Geological Survey of Denmark and Greenland Bulletin 33, 2015, 57-60 57 Greenland ice sheet melt area from MODIS (2000–2014) Robert S. Fausto, Dirk van As, Jens A. Antoft, Jason E. Box, William Colgan and the PROMICE project team* Th e Greenland ice sheet is an excellent observatory for global climate change. Meltwater from the 1.8 million km2 large ice sheet infl uences oceanic temperature and salinity, nutrient fl uxes and global sea level (IPCC 2013). Surface refl ectivity is a key driver of surface melt rates (Box et al. 2012). Mapping of diff erent ice-sheet surface types provides a clear indicator of where changes in ice-sheet surface refl ectivity are most prominent. Here, we present an updated version of a sur- face classifi cation algorithm that utilises NASA’s Moderate- resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite to systematically monitor ice-sheet surface melt (Fausto et al. 2007). Our aim is to determine the areal extent of three surface types over the 2000–2014 period: gla- cier ice, melting snow (including percolation areas) and dry snow (Cuff ey & Paterson 2010). Monthly 1 km2 resolution surface-type grids can be downloaded via the CryoClim in- ternet portal (www.cryoclim.net). In this report, we briefl y describe the updated classifi cation algorithm, validation of surface types and inter-annual variability in surface types. Classification algorithm Th e algorithm uses normalised thresholds (Th ) from cali- brated radiances (MOD021KM) between the near-infrared band 5 (1230–1250 nm) and the visible band 10 (483– 493  nm). Th is updated classifi cation improves on Fausto et al. (2007) by implementing new surface type thresh- olds: Th dry snow≤0.86, 0.86 0.7 or T < –7°C), melting snow (0.7 > α > 0.55), and glacier ice (α < 0.55; Cuff ey & Paterson 2010). Employing the T criterion acknowledges the infl uence of diurnal cycles at the beginning of the melt season. As a validation example, the in situ albedo and nearest-pixel classifi cation at KAN_L in 2009 are presented in Figs 2 and 3, including a visual com- parison with the passive microwave melt area product (PMP) by Mote (2007). Th e KAN_L station, located c. 10 km from the ice sheet margin at 680 m elevation, transitions through all three sur- face types during the melt season, from dry snow to melting snow to glacier ice. Relative to the 2000–2014 period, the 2009 surface melt was normal in west Greenland, with maxi- mum melt areal extent in August. At KAN_L, the surface melted from May to August, with a daily mean albedo gen- erally between 0.5 and 0.6 (Fig. 2). Th e algorithm accuracy for the KAN_L site may be assessed by an error matrix (Ta- ble 1). Th e diagonal represents successful classifi cations, the total number represents all classifi cations and the ratio be- tween the sum of the diagonal and total is the accuracy. Th e algorithm yields 79% successful classifi cations at KAN_L, with an overall accuracy of 71%. Th e classifi cation algorithm performs best in the south and worst in the north, with ac- curacies of 87% (NUK_L) and 61% (KPC_U), respectively. Figure 2 illustrates changes in surface type during summer 2009, between 15 May and 14 September, according to the AWS data; all but two classifi cations were successful. Results and discussion Th e melt area from this algorithm and the PMP of Mote (2007), illustrated in Fig. 3 for 12 July 2012, are consistent with the reported melt area by Nghiem et al. (2012), who documented that 98.6% of the ice-sheet surface had melting. Th e GST also demonstrates close visual correspondence with PMP for the 2000–2014 MODIS period (Fig. 4). In Fig. 4 we have plotted the yearly maximum values of the GST, GSTmax and GSTmin products, as well as the PMP maxi- mum extent of Greenland melt area. Th e increasing trends of GSTmax and GSTmin indicate a rising frequency of melt events and increasing summer melt, which is corroborated by the PMP which is comparable with GSTmax. Th e trend for PMP between 1979 and 2000 and 2000 and 2012 are almost identical making the PMP and GSTmax trends comparable. Overall, an expansion of the melt area to higher elevations is apparent (Fig. 4). Fausto et al. (2007) suggested that a sub-monthly GST product is non-optimal, because missing data due to cloud cover is the primary problem in determining the melt area. When trying to characterise all of Greenland, Hall et al. (2012) also found clear-sky, day-count problems, and also suggested that a sub-monthly product would have signifi cant uncertainty. However, uncertainties associated with the dif- A lb ed o C la ss ifi ca ti o n 3 2 1 1 0.8 0.6 0.4 0.2 0 91 111 131 151 171 191 211 231 251 271 91 111 131 151 171 191 211 231 251 271 Day of year Day of year Fig. 2. Daily GST classification for 2009 of the K AN_L pixel and albedo measured at the K AN_L automatic weather station. 1: glacier ice. 2: melt- ing snow. 3: dry snow. Table 1. Error matrix for the assessment of KAN_L GST\AWS* Glacier ice Melting snow Dry snow Total Glacier ice 32 14 0 46 Melting snow 3 33 1 37 Dry snow 0 5 21 26 * GST: Greenland surface type AWS: automatic weather station 59 ferent surface types are assessed with the number of observa- tions and standard deviation for each cloud-free pixel of the GST product (Fausto et al. 2007). Th e MODIS data have the advantages of high spatial resolution (1 km2), pan-ice sheet coverage and quasi-daily temporal coverage, while the footprints of the in situ measurements are small. Th e AWS surface type classifi cations are therefore not an ideal ground truth for the surface classifi cation. Furthermore, whereas both GST and PMP melt area products can give daily re- sults, the PMP surface microwave emittance originates not only from the surface but the top metre of the snow or fi rn, and is infl uenced by the water content in the snow during the previous days (Mote 2007). MODIS classifi cation is sensi- tive to cloud cover, but the spatial resolution of PMP is 625 times coarser than GST. During the melt period, exposed glacier ice in the ablation zone can have sub-zero tempera- tures. Such areas are included in the melting area, because the algorithm only makes use of the visual and near-infrared spectrum. Hence the melt area that we map might be more representative of the cumulative melt area during the melt period. However, if exposed, glacier ice in the ablation zone is covered by snow it will be mapped as non-melting areas. An August anomaly in monthly GST is evident during the 2010–2014 period. All August images indicate a noisy melting snow classifi cation in the northern ice sheet (not shown), which is most likely due to false classifi cation. How- ever, anomalous, high concentrations of dust or reddish ma- terial have been observed on the ice sheet during recent late summers (Dumont et al. 2014). Increasing dust concentra- tions are problematic for the fi xed threshold algorithm we employ, because of enhanced absorption in near infrared wavelengths. Despite this possible biased source, an increas- ing trend in the melt area for the MODIS and PMP periods (Fig. 4) is consistent with increasing Greenland mass loss due to surface processes (Tedesco et al. 2013). Both independent, remotely sensed observations (Hall et al. 2012) and in situ observations (McGrath et al. 2013) show that the Greenland melt area is expanding to higher elevations. Fig. 4. Yearly maximum melt area values and trends according to Green- land surface type (GST), maximum melt extent (GSTmax), minimum melt extent (GSTmin) and passive microwave melt area product (PMP). 60°W 30°W 60°N 75°N A B No data/clouds Melting snow Dry snow Glacier ice Surface melting Non-melting areas Fig. 3. Melt area on the Greenland ice sheet for 12 July 2012 A: Passive microwave melt area product (PMP). B: Greenland surface type classification. M e lt a re a ( k m 2 ) 1800 1600 1400 1200 1000 800 600 400 200 0 1979 1986 1993 2000 2007 2014 Time (year) GST GSTmax GSTmin PMP y = 9.0x − 17533 y = 35.3x − 69982 y = 3.3x − 6459 y = 22.1x − 43255 6060 Conclusions Th e MODIS data can yield daily, automated classifi cations of the Greenland ice sheet into bare ice, melting and dry snow areas. Validation indicates that the surface classes are useful as ice-sheet climate indicators. Th e surface-type products are complementary to existing ice-surface temperature (Hall et al. 2012) and melt-area (Mote 2007) products. Acknowledgements Th e Programme for Monitoring of the Greenland Ice Sheet (PROMICE) is funded by the Geological Survey of Denmark and Greenland (GEUS) and the Danish Ministry of Climate, Energ y and Building under Dan- ish Cooperation for Environment in the Arctic (DANCEA), and is con- ducted in collaboration with the National Space Institute (DTU Space) and Asiaq (Greenland Survey). Th e NUK and K AN stations were/are (co-)funded by the Greenland Climate Research Centre (GCRC) and the Greenland Analogue Project (GAP), respectively. Th anks to T. Mote for making the passive microwave product (PMP) available. Th is study was funded by DK ESA-PRODEX under the CryoClim project. References Ahlstrøm, A.P. et al. 2008: A new programme for monitoring the mass loss of the Greenland ice sheet. Geological Survey of Denmark and Green- land Bulletin 15, 61–64. 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Hall, D.K., Comiso, J.C., DiGirolamo, N.E., Shuman, C.A., Key, J.R. & Koenig, L.S. 2012: A satellite-derived climate-quality data record of the clear-sky surface temperature of the Greenland ice sheet. Journal of Climate 25, 4785–4798. IPCC 2013: Climate Change 2013: Th e physical science basis. Working Group I contribution to the Fift h Assessment Report of the Intergov- ernmental Panel on Climate Change, 1535 pp. Cambridge University Press. McGrath, D., Colgan, W., Bayou, N., Muto, A. & Steff en, K. 2013: Recent warming at Summit, Greenland: global context and implications. Geo- physical Research Letters 40, 2091–2096. Mote, T.L. 2007: Greenland surface melt trends 1973–2007: evidence of a large increase in 2007. Geophysical Research Letters 34, L22507. Nghiem, S.V., Hall, D.K., Mote, T.L., Tedesco, M., Albert, M.R., Keegan, K., Shuman, C.A., DiGirolamo, N.E. & Neumann, G. 2012: Th e ex- treme melt across the Greenland ice sheet in 2012. Geophysical Re- search Letters 39, L20502. Tedesco, M., Fettweis, X., Mote, T., Wahr, J., Alexander, P., Box, J.E. & Wouters, B. 2013: Evidence and analysis of 2012 Greenland records from spaceborne observations, a regional climate model and reanalysis data. Th e Cryosphere 7, 615–630. Authors’ address Geological Survey of Denmark and Greenland, Øster Voldgade 10, DK-1350 Copenhagen K, Denmark. E-mail: rsf@geus.dk