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Interannual Variability of Northern Hemisphere Storm Tracks in Coarse-Gridded Datasets

Interannual Variability of Northern Hemisphere Storm Tracks in Coarse-Gridded Datasets Hindawi Publishing Corporation Advances in Meteorology Volume 2013, Article ID 545463, 15 pages http://dx.doi.org/10.1155/2013/545463 Research Article Interannual Variability of Northern Hemisphere Storm Tracks in Coarse-Gridded Datasets 1 2 Timothy Paul Eichler and Jon Gottschalck Department of Earth and Atmospheric Sciences, Saint Louis University, 3642 Lindell Boulevard, O’Neil Hall 205, St. Louis, MO 63108, USA NOAA’s National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center, 5830 University Research Court, College Park, MD 20740, USA Correspondence should be addressed to Timothy Paul Eichler; teichler@slu.edu Received 20 August 2013; Revised 22 October 2013; Accepted 12 November 2013 Academic Editor:IgorI.Mokhov Copyright © 2013 T. P. Eichler and J. Gottschalck. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Extratropical cyclones exert a large socioeconomic impact. It is therefore important to assess their interannual variability. We generate cyclone tracks from the National Center for Environmental Prediction’s Reanalysis I and the European Centre for Medium Range Prediction ERA-40 reanalysis datasets. To investigate the interannual variability of cyclone tracks, we compare the eeff cts of El Nino ˜ , the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), and the Pacific North American Pattern (PNA) on cyclone tracks. Composite analysis shows similar results for the impacts of El Nino, NAO, and the PNA on NH storm tracks. Although it is encouraging, we also found regional differences when comparing reanalysis datasets. The results for the IOD suggested a wave-like alteration of cyclone frequency across the northern US/Canada possibly related to Rossby wave propagation. Partial correlation demonstrates that although El Nino ˜ aec ff ts cyclone frequency in the North Pacific and along the US east coast, its impact on the North Pacific is accomplished via the PNA. Similarly, the PNA’s impact on US east coast storms is modulated via El Nino ˜ . In contrast, the impacts of the NAO extend as far west as the North Pacific and are not influenced by either the PNA or El Nino ˜ . 1. Introduction and Higgins [21], Changnon et al. [22], and Tilinina et al. [23]) or 850 hPa vorticity (Hodges [24–27], Hoskins et al. A key issue in assessing present and future climate is in exam- [28], Mesquita et al. [29], and Hodges et al. [30]). ining the climatology, variability, and trends in the character- Advantages and disadvantages of cyclone methodologies istics of extratropical cyclones. eTh history of cyclone tracks depend on the application of the research. For example, research has two preferred methods to define cyclones. eTh Hodges et al. [30]notethat850hPavorticity becomesquite first is the Eulerian method, which uses band-passed 500 hPa noisy at high-resolution requiring a reduction in spatial height field and strongest wave activity to define a cyclone resolution. On the other hand, tracking 850 hPa vorticity (Blackmon [1], Blackmon et al. [2], Wallace et al. [3]), Hoskins as opposed to MSLP captures more systems, especially and Valdes [4], Chang and Fu [5], Chang [6], Rao et al. [7], weaker ones (e.g., Hoskins et al. [28] and Hodges et al. C. S. Frederiksen and J. S. Frederiksen [8], and Nakamura [30]). eTh ability for higher-resolution reanalysis datasets to and Shimpo [9]). In contrast, a Lagrangian approach tracks capture weaker cyclones was also discussed by Tilinina et al. cyclones by utilizing diagnostics such as minimum sea-level [23], who compared storm track frequency among several pressure (MSLP) (Petterson [10], Klein [11], Mather et al. [12], reanalysis datasets. Tilinina et al. [23] found that the high- Reitan [13], Zishka and Smith [14], Sanders and Gyakum resolution NASA MERRA data produced the greatest number [15], Whittaker and Horn [16], Lambert [17], Murray and of cyclones, with much of the increase due to the inclusion Simmonds [18], Chen et al. [19], Hirsch et al. [20], Eichler of more shallow cyclones relative to the coarse-gridded 2 Advances in Meteorology reanalysis datasets. With regard to intensity, Akperov and and Yamagata [48] found that the teleconnections in several Mokhov [31] found that ERA-INTERIM data produced more areas were in opposite phase to ENSO. Interestingly, Anna- intense cyclones than ERA-40 or NCEP/NCAR reanalysis malai and Okajima [51]found anegativePNA response in data. eTh y also found that ERA-INTERIM produced more theECHAM5model in response to Indian Oceanwarming. smaller (<200 Km) cyclones than ERA-40 and NCEP/NCAR Min et al. [52] suggested that a Rossby wave train propagates reanalysis data, because of the higher spatial resolution of from northeast of India to Canada in response to SST changes ERA-INTERIM data. Akperov and Mokhov [31]alsoillus- linked to the IOD. trate the potential issues of choosing a tracking methodology, Given various assimilation systems, satellite data imple- as they found that when comparing three dieff rent storm mentation, and instrument errors, it is somewhat surprising tracking routines, the routine developed by Serreze [32] that Dee et al. [53]statedthatfew comparisonshavebeen produced less storms than two other routines. Neu et al. [33] made between the various datasets. Therefore, this study compared cyclone tracks over a number of methodologies compares the interannual variability of NH cyclone tracks and found that there was better agreement with regard to in the Reanalysis I and ERA-40 between coarse-gridded deep cyclones, with less agreement for weaker cyclones. reanalysis datasets. Although future work will focus on high- With regard to the total number of cyclones, Neu et al. resolution datasets such as the CFS reanalysis and JRA25 [33] also found qualitative agreement in terms of patterns, datasets, we feel it is prudent to rfi st assess the coarse-gridded although there were large differences when comparing the datasets because of the following. (a) We are examining storm totalnumberofcyclones. Perhapsthe best summaryofwhich track features compatible with spatial gridding of the reanal- cyclone track methodology to use was stated by Mesquita et ysis datasets. (b) The coarse-gridded datasets currently have al. [34]who quoted Leonardetal. [35]: “In carrying out an a longer temporal range than the high-resolution datasets intercomparison of depression tracking schemes great care (e.g., the Reanalysis I dataset extends back to 1950 while must be taken not to draw the misleading conclusions about the CFS reanalysis dataset extends back to 1979) making the merits and values of a particular scheme, since different them highly suitable for interannual variability studies. (c) users of the software will have different requirements.” Although there are many studies of storm track climatology, Although comparisons of cyclone track climatologies there are less studies on their interannual variability; this among reanalysis datasets are detailed in the literature, less is especially true when comparing interannual variability has been done regarding interannual variability. eTh impact between different reanalysis datasets. (d) Examination of of factors such as El Nino ˜ on cyclone tracks is determined the interannual variability of storm tracks in coarse-gridded by its ability to alter the jet stream. For example, the Pacicfi datasets will provide a foundation for comparing to higher- North-American Pattern (PNA) described by Wallace and resolution datasets. eTh organization of our paper is as Gutzler [36]was foundtobelinkedtoElNino by Horel and follows. Section 2 describes the methodology for generat- Wallace [37]. Eichler and Higgins [21], Hirsch et al. [20], and ing cyclone tracks, developing cyclone track frequency and Noel and Changnon [38] found an increase in the frequency intensity climatologies, and analyzing interannual variability. of NH winter cyclones along the US east coast during El Section 3 explores differences in interannual variability as a Nin˜o and a decrease along the US southeast coast during La function of ENSO, NAO, PNA, and the IOD via composite Nina, ˜ which correspond to the positive and negative phase analysis and partial correlation. In Section 4,wereviewand of the PNA, respectively. Gulev et al. [39]statedthatcyclone discuss our results and present our conclusions. frequency over the Gulf of Mexico and the US east coast was maintained by the North Atlantic Oscillation (NAO) from 2. Methodology 1958 to 1978 and the PNA from 1979 to 1999. Alterations in jet stream strength and location are also Cyclone tracks were generated from 6-hourly reanalysis linked to the North Atlantic Oscillation (NAO), which refers data for Reanalysis I (1950–2010), Reanalysis II (1979–2010), to fluctuations in sea-level pressure (SLP) between Iceland andERA-40(1958–2001).Toensureconsistency among and the Azores described by Hurrell [40]and Hurrellet the comparisons, all of the datasets had identical spatial ∘ ∘ al. [41]. An example of NAO impact on cyclone tracks is resolution (2.5 Lat× 2.5 Lon). To track cyclones, we utilize a provided by Bradbury et al. [42], who examined regional Lagrangian approach developed by Serreze [32]and Serreze eeff cts of the NAO on New England and found a decrease in et al. [54] that determines the minimum sea-level pressure cyclone frequency in northwestern New England during the (MSLP) field relative to surrounding grid points to track negative NAO phase. cyclones. We utilized the same criterion in the cyclone track In addition to ENSO, the PNA, and the NAO, u fl ctuations algorithm as Eichler and Higgins [21]bychoosingaonehPa in SST variations in the tropical Indian Ocean have also been threshold for finding cyclones and a maximum propagation linked to global teleconnections. The Indian Ocean Dipole distance of 800 km between timesteps. Although this is an (IOD) refers to SST u fl ctuations between the eastern and overestimate of a distance a cyclone can travel between western tropical Indian Ocean first described by Saji et al. timesteps, it does allow for the possibility of center jumps [43]. eTh IODhas been foundtobelinkedtoteleconnections, (e.g., cyclones reforming on the opposite side of a mountain especially in the Southern Hemisphere (e.g., Ashok et al. [44], chain) and also accounts for the gridded nature of the data we Na et al. [45], Ashok et al. [46], and Liu et al. [47]). Global are using. teleconnections have been described in Saji and Yamagata Reanalysis datasets used in our assessment include [48], Yamagata et al. [49], and Yang et al. [50]. For the NH, Saji the National Center for Environmental Prediction (NCEP) Advances in Meteorology 3 Table 1: Cyclone track climatology reanalysis datasets. Note that Table 2: Years used in composite study for El Ni no ˜ , NAO, and IOD abbreviations include the beginning and end year of the dataset (e.g., (JFM except OND for IOD). NCEP1 5010 represents Reanalysis I data from 1950 to 2010). 1950, 1958, 1966, 1969, 1973, 1983, 1987, 1992, El Nino ˜ Dataset Abbreviation used in study 1998, 2010 La Nina ˜ Reanalysis I (1950–2010) NCEP1 5010 1950,1971, 1974,1976, 1989,1999,2000,2008 Reanalysis I (1958–2001) NCEP1 5801 Positive PNA 1977, 1981, 1983, 1984, 1987, 2010 Reanalysis I (1979–2010) NCEP1 7910 1950, 1951, 1952, 1954, 1955, 1956, 1957, 1959, Negative PNA Reanalysis I (1950–1978) NCEP1 5078 1962, 1965, 1967, 1969, 1971, 1972, 1974, 1975, 1976, 1979, 1982, 1989, 1990, 1996, 2002, 2009 ECMWF reanalysis (1958–2001) ERA40 5801 1950, 1957, 1959, 1961, 1967, 1973, 1974, 1976, Positive NAO 1983, 1989, 1990, 1992, 1993, 1994, 1995, 1997, 1998, 2000, 2002, 2008 Reanalysis I dataset (Kalnay et al. [55]) and the European Negative NAO Center’s ERA-40 reanalysis dataset (Uppala et al. [56]). To 1955, 1963, 1969, 1979, 1985, 1996, 2010 facilitate comparisons among the various reanalysis products, we subdivided thedatasetsasshown in Table 1 and described as follows. For NCEP1 5010, we extend the cyclone track Index (EIS) described in Kousky and Higgins [58], which analysis done by Eichler and Higgins [21]from2002to2010. is calculated by doubling the Ocean Nino ˜ Index (ONI). To We also createdasubset of theReanalysisIdatasetfrom detect impacts from El Nino ˜ , we composited cyclone track 1958 to 2001 for direct comparison to ERA-40. To assess frequency and intensity for the strongest El Nino ˜ and La possible changes in storm tracks due to temporal changes Nina ˜ periods and these are shown in Table 2.Toensurea in the coupling strength between ENSO and the NAO balance between sample size and using sufficiently strong (Pinto et al. [57]), or due to an altered temporal response ENSO events to detect potential signals, we composited in cyclone frequency as a function of the NAO (Gulev et moderate/strong El Nino ˜ (La Nina) ˜ events for EIS greater al. [39]), we created two subsets of NCEP1 5010: one from than or equal (less than or equal) to two (negative two). For 1950 to 1978 (NCEP1 5078) and the other from 1979 to 2010 the NAO and IOD, we utilized data from Jones et al. [59] (NCEP1 7910). andSajietal. [43], respectively.ThePNA Index wasobtained Similar to Eichler and Higgins [21], mean, seasonal from NOAA’s Climate Prediction Center (CPC), for which cyclone track frequencies were generated by binning cyclones the values were standardized by the 1981–2010 climatology ∘ ∘ into 5 Lat × 5 Lonboxes forwinter(JFM),spring (see http://www.cpc.ncep.noaa.gov/data/teledoc/pna.shtml (AMJ), summer (JAS), and fall (OND). To ensure direct for more details). Compositing the NAO, PNA, and IOD comparison with Re27910 and ERA40 5801 data, Reanal- was done according to standard deviation (SD). Defining ysis I climatologies were also tabulated for the period a proper cutoff for SD is somewhat arbitrary. For example, 1979–2010 (NCEP1 7910) and 1958–2001 (NCEP1 5801) to Noel and Changnon [38]used1SD forcompositing ENSO match Re27910 and ERA40 5801 time periods, respectively. events. Bai et al. [60]used0.5 SD to denfi e NAOevents, Although we do focus on cyclone climatology, the frequency while Serreze et al. [54] selected the top positive and bottom climatologies for each dataset are shown in Appendix A. negative seven years for NAO and dene fi d NAO phase Seasonal cyclone intensity was determined with the same by quartile. While the methodology used by Serreze et al. methodology used by Eichler and Higgins [21]. First, we [54] is attractive because it ensures an evenly distributed generated a gridded cyclone intensity climatology by binning sample size, it does not account for the possibility of the cyclone MSLP. Next, we developed a seasonal MSLP clima- nonsymmetry of positive/negative phases of the NAO (e.g., tology for each reanalysis dataset from monthly mean MSLP the positive events may be defined by a higher NAO value data. To eliminate potential errors due to trends, we applied relative to the criterion for the negative NAO). On the a linear regression to the seasonal mean MSLP climatology. otherhand, the0.5 SD cutoffusedbyBai et al.[ 60]may We then normalized our cyclone track intensity climatol- allow for the inclusion of events that will not give sucffi ient ogy by subtracting the seasonal regressed MSLP for the separation from neutral events. As a compromise, we den fi ed periods 1950–2010 (NCEP1 5010), 1979–2010 (NCEP1 7910), NAO, PNA, and IOD events by 0.75 SD. Although slightly and 1958–2001 (NCEP1 5801 and ERA40 5801). As was the more strict than that used by Bai et al. [60], it still provides case for storm track frequency climatology, the intensity an adequate sample size (see Table 2). To assess statistical climatologies for each dataset are shown in Appendix B. significance of the composites, we again apply the 𝑡 -score To evaluate the interannual variability of storm track used by Bai et al. [60]. Results are considered significant for frequency and intensity, we utilized the January through a 2-tailed test criterion of 90 percent. March (JFM) storm track data for each year. eTh choice of Finally, we performed a partial correlation analysis to JFM was due to NH cyclone tracks being most influenced quantify the impacts of El Nin˜o, PNA, and NAO on cyclone by external forcing in winter. However, we also generate track frequency found in the composite analysis. Partial October through December (OND) frequency and intensity correlation, which is a method to filter out external eeff cts, climatologies, since the Indian Ocean Dipole (IOD) is the was used by Ashok et al. [46] for Southern Hemisphere strongest during NH autumn [52]. The strongest El Ni no ˜ and cyclone tracks to eliminate the IOD when determining El La Nina ˜ periods were found by using the ENSO Intensity Nino ˜ relationships and vice versa. For our study, we show 4 Advances in Meteorology and extending across the north central Pacific, with sig- nificantly less cyclones over the Gulf of Alaska (Figures 1(a) and 1(b)). This pattern suggests a positive (negative) Pacific North American pattern (Wallace and Gutzler [ 36]), with a southward (northward) displaced cyclone track in the North Pacific with increased (decreased) cyclones along the east coast during El Nino ˜ (La Nina). ˜ More frequent cyclones are also seen in the eastern Mediterranean and over (a) Italy; the latter being prominent for NCEP1 5801 suggesting that assimilation differences between NCEP reanalysis and ECMWF reanalysis are playing a role (compare Figure 1(a) with Figure 1(b)). Another interesting feature is over the North Atlantic, where there are increased cyclones in the North Atlantic, with less cyclones just south of Greenland (Figures 1(a) and 1(b)). This resembles a negative phase of the NAO and is consistent with the results found by Rogers [61], who linked the Southern Oscillation (SO) with the NAO. As (b) will be demonstrated when examining partial correlations, this result will be dependent on the time period chosen for the analysis suggesting a nonrobust relationship. eTh SLP composites for El Ni no ˜ relative to La Nina ˜ are generally consistent across both datasets, with more intense cyclones in the North Pacicfi and North Atlantic from 35 N to 50 N and less intense cyclones across much of Canada and ∘ ∘ the North Atlantic from 60 Nto70 N (Figures 1(c) and 1(d)). (c) The increase in intensity in the North Pacific is consistent with a stronger Aleutian low occurring during El Nino ˜ , while the increased intensity of cyclones along the US east coast is consistent with an enhanced east coast cyclone track during El Nin˜o consistent with the results of Eichler and Higgins [21]. The decrease in cyclone intensity across Canada during La Nina ˜ relative to El Nin˜o is indicative of a northward- displaced polar jet during La Nina. ˜ As was the case for the El (d) Nino ˜ frequency composite, the decrease (increase) in cyclone Figure 1: NH winter (JFM) El Ni no-L ˜ a Nina ˜ cyclone track intensity south of Greenland (across the mid-North Atlantic) frequency composite for (a) NCEP1 5801, (b) like (a) but for resembles the negative phase of the NAO (Rogers [61]). ∘ ∘ ERA40 5801.Units:no. of cyclones per5 Lat× 5 Lon box. Hatched For the NAO (Figure 2), the most notable impact is the areas are significant at 90% for 2-tailed 𝑡 -test. (c) NH winter (JFM) frequency dipole in the Atlantic suggesting that more (less) El Nino-L ˜ a Nin˜a cyclone track intensity composite for NCEP1 5801. frequent cyclones occur south of Greenland, with less (more) (d) like (c) but for ERA40 5801.Units:hPa.Hatched areasare cyclones west of Europe consistent with a northward (south- significant at 90% for 2-tailed 𝑡 -test. ward) displacement of the cyclone track during positive (neg- ative) NAO (Figures 2(a) and 2(b)). This agrees with Luo et al. [62], who compared North Atlantic cyclone tracks during a period when the NAO was trending upward (1978–1990) (1) El Nino ˜ correlations eliminating PNA and NAO, (2) PNA with a period when the NAO was trending downward (1991– eliminating El Nino,and(3)theNAOeliminatingPNAandEl 2009) and concluded that the North Atlantic cyclone track Nino ˜ . Since the IOD was found to have weak correlations, we was more intense when the NAO was trending downward. didnot includethe IODinthe analysis.Sucffi ienttemporal Ourresults arealsoconsistentwithWalterand Graf [63], who size allowed us to compare different time periods in the same examined teleconnection patterns related to the strength of dataset (i.e., NCEP1 7910 versus NCEP1 5078) in addition to the polar vortex and found that, when the vortex was strong, comparing two different reanalysis datasets (i.e., ERA15801 cyclone tracks extend northward into the Arctic Ocean, with versus NCEP1 5801). a secondary track over the Denmark Strait. An interesting feature in our NAO composite is that it is not confined strictly to the NAO centers of action in the 3. Interannual Variability North Atlantic (Figures 2(a) and 2(b)). For example, the pos- 3.1. Composite Analysis. Figure 1 shows cyclone track fre- itive area south of Greenland extends west-southwestward to quency and intensity composites for El Nino ˜ relative to Central Canada, while the negative area in the North Atlantic extends eastward through the Mediterranean. A significant La Nina ˜ for NCEP1 5801 and ERA40 5801. Both datasets show signicfi antly more cyclones along the US east coast increase in cyclones during NAO positive relative to NAO 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 10 Advances in Meteorology 5 ∘ ∘ intense cyclones are found generally along 70 Nfrom120 W to 70 E for positive NAO relative to negative NAO. Less intense cyclones for positive NAO relative to negative NAO arefound across theNorth Atlantic along40 Neastward to western Europe in both datasets (Figures 2(c) and 2(d)). As was the case for the frequency composite, a southward displacement of the polar jetstream is indicated for a negative NAO. Impacts of a negative NAO on cyclone tracks were investigated by Seager et al. [64], who found that precipitation (a) anomalies in the western and southeastern US are due to a southwardshiftinthecyclonetrack.Nonsignicfi antdecreases in intensity are located east of this zone across much of central Europe eastward to China (Figures 2(c) and 2(d)). Although there is good agreement among all of the datasets, regional differences are evident such as an area of more intense storms in positive NAO relative to negative NAO extending further west from Hudson Bay to Montana in NCEP1 5801 but not in ERA40 5801 (compare Figure 2(c) (b) with Figure 2(d)). Assimilation/model differences are likely culprits for this difference. PNA frequency and intensity composites are shown in Figure 3. Results from both datasets are consistent, with an increase (decrease) in cyclones in the central North Pacific, adecrease(increase)incyclonesinthe Gulf of Alaska eastward across the northern US, and an increase (decrease) in cyclones along the US east coast during positive (negative (c) PNA) (Figures 3(a) and 3(b)). Given the configuration of troughs and ridges associated with positive/negative phases of the PNA, this result is not surprising. However, we will see that when analyzing partial correlation, the east coast signal in cyclone track frequency will vanish when eliminating El Nino ˜ . For the PNA intensity composite, more (less) intense cyclones arefound in theNorth Pacicfi forbothdatasets associated with a stronger (weaker) Aleutian low dur- (d) ing positive (negative) PNA (Figures 3(c) and 3(d)). An increase (decrease) in intensity for positive (negative) PNA Figure 2: NH winter (JFM) NAO positive-NAO negative cyclone track frequency composite for (a) NCEP1 5801, (b) like (a) but for is also evident across the northwestern US/southwestern ∘ ∘ ERA40 5801.Units:no. of cyclones per5 Lat× 5 Lon box. Hatched Canada for NCEP1 5801 but not for ERA40 5801 (compare areas are significant at 90% for 2-tailed 𝑡 -test. (c) NH winter (JFM) Figure 3(c) with Figure 3(d)). Data assimilation/model dieff r- NAO positive-NAO negative cyclone track intensity composite for ences between the ERA-40 and NCEP reanalyses are likely NCEP1 5801. (d) like (c) but for ERA40 5801.Units:hPa.Hatched causes in this data-sparse area. Further downstream, the areas are significant at 90% for 2-tailed 𝑡 -test. eeff cts of the PNA on cyclone intensity are more muted, with suggestions of a (nonsignicfi ant) response of storm intensity to PNA from Iceland southward to the northeastern Atlantic negative yearsisalsonoted from Japannortheastward to occurring in NCEP1 5801 (Figure 3(c)). Overall, the PNA intensity composites suggest a robust signal across the North the western tip of the Aleutians in both datasets, with the area of signicfi ance conn fi ed to south of the Aleutians for Pacific, with a decreased response further downstream, with ERA40 5801 (Figure 2(b)). Overall, the large spatial extent differences between NCEP and ERA-40 reanalysis suggesting that some of the cyclone intensity response to PNA be a by- of statistical significance, coupled with the prevalence of several overlapping signicfi ant features in all of the reanalysis product of assimilation differences. datasets, speaks to the hemispheric extent of the positive Since the IOD’s center of action is far removed from NH NAO impact on cyclone tracks. Since there is a risk of mid-latitude cyclone tracks, there was little impact of the contamination from external forcing such as El Nino ˜ and IOD on NH cyclone track frequency and intensity in the correlation and intensity composites (not shown). However, the PNA, partial correlation will be useful in verifying the hemispheric extent to our NAO composite. frequency composite analysis did reveal an interesting pat- Similar to the NAO frequency composites, the NAO tern across Canada and the northern US (Figure 4). Figure 4 reveals a banded structure, with increased (decreased) intensity composites extend beyond the centers of action in the North Atlantic (Figures 2(c) and 2(d)). For example, more cyclones over Central Canada northeastward to Hudson 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W −10 −7 −4 −1 1 4 7 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 10 6 Advances in Meteorology (a) 120W 110W 100W 90W 80W 70W 60W 50W 40W (a) (b) 120W 110W 100W 90W 80W 70W 60W 50W 40W (b) (c) Figure 4: OND cyclone track frequency composite for (a) IOD positive relative to negative for NCEP1 5801 (shaded). 2-tailed 90% significance (hatched), (b) like (a) but for ERA40 5801 Units: ∘ ∘ number of storms per winter for a 5× 5 box. is suggestive of a Rossby wave train response to IOD as suggested by Saji and Yamagata [48], Min et al. [52], and Small (d) et al. [65]. More research using higher-resolution reanalysis datasets and model simulations is needed to further explore Figure 3: NH winter (JFM) PNA positive-PNA negative cyclone any potential IOD/NH cyclone relationships. track frequency composite for (a) NCEP1 5801, (b) like (a) but for ∘ ∘ ERA40 5801.Units:no. of cyclones per5 Lat× 5 Lon box. Hatched areas are significant at 90% for 2-tailed 𝑡 -test. (c) NH winter (JFM) 3.2. Partial Correlation Analysis. The correlation between NAO positive-NAO negative cyclone track intensity composite for cyclone track frequency and El Nino ˜ (eliminating PNA NCEP1 5801. (d) like (c) but for ERA40 5801.Units:hPa.Hatched and NAO) is shown in Figure 5.For alldatasets, abandof areas are significant at 90% for 2-tailed 𝑡 -test. positive correlations containing large areas of significance extend from the Gulf of Mexico northeastward along the southeastern US coast, although it is much weaker in Bay, decreased (increased) cyclones from the central US NCEP1 7910 (Figure 5(c)). When comparing NCEP1 5801 to the southeastern tip of Hudson Bay, and increased with ERA40 5801,the responsesare quitesimilar,although (decreased) cyclones from the US mid-Atlantic coast to there is a significant negative correlation south of Greenland Nova Scotia during positive (negative) IOD. Interestingly, in ERA40 5801 (compare Figure 5(a) with Figure 5(b)). thenegativebandfromthe centralUStosoutheastern Differences between NCEP1 7910 and NCEP1 5078 Hudson Bay in NCEP1 5801 resembles a “lee cyclogenesis” include negative correlations across the U.S/Canadian cyclone track, while the negative area originates further north border in NCEP1 5078, which is shifted poleward across in ERA40 5801 and resembles an “Alberta Clipper” track central Canada in NCEP1 7910 (compare Figure 5(c) with (compare Figure 4(a) with Figure 4(b)). Figure 5(d)). This may be related to low-level warming at Given that statistically significant areas are fairly limited high-latitudes during the 1980’s and 1990’s as suggested by in arealcoveragefor thebandedstructuresseeninIOD com- Greatbatch et al. [66], which would result in a poleward posites, caution needs to be applied to interpreting the results. shift of the mid-latitude baroclinic zone. Major differences However, they are evident across all of the datasets despite were also found in all datasets with respect to the El Nino ˜ their fairly localized regional extent. The banded structure frequency composite analysis including (1) the absence of 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 −10 −7 −4 −1 1 4 7 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 10 Advances in Meteorology 7 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (a) (a) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (b) (b) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (c) (c) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (d) (d) Figure 6: Partial correlation coefficient for JFM cyclone fre- Figure 5: Partial correlation coefficient for JFM cyclone fre- quency versus PNA (excluding ENSO and NAO). Correlation quency versus ENSO (excluding PNA and NAO). Correlation (shaded). 2-tailed 90% significance (hatched) for (a) NCEP1 5801, (shaded). 2-tailed 90% significance (hatched) for (a) NCEP1 5801, (b) ERA40 5801, (c) NCEP1 7910, and (d) NCEP1 5078. (b) ERA40 5801, (c) NCEP1 7910, and (d) NCEP1 5078. positive correlations in the North Pacific where the composite when comparing NCEP1 7910 with NCEP1 5078 (Figures analysis suggests a well-defined cyclone track during El Ni no ˜ 6(c) and 6(d)). For example, NCEP1 5078 shows a large and (2) a less distinct zone of negative correlation across area of positive correlation across central and northern the northern US/southern Canada, where the composites Europe, which is completely absent in NCEP1 7910 (compare suggest a more active cyclone track during La Nina ˜ stretching Figure 6(c) with Figure 6(d)). The positive correlations across from theGulfofAlaskaeastwardintothe NorthAtlantic Europe in NCEP1 5078 are consistent with the negative phase (compare Figure 5 with Figure 1 forbothpoints).Aswillbe of the NAO, suggesting a negative correlation between the shown subsequently, the elimination of the PNA from the El NAO and PNA during this period. This is consistent with Nino ˜ correlation plays a role in the differences between the Pinto et al. [57], who found a signicfi ant negative correlation composite analysis and the partial correlation analysis. between the NAO and PNA in three simulations of the For the PNA correlation, signicfi ant positive correla- ECHAMmodel.Pinto et al.[57]found this relationship tions are found in the North Pacicfi south of the Aleu- lacking when assessing ERA-40 and NCEP reanalysis data tians, with negative correlations northeast of Japan for all from 1973 to 1994, which was attributed to an inactive period datasets (Figure 6). ERA40 5801 and NCEP1 5801 produced in the coupling strength between NAO and PNA. The lack of similar responses, although significant negative correlations this feature in 1979–2010 suggests that the coupling strength extended as far west as Iran in NCEP1 5801 (compare between the PNA and NAO was weak from 1979 to 2010 Figure 6(a) with Figure 6(b)). Larger differences are noted and strong from 1950 to 1978. When comparing the PNA 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 40 40 20 20 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 40 40 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 8 Advances in Meteorology −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (a) (d) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (b) (e) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (c) (f) Figure 7: (a)–(c) Correlation coefficient for JFM cyclone frequency versus ENSO for (a) total correlation, (b) excluding PNA, (c) excluding PNA and NAO. (d)–(f) Correlation coefficient for JFM cyclone frequency versus PNA for (d) total correlation, (e) excluding NAO, and (f) excluding NAO and ENSO. Correlation (shaded) 2-tailed 95% significance (hatched). −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (a) (b) −0.5 0.1 0.2 0.3 0.4 0.5 −0.5 0.1 0.2 0.3 0.4 0.5 −0.4 −0.3 −0.2 −0.1 −0.4 −0.3 −0.2 −0.1 (c) (d) Figure 8: Correlation coefficient for JFM cyclone frequency versus NAO (excluding PNA and ENSO). Correlation (shaded) 2-tailed 95% significance (hatched) for (a) NCEP1 5801, (b) ERA40 5801, (c) NCEP1 7910, and (d) NCEP1 5078. correlation to the PNA composite, a major difference is seen As discussed above, the El Nino ˜ and PNA correlations along the US east coast, where the composite analysis shows showed major differences with their composites in specific an increase (decrease) in cyclones for positive (negative) regions. To investigate further, we recomputed the El Nino ˜ PNA. However, the correlation analysis lacks this feature correlation for NCEP1 5010 with (1) no factors eliminated, (compare Figure 6 with Figure 3). that is, a full correlation, (2) a partial correlation with 80 80 60 60 40 40 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 40 40 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 60 60 40 40 20 20 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 20 20 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 Advances in Meteorology 9 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 (a) (b) 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 (c) (d) Figure 9: Continued. 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 OND JAS AMJ JFM SON JAS AMJ JFM OND JAS AMJ SON JAS AMJ JFM JFM 10 Advances in Meteorology 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 (e) (f) Figure 9: Cyclone track frequency climatology (shaded) for (a) NCEP1 5010 (JFM, AMJ, JAS, OND), (b) like (a) but for NCEP1 5801, and (c) ∘ ∘ like (a) but for ERA40 5801. Units: number of storms per winter for a 5× 5 box. Cyclone track frequency climatology (shaded) for (d) like ∘ ∘ (a) but for Re27910, (e) like (a) but for NCEP1 7910, and (f) like (a) but for NCEP1 5078. Units: number of storms per winter for a 5 × 5 box. the PNA removed, and (3) a partial correlation with the North Pacific and along the US east coast and negative PNA and NAO removed (Figures 7(a)–7(c)). When assessing correlations northeast of Japan and across the northern US the full correlation (Figure 7(a)), the results resemble the (compare Figure 7(d) with Figure 3). When eliminating the composite analysis (compare with Figure 1), with positive NAO, little difference is seen, demonstrating that the NAO correlations in the central North Pacific and along the does not play a significant role in cyclone tracks when US east coast and negative correlations from the Gulf of assessing the PNA (Figure 7(e)). However, this result may Alaska eastward across the northern US. When the PNA is also be theresultofalack of coupling betweenthe PNA removed (Figure 7(b)), the North Pacicfi and northern US and cyclone frequency tracks for this period in the reanalysis correlations are eliminated, leaving only the US east coast ˜ data as suggested by Pinto et al. [57]. When El Nino and correlation. Eliminating the NAO (Figure 7(c)) does little to theNAO areeliminated(Figure 7(f)), much of the signal the correlation pattern, which indicates that the NAO does across the northern US and along the US east coast vanishes. notplayamajorroleinthe El Nino ˜ correlation. Since El eTh se results are consistent with the El Ni no ˜ analysis in Nin˜o affects the PNA, the full correlation shows both of these Figures 7(a)–7(c) and suggest that, by itself, the PNA has effects. However, the elimination of the PNA suggests that a significant eeff ct on cyclone tracks in the North Pacific. the North Pacific/northern US correlations are only indirectly However, El Nino ˜ needs to be operative to produce eeff cts caused by El Nino ˜ via the PNA, while the US east coast further downstream across the northern US/US east coast. correlation is a direct result of anomalous heating in the Unlike thePNA andElNino ˜ correlations, the NAO Equatorial Pacific due to El Ni no ˜ . partial correlation is more robust when compared with the In a similar fashion, we also recomputed the PNA NAO composite analysis (Figure 8). For example, significant correlation as a (1) full correlation with no factors eliminated positive correlations are found south of Greenland, while (2) partial correlation removing effects of the NAO, and (3) significant negative correlations occur in the central North partial correlation eliminating El Nino ˜ and the NAO (Figures Atlantic, consistent with the NAO composite analysis for all 7(d)–7(f)). The full correlation ( Figure 7(d)) is similar to the datasets (compare Figure 8 with Figure 2). Positive correla- composite analysis, with positive correlations in the central tions (with areas of significance) are also found in the central 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 OND JAS AMJ JFM OND JAS AMJ JFM Advances in Meteorology 11 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 (a) (b) 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 (c) (d) Figure 10: Continued. 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 OND JAS AMJ JFM OND JAS AMJ JFM OND JAS AMJ OND JAS AMJ JFM JFM 12 Advances in Meteorology 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 (e) (f) Figure 10: Cyclone track intensity climatology (shaded). Intensity derived by subtracting regressed climatological mean from cyclone track mean (units: hPa) for (a) NCEP1 5010 (JFM, AMJ, JAS, OND), (b) like (a) but for NCEP1 5801, and (c) like (a) but for ERA40 5801. Cyclone track intensity climatology (shaded). Intensity derived by subtracting regressed climatological mean from cyclone track mean (units: hPa) for (d) like (a) but for Re27910, (e) like (a) but for NCEP1 7910, and (f) like (a) but for NCEP1 5078. North Pacicfi ( Figure 8), which is far removed from the NAO composite analysis also showed that caution is advised when teleconnection areas. However, some differences are seen assessing interannual variability for regional spatial scales, between NCEP1 7910 and NCEP1 5078, with NCEP1 7910 sinceregions of signicfi ancevaryeitherasaresult of thetime showing a zone of negative correlation across Asia relative period chosen for the composite analysis or from differences to NCEP1 5078 (compare Figure 8(c) with Figure 8(d)). An in model/data assimilation techniques between the reanalysis area of positive correlation in the North Pacific is also datasets themselves. This was especially true when comparing displaced eastward to the Gulf of Alaska in Re5078 relative intensity composites, such as the PNA intensity composite to NCEP1 7910 (compare Figure 8(c) with Figure 8(d)). A differences between ERA-40 and NCEP reanalysis datasets. possible reason for these differences is an altered response of Partial correlation was useful in verifying which com- cyclone track frequency to the NAO in different time periods ponents of the composite analysis were most robust. The similar to the southeastward shift of the cyclone track in 1958– PNA exhibited a positive correlation with cyclone frequency 1978 relative to 1979–1999 found by Gulev et al. [39]. across Europe in the 1950–1978 period compared with 1979– 2010, suggesting that the coupling strength between the NAO and PNA was greater in the former period than the latter 4. Conclusions period.For theNAO,the correlations were most similarto Composite analyses showed surprisingly good agreement the composite analysis suggesting that El Nino ˜ and the PNA do not play a significant role in the NAO’s impact on cyclone especially for El Nin˜o,NAO,and thePNA.Interestingly, NAO impacts were also noted as far west as the North tracks. However, the El Nino ˜ correlation was not consistent with the El Nino ˜ composite in the North Pacific nor was the Pacific, which was confirmed by partial correlation analysis. PNA correlation consistent with the PNA composite across eTh results for the IOD showed some interesting features across the northern US/Canada possibly related to Rossby the northern US/US east coast. 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Interannual Variability of Northern Hemisphere Storm Tracks in Coarse-Gridded Datasets

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Copyright © 2013 Timothy Paul Eichler and Jon Gottschalck. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Hindawi Publishing Corporation Advances in Meteorology Volume 2013, Article ID 545463, 15 pages http://dx.doi.org/10.1155/2013/545463 Research Article Interannual Variability of Northern Hemisphere Storm Tracks in Coarse-Gridded Datasets 1 2 Timothy Paul Eichler and Jon Gottschalck Department of Earth and Atmospheric Sciences, Saint Louis University, 3642 Lindell Boulevard, O’Neil Hall 205, St. Louis, MO 63108, USA NOAA’s National Weather Service, National Centers for Environmental Prediction, Climate Prediction Center, 5830 University Research Court, College Park, MD 20740, USA Correspondence should be addressed to Timothy Paul Eichler; teichler@slu.edu Received 20 August 2013; Revised 22 October 2013; Accepted 12 November 2013 Academic Editor:IgorI.Mokhov Copyright © 2013 T. P. Eichler and J. Gottschalck. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Extratropical cyclones exert a large socioeconomic impact. It is therefore important to assess their interannual variability. We generate cyclone tracks from the National Center for Environmental Prediction’s Reanalysis I and the European Centre for Medium Range Prediction ERA-40 reanalysis datasets. To investigate the interannual variability of cyclone tracks, we compare the eeff cts of El Nino ˜ , the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), and the Pacific North American Pattern (PNA) on cyclone tracks. Composite analysis shows similar results for the impacts of El Nino, NAO, and the PNA on NH storm tracks. Although it is encouraging, we also found regional differences when comparing reanalysis datasets. The results for the IOD suggested a wave-like alteration of cyclone frequency across the northern US/Canada possibly related to Rossby wave propagation. Partial correlation demonstrates that although El Nino ˜ aec ff ts cyclone frequency in the North Pacific and along the US east coast, its impact on the North Pacific is accomplished via the PNA. Similarly, the PNA’s impact on US east coast storms is modulated via El Nino ˜ . In contrast, the impacts of the NAO extend as far west as the North Pacific and are not influenced by either the PNA or El Nino ˜ . 1. Introduction and Higgins [21], Changnon et al. [22], and Tilinina et al. [23]) or 850 hPa vorticity (Hodges [24–27], Hoskins et al. A key issue in assessing present and future climate is in exam- [28], Mesquita et al. [29], and Hodges et al. [30]). ining the climatology, variability, and trends in the character- Advantages and disadvantages of cyclone methodologies istics of extratropical cyclones. eTh history of cyclone tracks depend on the application of the research. For example, research has two preferred methods to define cyclones. eTh Hodges et al. [30]notethat850hPavorticity becomesquite first is the Eulerian method, which uses band-passed 500 hPa noisy at high-resolution requiring a reduction in spatial height field and strongest wave activity to define a cyclone resolution. On the other hand, tracking 850 hPa vorticity (Blackmon [1], Blackmon et al. [2], Wallace et al. [3]), Hoskins as opposed to MSLP captures more systems, especially and Valdes [4], Chang and Fu [5], Chang [6], Rao et al. [7], weaker ones (e.g., Hoskins et al. [28] and Hodges et al. C. S. Frederiksen and J. S. Frederiksen [8], and Nakamura [30]). eTh ability for higher-resolution reanalysis datasets to and Shimpo [9]). In contrast, a Lagrangian approach tracks capture weaker cyclones was also discussed by Tilinina et al. cyclones by utilizing diagnostics such as minimum sea-level [23], who compared storm track frequency among several pressure (MSLP) (Petterson [10], Klein [11], Mather et al. [12], reanalysis datasets. Tilinina et al. [23] found that the high- Reitan [13], Zishka and Smith [14], Sanders and Gyakum resolution NASA MERRA data produced the greatest number [15], Whittaker and Horn [16], Lambert [17], Murray and of cyclones, with much of the increase due to the inclusion Simmonds [18], Chen et al. [19], Hirsch et al. [20], Eichler of more shallow cyclones relative to the coarse-gridded 2 Advances in Meteorology reanalysis datasets. With regard to intensity, Akperov and and Yamagata [48] found that the teleconnections in several Mokhov [31] found that ERA-INTERIM data produced more areas were in opposite phase to ENSO. Interestingly, Anna- intense cyclones than ERA-40 or NCEP/NCAR reanalysis malai and Okajima [51]found anegativePNA response in data. eTh y also found that ERA-INTERIM produced more theECHAM5model in response to Indian Oceanwarming. smaller (<200 Km) cyclones than ERA-40 and NCEP/NCAR Min et al. [52] suggested that a Rossby wave train propagates reanalysis data, because of the higher spatial resolution of from northeast of India to Canada in response to SST changes ERA-INTERIM data. Akperov and Mokhov [31]alsoillus- linked to the IOD. trate the potential issues of choosing a tracking methodology, Given various assimilation systems, satellite data imple- as they found that when comparing three dieff rent storm mentation, and instrument errors, it is somewhat surprising tracking routines, the routine developed by Serreze [32] that Dee et al. [53]statedthatfew comparisonshavebeen produced less storms than two other routines. Neu et al. [33] made between the various datasets. Therefore, this study compared cyclone tracks over a number of methodologies compares the interannual variability of NH cyclone tracks and found that there was better agreement with regard to in the Reanalysis I and ERA-40 between coarse-gridded deep cyclones, with less agreement for weaker cyclones. reanalysis datasets. Although future work will focus on high- With regard to the total number of cyclones, Neu et al. resolution datasets such as the CFS reanalysis and JRA25 [33] also found qualitative agreement in terms of patterns, datasets, we feel it is prudent to rfi st assess the coarse-gridded although there were large differences when comparing the datasets because of the following. (a) We are examining storm totalnumberofcyclones. Perhapsthe best summaryofwhich track features compatible with spatial gridding of the reanal- cyclone track methodology to use was stated by Mesquita et ysis datasets. (b) The coarse-gridded datasets currently have al. [34]who quoted Leonardetal. [35]: “In carrying out an a longer temporal range than the high-resolution datasets intercomparison of depression tracking schemes great care (e.g., the Reanalysis I dataset extends back to 1950 while must be taken not to draw the misleading conclusions about the CFS reanalysis dataset extends back to 1979) making the merits and values of a particular scheme, since different them highly suitable for interannual variability studies. (c) users of the software will have different requirements.” Although there are many studies of storm track climatology, Although comparisons of cyclone track climatologies there are less studies on their interannual variability; this among reanalysis datasets are detailed in the literature, less is especially true when comparing interannual variability has been done regarding interannual variability. eTh impact between different reanalysis datasets. (d) Examination of of factors such as El Nino ˜ on cyclone tracks is determined the interannual variability of storm tracks in coarse-gridded by its ability to alter the jet stream. For example, the Pacicfi datasets will provide a foundation for comparing to higher- North-American Pattern (PNA) described by Wallace and resolution datasets. eTh organization of our paper is as Gutzler [36]was foundtobelinkedtoElNino by Horel and follows. Section 2 describes the methodology for generat- Wallace [37]. Eichler and Higgins [21], Hirsch et al. [20], and ing cyclone tracks, developing cyclone track frequency and Noel and Changnon [38] found an increase in the frequency intensity climatologies, and analyzing interannual variability. of NH winter cyclones along the US east coast during El Section 3 explores differences in interannual variability as a Nin˜o and a decrease along the US southeast coast during La function of ENSO, NAO, PNA, and the IOD via composite Nina, ˜ which correspond to the positive and negative phase analysis and partial correlation. In Section 4,wereviewand of the PNA, respectively. Gulev et al. [39]statedthatcyclone discuss our results and present our conclusions. frequency over the Gulf of Mexico and the US east coast was maintained by the North Atlantic Oscillation (NAO) from 2. Methodology 1958 to 1978 and the PNA from 1979 to 1999. Alterations in jet stream strength and location are also Cyclone tracks were generated from 6-hourly reanalysis linked to the North Atlantic Oscillation (NAO), which refers data for Reanalysis I (1950–2010), Reanalysis II (1979–2010), to fluctuations in sea-level pressure (SLP) between Iceland andERA-40(1958–2001).Toensureconsistency among and the Azores described by Hurrell [40]and Hurrellet the comparisons, all of the datasets had identical spatial ∘ ∘ al. [41]. An example of NAO impact on cyclone tracks is resolution (2.5 Lat× 2.5 Lon). To track cyclones, we utilize a provided by Bradbury et al. [42], who examined regional Lagrangian approach developed by Serreze [32]and Serreze eeff cts of the NAO on New England and found a decrease in et al. [54] that determines the minimum sea-level pressure cyclone frequency in northwestern New England during the (MSLP) field relative to surrounding grid points to track negative NAO phase. cyclones. We utilized the same criterion in the cyclone track In addition to ENSO, the PNA, and the NAO, u fl ctuations algorithm as Eichler and Higgins [21]bychoosingaonehPa in SST variations in the tropical Indian Ocean have also been threshold for finding cyclones and a maximum propagation linked to global teleconnections. The Indian Ocean Dipole distance of 800 km between timesteps. Although this is an (IOD) refers to SST u fl ctuations between the eastern and overestimate of a distance a cyclone can travel between western tropical Indian Ocean first described by Saji et al. timesteps, it does allow for the possibility of center jumps [43]. eTh IODhas been foundtobelinkedtoteleconnections, (e.g., cyclones reforming on the opposite side of a mountain especially in the Southern Hemisphere (e.g., Ashok et al. [44], chain) and also accounts for the gridded nature of the data we Na et al. [45], Ashok et al. [46], and Liu et al. [47]). Global are using. teleconnections have been described in Saji and Yamagata Reanalysis datasets used in our assessment include [48], Yamagata et al. [49], and Yang et al. [50]. For the NH, Saji the National Center for Environmental Prediction (NCEP) Advances in Meteorology 3 Table 1: Cyclone track climatology reanalysis datasets. Note that Table 2: Years used in composite study for El Ni no ˜ , NAO, and IOD abbreviations include the beginning and end year of the dataset (e.g., (JFM except OND for IOD). NCEP1 5010 represents Reanalysis I data from 1950 to 2010). 1950, 1958, 1966, 1969, 1973, 1983, 1987, 1992, El Nino ˜ Dataset Abbreviation used in study 1998, 2010 La Nina ˜ Reanalysis I (1950–2010) NCEP1 5010 1950,1971, 1974,1976, 1989,1999,2000,2008 Reanalysis I (1958–2001) NCEP1 5801 Positive PNA 1977, 1981, 1983, 1984, 1987, 2010 Reanalysis I (1979–2010) NCEP1 7910 1950, 1951, 1952, 1954, 1955, 1956, 1957, 1959, Negative PNA Reanalysis I (1950–1978) NCEP1 5078 1962, 1965, 1967, 1969, 1971, 1972, 1974, 1975, 1976, 1979, 1982, 1989, 1990, 1996, 2002, 2009 ECMWF reanalysis (1958–2001) ERA40 5801 1950, 1957, 1959, 1961, 1967, 1973, 1974, 1976, Positive NAO 1983, 1989, 1990, 1992, 1993, 1994, 1995, 1997, 1998, 2000, 2002, 2008 Reanalysis I dataset (Kalnay et al. [55]) and the European Negative NAO Center’s ERA-40 reanalysis dataset (Uppala et al. [56]). To 1955, 1963, 1969, 1979, 1985, 1996, 2010 facilitate comparisons among the various reanalysis products, we subdivided thedatasetsasshown in Table 1 and described as follows. For NCEP1 5010, we extend the cyclone track Index (EIS) described in Kousky and Higgins [58], which analysis done by Eichler and Higgins [21]from2002to2010. is calculated by doubling the Ocean Nino ˜ Index (ONI). To We also createdasubset of theReanalysisIdatasetfrom detect impacts from El Nino ˜ , we composited cyclone track 1958 to 2001 for direct comparison to ERA-40. To assess frequency and intensity for the strongest El Nino ˜ and La possible changes in storm tracks due to temporal changes Nina ˜ periods and these are shown in Table 2.Toensurea in the coupling strength between ENSO and the NAO balance between sample size and using sufficiently strong (Pinto et al. [57]), or due to an altered temporal response ENSO events to detect potential signals, we composited in cyclone frequency as a function of the NAO (Gulev et moderate/strong El Nino ˜ (La Nina) ˜ events for EIS greater al. [39]), we created two subsets of NCEP1 5010: one from than or equal (less than or equal) to two (negative two). For 1950 to 1978 (NCEP1 5078) and the other from 1979 to 2010 the NAO and IOD, we utilized data from Jones et al. [59] (NCEP1 7910). andSajietal. [43], respectively.ThePNA Index wasobtained Similar to Eichler and Higgins [21], mean, seasonal from NOAA’s Climate Prediction Center (CPC), for which cyclone track frequencies were generated by binning cyclones the values were standardized by the 1981–2010 climatology ∘ ∘ into 5 Lat × 5 Lonboxes forwinter(JFM),spring (see http://www.cpc.ncep.noaa.gov/data/teledoc/pna.shtml (AMJ), summer (JAS), and fall (OND). To ensure direct for more details). Compositing the NAO, PNA, and IOD comparison with Re27910 and ERA40 5801 data, Reanal- was done according to standard deviation (SD). Defining ysis I climatologies were also tabulated for the period a proper cutoff for SD is somewhat arbitrary. For example, 1979–2010 (NCEP1 7910) and 1958–2001 (NCEP1 5801) to Noel and Changnon [38]used1SD forcompositing ENSO match Re27910 and ERA40 5801 time periods, respectively. events. Bai et al. [60]used0.5 SD to denfi e NAOevents, Although we do focus on cyclone climatology, the frequency while Serreze et al. [54] selected the top positive and bottom climatologies for each dataset are shown in Appendix A. negative seven years for NAO and dene fi d NAO phase Seasonal cyclone intensity was determined with the same by quartile. While the methodology used by Serreze et al. methodology used by Eichler and Higgins [21]. First, we [54] is attractive because it ensures an evenly distributed generated a gridded cyclone intensity climatology by binning sample size, it does not account for the possibility of the cyclone MSLP. Next, we developed a seasonal MSLP clima- nonsymmetry of positive/negative phases of the NAO (e.g., tology for each reanalysis dataset from monthly mean MSLP the positive events may be defined by a higher NAO value data. To eliminate potential errors due to trends, we applied relative to the criterion for the negative NAO). On the a linear regression to the seasonal mean MSLP climatology. otherhand, the0.5 SD cutoffusedbyBai et al.[ 60]may We then normalized our cyclone track intensity climatol- allow for the inclusion of events that will not give sucffi ient ogy by subtracting the seasonal regressed MSLP for the separation from neutral events. As a compromise, we den fi ed periods 1950–2010 (NCEP1 5010), 1979–2010 (NCEP1 7910), NAO, PNA, and IOD events by 0.75 SD. Although slightly and 1958–2001 (NCEP1 5801 and ERA40 5801). As was the more strict than that used by Bai et al. [60], it still provides case for storm track frequency climatology, the intensity an adequate sample size (see Table 2). To assess statistical climatologies for each dataset are shown in Appendix B. significance of the composites, we again apply the 𝑡 -score To evaluate the interannual variability of storm track used by Bai et al. [60]. Results are considered significant for frequency and intensity, we utilized the January through a 2-tailed test criterion of 90 percent. March (JFM) storm track data for each year. eTh choice of Finally, we performed a partial correlation analysis to JFM was due to NH cyclone tracks being most influenced quantify the impacts of El Nin˜o, PNA, and NAO on cyclone by external forcing in winter. However, we also generate track frequency found in the composite analysis. Partial October through December (OND) frequency and intensity correlation, which is a method to filter out external eeff cts, climatologies, since the Indian Ocean Dipole (IOD) is the was used by Ashok et al. [46] for Southern Hemisphere strongest during NH autumn [52]. The strongest El Ni no ˜ and cyclone tracks to eliminate the IOD when determining El La Nina ˜ periods were found by using the ENSO Intensity Nino ˜ relationships and vice versa. For our study, we show 4 Advances in Meteorology and extending across the north central Pacific, with sig- nificantly less cyclones over the Gulf of Alaska (Figures 1(a) and 1(b)). This pattern suggests a positive (negative) Pacific North American pattern (Wallace and Gutzler [ 36]), with a southward (northward) displaced cyclone track in the North Pacific with increased (decreased) cyclones along the east coast during El Nino ˜ (La Nina). ˜ More frequent cyclones are also seen in the eastern Mediterranean and over (a) Italy; the latter being prominent for NCEP1 5801 suggesting that assimilation differences between NCEP reanalysis and ECMWF reanalysis are playing a role (compare Figure 1(a) with Figure 1(b)). Another interesting feature is over the North Atlantic, where there are increased cyclones in the North Atlantic, with less cyclones just south of Greenland (Figures 1(a) and 1(b)). This resembles a negative phase of the NAO and is consistent with the results found by Rogers [61], who linked the Southern Oscillation (SO) with the NAO. As (b) will be demonstrated when examining partial correlations, this result will be dependent on the time period chosen for the analysis suggesting a nonrobust relationship. eTh SLP composites for El Ni no ˜ relative to La Nina ˜ are generally consistent across both datasets, with more intense cyclones in the North Pacicfi and North Atlantic from 35 N to 50 N and less intense cyclones across much of Canada and ∘ ∘ the North Atlantic from 60 Nto70 N (Figures 1(c) and 1(d)). (c) The increase in intensity in the North Pacific is consistent with a stronger Aleutian low occurring during El Nino ˜ , while the increased intensity of cyclones along the US east coast is consistent with an enhanced east coast cyclone track during El Nin˜o consistent with the results of Eichler and Higgins [21]. The decrease in cyclone intensity across Canada during La Nina ˜ relative to El Nin˜o is indicative of a northward- displaced polar jet during La Nina. ˜ As was the case for the El (d) Nino ˜ frequency composite, the decrease (increase) in cyclone Figure 1: NH winter (JFM) El Ni no-L ˜ a Nina ˜ cyclone track intensity south of Greenland (across the mid-North Atlantic) frequency composite for (a) NCEP1 5801, (b) like (a) but for resembles the negative phase of the NAO (Rogers [61]). ∘ ∘ ERA40 5801.Units:no. of cyclones per5 Lat× 5 Lon box. Hatched For the NAO (Figure 2), the most notable impact is the areas are significant at 90% for 2-tailed 𝑡 -test. (c) NH winter (JFM) frequency dipole in the Atlantic suggesting that more (less) El Nino-L ˜ a Nin˜a cyclone track intensity composite for NCEP1 5801. frequent cyclones occur south of Greenland, with less (more) (d) like (c) but for ERA40 5801.Units:hPa.Hatched areasare cyclones west of Europe consistent with a northward (south- significant at 90% for 2-tailed 𝑡 -test. ward) displacement of the cyclone track during positive (neg- ative) NAO (Figures 2(a) and 2(b)). This agrees with Luo et al. [62], who compared North Atlantic cyclone tracks during a period when the NAO was trending upward (1978–1990) (1) El Nino ˜ correlations eliminating PNA and NAO, (2) PNA with a period when the NAO was trending downward (1991– eliminating El Nino,and(3)theNAOeliminatingPNAandEl 2009) and concluded that the North Atlantic cyclone track Nino ˜ . Since the IOD was found to have weak correlations, we was more intense when the NAO was trending downward. didnot includethe IODinthe analysis.Sucffi ienttemporal Ourresults arealsoconsistentwithWalterand Graf [63], who size allowed us to compare different time periods in the same examined teleconnection patterns related to the strength of dataset (i.e., NCEP1 7910 versus NCEP1 5078) in addition to the polar vortex and found that, when the vortex was strong, comparing two different reanalysis datasets (i.e., ERA15801 cyclone tracks extend northward into the Arctic Ocean, with versus NCEP1 5801). a secondary track over the Denmark Strait. An interesting feature in our NAO composite is that it is not confined strictly to the NAO centers of action in the 3. Interannual Variability North Atlantic (Figures 2(a) and 2(b)). For example, the pos- 3.1. Composite Analysis. Figure 1 shows cyclone track fre- itive area south of Greenland extends west-southwestward to quency and intensity composites for El Nino ˜ relative to Central Canada, while the negative area in the North Atlantic extends eastward through the Mediterranean. A significant La Nina ˜ for NCEP1 5801 and ERA40 5801. Both datasets show signicfi antly more cyclones along the US east coast increase in cyclones during NAO positive relative to NAO 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 10 Advances in Meteorology 5 ∘ ∘ intense cyclones are found generally along 70 Nfrom120 W to 70 E for positive NAO relative to negative NAO. Less intense cyclones for positive NAO relative to negative NAO arefound across theNorth Atlantic along40 Neastward to western Europe in both datasets (Figures 2(c) and 2(d)). As was the case for the frequency composite, a southward displacement of the polar jetstream is indicated for a negative NAO. Impacts of a negative NAO on cyclone tracks were investigated by Seager et al. [64], who found that precipitation (a) anomalies in the western and southeastern US are due to a southwardshiftinthecyclonetrack.Nonsignicfi antdecreases in intensity are located east of this zone across much of central Europe eastward to China (Figures 2(c) and 2(d)). Although there is good agreement among all of the datasets, regional differences are evident such as an area of more intense storms in positive NAO relative to negative NAO extending further west from Hudson Bay to Montana in NCEP1 5801 but not in ERA40 5801 (compare Figure 2(c) (b) with Figure 2(d)). Assimilation/model differences are likely culprits for this difference. PNA frequency and intensity composites are shown in Figure 3. Results from both datasets are consistent, with an increase (decrease) in cyclones in the central North Pacific, adecrease(increase)incyclonesinthe Gulf of Alaska eastward across the northern US, and an increase (decrease) in cyclones along the US east coast during positive (negative (c) PNA) (Figures 3(a) and 3(b)). Given the configuration of troughs and ridges associated with positive/negative phases of the PNA, this result is not surprising. However, we will see that when analyzing partial correlation, the east coast signal in cyclone track frequency will vanish when eliminating El Nino ˜ . For the PNA intensity composite, more (less) intense cyclones arefound in theNorth Pacicfi forbothdatasets associated with a stronger (weaker) Aleutian low dur- (d) ing positive (negative) PNA (Figures 3(c) and 3(d)). An increase (decrease) in intensity for positive (negative) PNA Figure 2: NH winter (JFM) NAO positive-NAO negative cyclone track frequency composite for (a) NCEP1 5801, (b) like (a) but for is also evident across the northwestern US/southwestern ∘ ∘ ERA40 5801.Units:no. of cyclones per5 Lat× 5 Lon box. Hatched Canada for NCEP1 5801 but not for ERA40 5801 (compare areas are significant at 90% for 2-tailed 𝑡 -test. (c) NH winter (JFM) Figure 3(c) with Figure 3(d)). Data assimilation/model dieff r- NAO positive-NAO negative cyclone track intensity composite for ences between the ERA-40 and NCEP reanalyses are likely NCEP1 5801. (d) like (c) but for ERA40 5801.Units:hPa.Hatched causes in this data-sparse area. Further downstream, the areas are significant at 90% for 2-tailed 𝑡 -test. eeff cts of the PNA on cyclone intensity are more muted, with suggestions of a (nonsignicfi ant) response of storm intensity to PNA from Iceland southward to the northeastern Atlantic negative yearsisalsonoted from Japannortheastward to occurring in NCEP1 5801 (Figure 3(c)). Overall, the PNA intensity composites suggest a robust signal across the North the western tip of the Aleutians in both datasets, with the area of signicfi ance conn fi ed to south of the Aleutians for Pacific, with a decreased response further downstream, with ERA40 5801 (Figure 2(b)). Overall, the large spatial extent differences between NCEP and ERA-40 reanalysis suggesting that some of the cyclone intensity response to PNA be a by- of statistical significance, coupled with the prevalence of several overlapping signicfi ant features in all of the reanalysis product of assimilation differences. datasets, speaks to the hemispheric extent of the positive Since the IOD’s center of action is far removed from NH NAO impact on cyclone tracks. Since there is a risk of mid-latitude cyclone tracks, there was little impact of the contamination from external forcing such as El Nino ˜ and IOD on NH cyclone track frequency and intensity in the correlation and intensity composites (not shown). However, the PNA, partial correlation will be useful in verifying the hemispheric extent to our NAO composite. frequency composite analysis did reveal an interesting pat- Similar to the NAO frequency composites, the NAO tern across Canada and the northern US (Figure 4). Figure 4 reveals a banded structure, with increased (decreased) intensity composites extend beyond the centers of action in the North Atlantic (Figures 2(c) and 2(d)). For example, more cyclones over Central Canada northeastward to Hudson 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W −10 −7 −4 −1 1 4 7 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 10 6 Advances in Meteorology (a) 120W 110W 100W 90W 80W 70W 60W 50W 40W (a) (b) 120W 110W 100W 90W 80W 70W 60W 50W 40W (b) (c) Figure 4: OND cyclone track frequency composite for (a) IOD positive relative to negative for NCEP1 5801 (shaded). 2-tailed 90% significance (hatched), (b) like (a) but for ERA40 5801 Units: ∘ ∘ number of storms per winter for a 5× 5 box. is suggestive of a Rossby wave train response to IOD as suggested by Saji and Yamagata [48], Min et al. [52], and Small (d) et al. [65]. More research using higher-resolution reanalysis datasets and model simulations is needed to further explore Figure 3: NH winter (JFM) PNA positive-PNA negative cyclone any potential IOD/NH cyclone relationships. track frequency composite for (a) NCEP1 5801, (b) like (a) but for ∘ ∘ ERA40 5801.Units:no. of cyclones per5 Lat× 5 Lon box. Hatched areas are significant at 90% for 2-tailed 𝑡 -test. (c) NH winter (JFM) 3.2. Partial Correlation Analysis. The correlation between NAO positive-NAO negative cyclone track intensity composite for cyclone track frequency and El Nino ˜ (eliminating PNA NCEP1 5801. (d) like (c) but for ERA40 5801.Units:hPa.Hatched and NAO) is shown in Figure 5.For alldatasets, abandof areas are significant at 90% for 2-tailed 𝑡 -test. positive correlations containing large areas of significance extend from the Gulf of Mexico northeastward along the southeastern US coast, although it is much weaker in Bay, decreased (increased) cyclones from the central US NCEP1 7910 (Figure 5(c)). When comparing NCEP1 5801 to the southeastern tip of Hudson Bay, and increased with ERA40 5801,the responsesare quitesimilar,although (decreased) cyclones from the US mid-Atlantic coast to there is a significant negative correlation south of Greenland Nova Scotia during positive (negative) IOD. Interestingly, in ERA40 5801 (compare Figure 5(a) with Figure 5(b)). thenegativebandfromthe centralUStosoutheastern Differences between NCEP1 7910 and NCEP1 5078 Hudson Bay in NCEP1 5801 resembles a “lee cyclogenesis” include negative correlations across the U.S/Canadian cyclone track, while the negative area originates further north border in NCEP1 5078, which is shifted poleward across in ERA40 5801 and resembles an “Alberta Clipper” track central Canada in NCEP1 7910 (compare Figure 5(c) with (compare Figure 4(a) with Figure 4(b)). Figure 5(d)). This may be related to low-level warming at Given that statistically significant areas are fairly limited high-latitudes during the 1980’s and 1990’s as suggested by in arealcoveragefor thebandedstructuresseeninIOD com- Greatbatch et al. [66], which would result in a poleward posites, caution needs to be applied to interpreting the results. shift of the mid-latitude baroclinic zone. Major differences However, they are evident across all of the datasets despite were also found in all datasets with respect to the El Nino ˜ their fairly localized regional extent. The banded structure frequency composite analysis including (1) the absence of 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W 0 −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 0 60E 120E 180 120W 60W −2.5 −2 −1.5 −1 −0.5 0.5 1 1.5 2 2.5 −10 −7 −4 −1 1 4 7 0 60E 120E 180 120W 60W 0 −10 −7 −4 −1 1 4 7 10 Advances in Meteorology 7 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (a) (a) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (b) (b) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (c) (c) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (d) (d) Figure 6: Partial correlation coefficient for JFM cyclone fre- Figure 5: Partial correlation coefficient for JFM cyclone fre- quency versus PNA (excluding ENSO and NAO). Correlation quency versus ENSO (excluding PNA and NAO). Correlation (shaded). 2-tailed 90% significance (hatched) for (a) NCEP1 5801, (shaded). 2-tailed 90% significance (hatched) for (a) NCEP1 5801, (b) ERA40 5801, (c) NCEP1 7910, and (d) NCEP1 5078. (b) ERA40 5801, (c) NCEP1 7910, and (d) NCEP1 5078. positive correlations in the North Pacific where the composite when comparing NCEP1 7910 with NCEP1 5078 (Figures analysis suggests a well-defined cyclone track during El Ni no ˜ 6(c) and 6(d)). For example, NCEP1 5078 shows a large and (2) a less distinct zone of negative correlation across area of positive correlation across central and northern the northern US/southern Canada, where the composites Europe, which is completely absent in NCEP1 7910 (compare suggest a more active cyclone track during La Nina ˜ stretching Figure 6(c) with Figure 6(d)). The positive correlations across from theGulfofAlaskaeastwardintothe NorthAtlantic Europe in NCEP1 5078 are consistent with the negative phase (compare Figure 5 with Figure 1 forbothpoints).Aswillbe of the NAO, suggesting a negative correlation between the shown subsequently, the elimination of the PNA from the El NAO and PNA during this period. This is consistent with Nino ˜ correlation plays a role in the differences between the Pinto et al. [57], who found a signicfi ant negative correlation composite analysis and the partial correlation analysis. between the NAO and PNA in three simulations of the For the PNA correlation, signicfi ant positive correla- ECHAMmodel.Pinto et al.[57]found this relationship tions are found in the North Pacicfi south of the Aleu- lacking when assessing ERA-40 and NCEP reanalysis data tians, with negative correlations northeast of Japan for all from 1973 to 1994, which was attributed to an inactive period datasets (Figure 6). ERA40 5801 and NCEP1 5801 produced in the coupling strength between NAO and PNA. The lack of similar responses, although significant negative correlations this feature in 1979–2010 suggests that the coupling strength extended as far west as Iran in NCEP1 5801 (compare between the PNA and NAO was weak from 1979 to 2010 Figure 6(a) with Figure 6(b)). Larger differences are noted and strong from 1950 to 1978. When comparing the PNA 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 40 40 20 20 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 40 40 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 8 Advances in Meteorology −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (a) (d) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (b) (e) −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (c) (f) Figure 7: (a)–(c) Correlation coefficient for JFM cyclone frequency versus ENSO for (a) total correlation, (b) excluding PNA, (c) excluding PNA and NAO. (d)–(f) Correlation coefficient for JFM cyclone frequency versus PNA for (d) total correlation, (e) excluding NAO, and (f) excluding NAO and ENSO. Correlation (shaded) 2-tailed 95% significance (hatched). −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 −0.5 −0.4 −0.3 −0.2 −0.1 0.1 0.2 0.3 0.4 0.5 (a) (b) −0.5 0.1 0.2 0.3 0.4 0.5 −0.5 0.1 0.2 0.3 0.4 0.5 −0.4 −0.3 −0.2 −0.1 −0.4 −0.3 −0.2 −0.1 (c) (d) Figure 8: Correlation coefficient for JFM cyclone frequency versus NAO (excluding PNA and ENSO). Correlation (shaded) 2-tailed 95% significance (hatched) for (a) NCEP1 5801, (b) ERA40 5801, (c) NCEP1 7910, and (d) NCEP1 5078. correlation to the PNA composite, a major difference is seen As discussed above, the El Nino ˜ and PNA correlations along the US east coast, where the composite analysis shows showed major differences with their composites in specific an increase (decrease) in cyclones for positive (negative) regions. To investigate further, we recomputed the El Nino ˜ PNA. However, the correlation analysis lacks this feature correlation for NCEP1 5010 with (1) no factors eliminated, (compare Figure 6 with Figure 3). that is, a full correlation, (2) a partial correlation with 80 80 60 60 40 40 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 60 60 40 40 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 60 60 40 40 20 20 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 80 80 20 20 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 Advances in Meteorology 9 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 (a) (b) 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 (c) (d) Figure 9: Continued. 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 OND JAS AMJ JFM SON JAS AMJ JFM OND JAS AMJ SON JAS AMJ JFM JFM 10 Advances in Meteorology 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 80N 80N 60N 60N 40N 40N 20N 20N 1 23456789 10 1 23456789 10 (e) (f) Figure 9: Cyclone track frequency climatology (shaded) for (a) NCEP1 5010 (JFM, AMJ, JAS, OND), (b) like (a) but for NCEP1 5801, and (c) ∘ ∘ like (a) but for ERA40 5801. Units: number of storms per winter for a 5× 5 box. Cyclone track frequency climatology (shaded) for (d) like ∘ ∘ (a) but for Re27910, (e) like (a) but for NCEP1 7910, and (f) like (a) but for NCEP1 5078. Units: number of storms per winter for a 5 × 5 box. the PNA removed, and (3) a partial correlation with the North Pacific and along the US east coast and negative PNA and NAO removed (Figures 7(a)–7(c)). When assessing correlations northeast of Japan and across the northern US the full correlation (Figure 7(a)), the results resemble the (compare Figure 7(d) with Figure 3). When eliminating the composite analysis (compare with Figure 1), with positive NAO, little difference is seen, demonstrating that the NAO correlations in the central North Pacific and along the does not play a significant role in cyclone tracks when US east coast and negative correlations from the Gulf of assessing the PNA (Figure 7(e)). However, this result may Alaska eastward across the northern US. When the PNA is also be theresultofalack of coupling betweenthe PNA removed (Figure 7(b)), the North Pacicfi and northern US and cyclone frequency tracks for this period in the reanalysis correlations are eliminated, leaving only the US east coast ˜ data as suggested by Pinto et al. [57]. When El Nino and correlation. Eliminating the NAO (Figure 7(c)) does little to theNAO areeliminated(Figure 7(f)), much of the signal the correlation pattern, which indicates that the NAO does across the northern US and along the US east coast vanishes. notplayamajorroleinthe El Nino ˜ correlation. Since El eTh se results are consistent with the El Ni no ˜ analysis in Nin˜o affects the PNA, the full correlation shows both of these Figures 7(a)–7(c) and suggest that, by itself, the PNA has effects. However, the elimination of the PNA suggests that a significant eeff ct on cyclone tracks in the North Pacific. the North Pacific/northern US correlations are only indirectly However, El Nino ˜ needs to be operative to produce eeff cts caused by El Nino ˜ via the PNA, while the US east coast further downstream across the northern US/US east coast. correlation is a direct result of anomalous heating in the Unlike thePNA andElNino ˜ correlations, the NAO Equatorial Pacific due to El Ni no ˜ . partial correlation is more robust when compared with the In a similar fashion, we also recomputed the PNA NAO composite analysis (Figure 8). For example, significant correlation as a (1) full correlation with no factors eliminated positive correlations are found south of Greenland, while (2) partial correlation removing effects of the NAO, and (3) significant negative correlations occur in the central North partial correlation eliminating El Nino ˜ and the NAO (Figures Atlantic, consistent with the NAO composite analysis for all 7(d)–7(f)). The full correlation ( Figure 7(d)) is similar to the datasets (compare Figure 8 with Figure 2). Positive correla- composite analysis, with positive correlations in the central tions (with areas of significance) are also found in the central 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 OND JAS AMJ JFM OND JAS AMJ JFM Advances in Meteorology 11 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 (a) (b) 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 (c) (d) Figure 10: Continued. 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 0 60E 120E 180 120W 60W 0 OND JAS AMJ JFM OND JAS AMJ JFM OND JAS AMJ OND JAS AMJ JFM JFM 12 Advances in Meteorology 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 80N 80N 60N 60N 40N 40N 20N 20N −24 −20 −16 −12 −8 −4 −24 −20 −16 −12 −8 −4 (e) (f) Figure 10: Cyclone track intensity climatology (shaded). Intensity derived by subtracting regressed climatological mean from cyclone track mean (units: hPa) for (a) NCEP1 5010 (JFM, AMJ, JAS, OND), (b) like (a) but for NCEP1 5801, and (c) like (a) but for ERA40 5801. Cyclone track intensity climatology (shaded). Intensity derived by subtracting regressed climatological mean from cyclone track mean (units: hPa) for (d) like (a) but for Re27910, (e) like (a) but for NCEP1 7910, and (f) like (a) but for NCEP1 5078. North Pacicfi ( Figure 8), which is far removed from the NAO composite analysis also showed that caution is advised when teleconnection areas. However, some differences are seen assessing interannual variability for regional spatial scales, between NCEP1 7910 and NCEP1 5078, with NCEP1 7910 sinceregions of signicfi ancevaryeitherasaresult of thetime showing a zone of negative correlation across Asia relative period chosen for the composite analysis or from differences to NCEP1 5078 (compare Figure 8(c) with Figure 8(d)). An in model/data assimilation techniques between the reanalysis area of positive correlation in the North Pacific is also datasets themselves. This was especially true when comparing displaced eastward to the Gulf of Alaska in Re5078 relative intensity composites, such as the PNA intensity composite to NCEP1 7910 (compare Figure 8(c) with Figure 8(d)). A differences between ERA-40 and NCEP reanalysis datasets. possible reason for these differences is an altered response of Partial correlation was useful in verifying which com- cyclone track frequency to the NAO in different time periods ponents of the composite analysis were most robust. The similar to the southeastward shift of the cyclone track in 1958– PNA exhibited a positive correlation with cyclone frequency 1978 relative to 1979–1999 found by Gulev et al. [39]. across Europe in the 1950–1978 period compared with 1979– 2010, suggesting that the coupling strength between the NAO and PNA was greater in the former period than the latter 4. Conclusions period.For theNAO,the correlations were most similarto Composite analyses showed surprisingly good agreement the composite analysis suggesting that El Nino ˜ and the PNA do not play a significant role in the NAO’s impact on cyclone especially for El Nin˜o,NAO,and thePNA.Interestingly, NAO impacts were also noted as far west as the North tracks. However, the El Nino ˜ correlation was not consistent with the El Nino ˜ composite in the North Pacific nor was the Pacific, which was confirmed by partial correlation analysis. PNA correlation consistent with the PNA composite across eTh results for the IOD showed some interesting features across the northern US/Canada possibly related to Rossby the northern US/US east coast. 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