by the NSF (ARC#1023592). P.J.K. and K.E.M. were supported by the Canadian Sea Ice and Snow Evolution Network, which is funded by the Natural Science and Engineering Research Council of Canada. T.O. was funded by Environment and Climate Change Canada (GCXE17S038). L.S. was supported by the National Oceanic and Atmospheric Administration’s Climate Program Office
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