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weeks and can last longer than 2 months ( Baldwin and Dunkerton 2001 ). This effect is often measured by an index of the Arctic Oscillation (AO; Thompson and Wallace 1998 ), which describes the dominant pattern of nonseasonal sea level pressure variations north of 20°N. It has been reported that the surface pressure signal shifts to a negative pattern of the AO after SSW events ( Limpasuvan et al. 2004 ). However, the negative phase of the AO, which signals a weak tropospheric polar vortex, does
weeks and can last longer than 2 months ( Baldwin and Dunkerton 2001 ). This effect is often measured by an index of the Arctic Oscillation (AO; Thompson and Wallace 1998 ), which describes the dominant pattern of nonseasonal sea level pressure variations north of 20°N. It has been reported that the surface pressure signal shifts to a negative pattern of the AO after SSW events ( Limpasuvan et al. 2004 ). However, the negative phase of the AO, which signals a weak tropospheric polar vortex, does
zones of heavier precipitation. A variety of mesoscaleand regional aspects of Puget Sound snowstorms are described including arctic fronts, coastal troughing andcyclogenesis, and additional mesoscale topographic effects. There is a strong correlation between El Nifio/Southern Oscillation and snowfall over Puget Sound, with lesser (greater) snow amounts during El Nifio (LaNifia) years.1. Introduction Predicting snowfall over the Puget Sound lowlandsis one of the most challenging and important
zones of heavier precipitation. A variety of mesoscaleand regional aspects of Puget Sound snowstorms are described including arctic fronts, coastal troughing andcyclogenesis, and additional mesoscale topographic effects. There is a strong correlation between El Nifio/Southern Oscillation and snowfall over Puget Sound, with lesser (greater) snow amounts during El Nifio (LaNifia) years.1. Introduction Predicting snowfall over the Puget Sound lowlandsis one of the most challenging and important
physics or data assimilation may apparently degrade the forecast skill. Therefore, it is very important to improve and check the quality of both the model (background) and analysis (observations) when evaluating the forecast. Finally, we investigated the impacts of the middle atmosphere on the troposphere by calculating the Arctic Oscillation (AO; Thompson and Wallace 1998 ) index, as was done in KF , but for the 5-day period. We do not expect as large a difference as was reported in KF because
physics or data assimilation may apparently degrade the forecast skill. Therefore, it is very important to improve and check the quality of both the model (background) and analysis (observations) when evaluating the forecast. Finally, we investigated the impacts of the middle atmosphere on the troposphere by calculating the Arctic Oscillation (AO; Thompson and Wallace 1998 ) index, as was done in KF , but for the 5-day period. We do not expect as large a difference as was reported in KF because
://www.cdc.noaa.gov/cgi-bin/Composites/printpage.p1 ). September, October, and November were composited individually and grouped together to see what atmospheric and oceanic features prevailed in the years prior to the 10 most active and 10 most inactive years. NTC was the tropical cyclone parameter utilized to classify years. Several features that became evident from the composite maps were anomalously high heights in northern latitudes, indicating a negative Arctic Oscillation (AO) ( Thompson and Wallace 1998 ) and North Atlantic Oscillation
://www.cdc.noaa.gov/cgi-bin/Composites/printpage.p1 ). September, October, and November were composited individually and grouped together to see what atmospheric and oceanic features prevailed in the years prior to the 10 most active and 10 most inactive years. NTC was the tropical cyclone parameter utilized to classify years. Several features that became evident from the composite maps were anomalously high heights in northern latitudes, indicating a negative Arctic Oscillation (AO) ( Thompson and Wallace 1998 ) and North Atlantic Oscillation
climate and the NAO are predictable months ahead, with a high proportion of the variance being accounted for by the models ( Scaife et al. 2014 ). A number of potential predictors have been identified: El Niño–Southern Oscillation (ENSO; e.g., Bell et al. 2009 ), spring North Atlantic sea surface temperatures (SSTs; e.g., Rodwell and Folland 2002 ), tropical volcanic eruptions (e.g., Robock and Mao 1995 ), Arctic sea ice extent (e.g., Strong and Magnusdottir 2011 ), the stratospheric quasi
climate and the NAO are predictable months ahead, with a high proportion of the variance being accounted for by the models ( Scaife et al. 2014 ). A number of potential predictors have been identified: El Niño–Southern Oscillation (ENSO; e.g., Bell et al. 2009 ), spring North Atlantic sea surface temperatures (SSTs; e.g., Rodwell and Folland 2002 ), tropical volcanic eruptions (e.g., Robock and Mao 1995 ), Arctic sea ice extent (e.g., Strong and Magnusdottir 2011 ), the stratospheric quasi
corrected. In addition, the number of predictors that have extraseasonal effects on summer rainfall in China was limited in order to keep the predictions of the regression model as satisfactory and stable as possible. As detailed below, the predictors included three precursory factors from the previous winter and synchronous numerical prediction results for summer rainfall. As is well known, the Antarctic Oscillation (AAO) and the Arctic Oscillation (AO) are the dominant modes of atmospheric circulation
corrected. In addition, the number of predictors that have extraseasonal effects on summer rainfall in China was limited in order to keep the predictions of the regression model as satisfactory and stable as possible. As detailed below, the predictors included three precursory factors from the previous winter and synchronous numerical prediction results for summer rainfall. As is well known, the Antarctic Oscillation (AAO) and the Arctic Oscillation (AO) are the dominant modes of atmospheric circulation
1989 were not reported and therefore March HDDs are not included as a potential predictor. The Arctic Oscillation (AO) also is not included as a potential predictor because it is statistically similar to the North Atlantic Oscillation (NAO), and recent analyses by Ambaum et al. (2001) suggest the NAO is more physically reasonable than the AO. Total (CT) and multiyear ice (MYI) concentration data used as potential predictors were obtained from the National Snow and Ice Data Center (NSIDC). Total
1989 were not reported and therefore March HDDs are not included as a potential predictor. The Arctic Oscillation (AO) also is not included as a potential predictor because it is statistically similar to the North Atlantic Oscillation (NAO), and recent analyses by Ambaum et al. (2001) suggest the NAO is more physically reasonable than the AO. Total (CT) and multiyear ice (MYI) concentration data used as potential predictors were obtained from the National Snow and Ice Data Center (NSIDC). Total
meteorological characteristics of such events. The rest of the article proceeds as follows. Section 2 establishes the temporal and spatial characteristics of the tornadoes to be included in the climatology. Next, results are given from two core perspectives. Section 3 assesses the temporal and spatial trends in cold-season tornadoes and their relationship to teleconnection patterns, specifically ENSO and the Arctic Oscillation (AO). Section 4 uses data from the NCEP–NCAR reanalysis dataset to
meteorological characteristics of such events. The rest of the article proceeds as follows. Section 2 establishes the temporal and spatial characteristics of the tornadoes to be included in the climatology. Next, results are given from two core perspectives. Section 3 assesses the temporal and spatial trends in cold-season tornadoes and their relationship to teleconnection patterns, specifically ENSO and the Arctic Oscillation (AO). Section 4 uses data from the NCEP–NCAR reanalysis dataset to
shown that sea ice variability is largely affected by the atmospheric circulation ( Fang and Wallace 1994 ; Deser et al. 2000 ; Serreze et al. 2003 ; Ding et al. 2017 ). For example, the Arctic Oscillation (AO) can affect Arctic sea ice variability ( Rigor et al. 2002 ; Mysak and Venegas 1998 ; Serreze et al. 2003 ; Rigor and Wallace 2004 ). Other low-frequency large-scale climate variations (e.g., Arctic dipole, ENSO, PNA) also have an impact on Arctic sea ice ( Wu et al . 2006 ; Wang and
shown that sea ice variability is largely affected by the atmospheric circulation ( Fang and Wallace 1994 ; Deser et al. 2000 ; Serreze et al. 2003 ; Ding et al. 2017 ). For example, the Arctic Oscillation (AO) can affect Arctic sea ice variability ( Rigor et al. 2002 ; Mysak and Venegas 1998 ; Serreze et al. 2003 ; Rigor and Wallace 2004 ). Other low-frequency large-scale climate variations (e.g., Arctic dipole, ENSO, PNA) also have an impact on Arctic sea ice ( Wu et al . 2006 ; Wang and
1. Introduction The Arctic is experiencing rapid changes in its harsh climate and environment, for example, the observed annual averaged near-surface temperatures at Svalbard are now increasing at between 1.04° and 1.76°C decade −1 ( Hanssen-Bauer et al. 2019 ). Anticipated increases in ship traffic, resource exploitation, tourism, and other activities ( WMO 2017 ) call for accurate and reliable weather predictions for safe and efficient operations. Despite improved Arctic forecast skill in
1. Introduction The Arctic is experiencing rapid changes in its harsh climate and environment, for example, the observed annual averaged near-surface temperatures at Svalbard are now increasing at between 1.04° and 1.76°C decade −1 ( Hanssen-Bauer et al. 2019 ). Anticipated increases in ship traffic, resource exploitation, tourism, and other activities ( WMO 2017 ) call for accurate and reliable weather predictions for safe and efficient operations. Despite improved Arctic forecast skill in
