Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic

INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 26: 1027–1049 (2006)

Published online 6 March 2006 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1306

CHANGES IN SYNOPTIC WEATHER PATTERNS IN THE POLAR REGIONSIN THE TWENTIETH AND TWENTY-FIRST CENTURIES, PART 1: ARCTIC

JOHN J. CASSANO,a,* PETTERI UOTILAb and AMANDA LYNCHb

a Cooperative Institute for Research in Environmental Sciences and Department of Atmospheric and Oceanic Sciences, University ofColorado, Boulder, CO, USA

b School of Geography and Environmental Science, Monash University, Monash, Australia

Received 30 April 2005Revised 30 November 2005Accepted 1 December 2005

ABSTRACT

An analysis of late twentieth and twenty-first century predictions of Arctic circulation patterns in a ten-model ensembleof global climate system models, using the method of self-organizing maps (SOMs), is presented. The model simulationswere conducted in support of the fourth assessment report of the intergovernmental panel on climate change (IPCC). Theanalysis demonstrates the utility of SOMs for climate analysis, both as a tool to evaluate the accuracy of climate modelpredictions, and to provide a useful alternative view of future climate change.

It is found that not all models accurately simulate the frequency of occurrence of Arctic circulation patterns. Someof the models tend to overpredict strong high-pressure patterns while other models overpredict the intensity of cycloniccirculation regimes. In general, the ensemble of models predicts an increase in cyclonically dominated circulation patternsduring both the winter and summer seasons, with the largest changes occurring during the first half of the twenty-firstcentury. Analysis of temperature and precipitation anomalies associated with the different circulation patterns revealscoherent patterns that are consistent with the different circulation regimes and highlight the dependence of local changesin these quantities to changes in the synoptic scale circulation patterns. Copyright 2006 Royal Meteorological Society.

KEY WORDS: Arctic; synoptic climatology; climate change; global climate model

1. INTRODUCTION

In 2001, at its eighteenth session, the Intergovernmental Panel on Climate Change (IPCC) agreed to prepare afourth comprehensive assessment report (AR4) of the scientific, technical, and socioeconomic understandingof anthropogenic climate change and its consequences. A key element of the physical science basis of thisassessment has been the development of global projections by climate system models from around the world.For these projections to contribute to our understanding of the sensitivity of the system, it is important tocharacterize in detail the range of performance of these models in simulating observed and future change.Such an undertaking, in the context of the complex system that is the earth’s climate, can only be attemptedthrough the collective effort of a community of scientists analyzing all aspects of model behavior. This paperis one such contribution to that effort, and we choose as our focus a description of changes in the circulationof the high northern latitudes simulated by an ensemble of global climate system models. The companionpaper (Lynch et al., 2005) addresses the circulation of the high southern latitudes.

It is now well known that the Arctic region demonstrates many of the expected consequences of thepolar amplification of global climate change (Serreze et al., 2000; ACIA, 2004; Hinzman et al., 2005). Thesechanges have global implications, both as a model for the detection of anthropogenic climate forcing and in the

* Correspondence to: John J. Cassano, Cooperative Institute for Research in Environmental Sciences, University of Colorado, 216 UCB,Boulder, CO 80309, USA; e-mail: [email protected]

Copyright 2006 Royal Meteorological Society

1028 J. J. CASSANO, P. UOTILA AND A. LYNCH

broader sense through the effects on freshwater cycling, thermohaline circulation, the terrestrial carbon cycle,and biodiversity. One manifestation of this observed change is to be found in the atmospheric circulation ofthe northern high latitudes, which may be described in a number of ways (e.g. Walsh et al., 1996; Thompsonand Wallace, 1998; McCabe et al., 2001). This aspect of Arctic change is of particular importance becauseof its role in the modulation of Arctic sea ice distribution and North Atlantic ice export, and the subsequentimpacts on the global thermohaline circulation. For example, Walsh and Crane (1992) and Bitz et al. (2002)describe the sensitivity of simulated Arctic sea ice to changes in atmospheric circulation patterns. Bitz et al.(2002) highlight the importance of accurate simulation of high sea-level pressure (SLP) over the BeaufortSea in winter and low SLP over the Arctic Ocean in summer for accurate simulation of sea ice thickness inthe Arctic basin. The results presented here describe changes in Arctic circulation of the present and futureas represented in global climate models through the lens of synoptic climatology.

The field of synoptic climatology provides a powerful method to study the climate of a region by stratifyinglarge volumes of data (daily or higher temporal resolution fields of the atmospheric state) into a small numberof categories on a physically meaningful basis. Such an approach provides important information on theweather processes that control the local climate, which may often be hidden by monthly or seasonal meanfields (Barry and Perry, 2001; Hanson et al., 2004). An important step in this type of analysis is developinga robust classification scheme that can be applied to large volumes of data. Barry and Perry (2001), andreferences therein, provide a detailed overview of synoptic climatology and its applications, but we summarizethe important points below.

Most commonly, cyclone track and cyclone event climatologies have been developed using objectivealgorithms such as SLP, SLP Laplacian, vorticity, or potential vorticity minimization (e.g. Serreze et al.,1993; Sinclair, 1994; Serreze et al., 1997; Lambert et al., 2002; Paciorek et al., 2002; Cao and Zhang, 2004;Zhang et al., 2004). Some tracking schemes have been applied to both cyclones and anticyclones (Pezza andAmbrizzi, 2003). These approaches have been demonstrated to be physically consistent and reproducible, andhence create highly useful data sets. An alternative approach has been the application of synoptic timescalefilters to pressure or height data and analyzing the variance of the filtered data (Trenberth, 1991; Nakamuraand Shimpo, 2004). Other authors have argued for the use of unfiltered data (Berbery and Vera, 1996;Rao et al., 2002), but in any case the relationship of these variances to cyclone and anticyclone trajectoriesremains problematic (Wallace et al., 1988). A more general method for analyzing the circulation as a whole(as opposed to only cyclones, or cyclone and anticyclone centers) is the use of empirical orthogonal function(EOF) analysis (e.g. Kidson and Sinclair, 1995; Thompson and Wallace, 1998; Vera, 2003; Carvalho et al.,2005). Such approaches have been useful in identifying connections to large-scale modes of variability suchas the Arctic and Antarctic Oscillations and El Nino-Southern Oscillation (ENSO).

Research efforts are underway to evaluate the IPCC global climate system model simulations with manyof the techniques discussed above as part of the larger analysis effort for the IPCC AR4. In this paper, weuse the method of self-organizing maps (SOMs) (Kohonen, 2001) to derive a synoptic climatology for theArctic from an ensemble of current and twenty-first century climate simulations conducted in support ofthe IPCC AR4. The SOM technique employs a neural network algorithm that uses unsupervised learning todetermine generalized patterns in data. We analyze the distribution of Arctic synoptic weather patterns in thelate twentieth century in a range of climate models and reanalyses, and changes in synoptic weather patternsover the twenty-first century on the basis of climate model predictions. The synoptic pattern classificationtechnique is used to create a continuum of 35 synoptic patterns on the basis of daily SLP data for the seasonsdefined by December, January and February (DJF) and June, July and August (JJA). The analysis is thenused as a framework to analyze trends in temperature and precipitation over the same time periods. Tenmodels participating in the IPCC Model Analysis1 project were selected for the study, and the future scenarioused is the Special Report on Emissions Scenarios (SRES) A1B (Nakicenovic and Swart, 2000). (The A1scenario family represents rapid economic growth, a global population that peaks in midcentury, and therapid introduction of new technologies. The A1B group is a representative scenario that postulates a balancebetween fossil-intensive and nonfossil energy sources.) We expect that useful comparisons between the SOM-based analysis presented here and other analyses using the methods discussed in the previous paragraph willbe carried out in the future and will highlight the advantages and disadvantages of the different analysis

Copyright 2006 Royal Meteorological Society Int. J. Climatol. 26: 1027–1049 (2006)

ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 1029

methods. By using a diverse set of analysis tools, the climate science community should be able to betterunderstand and evaluate the predicted climate change for the twenty-first century.

The next section summarizes the data and methods used in this analysis, including a detailed discussionof the SOM algorithm, and presents the master synoptic pattern classifications that result for each season.Section 3 describes the ways in which the contemporary climate simulations are distributed across the map incomparison to each other and to the European Center for Medium-Range Weather Forecasts 40-year Reanalysis(ERA-40) and National Centers for Environmental Prediction/National Center for Atmospheric Research(NCEP/NCAR) reanalysis (NNR) products. Section 4 describes the changes in circulation in the twenty-firstcentury as predicted by the ten models, and relates these changes to predicted changes in temperature andprecipitation. Additional research will be required to further understand the relationships between the predictedchanges in the Arctic atmospheric circulation and other components of the Arctic climate system such as thefreshwater cycle, sea ice dynamics, and oceanic processes.

2. METHODS

2.1. Data

The synoptic climatology and analysis in this paper is based primarily on coupled atmosphere-ocean generalcirculation model output archived at the Program for Climate Model Diagnostics and Intercomparisons(PCMDI) in support of the IPCC’s fourth assessment report. Daily fields of SLP, surface temperature,and precipitation amount for the DJF and JJA seasons for the time periods 1991–2000, 2046–2055, and2091–2100 were retrieved from the PCMDI archive for ten models (Table I). This model data was interpolatedto an Equal-Area Scalable Earth Grid (EASE grid2) of 42 × 42 points, centered on the pole, with 200 km gridspacing. This analysis domain extends from the pole to 51°N latitude at 0°, 90 °E, 180°, and 90 °W longitudes.Single model realizations for each of the models were used, except for the NCAR CCSM3 model for whicheight realizations were retrieved for use in the analysis. The model outputs for the 1991–2000 period weretaken from climate of the twentieth century model experiments (20C3M) while the twenty-first century datawere taken from 720 ppm CO2 stabilization experiments (SRES A1B) (Nakicenovic and Swart, 2000).

Global atmospheric reanalysis data from ERA-40 (Simmons and Gibson, 2000) for the period 1991–2000and from the NNR (Kalnay et al., 1996) for the period 1957–1997 are also used. This data is used to evaluatethe ability of the Global Climate Models (GCMs) to simulate the correct distribution of synoptic circulationpatterns for the 1991–2000 period, and to provide a comparison of data for this decade in the twentieth centuryto a longer record in the second half of the twentieth century. The reanalysis data were also interpolated tothe same grid as the model output data.

Table I. List of models used for IPCC simulations analyzed in this paper. The column labeled n lists the number ofmodel realizations used in the analysis presented in this paper. Additional information about each model can be found at

the web sites listed in the table

Model name n Website

CNRM-CM3 1 http://www.cnrm.meteo.fr/scenario2004/indexenglish.htmlGFDL-CM2.0 1 http://nomads.gfdl.noaa.gov/CM2.X/GFDL-CM2.1 1 http://nomads.gfdl.noaa.gov/CM2.X/GISS-AOM 1 http://aom.giss.nasa.govGISS-ER 1 http://www.giss.nasa.gov/tools/modelE/IPSL-CM4 1 http://dods.ipsl.jussieu.fr/omamce/IPSLCM4/MIROC3.2 (hires) 1 http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdfMIROC3.2 (medres) 1 http://www.ccsr.u-tokyo.ac.jp/kyosei/hasumi/MIROC/tech-repo.pdfMRI-CGCM3.2.2 1 http://www.mri-jma.go.jp/Dep/cl/cl4/publications/yukimoto pap2001.pdfNCAR CCSM3 8 http://www.ccsm.ucar.edu



2.2. Description of the self-organizing map algorithm

The SOM algorithm is a neural network algorithm that uses an unsupervised learning process to findgeneralized patterns in data. Formally, the SOM may be described as a nonlinear mapping of high-dimensionalinput data onto the elements of a regular low-dimensional array (Kohonen, 2001). In this analysis, thehigh-dimensional input data consists of a time series of three decades of daily gridded SLP fields from anensemble of global climate system models, and thus represents both spatial and temporal dimensions. Theuse of self-organizing neural networks to analyze and organize circulation data represents a new way tocreate comprehensive and useful synoptic climatologies (Barry and Perry, 2001; Hewitson and Crane, 2002).SOMs have been used across a wide range of disciplines (Oja et al., 2003; Kaski et al., 1998), but arenewer to climate research. Hewitson and Crane (2002) used the SOM technique to classify synoptic patternsover the east coast of the United States, and to relate these patterns to daily precipitation at State College,Pennsylvania. Cavazos (1999, 2000) used SOMs to identify and classify patterns representative of extremewintertime precipitation in Central America and the Balkans respectively, identifying large-scale circulationanomalies associated with local extreme precipitation events. Ambroise et al. (2000) used SOMs for cloudclassification. Malmgren and Winter (1999) used SOMs to classify climate zones in Puerto Rico. Crane andHewitson (2003) used SOMs to analyze precipitation data from the mid-Atlantic and northeastern UnitedStates, while Reusch et al. (2005) applied the SOM methodology to aid in interpreting Antarctic ice coredata.

The mode in which SOMs are used here can be most directly compared to cluster analysis. The SOMclassification approach is characterized by a tendency to categorize the distributions more uniformly, comparedto traditional cluster analysis that groups most of the distributions into a few distinctive classes with theremaining distributions as outliers (Michaelides et al., 2001). In some cluster analysis tools, as seen inMichaelides et al. (2001) and Kalkstein et al. (1987), this can result in grouping rarer data points in the largercluster classes that are not necessarily representative of the rarer data points. For the study of infrequentextreme events, this characteristic of traditional cluster analysis techniques is a significant disadvantage. Oneof the strengths of the SOM is that there are no assumptions made as to the final structure of the clusters;in other words, the SOM does not try to make the data fit a predetermined data distribution or underlyingmodel. The SOM will place more classes in areas of high data density and fewer classes in areas of lowdata density, while still creating a final classification that spans the data space of the input data set. Thus, theSOM is an effective tool for organizing and finding patterns in data, which preserves the probability densityfunction of that data, and is applicable across a wide range of climate research studies.

Application of the SOM algorithm to a high-dimensional input data set results in the creation of alow-dimensional array, called a map. This map is a two-dimensional array of reference vectors that arerepresentative of the probability density function of the input data. For the application of the SOM algorithmin this paper, the input data are daily arrays of gridded SLP from an ensemble of global climate models andthe reference vectors contain spatial SLP data. The reference vectors are of the same dimension as the inputdata vectors (Hewitson and Crane, 2002); and for our application, the position of a data value in the referencevector corresponds to a fixed spatial location in the two-dimensional EASE grid. The resulting map is atwo-dimensional array of gridded SLP fields that are representative of the range of SLP patterns contained inthe input data set, and can be used to depict the probability density function of those patterns as a functionof time. The resulting maps generated by the SOM algorithm for the DJF and JJA seasons are shown inFigure 1. In each case, each panel in this figure represents a single reference vector, also called a node.

The SOM algorithm seeks to map a user-defined number of these reference vectors (here, 35) to adistribution of input data. The number of reference vectors chosen will depend on the intended applicationand the size of the data space spanned by the input data. The use of a small number of reference vectorswill result in a map that provides a broad generalization of the input data while a large number of referencevectors will result in a map with reference vectors that may be quite similar to adjacent reference vectors.

The reference vectors become characteristic of the input data by being ‘trained’ by the input data. Theprocess of training the SOM takes place in two phases. In the initial phase of training, a first-guess map iscreated using reference vectors (nodes) distributed across a two-dimensional subspace spanned by the twoprincipal eigenvectors of the input data vectors. As noted, we chose the size of the map to be 35 nodes



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(representing 35 synoptic patterns) in a 7 × 5 arrangement. Such a map is large enough to distinguish andseparate the important patterns in the data, such as varying intensity and positions of high- and low-pressurecenters, as elucidated by initial testing using both smaller and larger maps to determine the optimal map sizefor the intended analysis.

The second phase uses an iterative procedure to refine the first-guess map. During this phase, each vectorfrom the input data is compared to the initial reference vectors. The reference vector (node) that is mostsimilar to the input vector, on the basis of euclidian distance between the input and reference vector, isidentified. This reference vector is then modified to reduce the difference with the input vector. The degree towhich this reference vector is modified is based on a learning rate parameter in the algorithm. The adjacentreference vectors, which are within a user-specified radius to the identified reference vector, are also modifiedto a degree that diminishes with distance and in proportion to the learning rate parameter. This results in boththe identified node and the neighboring nodes being modified by the input vector. This procedure is repeatedfor all of the input vectors and the entire procedure is then iterated until the quantization error (that is, theerror with the smallest aggregated euclidian distances from the input data) is minimized. It should be notedthat several combinations of the learning rate, radius of the training area, and number of iterations were foundto create a final map with an acceptably small quantization error, but in all cases these maps were nearlyidentical. Therefore the final map created in this way is quite robust, and is not sensitive to the choice ofuser-specified parameters.

In the final product, the reference vectors have become ordered, i.e. the vectors that are close together inthe map have similar patterns; dissimilar patterns are far apart on the map. This organization of the map is aresult of the training procedure updating not only the reference vector that is most similar to the input vectorbut also updating adjacent reference vectors, and is thus a self-organization of the final map and inherentin the training procedure. In using the SOM algorithm to classify SLP patterns, similar synoptic circulationpatterns will be clustered together on the map (Figure 1). This is an important attribute of the approach, sinceit allows analysis not only on a node-by-node basis but also on a map area by map area basis, as appropriate.A thorough theoretical description of the SOM algorithm is found in Kohonen (2001) and further details onthe application of the SOM algorithm to climate data can be found in Hewitson and Crane (2002).

2.3. Development of ‘Master’ SOMs

The SOM algorithm is used to codify SLP patterns and trends in the Arctic for the DJF and JJA seasonsfor the periods 1991–2000, 2046–2055 and 2091–2100. As input to the SOM algorithm, we selected tenmodels, described in Section 2.1 and listed in Table I, and extracted daily SLP data for DJF and JJA for thethree time periods of interest. These data were used to create two 7 × 5 maps of 35 representative synopticpatterns that span the range of circulation variability for the DJF and JJA seasons (Figure 1). By includingmodel output from all of the models and the three time periods of interest, we ensure that the map thatis created will span the full range of synoptic circulation patterns present in the data. The maps created inthis way will be referred to as the master SOMs for DJF and JJA. (Slightly different color scales are usedto prevent any loss of detail in these maps, and hence these scales should be kept in consideration whenanalyzing the maps.) These master SOMs are a nonlinear projection of the probability density function of theinput data and display the primary atmospheric circulation features expected in the Arctic during the borealsummer and winter.

For the DJF master SOM (Figure 1(a)) patterns representative of strong high pressure over Asia and theArctic Ocean are present on the right side of the map, with the strongest high-pressure patterns in the upperright corner of the map. Patterns with a pronounced Icelandic low occur in the bottom three rows of the map,with a tendency for elongated storm tracks extending from the North Atlantic into the eastern Arctic basinclustered in the lower left corner of the SOM. Finally, patterns with a well-developed Aleutian low are foundon the left side of the map, with the strongest Aleutian low patterns in the lower central portion of the map.

For the JJA master SOM (Figure 1(b)) patterns representative of low pressure over Siberia, indicative ofthe Arctic front (Serreze et al., 2001) (lower left and central columns), low pressure over the Arctic Oceanand/or around Greenland (upper left corner), and high pressure over the Arctic Ocean and/or Greenland (lower



right corner) are evident. Generally weak circulation patterns over the Arctic are represented by nodes in theupper right corner of the map.

The master SOMs for both seasons include all of the major circulation patterns expected for the Arctic.Master maps based on other subsets of the available data, the reanalyses for example, did not produce asbroadly representative a map as the master SOM created by using all ten models’ realizations. The useof a relatively large SOM allows for subtle changes in these patterns to be represented on the map, withvariations in the intensity and position of the dominant pressure centers being identified on the map. Furthergeneralization is also possible by considering the euclidian distance between adjacent nodes and groupingthose nodes within a specified minimum distance. We have computed a distortion surface that shows the

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euclidian distance between nodes of the map projected to a set of 2-d vectors using a Sammon mappingscheme (Sammon, 1969) (Figures 2(b) and 4(b)), which is also useful for identifying groups of similar nodes,and is discussed in more detail below.

3. TWENTIETH CENTURY CIRCULATION: MODELS AND REANALYSES

Once the master SOM has been defined, data from individual models, the entire ten-model ensemble,atmospheric reanalyses, or for different time periods can be mapped to the SOM, allowing the residencefrequency of each synoptic pattern represented by the individual nodes to be determined for a particulardata set.

3.1. DJF circulation

Figure 2(a) displays the residence frequency of each node for the ten-model ensemble for the DJF1991–2000 period. This figure indicates that nodes near the top right corner of the map, associated withstrong high pressure over Asia and/or the Arctic Ocean, and the top left corner of the map, associated witha moderate Aleutian low and high pressure over western Canada and Alaska, occur most frequently, withresidence frequencies generally greater than 3%. Note that if all circulation types occurred equally frequently,each node would display a residence frequency of 2.86%. All of the nodes in the top two rows of theSOM, except nodes (4,0), (0,1), and (3,1) have a residence proportion that is significantly greater than theexpected value of 2.86% at the 95% confidence level. Nodes that represent strong Icelandic low patterns,across the lower two rows of the map, have residence frequencies of 1.5 to 2.6%, and all but node (6.3) occursignificantly less frequently than the expected value of 2.86% at the 95% confidence level. The Sammon mapfor the DJF master SOM shown in Figure 2(b) shows that nodes in the upper left corner of the map are closerin the euclidian sense than nodes in other portions of the map. The information displayed on the Sammonmap is useful in interpreting the relationship between groups of circulation patterns, and generally conformsto expectations that arise simply from viewing the node SLP patterns.

These model ensemble node residence frequencies can be compared to residence frequencies calculatedwith reanalysis data. Figure 3 shows the residence frequency plot for NNR for the period 1958–1997 and theERA-40 reanalysis for the period 1991–2000. The differing time periods used for the ERA-40 and NNR werechosen to match the time period from the IPCC model data analyzed in this paper (ERA-40) and to providea longer data set representative of the late twentieth century (NNR). Despite the different time periods used

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Figure 3. Node residence proportions for the NCEP/NCAR reanalysis (NNR) 1958–1997 and the ERA-40 reanalysis 1991–2000,showing (a) NNR DJF and (b) ERA-40 DJF. Residence proportions outside the range 2.32–3.39% (for NNR) and 1.78–3.94% (forERA-40) are significantly different from the expected value of 2.86% at the 95% confidence level. (c) Node residence proportions for

the NNR for 1979–1997 showing differences between the positive and negative phases of the Arctic Oscillation during DJF



and different reanalysis procedures, the residence frequency plots are qualitatively similar. Comparison ofnode residence frequencies for the ERA-40 and NNR data for the common period of 1991–2000 (not shown)indicate a mean absolute difference in residence frequency of 0.5% between the reanalyses. Both reanalyseshave high residence frequencies (generally greater than 3%) for nodes on the right side of the map, excludingthe top right corner of the map (somewhat weaker and spatially confined Icelandic low patterns), and upperleft corner of the map (moderate to strong Aleutian low patterns with limited areas of high pressure over theArctic land areas). Both reanalyses have low residence frequencies for nodes with very strong high pressuredominating the Arctic (far upper right corner; nodes (4,0), (5,0), and (6,0)) and nodes with an extended NorthAtlantic storm track and/or strong Aleutian low (lower left corner). The residence frequencies of the modelensemble differs from the reanalyses in that the models predict a greater occurrence of strong, sometimesexcessively strong, Arctic high-pressure patterns (upper right) at the expense of Icelandic low patterns (middleright). This is consistent with results presented by Bitz et al. (2002) that indicated that mean SLP for DJFin eight Atmospheric Model Intercomparison Project (AMIP1) models was too high over the Arctic Oceanrelative to NNR and ERA-15 reanalyses. In general, the distribution of node residence proportions for theensemble model data is more uniform across the map than for the reanalyses. Since the master SOM wastrained using the ensemble model data, and in some cases the model data represents atmospheric circulationregimes that are either not observed or rarely observed, it should be expected that the model ensemblewould more uniformly fill the SOM space, whereas the reanalysis data would have quite low node residenceproportions for simulated circulation regimes that are either unrealistic or occur very infrequently. Noderesidence proportion maps for individual model realizations (not shown) are also less uniform than those forthe ensemble, because any single model realization is less likely to represent the broad range of synopticcirculation patterns shown in Figure 1 than a multimodel ensemble.

Differences in the node residence frequencies for the NNR data for DJF seasons with positive and negativephases of the Arctic Oscillation (AO) in January (Figure 3(c)) show appreciable differences in the dominantsynoptic circulation patterns for these two phases of the AO. For the positive phase of the AO, nodes acrossthe bottom and far left side of the map (those nodes associated with a synoptic pattern dominated by theIcelandic low, the Aleutian low, and/or low pressure in the Arctic basin) have a higher residence frequencythan for the cases with a negative phase of the AO. This matches the broad circulation changes expected bythe changing phase of the AO.

3.2. JJA circulation

Figure 4(a) displays the residence frequency of each node for the ten-model ensemble for the JJA1991–2000 period. The circulation patterns that occur most frequently are those along the bottom and rightedges of the map, with weaker maxima in the node residence frequency in the center of the map. All ofthe nodes along the bottom and right edges of the map, except node (6,2) have node residence proportionssignificantly greater than the expected value of 2.86% at the 95% confidence level. These nodes correspond tocirculations dominated by a strong expression of the Arctic front over Siberia (lower left and center; Serrezeet al., 2001), high pressure over Greenland and/or the Arctic Ocean (lower right), or weak circulation patterns(upper right). Patterns associated with low pressure over the Arctic Ocean and around Greenland (upper leftportion of the map) are less frequent in the model ensemble than the expected node frequency of occurrenceof 2.86%. The Sammon map for the JJA master SOM shown in Figure 4(b) shows that nodes in the upperright corner of the map are closer in the euclidian sense than nodes in other portions of the map, and thisgenerally conforms to expectations that arise simply from viewing the node SLP patterns in Figure 1(b).

The residence frequencies for the ERA-40 and NNR data for JJA are shown in Figure 5. As for the DJFseason, both the ERA-40 and NNR residence frequency plots are qualitatively similar to each other despiterepresenting different time periods and reanalysis systems. Comparison of the node residence frequenciesfor the two reanalysis data sets for the common period 1991–2000 (not shown) indicate a mean absolutedifference of the residence frequency of 0.3%. However, the reanalysis residence frequency patterns are quitedifferent from the model ensemble pattern shown in Figure 4. The reanalysis data tends to be dominated bycyclonic circulation patterns over the central and eastern Arctic (upper left, excluding the leftmost column



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Figure 4. (a) Node residence proportions during the period 1991–2000 for all ten models for JJA. Residence proportions outside therange 2.56–3.15% are significantly different from the expected value of 2.86% at the 95% confidence level. (b) Sammon map for JJA

SOM

of the map) or by weak circulation (far upper right), with a lesser maximum for the strong Siberian summerfront (lower left, excluding the leftmost column of the map, and center).

3.3. Individual models – DJF

It is important to remember that Figure 2 displays results for the ten-model ensemble, but that individualmodels can differ quite dramatically from this ensemble behavior. While plots of the node residencefrequencies for each of the individual model members of the ensemble are not shown here (in the interest ofspace), these results will be discussed below.



Of the ten models, three models (GISS-AOM, GFDL-CM2.1, and CNRM-CM3) have a pattern of noderesidence frequencies that is most similar to the ERA-40 and NNR reanalysis results shown in Figure 3. Twoof the models (MRI-CGCM2-3.2 and GISS-ER) have circulations dominated by the nine nodes in the upperright corner of the map (65 to 81% residence frequency) representing strong Arctic high pressure across abroad region. These two models also display very low node residence frequencies for any of the cyclonicallydominated patterns on the remainder of the map space. The CCSM3 model, considered both as an eight-modelensemble and as individual model realizations, has the largest residence frequencies across the lower portionof the map, with distinct maxima in the lower left corner. These nodes correspond to an extended NorthAtlantic storm track and strong Icelandic low. The Aleutian low is also well represented in these patterns.The two MIROC3.2 model simulations are dominated by Aleutian low circulation patterns that extend fromthe upper left to lower central portion of the map, with low frequency of occurrence of both a pronouncedIcelandic low and a strong Arctic high-pressure patterns. The last two models from the ensemble (IPSL-CM4and GFDL-CM2.0) have the largest node residence frequencies across the top of the map (weak Aleutian lowand strong Arctic high-pressure patterns).

3.4. Individual models – JJA

Given the poor agreement between the residence proportions for the different circulation patterns in theensemble model data and the reanalyses for the JJA season (Section 3.2), it is worth considering individualmodel results to determine if certain models are able to accurately reproduce the distribution of current climatecirculation patterns in the Arctic. Two models (CCSM3 and MIROC3.2 (medres)) produce a circulation patterndistribution that is qualitatively most similar to that found in the reanalysis data, although both models tendto have higher residence proportions in the lower left corner of the map (low pressure over Siberia) andlower residence proportions in the upper right corner of the map (weak circulation pattern) compared to thereanalyses. Three other models (MIROC3.2 (hires), GFDL-CM2.0, and GFDL-CM2.1) have somewhat similarpatterns of node residence proportion to the reanalyses, although these models tend to shift the maximumresidence proportion one to two columns to the right on the map, resulting in fewer occurrences of deep lowpressure over the Arctic Ocean compared to the reanalyses. The remaining five models (GISS-ER, GISS-AOM, CNRM-CM3, MRI-CGCM3.2.3, and IPSL-CM4) have summer circulations dominated by nodes on theright side of the map (either strong high pressure over the Arctic Ocean and Greenland or a weak circulation).The large residence proportion for the nodes on the lower right corner of the map in these models is in starkcontrast to the reanalysis results (Figure 5), while the large residence proportion for nodes in the upper rightcorner is in better agreement with the reanalyses.

4. TWENTY-FIRST CENTURY TRENDS

With the biases and intermodel differences in mind, we now consider changes in the frequency of occurrenceof particular circulation regimes in the SRES A1B scenario IPCC model simulations, and temperature andprecipitation anomalies associated with the different circulation patterns. All of the changes presented in thissection are calculated from the model predictions only and do not use the twentieth century reanalysis data.The use of only model data for this analysis was desirable as it prevented the introduction of artificial trendsthat would arise solely from differences between the twentieth century model and reanalysis data discussedin Section 3.

4.1. DJF trends

Figure 6 displays the relative change in node residence frequency between the 1991–2000 and 2046–2055time periods and the 2046–2055 to 2091–2100 time periods, with significant trends shown in gray. Therelative change is calculated as the difference in the node residence frequency between the two time periodsdivided by the averaged node residence frequency for the two time periods. The largest relative changes occurin the first half of the twenty-first century, consistent with changes in global temperature predicted for the



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(a) (b)

Figure 5. Node residence proportions for the NCEP/NCAR reanalysis (NNR) 1958–1997 and the ERA-40 reanalysis 1991–2000,showing (a) NNR JJA and (b) ERA-40 JJA. Residence proportions outside the range 2.32–3.39% (for NNR) and 1.78–3.94% (for

ERA-40) are significantly different from the expected value of 2.86% at the 95% confidence level

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Figure 6. Percentage difference in node residence proportions for DJF for the time periods (a) 2046–2055 versus 1991–2000 and(b) 2091–2100 versus 2046–2055. Statistically significant trends at the 95% confidence level are highlighted in gray

SRES A1B scenario (ACIA, 2004). The largest relative increases, in excess of 30%, are found in the lowerleft portion of the map and represent extended North Atlantic storm track patterns. These nodes correspond topatterns that did not have a large residence proportion in either the multimodel ensemble or in the reanalysisdata for the period of 1991–2000. The largest decreases, in excess of −30%, are found for nodes in the



upper right corner of the map (strong Arctic high-pressure patterns). These are patterns that the ensembleof models tended to overpredict relative to the reanalysis data (Figures 2(a) and 3) in the last decade of thetwentieth century. This is consistent with an increasingly positive AO index, as has been suggested by earlierobservational (e.g. Walsh et al., 1996; Thompson and Wallace, 2000) and modeling studies (e.g. Gillett et al.,2002, 2003). In the second half of the twenty-first century, the trend of decreasing occurrence of strong Arctichigh-pressure patterns continues, although with smaller magnitude. Moderate increases in the node residencefrequency is found from the lower right to upper left corner of the map (Icelandic and Aleutian low patterns)with some decreases in the extended North Atlantic storm track pattern that increased in the first half of thecentury. Overall, in the twenty-first century, the ensemble of simulations indicates a large relative decreasein the occurrence of strong Arctic high-pressure patterns with large increases in strong Icelandic low patterns(both spatially confined and with an extended storm track into the eastern Arctic basin). Such a change wouldbe congruent with an expectation for increasingly positive AO and North Atlantic Oscillation (NAO) phasesin the coming century.

Temperature and precipitation anomalies for each node were calculated as the difference between the meanof the model data that mapped to a particular node (i.e. for all samples that had a SLP pattern that correspondedto a particular node in Figure 1) and the mean of the entire ensemble data set (all of the models for all threedecades of interest). Figure 7 displays the temperature and precipitation anomalies for each node for DJF,and illustrate the spatial patterns of temperature and precipitation anomalies for the 35 circulation patterns(nodes) identified in the SOM analysis. The most striking feature of the temperature anomaly map is theorganization of temperature anomalies such that cold anomalies are associated with nodes in the upper rightcorner of the map and warm anomalies are associated with nodes in the lower left corner of the map. Thecold anomalies, in excess of 5 K, are found over Eurasia, the Arctic basin, and northwestern North Americaand are associated with strong Arctic high-pressure patterns. The warm anomalies, in excess of 5 K, aremost pronounced over Eurasia with some warming in northwestern North America as well. The coherenceof this mapping of temperature anomalies to the SOM expresses the strong relationship between circulationand temperature changes. In this case, these warm patterns are driven by the extended North Atlantic stormtrack, which acts to transport warm air into Eurasia, and a strong Aleutian low which advects warm air intonorthwestern North America. On the basis of the changing residence frequency of the nodes in the twenty-firstcentury (Figure 6), this suggests an increase in circulation patterns that favor warm conditions in Eurasia andnorthwestern North America, with a reduced occurrence of circulation regimes that are associated with thecoldest temperatures in Eurasia and North America.

Precipitation anomalies display a similarly coherent signal relative to the master SOM. The largestprecipitation anomalies (Figure 7(b)) are found along the west coast of North America and are driven bychanges in the circulation over the North Pacific Ocean. Circulation patterns with a pronounced Aleutian loware generally associated with positive precipitation anomalies along the Canadian west coast and in southeastAlaska, although slight shifts in the position of this low-pressure center can shift the positive precipitationanomaly further north and west leaving the west coast of Canada with reduced precipitation (e.g. nodes (0,2),(0,3), and (0,4)). Circulation patterns with a strong and/or extended Icelandic low are also associated withpositive precipitation anomalies in the North Atlantic, along the east coast of Greenland, and extending intoScandinavia. Negative precipitation anomalies are found in the North Atlantic for nodes in the upper rightcorner of the map, which are dominated by strong high pressure. Given the increasing frequency of occurrenceof nodes in the lower left corner and bottom portions of the map, this suggests an increase in circulationpatterns that favor increased precipitation along the east coast of Greenland, in the North Atlantic extendinginto Scandinavia, and along the western coast of North America. The decreasing frequency of occurrence ofnodes in the upper right corner of the map implies a reduction in circulation patterns that are associated withreduced precipitation events in the North Atlantic, around Greenland, and along the northwestern coast ofNorth America.

4.2. JJA trends

Relative changes in the node residence proportion over the twenty-first century for the JJA season are shownin Figure 8 (significant trends are shown in gray). As for the DJF season, the largest changes occur in the first



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half of the twenty-first century (between the time periods 1991–2000 and 2046–2055) (Figure 8(a)), withsmaller changes in the second half (Figure 8(b)). In the twenty-first century, circulation patterns representedby nodes in the upper left corner of the map are predicted to have an increased frequency of occurrence.These circulation patterns correspond to nodes that were generally underpredicted by the multimodel ensemblecompared to the reanalysis data for the period 1991–2000. Circulation patterns associated with nodes acrossmost of the bottom of the map are projected to have a decreased frequency of occurrence, and correspondto nodes that were overpredicted by the models relative to the reanalysis data. In general, these changesrepresent an increased occurrence of patterns with low pressure over the Arctic Ocean and around Greenlandand a decreased occurrence of patterns associated with high pressure over the Arctic Ocean and Greenland.This analysis of changes in node residence proportion for JJA over the twenty-first century must be viewedwith caution, given the level of disagreement between the modeled and observed node residence proportionduring the latter part of the twentieth century (Figures 4 and 5). Analysis of only those ensemble membersthat more closely reproduced the current JJA node residence proportion (as discussed in Section 3.4) revealedsimilar patterns as those discussed above for the entire ensemble, and provides some confidence in the resultsfor the entire ensemble.

The temperature and precipitation anomalies associated with each node for JJA are shown in Figure 9. Notethe more restricted scale used in Figure 9 compared to the boreal winter anomalies in Figure 7, reflecting thesmaller differences between circulation patterns during the summer season. The nodes in the upper left cornerof the map, which the ensemble predicts to have an increased frequency of occurrence in the twenty-firstcentury, are associated with warm anomalies north of Scandinavia and extending into the eastern Arctic basinand cold anomalies over much of Alaska and portions of northwestern Canada. This pattern of temperatureanomalies is consistent with the temperature advection patterns associated with an SLP pattern dominated bylow pressure over the Arctic Ocean. Nodes with a decreasing frequency of occurrence (bottom right portionof map) are associated with cold anomalies in the eastern Arctic basin and warm anomalies over Alaskaand eastern Siberia, and are consistent with the SLP pressure patterns dominated by high pressure over theArctic Ocean. The ensemble model predictions analyzed in this paper thus indicate a trend toward circulationpatterns that favor cooler conditions (less warming) in Alaska over the next century relative to locations inthe eastern Arctic. This tendency is counter to the recent strong warming trend observed in Alaska (ACIA,

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Figure 8. Percentage difference in node residence proportions for JJA for the time periods (a) 2046–2055 versus 1991–2000 and(b) 2091–2100 versus 2046–2055. Statistically significant trends at the 95% confidence level are highlighted in gray



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2004; Hinzman et al., 2005), although some data records do suggest that this trend has recently moderated(Lynch et al., 2004).

Nodes with positive precipitation anomalies over Alaska and the west coast of Canada, the Greenlandice sheet, and western Scandinavia (upper left corner of map) are projected to occur more frequently in thetwenty-first century by the IPCC model ensemble, although the spatial pattern of the precipitation anomaliesis quite variable for these nodes. These precipitation anomalies are sensitive to the details of the circulationpattern represented by the different nodes. For example, node (0,0) is characterized by low pressure northeastof Greenland and increased precipitation along the west coast of Greenland. In contrast, nodes (2,0) and(3,0), with low-pressure troughs along both the east and west coasts of Greenland are associated withpositive precipitation anomalies in eastern Greenland. Nodes with negative precipitation anomalies oversouthern coastal Alaska and the west coast of Canada (bottom center and right) are predicted to have adecreased frequency of occurrence by the ensemble of IPCC models in the twenty-first century. These nodesare dominated by high SLP over the Arctic basin, consistent with the negative precipitation anomalies.Precipitation anomalies in the large watersheds that drain into the Arctic Ocean (Ob, Yenissei, Lena, andMackenzie rivers) indicate mixed precipitation anomalies for the circulation patterns that show either largeincreases (upper left corner) or large decreases (lower right corner) in frequency of occurrence over the twenty-first century. Further analysis of the predicted changes in atmospheric circulation regimes and the relationshipto precipitation in the large Arctic basins is an area that will require additional research in the future.

5. CONCLUSIONS

The method of SOMs was used to analyze synoptic circulation patterns in the Arctic during the summer(JJA) and winter (DJF) seasons. This analysis used daily SLP data from ten global climate system models aswell as the ERA-40 and NNR reanalysis data. The SOM algorithm was used to create an objective synopticclimatology of the model data for the time periods 1991–2000, 2046–2055, and 2091–2100. A total of35 synoptic patterns were classified for each season (Figure 1), and this synoptic classification was foundto be insensitive to changes in the SOM algorithm parameters. The SOM methodology was found to bea useful tool for diagnosing differences between the ensemble model simulations and reanalysis data, foridentifying differences between individual ensemble members, and for assessing predicted changes in theArctic circulation in the twenty-first century. This method also allowed for changes in circulation patterns tobe related to spatial patterns in anomalies of surface temperature and precipitation.

For the 1991–2000 DJF seasons the ten-model ensemble displayed a similar distribution of synopticpatterns to the ERA-40 and NNR analyses (Figures 2 and 3), although the models tended to overpredict thefrequency of occurrence of strong Arctic high-pressure patterns at the expense of patterns with an Icelandiclow. Analysis of individual model synoptic climatologies revealed a range of model skill, with some modelsunable to predict a reasonable distribution of synoptic patterns (either dominated by too many strong Arctichigh-pressure patterns or by too many strong cyclonic patterns – both Icelandic low and Aleutian low). Threeof the 10 models evaluated in this study (GISS-AOM, GFLD-CM2.1, and CNRM-CM3) simulated a synopticclimatology that was qualitatively similar to that in the reanalysis data.

For the 1991–2000 JJA seasons, the ten-model ensemble did not predict a distribution of synoptic patternsthat matched the pattern found in the reanalysis data (Figures 4 and 5). Analysis of the model predictionsindicated that circulation patterns dominated by an Arctic front over Siberia, high pressure over the ArcticOcean and/or Greenland, and generally weak circulation patterns are the dominant circulation regimessimulated in the Arctic summer. This is in contrast to the reanalysis data that are dominated by strongcyclonic circulation patterns over the central and eastern Arctic. Five models in the ten-model ensemblewere able to reasonably reproduce the synoptic climatology observed in the reanalysis data (NCAR CCSM3,MIROC3.2 (medres), MIROC3.2 (hires), GFDL-CM2.0, and GFDL-CM2.1), while the remaining modelswere unable to simulate even the basic features of the Arctic synoptic climatology and were dominated byeither strong high pressure over the Arctic basin or weak circulation patterns.



Changes in the frequency of occurrence of the Arctic synoptic circulation patterns in the twenty-firstcentury were evaluated using SRES A1B scenario IPCC model simulations (Figures 6 and 8). Temperatureand precipitation anomalies associated with the different circulation patterns (Figures 7 and 9) were alsoidentified and used to evaluate local changes in these parameters based on changes in the model predictedsynoptic climatology. For both seasons, the largest changes in the synoptic circulation patterns were found tooccur during the first half of the twenty-first century, with similar signs but smaller magnitude trends duringthe second half of the century.

During the DJF season, the ten-model ensemble predicts a decreased frequency of occurrence of the strongArctic high-pressure patterns and an increase in the occurrence of patterns dominated by a North Atlanticstorm track that extends into the eastern Arctic basin. These changes in the circulation patterns correspondto warm temperature anomalies in Eurasia and western North America, and positive precipitation anomaliesalong the east coast of Greenland, from the North Atlantic extending into Scandinavia, and along the westerncoast of North America.

During the JJA season, the ten-model ensemble predicts an increased frequency of occurrence of patternswith low pressure over the Arctic Ocean and around Greenland. This change in the circulation pattern in theArctic was found to correspond to warm anomalies north of Scandinavia and extending into the eastern Arcticbasin, with cold anomalies over much of Alaska and northwestern Canada. The circulation patterns that areprojected to increase most in the twenty-first century are associated with positive precipitation anomalies overAlaska, the west coast of Canada, the Greenland ice sheet, and western Scandinavia, although the precipitationanomaly patterns are less spatially coherent than the temperature anomalies and are more sensitive to smallchanges in the details of the circulation pattern.

Future work will expand upon this analysis, with an emphasis on the impacts of changing atmosphericcirculation patterns on other components of the Arctic climate system. Two key areas of interest are changesin atmospheric forcing of the Greenland ice sheet mass balance and changes in the freshwater cycle of theArctic. Sea ice transport within and export from the Arctic basin is another area for future analysis. Analysisof individual model and ensemble member trends was not presented here, in the interest of space, but willbe an avenue of future research.

ACKNOWLEDGEMENTS

We acknowledge the international modeling groups for providing their data for analysis, the PCMDI forcollecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modelling (WGCM)and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing themodel data analysis activity, the IPCC WG1 TSU for technical support, and the suggestions of two anonymousreviewers. The IPCC Data Archive at Lawrence Livermore National Laboratory is supported by the Officeof Science, US Department of Energy. This research uses data provided by the Community Climate SystemModel project (www.ccsm.ucar.edu), supported by the Directorate for Geosciences of the National ScienceFoundation and the Office of Biological and Environmental Research of the US Department of Energy. Thisanalysis was supported by research grants ARC FF0348550, NSF OPP-0100120 and NSF OPP-0229649.

NOTES

1. http://www-pcmdi.llnl.gov/ipcc/about ipcc.php2. http://nsidc.org/data/ease/ease grid.html

REFERENCES

ACIA. 2004. Impacts of a Warming Arctic: Arctic Climate Impact Assessment. Cambridge University Press, Cambridge: 139.Ambroise C, Seze G, Badran F, Thiria S. 2000. Hierarchical clustering of self-organizing maps for cloud classification. Neurocomputing

30: 47–52.Barry RG, Perry AH. 2001. Synoptic climatology and its applications. In Synoptic and Dynamic Climatology, Barry RG, Carleton AM

(eds). Routledge: London 547–603.



Berbery EH, Vera C. 1996. Characteristics of the Southern Hemisphere winter storm track with filtered and unfiltered data. Journal ofthe Atmospheric Sciences 53: 468–481.

Bitz CM, Fyfe JC, Flato GM. 2002. Sea ice response to wind forcing from AMIP models. Journal of Climate 15: 522–536.Cao ZH, Zhang DL. 2004. Tracking surface cyclones with moist potential vorticity. Advances in Atmospheric Sciences 21: 830–835.Carvalho LMV, Jones C, Ambrizzi T. 2005. Opposite phases of the antarctic oscillation and relationships with intraseasonal to

interannual activity in the tropics during the austral summer. Journal of Climate 18: 702–718.Cavazos T. 1999. Large-scale circulation anomalies conducive to extreme precipitation events and derivation of daily rainfall in

northeastern Mexico and southeastern Texas. Journal of Climate 12: 1506–1523.Cavazos T. 2000. Using self-organizing maps to investigate extreme climate events: An application to wintertime precipitation in the

Balkans. Journal of Climate 13: 1718–1732.Gillett NP, Allen MR, Williams KD. 2002. The role of stratospheric resolution in simulating the Arctic oscillation response to greenhouse

gases. Geophysical Research Letters 29(1500):, DOI:10.1029/2001GL014444.Gillett NP, Allen MR, Williams KD. 2003. Modelling the atmospheric response to doubled CO2 and depleted stratospheric ozone using

a stratosphere-resolving coupled GCM. Quarterly Journal of the Royal Meteorological Society 129: 947–966.Hanson CE, Palutikof JP, Davies TD. 2004. Objective cyclone climatologies of the North Atlantic – a comparison between the ECMWF

and NCEP reanalysis. Climate Dynamics 22: 757–769.Hewitson BC, Crane RG. 2002. Self-organising maps: Applications to synoptic climatology. Climate Research 22: 13–26.Hinzman L, Bettez ND, Bolton WR, Chapin FS, Dyurgerov MB, Fastie CL, Griffith B, Hollister RD, Hope A, Huntington HP,

Jensen AM, Jia GJ, Jorgenson T, Kane DL, Klein DR, Kofinas G, Lynch AH, Lloyd AH, McGuire AD, Nelson FE, Oechel WC,Osterkamp TE, Racine CH, Romanovsky VE, Stone RS, Stow DA, Sturm M, Tweedie CE, Vourlitis GL, Walker MD, Walker DA,Webber PJ, Welker JM, Winker KS, Yoshikawa K. 2005. Evidence and implications of recent climate change in terrestrial regions ofthe Arctic. Climatic Change 72: 251–298, DOI: 10.1007/s10584-005-5352-2.

Kalkstein LS, Tan G, Skindlov JA. 1987. An evaluation of three clustering procedures for use in synoptic climatological classification.Journal of Climate and Applied Meteorology 26: 717–730.

Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A,Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D. 1996. TheNCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77: 437–471.

Kaski S, Honkela T, Lagus K, Kohonen T. 1998. WEBSOM – Self-organizing maps of document collections. Neurocomputing 21:101–117.

Kidson JW, Sinclair MR. 1995. The influence of persistent anomalies on Southern Hemisphere storm tracks. Journal of Climate 8:1938–1950.

Kohonen T. 2001. Self-Organizing Maps. Springer: Berlin 501.Lambert SJ, Sheng J, Boyle J. 2002. Winter cyclone frequencies in thirteen models participating in the Atmospheric Model

Intercomparison Project (AMIP1). Climate Dynamics 19: 1–16.Lynch AH, Uotila P, Cassano JJ. 2005. Changes in synoptic weather patterns in the polar regions in the 20th and 21st centuries, Part

2: Antarctic. International Journal of Climatology. (in press).Lynch AH, Brunner RD, Cassano EN, Jensen A, Koslow MR, Lestak L, Manley WF, Maslanik JA, Mearns LO, Pocernich M,

Sheehan G, Sturtevant P, Tebaldi C. 2004. Barrow Climatic and Environmental Conditions and Variations – A Compendium . TechnicalEdition, 124.

Malmgren BA, Winter A. 1999. Climate zonation in Puerto Rico based on principal components analysis and an artificial neural network.Journal of Climate 12: 977–985.

McCabe GJ, Clark MP, Serreze MC. 2001. Trends in Northern Hemisphere surface cyclone frequency and intensity. Journal of Climate14: 2763–2768.

Michaelides SC, Pattichis CS, Kleovoulou G. 2001. Classification of rainfall variability by using artificial neural networks. InternationalJournal of Climatology 21: 1401–1414.

Nakamura H, Shimpo A. 2004. Seasonal variations in the Southern Hemisphere storm tracks and jet streams as revealed in a reanalysisdataset. Journal of Climate 17: 1828–1844.

Nakicenovic N, Swart R (eds). 2000. Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios. CambridgeUniversity Press: Cambridge.

Oja M, Kaski S, Kohonen T. 2003. Bibliography of self-organizing map (SOM) papers: 1998–2001 addendum. Neural ComputingSurveys 3: 1–156.

Paciorek CJ, Risbey JS, Ventura V, Rosen RD. 2002. Multiple indices of Northern Hemisphere cyclone activity, Winters 1949–99.Journal of Climate 15: 1573–1590.

Pezza AB, Ambrizzi T. 2003. Variability of Southern Hemisphere cyclone and anticyclone behavior: Further analysis. Journal of Climate16: 1075–1083.

Rao VB, do Carmo AMC, Franchito SH. 2002. Seasonal variations in the Southern Hemisphere storm tracks and associated wavepropagation. Journal of the Atmospheric Sciences 59: 1029–1040.

Reusch DB, Hewitson BC, Alley RB. 2005. Towards ice-core-based synoptic reconstructions of West Antarctic climate with artificialneural networks. International Journal of Climatology 25: 581–610.

Sammon J. 1969. A non-linear mapping for data structure and analyses. IEEE Transactions on Computers 18: 401–409.Serreze MC, Box JE, Barry RG, Walsh JE. 1993. Characteristics of Arctic synoptic activity, 1952–1989. Meteorology and Atmospheric

Physics 51: 147–164.Serreze MC, Carse F, Barry RG, Rogers JC. 1997. Icelandic low cyclone activity: Climatological features, linkages with the NAO, and

relationships with recent changes in the Northern Hemisphere circulation. Journal of Climate 10: 453–464.Serreze MC, Lynch AH, Clark MP. 2001. The Arctic frontal zone as seen in the NCEP-NCAR reanalysis. Journal of Climate 14:

1550–1567.Serreze MC, Walsh JE, Chapin FS, Osterkamp T, Dyurgerov M, Romanovsky V, Oechel WC, Morison J, Zhang T, Barry RG. 2000.

Observational evidence of recent change in the northern high-latitude environment. Climatic Change 46: 159–207.



Sinclair MR. 1994. An objective cyclone climatology for the Southern Hemisphere. Monthly Weather Review 122: 2239–2256.Simmons AJ, Gibson JK. 2000. The ERA-40 Project Plan. ECMF, Reading. ERA-40 Project Report Series No. 1, 63.Thompson DWJ, Wallace JM. 1998. The Arctic oscillation signature in the wintertime geopotential height and temperature fields.

Geophysical Research Letters 25: 1297–1300.Thompson DWJ, Wallace JM, Hegerl GC. 2000. Annular modes in the extratropical circulation. Part II: Trends. Journal of Climate 13:

1018–1036.Trenberth KE. 1991. Storm tracks in the Southern Hemisphere. Journal of the Atmospheric Sciences 48: 2159–2178.Vera C. 2003. Interannual and interdecadal variability of atmospheric synoptic-scale activity in the Southern Hemisphere. Journal of

Geophysical Research 108: Art. No. 8077. DOI: 10.1029/2000JC000406.Wallace JM, Lim G-H, Blackmon ML. 1988. Relationship between cyclone tracks, anticyclone tracks and baroclinic waveguides. Journal

of the Atmospheric Sciences 45: 439–462.Walsh JE, Crane RG. 1992. A comparison of GCM simulations of Arctic climate. Geophysical Research Letters 19: 29–32.Walsh JE, Chapman WL, Shy TL. 1996. Recent decrease of sea level pressure in the central Arctic. Journal of Climate 9: 480–486.Zhang X, Walsh JE, Zhang J, Bhatt US, Ikeda M. 2004. Climatology of interannual variability of Arctic cyclone activity: 1948–2002.

Journal of Climate 17: 2300–2317.


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Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic