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Variability and Trends of the Summer Melt Period of Antarctic Ice Margins since 1980 from Microwave Sensors
OLIVIER TORINESI,MICHEL FILY, AND CHRISTOPHE GENTHON Laboratoire de Glaciologie et GeÂophysique de I'Environnement/CNRS, Universite Joseph Fourier, Saint Martin d'Heres, France
(Manuscript received 15 February 2001, in ®nal form 5 September 2002)
ABSTRACT The density and range of observations made by meteorological stations is insuf®cient to fully characterize decadal climate variability in Antarctica. Satellite-borne instruments, which offer a high spatial and temporal density of information, can contribute complementary data for characterizing Antarctic climate change. Here, partial melting of Antarctic snow, which signi®cantly affects the microwave emissivity of the surface, is identi®ed and counted over 18 yr in the 20-yr period 1980±99. The cumulated product of the surface area affected by melting and the duration of the melting event, called cumulative melting surface (CMS), is one of the three melt indices de®ned and discussed here. On average over the last 20 yr, the Antarctic CMS has decreased by 1.8% Ϯ 1% yrϪ1, a result that is consistent with a mean January cooling of the continent recently identi®ed from infrared satellite data. In addition, the interannual signatures of the Antarctic Oscillation, and possibly of the Southern Oscillation, are found in the melt indices.
1. Introduction available online at http://nsidc.org/NASA/GUIDE/docs/ datasetdocuments/smmrpath®ndertbs.html#7), a re- The Antarctic coasts and shelves undergo snow melt- gion of no interest for the surface melting studies. Melt- ing during the summer months of December and Jan- ing, or even moistening of the surface snow grains, can uary. There is virtually no melting at other times of the have a signi®cant impact on the microwave emissivity year except in the Antarctic Peninsula. Surface melting of the surface (Mote et al. 1993; Zwally and Fiegles is not known to signi®cantly in¯uence the mass balance 1994). As a consequence, the SMMR and SSM/I data of the Antarctic ice sheet and shelves, by contrast to provide signals sensitive to changes in the surface en- Greenland. However, surface air temperatures have been ergy balance of Antarctica at temperatures close to melt- reported to be on the rise at many stations in Antarctica, ing over the last 20 yr. For instance, a systematic in- though mainly in the Bellinghausen±Antarctic Peninsula crease in the duration of the summer melt season over area (Raper et al. 1984; King 1994; Jones 1995; Jacobs 1979±91 in the Antarctic Peninsula (Ridley 1993) and and Comiso 1997; Skvarca et al. 1998). Elsewhere, cool- a small and barely signi®cant decline over 1978±87 on ing has been reported (Comiso 2000; Doran et al. 2002). the Ronne and Ross ice shelves (Zwally and Fiegles As temperature changes, the length of the summer melt- 1994, hereinafter ZF94) have been identi®ed. Although ing period may change and thus contribute to charac- longer-term warming trends have been reported, direct terize summer climate change at Antarctic coasts and surface temperature retrieval from IR satellite data sug- shelves. gest that the Antarctic continent has been cooling (Jan- More than 20 yr of spaceborne microwave radiometer uary temperature) over the last 20 yr (Comiso 2000), a observations of the surface of the earth are now available trend that is compatible with the sea ice temporal evo- from the Scanning Multichannel Microwave Radiometer lution and screen temperature data. Our objective, here, (SMMR) and the Special Sensor Microwave Imager is to detect and quantify temporal and spatial variations (SSM/I) sensors (Maslanik and Stroeve 2000). Over this of the melting areas over the last 20 yr. period, the polar-orbiting satellites have provided almost The satellite data and ground observations used to full spatial and daily or semidaily coverage of the Ant- derive and verify the melting time series are presented arctic, except for a small cap south of 87ЊS for the in section 2. Data processing to extract melting occur- SSM/I sensor and 84ЊS for SMMR (more information ences is described in section 3. The results, including tables, maps and plots of interannual variability, and trends for the Antarctic summer period, are presented Corresponding author address: Michel Fily, LGGE/CNRS, Univ- ersite Joseph Fourier, BP96, 38402 Saint Martin d'Heres, Cedex and discussed in section 4. A general conclusion sum- France. marizes the results and expands the perspectives in sec- E-mail: ®[email protected] tion 5.
᭧ 2003 American Meteorological Society
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TABLE 1. List of Antarctic sites at which meteorological data are melting snow, the penetration depth is strongly reduced. compared with satellite data. For a given frequency, small amounts of liquid water Station Lat Lon in snow cause a larger increase of brightness temper- ature at horizontal than at vertical polarization (ZF94). Rothera 67.57ЊS 68.13ЊW Larsen 66.95ЊS 60.91ЊW The depolarization effect is due to a change in dielectric Base Belgrano 77.87ЊS 34.62ЊW properties at the air±snow interface when snow is wet Halley 75.50ЊS 26.65ЊW (Steffen et al. 1993; Abdalati and Steffen 1997). Dif- Nordenskiold base 73.05ЊS 13.38ЊW ferential polarization emissivity due to melting is further Neumayer 70.62ЊS 8.37ЊW Novolazarevskaya 70.77ЊS 11.83ЊE discussed in section 3. Maitri 70.77ЊS 11.75ЊE Preprocessed data distributed by NSIDC are available Syowa 69.00ЊS 39.58ЊE on a rectangular grid on a polar stereographic projection Molodeznaja 67.67ЊS 45.85ЊE covering most of Antarctica. Each pixel is a 25 km ϫ Mawson 67.60ЊS 62.90ЊE 25 km square. All observations made within a day and Zhongshan weather 69.40ЊS 76.40ЊE Davis 68.60ЊS 78.00ЊE within a given grid square are accumulated to provide Mirnyj 66.55ЊS 93.02ЊE a mean daily sample. Casey 66.28ЊS 110.52ЊE Gill 79.99ЊS 178.61ЊW Elaine 83.10ЊS 178.60ЊW b. In situ data Lettau 82.52ЊS 174.45ЊW There is no available direct observation of surface Ferrel 77.95ЊS 170.80ЊW melting in Antarctica. Melting can occur only if the surface temperature is at 0ЊC, but very few meteoro- logical stations in Antarctica, and apparently none in 2. The data coastal Antarctica, report surface (skin) temperature. a. Remote sensing data Temperature closest to the surface is generally reported at 2 or 3 m. If the wind is not strong enough to ef®ciently For the period from April 1979 to July 1987, the data mix the air in the lowest few meters, the temperature of SMMR on the Nimbus-7 satellite have been used. difference between the surface and 2±3 m in the at- Data from SSM/I on three satellites of the Defense Me- mosphere can be signi®cant. In particular, in case of teorological Satellite Program (DMSP), F-8 (August surface inversion, the temperature recorded at 2±3 m 1987±December 1991), F-11 (January 1992±June may be positive with no melting at the surface. Alter- 1995), and F-13 (July 1995±March 1999), complete an nately, melt may occur with negative air temperature almost continuous 20-yr series. These data are distrib- when the radiation balance is positive. Using atmo- uted by the National Snow and Ice Data Center (NSIDC, spheric temperature to detect surface melting thus bears Boulder, Colorado; Maslanik and Stroeve 2000). Dif- some uncertainty that must be kept in mind, even if ferent instruments on different platforms must be cross- surface air mixing seems to be effective most of the calibrated. The SSM/I F-8 data provide a baseline time on the coasts. against which other data are adjusted using calibration Stations have been selected to, as much as possible, coef®cients from Jezek et al. (1991) for SMMR, Ab- verify satellite-derived melting in widely different re- dalati and Steffen (1995) for SSM/I F-11, and Colton gions. When several stations are available in the same and Poe (1999) for SSM/I F-13. In any case, the melt area (e.g., on the Ross ice shelf), those farthest from detection algorithm, as described in section 3, is inde- rocks and having the most homogeneous environment pendent of sensor calibration. on scales of a satellite grid square have been preferred. The SMMR and SSM/I instruments provide mea- Data from both staffed and automatic weather stations surements of the energy emitted by the earth's surface [(MWS) and (AWS)] are available. Data from AWS at several frequencies and at both vertical (V) and hor- have been downloaded from ftp://ice.ssec.wisc.edu and izontal (H) polarizations [18SMMR or 19.35SSM/I (V, H), MWS data have been retrieved from the European Cen- 22.2 (V), 37.0 (V, H), and 85.5 GHz (V, H); Maslanik tre for Medium-Range Weather Forecasts (ECMWF) ob- and Stroeve 2000]. Previous work on snow/icemelt de- servation archive. The frequency of reports varies with tection (ZF94; Abdalati and Steffen 1997) suggests fo- stations. To compare with satellite data, we only select cusing on the 19- and 37-GHz frequencies. The 22-Ghz the maximum recorded daily temperature, which is less channel is used only for water vapor and the 85-GHz prone to the inversion effect and corresponds to the time frequency is too in¯uenced by water vapor and clouds when melting occurs. Table 1 list the geographic lo- (scattering) in the atmosphere (MaÈtzler 2000). The en- cations of all stations used. ergy emitted by the snow and received by the space- borne instrument comes from different layers of snow. 3. Processing of the microwave data The depth seen by the instrument (penetration depth; Steffen et al. 1993) depends on the frequency of the a. Data selection process signal and on the water content. The lower the fre- Electric power supply limitations of Nimbus-7 have quency, the more responsive it is to melt onset. For only allowed operation of SMMR every other day (until
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FIG. 1. Number of melting days vs in situ max daily temperatures for four representative stations and for the chosen threshold (3).
July 1987). In order to take into account the missing For 19 GHz, horizontal polarization, the emissivity days, ZF94 twice counted each occurrence of melting. of bare soils (approximately 0.9) or ocean (0.6) is dif- For convenience, we simply linearly interpolate bright- ferent from the emissivity of dry snow or ice (0.8) (MaÈt- ness temperatures over missing days. Statistically, there zler 1987). Melt signals in a grid square potentially should be no signi®cant difference between the two containing signi®cant rock or ocean surfaces are thus methods. In addition, several gaps are found in the series not reliable. A land±sea mask provided by NSIDC (Mas- (usually due to instrument failures). Most gaps last only lanik and Stroeve 2000) is used to avoid ocean pixels. 1 or 2 days. For gaps of duration less than 3 days, the Because most of the signi®cant rock outcrops are as- missing brightness temperatures are linearly interpolat- sociated with boulders and mountains peaking higher ed. For gaps equal or longer than 3 days, linear inter- than 1500 m of altitude, where melting is unlikely, a polation may induce signi®cant errors. Years with gaps topographic map is used to eliminate surfaces higher longer than a month in a row, for which summer is badly than 1500 m. undersampled, are simply ignored. As a consequence, the 1981/82 and 1987/88 summers are not taken into b. The algorithm account in the present study. For shorter gaps, in order to avoid spurious interannual trends, the missing days The 19-GHz horizontal polarization channel is chosen of a particular year are removed from all the other years to detect the melt onsets. Melt induces large increases in the 20-yr series. For instance, in 1988, the period of brightness temperature. An annually and regionally from 24 to 28 December is unavailable everywhere at varying threshold is thus calculated and all values of the coast of Antarctica, except in the region of the Ross brightness temperature above the annual mean plus this ice shelf. The data for 24±28 December are thus dropped threshold are associated with melting. This threshold is for all years, everywhere except in the Ross region. This proportional to the standard deviation of the signal, thus reduction of the time series, repeated for all gaps, results taking into account the spatial variability of its ampli- in December being particularly affected (only 14 days tude. in most regions). Sampling reduction results in under- The annual mean 19-GHz H brightness temperature estimating the frequency of summer melting events. is calculated for each year from 1 April to 31 March in Sensitivity experiments are described in section 4 to each grid square for the 20-yr series. By calculating an both tentatively correct this underestimation and test the annual mean, we take into account interannual varia- robustness of the interannual variability and trends es- tions of the signal. In some regions like on the Larsen timated from the reduced series. ice shelf, melting lasts as much as four months (No- All grid squares are sampled at least once a day, but vember±February). In such cases, means are badly bi- the density of satellite tracks at the surface increases ased toward values typical of melting and do not re¯ect with latitude. For melting events lasting signi®cantly average values of unaffected surfaces. Strong melt sig- less than a day, for example, transient tenuous moist- nals are thus simply ®ltered out by eliminating values ening of surface snow grains near midday, the proba- more than 30 K above the mean (the 30-K threshold is bility of missing the event thus increases when latitude derived from the ZF94 study as discussed below). The decreases. Also, measurement failures inducing false re- process is recursive: the ®rst time, the mean is calculated ports [e.g., meltinglike peaks of brightness temperature using all the values (without any ®lter); then strong melt
(Tb) in full winter] occur randomly. Such events last signals are ®ltered out and the calculation is repeated generally one day only and can be avoided if melting twice. Then, for each year and each grid square, the events recorded over less than two successive days are standard deviation is computed using the same ®lter. ignored. The threshold T above which a brightness temperature
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FIG. 2. Map of the mean duration of surface melting (days) for the 18 yr of data available, plus the location of the seven zones. The color version of this ®gure was available online at http//lgge.obs.ujf-grenoble.fr/ϳchristo/teledet/torinesi.htm at the time of writing. value is considered to be a melt signal is the mean (M) approach was proposed by ZF94. A spatially and tem- plus N standard deviations (): T ϭ M ϩ N. porally constant threshold (30 K) is selected and all Although M, , and, thus, T vary with year and grid values of the 19-GHz horizontal polarization (19H) square, the value of the constant N does not. Meteo- channel above the 9-yr mean plus this threshold are rological observations are used to select and adjust N, associated with melting. We found that an adaptative so that, as much as possible, all and only real melting instead of a constant threshold is more sensitive to the events are detected. The N is chosen so that most melt spatial variability of the signal amplitude. Also, the an- events correspond to daily maximum temperatures nual adaptative threshold minimizes problems due to above Ϫ5ЊC as reported by weather stations at test sites instrumental drifts or snow-cover evolution, which is listed in Table 1. For N ϭ 3, the percentage of tem- one major improvement on the ZF94 algorithm. Even peratures above Ϫ5ЊC for all the available stations is though the air temperatures are not unequivocally re- greater or equal to 98%. This percentage remains high lated to surface melting, a fair semiquantitative relation (85%) for temperatures above 0ЊC. Figure 1 illustrates is suggested by Fig. 1. how selective the N ϭ 3 threshold is at four stations only, but all the available stations show similar results. 4. Spatial and temporal variability of surface The threshold for melting detection as described snowmelting above differs signi®cantly from the threshold used in previous studies. Abdalati and Steffen (1997) proposed a. Processing of the melting signal a melt-detection algorithm based on the cross-polarized Removing the randomly missing data periods of one gradient ratio particular year from all other years (the reduced series in section 3) avoids biasing the interannual variability Tbb(19H) Ϫ T (37V) XPGR ϭ and trends of the melt series. In the following, the results T (19H) ϩ T (37V) bb will be referred to as those of the ``reduced'' calcula- with an empirical threshold. The XPGR appears to work tions. The reduced calculations do not apply to the full well for massive melting as in the peninsula but does summer. A number of other calculations were made to not give good results elsewhere. A more straightforward ensure that the unbiased trends obtained from the re-
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FIG. 3. Surface melting anomaly (days per 25 ϫ 25 km2 pixel) during the summer periods between 1979/80 and 1988/89 for four different zones (see Fig. 2), calculated by the ®lled-in calculation. The summers of 1981/82 and 1987/ 88 are not displayed owing to too many missing data (see Web site listed in Fig. 2 for color version).
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FIG. 4. As in Fig. 3 but during the summer periods between 1989/90 and 1998/99 (see Web site listed in Fig. 2 for color version).
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FIG. 5. CMS (106 day km2), MMS (km2), and MMD (day) for three zones plus the whole continent; reduced calculation. Linear regressions and relative slopes (% yrϪ1) values are plotted when statistically signi®cant (see Tables 4, 5, and 6). Warm events (%) are calculated with the max daily temperatures of Rothera Point for the peninsula area, Syowa and Molodeznaja for DML, and Lettau and Ferrel for the Ross area. The summer of 1979/80 is labeled 1980 and so on.
duced series are robust. For instance, in order to avoid are presented (e.g., Figs. 5,6), the reduced calculations the problem of the many missing days of December, are preferred. calculations restricted to the second part of summer Because the interannual variability of melting is spa- (January±March) were done, which broadly con®rm the tially coherent over large regions (Figs. 3±4), we select calculations obtained with the reduced series. Also, seven areas over which synthetic calculations are per- complete series were constructed by ®lling gaps of a formed (Fig. 2). These areas coincide with those se- particular year with the brightness temperatures aver- lected by ZF94, so that our results can extend theirs. aged over the corresponding days of the other years. Those areas are designed as follows: peninsula, Filch- The results, hereinafter referred to as those from ``®lled- ner-Ronne, Dronning Maud Land (DML), Amery, in'' calculations, will be presented (Tables 4±6) and Wilkes Land, Ross and Marie Byrd Land (MBL). discussed along with, and as a sensitivity test with re- Figure 2 shows the mean annual number of melting spect to, those of the reduced calculations. In fact, when days over the full 18-yr period (20 yr minus the summers a cumulative result is presented (e.g., the mean total 1981/82 and 1987/88; see section 3). Maps of spatial number of melting days in summer, Fig. 2), the ®lled- extent and duration anomalies of surface melting, are in option, rather than the necessarily underestimated re- given in Figs. 3 and 4 for four different areas and for duced option, is preferred. When variability and trends each summer period between 1979/80 and 1998/99
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FIG. 6. As in Fig. 5 but for four different zones. Warm events (%) are calculated with the max daily temperatures of Belgrano for the Filchner area; Mawson, Zhongshan, and Davis for Amery; and Casey Air Strip for the Wilkes area.
(with two missing summers; see section 3a). The re- is calculated by dividing the CMS by the MMS of gional dependence of temperature, and thus of surface the summer. melting can be illustrated by the very different duration In Figs. 5 and 6, we plot the evolutions of those three of the melting period of the peninsula region (50 days indices for the seven zones. An eighth series of charts on average) versus the rest of the coasts and shelves represents Antarctica as a whole (Fig. 5). Meteorolog- (from 5 to 20 days; Table 6). ical weather stations were selected in each zone for Three different synthetic parameters of interest are which the percentage of daily maximum temperatures discussed: above the summer mean was calculated to compare with 1) The cumulative melting surface (CMS; day km2)is melt characteristics (see section 3). This is also shown the annual sum of the pixel days where melting oc- in Figs. 5 and 6. curs, multiplied by the pixel area (25 ϫ 25 km2). 2) The maximum melting surface (MMS; km2)isthe b. Melt variability and trends surface over which melting is detected at least once during a summer. Depending on the zone, the information given by the 3) The mean melt duration (MMD; day) yields infor- melt indices is not the same. For instance, the CMS of mation about the duration of the melting period and the peninsula is mainly dependent on the duration of
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TABLE 2. Periods (yr) and phases (Њ) calculated by MTM on TABLE 3. Signi®cant correlations (above the 99% con®dence level) complete melting index series, above the 90% signi®cance level. between the summer-mean AOI (Oct±Jan) and the surface melting index. CMS MMD MMS rr Area Period Phase Period Phase Period Phase detrended Peninsula MMS Ϫ0.60 Peninsula 2.3 175 2.4 133 DML CMS Ϫ0.61 Filchner 4.2 4 MMD Ϫ0.66 DML 2.1 233 2 90 Amery CMS Ϫ0.70 Amery 2.1 207 2.1 210 3 325 MMD Ϫ0.68 Wilkes 4.8 204 5.4 19 2.3 356 Wilkes CMS Ϫ0.61 Ross MMD Ϫ0.73 Ϫ0.61 MBL 2.6 217 2.6 334 2.6 310 Ross CMS Ϫ0.68 Ϫ0.64 Antarctic 2.5 47 MMS Ϫ0.77 Ϫ0.72 MBL CMS Ϫ0.69 Ϫ0.81 MMS Ϫ0.63 Ϫ0.69 MMD Ϫ0.67 Ϫ0.77 melting because the melting surface variability is low. Antarctic CMS Ϫ0.84 Ϫ0.81 On the other hand, the large ice shelves undergo very MMS Ϫ0.68 Ϫ0.61 short melting periods, but their melting surface is highly MMD Ϫ0.65 Ϫ0.64 variable. ZF94 report on Antarctic melt over the period 1978± 87. We use a more sensitive and accurate detection al- eling waves, is not applicable. As a ®rst approach to gorithm (see section 3) and various alternate data pro- objective analysis of the interannual variability in the cessing to show that the means, variability, and trends melt indices, we simply use spectral analysis on the bulk (maxima and minima, order of magnitude, and sign of indices. Common periodic variability in the different the trends; Tables 4±6) are at least qualitatively robust. zones should sign in with similar periods. Also, a cir- We thus extend ZF94 results not only in time, but also cumpolar traveling wave, for example, melt variability in signi®cance. related to the Antarctic circumpolar wave (Gloersen and White 2001), should sign in with a phase evolving broadly linearly with time around Antarctica. Because 1) INTERANNUAL VARIABILITY the series are short, we use the multitaper method Interannual variability for CMS in ZF94 (their Figs. (MTM; Thomson 1982), which is sensitive and provides 6 and 7) and in our calculations (our Figs. 5 and 6) are estimates of the statistical signi®cance of detected fre- very similar over the common time period covered. The quencies. ®rst two summers in common (1979/80 and 1980/81) The results are displayed in Table 2. Periods of 2±3 behave similarly in all seven zones, showing a clear yr are mainly found, consistent with visual inspection melt decrease. Then, as pointed out by ZF94, a 2-yr of the series. The mean Antarctic MMD index has a oscillation is found for the summers 1982/83, 1984/85, 2.5-yr period. Longer periods (4±5 yr) are also found. and 1986/87. This oscillation is seen in all zones and However, beyond this, no clear spatially coherent pic- in the whole of Antarctica, except for Filchner and ture emerges from Table 2. In particular, although the Wilkes. This is also in agreement with ZF94. phases vary from region to region, they are not consis- The next decade undergoes the strongest summer tent with a circumpolar traveling wave. melting for all the zones. It takes place in 1992/93 in Dynamical connections between the Tropics and the the peninsula and Marie Byrd Land zones and is not high southern latitudes at the El NinÄo±Southern Oscil- seen anywhere else (it even coincides with a very weak lation (ENSO) pace have been found in the Paci®c sector melting period elsewhere except for the Wilkes zone). (e.g., Trenberth and Caron 2000). Correlation of Ant- On the contrary, in the DML, Ross, Filchner, Amery, arctic climate variability with the Southern Oscillation and Wilkes zones, it takes place one year earlier, in 1991/ index (SOI) have also been suggested (e.g., Yuan and 92. Martinson 2000; Bromwich et al. 2000). Negative cor- Principal components analysis (PCA) and calculation relations between melt indices and the summer-mean of empirical orthogonal functions (EOF) are common SOI are found above the 99% signi®cance level in only data processing methods to extract modes of variability two regions (Amery and Ross) and for the Antarctic as and spatial patterns of coherency from spatially distrib- a whole. However, because we looked for correlations uted time series. Unfortunately, such methods are not in eight regions and for three indices, we signi®cantly appropriate for melt time series. Because melting is a increase the likelihood of ®nding merely random cor- threshold process, it is not normally distributed, and, relations and thus decrease the statistical signi®cance of for instance, in some pixels the time series contain zeros the correlations. In particular, the correlations are not for all but a few years. For the same reason, complex robust with respect to the various melt indices (i.e., in EOF analysis (e.g., Yuan and Martinson 2000) to detect most cases, a signi®cant correlation is found for one evolutionary patterns of correlation, for example, trav- index only). In addition, over 1980±99, signi®cant
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TABLE 4. CMS index calculations. Trend, : std dev of the trend, RT: relative trend [%-trend ϫ 100/(mean over the 20 yr)], S: signi®cance of the trend (%) derived from the t test, and mean value over the 20 yr for the reduced calculation. Mean value over 20 yr, RT (%), and S (%) for the ®lled-in calculation. Trends are given when S Ͼ 85%; 99ϩ when S is very close to 100%.
CMS (106 day km2) Reduced calculation Filled-in calculation
Trend trend RT S Mean RT S Area (106 day km2 yrϪ1) (%) (%) (106 day km2) (%) (%) Peninsula Ð 10.87 14.17 Ð Filchner Ð 0.47 0.58 Ð DML Ϫ0.17 0.07 Ϫ5.0 95 3.30 4.25 Ϫ5.8 95 Amery Ϫ0.14 0.03 Ϫ7.2 99ϩ 1.88 2.45 Ϫ7.4 99ϩ Wilkes Ϫ0.06 0.03 Ϫ3.7 95 1.51 2.11 Ϫ3.8 95 Ross Ð 0.94 1.56 Ð MBL Ð 1.86 2.17 Ð Antarctic Ϫ0.4 0.2 Ϫ1.8 90 20.83 27.29 Ϫ2.2 90 trends are seen in the melt indices (section 4c) and in itudes in the Southern Hemisphere in summer. A lower the SOI, possibly affecting the signi®cance of correla- correlation in the peninsula than elsewhere possibly re- tions in terms of year-to-year variability. However, the ¯ects that the Antarctic Oscillation has less amplitude correlations are weakly modi®ed, and in fact, slightly at lower latitude. The correlations are systematically improved, when 20-yr trends are removed. Our melt negative, and in some cases very high, indicating that results thus appear to hint at a possible implication of higher pressure at the Antarctic periphery coincides with the SOI in Antarctic climate variability. less melting. The coastal weather patterns (temperature, In fact, the striking 2-yr oscillation seen around 1985 surface energy balance, and thus, e.g., cloudiness) that in several regions and melt indices is not a characteristic are associated with pressure higher than normal and that of the SOI, but rather bears some resemblance to the can affect summer melting thus need to be investigated. dominant (annular) mode of oscillation of the Antarctic As for the SOI, correlations after removing long-term atmospheric pressure, often simply referred to as the trends were calculated, this time with consequences in Antarctic Oscillation and quanti®ed by the Antarctic some regions. On average over Antarctica, though, Oscillation index (AOI; Gong and Wang 1999). Visual much of the AOI±melt correlation is re¯ected on a year- comparison of the melt series with the AOI suggests to-year basis. other similarities in the 1980±99 period, and this is con- ®rmed by correlations signi®cant at the 99% level in all regions (Table 3). The correlations are stronger and 2) TRENDS more convincing (e.g., more robust with respect to melt index) than for the SOI. Interestingly, correlations be- For each index, in each zone, we determine a 20-yr tween a 4-month running mean of the AOI and surface trend by calculating a linear regression (slope and stan- melt indices were blindly searched for with various dard deviation of the slope) across the 18 available phase leads and lags, but signi®cant correlations showed years. We estimate the signi®cance of the results from only for phases near zero, that is with the AOI averaged a t test (Tables 4±6). Trends with a con®dence level over the four summer months in common with melt below 85% are not considered to be signi®cantly dif- calculations. (October±January). Therefore, there ap- ferent from 0. We draw the attention of the reader to pears to be a relation between summer melt and the the fact that the trends are affected by missing data distribution of air mass between the mid- and high lat- (especially in December). However, trends computed
TABLE 5. As in Table 4 but for MMS. MMS Reduced calculation Filled-in calculation
Trend trend RT S Mean RT S Area (106 km2 yrϪ1) (%) (%) (106 km2) (%) (%) Peninsula Ϫ0.002 0.0008 Ϫ0.6 95 0.28 0.29 Ϫ0.6 90 Filchner Ð 0.12 0.13 Ð DML Ð 0.28 0.29 Ϫ2.2 90 Amery Ϫ0.004 0.001 Ϫ3.1 99 0.12 0.13 Ϫ2.6 98 Wilkes Ð 0.15 0.17 Ð Ross Ϫ0.01 0.006 Ϫ6.8 85 0.15 0.18 Ϫ6.8 85 MBL Ð 0.18 0.18 Ð Antarctic 1.28 1.37 Ð
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TABLE 6. As in Table 4 but for MMD. MMD (day) Reduced calculation Filled-in calculation
Trend trend RT S Mean RT S Area (day yrϪ1) (%) (%) (day) (%) (%) Peninsula ϩ0.5 0.3 1.2 85 38.9 49.3 Ð Filchner Ϫ0.08 0.03 Ϫ2.3 99 3.7 4.1 Ð DML Ϫ0.5 0.15 Ϫ4.1 99 10.7 13.0 Ϫ4.6 98 Amery Ϫ0.8 0.2 Ϫ5.4 99ϩ 14.3 17.5 Ϫ5.7 99ϩ Wilkes Ϫ0.4 0.1 Ϫ4.0 99ϩ 9.7 12.1 Ϫ3.9 99ϩ Ross 5.0 6.2 MBL 9.9 11.1 Antarctic 16.3 19.8
from ®lled-in rather than reduced series (section 4) are 1992/93 (peninsula and MBL) are clearly seen in the qualitatively similar. meteorological data. From a general point of view, the MMS, the MMD, Summer mean (December±January) temperature for and, thus, the CMS have all decreased over Antarctica. four zones [peninsula, DML, Amery, and Wilkes; data For the Antarctic CMS, the decrease amounts to ap- from J. Jacka, Antarctic Comparative Research Centre proximately Ϫ1.8% Ϯ 1% yrϪ1 (Fig. 5). This con®rms (CRC); more information available online at http:// the work of ZF94 who found Ϫ2.4% yrϪ1 for the ®rst www.antcrc.utas.edu.au/ϳjacka/temperature.html] are 9 yr of the period. The MMS trend of the whole Ant- also compared with the annual ®lled-in CMS over the arctica is also negative but below the signi®cance 18-yr period. The results of linear ®ts are given in Fig. threshold. The MMD shows no trend at all, suggesting 7 {CMS ϭ Slope ϫ (͗T Њ͘Dec±Jan Ϫ Tthreshold), where CMS that the melting-period duration over the whole of Ant- is in 10 6 day km 2 , Slope is in 10 6 day km 2 ЊC Ϫ1 , and Ϫ1 arctica is stable (approximately 20 day yr , from the ͗T Њ͘Dec±Jan [summer mean (December±January) tem- ®lled-in calculations) whereas the melting surfaces are perature] and Tthreshold are in ЊC}, in which correspond- shrinking. ing plots are also displayed. The DML area has the Only the peninsula zone shows positive trends: the most important mean slope. On average over the four CMS trend is positive but not signi®cant; MMD in- zones, melting increases by 9.3 ϫ 10 6 day km 2 (sum creases by 1.2% Ϯ 0.7% yrϪ1 (Table 6), which means of the four slopes) when mean summer temperature that the length of the melting period increases. At the increases by 1ЊC. The Tthreshold re¯ects the average sum- same time, the MMS decreases, and this combination mer temperature below which no signi®cant melting explains why the CMS shows no signi®cant positive occurs. The peninsula, DML, and Wilkes areas have trend. On the contrary, the indices of the other zones mean Tthreshold close to Ϫ4.0ЊC, versus only Ϫ1.3ЊC for decrease strongly (Ϫ3% to Ϫ7%). All trends in MBL Amery. ZF94 make a similar analysis over the period are statistically insigni®cant. of 1978±87. The two studies con®rm a good correlation between the temporal evolution of the regional CMS indices and the local air temperature. c. Relation between melt indices and meteorological The climate signi®cance of our results is further re- data inforced if compared with the annual trends reported by Meteorological data (Table 1) have been used to cal- Comiso (2000) from meteorological data over the last ibrate the melt threshold of the algorithm used in this 20 yr. A positive trend is found at Rothera Point (0.090Њ study (section 3). The consistency of the variability Ϯ 0.027ЊCyrϪ1). This may be related to a signi®cant and trends in the melt indices with the meteorological positive MMD trend for the peninsula region, although data is now tested. For that purpose, we calculate the the MMS slowly decreases. Trends reported by Comiso percentage of warm events per summer (November± (2000) as signi®cantly different from 0 in the DML February) for a selection of meteorological stations in (Syowa station), Wilkes (Casey station), and Filchner- each zone, except MBL, for which no appropriate data Ronne (Halley Bay station) zones are negative, ranging were found. A warm event is detected when air tem- from Ϫ0.040ЊϮ0.021Њ to Ϫ0.079ЊϮ0.025ЊCyrϪ1. perature is above the mean December±January tem- Figure 7 also displays how the Antarctic CMS relates perature of the station. The alternation of warm and to the Microwave Sounding Unit (MSU; Christy et al. cold years as detected in the microwave and the me- 2000) global Antarctic tropospheric temperature (De- teorological data are in general agreement (Figs. 5 and cember±February mean anomalies). MSU temperatures 6). Thus, 2- and 2.5-yr oscillations are often con®rmed are less subject to cloud clearing, aerosol, and contam- (peninsula, DML, Amery), and the strong melting pe- ination than are IR measurements and are representative riods of 1991/92 (Filchner, DML, and Amery) and of the energy available for melting at the surface on a
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FIG. 7. Correlation between the CMS (106 day km2) and the Dec±Jan temperature average (T; ЊC) of 11 stations and four zones. Argentine Island station (65Њ15ЈS, 64Њ16ЈW) was not used earlier because no daily data were available. (bottom right) Antarctic CMS vs Dec±Feb tropospheric temperature anomalies (70Њ±80ЊS). The percentage of signi®cance (S;99ϩ when S is very close to 100%) is also given. more global scale than meteorological stations. The cor- temporal structure and of the nature of climate change relation is high (r ϭ 0.82), highly signi®cant, and pos- expands. Here, we have contributed to characterizing itive, con®rming the results reported above. climate variability and change in Antarctica by building a history of summer melt events over the last 20 yr of the twentieth century. This is performed by processing 5. Discussion and conclusions microwave brightness temperature data from space- The monitoring and depiction of climate variability borne instruments. Satellite data allow large spatial and and change improve as our knowledge of the spatial and temporal coverage; however, the very nature of melt in
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Antarctica limits this coverage to the summer coastal, tent (Cavalieri et al. 1997). Our study thus contributes shelf, and peninsula regions. We show that the inter- to characterizing a previously identi®ed cooling trend annual variability of melt events has spatial coherence in Antarctica over the last 20 yr of the twentieth century. over large regions and has partial coherence over the Reported longer-term trends suggest a mean warming whole Antarctic ice sheet. Also, 2- to 5-yr oscillations over the second half of the century (e.g., Comiso 2000). are identi®ed in the series, which are reminiscent of Global temperatures are also seen to increase, and this those reported by Comiso (2000), Cavalieri et al. (1997), is mostly associated with increased greenhouse gas con- and White and Peterson (1996) for sea ice extent. Strong centration (Houghton et al. 2001). Some climate models melt events occur during the period 1991±93. The sig- suggest that climate change may be slower around Ant- natures of the Antarctic Oscillation and also possibly, arctica than in other regions of the globe because of the but less clearly, the Southern oscillation are found in ef®cient uptake of additional surface heat by oceanic this variability. On the other hand, there is no clear sign convection (e.g., Manabe et al. 1992). In addition, the of a circumpolar traveling wave, for example, the Ant- coastal climate of Antarctica may ¯uctuate in response arctic circumpolar wave. to naturally changing patterns of the atmospheric cir- A decreasing trend is found in four out of seven main culation, blurring the signature of anthropogenic warm- regions of Antarctica. The melting duration at sites ing. Altogether, the climate interpretation of our evi- where melting occurs ranges from about 50 days in the dence and other evidence of Antarctic climate trends in peninsula (with maxima up to 100), to less than 10 days the last 20 yr or more is not obvious. Climate models in the Filchner-Ronne and the Ross ice shelves. On av- and meteorological analyses, which give access to syn- erage over the ice sheet, this number is about 20 days thetic data and (for climate models) climate processes, and does not signi®cantly change over the 20-yr period. are natural complements to the ®eld of remotely sensed On the other hand, the surface affected by melt de- data in such interpretational work. creases over time, from about 1.6 ϫ 106 km2 at the beginning to about 1.0 ϫ 106 km2 at the end of the Acknowledgments. This work is supported by the period (i.e., from 11.4% to 7.2% of the total continental French Programme National d'Etude de la Dynamique surface). A decrease in summer melt characteristics (du- du Climat, project ``Anthropique.'' Three anonymous ration or extent) is particularly signi®cant in the Dron- reviewers contributed to improve the quality of the pa- ning Maud Land, Wilkes Land, Amery, and Ross re- per. gions. No region shows any signi®cant increase, except possibly the peninsula for the mean melt duration. The interannual variability shown by melt characteristics ap- REFERENCES pears consistent with the interannual variability of the MSU tropospheric temperatures and the number of Abdalati, W., and K. Steffen, 1995: Passive microwave-derived snow melt regions on the Greenland ice sheet. Geophys. Res. Lett., warm temperature events at a few meteorological sta- 22, 787±790. tions where relevant data are available. Because melt is ÐÐ, and ÐÐ, 1997: Snow melt on the Greenland ice sheet as the result of a positive surface energy balance when derived from passive microwave satellite data. J. 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