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

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  • INTERNATIONAL JOURNAL OF CLIMATOLOGYInt. J. Climatol. 26: 10271049 (2006)Published online 6 March 2006 in Wiley InterScience ( DOI: 10.1002/joc.1306


    JOHN J. CASSANO,a,* PETTERI UOTILAb and AMANDA LYNCHba Cooperative Institute for Research in Environmental Sciences and Department of Atmospheric and Oceanic Sciences, University of

    Colorado, Boulder, CO, USAb School of Geography and Environmental Science, Monash University, Monash, Australia

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


    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


    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 earths 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:

    Copyright 2006 Royal Meteorological Society


    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: 10271049 (2006)


    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 IPCCs fourth assessment report. Daily fields of SLP, surface temperature,and precipitation amount for the DJF and JJA seasons for the time periods 19912000, 20462055, and20912100 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 51N 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 19912000 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 19912000and from the NNR (Kalnay et al., 1996) for the period 19571997 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 19912000 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 1 1 1 http://aom.giss.nasa.govGISS-ER 1 1 (hires) 1 (medres) 1 1 pap2001.pdfNCAR CCSM3 8

    Copyright 2006 Royal Meteorological Society Int. J. Climatol. 26: 10271049 (2006)


    2.2. Description of the self-organizing map algorithmThe 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-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.



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