The use of sea surface temperatures to track oceanic heat anomalies


Tore Furevik

University of Bergen, Geophysical Department, Bergen, Norway


Nansen Environmental and Remote Sensing Center, Solheimsviken, Norway




Gridded sea surface temperature (SST) data are used in a study of the large scale temperature variability in the Nordic Seas, and the transport of heat anomalies towards the Arctic Ocean. A complex principal component analysis is applied to identify coherent structure of variability in the SST field. The leading mode accounting for 39 percent of the variance, reveals a band of high correlations at increasing phase lags along the west coast of Norway and into the Barents Sea, Fram Strait and Greenland Sea. Propagation speeds agree with other transport estimates, indicating that the SST anomalies are the signatures of upper ocean heat anomalies advected by the mean flow from north of Scotland towards the Barents Sea. Data from several regular hydrographical sections are supporting the SST observations. Composite maps of anomalous cold and warm years show that forcing from the local wind field anomalies is likely to produce the observed amplification in the SST anomalies in the Barents Sea and along the East Greenland coast.



1. Introduction

The flow of warm and salty Atlantic Water (AW) through the Faroe-Scotland Gap, is the key parameter for the mild climate and the rich biomass production in the Nordic Seas (the Greenland-Iceland-Norwegian Seas), and the adjacent Barents and North Seas (Figure 1, left).


In the Nordic Seas the AW cools and becomes denser, and as the end product spills over the ridges between Greenland and Scotland, it makes a significant contribution to the renewal of the deep waters of the North Atlantic, and to the global thermohaline circulation.


Associated with a decrease in the sea level pressure (SLP) over the Nordic Seas during the recent decades, a larger amount of Polar Water (PW) is found to leak into the interior of the Nordic Seas (Blindheim et al., 1999), giving a displacement of the front between the AW and the PW towards the Norwegian Coast. The increased cyclonic wind forcing seems to give an increased flow of warmer AW to the Arctic Ocean, mainly through the Barents Sea (Furevik, 1998; Zhang et al., 1998), resulting in the observed warmer and thicker AW layer in the Arctic Ocean (Carmack et al., 1997; Swift et al., 1997). Recently McPhee et al. (1998) have reported of a warmer and fresher surface layer, thinner sea ice and large areas of open waters in the western Arctic. Kwok and Rothrock (1999) observed an increased ice flux south through the Fram Strait, and Johannesen et al. (1999) a 14 percent reduction in the areal extent of the multi-year Arctic sea ice since 1978.


Changes in the sea ice distribution will have enormous effects upon the atmosphere-ocean heat fluxes, and give climatic consequences far outside the Arctic region. It is therefore of vital importance to understand the nature of the heat anomalies in the Nordic Seas in terms of amplitudes, generation mechanisms and propagation. This is the main objective for this study. A further discussion is given in Furevik (1999a, 1999b).


Figure 1: Left: Map of the Nordic Seas based upon the ETOPO5 data set. Isobaths are drawn at 250, 500, 1000, 1500, and 3000 m. Red arrows illustrate the flow of warm Atlantic Water, blue arrows the flow of cold, Polar Water. Right: Winter mean SLP (mb) calculated from the NCEP/NCAR data set (upper), and winter mean SST (°C) calculated from the IGOSS data set (lower). The shading indicates areas with standard deviation of winter mean SLP exceeding 5 mb and 6 mb respectively, and of winter mean SST exceeding 0.5°C and 1°C. The thick solid line in the SST plot shows the winter mean ice edge (50 percent cover) as calculated from the NCEP/NCAR data, while the thick dashed lines show the minimum and maximum extent of the winter mean ice edge during the 17 winters studied. Linear trends are removed prior to calculations of the standard deviations.



2. Data and analysis

The data sets used in this study are: (i) Monthly mean SST fields blended from ship, buoy and bias corrected satellite data from the Integrated Global Ocean Services System (IGOSS) Products Bulletin data set, gridded onto 1ºx1º grid boxes (Reynolds and Smith, 1994). (ii) Monthly mean fields of SLP, sea ice cover and 10 m wind field from the National Center for Environmental Prediction/ National Center for Atmospheric Research (NCEP/NCAR) reanalysis project (Kalnay et al., 1996). The SLP data set is on a 2ºx2º grid, while the other data sets are on grids approximately 1.9ºx1.9º. (iii) Hydrographical data from the Institute of Marine Research, Bergen, and the Marine Laboratory, Aberdeen. For this study data from 17 winters from 1982 to 1998 are used. This period was limited by the length of the IGOSS record.


The winter mean of SST, SLP, and ice edge position, together with areas of large variability, are shown in Figure 1 (right). The SLP plot shows the well known Greenland High (SLP>1018 mb), and the Icelandic Low (SLP<996 mb) which stretches as a trough into the Nordic Seas. Maximum variability is found over the central Nordic Seas, with standard deviation exceeding 6 mb. The SSTs are ranging from above 9ºC west of Scotland, to the freezing point in the marginal ice zone. Largest variability is found in the frontal zones between the AW and PW regimes.


Patterns of coherent structures in the SST winter data, are analysed in terms of complex principal components (CPCs). While standard (real) principal component analyses return patterns of standing oscillations only, the CPC method detects travelling waves and irregularly occurring features as well (Horel, 1984). The following procedure is followed: (i) The gridded SST data are weighted by the square root of the cosine of latitude, so equal areas are afforded equal weights in the analysis. (ii) A complex data set is formed, where the real and imaginary parts are the weighted SSTs and their quadrature function (the Fourier components rotated 90º) respectively. (iii) The complex covariance matrix is formed, con­taining all combinations of covariances between the time series in each grid point. (iv) From the covariance matrix, the CPCs and the complex empirical orthogonal functions (CEOFs) are calculated using singular value decomposition. (v) Each CEOF will now be the complex data set regressed upon the corresponding CPC. In the plots shown, they are divided by the weighting function and multiplied by the standard deviation of the CPCs. As pointed out by Thompson and Wallace (1999), the plotted functions are not strictly orthogonal, but the real part will be the SST anomaly associated with one standard deviation in the CPC.



3. Heat propagation in the Nordic Seas

The leading mode of SST variability is shown in Figure 2. While 39 percent of the total variance in the complex data set is accounted for by this mode, it explains more than 70 percent of the local variance in some of the areas with largest variability (not shown). The amplitudes of the regression coefficients have maxima in the Barents Sea, Greenland Sea and in the Denmark Strait, where confidence level is better than 95 percent. The phase angles of the regression coefficients show a well organised pattern, as they are increasing in a cyclonic sense along the band of high correlation (Figure 2b). As the CPC vector is rotating in a clockwise direction (Figure 2c), the SST signals move northward along the Norwegian coast, and into the Barents Sea, Fram Strait, or across the Norwegian and Greenland Seas, and south along the East Greenland coast. The CPC is seen to consist of almost two complete cycles during the 17 years which are studied, with periods 5 and 12 years respectively.


Figure 2: The leading CEOF mode of the winter mean SSTs 1982-1998, accounting for 39 percent of the total variance. (a) Magnitude (°C) and (b) phase angle (deg) of the complex SST winter anomalies regressed upon the standardised first principal component. Thin dashed lines mark the boundary of the 95 percent confidence limit. SST winter anomalies along the dashed lines are shown in Figure 3. (c) Magnitude and phase angle (deg) of the standardised first principal component. (d) The percentage of the total variance explained by each CEOF. Note that the part of the SST variability explained by this mode, is simply the complex pattern in a,b, multiplied by the complex time series in c.



Plots of the SST anomalies along the pathways of the AW flow from the Faroe-Scotland Gap, along the Norwegian coast and into the Fram Strait and Barents Sea (thick dashed lines in Figure 2a,b), give a more detailed picture of the coherent structure of variability. In Figure 3a the observed SST anomalies along the pathway ending in the Fram Strait indicate northward propagating features. They are better visualised by the SST anomalies reconstructed from the leading CEOF (Figure 3b). During the first cycle, the SST maximum northwest of Scotland leads the Svinøy maximum by 12 months, Gimsøy by 18 months and Sørkapp by more than 2 years, indicating propagation speeds between 2 cm/s and 5 cm/s. During the second cycle, time lags are doubled, and the propagation speeds are therefore reduced by 50 percent.


Figure 3: Upper: Time-distance (Hovmoeller) diagram of actual SST anomalies along the track ending in the Fram Strait (a), and reconstructed from the leading CEOF the SST anomalies along the same track (b) and along the track ending in the Barents Sea (c). Colour bars are in units of  ºC. Dashed lines indicate the positions of the hydro­graphical data. (d) Mean temperature anomalies in the core of the AW (50m-200m) for the Scotland-Faroe (SF), Svinøy (SV), Gimsøy (GI), Bjørnøya-Fugløya (BF), and Sørkapp (SK) hydrographical sections (see Figure 1, left). (e) Mean temperatures at several depths in the BF section, together with sectional mean SST (thick black line) calculated from the IGOSS data. The data in d,e are 3 years low pass filtered.



Observed temperature anomalies in the core of AW in several hydrographical sections along the flow, and at several depths in the Barents Sea opening, are shown in Figure 3d,e. While the first warm anomaly (W1) shows a 2 years lag between the SF and the SK sections, the cold anomaly of the late 1980s (C1) and the warm anomaly of the early 1990s (W2) are different. This is also evident in the BF section (Figure 3e), where W1 occurs at all depths simultaneously, while C1 and clearly W2 show a phase lag from the surface waters towards the depth.


In Figure 3c SST anomalies along the track ending in the Barents Sea are shown. Note the amplification of the anomalies as they enter the Barents Sea. The reason for this is investigated in Figure 4, where composite maps of 4 cold years (1982, 1987, 1988, and 1989) and 4 warm years (1984, 1991, 1993, and 1995) are shown. Cold years are evidently associated with weakened westerlies in the southern part of the Nordic Seas, and weakened northerlies in the northwestern part, thus representing years with a weak phase of the North Atlantic Oscillation (NAO). Cold air outbreaks over the Barents Sea and anomalous strong northeasterlies along the East Greenland marginal ice zone, evidently produce anomalous cold water here, and increase ice production. The four cold years are the years in the study period with largest extent of sea ice in the Barents Sea Loeng (1998).



Figure 4: Map of the mean SST winter anomaly, 10 m wind anomaly (arrows), and ice edge (thick line) for cold winters (a) and warm winters (b). Negative SST anomalies are drawn as dashed lines, positive as solid lines. Equidistance is 0.25°C. Shaded areas show anomalous strong wind speeds. For selection criteria, see text.



Warm years are associated with a positive NAO phase and enhanced westerlies, and decreased northerlies over the marginal ice zone. The area of maximum SST anomaly is the position of the maximum wind stress curl anomaly, which may indicate that the warm anomaly is generated by upwelling of deeper and warmer water.


A 180° phase reversal across Iceland is evident in the composite fields, and may be due to wind-forced variability in the East Icelandic Current, as proposed by Blindheim et al. (1999).



4. Final remarks

The SST signals are found propagating in a cyclonic direction in the Nordic Sea. They are first seen in the southern part of the area, indicating that they are generated by anomalous atmospheric fluxes linked to the NAO phase, or by mixing with warmer sub-surface waters. The SST anomalies are then transported into the Barents Sea, Fram Strait and Greenland Sea, where the original SST signals are strongly amplified or masked by the local atmospheric forcing.


It is uncertain to what degree the propagating of the heat anomalies will influence upon the atmosphere, and if they are taking active parts in decadal scale climate variability observed in ice and SLP in the Arctic (Mysak and Venegas, 1998), or in the newly described Barents Oscillation (Skeie, 1999).


An output from a coupled atmosphere-ocean model with focus on the North Atlantic and the Nordic Seas, should be analysed in order to (i) quantify the relative effects of oceanic heat advection and atmospheric forcing on the SST anomalies, (ii) quantify atmospheric responses of heat anomalies advected by the ocean.





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