JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 111, F01003, doi:10.1029/2005JF000318, 2006
Spatiotemporal variations of snowmelt in Antarctica derived from satellite scanning multichannel microwave radiometer and Special Sensor Microwave Imager data (1978–2004) Hongxing Liu and Lei Wang Department of Geography, Texas A&M University, College Station, Texas, USA
Kenneth C. Jezek Byrd Polar Research Center, Ohio State University, Columbus, Ohio, USA Received 30 March 2005; revised 6 September 2005; accepted 4 November 2005; published 21 January 2006.
[1] We derived the extent, onset date, end date, and duration of snowmelt in Antarctica from 1978 to 2004 using satellite passive microwave scanning multichannel microwave radiometer (SMMR) and Special Sensor Microwave Imager (SSM/I) data. A wavelet- transform-based method was developed to determine and characterize melt occurrences. About 9–12% of the Antarctic surface experiences melt annually. This is more than twice the surface melt extent measured in Greenland. Seasonally, surface melt primarily takes place in December, January, and February and peaks in early January. Regression analysis over the 25 year period of study reveals a negative interannual trend in surface melt. Nevertheless, the trend inference is not statistically significant. Large year-to-year fluctuations characterize the interannual variability. Extremely high melt occurred in the 1982/1983 and 1991/1992 summers, while extremely weak melt occurred in the 1999/ 2000 summer. A strong correlation with air temperature suggests that the melt index can serve as a diagnostic indicator for regional temperature variations. Periodic melting has been observed over Ross Ice Shelf, Ronne-Filchner Ice Shelf, the West Antarctic ice streams, and outlet glaciers in the Transantarctic Mountains. The Antarctic Peninsula, West Ice Shelf, Shackleton Ice Shelf, Amery Ice Shelf, and the ice shelf along the Princess Ragnhild Coast experienced the most persistent and intensive melt and should be closely monitored for their stability in the future, given the recent disintegration of the Larsen Ice Shelf A and B. Citation: Liu, H., L. Wang, and K. C. Jezek (2006), Spatiotemporal variations of snowmelt in Antarctica derived from satellite scanning multichannel microwave radiometer and Special Sensor Microwave Imager data (1978–2004), J. Geophys. Res., 111, F01003, doi:10.1029/2005JF000318.
1. Introduction disintegration of the Wordie Ice Shelf during the 1980s [Doake and Vaughan, 1991] and the catastrophic collapse [2] Portions of the Antarctic Ice Sheet annually experi- of Larsen Ice Shelf A in 1995 [Vaughan and Doake, 1996] ence surface melting. Wet snow has a low visible and near- and Larsen Ice Shelf B in 2002 [MacAyeal et al., 2003]. infraredalbedoandabsorbsasmuchas3timesmore These ice shelves survived thousands of years of climate incoming solar radiation than dry snow with a high albedo variations before their recent disintegrations [Gilbert and [Steffen, 1995]. The variation in snowmelt extent and Domack, 2003]. On the basis of the observations from duration affects the Earth’s radiation budget and hence is a sequential satellite images, Scambos et al. [2000] concluded factor in global climate changes. Furthermore, the Antarctic that meltwater ponding on the surface during the summer Ice Sheet and its surrounding ice shelves are extremely seasons fills and magnifies the ice crevasses and provides the sensitive to both atmospheric and seawater temperature conditions for the ice shelf disintegration. Their findings changes. Mercer [1978] postulated that the Antarctic ice suggest that snowmelt amount could serve as an indicator of shelves would be the component of the Antarctic glacier the stability of the ice shelves and perhaps as a proxy system most responsive to ‘‘greenhouse’’ warming. He indication of climate change. anticipated a southerly retreat of the ice shelf margins [3] Antarctica is the coldest, windiest and, on average, starting with the ice shelves of the Antarctic Peninsula. His highest continent on the Earth. Harsh climate, inaccessibil- anticipation seems especially prescient given the rapid ity, long dark polar nights in winters, and logistical diffi- culties impose serious difficulties and challenges for Copyright 2006 by the American Geophysical Union. traditional field observations. With the ability to peer 0148-0227/06/2005JF000318$09.00 through clouds and observe day and night, satellite passive
F01003 1of20 F01003 LIU ET AL.: SNOWMELT IN ANTARCTICA F01003 microwave sensors provide an ideal instrument for mapping wave emissivity of a snowpack increases rapidly in snowmelt in often cloudy polar regions. The scanning response to the introduction of a small amount of moisture. multichannel microwave radiometer (SMMR) onboard the The increase in the liquid water content makes the wet Nimbus 7 satellite recorded microwave radiation every snow act as a black body and emit more energy, resulting other day from October 1978 to August 1987 [Knowles et in a distinct rise in brightness temperature [Zwally and al., 2002]. The Special Sensor Microwave/Imager (SSM/I) Gloersen, 1977; Ulaby et al., 1986]. The microwave from the Defense Meteorological Satellite Program (DMSP) emission drops either as the liquid fraction increases or [Armstrong et al., 2003] recorded daily radiation from July upon refreezing, because the ability of the ice particles to 1987 to the present. The availability of spatially coherent scatter radiation emitted from deeper in the snow is and temporally continuous passive microwave data provides reestablished. The brightness temperature increase at the a viable means for investigating the intra-annual and inter- melt onset and decrease at the time of freeze-up are annual variability of surface melt. Using SMMR and SSM/I detectable by microwave sensors at frequencies in excess data, surface melt over the Greenland Ice Sheet has been of 10 GHz [Ulaby et al., 1986; Abdalati and Steffen, 1997]. thoroughly examined by various researchers [Mote et al., [6] When the daily brightness temperature (Tb) is plotted 1993; Mote and Anderson, 1995; Abdalati and Steffen, as a one-dimensional time series curve, strong edges (dis- 1995, 1997, 2001; Joshi et al., 2001]. However, only a continuities) emerge on the curve at the onset of melt and at limited number of studies have reported on Antarctic the time of freeze-up. Curve A in Figure 1 shows the surface melt. Zwally and Fiegles [1994] mapped the length brightness temperature variation for a wet pixel that expe- of the melt season over the Antarctic continent using rienced melting during the austral summer. The most SMMR data spanning the period 1978–1987. Ridley prominent feature of this curve is that the rapid increase [1993] examined SMMR and SSM/I data spanning 1978– in Tb at the onset of snowmelt in early summer stands in 1991 over the Antarctic Peninsula. Fahnestock et al. [2002] sharp contrast to the lower Tb observed during nonmelt analyzed snowmelt on ice shelves of the Antarctic Peninsula conditions of spring and winter, forming a strong upward during the period 1978–2000. Torinesi et al. [2003] exam- edge on the time series curve. An apparent prolonged period ined the spatial variability of surface melt duration in (plateau) of elevated brightness temperatures is observed Antarctica during 1980–1999. during the summer. During late summer, a dramatic de- [4] Our research updates the previous investigations with crease in brightness temperatures corresponding to snow an extended time period of passive microwave records using refreezing creates an obvious downward edge on the curve. a more sensitive and more accurate melt detection and Curve B in Figure 1 shows the brightness temperature tracking method. The surface melt analysis is extended over variation for a typical dry pixel, where the physical tem- the period 1978–2004 across the entire Antarctic continent perature of snow remains below the melting point all the and all the surrounding ice shelves. A wavelet transform time. Abrupt changes (strong edges) are absent for the dry based method is employed in our melt analysis. With this pixel, and its brightness temperature varies gently and new method, we are not only able to derive melt extent and smoothly. melt duration with an improved accuracy, but are also able to create time series maps for melt onset date and melt end 2.2. Channel Selection date during 1978–2004. On the basis of 25 years of surface [7] The SMMR sensor has ten microwave channels, melt information, we analyze the spatial and temporal trend including both vertical and horizontal polarizations for and variability of snowmelt. We also characterize the 6.6 GHz, 10.7 GHz, 18 GHz, 21 GHz and 37 GHz. The seasonal melting cycle and identify the most extensive SSM/I sensor has seven channels, the vertical polarization and intensive melt zones in Antarctica. The timing and for 22 GHz and both polarizations for 19 GHz, 37 GHz extent of the extreme melt events in the past two and a half and 85 GHz. Although the 85 GHz channel has the best decades are also determined and discussed. Finally, we spatial resolution (14 km), it tends to be contaminated by examine the relationship of surface melt extent and duration water vapor and clouds in the atmosphere [Ma¨tzler, 2000]. with near-surface air temperature at a regional scale. [8] We compared the brightness temperatures of all the channels for the SMM/I sensor at various locations. As demonstrated in Figure 2, the low-frequency channels are 2. Methodology more responsive to melt onset than high-frequency chan- 2.1. Melt-Induced Edges on Time Series Curve of nels. This is primarily due to the fact that absorption and Brightness Temperature hence emission are greater at the higher frequencies and [5] A passive microwave sensor detects the naturally there is less difference between wet and dry snow condi- emitted microwave energy from snowpack within its field tions for the higher-frequency channels. Although the of view. Passive microwave data are brightness temper- emissivities for wet snow at horizontal and vertical polar- atures calibrated from recorded microwave energy. The izations are nearly equal, the emissivity of dry firn at relationship between the microwave brightness temperature horizontal polarization is significantly lower than the verti- (Tb) and near-surface physical temperature (Ts) of snowpack cal polarization [Zwally and Fiegles, 1994]. Therefore the can be represented by the first-order Rayleigh-Jeans ap- transition from dry snow to wet snow causes a greater proximation [Zwally and Gloersen, 1977]: increase in horizontal brightness temperatures than vertical brightness temperatures at the same frequency (Figure 2). T ¼ eT ð1Þ b s Overall, we observed that the brightness temperature of the where e is the emissivity of the near-surface snowpack. The 19 GHz horizontally polarized channel exhibits the sharpest physical basis for snowmelt algorithms is that the micro- transitions at the melt onset and at the time of refreezing.
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Figure 1. Daily brightness temperature variations of the 19 GHz horizontal channel of the SSM/I for typical wet and dry pixels. The wet pixel is located on the Antarctic Peninsula, and the dry pixel is located in the interior Antarctic Ice Sheet. The horizontal axis is the sequential number of days from 1 July 2001 to 30 June 2002.
Since our melt detection algorithms rely on the edge curves of daily brightness temperature and determined the strength of temperature transitions induced by melt and timing of melt and refreeze occurrences. refreeze events, we chose to use the 19 GHz horizontally [10] On the basis of the wavelet transform of the daily polarized channel of the SSMI data (18 GHz for SMMR) in brightness temperature time series, we performed a multi- our analysis. scale edge detection for deriving snowmelt onset date, melt end date, melt duration, and melt extent [Liu et al., 2005]. In 2.3. Multiscale Wavelet-Transform-Based Method the same vein as the work of Joshi et al. [2001], our method [9] In the past decade, various algorithms have been is based on the observation that strong and significant edges proposed for extracting snowmelt information from multi- in the brightness temperature (Tb) time series curve signify channel satellite passive microwave data. To detect snow- snow melting and refreezing events. We first decompose the melt occurrence, many researchers have employed a single time series brightness temperature values into multiscale channel brightness temperature and a threshold empirically components through a wavelet transform. Then, the edges determined by field observations or physical experiments are tracked and analyzed across scales. To differentiate [e.g., Mote et al., 1993; Mote and Anderson, 1995; Ridley, significant edges corresponding to melting events from 1993; Zwally and Fiegles, 1994; Torinesi et al., 2003]. weak edges associated with random signal perturbation Some researchers have also used a composite index derived and noise, an optimal edge threshold is statistically deter- from two channels of passive microwave data, including the mined by variance analysis and a bimodal Gaussian curve normalized gradient ratio (GR) [Steffen et al., 1993] and the fitting technique [Liu et al., 2005]. On the basis of the cross polarization gradient ratio (XPGR) [e.g., Abdalati and principle of spatial autocorrelation, we also developed a Steffen, 1997, 2001; Fahnestock et al., 2002]. Ramage and spatial neighborhood operator for detecting and correcting Isacks [2003] introduced diurnal amplitude variations the possible errors brought about by strong noise or hetero- (DAVs) in snowmelt analysis, which are the running differ- geneity of data pixels [Liu et al., 2005]. Through detecting ences between early morning and late afternoon brightness and tracking strong and significant edges in the brightness temperature observations. Joshi et al. [2001] applied a temperature time series curve, our algorithm is able to derivative of Gaussian (DOG) edge detector to time series determine whether and when a pixel experienced melt.
Figure 2. Comparison of brightness temperatures for channels of different frequencies and polarizations.
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Table 1. Orbital Parameters of Nimbus 7, DMSP-F8, DMSP-F11, and DMSP-F13 Satellites Parameter Nimbus 7 DMSP-F8 DMSP-F11 DMSP-F13 Sensor SMMR SSM/I SSM/I SSM/I Nominal altitude, km 955 850 840 860 Inclination 99 98.8 98.8 98.8 Earth incidence angle 50.2 53.1 52.8 53.4 Orbital period, min 104 101.8 101.9 102 Ascending equatorial 1200, local noon 0615, dawn 1811, dusk 1742, dusk crossing time, LT Descending equatorial 2400, midnight 1815, dusk 0611, dawn 0542, dawn crossing time, LT Temporal resolution every other day daily daily daily Launch date 25 Oct 1978 18 Jun 1987 28 Nov 1991 24 Mar 1995 Swath width, km 780 1400 1400 1400 Temporal coverage 25 Oct 1978 to 9 Jul 1987 to 3 Dec 1991 to 3 May 1995 to 20 Aug 1987 30 Dec 1991 30 Sep 1995 31 Dec 2003
The melt onset date is identified by locating the position of less noisy, compared with previous similar studies [Liu et the first significant upward edge along the time axis. The al., 2005]. melt end date is indicated by the last significant downward [12] Our method has been implemented using C++ edge along the time axis. The melt duration, namely, the programming language. A detailed description and evalua- total melt days in the year, is estimated by tracking and tion of the wavelet transform based method has been accumulating the time intervals between significant upward presented by Liu et al. [2005]. edge and downward edge pairs. It excludes the refreezing periods between the melt onset date and the melt end date. 3. Melt Information Extraction From Satellite [11] Our wavelet transform based edge detection method has multiple advantages over conventional snowmelt algo- Passive Microwave Data rithms. First, our method explicitly searches and tracks [13] The satellite passive microwave data used in our strong edges on brightness temperature curves across scales analysis include the SMMR data collected by the NASA and therefore provides a more precise estimate for the melt Nimbus 7 satellite and the SSM/I data collected by the onset date, melt end date and melt duration. This is in DMSP-F8, DMSP-F11, and DMSP-F13 satellites. We contrast to some previous melt studies that use a melt index processed the data spanning the period from 26 October with an absolute brightness threshold to detect melt occur- 1978 to 30 June 2004. The temporal coverage of each rence. Because of the variation in emissivity of the snow- sensor is listed in Table 1. We employed the 18 GHz pack at different locations, the use of a fixed absolute horizontal polarization channel from the SMMR data and brightness temperature as a melt threshold often causes the 19 GHz horizontal polarization channel from the SSM/ errors in the timing of melt occurrences. As shown by Liu I data. et al. [2005], the conventional melt index threshold method [14] The observations from both ascending and descend- tends to produce too late a melt onset date and too early a ing orbits are available. We chose the observations that melt end date for each melt event and hence too short a melt occur closer in time to the strongest melt period for most duration. Second, our statistical approach produces a more sensitive detection of snowmelt. Those include the obser- objective and reliable edge threshold compared with the vations from the Nimbus 7 SMMR ascending orbits, trial-and-error process used by Joshi et al. [2001] for edge DMSP-F8 descending orbits, DMSP-F11 and DMSP-F13 threshold selection. It also circumvents the problems and ascending orbits. As shown in Table 1, observations of frequent unavailability of field observations for threshold Antarctica were made near local noon by the SMMR determination. In most previous studies, the melt threshold ascending orbits and at dusk (about 17:30 local time) by value was empirically determined from field observations of the DMSP-F8 descending orbits and the DMSP-F11 and the volumetric water content of a snowpack or near-surface DMSP-F13 ascending orbits. air temperature at one or more specific sites. Since one pixel [15] The continuous passive microwave data over 1978– from the satellite passive microwave data covers a relatively 2004 were acquired by four sensors. These sensors are large ground area, e.g., 25 km by 25 km for SSM/I data, the different in terms of the incidence angle, over-flight time, in situ point observations of the water content of snowpack or effective field of view. In addition, the 18 GHz horizon- or air temperature may not be representative for the entire tally polarized channel of the SMMR sensor is used to ground area covered by the pixel of passive microwave match the 19 GHz channel of the SSM/I sensors. For the data, resulting in a biased threshold value. Because of the sake of consistent analysis, the microwave brightness tem- hostile environment in polar regions, acquisition of field peratures from these sensors are recalibrated. On the basis observations is extremely difficult and costly. Furthermore, of the overlapping observations, Jezek et al. [1991] com- the explicit incorporation of spatial contextual reasoning puted linear regression equations for all channels between through a neighborhood operation enables our method to the SMMR and SSM/I F8 sensors to remove systematic check and remove potential errors from purely temporal differences. Abdalati et al. [1995] conducted the similar analysis of brightness temperature time series. Therefore our work between the SSMI F8 and F11 sensors. Using the melt computation results are spatially more consistent and SSM/I F8 data as a baseline, we converted the SMMR,
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Table 2. Regression Coefficients for Data Adjustments Between Different Sensors Correlation Conversion Slope Intercept Coefficient SMMR 18 GHz horizontal to SSM/I F-8 1.06 2.79 R > 0.99 19 GHz horizontal SMM/I F-11 and F-13 19 GHz horizontal 1.008 1.17 R > 0.99 to SSM/I F-8 19 GHz horizontal
SSM/I F11 and SSM/I F13 data into SSM/I F8 equivalent occurrence, we compiled an average melt duration map values using the regression coefficients derived by Jezek et for the period 1978–2004 (Figure 3). The shaded area al. [1991] and Abdalati et al. [1995], as shown in Table 2. shows the total melt extent, which consists of grid cells The brightness temperature differences between SSM/I F11 that were detected as melting for at least one day on average and SSM/I F13 are insignificant, due to very similar orbital during summers from 1978 to 2004. The total melt extent is characteristics (Table 1). Therefore the same set of regres- 2,359,375 km2, covering 17.27% of the continent. The grey sion coefficients between SSM/I F11 and SSM/I F8 level of each grid cell represents the average melt duration are applied to the conversion between SSM/I F13 and (melt days) per year during 1978–2004. SSM/I F8. [19] Relatively extensive and continuous melt areas in- [16] The SMMR and SSM/I data have been processed clude the Larsen Ice Shelf on the Antarctic Peninsula, into Equal Area Scalable Earth (EASE)-Grid Temperature George VI Ice Shelf and Wilkins Ice Shelf around Alexan- Brightness (Tb) products at the National Snow and Ice Data der Island, Amery Ice Shelf, West Ice Shelf, Shackleton Ice Center (NSIDC). A grid cell for the 18/19 GHz channels Shelf, ice shelves along Queen Maud Land, and Ross Ice covers a 25 km by 25 km ground area. A grid with 177 Shelf (Figure 3). Melt areas are also scattered along the 221 cells retrieved from the EASE-GRID Southern Hemi- coasts of Wilkes Land and Marie Byrd Land, on the Ronne- sphere data fully covers the Antarctic continent and a small Filchner Ice Shelf, and in glacial valleys in the Trans- portion of the surrounding ocean. To eliminate ocean antarctic Mountains. There exist strong regional variations contamination, we applied an Antarctic coastline extracted in melt duration. On average, 25% of the melt areas only from a Radarsat SAR image mosaic [Liu and Jezek, 2004] experienced melting of shorter than 1 week each year, and to mask the EASE-Grid data. The melt computation was about 71% of the melt zones experienced melting of shorter carried out only for pixels that lie on the ice shelves and/or than 30 days (Figure 4). Only about 9% of the melt zones the ice sheet. experienced more than 60 days of melting. Ice shelves on [17] The SMMR data are available every other day. the Antarctic Peninsula, Amery Ice Shelf, Shackleton Ice Brightness temperatures for the alternate missing days are Shelf, West Ice Shelf, and the ice shelf along the Princess calculated through linear interpolation from adjacent days. Ragnhild Coast experienced the longest periods of melting Also, there are instances of missing data for some days in on average, due to their relatively low latitude and low the SSM/I EASE Grid data, which are indicated by a value elevation (Figure 3). Significant parts of the Ross Ice Shelf of 0. To remove the false edges induced by 0 values on the and the Ronne-Filchner Ice Shelf experienced melting, time series curve, we replace the 0 values with the linearly although average annual melt duration was less than one interpolated brightness temperatures. For each pixel, we week per year. extract the corresponding brightness temperature values [20] In a Geographical Information System (GIS) envi- from daily SMMR and SSM/I grids to form 365 days of ronment, we calculated the distance of each melt pixel to the Tb time series. The time series starts with 1 July and ends on coastline (ice sheet or ice shelf margin) [Liu and Jezek, 30 June of the next year in order to center the Antarctic 2004] and the elevation value of each melt pixel from the austral summer season in the middle of the Tb curve. The Antarctic digital elevation model [Liu et al., 1999]. The brightness temperature data of the 18/19 GHz channels are relationships of the melt duration and melt occurrence rate sequentially processed pixel by pixel for every year using with the topography and proximity to the ocean were the wavelet transform based method. Three output grids are analyzed based on all melt pixels. As shown in Figure 5, produced for each year: melt onset date grid, melt end date the melt duration and melt occurrence rate decrease with the grid, and melt duration grid. Because the SSM/I data were increasing distance from the coast. For the continental ice missing for 28 days in December, 1987, the melt calculation sheet, surface melting can extend inland up to 600 km from for the 1987/1988 austral summer is incomplete. Therefore the coastline. However, 60% of the melt areas are concen- this year is not included in the following analysis of trated within 100 km wide coastal strip, and 90% of the melt spatiotemporal variability. areas are located within 300 km of the coastal ice margins. Seventy-one percent of the area that falls within the 100 km wide coastal zone experienced surface melting. The average 4. Spatiotemporal Variation of Snowmelt in duration is about 20 days for the melt zones that are located Antarctica less than 100 km from the coastline. Snowmelt was ob- 4.1. Snowmelt Extent and Duration served on the ice sheet to an elevation as high as 1500 m [18] By processing the SMMR and SSM/I data, we are above the mean sea level on the Antarctic Peninsula and in able to map the spatial extent of snowmelt across the the Transantarctic Mountains, but 60% of the melt areas are continent on a daily, monthly or seasonal basis. In order located below 200 m in elevation (Figure 6). About 95% of to gain an insight into the spatial variability in melt the snow surface with an elevation below 50 m experienced
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Figure 3. Average annual melt duration during austral summers from 1978 to 2004. melting, having an average melt duration of 29 days. In high elevation range have relatively high melt duration general, the melt duration and frequency in Antarctica (about 15 days) due to their low latitude. decreases with the rise in elevation as might be expected based on the atmospheric temperature lapse rate (Figure 6). 4.2. Seasonal Melt Cycle and Spatial Variation of However, this elevation dependence pattern is complicated Snowmelt Onset and End Dates by the latitudinal effect. The melt zones with an elevation [21] We delineated the daily melt extents for every year between 50 m and 100 m are mainly distributed in the Ross from 1978 to 2004, except for the year 1987/1988. To Ice Shelf and Ronne-Filchner Ice Shelf. Because of the high understand the typical seasonal cycle of surface melt, the latitude, the melt zones in this elevation range have rela- melt extent values of corresponding days of each year are tively lower melt duration (about 10 days) (Figure 6). The averaged across the past 25 years to produce mean daily melt zones with an elevation above 1000 m are mainly melt extents. Days are sequentially labeled with 1 July as located in the Antarctic Peninsula. The melt zones in this the first day and 30 June of the next year as the last day.
Figure 4. Frequency distribution of surface melt duration in Antarctica.
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Figure 5. Relationship of surface melt with distance to coastal margin. The melt duration curve shows average melt days for melt pixels that are located at different distance ranges from the coast. The melting distribution rate curve indicates the percentage of melt pixels that are located at a specified range in relation to the total number of melt pixels in Antarctica. The melting occurrence rate curve shows the percentage of the melt pixels in relation to the total number of melt and nonmelt pixels within a specified range of distance to the coast.
[22] The daily variation of 25-year mean melt extent is refreezing begins to occur and spatial coverage of melt displayed in Figure 7 to show the typical seasonal melt drops off steadily. Melt typically terminates in late February distribution cycle. Slight melt occurred in late October and or early March for most melt zones. November. In early December, the melt begins to spread [23] Most surface melting in Antarctica occurs during and the melt extent expands rapidly. Melt reaches its December, January and February (Figure 7). The 25 year maximum coverage in early January. From early February, mean melt extent and mean frequency of melt occurrence
Figure 6. Relationship of surface melt with surface elevation. The average melt duration curve shows average melt days for melt pixels within a specified range of elevation values. The melting distribution rate curve indicates the percentage of melt pixels within a specified range of elevation values in relation to the total number of melt pixels in Antarctica. The melting occurrence rate curve shows the percentage of the melt pixels in relation to the total number of melt and nonmelt pixels within a specified range of elevation.
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Figure 7. Seasonal variation of mean melt extent averaged over 1978–2004. The vertical axis is the average area of melt, and the horizontal axis is the sequential number of days starting with 1 July. within each of these three summer months are shown in February. The melt occurrence frequency is considerably Figures 8a–8c. The mean melt frequency indicates the higher in January than in December. By February, only the percentage of melt days in each summer month averaged ice shelves on the Antarctic Peninsula still have sustained over 1978–2004. The mean melt frequency can be inter- high melt frequency, and other melt zones have much preted as the melt occurrence probability on a day within an reduced melt occurrences (Figures 8a–8c). austral summer month. The melt extent in January is similar [24] We have produced melt onset date and melt end date to that in December but is over 2 times as large as in maps for each year from 1978 to 2004 (Figures 9 and 10).
Figure 8a. Melt extent and occurrence frequency in major summer months: December.
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Figure 8b. Melt extent and occurrence frequency in major summer months: January.
Figure 8c. Melt extent and occurrence frequency in major summer months: February. The gray level shows the percentage of melt days in each month.
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Figure 9. Melt onset date in Antarctica during 1978–2004. F01003 I TA. NWETI ANTARCTICA IN SNOWMELT AL.: ET LIU 1o 20 of 11 F01003
Figure 10. Melt end date in Antarctica during 1978–2004. F01003 I TA. NWETI ANTARCTICA IN SNOWMELT AL.: ET LIU 2o 20 of 12 F01003
Figure 11. Melt duration in Antarctica during 1978–2004. F01003 LIU ET AL.: SNOWMELT IN ANTARCTICA F01003
Figure 12. Interannual variability of snowmelt with fitted trend lines during 1978–2004. R is the correlation coefficient, and CI is the 95% confidence interval of the slope coefficient of the regression line. (a) Entire year, (b) December, (c) January, and (d) February.
These maps demonstrate respectively the expansion and information, it provides an overall measurement of melt contraction of melt zones in space and time. As expected, amount for a region. melt zones expand generally from the coast toward the [26] We computed the melt extent and melt index on a interior, from lower latitude to higher latitude, and from yearly basis from 1978 to 2004 (Figure 12). Interannual lower elevation to higher elevation. Melt zones contract changes in the snowmelt are investigated by examining the in an opposite direction. This is because the air temper- time series of the melt extent and melt index. To uncover ature increases and decreases approximately according to possible interannual trends, we conducted linear regression this pattern as the summer season progresses [Comiso, analysis on the time series of melt extent and melt index 2000]. using the least squares method. Over the 25 year period, the melt index decreases by about 274,820 day km2 (0.66% of 4.3. Interannual Variability and Extreme Melt Years the mean) annually, and the melt extent shows a slight [25] The spatiotemporal variations in melt duration for decrease of 8,600 km2 (0.63% of the mean) annually. the Antarctic continent are shown in Figure 11. The melt Breaking annual data into separate summer months, we extent and melt index are used to quantify the interannual found out that both the melt index and melt extent exhibited variability of the snowmelt. The areal extent of melt for a negative trend in December and January and a positive each year is calculated by summing the areas of all pixels trend in February. However, caution must be exercised in that experienced at least one day of melting in the year. interpreting the trend analysis results. As indicated by small The concept of melt index introduced by Zwally and correlation coefficients (R) and 95% confidence intervals of Fiegles [1994] is used to measure the total melt amount. the fitted slope coefficients (Figure 12), the fitted trend lines The melt index MI for a year is defined in equation (2) are not statistically significant. as [27] Lack of statistical significance in the trend study reflects large interannual variability in melt occurrence. We XN calculated the median and median absolute deviation of the MI ¼ A mdi ð2Þ melt extent and melt index during 1978–2004 to quantify i¼1 the interannual variability. Because of the presence of anomalous melt years, the median is a more robust estimator where A is the areal size of one grid cell, mdi is the number for the central tendency of annual melt magnitude than the of melt days (duration) within a year for the pixel i, and N is mean, and the median absolute deviation (MAD) is a more the total number of melt pixels for the year. As the melt robust measure of variability (dispersion) than the standard index incorporates both the melt extent and duration deviation. The median absolute deviation is defined as the
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Figure 13. Seasonal variation of melt extent for different regions averaged over 1978–2004. The vertical axis is the average area of melt, and the horizontal axis is the sequential number of days. median of the absolute deviations about the median [Barnett 3.5. This year has the least melt extent and duration and Lewis, 1994]. On the basis of the median and MAD, we during the past 25 years. developed an anomaly indicator to identify anomalous melt years: 4.4. Regional Differences in Surface Melt [29] We divided the Antarctic continent into eight regions according to spatial continuity and similarity in seasonal MAD ¼ median MEj median MEj ð3Þ j j melt cycle (Figure 13). The seasonal melt cycle for each region is described by the mean daily melt extent averaged over 1978–2004. As shown in Figure 13, the Antarctic MEj median MEj j Peninsula region has the longest melt season, spanning from AI ¼ ð4Þ j MAD late October until early March of the next year. This region is the first to show melting, the last to show refreezing. In where AIj is the anomaly indicator for the year j, and MEj is contrast, the Ronne-Filchner Ice Shelf region and the Ross the melt extent for the year j. Similarly, we can calculate the Ice Shelf region have the shortest melt season, mainly anomaly indicator in terms of melt index MIj, which occurring in late December and January. The melt season replaces the melt extent MEj in equations (3) and (4). of other regions is primarily December, January, and the [28] We label a year as an extremely high melt year if the early part of February. anomaly indicator AIj for that year is greater than 3.0. [30] For each region, we calculate the annual melt extent Namely, the melt extent MEj or the melt index MIj for that and melt index between 1978 and 2004, and trend lines are year is greater than the median plus 3 times the median also fitted (Figure 14). Most regions, including the Antarctic absolute deviation. We label a year as an extremely low melt Peninsula region, the Amery Ice Shelf region, the Queen year if the anomaly indicator AIj for that year is less than Maud Land region, the Marie Byrd Land region, and the 3.0. In this way, we identified 1982/1983 and 1991/1992 Ross Ice Shelf region, exhibited a negative interannual trend to be extremely high melt years for Antarctica. The greatest in both melt extent and melt index. Only Ronne-Filchner Ice melt extent and duration over 25 year period occurred in Shelf region had a positive trend in both melt extent and 1991/1992 summer. Its melt extent reached 2,315,000 km2, melt index. For the Shackleton Ice shelf region (including and the anomaly indicator of the melt extent is 6.26. The the West Ice Shelf) and the Wilkes Land region, the melt melt index for this austral year is 69,375,625 day km2, index has a negative trend, while the melt extent has a slight and the anomaly indicator calculated from the melt positive trend. Again, the trends identified from the regres- index is 4.72. The austral year 1999/2000 is identified sion analysis are not statistically significant (Figure 14). as the extremely low melt year. The melt extent is only [31] We calculated the median, MAD, and coefficient of 697,500 km2, and the anomaly indicator of melt extent is variation (CV) in terms of melt extent, melt index, and melt
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Figure 14. Interannual variability in melt extent and melt index with fitted trend lines for eight Antarctic regions. R is the correlation coefficient, and CI is the 95% confidence interval of the slope coefficient of the regression line. The regions are shown in Figure 13. duration over the past 25 years for each region (Table 3). identified extremely high and extremely low melt years The CV is defined as the ratio of the MAD to the for each region (Table 3). median. It indicates the relative interannual variability. [32] The most extensive, intensive and consistent melting Using the anomaly indicator defined earlier, we also in Antarctica occurred in the Antarctic Peninsula region. Its
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Table 3. Snowmelt Variability in Different Regions in During 1978–2004 Statistics Peninsula Ronne-Filchner Queen Maud Land Amery Shackleton Wilkes Land Ross Marie Byrd Land Total Melt extent, km2
Median 315625 87500 317188 91563 73750 109375 68125 208750 1277500 ANTARCTICA IN SNOWMELT AL.: ET LIU Median absolute deviation 24688 32813 40625 11250 5313 19688 68125 41406 165625 Coefficient of variation 0.0782 0.3750 0.1281 0.1229 0.0720 0.1800 1.0000 0.1984 0.1296 Melt index, day km2 Median 18357188 658750 6750625 2790625 3045625 2490938 426875 4208125 39265938 Median absolute deviation 2415000 390625 1910625 838750 498750 831875 426875 1962813 6377501 Coefficient of variation 0.1316 0.5930 0.2830 0.3006 0.1638 0.3340 1.0000 0.4664 0.1624 Melt duration, days 6o 20 of 16 Median 59.60 8.52 21.39 28.03 39.48 21.08 6.31 14.23 30.26 Median absolute deviation 5.39 1.36 4.09 4.81 5.80 3.51 2.56 4.82 1.27 Coefficient of variation 0.0905 0.1593 0.1911 0.1715 0.1468 0.1667 0.4062 0.3385 0.0419 Extremely high melt years 1992/1993 1991/1992, 1991/1992, 1982/1983 1989/1990, 1989/1990 1991/1992, 1997/1998 1991/1992, (in order) 1990/1991, 2003/2004, 1991/1992 1983/1984, 1982/1983, 1982/1983 1996/1997 1984/1985 1992/1993, 1979/1980, 1991/1992, 1988/1989 2001/2002 Extremely low melt none 2003/2004, 1999/2000 1999/2000 1999/2000 1999/2000 1983/1984, 2001/2002 1999/2000 years (in order) 1999/2000, 1985/1986, 1993/1994, 1993/1994, 2000/2001 1995/1996, 1998/1999, 1999/2000, 2000/2001 F01003 F01003 LIU ET AL.: SNOWMELT IN ANTARCTICA F01003
Figure 15. Relationship of surface melt index with monthly mean temperature of December and January. melt extent and duration are the highest among the regions ity in melt extent. In the Ronne-Filchner Ice Shelf region, and its interannual variability of melt extent, melt index and the extensive melting occurred in the 1990/1991, 1991/ melt duration is the smallest. The sustained melting is 1992 and 1996/1997 austral summers. In these years, melt attributable to the gentle surface slope on Peninsula ice zones extended along the Ronne Ice Shelf inland up to the shelves and the greater solar radiation intensity at the lower Evans Ice Stream and along the Filchner Ice Shelf up to the latitudes and, possibly, the advection of the warm humid air Slessor Glacier valley. In contrast, no melting or only a very masses from northerly directions. Scambos et al. [2000] small extent of melting occurred during 1993/1994, 1999/ suggested that recent breakups of ice shelves on the 2000, 2000/2001, and 2003/2004 austral summers. In the Antarctic Peninsula [Vaughan and Doake, 1996; MacAyeal Ross Ice Shelf region, the interannual variability in melt et al., 2003] were correlated with the ponded water brought extent is even greater. In the extremely high melt years, about by long and extensive melt on the ice shelves. Three such as 1982/1983 and 1991/1992 austral summers, spa- other regions with steady and extensive melt are the Amery tially continuous melt spread over most of the Ross Ice Ice Shelf region, the Queen Maud Land region, the Shelf. Short-duration melting periodically covered the West Shackleton Ice Shelf region. For these regions, melt extent Antarctic Ice Streams, including the Echelmeyer Ice and duration are also quite large and consistent from year to Stream, MacAyeal Ice Stream, the Binschadler Ice Stream, year. In comparison, the Marie Byrd Land region and the Kamb Ice Stream, and Whillans Ice Stream. However, in Wilkes Land region have a short melt duration. In these two some years the entire Ross Ice Shelf region remained frozen regions, surface melting is scattered along a narrow band of and no melt was detected (Table 3 and Figure 14). Sporadic the coast. Of the eight regions, the Ronne-Filchner Ice Shelf melting was also detected in the glacial valleys in the region and especially the Ross Ice Shelf region have the Transantarctic Mountains up to 85 S, despite the high shortest melt duration and the greatest interannual variabil- latitude and high elevation. Melt patches and their spatial
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Table 4. Statistics for Regression Analysis Between Melt Index and Station Monthly Mean Temperature for Five Major Regions Region Peninsula Queen Maud Amery Shackleton Wilkes Weather station Rothera Syowa Mawson Casey Casey (67.57 S, (69.0 S, (67.60 S, (66.25 S, (66.25 S, 68.13 W) 39.58 E) 62.87 E) 110.53 E) 110.53 E) Temperature (December– January), C Mean 0.92 1.23 0.48 0.44 0.44 Standard deviation 0.58 0.78 0.89 1.03 1.03 Melt index, 106 day km2 Mean 7.027 3.737 1.352 1.423 1.297 Standard deviation 1.188 1.742 0.632 0.360 0.649 R 0.60 0.81 0.84 0.79 0.67 R2 0.36 0.66 0.71 0.62 0.45 Intercept coefficient (106 5.900 5.950 1.602 1.538 1.482 day km2) Slope coefficient, 106 day 1.230 1.804 0.616 0.283 0.420 km2 C 1 Standard error 0.344 0.276 0.087 0.048 0.102 t value 3.58 6.53 7.09 5.87 4.12 p value 0.0015919 0.0000015 0.0000005 0.0000079 0.0004842 95% confidence interval [0.519, 1.942] [1.231, 2.377] [0.435, 0.796] [0.183, 0.383] [0.208, 0. 632] Melt temperature threshold, C 4.8 3.3 2.6 5.4 3.5 growth and decay in the Transantarctic Mountains appear temperatures of December and January as the predictor linked to the locations of storm tracks. The adiabatic (independent) variable. The regression analysis results are warming of Katabatic winds flowing downslope and the shown in Figure 15 and Table 4. A clear positive correlation relatively low albedo of the ice are conducive to local between surface melt index and air temperatures is indicated temperature rise and to intensive solar insolation, which are by high correlation coefficients. The statistical significance capable of supplying sufficient heat to produce temporary of the linear relationship is confirmed at over 99% confi- surface melting. dence level by Student’s t tests (Table 4). Alternating extremely warm and cold summer temperatures correspond 5. Correlation Between Surface Melt and to extremely high and low melt years. As shown in Figure 15, Regional Climate the surface melt index generally increases as summer tem- perature increases. The slope of the regression line indicates [33] The relationship between surface melt occurrence the quantity of surface melt increase by 1 C summer tem- and regional climate was analyzed by examining the corre- perature rise. The melt associated with a warming of 1 Cis lation of surface melt extent and melt index with concordant close to one standard deviation of the natural interannual historical near-surface air temperatures recorded by weather variability of the melt index (Table 4). The 95% confidence stations. In our analysis, four manned stations were used, interval is also computed for the slope coefficient for each and their geographical locations are marked in Figure 13. region. For the five primary melt regions listed in Table 4, we The meteorological data for these stations were obtained infer at the 95% confidence that a 1 C summer temperature from the Web site of the Antarctic Cooperative Research increase is accompanied by a melt index increase between Center (http://aadc-maps.aad.gov.au/aadc/portal/) and the 2,580,000 to 6,130,000 day km2. Web site of the British Antarctic Survey (BAS) (http:// [35] The intercept of the regression equations can be www.antarctica.ac.uk/met/data.html). The monthly mean used to estimate the average monthly temperatures (at the station temperature was used to represent the regional selected station) required for the occurrence of surface melt climate condition. Because of very limited temperature in the corresponding region. Surface melting occurs for the records and strong fluctuations in surface melt, regression Antarctic Peninsula region when the monthly mean temper- analysis was not conducted for the Ross Ice Shelf region, ature during December and January at Rothera station is the Ronne-Filchner Ice Shelf Region and the Marie Byrd above 4.8 C, for the Amery Ice Shelf region when the Land region. For other five regions, we first performed a monthly average temperature at Mawson station is above regression analysis between regional melt index and the 2.6 C, and for the Queen Maud Land region when the selected station air temperatures, respectively, for December monthly average temperature at Syowa station is above and January. A statistically significant linear relationship 3.3 C. The surface melting is negligible for most of regions between melt index and the monthly mean temperature when the monthly mean temperature is lower than 5 C. exists for both December and January. The correlation This finding is similar to that of Zwally and Fiegles [1994]. strength for December is considerably higher than that for [36] Positive correlation is also found between the melt January for all the five regions. extent and the monthly mean temperature for all regions. [34] To reveal the overall relationship between the sum- However, the strength of correlation is not strong. The mer surface melt and near-surface air temperature, a linear correlation coefficient R is below 0.5 for most of regions, regression equation is fitted for each of the five regions except for the Amery Ice Shelf region (0.6) and the Queen using the average December and January melt index as the Maud Land region (0.53). The weaker linear correlation response variable and the average of the monthly mean between the melt extent and the monthly mean temperature
18 of 20 F01003 LIU ET AL.: SNOWMELT IN ANTARCTICA F01003 is probably the result of topographic effects. With increas- situations in the Ross Ice Shelf region, the Ronne-Filchner ingly higher temperature, the spread of melt further inland Ice Shelf region, and the Wilkes Land region. These three would occur at a slower rate than estimated by linear regions have the greatest interannual variability in melt regression models due to the relatively high surface slope extent. Other regions, especially the Antarctic Peninsula at the coastal zone of the continental ice sheet. region, have relatively stable and consistent melt occurrence from year to year. [41] Spatially, snowmelt mainly occurred on the Antarctic 6. Discussion and Conclusions peripheral ice shelves, and most of the grounded ice sheet [37] In this research, we derived the melt extent, melt remains frozen throughout all the seasons. In most years, onset date, melt end date and melt duration during 1978– melt occurred only over 9–12% of the continent. In the past 2004 by applying our wavelet transform based method. The 25 years, about 17% of the Antarctic snow surface in total quantitative information about snowmelt extent and dura- has experienced surface melting. This is in strong contrast to tion will be useful input for modeling and predicting climate Greenland where melt normally occurred over 40% of the and sea level changes. ice sheet each year, and cumulatively about 66% of the [38] Our analysis shows that surface melt on the Antarctic Greenland ice sheet has experienced surface melt during ice sheet and its surrounding ice shelves are linked to 1979–1991 [Mote et al., 1993]. The limited melt extent on regional climate changes. The strong positive correlation the Antarctic ice sheet is mainly due to its high latitude and with the near-surface air temperature suggests that the melt relatively high surface slope in the coastal zone, which index can serve as a diagnostic indicator of changing limits the expansion of melt zones toward the interior of the climate as recommended by Zwally and Fiegles [1994]. ice sheet. Nevertheless, it should be noted that, despite the Although the observed melting is correlated with regional small fraction being melt zone, the absolute melt extent in air temperatures overall, some scattered short-duration melt Antarctica is over two times as large as in Greenland. patches on the Ross Ice Shelf and in the Transantarctic [42] Our numerical analysis demonstrated that the lati- Mountains cannot be explained by synoptic scale climate tude, surface elevation, and proximity to the ocean are three conditions. Melting associated with warm synoptic-scale primary factors shaping the spatial pattern of the melt meteorological conditions is commonly more uniform over occurrence and duration in Antarctica. With increasing larger areas at the same surface elevation and progresses in latitude, elevation and/or distance to the coast, the melt terms of surface elevation and distance to the coast. The onset date is generally later, melt end date is generally scattered and spatially discontinuous melt pattern in these earlier, and duration is generally longer. Seasonally, surface regions is likely affected by sporadic katabatic wind heating melt in Antarctic continent takes place primarily during of the surface. December, January, and February and peaks in the early [39] Examination of melt extent and melt index time January. series reveals a negative interannual trend over the past [43] Surface melting is closely related to the stability 25 years in Antarctica. This finding compares favorably to of the Antarctic glacial system and global sea level the slight cooling trend derived by Comiso [2000] with the changes. The Larsen Ice Shelf, the George VI Ice Shelf AVHHR infrared data from 1979 to 1998. At a regional and the Wilkins Ice Shelf on the Antarctic Peninsula scale, our data analysis indicates that only the Ronne- have experienced the most intensive surface melting in the Filtcher Ice Shelf region had a positive trend, and all other past 25 years. Scambos et al. [2000] and MacAyeal et al. regions exhibited a negative trend in surface melt. These [2003] show that ponding water resulting from intensive regional trends are in agreement to the findings of Zwally melting during summers provides the conditions for ice and Fiegles [1994] (9 years of SMMR data) and of Torinesi shelves to break up. Our melt analysis indicates that melting et al. [2003] (18 years of SMMR and SSM/I data). Through on the Shackleton Ice Shelf, West Ice Shelf, and lower part of examining passive microwave data from 1978 to 1991, the Amery Ice Shelf, the ice shelf along the Princess Ridley [1993] concluded a positive trend in melt duration Ragnhild Coast in Queen Maud Land was also quite inten- for three small ice shelves on the Antarctic Peninsula with a sive and persistent in the past 25 years. If the surface melting confidence level of lower than 85%. His result is likely is further intensified, these ice shelves may be susceptible to biased by a short period of the surface melt increase during similar disintegration and therefore should be closely mon- 1985–1990. For the Antarctic Peninsula the slight negative itored in the future. In addition, we observed that extensive trend over the past 25 years derived from our study agrees surface melting periodically occurred over the Ross Ice with that of Comiso [2000], who revealed a similar trend Shelf, the Ronne-Filchner Ice Shelf, the West Antarctic ice during 1979–1998 for most of the Peninsula at the pixel streams, and outlet glaciers in the Transantarctic Mountains, level. Like previous research [Zwally and Fiegles, 1994; despite the high latitude. Because of the very gentle surface Torinesi et al., 2003] the negative melting trends inferred slope, the Ross Ice Shelf and the Ronne-Filchner Ice Shelf from our least squares regressions are not statistically are most responsive to extremely warm summers. Additional significant. increases in melt extent on these two giant ice shelves [40] The most prominent feature in the surface melt time associated with warming summers would be much greater series over the past 25 years is a large degree of year-to-year than other regions. The West Antarctic ice streams and fluctuations. Alternating high and low melting years rule out valley glaciers in the Transantarctic Mountains are the most reliable statistical inference of melt trends. With a sensible dynamic and fast moving in the Antarctic continent. The anomaly indicator, we identified the anomalous melting Ross Ice Shelf is the main outlet for these fast moving ice years in Antarctica. The overall interannual variability in streams. The impacts of periodic surface melting on the melt extent in Antarctica is largely dictated by the melt dynamic nature of these ice streams and on the stability of
19 of 20 F01003 LIU ET AL.: SNOWMELT IN ANTARCTICA F01003 the Ross Ice Shelf are largely unknown at present. Given Liu, H., and K. C. Jezek (2004), A complete high-resolution coastline the ongoing debate concerning global climate and uncer- of Antarctica extracted from orthorectified Radarsat SAR imagery, Photogramm. Eng. Remote Sens., 70(5), 605–616. tain trend inference of surface melting at present, contin- Liu, H., K. Jezek, and B. Li (1999), Development of Antarctic digital ued monitoring of surface melt of the Antarctic continent elevation model by integrating cartographic and remotely sensed data: is of particular value for a reliable evaluation of the A GIS-based approach, J. Geophys. Res., 104(B10), 23,199–23,213. Liu, H., L. Wang, and K. Jezek (2005), Wavelet-transform based edge magnitude and direction of long-term melting and climate detection approach to derivation of snowmelt onset, end and duration change trends. from satellite passive microwave measurements, Int. J. Remote Sens., 26(21), 4639–4660. MacAyeal, D. R., T. A. Scambos, C. L. Hulbe, and M. A. Fahnestock [44] Acknowledgments. This work was supported by NASA grant (2003), Catastrophic ice-shelf break-up by an ice-shelf-fragment-capsize NAG5-10112 and NSF grant 0126149. The authors want to thank the mechanism, J. Glaciol., 49(164), 22–36. National Snow and Ice Data Center (NSIDC) in Boulder, Colorado, for Ma¨tzler, C. (2000), A simple snowpack/cloud reflectance and transmittance providing the SMMR and SSM/I EASE-Grid brightness temperature data model from microwave to ultraviolet: The ice-lamella pack, J. Glaciol., for this research project. 46, 20–24. Mercer, J. H. (1978), West Antarctic ice sheet and CO2 greenhouse effect: A threat of disaster, Nature, 271(5643), 321–325. References Mote, T. L., and M. R. Anderson (1995), Variations in snowpack melt on Abdalati, W., and K. 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Bolzan (2001), An edge detection technique to estimate melt duration, season and melt extent on the Greenland ice sheet using passive microwave data, Geophys. Res. Lett., 28(18), 3497–3500. K. C. Jezek, Byrd Polar Research Center, Ohio State University, Knowles, K., E. Njoku, R. Armstrong, and M. J. Brodzik (2002), Nimbus-7 Columbus, OH 43210, USA. ([email protected]) SMMR Pathfinder Daily EASE-Grid Brightness Temperatures, http:// H. Liu and L. Wang, Department of Geography, Texas A&M University, www-nsidc.colorado.edu/data/nsidc-0071.html, Natl. Snow and Ice Data College Station, TX 77843, USA. ([email protected]; wanglei@geog. Cent., Boulder, Colo. tamu.edu)
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