Tropical cyclone effects on Arctic Sea ice variability

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi: /2012gl052987, 2012 Tropical cyclone effects on Arctic Sea ice variability Enrico Scoccimarro, 1,2 Silvio Gualdi, 1,2 and Antonio Navarra 1,2 Received 2 July 2012; revised 31 July 2012; accepted 1 August 2012; published 13 September [1] In recent years increasing interest has been put on the role that intense Tropical Cyclones can play in the climate system. The following study is aimed at highlighting the effects of strong Tropical Cyclones over the Tropical Atlantic on the mean climate. Their composite effect on the surface winds is made apparent by a wide cyclonic perturbation that affects a large portion of the Atlantic tropical Ocean. Teleconnection patterns, which are visible in the Sea Level Pressure anomalies associated with this Tropical Composite Cyclone, appear to link the activity of the hurricanes to the Arctic Ocean. A significant negative correlation between the energy dissipated by hurricanes in the Tropical atmosphere and the sea ice cover along the Transpolar Drift Stream path, has also been found. Citation: Scoccimarro, E., S. Gualdi, and A. Navarra (2012), Tropical cyclone effects on Arctic Sea ice variability, Geophys. Res. Lett., 39,, doi: / 2012GL Introduction [2] Tropical cyclones (TCs) are an important component of the Earth s climate, and many studies have been carried out in order to describe the effects that climate variability and climate change may have on them [Knutson et al., 2010]. There is also evidence that TCs have a role in modulating the meridional heat transport in the northern hemisphere through their interplay with the oceans [Emanuel, 2001; Sriver and Huber, 2007; Scoccimarro et al., 2011], thus affecting the climate system [Hu and Meehl, 2009]. In this paper we investigate some new aspects of the relationship between TCs and climate. The fingerprint of a wide cyclonic structure (here referred to as Tropical Composite- Cyclone, TCC) emerges after computing the composite of the anomalies in the speed of the zonal 10 m wind associated with all the observed occurrences of hurricanes during the period in the Atlantic basin (Figure 1). This links the TCs activity in the Tropical Atlantic to the large scale atmospheric circulation. It is well known that tropical atmospheric variability can affect high latitude processes [Trenberth et al., 1998] and teleconnection patterns can extend from the tropics up to polar latitudes [Wang, 2002; Liu et al., 2002]. Recent studies suggest that poleward heat transport associated with Rossby waves excited by tropical convection plays an important role for the enhanced high latitude warming [Ding et al., 2011; Schneider et al., 2011], moreover Screen et al. [2012] suggests that the Arctic climate change is 1 Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy. 2 Istituto Nazionale di Geofisica e Vulcanologia, Bologna, Italy. Corresponding author: E. Scoccimarro, Centro Euro-Mediterraneo sui Cambiamenti Climatici, V.le A. Moro 44, IT Bologna, Italy. (enrico.scoccimarro@bo.ingv.it) American Geophysical Union. All Rights Reserved /12/2012GL linked to remote influences. Further evidence of the linkage between tropical convection and the Arctic region has been highlighted by Yoo et al. [2011] investigating the contribution of the Madden-Julian Oscillation variability to the Arctic amplification. In particular enhanced and localized tropical convection can trigger high-latitude warming through the excitation of poleward-propagating planetary-scale Rossby waves, which transport heat poleward [Lee et al., 2011].Since TCs are known to be a source of Rossby waves [Sardeshmukh and Hoskins, 1988], thus inducing potential communication with higher latitudes, we aim at investigating their possible influence on the Arctic climate. This analysis focuses on the TCC effect on surface winds, sea level pressure and sea ice cover and drift. 2. Data and Methodology 2.1. Reference Data [3] In this study we use atmospheric reanalysis and observational data-sets of tropical cyclone and sea ice cover. Specifically, we consider horizontal wind velocities (U and V), vertical velocities and sea level pressure (SLP), collected daily from the ERA-Interim Archive at ECMWF [Berrisford et al., 2009]. The ERA-Interim atmospheric model and reanalysis system is based on a numerical model (IFS, ecmwf.int/research) with 60 levels in the vertical (top level at 0.1 hpa) and a T255 spherical-harmonic representation for the basic dynamical fields. It is well known that there are discrepancies in TC position and intensity between reanalysis and observations [Schenkel and Hart, 2011]. However the ERA-Interim data set has been previously used to represent TC activity in both hemispheres [Garde et al., 2010; Daloz et al., 2010] and results are in good agreements with observations in the Atlantic basin [Daloz et al., 2010]. [4] Data on the observed TC tracks, distribution and intensity are from the National Hurricane Center (NHC). From the sea ice cover (ICECOV) we use the monthly Bootstrap sea ice concentrations for NIMBUS-7 SMMR and DMSP SSM/I [Comiso, 1999] (updated 2008) data, provided by the National Snow and Ice Data Center (NSIDC) on a Km grid, while for the sea ice velocity, we use gridded ice motion fields from the Polar Pathfinder Daily 25-km Equal-Area Scalable Earth Grid (EASE- grid) Sea Ice Motion Vectors dataset, available from the NSIDC. The period analyzed in here is (hereafter PRESCLI) and in the rest of the paper, for the sake of simplicity, we will refer to the above-mentioned data as observations. [5] The main regions considered are the North Atlantic (0 N 60 N E 360 E) and the Arctic (60 N 90 N- 0 E 360 E) basins. Furthermore, in order to better characterize the impact of the TC activity in the Arctic, sub-regions corresponding to Beaufort Sea, East Siberian Sea, eastern Arctic, Kara and Barents Seas, central Arctic and Fram Strait, are defined following Arfeuille et al. [2000]. 1of6

2 Figure 1. Composite of zonal velocity anomalies, U TC (shaded), induced by hurricanes (cat > = 1, 180 green stars) over PRESCLI. Magenta contours indicate June-December zonal velocity U climatology. U and U TC units are [m/s] Methodology [6] Over the PRESCLI period we detected the TCs with a sustained surface wind speed greater than 33 m/s (hurricane cat. 1 5) in the Atlantic basin. We found 180 TCs reaching hurricane intensity during the PRESCLI period, corresponding to about 6.5 hurricanes per year [Webster et al., 2005]. [7] In our analysis, we intend to assess the integrated effect of the TC activity in the tropical Atlantic (ATL) on the mean climate of the Northern Hemisphere with a particular focus over the Arctic region. Thus it is first necessary to define the circulation anomalies associated with the TC activity in the ATL basin. The daily anomaly of the observed field A associated with the occurrence of TCs in the ATL is defined as the difference between the mean daily value of A found when a TC is detected in this basins, and the corresponding daily climatological value of A. In other words: ða TC Þ ¼ ða TC Þ hai where (A TC ) is the daily anomaly of A when a hurricane is active in the Atlantic basin, (A TC ) is the daily mean value of A when a hurricane is detected in ATL, and A is the daily climatological value of A for the PRESCLI period. The composite anomaly A TC is then calculated as the mean of the daily anomalies (A TC ). [8] In the rest of the paper the term anomaly will refer to this type of calculation method to identify the composite effect of TCs. [9] The statistical significance mentioned through the paper is verified at the 90% level with a bootstrap method for both anomalies and correlations. 3. Results [10] One of the most relevant signatures left by a TC is the strong perturbation in the speed of the surface wind. The distance of the strongest winds from the TC center typically [Anthes, 1982] ranges between 10 and 100 km. By computing the 10 meter zonal wind anomalies (U TC, computed as described in section 2.2), it becomes apparent (Figure 1) that the meridional influence of the resulting TCC structure extends from about 10 N to about 45 N, with a radius of maximum induced winds anomaly around 900 km. The region affected by the TCC shown in Figure 1 is approximately one order of magnitude larger than the radius of maximum winds associated with a single TC. The center of the TCC is located at about 26 N, the latitude where the probability density function of the latitudinal location of hurricane centers reaches its maximum (not shown). The maximum U TC values have a magnitude comparable to the climatological June December zonal wind velocity (contour lines in Figure 1). This highlights the relevance of the possible TC integrated effect on the mean circulation. [11] The TCC structures identified by the compositing technique occur at basin wide spatial scales, and we will now focus on their effect on the mean climate. The induced anomaly in the sea level pressure (SLP TC, Figure 2) reveals an anomalous wave train, extending from the tropics to the Arctic region. Large negative anomalies ( 1 hpa) are found over the tropical and central Atlantic, and large positive anomaly (+1.8 hpa) are found over the Barents sea. The extension of robust Sea Level Pressure TC-induced anomaly patterns leads to a dynamical teleconnection between the tropical regions and higher latitudes. The teleconnection is also visible in the atmospheric vertical velocity field, where the TCC induced anomalies form large scale meridional cells (not shown) is consistent with the surface SLP anomalies. [12] In order to assess the effect of hurricanes on the Arctic climate we are focusing on September, as during this month the TC activity has a peak in the Northern Hemisphere (72 of the 180 strong TCs registered occur in September). Also, September corresponds to the minimum in the observed 2of6

3 Figure 2. Composite of sea level pressure anomalies SLP TC, induced by hurricanes (cat. > = 1) over PRESCLI. Magenta contours indicate June December SLP climatology. Black contours indicate significant anomalies. SLP and SLP TC Units are [hpa]. annual cycle of the sea ice cover in the Northern Hemisphere. In Figure 3, the SLP September climatology for the period (panel a, white contours) is shown together with the September SLP TC anomaly (panel a, shaded). The positive anomaly on the Barents and Greenland Seas (up to 1.8 hpa), and the negative pattern (up to 1 hpa) centered on the East Siberian Sea are associated with a wind anomaly extending over a large part of the Arctic (green arrows in Figure 3b) which, in turn, can interfere with the Transpolar Drift Stream (TDS). TDS is a wind driven ocean current responsible for the advection of the sea ice out of the Arctic Ocean into the Nordic Seas through the Fram Strait [Mysak and Venegas, 1998; Köberle and Gerdes, 2003]. Strong TDS conditions correspond to large export of sea ice from the eastern and central Arctic into the Fram Strait [Arfeuille et al., 2000]. [13] In order to investigate the possible influence of TCC on the sea ice cover in the Arctic basin, we use an index measuring the power associated to TCs activity: the Power Dissipation Index (PDI) [Emanuel, 2005]. We computed the correlation map between the September sea ice cover in the Northern Hemisphere and the PDI associated with the hurricanes occurring in September over the ATL basin (Figure 3b, shaded). The PDI is usually computed as yearly integral and can be considered as an approximated measure of the annual energy dissipated by TCs in the atmosphere. The ICECOV-PDI correlation map (only significant values are shown) computed for the PRESCLI period shows evident negative patterns (Figure 3b, shaded) over Beaufort, Eastern and Central Arctic Seas. Negative correlation coefficients between sea ice cover and PDI indicate that years with high September TC activity correspond to reduced sea ice cover. Over the Beaufort sea, the negative correlation extends to the southern brunch of the anticyclonic Beaufort sea ice gyre [Rigor et al., 2002]; this part of the gyre is responsible for sea ice advection into the TDS during the summer [Asplin et al., 2009; Screen et al., 2011]. [14] In the Beaufort, Eastern and Central Arctic Seas, where the ICECOV-PDI correlation is lower than 0.5, the meridional wind velocity anomaly is predominantly northward, and the zonal wind velocity anomaly is predominantly westward as indicated by the green arrows in Figure 3b. This induced effect in the northward winds over the Beaufort eastern Arctic Sea might induce advection of sea ice into the TDS path (green arrows in Figure 3), leading to an increase in the transport from the Beaufort eastern Arctic region to the central Arctic. This is confirmed by the large (up to 0.75) ICECOV-PDI negative correlation over these regions (Figure 3b). Similar correlation patterns are found over the same regions using detrended time series (see auxiliary material, Figure S1) 1, though now the maximum negative correlation ( 0.65) appears to be slightly smaller than in the non detrended case ( 0.75). The positive correlation in the Kara Sea, on the other hand, appears to be larger. The described potential advection of sea ice into the TDS is highlighted by the anomalies induced in the observed sea ice motion fields: over Eastern and Central Arctic regions, an extended area covered by a significant induced anomaly in the sea ice velocity (see additional material, Figures 2 and 3), coherent with the anomalies induced in the winds (green arrows in Figure 3b), has been found. 4. Discussion [15] The analysis carried out on atmospheric circulation TC-induced anomalies suggests a possible link between 1 Auxiliary materials are available in the HTML. doi: / 2012GL of6

4 Figure 3. (a) September SLP average (white contours) and SLP TC (shaded), Units are [hpa]. Black contours indicate significant anomalies. (b) ICECOV-PDI correlation (shaded) and TCC induced Wind anomaly (green arrows). Magenta lines indicate ICECOV-PDI correlation contours equal to 0.5. Only significant patterns are shown. Tropical Cyclones activity and the Arctic region. The dynamical mechanism at the basis of this large scale teleconnection can be explained considering the role of TCs as important source of Rossby waves. Rossby Wave Source (RWS) are derived from the absolute vorticity and the divergent (irrotational) component of wind [Sardeshmukh and Hoskins, 1988]. The transit of a tropical cyclone into the midlatitudes may result in a high-amplitude Rossby wave that can extend to near-hemispheric scales [Harr and Dea, 2009], and the excitation of Rossby wave trains by recurving TCs (TCs transitioning from a generally westward motion to a generally eastward motion) upon midlatitudes may provide a major component of the emanation of energy to high latitudes [Riemer and Jones, 2010; Ko and Hsu, 2010]. [16] The integrated effect of TCs as a RWS [Lu and Kim, 2004; Shimizu and de Albuquerque Cavalcanti, 2011] is highlighted by the 200mb RWS composite anomaly induced by recurving TCs (see Figure 4), which shows a large positive pattern near the eastern coast of North America, between 30 N and 50 N. [17] The SLP composite anomaly induced by Atlantic hurricanes (Figure 2) shows a 1.4 hpa maximum over the Barents Sea. As shown by Wu et al. [2006], in the Arctic region, the first mode of SLP variability (EOF-1) is the Arctic Oscillation (AO), and the second-leading mode (EOF-2) is the Arctic Dipole Anomaly (DA). Both the Arctic DA and the AO have been shown to be linked to the sea ice variability in the Arctic. Specifically, Wu et al. [2006] and Watanabe et al. [2006] illustrate the relationship between the DA and the sea ice volume variability in the Barents Sea, moreover Wang et al. [2009] discuss the DA relationship with Arctic summer sea ice extent. The role of AO in driving the sea ice export out of the Arctic Basin into the Nordic seas and Barents Sea has been also extensively discussed in previous works [Rigor et al., 2002; Holland, 2003; Koenigk et al., 2009]. In addition Maslanik et al. [2007] pointed out the importance of understanding factors that affect regional atmospheric circulation and multidecadal variability in the Arctic. The amplitude of the SLP anomalies associated with AO and DA is of a few hpa. This analysis reveals that TCC induced SLP anomalies are comparable with the first two modes of SLP variability over the Arctic Ocean. [18] It is argued that SLP anomalies induced by TCC at high latitudes may interfere, at least, with the drift of the sea ice by affecting the wind circulation over the Arctic Ocean (Figure 3). Other works, indeed suggest that the interannual variability in mean sea ice extent and volume in the Arctic Basin is primarily caused by the variation in the wind-driven sea ice transport [Maslanik and Dunn, 1997; Vihma et al., 2012]. Also modelling results [Arfeuille et al., 2000] have inferred that the interannual anomalies in the Arctic sea ice velocity fields follow the isobars of the SLP anomalies. The positive SLP anomaly induced by TCC over NE Europe (Figure 2) may also have a weakening effect on the Northern Hemisphere storm track causing a decrease of the number of cyclones entering the Arctic. 5. Conclusions [19] In this study, we showed that the integrated effect of TC activity in the Tropical Atlantic can substantially influence the atmospheric circulation at high latitudes, and perturb the sea ice distribution in the Arctic. A large Composite- Cyclonic structure (TCC) in the Atlantic was found using observed data sets. The TCC fingerprint on the zonal winds is coherent with the minimum of Sea Level Pressure associated with the TCC center in the sub-tropical part of the Atlantic ocean. The anomaly induced by TCC in the Sea Level Pressure shows teleconnections between the tropical Atlantic belt and the Arctic. [20] The location and magnitude of the SLP anomalies induced by TC suggest a possible interaction with the modes of SLP variability in the Northern Hemisphere. For instance, the SLP anomalies associated with the Arctic Oscillation and the Arctic Dipole Anomaly modes have a magnitude which is comparable to the observed anomaly in SLP induced by the Atlantic Tropical Composite Cyclone. [21] The direct effects of hurricanes on the interannual variability of the Arctic sea ice cover were surveyed through an analysis of Sea Level Pressure and surface wind anomalies 4of6

5 Figure 4. Rossby Wave Source anomaly induced by recurving TCs over PRESCLI. Units are [s-2]. induced by TCC, suggesting a link between the interannual variability of the hurricane energy (expressed in terms of accumulated PDI) and the sea ice cover in the Arctic Ocean in September. During the years with high registered PDI values, an advection of the sea ice driven by local TCC induced wind into the TDS appears to increase sea ice transport from the Beaufort and Eastern Arctic into the Central Arctic. [22] In absence of large scale data sets of sea ice thickness, coupled model experiments are a viable tool to aid investigation on how the Atlantic TC activity might influence the sea ice in the Arctic. [23] Acknowledgments. We gratefully acknowledge the support of Italian Ministry of Education, University and Research and Ministry for Environment, Land and Sea through the project GEMINA and the Italian Ministry for Environment, Land and Sea through the project SIDS2. [24] The Editor thanks the two anonymous reviewers for their assistance in evaluating this paper. References Anthes, R. A. (1982), Tropical Cyclones: Their Evolution, Structure and Effects, Meteorol. Monogr., vol. 212, no. 41, 208 pp., Am. Meteorol. Soc., Boston, Mass. Arfeuille, G., L. A. Mysak, and L.-B. Tremblay (2000), Simulation of the interannual variability of the wind driven Arctic sea ice cover during , Clim. Dyn., 16, , doi: /pl Asplin, M. G., J. V. Lukovich, and D. G. Barber (2009), Atmospheric forcing of the Beaufort Sea ice gyre: Surface pressure climatology and sea ice motion, J. Geophys. Res., 114, C00A06, doi: /2008jc [Printed 115(C1), 2010.] Berrisford P., D. Dee, K. Fielding, M. Fuentes, P. Kallberg, S. Kobayashi, and S. Uppala (2009), The ERA-Interim archive, ERA Rep. Ser.1, Eur. Cent. for Medium Range Forecasts, Reading, U. K. Comiso, J. (1999), Bootstrap sea ice concentrations for NIMBUS-7 SMMR and DMSP SSM/I, October 1978 through December 2007, org/data/docs/daac/nsidc0079_bootstrap_seaice.gd.html, Natl. Snow and Ice Data Cent., Boulder, Colo. Daloz, S. A., F. Chauvin, and F. Roux (2010), Tropical cyclone rainfall in the observations, reanalysis and ARPEGE simulations in the North Atlantic Basin, Hurricanes Clim. Change, 2, 57 79, doi: / _4. Ding, Q., E. J. Steig, D. S. Battisti, and M. Kuttel (2011), Winter warming in West Antarctica caused by central tropical Pacific warming, Nat. Geosci., 4(6), , doi: /ngeo1129. Emanuel, K. A. (2001), Contribution of tropical cyclones to meridional heat transport by the oceans, J. Geophys. Res., 106(D14), 14,771 14,781. Emanuel, K. A. (2005), Increasing destructiveness of tropical cyclones over the past 30 years, Nature, 436, , doi: /nature Garde, L. A., A. B. Pezza, I. Simmonds, and N. E. Davidson (2010), A methodology of tracking transitioning cyclones, IOP Conf. Ser. Earth Environ. Sci., 11, , doi: / /11/1/ Harr, P., and J. M. Dea (2009), Downstream development associated with extratropical transition of tropical cyclone over the western North Pacific, Mon. Weather Rev., 137, , doi: /2008mwr Holland, M. M. (2003), The North Atlantic Oscillation Arctic Oscillation in the CCSM2 and its influence on Arctic climate variability, J. Clim., 16, , doi: / (2003)016<2767:tnaooi>2.0. CO;2. Hu, A., and G. A. Meehl (2009), Effect of the Atlantic hurricanes on the oceanic meridional overturning circulation and heat transport, Geophys. Res. Lett., 36, L03702, doi: /2008gl Knutson, T. R., J. L. McBride, J. Chan, K. Emanuel, G. Holland, C. Lansea, I. Held, J. P. Kossin, A. K. Srivastava, and M. Sugi (2010), Tropical cyclones and climate change, Nat. Geosci., 3, , doi: / NGEO779. Ko, K. C., and H. H. Hsu (2010), Downstream development of the summertime tropical cyclone/submonthly wave pattern in the extratropical North Pacific, J. Clim., 23, , doi: /2009jcli Köberle, C., and R. Gerdes (2003), Mechanisms determining the variability of Arctic sea ice conditions and export, J. Clim., 16, , doi: / (2003)016<2843:mdtvoa>2.0.co;2. Koenigk, T., U. Mikolajewicz, J. H. Jungclaus, and A. Kroll (2009), Sea ice in the Barents Sea: Seasonal to interannual variability and climate feedbacks in a global coupled model, Clim. Dyn., 32, , doi: /s Lee, S., T. Gong, N. Johnson, S. Feldstein, and D. Pollard (2011), On the possible link between tropical convection and the Northern Hemisphere Arctic surface air temperature change between , J. Clim., 24(16), , doi: /2011jcli Liu, J. P., X. J. Yuan, D. Rind, and D. G. Martinson (2002), Mechanism study of the ENSO and southern high latitude climate teleconnections, Geophys. Res. Lett., 29(14), 1679, doi: /2002gl of6

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