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

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 6: (6) Published online 6 March 6 in Wiley InterScience ( DOI: 1.1/joc.136 CHANGES IN SYNOPTIC WEATHER PATTERNS IN THE POLAR REGIONS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES, PART 1: ARCTIC JOHN J. CASSANO, a, * PETTERI UOTILA b and AMANDA LYNCH b a Cooperative Institute for Research in Environmental Sciences and Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO, USA b School of Geography and Environmental Science, Monash University, Monash, Australia Received 3 April 5 Revised 3 November 5 Accepted 1 December 5 ABSTRACT An analysis of late twentieth and twenty-first century predictions of Arctic circulation patterns in a ten-model ensemble of global climate system models, using the method of self-organizing maps (SOMs), is presented. The model simulations were conducted in support of the fourth assessment report of the intergovernmental panel on climate change (IPCC). The analysis demonstrates the utility of SOMs for climate analysis, both as a tool to evaluate the accuracy of climate model predictions, 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. Some of the models tend to overpredict strong high-pressure patterns while other models overpredict the intensity of cyclonic circulation regimes. In general, the ensemble of models predicts an increase in cyclonically dominated circulation patterns during both the winter and summer seasons, with the largest changes occurring during the first half of the twenty-first century. Analysis of temperature and precipitation anomalies associated with the different circulation patterns reveals coherent patterns that are consistent with the different circulation regimes and highlight the dependence of local changes in these quantities to changes in the synoptic scale circulation patterns. Copyright 6 Royal Meteorological Society. KEY WORDS: Arctic; synoptic climatology; climate change; global climate model 1. INTRODUCTION In 1, at its eighteenth session, the Intergovernmental Panel on Climate Change (IPCC) agreed to prepare a fourth comprehensive assessment report (AR4) of the scientific, technical, and socioeconomic understanding of anthropogenic climate change and its consequences. A key element of the physical science basis of this assessment 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 to characterize 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 attempted through the collective effort of a community of scientists analyzing all aspects of model behavior. This paper is one such contribution to that effort, and we choose as our focus a description of changes in the circulation of the high northern latitudes simulated by an ensemble of global climate system models. The companion paper (Lynch et al., 5) addresses the circulation of the high southern latitudes. It is now well known that the Arctic region demonstrates many of the expected consequences of the polar amplification of global climate change (Serreze et al., ; ACIA, 4; Hinzman et al., 5). These changes 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, 16 UCB, Boulder, CO 839, USA; cassano@cires.colorado.edu Copyright 6 Royal Meteorological Society

2 18 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 of the northern high latitudes, which may be described in a number of ways (e.g. Walsh et al., 1996; Thompson and Wallace, 1998; McCabe et al., 1). This aspect of Arctic change is of particular importance because of its role in the modulation of Arctic sea ice distribution and North Atlantic ice export, and the subsequent impacts on the global thermohaline circulation. For example, Walsh and Crane (199) and Bitz et al. () describe the sensitivity of simulated Arctic sea ice to changes in atmospheric circulation patterns. Bitz et al. () highlight the importance of accurate simulation of high sea-level pressure (SLP) over the Beaufort Sea in winter and low SLP over the Arctic Ocean in summer for accurate simulation of sea ice thickness in the Arctic basin. The results presented here describe changes in Arctic circulation of the present and future as 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 stratifying large volumes of data (daily or higher temporal resolution fields of the atmospheric state) into a small number of categories on a physically meaningful basis. Such an approach provides important information on the weather processes that control the local climate, which may often be hidden by monthly or seasonal mean fields (Barry and Perry, 1; Hanson et al., 4). An important step in this type of analysis is developing a robust classification scheme that can be applied to large volumes of data. Barry and Perry (1), and references therein, provide a detailed overview of synoptic climatology and its applications, but we summarize the important points below. Most commonly, cyclone track and cyclone event climatologies have been developed using objective algorithms 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., ; Paciorek et al., ; Cao and Zhang, 4; Zhang et al., 4). Some tracking schemes have been applied to both cyclones and anticyclones (Pezza and Ambrizzi, 3). These approaches have been demonstrated to be physically consistent and reproducible, and hence create highly useful data sets. An alternative approach has been the application of synoptic timescale filters to pressure or height data and analyzing the variance of the filtered data (Trenberth, 1991; Nakamura and Shimpo, 4). Other authors have argued for the use of unfiltered data (Berbery and Vera, 1996; Rao et al., ), but in any case the relationship of these variances to cyclone and anticyclone trajectories remains 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, 3; Carvalho et al., 5). Such approaches have been useful in identifying connections to large-scale modes of variability such as 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 many of the techniques discussed above as part of the larger analysis effort for the IPCC AR4. In this paper, we use the method of self-organizing maps (SOMs) (Kohonen, 1) to derive a synoptic climatology for the Arctic from an ensemble of current and twenty-first century climate simulations conducted in support of the IPCC AR4. The SOM technique employs a neural network algorithm that uses unsupervised learning to determine generalized patterns in data. We analyze the distribution of Arctic synoptic weather patterns in the late twentieth century in a range of climate models and reanalyses, and changes in synoptic weather patterns over the twenty-first century on the basis of climate model predictions. The synoptic pattern classification technique is used to create a continuum of 35 synoptic patterns on the basis of daily SLP data for the seasons defined by December, January and February (DJF) and June, July and August (JJA). The analysis is then used as a framework to analyze trends in temperature and precipitation over the same time periods. Ten models participating in the IPCC Model Analysis 1 project were selected for the study, and the future scenario used is the Special Report on Emissions Scenarios (SRES) A1B (Nakicenovic and Swart, ). (The A1 scenario family represents rapid economic growth, a global population that peaks in midcentury, and the rapid introduction of new technologies. The A1B group is a representative scenario that postulates a balance between fossil-intensive and nonfossil energy sources.) We expect that useful comparisons between the SOMbased analysis presented here and other analyses using the methods discussed in the previous paragraph will be carried out in the future and will highlight the advantages and disadvantages of the different analysis Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

3 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 19 methods. By using a diverse set of analysis tools, the climate science community should be able to better understand 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 discussion of 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 in comparison to each other and to the European Center for Medium-Range Weather Forecasts 4-year Reanalysis (ERA-4) 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-first century as predicted by the ten models, and relates these changes to predicted changes in temperature and precipitation. Additional research will be required to further understand the relationships between the predicted changes in the Arctic atmospheric circulation and other components of the Arctic climate system such as the freshwater cycle, sea ice dynamics, and oceanic processes..1. Data. METHODS The synoptic climatology and analysis in this paper is based primarily on coupled atmosphere-ocean general circulation 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, 46 55, and 91 1 were retrieved from the PCMDI archive for ten models (Table I). This model data was interpolated to an Equal-Area Scalable Earth Grid (EASE grid )of4 4 points, centered on the pole, with km grid spacing. This analysis domain extends from the pole to 51 N latitude at, 9 E, 18, and 9 W longitudes. Single model realizations for each of the models were used, except for the NCAR CCSM3 model for which eight realizations were retrieved for use in the analysis. The model outputs for the 1991 period were taken from climate of the twentieth century model experiments (C3M) while the twenty-first century data were taken from 7 ppm CO stabilization experiments (SRES A1B) (Nakicenovic and Swart, ). Global atmospheric reanalysis data from ERA-4 (Simmons and Gibson, ) for the period 1991 and from the NNR (Kalnay et al., 1996) for the period are also used. This data is used to evaluate the ability of the Global Climate Models (GCMs) to simulate the correct distribution of synoptic circulation patterns for the 1991 period, and to provide a comparison of data for this decade in the twentieth century to a longer record in the second half of the twentieth century. The reanalysis data were also interpolated to the 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 of model 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 GFDL-CM. 1 GFDL-CM GISS-AOM 1 GISS-ER 1 IPSL-CM4 1 MIROC3. (hires) 1 MIROC3. (medres) 1 MRI-CGCM pap1.pdf NCAR CCSM3 8 Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

4 13 J. J. CASSANO, P. UOTILA AND A. LYNCH.. Description of the self-organizing map algorithm The SOM algorithm is a neural network algorithm that uses an unsupervised learning process to find generalized patterns in data. Formally, the SOM may be described as a nonlinear mapping of high-dimensional input data onto the elements of a regular low-dimensional array (Kohonen, 1). In this analysis, the high-dimensional input data consists of a time series of three decades of daily gridded SLP fields from an ensemble of global climate system models, and thus represents both spatial and temporal dimensions. The use of self-organizing neural networks to analyze and organize circulation data represents a new way to create comprehensive and useful synoptic climatologies (Barry and Perry, 1; Hewitson and Crane, ). SOMs have been used across a wide range of disciplines (Oja et al., 3; Kaski et al., 1998), but are newer to climate research. Hewitson and Crane () used the SOM technique to classify synoptic patterns over the east coast of the United States, and to relate these patterns to daily precipitation at State College, Pennsylvania. Cavazos (1999, ) used SOMs to identify and classify patterns representative of extreme wintertime precipitation in Central America and the Balkans respectively, identifying large-scale circulation anomalies associated with local extreme precipitation events. Ambroise et al. () used SOMs for cloud classification. Malmgren and Winter (1999) used SOMs to classify climate zones in Puerto Rico. Crane and Hewitson (3) used SOMs to analyze precipitation data from the mid-atlantic and northeastern United States, while Reusch et al. (5) applied the SOM methodology to aid in interpreting Antarctic ice core data. The mode in which SOMs are used here can be most directly compared to cluster analysis. The SOM classification approach is characterized by a tendency to categorize the distributions more uniformly, compared to traditional cluster analysis that groups most of the distributions into a few distinctive classes with the remaining distributions as outliers (Michaelides et al., 1). In some cluster analysis tools, as seen in Michaelides et al. (1) and Kalkstein et al. (1987), this can result in grouping rarer data points in the larger cluster classes that are not necessarily representative of the rarer data points. For the study of infrequent extreme events, this characteristic of traditional cluster analysis techniques is a significant disadvantage. One of 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 underlying model. The SOM will place more classes in areas of high data density and fewer classes in areas of low data density, while still creating a final classification that spans the data space of the input data set. Thus, the SOM is an effective tool for organizing and finding patterns in data, which preserves the probability density function 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 a low-dimensional array, called a map. This map is a two-dimensional array of reference vectors that are representative of the probability density function of the input data. For the application of the SOM algorithm in this paper, the input data are daily arrays of gridded SLP from an ensemble of global climate models and the reference vectors contain spatial SLP data. The reference vectors are of the same dimension as the input data vectors (Hewitson and Crane, ); and for our application, the position of a data value in the reference vector corresponds to a fixed spatial location in the two-dimensional EASE grid. The resulting map is a two-dimensional array of gridded SLP fields that are representative of the range of SLP patterns contained in the input data set, and can be used to depict the probability density function of those patterns as a function of time. The resulting maps generated by the SOM algorithm for the DJF and JJA seasons are shown in Figure 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 a distribution of input data. The number of reference vectors chosen will depend on the intended application and the size of the data space spanned by the input data. The use of a small number of reference vectors will result in a map that provides a broad generalization of the input data while a large number of reference vectors 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. The process of training the SOM takes place in two phases. In the initial phase of training, a first-guess map is created using reference vectors (nodes) distributed across a two-dimensional subspace spanned by the two principal eigenvectors of the input data vectors. As noted, we chose the size of the map to be 35 nodes Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

5 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 131 (,) (1,) (,) (3,) (4,) (5,) (6,) (,1) (1,1) (,1) (3,1) (4,1) (5,1) (6,1) (,) (1,) (,) (3,) (4,) (5,) (6,) (,3) (1,3) (,3) (3,3) (4,3) (5,3) (6,3) (,4) (1,4) (,4) (3,4) (4,4) (5,4) (6,4) (a) hpa Figure 1. Master SOMs for (a) DJF and (b) JJA, using input data from all ten models and all three time slices. Blue shading represents values more than one standard deviation below the mean and red shading represents values more than one standard deviation above the mean. The maps in each panel are oriented such that the international date line points toward the center bottom of each panel Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

6 Copyright 6 Royal Meteorological Society (b) (1,) (1,3) (,) (,3) 1 (1,4) (1,1) (,1) (,4) (1,) (,) 15 (,4) (,3) (,) (,1) (,) Int. J. Climatol. 6: (6) hpa Figure 1. (Continued) 11 (3,4) (3,3) (3,) (3,1) (3,) 115 (4,4) 1 (5,4) (5,3) (5,) (4,) (4,3) (5,1) (5,) (4,1) (4,) 15 (6,4) (6,3) (6,) (6,1) (6,) 13 J. J. CASSANO, P. UOTILA AND A. LYNCH

7 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 133 (representing 35 synoptic patterns) in a 7 5 arrangement. Such a map is large enough to distinguish and separate the important patterns in the data, such as varying intensity and positions of high- and low-pressure centers, as elucidated by initial testing using both smaller and larger maps to determine the optimal map size for the intended analysis. The second phase uses an iterative procedure to refine the first-guess map. During this phase, each vector from the input data is compared to the initial reference vectors. The reference vector (node) that is most similar to the input vector, on the basis of euclidian distance between the input and reference vector, is identified. This reference vector is then modified to reduce the difference with the input vector. The degree to which this reference vector is modified is based on a learning rate parameter in the algorithm. The adjacent reference vectors, which are within a user-specified radius to the identified reference vector, are also modified to a degree that diminishes with distance and in proportion to the learning rate parameter. This results in both the identified node and the neighboring nodes being modified by the input vector. This procedure is repeated for all of the input vectors and the entire procedure is then iterated until the quantization error (that is, the error with the smallest aggregated euclidian distances from the input data) is minimized. It should be noted that several combinations of the learning rate, radius of the training area, and number of iterations were found to create a final map with an acceptably small quantization error, but in all cases these maps were nearly identical. Therefore the final map created in this way is quite robust, and is not sensitive to the choice of user-specified parameters. In the final product, the reference vectors have become ordered, i.e. the vectors that are close together in the map have similar patterns; dissimilar patterns are far apart on the map. This organization of the map is a result of the training procedure updating not only the reference vector that is most similar to the input vector but also updating adjacent reference vectors, and is thus a self-organization of the final map and inherent in the training procedure. In using the SOM algorithm to classify SLP patterns, similar synoptic circulation patterns will be clustered together on the map (Figure 1). This is an important attribute of the approach, since it 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 (1) and further details on the application of the SOM algorithm to climate data can be found in Hewitson and Crane ()..3. Development of Master SOMs The SOM algorithm is used to codify SLP patterns and trends in the Arctic for the DJF and JJA seasons for the periods 1991, and As input to the SOM algorithm, we selected ten models, described in Section.1 and listed in Table I, and extracted daily SLP data for DJF and JJA for the three time periods of interest. These data were used to create two 7 5 maps of 35 representative synoptic patterns that span the range of circulation variability for the DJF and JJA seasons (Figure 1). By including model output from all of the models and the three time periods of interest, we ensure that the map that is created will span the full range of synoptic circulation patterns present in the data. The maps created in this way will be referred to as the master SOMs for DJF and JJA. (Slightly different color scales are used to prevent any loss of detail in these maps, and hence these scales should be kept in consideration when analyzing the maps.) These master SOMs are a nonlinear projection of the probability density function of the input data and display the primary atmospheric circulation features expected in the Arctic during the boreal summer and winter. For the DJF master SOM (Figure 1(a)) patterns representative of strong high pressure over Asia and the Arctic Ocean are present on the right side of the map, with the strongest high-pressure patterns in the upper right 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 basin clustered in the lower left corner of the SOM. Finally, patterns with a well-developed Aleutian low are found on 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 of the Arctic front (Serreze et al., 1) (lower left and central columns), low pressure over the Arctic Ocean and/or around Greenland (upper left corner), and high pressure over the Arctic Ocean and/or Greenland (lower Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

8 134 J. J. CASSANO, P. UOTILA AND A. LYNCH right corner) are evident. Generally weak circulation patterns over the Arctic are represented by nodes in the upper 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 as broadly representative a map as the master SOM created by using all ten models realizations. The use of a relatively large SOM allows for subtle changes in these patterns to be represented on the map, with variations in the intensity and position of the dominant pressure centers being identified on the map. Further generalization is also possible by considering the euclidian distance between adjacent nodes and grouping those nodes within a specified minimum distance. We have computed a distortion surface that shows the (a) 4 6 (6,) (,) (6,4) (b) (,4) Figure. (a) Node residence proportions during the period 1991 for all 1 models for DJF. Residence proportions outside the range % are significantly different from the expected value of.86% at the 95% confidence level. (b) Sammon map for DJF SOM Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

9 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 135 euclidian distance between nodes of the map projected to a set of -d vectors using a Sammon mapping scheme (Sammon, 1969) (Figures (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 residence frequency of each synoptic pattern represented by the individual nodes to be determined for a particular data set DJF circulation Figure (a) displays the residence frequency of each node for the ten-model ensemble for the DJF 1991 period. This figure indicates that nodes near the top right corner of the map, associated with strong high pressure over Asia and/or the Arctic Ocean, and the top left corner of the map, associated with a moderate Aleutian low and high pressure over western Canada and Alaska, occur most frequently, with residence frequencies generally greater than 3%. Note that if all circulation types occurred equally frequently, each node would display a residence frequency of.86%. All of the nodes in the top two rows of the SOM, except nodes (4,), (,1), and (3,1) have a residence proportion that is significantly greater than the expected value of.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.6%, and all but node (6.3) occur significantly less frequently than the expected value of.86% at the 95% confidence level. The Sammon map for the DJF master SOM shown in Figure (b) shows that nodes in the upper left corner of the map are closer in the euclidian sense than nodes in other portions of the map. The information displayed on the Sammon map is useful in interpreting the relationship between groups of circulation patterns, and generally conforms to expectations that arise simply from viewing the node SLP patterns. These model ensemble node residence frequencies can be compared to residence frequencies calculated with reanalysis data. Figure 3 shows the residence frequency plot for NNR for the period and the ERA-4 reanalysis for the period The differing time periods used for the ERA-4 and NNR were chosen to match the time period from the IPCC model data analyzed in this paper (ERA-4) and to provide a longer data set representative of the late twentieth century (NNR). Despite the different time periods used (a) (b) (c) 4 6 Figure 3. Node residence proportions for the NCEP/NCAR reanalysis (NNR) and the ERA-4 reanalysis 1991, showing (a) NNR DJF and (b) ERA-4 DJF. Residence proportions outside the range % (for NNR) and % (for ERA-4) are significantly different from the expected value of.86% at the 95% confidence level. (c) Node residence proportions for the NNR for showing differences between the positive and negative phases of the Arctic Oscillation during DJF Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

10 136 J. J. CASSANO, P. UOTILA AND A. LYNCH and different reanalysis procedures, the residence frequency plots are qualitatively similar. Comparison of node residence frequencies for the ERA-4 and NNR data for the common period of 1991 (not shown) indicate a mean absolute difference in residence frequency of.5% between the reanalyses. Both reanalyses have high residence frequencies (generally greater than 3%) for nodes on the right side of the map, excluding the top right corner of the map (somewhat weaker and spatially confined Icelandic low patterns), and upper left corner of the map (moderate to strong Aleutian low patterns with limited areas of high pressure over the Arctic land areas). Both reanalyses have low residence frequencies for nodes with very strong high pressure dominating the Arctic (far upper right corner; nodes (4,), (5,), and (6,)) and nodes with an extended North Atlantic storm track and/or strong Aleutian low (lower left corner). The residence frequencies of the model ensemble differs from the reanalyses in that the models predict a greater occurrence of strong, sometimes excessively strong, Arctic high-pressure patterns (upper right) at the expense of Icelandic low patterns (middle right). This is consistent with results presented by Bitz et al. () that indicated that mean SLP for DJF in eight Atmospheric Model Intercomparison Project (AMIP1) models was too high over the Arctic Ocean relative to NNR and ERA-15 reanalyses. In general, the distribution of node residence proportions for the ensemble model data is more uniform across the map than for the reanalyses. Since the master SOM was trained using the ensemble model data, and in some cases the model data represents atmospheric circulation regimes that are either not observed or rarely observed, it should be expected that the model ensemble would more uniformly fill the SOM space, whereas the reanalysis data would have quite low node residence proportions for simulated circulation regimes that are either unrealistic or occur very infrequently. Node residence proportion maps for individual model realizations (not shown) are also less uniform than those for the ensemble, because any single model realization is less likely to represent the broad range of synoptic circulation 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 negative phases of the Arctic Oscillation (AO) in January (Figure 3(c)) show appreciable differences in the dominant synoptic circulation patterns for these two phases of the AO. For the positive phase of the AO, nodes across the bottom and far left side of the map (those nodes associated with a synoptic pattern dominated by the Icelandic low, the Aleutian low, and/or low pressure in the Arctic basin) have a higher residence frequency than for the cases with a negative phase of the AO. This matches the broad circulation changes expected by the changing phase of the AO. 3.. JJA circulation Figure 4(a) displays the residence frequency of each node for the ten-model ensemble for the JJA 1991 period. The circulation patterns that occur most frequently are those along the bottom and right edges of the map, with weaker maxima in the node residence frequency in the center of the map. All of the nodes along the bottom and right edges of the map, except node (6,) have node residence proportions significantly greater than the expected value of.86% at the 95% confidence level. These nodes correspond to circulations dominated by a strong expression of the Arctic front over Siberia (lower left and center; Serreze et al., 1), 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 left portion of the map) are less frequent in the model ensemble than the expected node frequency of occurrence of.86%. The Sammon map for the JJA master SOM shown in Figure 4(b) shows that nodes in the upper right corner of the map are closer in the euclidian sense than nodes in other portions of the map, and this generally conforms to expectations that arise simply from viewing the node SLP patterns in Figure 1(b). The residence frequencies for the ERA-4 and NNR data for JJA are shown in Figure 5. As for the DJF season, both the ERA-4 and NNR residence frequency plots are qualitatively similar to each other despite representing different time periods and reanalysis systems. Comparison of the node residence frequencies for the two reanalysis data sets for the common period 1991 (not shown) indicate a mean absolute difference of the residence frequency of.3%. However, the reanalysis residence frequency patterns are quite different from the model ensemble pattern shown in Figure 4. The reanalysis data tends to be dominated by cyclonic circulation patterns over the central and eastern Arctic (upper left, excluding the leftmost column Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

11 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES (a) 4 6 (,) (6,) (6,4) (,4) (b) Figure 4. (a) Node residence proportions during the period 1991 for all ten models for JJA. Residence proportions outside the range % are significantly different from the expected value of.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 summer front (lower left, excluding the leftmost column of the map, and center) Individual models DJF It is important to remember that Figure displays results for the ten-model ensemble, but that individual models can differ quite dramatically from this ensemble behavior. While plots of the node residence frequencies for each of the individual model members of the ensemble are not shown here (in the interest of space), these results will be discussed below. Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

12 138 J. J. CASSANO, P. UOTILA AND A. LYNCH Of the ten models, three models (GISS-AOM, GFDL-CM.1, and CNRM-CM3) have a pattern of node residence frequencies that is most similar to the ERA-4 and NNR reanalysis results shown in Figure 3. Two of the models (MRI-CGCM-3. and GISS-ER) have circulations dominated by the nine nodes in the upper right corner of the map (65 to 81% residence frequency) representing strong Arctic high pressure across a broad region. These two models also display very low node residence frequencies for any of the cyclonically dominated patterns on the remainder of the map space. The CCSM3 model, considered both as an eight-model ensemble and as individual model realizations, has the largest residence frequencies across the lower portion of the map, with distinct maxima in the lower left corner. These nodes correspond to an extended North Atlantic storm track and strong Icelandic low. The Aleutian low is also well represented in these patterns. The two MIROC3. model simulations are dominated by Aleutian low circulation patterns that extend from the upper left to lower central portion of the map, with low frequency of occurrence of both a pronounced Icelandic low and a strong Arctic high-pressure patterns. The last two models from the ensemble (IPSL-CM4 and GFDL-CM.) have the largest node residence frequencies across the top of the map (weak Aleutian low and strong Arctic high-pressure patterns) Individual models JJA Given the poor agreement between the residence proportions for the different circulation patterns in the ensemble model data and the reanalyses for the JJA season (Section 3.), it is worth considering individual model results to determine if certain models are able to accurately reproduce the distribution of current climate circulation patterns in the Arctic. Two models (CCSM3 and MIROC3. (medres)) produce a circulation pattern distribution that is qualitatively most similar to that found in the reanalysis data, although both models tend to have higher residence proportions in the lower left corner of the map (low pressure over Siberia) and lower residence proportions in the upper right corner of the map (weak circulation pattern) compared to the reanalyses. Three other models (MIROC3. (hires), GFDL-CM., and GFDL-CM.1) have somewhat similar patterns of node residence proportion to the reanalyses, although these models tend to shift the maximum residence proportion one to two columns to the right on the map, resulting in fewer occurrences of deep low pressure over the Arctic Ocean compared to the reanalyses. The remaining five models (GISS-ER, GISS- AOM, CNRM-CM3, MRI-CGCM3..3, and IPSL-CM4) have summer circulations dominated by nodes on the right 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 stark contrast to the reanalysis results (Figure 5), while the large residence proportion for nodes in the upper right corner 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 occurrence of particular circulation regimes in the SRES A1B scenario IPCC model simulations, and temperature and precipitation anomalies associated with the different circulation patterns. All of the changes presented in this section 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 trends that would arise solely from differences between the twentieth century model and reanalysis data discussed in Section DJF trends Figure 6 displays the relative change in node residence frequency between the 1991 and time periods and the to 91 1 time periods, with significant trends shown in gray. The relative change is calculated as the difference in the node residence frequency between the two time periods divided by the averaged node residence frequency for the two time periods. The largest relative changes occur in the first half of the twenty-first century, consistent with changes in global temperature predicted for the Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

13 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES (a) (b) Figure 5. Node residence proportions for the NCEP/NCAR reanalysis (NNR) and the ERA-4 reanalysis 1991, showing (a) NNR JJA and (b) ERA-4 JJA. Residence proportions outside the range % (for NNR) and % (for ERA-4) are significantly different from the expected value of.86% at the 95% confidence level (a) (b) Figure 6. Percentage difference in node residence proportions for DJF for the time periods (a) versus 1991 and (b) 91 1 versus Statistically significant trends at the 95% confidence level are highlighted in gray SRES A1B scenario (ACIA, 4). The largest relative increases, in excess of 3%, are found in the lower left portion of the map and represent extended North Atlantic storm track patterns. These nodes correspond to patterns that did not have a large residence proportion in either the multimodel ensemble or in the reanalysis data for the period of The largest decreases, in excess of 3%, are found for nodes in the Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

14 14 J. J. CASSANO, P. UOTILA AND A. LYNCH upper right corner of the map (strong Arctic high-pressure patterns). These are patterns that the ensemble of models tended to overpredict relative to the reanalysis data (Figures (a) and 3) in the last decade of the twentieth century. This is consistent with an increasingly positive AO index, as has been suggested by earlier observational (e.g. Walsh et al., 1996; Thompson and Wallace, ) and modeling studies (e.g. Gillett et al.,, 3). In the second half of the twenty-first century, the trend of decreasing occurrence of strong Arctic high-pressure patterns continues, although with smaller magnitude. Moderate increases in the node residence frequency 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 the century. Overall, in the twenty-first century, the ensemble of simulations indicates a large relative decrease in 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 would be congruent with an expectation for increasingly positive AO and North Atlantic Oscillation (NAO) phases in the coming century. Temperature and precipitation anomalies for each node were calculated as the difference between the mean of the model data that mapped to a particular node (i.e. for all samples that had a SLP pattern that corresponded to a particular node in Figure 1) and the mean of the entire ensemble data set (all of the models for all three decades 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 the organization of temperature anomalies such that cold anomalies are associated with nodes in the upper right corner of the map and warm anomalies are associated with nodes in the lower left corner of the map. The cold anomalies, in excess of 5 K, are found over Eurasia, the Arctic basin, and northwestern North America and are associated with strong Arctic high-pressure patterns. The warm anomalies, in excess of 5 K, are most pronounced over Eurasia with some warming in northwestern North America as well. The coherence of this mapping of temperature anomalies to the SOM expresses the strong relationship between circulation and temperature changes. In this case, these warm patterns are driven by the extended North Atlantic storm track, which acts to transport warm air into Eurasia, and a strong Aleutian low which advects warm air into northwestern North America. On the basis of the changing residence frequency of the nodes in the twenty-first century (Figure 6), this suggests an increase in circulation patterns that favor warm conditions in Eurasia and northwestern North America, with a reduced occurrence of circulation regimes that are associated with the coldest temperatures in Eurasia and North America. Precipitation anomalies display a similarly coherent signal relative to the master SOM. The largest precipitation anomalies (Figure 7(b)) are found along the west coast of North America and are driven by changes in the circulation over the North Pacific Ocean. Circulation patterns with a pronounced Aleutian low are generally associated with positive precipitation anomalies along the Canadian west coast and in southeast Alaska, although slight shifts in the position of this low-pressure center can shift the positive precipitation anomaly further north and west leaving the west coast of Canada with reduced precipitation (e.g. nodes (,), (,3), and (,4)). Circulation patterns with a strong and/or extended Icelandic low are also associated with positive precipitation anomalies in the North Atlantic, along the east coast of Greenland, and extending into Scandinavia. Negative precipitation anomalies are found in the North Atlantic for nodes in the upper right corner of the map, which are dominated by strong high pressure. Given the increasing frequency of occurrence of nodes in the lower left corner and bottom portions of the map, this suggests an increase in circulation patterns that favor increased precipitation along the east coast of Greenland, in the North Atlantic extending into Scandinavia, and along the western coast of North America. The decreasing frequency of occurrence of nodes in the upper right corner of the map implies a reduction in circulation patterns that are associated with reduced precipitation events in the North Atlantic, around Greenland, and along the northwestern coast of North America. 4.. JJA trends Relative changes in the node residence proportion over the twenty-first century for the JJA season are shown in Figure 8 (significant trends are shown in gray). As for the DJF season, the largest changes occur in the first Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)

15 Copyright 6 Royal Meteorological Society (1,4) (,4) 4 (1,3) (,3) 6 (1,) (,) (,4) (,3) K (3,4) (3,3) (3,) (3,1) (3,) (4,4) (4,3) (4,) (4,1) (4,) 4 6 (5,4) (5,3) (5,) (5,1) (5,) (6,4) (6,3) (6,) (6,1) (6,) Figure 7. (a) Temperature and (b) precipitation anomalies for DJF associated with each node on the DJF master SOM (Figure 1(a)). Blue shading represents values more than one standard deviation below the mean and red shading represents values more than one standard deviation above the mean. The maps in each panel are oriented such that the International Date Line points toward the center bottom of each panel (a) (,1) (1,1) (,1) (,) (,) (1,) (,) ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 141 Int. J. Climatol. 6: (6)

16 Copyright 6 Royal Meteorological Society (b) Figure 7. (Continued) mm day (5,4) (4,4) (3,4) (,4) (1,4) (,4).5 (5,3) (4,3) (3,3) (,3) (1,3) (,3) 1 (6,) (5,) (4,) (3,) (,) (1,) (,) 15 (6,1) (5,1) (4,1) (3,1) (,1) (1,1) (,1) (6,4) (6,3) (6,) (5,) (4,) (3,) (,) (1,) (,) 14 J. J. CASSANO, P. UOTILA AND A. LYNCH Int. J. Climatol. 6: (6)

17 ARCTIC SYNOPTIC WEATHER PATTERNS IN THE TWENTIETH AND TWENTY-FIRST CENTURIES 143 half of the twenty-first century (between the time periods 1991 and 46 55) (Figure 8(a)), with smaller changes in the second half (Figure 8(b)). In the twenty-first century, circulation patterns represented by 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 ensemble compared to the reanalysis data for the period Circulation patterns associated with nodes across most of the bottom of the map are projected to have a decreased frequency of occurrence, and correspond to nodes that were overpredicted by the models relative to the reanalysis data. In general, these changes represent an increased occurrence of patterns with low pressure over the Arctic Ocean and around Greenland and 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 viewed with caution, given the level of disagreement between the modeled and observed node residence proportion during the latter part of the twentieth century (Figures 4 and 5). Analysis of only those ensemble members that more closely reproduced the current JJA node residence proportion (as discussed in Section 3.4) revealed similar patterns as those discussed above for the entire ensemble, and provides some confidence in the results for the entire ensemble. The temperature and precipitation anomalies associated with each node for JJA are shown in Figure 9. Note the more restricted scale used in Figure 9 compared to the boreal winter anomalies in Figure 7, reflecting the smaller differences between circulation patterns during the summer season. The nodes in the upper left corner of the map, which the ensemble predicts to have an increased frequency of occurrence in the twenty-first century, are associated with warm anomalies north of Scandinavia and extending into the eastern Arctic basin and cold anomalies over much of Alaska and portions of northwestern Canada. This pattern of temperature anomalies is consistent with the temperature advection patterns associated with an SLP pattern dominated by low pressure over the Arctic Ocean. Nodes with a decreasing frequency of occurrence (bottom right portion of map) are associated with cold anomalies in the eastern Arctic basin and warm anomalies over Alaska and eastern Siberia, and are consistent with the SLP pressure patterns dominated by high pressure over the Arctic Ocean. The ensemble model predictions analyzed in this paper thus indicate a trend toward circulation patterns that favor cooler conditions (less warming) in Alaska over the next century relative to locations in the eastern Arctic. This tendency is counter to the recent strong warming trend observed in Alaska (ACIA, (a) (b) Figure 8. Percentage difference in node residence proportions for JJA for the time periods (a) versus 1991 and (b) 91 1 versus Statistically significant trends at the 95% confidence level are highlighted in gray Copyright 6 Royal Meteorological Society Int. J. Climatol. 6: (6)