THE ANNUAL CYCLE AND INTERANNUAL VARIABILITY OF ATMOSPHERIC PRESSURE IN THE VICINITY OF THE NORTH POLE

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 23: (23) Published online 9 July 23 in Wiley InterScience ( DOI: 1.12/joc.942 THE ANNUAL CYCLE AND INTERANNUAL VARIABILITY OF ATMOSPHERIC PRESSURE IN THE VICINITY OF THE NORTH POLE RICHARD I. CULLATHER a, * and AMANDA H. LYNCH a,b a Cooperative Institute for Research in Environmental Sciences, University of Colorado at Boulder, Boulder, CO, USA b Program in Atmospheric and Oceanic Sciences, University of Colorado at Boulder, Boulder, CO, USA Received 17 September 22 Revised 3 June 23 Accepted 3 June 23 ABSTRACT A comparison of National Centers for Environmental Prediction National Center for Atmospheric Research reanalysis six-hourly sea-level pressure data with former Soviet drifting station observations over the central Arctic Basin reveals high monthly correlations throughout the period , but also a preferred winter season negative bias of about 1.4 hpa. Using the reanalysis, supplemented by Arctic Ocean Buoy Program fields and in situ observations, a generalized depiction of the annual cycle of pressure fields over the Arctic may be constructed. Above the Canada Basin Laptev Sea side of the Arctic, the annual cycle of surface pressure is dominated by the first harmonic, which has an amplitude of about 5 hpa and maximum pressure occurring in March. Along the periphery of northern Greenland and extending to the North Pole, a weak semiannual cycle is found in surface pressure with maxima in May and November. The presence of the semiannual variation over time is highly variable. Dynamically, this progression of the annual cycle may be attributed to the transfer of atmospheric mass from Eurasia and into the Canadian Archipelago in spring and the reverse condition in autumn. Over the central Arctic Basin, springtime pressure increases result from an enhanced poleward mass transport from Eurasia. An increase of equatorward transport over the Canadian Archipelago in May and June results in central Arctic pressure decreases into summer. A less distinct temporal separation between the poleward Canadian transport and the equatorward Eurasian transport results in the weaker second pressure maximum in autumn. On interannual time-scales, atmospheric mass over the central Arctic is exchanged with the storm track centres of action in the North Atlantic and North Pacific. In particular, the large decrease in central Arctic Basin sea-level pressure during the late 198s is due to a large transfer of atmospheric mass into the North Pacific. Copyright 23 Royal Meteorological Society. KEY WORDS: Arctic Basin; annual cycle; atmospheric mass; surface pressure; harmonic analysis; numerical reanalyses; North Atlantic oscillation; Arctic Ocean oscillation 1. INTRODUCTION This study examines atmospheric pressure variability over the Arctic Basin and surrounding land surfaces on annual and interannual time scales. The low-frequency variability of the Northern Hemisphere sea-level pressure field has been the subject of extensive research in recent years (e.g. Proshutinsky and Johnson, 1997; Honda et al., 21; Wallace and Thompson, 22), due in part to the increased availability of historical climate data and the proliferation of gridded numerical analyses. A great many of these studies have documented the interannual variability of the wintertime sea-level pressure field, with particular interest in atmospheric teleconnection patterns such as the North Atlantic oscillation (NAO; van Loon and Rogers, 1978) and the recently considered Arctic oscillation (AO; Thompson and Wallace, 1998). * Correspondence to: Richard I. Cullather, National Center for Atmospheric Research, PO Box 3, Boulder, CO 837 3, USA; cullat@ucar.edu Copyright 23 Royal Meteorological Society

2 1162 R. I. CULLATHER AND A. H. LYNCH The motivation for this investigation is focused on three points. The first point is in the requirements for validation of regional and global climate models. Gridded atmospheric numerical analyses are frequently used in diagnostic studies and in the validation of modelled general circulation in the Arctic (e.g. Walsh, 1995; Briegleb and Bromwich, 1998). The numerical analyses themselves have only recently been examined for their ability to reproduce in situ observations. These validation studies are associated with the Surface Heat Budget of the Arctic (SHEBA) field observations and with examination of upper-level wind data (Wylie, 21; Francis, 22). Validation using long-term atmospheric pressure data over the central Arctic Basin has not been performed. The second motivation deals with the interest in other modelled and observational fields. As explained by Walsh (2), the atmospheric hydrologic cycle over the Arctic is intimately related to the large-scale circulation. Features in the sea-level pressure field are reflected in the atmospheric moisture transport (e.g. Hurrell, 1995), and thus have a substantial influence on the geography of atmospheric moisture sources and sinks. The hydrologic cycle of the Arctic is currently the subject of an intensive research program (Vörösmarty et al., 21). Similarly, sea-ice advection is strongly influenced by the overlying atmospheric circulation fields (e.g. Maslanik et al., 1996). Understanding these fields and their variability, and the ability to simulate their spatial and temporal evolution accurately, necessitates an understanding of the atmospheric pressure field. The third point is concerned with the natural variability of the circulation and its response to changes in forcing. A method for obtaining confidence in simulations of interannual variability is first to validate against the observed annual cycle. In particular, successful validation of the mean annual cycle is a desirable property for numerical models (e.g. Chen et al., 1995). The reproduction of high-frequency variability, from diurnal up to the annual cycle, is a necessary condition for successful simulations of feedback processes. The annual cycle of atmospheric pressure in the Arctic, however, is not well known. The annual cycle is the subject of earlier seminal studies, including Vowinckel and Orvig (197), various climatic atlases produced in the former Soviet Union, and a few significant references in the literature (e.g. Walsh, 1978). The topic has been revisited using numerical analyses to examine relations to oceanic variability (Polyakov et al., 1999), but not for the purpose of examining the veracity of earlier findings. Thus, an important aspect of this paper concerns the mean annual cycle. Some of the methods used in the study of the annual cycle are additionally exploited for examining the interannual variability. In looking at the interannual variability, we are concerned with evaluating the significance of the averaged depiction. In summary, the questions addressed in this study are as follows: How well do the numerical analyses reproduce in situ observations over the central Arctic Basin? What is the geography of the mean annual cycle? Are the mechanisms for the spatial discrepancies in the annual cycle apparent? Does the annual cycle vary substantially from year to year? What are the dynamic mechanisms of interannual variability? An evaluation of the data sets used has obvious implications for the analysis that follows, and this is provided in Section 2. Additionally, a method is described for obtaining the divergent component of the atmospheric mass flux that is responsible for the seasonal changes in the surface pressure distribution. Section 3 then provides the depiction of the mean annual cycle. In Section 4, the interannual variability of the diagnosed annual cycle is given. In an additional analysis, the atmospheric mass flux procedures used in examining the annual cycle are applied to evaluate the interannual variability over the Arctic Basin. Section 5 provides a discussion of the major results. 2. DATA AND METHODS 2.1. NCEP NCAR reanalysis The primary data set for this study is comprised of the surface and sea-level pressure fields of the (National Centers for Environmental Prediction (NCEP) National Center for Atmospheric Research (NCAR) reanalysis

3 ATMOSPHERIC PRESSURE VARIABILITY 1163 for the period (Kalnay et al., 1996; Kistler et al., 21). Reanalysis standard-level geopotential height and precipitable water fields are additionally used for diagnostic purposes, as explained below. These data were obtained from the National Oceanic and Atmospheric Administration (NOAA) Climatic Data Center at a horizontal resolution of The geopotential height data were obtained at 17 standard pressure levels. These data have been used extensively in previous high-latitude studies, due in part to their extended time series (e.g. Thompson and Wallace, 1998). There are significant caveats, however. Of the known difficulties with the data set that have been documented by NCEP, the most critical to this study appear to be the omission of numerous surface pressure observations for the period and the absence of interannually varying snow cover for the period (W. Ebisuzaki, personal communication, 1998). Changes to the observational network presented by the introduction of satellite data in the mid 197s may also be important. The pressure data omissions for the early period are a significant concern for the data-sparse northern high latitudes. An investigation is conducted here using the Soviet drifting camp measurements (Kahl et al., 1999). The drifting camps set atop the pack ice are referred to as North Pole, or NP stations. Figure 1 shows drift tracks of NP stations operating in the basin from 195 until Stations NP-2 through NP-3 operated almost continuously from 195 to 199 in the central Arctic Basin. Additionally, NP-31 recorded observations from 1988 to 1991 in the Beaufort Sea area. A comparison is made between station sea-level pressure observations and six-hourly reanalysis data at the nearest grid point. Computed monthly correlations between times series of six-hourly reanalysis and observational data are greater than.987 for the entire 4 year period. It is readily apparent that the observations have been incorporated into the reanalyses. There is, however, a consistent cold season bias, as shown in Figure 2(a). The largest differences are in March, where the reanalyses are, on average, 1.4 hpa less than observed values. This represents about 14% of the amplitude of the annual cycle that is experienced by the drifting NP-stations. A time series of the annual average bias is shown in Figure 2(b). The abrupt changes in the bias are associated with the latitude of the operating stations. Station NP-31, for example, operated in the Beaufort Sea near the end of the time series. The association of the data biases with the cold season suggests a difference in sea-level pressure reduction methods; however, the discrepancies are larger than one would expect for the station elevations (between 6 and 12 m). There are significant biases associated with reanalyses of near-surface air temperatures collocated with the NP-stations, but the temperature differences are not in phase with the pressure biases. Comparisons of 2 m reanalysis temperatures with NP-station values for (not shown) indicate that the NCEP data are warmer than observations from January through to August and colder from September through to December. Maximum positive biases of greater than 2.5 C occur in April and May, and a negative bias of greater than 3.5 C occurs in October. As with the pressure data comparison, the annual cycle of temperature biases is very robust among different years and stations. As shown in Figure 2, the seasonal discrepancy in pressure observations is small but significant. The results presented here using the reanalysis have been qualitatively reproduced using the Arctic Ocean Buoy Program (AOBP) data set, which is available for the period (Walsh et al., 1996). These data are produced as an optimal interpolation of Arctic buoy data and the NP station observations. The AOBP network consists of 1 to 3 buoys and has been maintained since The AOBP gridded data, however, only cover from 7 N to the pole and are supplemented by the NCEP operational analyses. Several additional observational data sets have been used to complement and verify results obtained with the reanalysis data. These include the Historical Arctic Rawinsonde Archive (HARA; Serreze et al., 1995) and Greenland automatic weather station (AWS) data (Shuman et al., 21). Figure 1 depicts six upper-air stations in the North Atlantic and Baffin Bay region examined in this study, as well as the Greenland AWS stations. The discrepancies with the reanalysis data present a substantial hindrance to quantitative study of the central Arctic sea-level pressure variability. There appears to be a real need for an Arctic-specific data reanalysis program, as has recently been proposed (e.g. Morison et al., 21) Computations and approach For the presentation of the results, two periods are examined, i.e and , with greater confidence being placed on the latter time period in the examination of the mean annual cycle. This latter time period also accommodates comparison with the AOBP data set.

4 1164 R. I. CULLATHER AND A. H. LYNCH Figure 1. Map of the Arctic Basin. Rawinsonde stations used in this study are shown as open circles. Greenland automatic weather stations are indicated with stars. Thick lines in the central basin indicate the drift tracks of NP stations. The grid denotes every 1 of latitude and longitude The procedure used here is to characterize the annual cycle averaged over the more reliable period, and then to examine the variability of the annual cycle over the whole of the available observational period. It is recognized that the averaging period will be significantly biased by the climatic variability during that time. For example, it has been noted by other authors that the mean statistics differ substantially depending on the averaging period used (Polyakov et al., 1999). Indices of the NAO were strongly positive during nearly the entire time period (i.e. Greenland below ; Hurrell and van Loon, 1997). Additionally, sea-level pressure in the North Pacific transitioned from mostly negative to positive anomalies in the late 198s (Trenberth and Hurrell, 1994). For these reasons the interannual variability question is explored in some detail in Section 4. Van den Dool and Saha (1993) provide an overview of the issues involved in examining pressure variations. The physically significant variable is surface pressure, but its spatial distribution mostly reflects the surface topography. The computation of sea level pressure is thus required, although it must be kept in mind that the reduction process may fabricate or destroy atmospheric mass.

5 ATMOSPHERIC PRESSURE VARIABILITY 1165 (a) 1 NCEP Minus NP Stn SLP [hpa] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Month (b) 1 NCEP Minus NP Stn SLP [hpa] Figure 2. (a) Monthly mean sea-level pressure difference of NCEP reanalysis minus NP station observations, Bars indicate the monthly mean standard deviation. January and February are plotted twice for continuity. (b) Annual mean sea-level pressure difference of NCEP reanalysis minus NP station observations, The annual value is plotted for years where 11 or more months are sampled with at least 5 six-hourly observations Year The harmonic analysis methods used below for evaluating the annual cycle follow widely used procedures (e.g. Lanczos, 1956; Wilks, 1995). The time series for a particular location may be expressed in the form n/2 [ ( 2πkt y(t) = y + C k cos n k=1 ) φ k ] (1) where C k is the computed amplitude and φ k is the phase for each harmonic. The equation is reviewed here for the purpose of illustrating an important computation. As shown by van Loon (1972), a key statistic in harmonic analysis is the percentage of the total variance r 2 that each harmonic explains. This is to say that

6 1166 R. I. CULLATHER AND A. H. LYNCH a time series of a particular harmonic k is computed as ( ) 2πkt y k (t) = y + C k cos φ k (2) n The quantity r 2 is then computed between the actual time series and the time series produced by the harmonic. This is a useful technique for appraising the character of the time series. In attributing the variability in the surface pressure, a procedure is followed similar to that employed by van den Dool and Saha (1993). The continuity equation may be expressed as follows (e.g. Trenberth, 1991): P sfc t Psfc + v dp = g(e P) (3) where P sfc is surface pressure, is the horizontal divergence operator, v is the horizontal wind vector, g is the acceleration due to gravity, and E and P are the rates of evaporation and of precipitation per unit mass respectively. Equation (3) is more accurate than the commonly used approximation of setting the right-hand side to zero. The balance equation for water vapour may be written as W t + 1 Psfc qv dp = E P (4) g where W is precipitable water and q is specific humidity. Substituting Equation (4) into Equation (3), rearranging, and applying temporal averaging yields: ( Psfc t g ) W Psfc = (1 q)v dp t (5) where the angled brackets denote a temporal average. Thus, surface pressure tendencies computed from monthly and annual averages may be decomposed into contributions from a precipitable water storage term minus the divergence of the vertically integrated dry air mass flux. Reanalysis values of surface pressure and precipitable water are used to compute the left-hand side of Equation (5). With the left-hand side of Equation (5) known for all points on the sphere, the irrotational component of the vector field within the divergence operator on the right-hand side may be reconstructed using spherical harmonics (Adams and Swarztrauber, 1997). A rigorous mathematical treatment of the procedure for computing the inverse divergence is given by Swarztrauber (1993). This vector field represents the divergent component of the dry air mass flux as computed from monthly and annually averaged tendencies, and is expressed in units of hpa m month 1. Unlike van den Dool and Saha (1993), the resulting vector field is not normalized by the surface pressure. Alternatively, one may have directly computed the right-hand side of Equation (5) using the horizontal wind field and specific humidity analysed at each vertical level. There are some significant obstacles, however. As noted by Trenberth (1991), dry air mass is not necessarily conserved in the numerical analyses. This would necessitate a correction procedure to the divergent wind field. The advantage of the method used here is that dry air mass is exactly conserved. The meaning of the resulting vector field should be clearly understood. The divergent transport represents that component of the mass flux that is responsible for the monthly and annual changes to the surface pressure field. As such it is an effective tool for examining seasonal changes to the pressure field. It is expected that this component is small compared with the rotational component, which is generally contour-parallel to the surface pressure field. The reader is referred to van den Dool and Saha (1993) for additional comment on the procedure. Only the dry air mass is conserved in the atmosphere, not the total air mass. The use of the reanalysis precipitable water field in Equation (5), however, requires some additional information regarding the data quality. An evaluation of the precipitable water field is given by Bromwich et al. (2). In comparison with the European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis and the National Aeronautics

7 ATMOSPHERIC PRESSURE VARIABILITY 1167 and Space Administration Water Vapor Product (NVAP; Randel et al., 1996), mean values for the north polar cap were found to be comparable. However, significant spatial discrepancies were noted, particularly over lower latitude land surfaces. All three data sets suffer similar remote-sensing limitations in identifying the sea-ice edge and differentiating cloudiness from clear sky conditions. Additional critical evaluation of the reanalysis precipitable water field may be found in the literature (e.g. Groves and Francis, 22). For our purposes, however, a simple test is to apply the simplification to Equation (3) (e.g. set the precipitable water tendency term in Equation (5) to zero) and compare the resulting computed vector fields. This has been performed on the monthly mean fields. In general, the spatial distribution of the vector fields is qualitatively similar over the northern high latitudes, but the magnitude of the vectors differs by approximately 1 to 5%. Therefore, it is not expected that errors in the precipitable water field will qualitatively affect the results presented. Note again for emphasis that the resulting vector diagrams present an Eulerian depiction of the atmospheric mass fluxes without consideration of the rotational component. Hovmöller diagrams are used in Sections 3.3 and 4 as a summary tool for evaluating the variability of these vector fields. As supplementary information, a general depiction of the Northern Hemisphere storm tracks has been computed using four-times daily sea-level pressure data using a band-pass filter (Duchon, 1979) to capture variability of 2 to 8 days duration. 3. MEAN ANNUAL CYCLE Figure 1 shows the Arctic Basin area of interest, including major coastal seas. Globally, the annual cycle of sea-level pressure has been examined extensively (e.g. Hsu and Wallace, 1976; Peixoto and Oort, 1992). In the midlatitudes, the annual cycle consists of an exchange of atmospheric mass between the land masses and the oceans, with higher sea-level pressure over land in winter that is concurrent with the intensification of the storm tracks over the oceans. Minima in the annual cycle occur over the central continents in summer and maxima occur over the oceans. This is also consistent with the concept of sea-level pressure variations being controlled by thermal forcing, i.e. relatively higher pressures collocated with colder temperatures and vice versa (e.g. Spar, 195; Held, 1983). It follows that the spring and autumn are marked by significant atmospheric mass fluxes across coastal regions. In contrast to the midlatitudes, the geography of the northern high latitudes introduces three significant influences on the annual cycle. These are: (1) the approximately zonal coastline orientation the major land masses of the Northern Hemisphere almost completely surround the Arctic Ocean to the south; (2) the presence of a quasi-permanent floating ice pack over the central Arctic Ocean, which may influence the land sea contrast; and (3) the pronounced annual cycle of incident solar radiation, with the polar night north of 8 N extending from mid October to late February The annual harmonic Figure 3 shows the amplitude and phase distribution for the first harmonic of the average annual cycle computed from NCEP NCAR reanalyses. Computations using AOBP data, also shown in Figure 3, are very similar to the reanalysis depictions, although it must be kept in mind that the AOBP data are sea-level pressure only, with resulting discrepancies over Greenland. In general, the concept of thermal forcing is consistent with the pressure variability over Eurasia, the lower latitudes of North America, and the Pacific and Atlantic Oceans. Maximum pressure is attained in Siberia in February, which is generally consistent with the timing of minimum temperatures. Pacific and Atlantic coastal regions are heavily influenced by the oceanic annual cycles. This is particularly true of the North American Pacific coast to the west of the Rocky Mountain range, which has a late-summer surface pressure maximum. The Arctic Ocean shows a significant contrast between the central basin Laptev Sea region and the North Atlantic Greenland Barents Sea area, with the boundary near the geographic location of the Lomonosov Ridge. The central basin area appears to show the influence of the nearby Siberian land surface. Surface pressure maxima over this area occur only about a month later than over Siberia. Additionally, the September pressure minimum reflects the track of cyclones forming over

8 1168 R. I. CULLATHER AND A. H. LYNCH (a) (b) AUG JUL JUN MAR FEB MAY APR MAR FEB JAN JUL DEC NOV OCT SEP (c) (d) AUG JUL JUN MAY MAR JUL APR MAR FEB JAN DEC NOV OCT SEP Figure 3. (a) Amplitude of the first harmonic of the mean annual cycle of surface pressure computed from reanalysis, Contour interval is 2 hpa. (b) Phase of first harmonic of the mean annual cycle computed from reanalysis, corresponding to the month of maximum surface pressure. (c) Same as (a) but with AOBP sea-level pressure data. (d) Same as (b) but with AOBP sea-level pressure data the Siberian Arctic front and migrating into the central basin via the Laptev Sea (Serreze and Barry, 1988). In contrast, the phase of the first harmonic on the North Atlantic side of the basin resembles midlatitude oceanic conditions. The amplitude of the first harmonic is greatest over the Eurasian land surface and the oceanic storm tracks. These areas of maximum amplitude of greater than 6 hpa correspond roughly to the wintertime centres of action, including the Icelandic and Aleutian low-pressure regions and the Siberian High. As noted previously, the averaging period mainly consists of the Greenland below conditions of the NAO, which correspond to an extended North Atlantic storm track into the Barents Sea area, shown in Figure 4. At high latitudes the monthly surface pressure tendency is dominated by the divergence term of Equation (3). As previously discussed, the dry air mass flux field depicts the exchange of mass between the North American and Eurasian land areas and the Atlantic and Pacific Oceans at midlatitudes (van den Dool and Saha, 1993). An intriguing aspect of the annual cycle at high latitudes is the dominance of the Eurasian land mass over the Arctic, as shown in Figure 5. In the spring, atmospheric mass is advected off Eurasia, across the Arctic Basin

9 ATMOSPHERIC PRESSURE VARIABILITY 1169 Figure 4. The root-mean-square of synoptically filtered NCEP reanalysis sea-level pressure data, averaged for December, January, and February The contour interval is.5 hpa. Regions containing values greater than 6 hpa are shaded and into the Canadian Archipelago, ultimately converging over the weakening North Atlantic storm track and the lower latitude Aleutian Low. The largest vector magnitudes for this month, of over 3 hpa m month 1, are found along the Eurasian coastline, and especially along the Siberian coast. The reverse condition occurs in the autumn with the development of the storm tracks. Surface pressure tendencies over the central Arctic Basin, which are significant in October, are then equated to the residual of the mass flux entering the basin from northern Greenland, the Canadian Archipelago, and the Beaufort Sea minus the flux crossing into Siberia and Eurasia The semiannual harmonic Walsh (1978: figure 2) used a 24 year average of the difference in zonally averaged monthly mean sea-level pressure at 9 N minus 7 N to illustrate a pronounced semiannual oscillation with near-equinoctial pressure maxima in April and November and minima in July and December. Reanalysis data reproduce this oscillation in Figure 6. The zonal average at 7 N combines disparate seasonal cycles over land and ocean. Essentially, the net result contains a maximum in April and a minimum in the later part of the year. In the difference plot this tends to amplify a secondary November maximum in the 9 N time series. The long-term average annual cycle for 9 N consists of a maximum in April and a weak secondary maximum in November, consistent

10 117 R. I. CULLATHER AND A. H. LYNCH (a) (b) Maximum vector size Figure 5. Contours of average surface pressure tendency for (a) April and (b) October, averaged for The contour interval is 1 hpa month 1. Negative contours are dashed. The vector field is of the computed divergent component of the monthly dry air mass flux. Maximum vector size corresponds to 38 hpa m month 1 with the conclusions of Hsu and Wallace (1976). For the data plotted in Figure 6, the semiannual harmonic explains 53% of the variance of the 9 N minus 7 N time series, and 39% of the variance in the 9 N time series alone. The geographic distribution of this type of variability is of some interest. The spatial distributions of the amplitude, variance, and phase for the Arctic are shown in Figure 7 for the surface pressure field. In the

11 ATMOSPHERIC PRESSURE VARIABILITY MSLP [hpa] N 7 N 12 MSLP Diff [hpa] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Month Figure 6. Average annual cycle of sea-level pressure at 9 N and 7 N computed from reanalysis data, and the difference. Vertical bars indicate the standard deviation for the averaging period January and February are plotted twice for continuity averaged fields, a semiannual harmonic occurs in the pressure variations along the periphery of northern Greenland, including the Greenland Sea, Fram Strait, the Lincoln Sea, and portions of the Canadian Archipelago. AOBP sea-level pressure data are consistent with the NCEP reanalysis for these locations (not shown). Additional locations are associated with high topography along the Alaska Range and in central Siberia. However, the semiannual harmonic for regions surrounding northern Greenland has relatively larger amplitude (greater than 3 hpa) compared with the Eurasian regions. The phase of the semiannual harmonic for the region surrounding northern Greenland is characterized by maxima in May and November. For the region of Siberia adjacent to the Laptev Sea, the semiannual harmonic has maxima in March and September. The combined first and second harmonics explain more than 9% of the variance for nearly the entire region shown in Figure 1. Figure 7(a), then, delineates those regions dominated by the first harmonic and the regions dominated by the semiannual harmonic. A small number of in situ observations have been examined to confirm the location of the variability. Of the North Atlantic upper-air stations shown in Figure 1, semiannual variability is most pronounced at Thule (76.53 N, W) and Danmarkshavn (76.77 N, W). The average annual cycle of geopotential height, normalized at each level for Danmarkshavn, is shown in Figure 8. At 1 hpa, the annual cycle of geopotential height is consistent with the analyses in showing a large maximum in late spring and a weaker maximum in November. Time series of pressure data from AWSs in central Greenland shown in Figure 1 indicate an annual cycle dominated by the first harmonic that is similar to the Danmarkshavn geopotential height data above the 8 hpa level. Harmonic analysis of the high-latitude semiannual cycle has previously been performed by Wikle and Chen (1996) using NCEP operational analyses, Wikle and Chen (1992) highlight centres of large

12 1172 R. I. CULLATHER AND A. H. LYNCH (a) (b) (c) Jan-Jul Jun-Dec May-Nov Apr-Oct Mar-Sep Feb-Aug Jan-Jul Figure 7. (a) The fraction of the total variance of surface pressure explained by the semiannual harmonic, computed from reanalysis data. Contours are every.1 beginning at.5. Regions containing values greater than.35 (roughly e 1 ) are shaded. Values greater than.45 (r >.67) are dark shaded. (b) Amplitude of the semiannual harmonic, computed from reanalysis surface pressure data, The contour interval is 1 hpa. (c) Phase of the semiannual harmonic, shown by month semiannual amplitude over high-latitude land surfaces in the 5 hpa geopotential height field, with particular emphasis on Alaska and areas adjacent to the North Pacific. The NCEP reanalysis data confirm these locations of semiannual amplitude maxima over the given time period, but the variance explained by the semiannual harmonic at 5 hpa is very low throughout the high latitudes of the Northern Hemisphere. As shown in Figure 8, the semiannual variability is present in geopotential height fields at very low altitudes only, and not aloft. In the North Pacific region, the presence of larger amplitudes for the semiannual harmonic in tropospheric geopotential height fields have been examined by Lanzante (1983, 1985), and are suggestive of an east west transfer in atmospheric mass. These exchanges are referred to as departures from the first harmonic, which explains greater than 8% of the variance (Lanzante, 1983: figure 2). Similarly, it has been found here that the annual cycle becomes dominated by the first harmonic above approximately the 9 hpa level. Similar examinations using reanalysis data at the North Pole and surrounding locations are qualitatively similar to that depicted in Figure 8.

13 ATMOSPHERIC PRESSURE VARIABILITY 1173 Figure 8. Average annual cycle of geopotential height for Danmarkshavn (76.77 N, W) from HARA rawinsonde data, The annual cycle is normalized to a range of to 1 at each pressure level. The vertical profile is produced by a log-interpolation of each sounding to 25 hpa levels and then averaging over the month. January and February are plotted twice for continuity Reanalysis average sea-level pressure fields for January, April, July, and October are shown in Figure 9. The semiannual variability over the central basin generally supports the description of the annual cycle given by Vowinckel and Orvig (197). In their depiction, the North Pole is dominated by the cyclonic flow around the Icelandic Low during winter months. Beginning in March, the central Arctic comes under the influence of the North American high-pressure area. During warm months, the central Arctic is shown to be under a feeble low-pressure area as the intensity of both high- and low-pressure areas become greatly diminished. The autumn season again finds relatively high pressure over the central Arctic The role of dry air mass transport in the semiannual cycle The mass exchange into and out of Eurasia and the semiannual oscillation may be reconciled by examining the meridional component of the divergent mass flux across 85 N, as shown in Figure 1; this summarizes the vector plots for an average annual cycle The 85 N meridian does not intersect with land surfaces. For reference, the components of the monthly surface pressure tendency are plotted above and the area-averaged monthly surface pressure values are plotted below. Note that the precipitable water tendency contribution is relatively small, but also that it is of the opposite sign to the dry air mass divergence contribution. This is in agreement with previous studies (e.g. Chen et al., 1997). Again, Figure 1 presents the mass flux asymmetry during the annual cycle, with the flux from the Eastern Hemisphere (EH) into the Western Hemisphere (WH) across the Arctic Basin in the spring and the reverse condition in the autumn. The contrasting fluxes are roughly bounded at and 18 longitude. There are, however, subtle discrepancies between the two sides of the basin that result in pressure changes over the annual cycle. The springtime

14 1174 R. I. CULLATHER AND A. H. LYNCH Figure 9. Sea-level pressure fields from reanalysis for January, April, July, and October, averaged over the period The contour interval is 2 hpa positive flux out of the EH is stronger and occurs earlier than the corresponding equatorward flux on the WH side, resulting in the increase in surface pressure from February to April. After April, the equatorward flux becomes predominant and the area-averaged surface pressure decreases into July. In contrast to the first half of the year, the onset of WH poleward and EH equatorward fluxes occur almost simultaneously. The poleward flux becomes larger after August, however, resulting in pressure rises into November. The smaller pressure decrease from December into February results from slightly larger equatorward flow on the WH side as the springtime pattern becomes re-established. These decreases are very small when averaged for the area poleward of 85 N, as shown at the bottom of Figure 1. During this short period, the flux is directed out of the Barents Sea area towards the Beaufort Sea direction, denoting an atmospheric mass evacuation out of the high North Atlantic storm track. This is consistent with the notion of a seesaw in atmospheric mass between the Atlantic and Pacific sides of the Arctic (Honda et al., 21).

15 ATMOSPHERIC PRESSURE VARIABILITY 1175 [hpa/mo] hpa Longitude g dw/dt Mass Conv 4 Jan Feb Mar Apr May Jun Jul Aug SepOct NovDecJan Feb Figure 1. The meridional component of the divergent dry air mass flux across 85 N computed from reanalyses, in hpa m month 1. The contour interval is 4 hpa m month 1. Absolute values greater than 8 hpa m month 1 are shaded, and absolute values greater than 16 hpa m month 1 are dark shaded. Negative contours are dashed. Above are the components of surface pressure tendency from Equation (3), averaged from 85 to 9 N. Below is the corresponding average monthly surface pressure 4. INTERANNUAL VARIABILITY A limited number of comments regarding the character of the interannual variability in atmospheric pressure at 9 N are given in the context of data validation, the transport of atmospheric mass, and the preceding harmonic analysis. First, it is found that the spatial coherence of the interannual variability is very limited. Figure 11 shows a one-point correlation map of annually averaged surface pressure using the North Pole as a reference. Not surprisingly, spatial autocorrelation is demonstrated in the vicinity of the pole, but correlation

16 1176 R. I. CULLATHER AND A. H. LYNCH Figure 11. Point correlation map of annual mean surface pressures with 9 N, from reanalysis data, Negative contours are dashed. The contour interval is every.1 Table I. Correlation of mean sea-level pressure at 9 N with various climate indices, Annual DJF MAM JJA SON NAO (Hurrell, 1995) NPO (Trenberth and Hurrell, 1994) AO (Thompson and Wallace, 1998) values drop off rapidly as one moves equatorward. Values greater than.6 do not extend beyond the central Arctic Basin. This is indicative of the numerous regional climate signals that influence Arctic climate. Contour lines extend farther south on the North Atlantic side. This may be interpreted as showing the influence of the North Atlantic storm track on the central Arctic. The influence of the North Atlantic may be further seen in correlations with various climate indices, as shown in Table I. For the NAO, negative correlation indicates that low pressure over the Arctic corresponds to lower pressure over Iceland relative to the Azores. For the North Pacific Oscillation (NPO), a negative correlation indicates that low pressure over the Arctic corresponds to anomalously high pressure in the North Pacific. The negative correlation with the AO indicates that lower Arctic pressure corresponds to a stronger

17 ATMOSPHERIC PRESSURE VARIABILITY 1177 annular mode. The NAO index (Hurrell, 1995) explains 41% of the time series of annual mean values at 9 N. By comparison, an index of Aleutian Low variability, the NPO (Trenberth and Hurrell, 1994) index, explains 2% of the variability. The hemispheric first principle component, referred to as the AO (Thompson and Wallace, 1998), is highly correlated with time series of North Pole pressure for both annual averages and for each season. Seasonally, the relations are strongest in the spring, when central Arctic pressures are the highest, whereas the autumn period, associated with the weak secondary maximum in pressure, is largely uncorrelated with the Aleutian and Icelandic Low regions. A second point is that the 54 year time series for the central basin exhibits low-frequency variability. Proshutinsky and Johnson (1997) describe two regimes of Arctic sea-ice drift within the central Arctic Basin. The wind-driven circulation patterns were found to oscillate from predominantly cyclonic circulation to anticyclonic, with the transition between regimes occurring at 5 7 year intervals. The interannual variability depicted by Proshutinsky and Johnson (1997) is documented using an index computed from modelled sea-ice drift and the pressure value analysed at the North Pole. Walsh et al. (1996) and Polyakov and Johnson (2) additionally employ a vorticity index for the central Arctic Ocean computed from the sea-level pressure analysed. This type of variability is referred to in the literature as the Arctic Ocean oscillation (AOO). An important caveat, and perhaps a point of confusion, is the use of different data sets in depicting this type of variability. Figure 12 shows the time series of the NCEP NCAR reanalysis sea-level pressure and the NCEP Northern Hemisphere operational analyses using a 3 year running mean. Proshutinsky and Johnson (1997) utilized values from the latter analyses, also employing a 3 year running mean. These data have been used in other studies of Arctic climate (e.g. Bromwich et al., 1993; Walsh et al., 1996). As shown in Figure 12, there are significant discrepancies between the two data sets. This is particularly true during the early 197s, when the operational analyses values were more than 3 hpa higher for a 3 year running mean. The consistency of biases with Soviet NP stations, as shown in Figure 2(b), indicates that the reanalysis data are more reliable. These discrepancies certainly do not invalidate the premise of the AOO. For example, Polyakov and Johnson (2) and Maslowski et al. (21) have further examined oceanic circulation responses to sea-level pressure consistent with AOO, using NCEP NCAR and ECMWF reanalysis data sets respectively. The time series of 6 4 NCEP/NCAR Reanalysis NCEP Operational Analysis Sea Level Pressure Anomaly [hpa] Figure 12. The 3 year centred running mean of sea-level pressure anomaly at 9 N from NCEP NCAR reanalyses (solid, ) and NCEP operational analyses (dashed, ). Both curves are referenced to the long-term operational analysis mean

18 1178 R. I. CULLATHER AND A. H. LYNCH regime shifts presented by Proshutinsky and Johnson (1997), however, may require revision. It is apparent from Figure 12 that there is higher frequency variability in Arctic sea-level pressure prior to A third point of significant interest is the attribution of Arctic interannual variability. Figure 13 shows the meridional component of the divergent mass flux across 8 N. The 8 N parallel is used to encompass the region of pressure falls indicated by Walsh et al. (1996) as explained below. There is a great deal of variability in the atmospheric fluxes as suggested by the time series of the sea-level pressure. However, it is shown in Figure 13 that there are preferred avenues of atmospheric mass transport in the vicinity of 6 E and, more distinctly, around 14 W (shown as 22 E) corresponding to mass exchanges with centres of action in the North Atlantic and North Pacific respectively. An examination of individual vector diagrams confirms these patterns. It is also suggested from Figure 13 that the larger mass flux exchanges involve the North Pacific. An important climate event in the time series of pressure variability shown in Figure 12 is the dramatic pressure drop over the central Arctic Basin in the late 198s, with significant low-pressure anomalies in the central Arctic extending into the 199s. Walsh et al. (1996) describe the spatial and temporal distribution of these pressure decreases. For the autumn and winter months, a decrease of more than 5 hpa occurred over a large area of the central Arctic Basin between the and time periods. Walsh et al. (1996) 36E 16 3E 12 24E E 4 12E 8 6E E Figure 13. Meridional component of the divergent mass flux across 8 N using annual mean reanalysis data, The contour interval is 2 hpa m month 1. Negative contours are dashed. Values with magnitude above 4 hpa m month 1 are shaded. The zero contour is omitted for clarity

19 ATMOSPHERIC PRESSURE VARIABILITY 1179 describe the sequence of consecutive years of negative pressure anomalies as unprecedented over the previous 45 years. It is suggested from Figure 13 that this pressure drop resulted from a large-scale atmospheric mass exchange with the North Pacific, and that the subsequent recovery to near-normal values in the 199s is also attributable to mass exchanges between the Aleutian Low and the central Arctic Basin. This has been confirmed through examination of computed vector fields over the time period (not shown). The largest fluxes shown in Figure 13, of greater than 1 hpa m month 1, advect the mass southwards over Alaska and the Yukon and into the Gulf of Alaska during This is consistent with the pressure increases in the North Pacific that were observed in the late 198s (Trenberth and Hurrell, 1994). The final point is with regard to the interannual variability of the semiannual oscillation. A time series of the annual cycle variance explained by semiannual harmonic is shown in Figure 14. It is found that the semiannual variability is a highly transient phenomenon. It is additionally apparent from Figure 14 that there are extended periods where the semiannual oscillation is either present or absent. The variations shown do not correlate well with climate indices, and are the subject of further study. 5. DISCUSSION This study has conducted a comparison of the NCEP NCAR reanalysis with central Arctic observations, a description of the mean annual and semiannual cycles, the annual variations in the divergent component of the dry air mass flux, and the exchange of atmospheric mass with North Atlantic and North Pacific centres of action. A significant conclusion of this paper is the importance of a comprehensive evaluation of gridded atmospheric fields over time. The discrepancies between operational and reanalysis time series are substantial on interannual time scales, and it is crucial to keep these discrepancies in mind when comparing studies using differing data sets. The information contained in Figure 12 and Table I suggests that the AO and the AOO share roughly the same time series. For the NCEP NCAR reanalysis, there is some reassurance in monthly.8 Variance Explained by Semiannual Harmonic Figure 14. Time series of the variance in monthly mean pressure at 9 N from NCEP NCAR reanalysis that is explained by the semiannual harmonic

20 118 R. I. CULLATHER AND A. H. LYNCH correlations of the NP-station data. However, the seasonal biases that have been determined are disturbing, and a full understanding require additional analysis of the causes and their influence on other atmospheric fields. Again, there appears to be a real need for an Arctic-specific atmospheric data reanalysis program. The spatial structure of the semiannual oscillation depicted along the periphery of the northern Greenland coast is consistent with the analysis of station data by Putnins (197), although his study was limited by the short time series available at the time. Semiannual variability has also been observed in the temperature lapse rates in northern coastal Greenland during rawinsonde field studies (K. Steffen, personal communication, 22). Weickmann and Chervin (1988) have classified semiannual variability into three general categories: tropical/subtropical, associated with variability in radiative forcing; the southern high latitudes, associated with contrasts in the energy budgets of Antarctica and the Southern Ocean; and the northern high latitudes, with reference to Lanzante s (1983) characterization of variability over the North Pacific. The mechanism for the northern coastal Greenland variability is difficult to discern. Unlike the Southern Ocean there is no corresponding region that is out of phase with the observed location, and hence suggestive of a land sea contrast mechanism. Although regions in Eurasia have annual cycles of surface pressure that are substantially attributed to semiannual variations and are roughly out of phase with northern coastal Greenland, the amplitudes are very small. Locations in the midlatitude North Atlantic Ocean are suggested by the reanalysis to have negative surface pressure correlations with the North Pole on annual and interannual (Figure 11) time scales. For example, Fuglister (1951) and Chase (1951) documented semiannual components in the Gulf Stream and the Bermuda Azores High respectively. It is thought that this type of variability in the Bermuda Azores High results in part from cross-equatorial mass exchanges, and it is hypothesized that this type of variability could then be teleconnected to higher latitudes (H. van Loon, personal communication, 23). This potential explanation is intriguing, although evidence of such a direct connection has not been assembled. Additionally, there is an expectation of a negative correlation between middle and high latitudes of the North Atlantic that is associated with changes in the location of the tropospheric jet (Marshall et al., 21). Any proposed mechanism would have to account for the spatial pattern, the limited vertical extent, and the extreme transient nature. It is suggested here that an interaction between the katabatic wind field of the Greenland ice sheet and the North Atlantic storm track during its autumnal development and springtime decay plays an important role. As shown by Bromwich et al. (1996), cyclones in close proximity to Greenland amplify the katabatic regime on all sides of the ice sheet. This would help to explain the halo pattern of semiannual variability around the periphery of Greenland and the vertical confinement to low levels of the atmosphere. However, one would then expect a correlation between the North Atlantic storm track as proxied by the NAO index and the variability of the semiannual oscillation shown in Figure 14; this is not the case. Such a comparison may be inherently flawed, however, because stations in close proximity to the semiannual variability are used in the computation of the NAO index, thus creating a circuitous comparison. Time series of sea-level pressure from Iceland have, in fact, been found to contain a significant semiannual component (Jonsson and Miles, 21). Further analysis of the Greenland katabatic wind climatology may shed additional light on this issue. An initial motivation of this study was to examine the relation of surface pressure variations to the atmospheric hydrologic cycle (Cullather et al., 2). A key issue of previous estimates of the atmospheric moisture budget for the north polar cap (7 9 N) has been disagreements in the annual cycle of fluxes in the vicinity of Greenland. This paper indicates that the coastal stations of northern Greenland are strongly influenced by semiannual variability, particularly for Thule, which is utilized when the Egedesminde rawinsonde station (69 N, 53 W) is not available for moisture transport estimates. The semiannual variability is not found to be present over Greenland beyond the coastal area. It is suggested that a spatial undersampling of moisture transports in the vicinity of Greenland may result in discrepancies for the average flux across 7 N. A preliminary harmonic analysis of computed P E (precipitation minus evaporation/sublimation) from reanalyses indicates that substantial semiannual variability is localized in the vicinity of the Fram Strait. With regard to the annual cycle of the divergent component of the dry air mass flux, the dominant influence of the Eurasian land mass is an interesting finding. The vector patterns shown by van den Dool and Saha (1993) are hemispheric in scale with directional orientations associated with the central continents and the oceanic storm tracks. The nature of the vector fields in the Arctic remains consistent with the previously