THE ANTARCTIC TEMPERATURE INVERSION

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOL. 16, (1996) THE ANTARCTIC TEMPERATURE INVERSION W. M. CONNOLLEY British Antarctic Survey, Natural Environment Research Council, High Cross, Cambridge, CB3 OET, UK Received I6 May 1995 Accepted 5 March 1996 ABSTRACT In the interior of the Antarctic ice sheet the surface temperature inversion averages over 25 C in the winter months. The negative buoyancy of the near-surface air drives the katabatic windflow, which has important consequences for the climate of Antarctica. Radiosonde measurements of the inversion are combined with recent GCM results in an attempt to assess the accuracy of proposed connections between the surface temperature and the inversion strength by comparing the limited observational verification data with the much wider coverage that a climate model allows. This indicates that, using multiannual data, the continent-wide Rh4S error of deducing the inversion strength from a regression technique is approximately 2.9"C, whereas using a method based upon differences between summer and winter temperaures has a RMS error of approximately 2.5"C. KEY WORDS: Antarctica; temperature inversion; regression; United Kingdom Meteorological GCM INTRODUCTION The strongest and most persistent surface, temperature inversion in the world occurs over the interior of Antarctica in winter. At Vostok (79"S, 107"E, 3420 m altitude) the average surface inversion for the 6 months May to October is 25 C. I define the inversion strength to be the difference between the maximum tropospheric temperature and the air temperature at standard screen height (i.e. 1.5 m). This inversion has important meteorological consequences. The negatively buoyant near-surface air flows off the dome-shaped Antarctic continent and, turned by Coriolis forces, leads to southerly and easterly winds around the coast of most of Antarctica. This in turn has a powerful effect on the production and distribution of sea-ice around the continent. Figure 1 shows the average temperature profile at Vostok in July 1989, together with the temperature profiles for the maximum and minimum inversions in that month. An accurate picture of the inversion is hard to obtain because there are few radiosonde stations in Antarctica. The only existing continent-scale map of inversion strength was produced by Phillpot and Zillman (1970; henceforth PZ). However, their map is based on direct measurements of the inversion from 21 radiosonde stations, only three of which are in the interior, supplemented by extrapolation. An alternative approach was taken by Jouzel and Merlivat (1984; henceforth JM) who were interested in the air temperature above the inversion in order to estimate ~3~~0 fractionation rates in precipitation to interpret ice-core records. They introduced the idea of estimating the inversion strength by regressing it against surface temperature. Since PZ, no new radiosonde stations have been established in the interior of Antarctica and no other new observational evidence has been produced. However, great progress has been made in atmospheric modelling. This paper combines observations from the 1980s together with data from a 10-year run of the UK Meteorological Office climate model. By using the fill output from the UK Meteorological Office Global Climate Model, I can obtain a complete map of the (climate model) inversion, surface and upper-air temperatures and can therefore test empirical relations between these variables. A restricted set of output, surface temperature only, is then used to generate maps of the inversion using the PZ and regression methods, which I compare with the climate model inversion. I show that both methods describe the climate model inversion strength quite well. The PZ method has a RMS error over the continent of about 2.5"C whereas the regression method is slightly CCC /96/ by the Royal Meteorological Society

2 1334 W. M. CONNOLLEY Temperature ("C) Figure 1. Temperature profiles at Vostok in July Thick line: average. Thin lines: profiles for maximum (solid) and minimum (dotted) inversion strength. worse with an RMS error of 24 C. The patterns of error between the two methods are different and vary somewhat between years. In certain areas the methods break down and by using climate model results we can identify these regions. The methods seem particularly susceptible to errors in the regions of the major ice shelves and the Lambert glacier, with peak errors of more than 6 C between the 'true' climate model inversion and that produced by the two methods. This may affect the surface wind patterns that have been deduced using the assumed inversion strengths. METHODS OF RELATING INVERSION STRENGTH TO SURFACE PARAMETERS Because of the sparsity of upper-air observations from the Antarctic both PZ and JM attempted to relate the inversion strength to the surface temperature, which is more widely available. Phillpot and Zillman (1 970) write: I J = ~ ZJ, + ATs - AT, where I= TM - Ts is the inversion strength, Ts the surface temperature, TM the maximum tropospheric temperature, and A is the difference between January and July. It is assumed that I,, is small, and using a value of 2.0"C produces only a minor error. Values of Ts, although not available for the whole continent, are more widely available than upper-air temperatures. The main assumption is that ATM can be approximated by a continent-wide value, which PZ suggest is 10-5"C, 12.OoC, and 12.5"C in June, July, and August respectively. Equation (1) predicts the inversion strength in July from the surface temperatures in January and July and an empirical constant representing the change in upper air temperature. An alternative way of deriving inversion strength is to assume a relation between surface temperature only and inversion strengh. This was done by Jouzel and Merlivat (1984) who, using the data of PZ, found (for annual averages) All temperatures are in "C. I = x Ts (2) (1)

3 ANTARCTIC TEMPERATURE INVERSION 1335 Table I. Names, locations, and heights of Antarctic stations Station name Longitude Latitude Height SANAE Georg von Neumayer Amundsen-Scott Halley Novolazarevskaja Syowa Molodeznaja Mawson Davis Mimyj Vostok Casey Dumont d Urville Leningradskaja McMurdo 2.35 W 8.37 W W E E E E E E E E E E E m 40 m 2800 m 32 m 130 m 21 m 40 m 16 m 13 m 30 m 3420 m 50 m 43 m 300 m 24 m Using the 15 stations around the coast and in the interior of East Antarctica from the data base of Antarctic radiosonde stations described in Connolley and King (1 993; henceforth CK) I find that I = x Ts - 2.1, 12 = (3) Station locations and heights are given in Table I. Figure 2 shows the two regression lines from equations (2) and (3). In both cases there is a scatter of coastal stations with small inversions (< 8 C) and the regression lines run perfectly through the two interior stations, Vostok and Amundsen-Scott. Note that a slightly different selection of coastal stations is used in the two cases. The two data sets show almost identical surface temperature averages for Vostok and Amundsen-Scott, but CK Surface Temperature ( C) Figure 2. Annual average inversion strength plotted against surface temperature for 15 Antarctic radiosonde stations. Solid line: best-fit regression line. Dotted line: regression line from JM. Data from CK, + ; data from PZ, 0.

4 1336 W. M. CONNOLLEY show stronger inversions, by 1 C for Amundsen-Scott and 1.5"C for Vostok. The most likely explanation for the discrepancy at the interior stations is that more recent soundings report more significant levels and allow a finer depiction of the inversion (this effect has been reported by Warren et al. (1995) for Arctic soundings). The change is almost certainly not just an effect of different years sampled. From the data of CK the mean f standard deviation of the inversion strength at Vostok is 18.4 f 0.4, whereas the PZ value is For the rest of this paper I shall use the data of CK. Jouzel and Merlivat (1984) were interested in 6"O fractionation and therefore in the annual average temperature above the inversion. However, the inversion is strongest and most meteorologically interesting in winter. Henceforth I shall present results for the month of July, in midwinter. Because of the coreless nature of the Antarctic winter this month is reasonably representative of the period May to October. Using the regression (JM) approach requires only the surface temperature and two empirical constants, whereas the PZ approach requires both summer and winter temperatures to be known. In practice this is no disadvantage because all sites where winter temperatures have been measured will also have measurements of summer temperatures. Both methods suffer from the small data base available for determining the empirical constants and the lack of independent validation data. RELATIONS BETWEEN OBSERVED TEMPERATURE AND INVERSION STRENGTH For July the station data give ZjUl = x Ts (4) with 2 = 0.94 (Figure 3). The coefficient multiplying T, in equation (4) is larger than in equation (3). This reflects the fact that upper air temperatures have a smaller annual cycle than surface temperatures. Figures 2 and 3 show that the annual or July inversion strength for all station data is well described by a linear fit to the surface temperature. However, the inversion strength varies both between station locations and throughout the year, and one regression relation cannot describe both variations. Figure 4 shows the variation of inversion strength with surface temperature for 15 Antarctic stations. Each data point represents 1 month's data and linear regession lines are fitted for each station. In December or January the inversion is small or zero at , 0 Surface Temperature ("C) Figure 3. Inversion strength plotted against surface temperature, monthly averaged data for up to six individual years for July for 15 stations.

5 ANTARCTIC TEMPERATURE INVERSION 1337 # +++ i Surface Temperature ("C) Figure 4. Inversion strength plotted against surface temperature, monthly averaged data for up to six individual years for all months for 15 stations. Station classes taken from Table 11: stars, interior; crosses, Weddell Sea coast; dots, East Antarctic coast. Solid lines: regression lines fitted to each station. Table 11. Slope and fit range for linear regression of monthly averaged inversion strength against surface temperature for individual stations, divided into three classes Station class Line slope 3 fit range Class members range Interior Vostok, Amundsen-Scott Weddell Sea Coast SANAE, Georg von Neumayer, Halley East Antarctic Coast I The rest every station, so the intercept of each regression line with the temperature axis is approximately the summer temperature at that station. The slopes of the lines fall into the three distinct geographically related categories shown in Table 11. Within each class the regression produces a good fit as temperatures varies during the year. There is no way of knowing whether other 'classes' of station climate exist that are not sampled by the radiosonde network. The variation of regression slope is not well correlated to latitude. Halley (slope -0.43, latitude 75.5"s) is nearly as far south as Vostok (slope -0.64, latitude 78.5"s) and is further north than McMurdo (slope -0.12, latitude 77.9"s). Notice that the fit to the regression line is better for higher line slopes. It would be interesting to study the interannual and interdecadal changes in the state of the inversion. However, the magnitude of the task of collecting, quality controlling, and analysing radiosonde observations is such that only a few years data are available, so such studies cannot be done at present. RELATIONS BETWEEN CLIMATE MODEL TEMPERATURE AND INVERSION STRENGTH Because observational data have such poor coverage over Antarctica I shall try to supplement them with atmospheric climate model data. Numerical weather prediction model data are not suitable because only standard meteorological levels are archived. In the interior in winter the inversion is typically only 50 hpa deep (Figure 1)

6 1338 W. M. CONNOLLEY and this cannot be resolved if only surface, 850 hpa, 700 hpa, and 500 hpa data are available. I therefore turn to climate model control simulations of the present climate. The GCM used in this study is the UK Meteorological Office Unified Model. This GCM is a grid-point model, used at its standard climate resolution of 2.5" (latitude) by 3.75" (longitude), with 19 vertical levels. The lowest four GCM levels are terrain-following or sigma levels, with sigma of 0-997, 0.975,0.930, and At Vostok, this corresponds to a pressure difference from the surface of 2, 16,45, and 85 ma, which provides a reasonable resolution of the temperature profile. The GCM provides a diagnosis of 1.5 m temperatures, which corresponds to the standard observed near-surface temperature. The configuration used in this study is atmosphere only, with climatologically varying sea-surface temperatures and sea-ice. Data are taken from a 10-year run. Seasonal and diurnal cycles are included, as are interactive clouds with prognostic water content. The GCM was described and its Antarctic climate evaluated in Connolley and Cattle (1994); a global description is given in Cullen (1993). In particular the annual average 1.5 m temperature was found to be close to real climatology (as shown by Radok et al., 1987), although too cold in the central high interior by up to 540 C. By examining the seasonal variation at Vostok compared with a 20-year climatology I showed that most of this error occurred in winter and that summer temperatures were accurate. In the sections below I shall assume that the GCM atmosphere is sufficiently realistic for us to draw conclusions from it about the real atmosphere. Using GCM data interpolated to station locations, the best-fit equation relating the GCM inversion to the GCM surface temperature in July (cf. equation (4) for observations) is I = x Ts which has an 3 value of The interannual standard deviation of the slope is 0.02 and that of the intercept is 0.7. Figure 5 shows the GCM temperatures plotted against inversion strength for July. For a given temperature the GCM predicts a lower inversion strength than observations. However, because the GCM surface temperature in the interior is generally lower than observed, at Vostok and Amundsen-Scott the GCM predicts inversions only slightly stronger than observed. Figure 6 is the same as Figure 4 but uses GCM data interpolated to station locations. It is clear that although the GCM correctly simulates the distinction between interior and coastal stations it fails to distinguish the two classes of coastal station seen on Figure 4. (5) Surface Temperature ("C) Figure 5. As for Figure 3 but using multiannd mean data from the UKMO GCM interpolated to IS station locations.

7 ANTARCTIC TEMPERATURE RWERSION Surface Temperature ("C) Figure 6. As for Figure 4 but using multiannual mean data from the UKMO GCM interpolated to 15 station locations. The advantage of GCM data is complete coverage. Figure 7 shows the GCM inversion strength in July. Here and subsequently the nine grid points that make up the Antarctic Peninsular have been omitted, leaving 741 grid points. Regressing inversion strength against surface temperature for all these points I find that IGCMsReg = x Ts - 4.8, (6) with rz = All GCM data are area weighted in order to compensate for the convergence of the grid towards the pole. Unsurprisingly, equation (6), with coefficients fitted from GCM data, produces a closer approximation to the GCM inversion than equation (4), with coefficients fitted from climatological data. To evaluate the goodness of fit of the regression method I shall use equation (6). Of course equation (6), because of the known defect in the GCM surface temperature, is not as good a relation for the real inversion as equation (4). The model surface temperature is too cold in winter and the model inversion strength is only slightly stronger than observed, so the best fit value for the constant ATM in equation (1) will be larger than the 12.0"C found from observations by PZ. When applying the PZ method to GCM data I have used a value of 15.55"C for the constant AT,, which is the best fit to the GCM data. VALIDATION OF THE METHODS USING GCM DATA It is difficult to difference graphically the GCM inversion (Figure 7) from that given by Figure 2 of PZ (and slightly more accessibly by figure 2.5 of Schwerdtfeger, 1984). The GCM inversion appears to be somewhat stronger than observations in the interior. The areas covered by the 25 C and 20 C isotherms are larger in the GCM, although at the Pole and Vostok the GCM and observations are in close agreement. Because I lack any other Observations in the interior it is impossible to know whether the GM or PZ is producing a better representation in areas where they disagree. To try to evaluate the effectiveness of the PZ technique I shall use equation (1) with GCM data to predict ZJul. This will be referred to as FCM.PZ whereas the true model inversion will be called FM. To do this, ATs is calculated exactly from GCM data, I,, is assumed to be 2 C and ATM is 1555 C. This latter value is used because it produces an error field with zero mean. The GCM indicates that the assumptions of PZ are broadly justified: the mean of ZJ, is 1.9"C, with standard deviation 0.9"C; the standard

8 1340 W. M. CONNOLLEY Figure 7. Inversion strength in July from UKMO GCM data. Isotherm interval 5 C. Figure 8. Difference between IOCM." and IKM. Contour interval 2 C; negative isotherms dotted; zero isotherm bold.

9 ANTARCTIC TEMPERATURE INVERSION 1341 Figure 9. Difference between IOCM.ma and ImM. Contour interval 2 C; negative isotherms dotted; zero isotherm bold. deviation of ATM is 2.3"C. Figure 8 shows the difference of FCM*" from FCM. The standard deviation over Antarctica is 2.5"C and? = This can be compared to approximating the inversion by regression using equation (6), with Ts taken from the GCM (denoted by PMvReg). The difference between this and FCM is shown in Figure 9. The standard error is 2.9"C, slightly greater than the value for F M v P Z - FCM. The? of the fit is Both ZGCM-PZ and ZGCM-Rcg produce a 'good' fit to the GCM inversion (< 3 C error) over large areas of the continent. ZGCM,PZ has errors of less than 3 C over 79 per cent of the continent, whereas FCMskg does slightly worse at 75 per cent. In individual years this conclusion is reversed: the mean f standard deviation for the percentage area with error less than 3 C is 64 f 7 for FCM-PZ but 70 f 4 for P'vRcg, using ATM and regression coefficients fitted to the year. If the multi-annual value of 15.55"C for ATM and the values in equation (6) are used for the PZ and regression methods then the fit is somewhat worse, with the PZ method affected more strongly: 60 f 8 for ZGCM." and 69 f 4 for ZGCM,Reg. The smoothing effect of taking multi-annual averages seems to benefit the PZ process more than the regression process. The following features of the error patterns of FM*" and ZGCM*Reg are present in all individual years as well as the 10-year average. ZGCM*PZ is too strong over the Ross ice shelf and the regions leading into the continental interior. It is too weak over much of West Antarctica and the escarpment region of East Antarctica. ZGCM,Reg is too strong along the coasts between 30"E and O"E, and on the eastern slopes of the Lambert glacier region at 75"E. It is too weak in the Lambert glacier valley and on the western slopes, and over the Ross and Ronne-Filchner ice shelves. The latter suggests that the GCM inversion is 'unexpectedly' strong over the flat ice shelves. This result may be compared with the work of Fortuin and Oerlemans (1990) who regressed temperature against elevation and latitude. If their regression relation for the whole of Antarctica is applied to the ice shelves the predicted temperatures are up to 10 C too warm. The anomalously strong inversion over the ice shelves thus appears to reflect the fact that surface temperatures in these regions are lower than those at equivalent latitudes and elevations elsewhere in Antarctica. CONCLUSIONS Using radiosonde data I have shown that a simple regression equation can be used to relate the surface inversion strength to surface temperature in July and have confirmed the results of Jh4 for annual averages. However,

10 1342 W. M. CONNOLLEY Figure 3 shows that there are at least three different classes of climatological relation between inversion strength and surface temperature through the year. The climate model produces a reasonable representation of the inversion, which is slightly stronger than that presented in PZ. It should be remembered that the map of PZ is based on very few data points. To try to evaluate the accuracy of the PZ method over the continental area I have compared the true climate model inversion with that generated by approximations using the regression method and the PZ method. I have shown that both methods produce a reasonably accurate representation of the climate model inversion. Using a 10-year average data set the RMS error of the PZ method is 2.5 C and that of the regression technique 2.9 C. In individual years the errors are slightly larger (averaging 3.3 C for PZ and 3.1 C for regression) and the PZ method generally performs worse than the regression method. For multi-annual data the PZ relation produces a slightly better representation and might be preferred because it is more physically based, although the regression relation is somewhat simpler. The errors of both methods appear to show some relation to topographic slope, which opens the possibility of including slope in the regression relation to produce a more accurate approximation. The accuracy of the ZGCM.PZ fit to the climate model inversion suggests that the PZ method is accurate to better than 3 C for about four-fifths of the continent. The regression method is accurate to 3 C over approximately three-quarters of the continent but appears susceptible to error in particularly interesting areas, such as the Lambert glacier region. This is a region of channelled katabatic flow both in the model and in reality. The results of PZ have been used as input to models of the Antarctic surface windflow (e.g. Parish and Bromwich, 1987). Although it is likely that the general pattern of winds would be little altered if an alternative temperature inversion field was used, there undoubtedly would be changes in the detail. ACKNOWLEDGEMENT Thanks are due to the staff of the Hadley Centre for their help in obtaining and interpreting the GCM data used in this study, in particular Mark Gallani who performed the model run. REFERENCES Connolley, W. M. and Cattle, H The Antarctic climate of the UKMO unified model, Antarc. Sci., 6, Connolley, W. M. and King, J. C Atmospheric water vapour transport to Antarctica inferred from radiosondes, Q. J. R. Meteorol. SOC., 119, Cullen, M. J. P The unified forecastlclimate model, Meteorol. Mag., 122, Fortuin, J. P. F. and Oerlemans, J Parameterization of the annual surface temperature and mass balance of Antarctica, Ann. Glaciol., 14, Jouzel, J. and Merlivat, L Deuterium and oxygen 18 in precipitation: modelling of the isotopic effects during snow formation, J. Geophys. Res., 89, Parish, T. R. and Bromwich, D. H The surface wind field over the Antarctic ice sheets, Nature, 328, Phillpot, H. R. and Zillman, J. W The surface temperature inversion over the Antarctic continent, J. Geophys. Res., 75, Radok, U., Jenssen, D. and McInnes, B On the Surging Potential of Polar Ice Streams, US Department of Commerce, Springfield, DOE/EW60197-H 1, 59 pp. Schwerdtfeger, W Weather and Climate of the Antarctic, Elsevier, Amsterdam, 261 pp. Warren, S. G., Mahesh, A. and Walden, V. P Heights of temperature inversions in the Arctic troposphere: multi-decadal trend?, in Fourth Conference on Polar Meteorology and Oceanography, Dallas, American Meteorological Society, pp