Synoptic weather pattern controls on temperature in Alaska

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116,, doi: /2010jd015341, 2011 Synoptic weather pattern controls on temperature in Alaska Elizabeth N. Cassano, 1 John J. Cassano, 1,2 and Matt Nolan 3 Received 16 November 2010; revised 10 March 2011; accepted 15 March 2011; published 8 June [1] Data from the National Centers for Environmental Prediction/National Center for Atmospheric Research and European Center for Medium Range Weather Forecasts 40 year reanalyses are used to relate large scale synoptic circulation patterns to local weather at several locations across Alaska. These results are compared to available National Weather Service observations to demonstrate the utility of this method such that it can be applied in future work at locations where local observations are not available. The focus of these comparisons is on surface observations of temperature. The results from the two reanalysis data sets match well to each other and to the observations. Synoptic patterns associated with warm/cold days at five National Weather Service stations representing different climate regions throughout Alaska are identified. In addition, a method to attribute a change in climate to circulation and noncirculation differences is applied to a known climate shift, the Pacific climate shift of 1976, which was associated with an increase in temperatures throughout Alaska. The results from this analysis show that general warming rather than changes in circulation is primarily responsible for the increase in temperatures after Citation: Cassano, E. N., J. J. Cassano, and M. Nolan (2011), Synoptic weather pattern controls on temperature in Alaska, J. Geophys. Res., 116,, doi: /2010jd Introduction [2] One of the purposes of climate studies is to understand the typical weather expected in a specific location. However, there is often a need to have knowledge of weather on shorter temporal scales and to understand the scenarios that will bring warm/cold days, e.g., for scientists studying ecosystem dynamics, residents of locations with no weather stations, Federal land managers, etc. In a place such as Alaska, this information can be difficult to obtain given that weather stations are much sparser than any other state in the United States, especially in arctic Alaska where only one weather station has been continuously operational without substantial missing observations over the past 50 years [McBean et al., 2005]. Scientists and land managers trying to understand the impacts of climate change here do not have sufficient station data to work with to explore the climate drivers of this change and therefore need alternative sources of information. For example, those studying shrub or tree line expansion, glacier volume change, permafrost degradation, and terrestrial and aquatic ecosystem dynamics have all noted substantial changes in their observables over the past 50 years, yet because of the paucity of nearby weather stations covering this time period, their ability to 1 Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, Colorado, USA. 2 Also at Department of Atmospheric and Oceanic Sciences, University of Colorado at Boulder, Boulder, Colorado, USA. 3 Institute of Northern Engineering, University of Alaska Fairbanks, Fairbanks, Alaska, USA. Copyright 2011 by the American Geophysical Union /11/2010JD demonstrate causal attribution to climate is hampered [e.g., Serreze et al., 2000; McBean et al., 2005]. [3] As has been well documented, there have been significant changes observed in the Arctic over the past 50 years [Overpeck et al., 1997; Serreze et al., 2000; Hinzman et al., 2005; Parkinson and Cavalieri, 2008], with Alaska experiencing general warming during this time [Stafford et al., 2000; Lynch et al., 2004; Wendler and Shulski, 2009; Wendler et al., 2010]. Much of the temperature increase observed in Alaska south of the Brooks Range occurred with a jump in temperature in association with a shift in the climate around 1976 [Hartmann and Wendler, 2005; Shulski and Wendler, 2007; Wendler and Shulski, 2009]. This jump in temperature was coincident with a shift in the Pacific Decadal Oscillation (PDO) that occurred at this time, which changed from a primarily negative phase to a primarily positive phase [Mantua and Hare, 2002]. Some of the changes associated with this climate shift were a deepening of the Aleutian Low and changes in sea surface temperatures (SSTs) in the tropical and northeastern Pacific [Miller et al., 1994; Mantua and Hare, 2002]. Hartmann and Wendler [2005] studied the climatology in Alaska in the context of this shift and found temperature increases over all of Alaska over the time period of study ( ). However, interannual temperature trends before and after 1976 showed cooling for both time periods except for Barrow, which has experienced warming since This suggests a different character of temperature change on either side of the Brooks Range. [4] Some previous studies have investigated the relationship between the larger synoptic circulation and surface temperature in Alaska. Stone [1997] showed that warmer temperatures in the western Arctic, including Alaska, are 1of19

2 associated with increased cyclonic activity in the North Pacific, which advects clouds northward. Colder temperatures are associated with anticyclonic flow in the Beaufort Sea. Two semipermanent synoptic features in the western Arctic, the Aleutian Low and the Beaufort High, are important controls on the climate in Alaska [Overland et al., 1999; Rodionov et al., 2005; Shulski et al., 2010]. A study of the summer temperatures in Alaska s interior showed that warm and dry conditions are associated with high pressure to the north/northeast of Alaska. Cool and moist conditions are associated with an eastward shift of the East Asian trough and a stronger than normal Pacific subtropical high that bring moist air from the southwest [Barber et al., 2004]. Studies relating large scale climate indices to surface temperature show that near normal to warmer temperatures are observed during El Niño winters [Papineau, 2001; Hess et al., 2001], with the El Niño Southern Oscillation signal moderated by the signal of the PDO [Papineau, 2001]. During La Niña winters, significantly colder than normal temperatures were observed statewide, though, in general, the impact of El Niño and La Niña are weak north of the Brooks Range [Papineau, 2001]. [5] Circulation pattern classification 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 synoptic drivers that control the local climate, which may be hidden by monthly or seasonal means of these fields [Barry and Perry, 2001; Hanson et al., 2004]. 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 [2001], and references therein, provide a detailed overview of synoptic climatology and its applications. [6] In this paper, the method of self organizing maps (SOMs) is used to relate large scale synoptic circulation to weather on a local, daily scale using the surface fields from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) and European Center for Medium Range Weather Forecasts 40 year (ERA40) global reanalysis data sets as a substitute for local station data. Since many regions of Alaska lack local station data (within 100 km), including most of the national parks in Alaska, most of Arctic Alaska, and most glaciated regions in the state, the use of reanalysis data or some other proxy is necessary. First, we compare the reanalysis surface fields to a variety of National Weather Service (NWS) station observations throughout the state. Then we use the SOM method (more information regarding the SOM method is located in section 2.1) to provide information on which synoptic scale weather patterns bring the warmest/coldest weather to the locations studied, what the frequency and seasonal distribution of those patterns are, and how the frequencies of synoptic patterns have changed over the past 50 years with a focus on changes around The ultimate goal of this research is to present a method that will aid scientists, land managers, and local populations in understanding synoptic scale weather patterns as they relate to climate change in Alaska. Another goal of this analysis is to demonstrate that the methodology and data used are appropriate and produce reasonable results so that future work can, for example, analyze synoptic weather patterns as they relate to observed changes in glacier mass balance at remote sites in Alaska where long term weather data are not available. Furthermore, this paper will provide a framework to understand observed changes in local climate (i.e., changes in the frequency of weather patterns or intrapattern changes). 2. Data and Methodology 2.1. Self Organizing Maps [7] Classification of data involves two steps: the definition of classes and the assignment of each case to the most appropriate class. For the first step, there are two methods by which classes are defined. The first is that classes are subjectively chosen a priori. This involves prior knowledge of how to best represent the data. In the second method, the different classes are determined during the classification process. In general, there are three main types of classification methods: subjective, objective, and a hybrid of these two methods in which classes are chosen subjectively but assigned by an objective or automated process [Huth et al., 2008]. Self organizing maps fall into the objective category and can further be described as a nonlinear method. Reusch et al. [2005a] compared the classification using the SOM method to that from using principal component analysis (PCA). They found the SOM classification to be more robust and that the PCA failed to adequately identify the known spatial patterns. [8] Philipp et al. [2010] compared different classification methods based on five basic properties describing the resulting classes. They found that subjective classifications differ considerably from all other methods. The resulting classification from the SOM showed less agreement with those from the other objective classifications; however, this was primarily attributed to an incomplete analysis, and they recommended reevaluation in the future. Beck and Philipp [2010] evaluated 16 circulation type classification methods representing four basic classification approaches and found the optimization algorithms (the category that SOMs fall into, though SOMs were not explicitly evaluated in this paper) should be preferably used for analyses such as longterm evaluation of circulation types. A comprehensive overview of different atmospheric circulation classification methods is given by Huth et al. [2008]. [9] For this analysis, we use SOMs [Kohonen, 2001] to create a synoptic climatology for Alaska and adjacent regions. Cassano et al. [2007] discussed the utility of using the SOM method for synoptic climatology studies in greater detail. Kohonen [2001] provided a detailed description of the SOM algorithm, and Hewitson and Crane [2002] provided additional information on the application of the SOM technique to climate data. Many other studies have also used SOMs for atmospheric analyses [e.g., Cavazos, 1999, 2000; Reusch et al., 2005a, 2005b; E. N. Cassano et al., 2006; J. J. Cassano et al., 2006; Lynch et al., 2006; Finnis et al., 2009; Schuenemann and Cassano, 2010]. [10] The SOM technique employs a neural network algorithm that uses unsupervised learning to determine generalized patterns in data. This technique reduces the dimensions of large data sets by grouping similar data records together and organizing them into a two dimensional array, referred to as a map (note the terms map and SOM will be 2of19

3 used interchangeably below). As a result, large, multidimensional data sets are reduced to more easily interpreted forms. Used in this way, the SOM algorithm may be considered a clustering technique, but unlike other clustering techniques, the SOM method does not need a priori decisions on data distribution and is instead trained when processing the data itself. The resulting classes are structured such that more classes are placed where the data density is highest. This has the advantage that the SOM classification does not result in the snowball effect, where one huge class is formed in an area of large data density with many smaller classes containing a small number or no observations in each class [Kalkstein et al., 1987; Huth et al., 2008]. The resulting map is organized such that similar synoptic patterns are located in the same portion of the map, with contrasting patterns located on opposite sides of the map (e.g., Aleutian lows in one corner of the SOM and Beaufort/Chukchi lows in the other corner). The final distribution of the patterns is arbitrary in the sense that the patterns could be located in any part of the map, though the final patterns will be the same (i.e., Aleutian lows will be represented but may be in any part of the map) and the most similar patterns will always be adjacent on the map. [11] The robustness of the SOM method is not sensitive to the resolution, quality, or time period of the data per se. The patterns that emerge from the SOM will depict the range of conditions represented in the input data space. Therefore, the usage of the SOM method, as is the case with any statistical method, is dependent on the quality of the input data to reproduce realistic results. In this application, the sea level pressure (SLP) field from the NCEP/NCAR and ERA40 reanalysis projects (described in more detail in section 2.2) is likely an accurate representation of reality and thus is adequate for our intended purpose of classifying the near surface synoptic circulation patterns. The SOM analysis can provide additional details of the synoptic climatology of an area than can be obtained from other methods such as averages. Since we are considering daily temperature, the use of monthly or seasonal circulation averages would likely mask details relevant to the understanding of the mechanisms that drive the daily weather. Another advantage to using the SOM method is the relative ease of interpreting the results. The different synoptic types that result from the analysis are displayed on a rectangular array that quickly shows the typical patterns that span the data space. Figure 1. indicated. MapofthestudyareawithNWSlocations 2.2. Data [12] Gridded daily mean SLP data from the NCEP/NCAR [Kalnay et al., 1996] and the ERA40 [Uppala et al., 2005] reanalysis projects were used to train the SOM to characterize synoptic patterns for the study area. The time period of the SLP data used was for the ERA40 data and for the NCEP/NCAR data. The SLP data from both reanalysis data sets were interpolated using the Cressman interpolation method to a polar stereographic grid with a resolution of 50 km. The interpolation was done so that each point would have equal weight in the SOM training. The data were originally on a latitude/longitude grid, so the points in the northern part of the domain would have greater weight than the southern part since more points would have been in a given area. The study area was chosen to be large enough to show the range of synoptic patterns that impact Alaska. In addition, daily 2 m temperature data from grid cells encompassing several NWS locations were extracted from the data set for the time periods (NCEP/NCAR) and (ERA40). [13] SLP was chosen for the analysis to focus on the relationship between the near surface circulation and the surface variables. SLP anomalies were used as the basis for the synoptic climatology because the SLP gradients, rather than absolute values of SLP, are responsible for determining the near surface circulation and therefore are of most interest in our analysis. SLP anomalies were calculated for each day by subtracting the mean SLP over the analysis domain for that day from the grid point values of SLP for the same day. The resulting field of SLP anomalies highlights the gradients, rather than the absolute magnitude, of SLP, and this can be used to define the synoptic patterns in the SOM algorithm. In addition, SLP values from locations with an elevation of greater than 500 m were filtered out of the analysis because of errors associated with the reduction of surface pressure to SLP for high elevation locations and locations with complex terrain [Wallace and Hobbs, 1977; Sangster, 1987; Pauley, 1998; Mohr, 2004]. This masking was not applied to the gridded temperature data that were mapped to the SOM once the final patterns were determined by the SOM analysis. [14] We compared surface air temperature from several NWS first order stations to the closest grid cell from the reanalysis data. The source for the NWS data was the National Climatic Data Center s Global Summary of the Day data set. This was done to determine the suitability of utilizing reanalysis data in lieu of surface station data to obtain meaningful results, as has been done at other remote locations [e.g., Radić and Hock, 2006; Hock et al., 2009]. The stations were selected to represent different climate regimes over the state of Alaska: Barrow (Arctic), Nome (west central coast), Fairbanks (interior or continental), Anchorage (Cook Inlet), and Juneau (south coast or maritime) [Shulski 3of19

4 Table 1. Elevation of Each NWS Station, the Latitude and Longitude of the NWS Station and the Closest Grid Point in the Reanalysis Data Sets, and the Distance Between the Actual Station Location and the Closest Grid Point a Station Elevation (masl) Elevation NCEP/NCAR Grid Cell (masl) Elevation ERA40 Grid Cell (masl) Actual Lat/Lon Grid Lat/Lon Distance Between Actual and Grid Lat/Lon (km) Barrow N/ W N/ W 9.1 Nome N/ W N/ W 34.4 Fairbanks N/ W N/ W 25.7 Anchorage N/ W N/ W 23.4 Juneau N/ W N/ W 13.6 a Abbreviations are masl, meters above sea level; lat/lon, latitude/longitude. and Wendler, 2007] (Figure 1). Table 1 shows the locations of each NWS station and the closest grid point in addition to the elevations of the stations for the observed and the two reanalysis data sets. The 2 m temperature from the reanalysis data sets was interpolated to the elevation of the surface observation using the standard atmosphere lapse rate. Table 2 shows the correlation between the NWS surface temperature and 2 m temperature data from both reanalysis data sets on an annual and seasonal basis. The correlations between the NCEP/NCAR and the NWS data were calculated for For the ERA40/NWS correlations, the time period of the calculation was The NCEP/NCAR correlations show good agreement between the data sets with the exception of the June July August (JJA) correlations, which are low except for Barrow. In many cases, the correlations are better between the NCEP/NCAR and NWS data than that for ERA40 data. Annual time series comparisons of temperatures show a cold bias in the reanalysis at Nome, Anchorage, and Juneau; a warm bias for ERA40 in Fairbanks; and the magnitudes of the time series matching well in Barrow for both data sets and in Fairbanks for the NCEP/NCAR (Figure 2). The observed trends also are adequately represented. A cold bias has been observed in general in the ERA40 over the Arctic [Bromwich et al., 2002; Bromwich et al., 2007], and a discontinuity in data assimilation in 1997 makes long term temperature trend analysis problematic [Screen and Simmonds, 2011]. Since our analysis is focused on the weather patterns that lead to warm/cold days, it is more important that the reanalysis data in general capture the trends and match the observations well in a relative sense rather than match the magnitudes. In Figure 2, the dashed line at 1976 shows a jump in temperature at the stations studied, which is well represented in the reanalysis data sets (more on this temperature jump in section 4). [15] Since our main interest is relating large scale climate data to daily weather, correlations between the NWS and reanalysis data are also calculated on a daily basis (Table 3). For the annual column, the correlations are calculated using all days available, and for each season, the correlations are calculated using just the days in that season. Also included in Table 3 are the mean biases calculated for each day for the time periods of for NCEP/NCAR NWS data and for the ERA40 NWS data. As was the case for the annual and seasonal time series data, the correlations show the data match quite well, and both reanalysis data sets have better daily correlations than annual/seasonal correlations with the NWS data. In general, cold biases are observed more often than warm biases, and the biases are comparable between the reanalysis data sets, with ERA40 being better for some stations and seasons and NCEP/ NCAR for others. Though the biases are somewhat large for some seasons and some stations, as has been found in other similar comparisons [e.g., Radić and Hock, 2006], the fact that the correlations and trends match well lends confidence that these surface fields are an adequate substitute for station data for many types of analyses, particularly our application of determining the relative weather that occurs in a location due to different synoptic patterns as determined by the ERA40 and NCEP/NCAR reanalysis data sets Data Analysis [16] For this analysis, a total of 35 SLP patterns were selected to represent the archetypal synoptic patterns for the study area (encompassing Alaska and surrounding regions), resulting in a 7 5 SOM (Figure 3). SOMs of this size have been found suitable for synoptic climatology studies since the SOM compactly displays the major circulation patterns, while still distinguishing important patterns in the data, such as varying intensity and positions of high and low pressure centers [Hewitson and Crane, 2002; J. J. Cassano et al., 2006; Lynch et al., 2006]. [17] Once the SOM algorithm has identified the patterns in the training data, the final step in the analysis is the association of each daily field of SLP with a single pattern on the SOM. The SLP data from each day is compared to each pattern on the SOM to determine which pattern it most closely matches. The squared difference between the daily SLP and the SOM node SLP is used as the measure of the similarity for this analysis. This results in a list of days associated with each SOM node. This list of days associated Table 2. Correlations for the Time Series of Annual and Seasonal Mean Surface Temperature Data Between the NWS Stations and Both Reanalysis Data Sets a Station Data Set Annual DJF MAM JJA SON Barrow NCEP ERA Fairbanks NCEP ERA Nome NCEP ERA Anchorage NCEP ERA Juneau NCEP ERA a The time period of the correlations is for the NCEP NCAR/ NWS data and for the ERA40/NWS data. Correlations in bold are significant at 99%, and the remaining correlations are significant at 95%. 4of19

5 Figure 2 5of19

6 Table 3. Daily Correlation and Biases for the Surface Temperature Data Between the NWS Stations and Both Reanalysis Data Sets a Data Set Annual DJF MAM JJA SON Barrow NCEP Correlation Bias (K) ERA Correlation Bias (K) Nome NCEP Correlation Bias (K) ERA Correlation Bias (K) Fairbanks NCEP Correlation Bias (K) ERA Correlation Bias (K) Anchorage NCEP Correlation Bias (K) ERA Correlation Bias (K) Juneau NCEP Correlation Bias (K) ERA Correlation Bias (K) a The time period of the correlations is for the NCEP NCAR/ NWS data and for the ERA40/NWS data. For the annual correlations, all days are used for the calculation; for the seasons, just the days in that season are used for the calculation. All correlations are statistically significant at 99%. with each node can then be used to determine how frequently each node occurs (node frequency of occurrence). [18] The list of days associated with each node can then be used to relate other fields to the SOM SLP patterns. For this study, the daily 2 m temperature anomaly from the NCEP/ NCAR and ERA40 reanalysis data sets for the stations of interest were calculated for each synoptic pattern identified by the SOM. The temperature anomaly was calculated by stripping the average daily mean analyzed over the entire time period from each day (i.e., the average temperature over the entire time period was calculated for a particular date, then this average was subtracted from each corresponding day s temperature). Using temperature anomalies in our study removed the seasonal temperature fluctuations and allowed us to explore, for example, which synoptic patterns are responsible for winter warm spells. 3. Synoptic Control on Local Weather 3.1. General Synoptic Patterns [19] The typical synoptic patterns for the study area are shown in Figure 3. Above each panel is the node number that will be used throughout the paper to identify each of the patterns. Along the right portion of the SOM are circulation types dominated by differing strengths and locations of the Aleutian Low (Figure 3, fifth to seventh columns). The Aleutian Low moves from being located in the Gulf of Alaska to westward over the Aleutian Islands, with the strongest lows located furthest to the right in the SOM. In the top left corner of the SOM are patterns with low pressure in the Beaufort/Chukchi seas and higher pressure in the southern portion of the domain. In the bottom left corner of the SOM are patterns with low pressure over the Canadian Archipelago and also to the southwest of the Aleutian Islands, with a high pressure center located in the northwest corner of the domain (over eastern Siberia) extending over Alaska. Patterns in the center of the SOM are transitional patterns between these dominant patterns. The SLP patterns shown in the SOM represent the major near surface circulation patterns expected in this region and are in agreement with previous climatologies [Overland and Hiester, 1980; Ledrew, 1983, 1985; Serreze and Barry, 2005]. [20] The list of days mapped to each pattern on the SOM can be used to determine how frequently each of the patterns occurs, both on an annual and seasonal basis (Figure 4). Figure 4 (top) shows the annual frequencies for both reanalysis data sets (NCEP/NCAR, Figure 4 (left), and ERA40, Figure 4 (right)) for the time period , which is the time period in which both data sets overlap. Comparing the two, the frequency distribution is very similar between the two data sets. The correlation coefficient between the two frequency distributions is 0.95, indicating that large scale weather patterns are well constrained within both reanalyses by measurements. The reason for exploring both reanalyses independently in this paper is that the numerical weather prediction models used to create the reanalyses use different schemes for representing processes such as cloud formation, turbulent fluxes, and radiative transfer, all of which can have large impacts on the reanalysis near surface state. As the purpose of this research is to understand surface conditions, we felt it important to assess the level of scatter between these models as an indication of the robustness of our results. Figure 2. Time series of the annual NWS surface temperature (solid black line) and the annual 2 m temperature interpolated to the elevation of the NWS stations from the two reanalysis data sets, NCEP/NCAR (dashed red line) and ERA40 (dashed blue line), for the five NWS stations studied (Barrow (top left), Nome (top right), Fairbanks (bottom left), Anchorage (bottom middle), and Juneau (bottom right)). The vertical dashed line indicates 1976, and the two solid vertical lines indicate the study period analyzing climate changes around 1976 as described in section 4. 6of19

7 Figure 3. Self organizing map of daily sea level pressure anomaly patterns created using the NCEP/NCAR SLP data ( ) and the ERA40 SLP data ( ). Blue shading indicates negative anomalies, and red shading indicates positive anomalies. The color scale represents a range of SLP anomalies from 45 to +45 hpa. The contour interval is 2 hpa. 7of19

8 Figure 4. Frequency of days that map to each SOM node annually for each reanalysis data set (top row; NCEP/NCAR on the right, ERA40 on the left) and seasonally from the NCEP/NCAR data set (bottom two rows). The time period shown is , which is the time period that overlaps both reanalysis data sets. [21] The patterns that occur most frequently are moderate to strong Aleutian lows centered in the Gulf of Alaska (nodes 4,5 and 5,5), patterns with a Beaufort/Chukchi low (node 1,1), and a pattern with low pressure over the Canadian Archipelago with broad low pressure over Alaska and high pressure to the west of Alaska (node 1,3). Summing the frequencies of all patterns with an Aleutian low (rightmost three columns plus nodes 4,4 and 4,5), this is the 8of19

9 most dominant pattern in this area (around 49%). Since the frequencies of patterns between the two data sets are so similar, just the NCEP/NCAR data are shown for the seasonal frequencies in the bottom 2 rows of Figure 4. During December January February (DJF) (Figure 4, middle left), the Aleutian Low is the most dominant pattern (right side of the SOM), while patterns with a Beaufort/Chukchi Low are quite infrequent (upper left corner of the SOM). Patterns that also occur frequently during DJF are those with low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands (lower left corner of the SOM). The most frequent patterns during JJA (Figure 4, bottom left) are an almost mirror image of those that occur most frequently during DJF. The most frequent patterns are Beaufort/Chukchi lows and patterns with broad low pressure over Alaska and eastern Siberia (upper left corner of the SOM). Aleutian lows occur infrequently during the summer, and the strongest Aleutian lows (nodes 7,2 though 7,5) do not occur at all. Thus, any changes in weather due to changes of the strongest Aleutian lows are restricted to affecting the colder parts of the year. Patterns that occur during March April May (MAM) and September October November (SON) (Figures 4, middle right, and 4, bottom right) are transitional patterns between these two states Analysis of 2 m Temperature Anomalies Mapped to the SOM [22] Mapping 2 m temperature anomalies calculated for all of the data sets (both reanalysis data sets and the NWS data) to the SOM reveal how each of the 35 archetypical weather patterns identified by the SOM influence local surface air temperature in Alaska (Figure 5). Figure 5 shows surface temperature anomalies mapped to the SOM for both the reanalysis data sets (NCEP/NCAR (Figure 5, left) and ERA40 (Figure 5, middle)) and the NWS data (Figure 5, right) with each row representing a different station. The time period for Figure 5 is , which is the time period for which all three data sets overlap. The individual values demonstrate the biases that were discussed in section 2, though the correlation of the values mapped to the SOM between the reanalysis and NWS data are quite high, ranging between and However, since the purpose of this paper is to determine the synoptic patterns that lead to warm/ cold anomalies at each of our stations studied, the fact that in a relative sense all of the data sets match very well suggest that the conclusions drawn from either reanalysis data set are robust. [23] The warmest anomalies at the most northern station studied (Barrow; Figure 5, first row) are associated with patterns that had a low pressure centered to the west of Alaska that brought southerly flow toward northern Alaska (nodes in the upper, central portion of the SOM). Patterns that place Barrow ahead of an approaching low pressure system from the west (nodes 2,1 and 3,1) or where Barrow is west of a ridge axis (node 2,2) also are associated with positive temperature anomalies. Cold temperature anomalies are associated with patterns with high pressure in the Beaufort/Chukchi seas, particularly Aleutian low patterns (nodes in the lower right portion of the SOM) and patterns with low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands with high pressure centered in the Chukchi Sea (lower left portion of the SOM). Flow into northern Alaska associated with these patterns is generally northerly. [24] Moving southwest to the coast, the warmest anomalies in Nome (Figure 5, second row) are associated with strong Aleutian lows centered near the tip of the Aleutian Island chain (upper right corner of the SOM). In general, patterns with positive temperature anomalies are those that bring southerly flow to Nome (central to upper right portion of the SOM). The coldest anomalies at Nome are associated with lows located further east (centered in the Gulf of Alaska; lower right portion of the SOM). Also, low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands with high pressure over eastern Siberia brings strong cold anomalies to Nome (node 1,5). Patterns that place Nome to the east of a ridge axis are also associated with negative temperature anomalies (nodes 1,1 and 1,2). [25] Moving inland, the warmest anomalies at Fairbanks (Figure 5, third row) are associated with strong Aleutian lows centered over the Aleutian Islands (nodes 7,2; 7,3; and 6,3). In general, patterns bringing southerly flow into central Alaska are associated with positive temperature anomalies (upper central to right portion of the SOM). The coldest temperatures are associated with low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands with high pressure over eastern Siberia (lower left corner of the SOM). Additionally, Aleutian lows centered in the Gulf of Alaska that are associated with easterly/northeasterly flow into Alaska s interior brought cold temperatures to Fairbanks (nodes 3,5 7,5). [26] The warmest temperature anomalies at Anchorage (Figure 5, fourth row) are associated with strong Aleutian lows located near the tip of the Aleutian Island chain (nodes 7,1 7,3), which places Anchorage ahead of lows approaching from the west and in the warm sector of the cyclone. In general, positive temperature anomalies are associated with these types of patterns, i.e., Anchorage ahead of an approaching cyclone (upper right portion of the SOM). The coldest temperature anomalies are associated with patterns with low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands with high pressure in the Chukchi Sea (nodes 1,5 and 2,5). This places Anchorage in an area of northerly flow on the leading edge of the high pressure system. Other patterns with negative temperature anomalies are those that place Anchorage to the east of a ridge axis (nodes 1,1 and 2,1) and Gulf of Alaska centered lows (bottom right portion of the bottom row of the SOM), which places Anchorage to the west of the low pressure system in the northerly flow regime and therefore cold sector of the cyclone. [27] For Juneau (Figure 5, fifth row), the warmest anomalies are associated with Aleutian lows that bring southerly flow to the panhandle of Alaska (lower right portion of the SOM). Similar to Anchorage, the coldest anomalies are associated with patterns with low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands with high pressure in the Chukchi Sea (nodes 1,5 and 2,5), which places Juneau in an area of northerly flow on the leading edge of the high pressure system. In contrast to the other stations studied, cold anomalies are associated with west to northwest shifted Aleutian lows with high pressure over northwestern Canada (top right row of the SOM). With this pattern, cold air 9of19

10 Figure 5. The 2 m temperature anomaly (K) from the NCEP/NCAR and ERA40 reanalyses and the NWS data mapped to the SOM for the time period Warm (cool) colors are positive (negative) anomalies. Each column represents a different data set. From left to right, the data sets are NCEP/NCAR, ERA40, and NWS data. Each row represents a different station. From top to bottom, the stations are Barrow, Nome, Fairbanks, Anchorage, and Juneau. 10 of 19

11 Table 4. The 2 m Temperature Anomaly Differences for Minus for NCEP/NCAR, ERA40, and NWS Data a Station Data Set 2 m Temperature Anomaly (K) Difference Barrow NCEP ERA NWS Nome NCEP ERA NWS Fairbanks NCEP ERA NWS Anchorage NCEP ERA NWS Juneau NCEP ERA NWS a Differences in bold are significant at 99%. is advected into the Alaskan panhandle from the north/ northeast. 4. Analysis of the 1976 Pacific Climate Shift in Alaska 4.1. Observations of This Shift [28] Numerous studies have noted that in 1976, coincident with a shift in the PDO index, air temperatures in Alaska increased significantly [Hartmann and Wendler, 2005; Shulski and Wendler, 2007; Wendler and Shulski, 2009]. These studies used surface weather station data, whereas here we explore this phenomena in several ways using the NCEP/NCAR and ERA40 reanalysis data sets in addition to the NWS data. Two 15 year periods encompassing 1976 ( and ) were chosen to analyze temperature changes in Alaska due to this climate shift. These time periods were chosen to maximize the climate signal within the constraints of data availability. [29] At each of the NWS stations studied in this paper, there was a shift in the mean temperature for 15 years before and after including 1976 (Table 4). For all stations and data sets, the mean temperature increased for the latter time period. For Nome, Fairbanks, and Juneau, the greatest increase in the mean was in the NWS data. For Anchorage, the greatest increase was in the ERA data set, though the ERA and NWS means were quite close. For Barrow, the NCEP data set showed the greatest increase in the mean. None of the difference in means was statistically significant at 99% (tested using a t test) for the NCEP data or any of the Barrow means but was statistically significant for the other four stations for the ERA and NWS data sets Attribution of the Observed Changes in Temperature [30] The first analysis performed was to determine how the synoptic circulation changed between these two time periods by analyzing changes in frequencies of the 35 patterns identified by the SOM. The differences in synoptic patterns between the two time periods are quite similar between the two data sets (Figure 6; correlation coefficient between the two data sets is 0.96). In Figure 6, reds are patterns that are more frequent after 1976 and blues are those less frequent. The darker red and blues indicates statistically significant differences. Statistical significance was tested as follows: given n 1 and n 2 daily records for the first and second time period, respectively, the total number of observations that map to any particular node is given by r 1 = n 1 p 1 and r 2 = n 2 p 2, where p is the frequency of occurrence for a particular node. Assuming these data sets result from two random, independent, binomial processes with node frequency variances p 1 (1 p 1 )/n 1 and p 2 (1 p 2 )/n 2, the hypothesis to be tested is that the frequency difference between the two time periods is zero. The random variable Figure 6. Percentage change in frequencies of patterns for minus for each reanalysis data set (NCEP/NCAR on the left, ERA40 on the right). Warm (cool) colors indicate positive (negative) differences. Dark red and blue indicates statistically significance differences at the 95% confidence level. 11 of 19

12 used as the test statistic, pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi which is at least approximately normal, is (p 1 p 2 )/ p 1 ð1 p 1 Þ=n 1 þ p 2 ð1 p 2 Þ=n 2. If this value exceeds 1.96, we reject the null hypothesis and deem the frequency differences between the two data sets to be significant at the 95% interval. After 1976, strong Aleutian lows are more frequent (right side of the SOM), particularly those that are shifted westward to be centered over the tip of the Aleutian Islands (upper right corner of the SOM). Less frequent after 1976 are weaker Aleutian lows (center of the SOM), particularly those that are centered in the Gulf of Alaska (lower central portion of the SOM). Also less frequent are patterns with low pressure west of Alaska (upper central portion of the SOM) and Beaufort/Chukchi lows (upper left corner of the SOM). [31] Two meter temperature anomaly differences around 1976 for the stations studied were mapped to the SOM (Figure 7). In Figure 7, warm (cool) colors are warmer (cooler) temperatures after 1976, with the thick black boxes around the numbers indicating statistical significance at the 95% level calculated using the Student s t test. In Barrow (Figure 7, first row), increasing temperatures were observed for almost every node, with many differences statistically significant. In Barrow, in general, the results matched quite well for both reanalysis data sets (correlation coefficients are 0.88 and 0.90 for the NCEP/NCAR/NWS and ERA40/NWS data sets, respectively). The warmest temperature increases were associated with strong westward shifted Aleutian lows (upper right corner of the SOM) and patterns with low pressure over the Canadian Archipelago and to the southwest of the Aleutian Islands (lower left corner of the SOM). [32] In Nome (Figure 7, second row), warm and cold patterns are spread pretty evenly throughout the SOM for both reanalysis data sets, while the NWS data shows warming for almost all SOM patterns (correlation coefficients are 0.78 and 0.87 for the NCEP/NCAR/NWS and ERA40/NWS data sets, respectively). The biggest difference between the two reanalysis data sets is NCEP/NCAR shows cooling for patterns with moderate to strong Gulf of Alaskacentered lows (lower right corner of the SOM). The greatest temperature increases after 1976 are associated with strong Aleutian lows centered over the center of the Aleutian Island chain westward (upper right corner of the SOM). Colder temperatures after 1976 are associated with low pressure systems west of Alaska (central upper portion of the SOM) and strong Gulf of Alaska centered, low pressure systems (lower right corner of the SOM). [33] In Fairbanks (Figure 7, third row), the ERA40 and NCEP/NCAR matched quite well with the NWS data (correlation coefficient of 0.93 for both data sets). The NCEP/NCAR analysis shows more patterns with cooling, some of these with statistically significant cooling. However, the general conclusions drawn are similar between the data sets. The cooling is associated with patterns with broad low pressure over Alaska (nodes 2,3 and 3,3) and low pressure to west of Alaska (nodes 2,2 and 3,2). Patterns that warmed after 1976 are, in general, patterns that occur primarily in the winter (right column and lower left corner of the SOM). Though there is cooling observed with some patterns, overall warming is observed. [34] In Anchorage (Figure 7, fourth row) and Juneau (Figure 7, fifth row), there are the greatest differences between the reanalysis data sets, though the correlation coefficients for the reanalysis and NWS data sets are high (0.94 for NCEP/NCAR/NWS and 0.91 for ERA40/NWS for Anchorage and 0.86 for NCEP/NCAR/NWS and 0.84 for Juneau). Qualitatively, ERA40 data match better with the NWS data than the NCEP/NCAR data do. The NWS data show warming for all patterns after The NCEP/NCAR data show cooling for many patterns, particularly those with broad low pressure over Alaska and low pressure to the west of Alaska (central upper left corner of the SOM). In Anchorage for all data sets, there is substantial warming associated with strong Aleutian low patterns (rightmost column of the SOM). For Juneau for all data sets, the greatest warming is associated with strong Gulf of Alaskacentered lows (lower right portion of the SOM). [35] While these analyses provide useful information on circulation and noncirculation changes (e.g., a global warming signal), the following analysis describes a technique to separate these changes and to determine which, if either, is the dominant cause of the observed changes [e.g., Cassano et al., 2007]. The following equations outline this technique, beginning with changes in frequency and temperature: f later ¼ f init þ Df ; T later ¼ T init þ DT: In these and the following equations, T init and T later are the temperatures for and , respectively; f init and f later are the frequencies for and , respectively; and N is the total number of nodes (35). The average temperature for each time period can be calculated using the SOM, where the summation is over all SOM nodes: T later ¼ XN i¼1 f lateri T lateri : Substituting the terms in equation (3) with those from equations (1) and (2) results in the following equation: T later ¼ XN i¼1 ð1þ ð2þ ð3þ ðf initi þ Df i ÞðT initi þ DT i Þ: ð4þ Expanding this equation results in the final equation used for the analysis presented below: T later ¼ PN i¼1 i¼1 A B C f initi T initi þ PN : ð5þ ðf initi DT i þ T initi Df i þ DT i Df i Þ The first term on the right hand side of the equation is the node contribution to the average temperature for the initial time period. This term describes how the frequencies of the patterns contribute to the average temperature for the initial time period, T init as in equation (3). For example, if a pattern has a very warm average temperature but does not occur very frequently, it will not contribute much to the overall average temperature for the time period. This added to the rest of the terms on the right hand side of the equation results in the mean for the later time period. Term A is the intrapattern (noncirculation) change in temperature (i.e., 12 of 19

13 Figure 7. Differences in 2 m temperature anomalies (K) for all three data sets (NCEP/NCAR (left), ERA40 (middle), and NWS (right) data) mapped to the SOM for minus Each row is a different station. From top to bottom, the stations are Barrow, Nome, Fairbanks, Anchorage, and Juneau. Warm (cool) colors indicate positive (negative) differences. Statistically significant differences at the 95% confidence level are indicated by a thick black box around those values. 13 of 19

14 Figure 8. Partitioning of the 2 m temperature anomaly (K) differences for minus using the NCEP/ NCAR reanalysis data (top left) into noncirculation (top right), circulation (bottom left), and combined (bottom right) temperature differences as described in the text. Warm (cool) colors are positive (negative) differences. 14 of 19

15 Figure 9. Partitioning of the 2 m temperature anomaly (K) differences for minus using the ERA40 reanalysis data (top left) into noncirculation (top right), circulation (bottom left), and combined (bottom right) temperature differences as described in the text. Warm (cool) colors are positive (negative) differences. 15 of 19

16 Table 5. Changes in Surface Temperature Anomaly (K) Around the 1976 Pacific Climate Shift Broken Down into Circulation/ Noncirculation/Combined Differences for the Stations Studied a Data Set Total Noncirculation Circulation Combined Barrow NCEP (95.17%) ( 1.44%) (6.28%) ERA (90.87%) ( 2.83%) (11.96%) NWS (94.77%) ( 1.61%) (6.43%) Nome NCEP ( 6.25%) (77.5%) (28.75%) ERA (74.88%) (17.15%) (8.08%) NWS (79.40%) (15.12%) (5.12%) Fairbanks NCEP (43.97%) (35.28%) (20.76%) ERA (84.08%) (9.78%) 0.08 (6.07%) NWS (86.44%) (6.71%) (6.91%) Anchorage NCEP (28.01%) (53.66%) (18.32%) ERA (83.745%) (11.27%) (4.98%) NWS (85.67%) (8.72%) (5.23%) Juneau NCEP (50.91%) (32.12%) (17.58%) ERA (89.16%) (8.09%) (2.87%) NWS (95.62%) (2.94%) (1.65%) a Percentages in the parentheses represent the percentage of the total change attributed to each category. changes in temperature that would occur if the pattern frequency were exactly the same). Term B is the temperature change for each pattern due to a change in frequency of that pattern, i.e., circulation changes. The final term, term C, is the change in temperature due to intrapattern and circulation changes working together, i.e., concurrent changes in pattern frequency and temperature. This term is referred to as the combined term. This analysis can be applied to data from an individual station as well as on a pointwise basis in a gridded data set (such as the reanalyses) to understand how these changes differ over a broad area [e.g., Cassano et al., 2007]. [36] This analysis was first performed over the entire study area using both the NCEP/NCAR (Figure 8) and ERA40 (Figure 9) reanalysis data sets. Comparisons between the two show very similar results. In general, temperatures increased between the two time periods for almost the entire area of study (Figures 8, top left, and 9, top left, which show the total temporal difference for each of the two data sets, respectively.). Comparing the results from the two data sets, for NCEP/NCAR the temperature increases were greatest along the North Slope of Alaska east to the coast of northeastern Canada (Figure 8, top left), but for ERA 40, the greatest temperature increases were focused in southern and central Alaska (Figure 9, top left). The warming was due primarily to the intrapattern contribution (Figures 8 and 9, top right; term A from equation (5)). Again, this term describes temperature changes for a particular pattern without any changes in the frequency of occurrence. Circulation changes led to cooling at the very northern tip of Alaska and slight warming over the rest of the state for both data sets (Figures 8 and 9, bottom left; term B from equation (5)). An interpretation of this analysis is if the only thing that changed between the two time periods was the circulation, cooling would have been observed at Barrow and less warming over the rest of the state than was actually observed. The combined term contributed a small amount to the warming/cooling observed (Figures 8 and 9, bottom right; term C from equation (5)). [37] This analysis was also performed for each of the stations studied and summed over all nodes (Table 5). This analysis bears out what is shown in Figures 8 and 9: the warming is due primarily to intrapattern changes with a smaller circulation and combined impact. Circulation changes led to cooling at Barrow. However, this analysis also does illustrate some key differences in the results between the two analyses. For NCEP/NCAR, the cause of the warming is split between terms A, B, and C from equation (5), except for Barrow where noncirculation changes are the dominant cause of the warming. ERA40 and the NWS station data match quite well and show the dominant cause of the warming after 1976 was noncirculation changes: in general, over 80% of the warming is due to noncirculation changes for both the ERA40 and NWS data, except for Nome where over 70% of the warming is due to noncirculation changes for both the ERA40 and NWS data. 5. Discussion and Conclusions [38] One of the goals of this paper was to describe the typical weather patterns that lead to warm/cold days at several NWS locations in Alaska. Of course, many variables impact surface temperature such as cloud cover and surface inversions that are often present in polar regions [Curry et al., 1996; Shupe and Intrieri, 2004; McBean et al., 2005; Liu et al., 2008], but this paper focuses on the synoptic controls on temperature. The stations studied were chosen to represent different climate regimes in the state. The SOM algorithm was used in conjunction with NCEP/NCAR and ERA40 reanalysis and NWS data to perform this analysis. An advantage of using the SOM technique over other synoptic typing tools is the SOM is a simple visual way to illustrate the typical synoptic patterns in an area and quickly determine the likely weather that will result from each pattern. Using the SOM as a classification method rather than something like a PCA gives the advantage that it is a more physically meaningful display of the data that can be directly compared to a given synoptic situation [Reusch et al., 2005a]. The SOM technique gives researchers simple tools for evaluating weather patterns in the context of the archetypal patterns identified. For example, a researcher knowing little more than the clockwise/counterclockwise rotation of high/ low pressure systems and that, in general, air over the Arctic Ocean is cold and over the Pacific Ocean is warm can fairly easily identify the dominant pathways of heat and moisture using the SOM patterns for their locations. [39] In general, the location and strength of the semipermanent features in the western Arctic of the Aleutian Low and Beaufort High have a great impact on the temperature experienced throughout the state, in agreement with previous studies [Overland et al., 1999; Rodionov et al., 2005; Shulski et al., 2010]. A comparison between the warm/cold anomalies at each of the stations studied shows how these main features impact different parts of the state. For all stations, negative temperature anomalies are associated with Gulf of Alaska centered, low pressure systems (lowest row 16 of 19