Using arctic sea ice concentration derived from passive microwave satellite observations in autumn and early winter over the 1979–2014 period, the arctic region was objectively classified into several smaller regions based on the interannual sea ice variability through self-organizing map analyses.Cognitive learning theory in the classroom the trend in regional sea ice extent (RSIE) in each region was removed using an adaptive, nonlinear, and nonstationary method called ensemble empirical mode decomposition, which captures well the accelerating decline of arctic rsies in recent decades.Cognitive learning theory in the classroom although the linear trend in RSIE is negative in all regions in both seasons, there are marked differences in RSIE trends and variability between regions, with the largest negative trends found during autumn in the beaufort sea, the barents-kara seas, and the laptev-east siberian seas.Cognitive learning theory in the classroom winter weather patterns associated with the nonlinearly detrended rsies show distinct features for different regions and tend to be better correlated with the autumn than early winter RSIE anomalies.Cognitive learning theory in the classroom sea ice losses in the beaufort sea and the barents-kara seas are both associated with a cooling of eurasia, but in the former case the circulation anomaly is reminiscent of a rossby wave train, whereas in the latter case the pattern projects onto the negative phase of the arctic oscillation.Cognitive learning theory in the classroom these results highlight the nonuniform changes in arctic sea ice and suggest that regional sea ice variations may play a crucial role for the winter weather patterns.Cognitive learning theory in the classroom

The significance and robustness of the link between arctic sea ice loss and changes in midlatitude weather patterns is investigated through a series of model simulations from the community atmosphere model, version 5.3, with systematically perturbed sea ice cover in the arctic.Cognitive learning theory in the classroom using a large ensemble of 10 sea ice scenarios and 550 simulations, it is found that prescribed arctic sea ice anomalies produce statistically significant changes for certain metrics of the midlatitude circulation but not for others.Cognitive learning theory in the classroom furthermore, the significant midlatitude circulation changes do not scale linearly with the sea ice anomalies and are not present in all scenarios, indicating that the remote atmospheric response to reduced arctic sea ice can be statistically significant under certain conditions but is generally nonrobust.Cognitive learning theory in the classroom shifts in the northern hemisphere polar jet stream and changes in the meridional extent of upper-level large-scale waves due to the sea ice perturbations are generally small and not clearly distinguished from intrinsic variability.Cognitive learning theory in the classroom reduced arctic sea ice may favor a circulation pattern that resembles the negative phase of the arctic oscillation and may increase the risk of cold outbreaks in eastern asia by almost 50%, but this response is found in only half of the scenarios with negative sea ice anomalies.Cognitive learning theory in the classroom in eastern north america the frequency of extreme cold events decreases almost linearly with decreasing sea ice cover. This study’s finding of frequent significant anomalies without a robust linear response suggests interactions between variability and persistence in the coupled system, which may contribute to the lack of convergence among studies of arctic influences on midlatitude circulation.Cognitive learning theory in the classroom

The köppen climate classification was developed based on the empirical relationship between climate and vegetation. This type of climate classification scheme provides an efficient way to describe climatic conditions defined by multiple variables and their seasonalities with a single metric.Cognitive learning theory in the classroom compared with a single variable approach, the köppen classification can add a new dimension to the description of climate variation. Further, it is generally accepted that the climatic combinations identified with the köppen classification are ecologically relevant.Cognitive learning theory in the classroom the classification has therefore been widely used to map geographic distribution of long term mean climate and associated ecosystem conditions.Cognitive learning theory in the classroom over the recent years, there has also been an increasing interest in using the classification to identify changes in climate and potential changes in vegetation over time.Cognitive learning theory in the classroom these successful applications point to the potential of using the köppen classification as a diagnostic tool to monitor changes in the climatic condition over various time scales.Cognitive learning theory in the classroom this work used a global temperature and precipitation observation dataset to reveal variations and changes of climate over the period 1901–2010, demonstrating the power of the köppen classification in describing not only climate change, but also climate variability on various temporal scales.Cognitive learning theory in the classroom it is concluded that the most significant change over 1901–2010 is a distinct areal increase of the dry climate (B) accompanied by a significant areal decrease of the polar climate (E) since the 1980s.Cognitive learning theory in the classroom the areas of spatially stable climate regions for interannual and interdecadal variations are also identified, which have practical and theoretical implications.Cognitive learning theory in the classroom