remote sensing

Article A Satellite-Based Climatology of Wind-Induced Surface Temperature Anomalies for the Antarctic

Günther Heinemann * , Lukas Glaw and Sascha Willmes

Environmental Meteorology, University of Trier, 54286 Trier, Germany * Correspondence: [email protected]; Tel.: +49-651-201-4623



Received: 15 May 2019; Accepted: 26 June 2019; Published: 28 June 2019


Abstract: It is well-known that katabatic winds can be detected as warm signatures in the surface temperature over the slopes of the Antarctic ice sheets. For appropriate synoptic forcing and/or topographic channeling, katabatic surges occur, which result in warm signatures also over adjacent ice shelves. Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) data are used to detect warm signatures over the Antarctic for the winter periods 2002–2017. In addition, high-resolution (5 km) regional climate model data is used for the years of 2002 to 2016. We present a case study and a climatology of wind-induced IST anomalies for the Ross Ice Shelf and the eastern Weddell Sea. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. Katabatic surges represent a strong climatological signal with a mean warm anomaly of more than 5K on more than 120 days per winter for the Byrd Glacier and the Nimrod Glacier on the Ross Ice Shelf. The mean anomaly for the Brunt Ice Shelf is weaker, and exceeds 5K on about 70 days per winter. Model simulations of the IST are compared to the MODIS IST, and show a very good agreement. The model data show that the near-surface stability is a better measure for the response to the wind than the IST itself.

Keywords: katabatic winds; MODIS ice surface temperatures; ice shelves; Antarctic

1. Introduction The near-surface wind field over the Antarctic ice sheet (Figure1a) is dominated by katabatic winds in the stably-stratified boundary layer [1–3], which is a climatological feature for the Antarctic continent [4]. Katabatic winds may rise up to gale force [5,6], and are important for the exchange of energy and momentum between the atmosphere and the underlying surface, but also for field work. While the classical katabatic (downslope) winds develop only over the slopes of the ice sheet, they can extend also over adjacent (flat) ice shelves, if appropriate synoptic forcing and/or topographic channeling is present. These are the so-called katabatic surges. Despite the fact that the katabatic wind is a negatively buoyant air layer, the strong wind associated with katabatic winds and surges leads to a warm signature of the surface temperature. The reason for this behavior is the strong turbulence in katabatic winds [7], which destroys or weakens the surface inversion compared to the weak-wind surroundings, where the surface inversion leads to much colder temperatures (Figure2). This leads to a warming close to the surface, while the katabatic wind layer as a whole is still negatively buoyant. Thus the regions of strong wind over ice sheets and ice shelves can be detected as warm signatures in satellite thermal infrared (TIR) imagery, particularly in winter, when the surface inversions are at their strongest (see [8]).

Remote Sens. 2019, 11, 1539; doi:10.3390/rs11131539 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1539 2 of 17 Remote Sens. 2019, 11, x FOR PEER REVIEW 2 of 17

(a)

(b) (c)

FigureFigure 1. 1. ((aa)) MapMap of the Antarctic with with topography topography (isolin (isolines).es). Permanent Permanent research research stations stations are are marked marked byby red red squares. squares. IceIce shelfshelf areasareas are light blue (FRIS = Filchner-RonneFilchner-Ronne Ice Ice Shelf, Shelf, RIS RIS = =RossRoss Ice Ice Shelf). Shelf). TheThe blue blue rectangles rectangles markmark thethe focusfocus areasareas ofof thisthis study. Data source: RAMP2 RAMP2 ([9]). ([9]). Part Part (b (b):): As As (a (),a ),but but forfor the the study study areaarea inin thethe easterneastern Weddell Sea with SWG SWG == Stancomb-WillsStancomb-Wills Glacier, Glacier, LI LI = =LyddanLyddan Island, Island, GGGG= = GoldsmithGoldsmith Glacier, SG = Slessor Glacier. Glacier. Part Part (c (c):): As As (a (a),), but but for for the the study study area area of of the the Ross Ross Ice Ice ShelfShelf with with BGBG= = ByrdByrd Glacier,Glacier, NG = NimrodNimrod Glacier. Glacier.

TheseThese warm warm signatures signatures in in TIR TIR data data were were first first explained explained by by [10 [10]] and and further further investigated investigated by by [11 [11––14]. They14]. They can also can be also found be found in high-resolution in high-resolution numerical numerical model simulationsmodel simulations [15]. An [15]. example An example of katabatic of warmkatabatic signatures warm signatures including aincluding katabatic a surgekatabatic on 2 surg Auguste on 20152 August is shown 2015 inis shown Figure3 in for Figure the area 3 for of the the easternarea of Weddellthe eastern Sea Weddell (compare Sea Figure (compare1b). The Figure katabatic 1b). The surge katabatic extends surge from extends the Stancomb-Wills from the Stancomb- Glacier overWills the Glacier Brunt over Ice Shelf. the Brunt Warm Ice katabatic Shelf. Warm signatures katabati arec presentsignatures over are the present slopes over of Coats the slopes Land andof Coats in the valleyLand associatedand in the withvalley the associated Goldsmith with Glacier. the Goldsm Underith favorable Glacier. synoptic Under favorable conditions, synoptic katabatic conditions, surges can travelkatabatic over surges long distances can travel over over the long flat distances ice shelves over and the can flat lead ice to shelves the formation and can of lead coastal to the polynyas formation [16 ]. of coastal polynyas [16].

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Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 17 Katabatic flows are also considered to contribute to the formation of mesocyclones in the coastal regionsKatabatic [1,12,17]. flows For are example, also considered [18] relate to the contribute formation to theof a formation mesocyclone of mesocyclones near Halley to in a the katabatic coastal Katabatic flows are also considered to contribute to the formation of mesocyclones in the coastal regionssurge at [Stancomb-Wills1,12,17]. For example, Glacier. [18 ] relate the formation of a mesocyclone near Halley to a katabatic regions [1,12,17]. For example, [18] relate the formation of a mesocyclone near Halley to a katabatic surge at Stancomb-Wills Glacier. surge at Stancomb-Wills Glacier.

Figure 2.2. Schematics of the vertical temperature structur structuree in the katabatic wind (black line) and a weak-wind ice surface (blue line). Figureweak-wind 2. Schematics ice surface of (blue the vertical line). temperature structure in the katabatic wind (black line) and a weak-wind ice surface (blue line).

Figure 3.3. Example of katabatickatabatic warmwarm signaturessignatures (Moderate(Moderate ResolutionResolution ImagingImaging SpectroradiometerSpectroradiometer (MODIS)(MODIS) IRIR image image 2 2 August August 2015, 2015, warm warm= dark, = dark, data da source:ta source: NASA NASA Worldview) Worldview) and topographyand topography from Figure 3. Example of katabatic warm signatures (Moderate Resolution Imaging Spectroradiometer Cryosat-2from Cryosat-2 data [data19]). [19]). (MODIS) IR image 2 August 2015, warm = dark, data source: NASA Worldview) and topography fromIn the Cryosat-2 present data study, [19]). Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface In the present study, Moderate Resolution Imaging Spectroradiometer (MODIS) ice surface temperature (IST) data are used for the detection of warm signatures over the Antarctic ice sheet and temperature (IST) data are used for the detection of warm signatures over the Antarctic ice sheet and the iceIn shelves.the present MODIS study, data Moderate are available Resolution since 2002Imaging and haveSpectroradiometer been used for studies(MODIS) of theice surface the ice shelves. MODIS data are available since 2002 and have been used for studies of the surface temperaturetemperature (IST) in the data Antarctic are used by e.g.,for the [20 detection–22]. [23] of used warm MODIS signatures IST for over the assessmentthe Antarctic of ice atmospheric sheet and temperature in the Antarctic by e.g., [20–22]. [23] used MODIS IST for the assessment of atmospheric the ice shelves. MODIS data are available since 2002 and have been used for studies of the surface reanalyses. Here we present an analysis of wind wind-induced-induced warming for the the winter winter periods periods 2002–2017. 2002–2017. temperature in the Antarctic by e.g., [20–22]. [23] used MODIS IST for the assessment of atmospheric The focus lies on a climatology of wind-induced IST anomalies for the Ross Ice Shelf (Figure 1c) and reanalyses. Here we present an analysis of wind-induced warming for the winter periods 2002–2017. the eastern Weddell Sea (Figure 1b). The focus lies on a climatology of wind-induced IST anomalies for the Ross Ice Shelf (Figure 1c) and the eastern Weddell Sea (Figure 1b).

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TheRemote focus Sens. lies2019, on 11, ax climatologyFOR PEER REVIEW of wind-induced IST anomalies for the Ross Ice Shelf (Figure1c)4 and of 17 the eastern Weddell Sea (Figure1b). 2. Materials and Methods 2. Materials and Methods 2.1. MODIS Surface Temperature Products 2.1. MODIS Surface Temperature Products We use the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature We use the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature product MOD11 (Terra satellite) and MYD11 (Aqua satellite), in the following referred to as MxD11 product MOD11 (Terra satellite) and MYD11 (Aqua satellite), in the following referred to as MxD11 [24], [24], and the ice surface temperature product MOD/MYD29 (Terra/Aqua), in the following referred and the ice surface temperature product MOD/MYD29 (Terra/Aqua), in the following referred to as to as MxD29 [25]. Both products are used as collection 6 Level-2 swath data, and have a spatial MxD29 [25]. Both products are used as collection 6 Level-2 swath data, and have a spatial resolution of resolution of 1 km at nadir. All swaths for the austral winter seasons (April to September) from 2002 1 km at nadir. All swaths for the austral winter seasons (April to September) from 2002 to 2017 were to 2017 were used south of 50°S, resulting in ca. 160 swaths per day. used south of 50◦S, resulting in ca. 160 swaths per day. In a first step, the data of the surface temperature from MxD11 and MxD29 were merged in order In a first step, the data of the surface temperature from MxD11 and MxD29 were merged in order to cover ice sheet and ice shelf regions seamlessly using the same swaths from each product. Ice to cover ice sheet and ice shelf regions seamlessly using the same swaths from each product. Ice surface surface temperature (IST) data from each swath were then projected to a polar stereographic grid temperature (IST) data from each swath were then projected to a polar stereographic grid with a pixel with a pixel resolution of 1.5 km for the whole of the Antarctic. A daily average was computed for resolution of 1.5 km for the whole of the Antarctic. A daily average was computed for each pixel using each pixel using the median of all available IST values in the daily stack. Figure 4 shows the mean the median of all available IST values in the daily stack. Figure4 shows the mean daily coverage daily coverage of cloud-free pixels for the whole Antarctic. The highest frequency of cloud-free pixels of cloud-free pixels for the whole Antarctic. The highest frequency of cloud-free pixels can be seen can be seen over the ice sheet for surface elevations higher than 2000 m. Since synoptic weather over the ice sheet for surface elevations higher than 2000 m. Since synoptic weather systems are often systems are often blocked by the high topography, frontal systems associated with clouds are rare blocked by the high topography, frontal systems associated with clouds are rare over the Antarctic over the Antarctic plateau region [26]. Although the MxD11 and MxD29 data were merged plateau region [26]. Although the MxD11 and MxD29 data were merged seamlessly, a difference in seamlessly, a difference in the cloud detection of these products becomes apparent. MxD11 seems to the cloud detection of these products becomes apparent. MxD11 seems to reject more pixels because reject more pixels because of cloudiness, leading to a jump in the frequency of cloud-free pixels of cloudiness, leading to a jump in the frequency of cloud-free pixels between ice shelves and the between ice shelves and the continental slopes. However, the mean coverage in the latter regions is continental slopes. However, the mean coverage in the latter regions is at least between 2–4 per day, at least between 2–4 per day, i.e., there is a good spatial coverage for monthly means. In order to i.e., there is a good spatial coverage for monthly means. In order to minimize errors caused by the minimize errors caused by the cloud detection in the different data sets, a minimum requirement of cloud detection in the different data sets, a minimum requirement of at least three observations per at least three observations per pixel per day was used. pixel per day was used.

Figure 4. Mean daily coverage 2002–2017 of cloud-free pixelspixels (topography asas isolinesisolines everyevery 500500 m).m).

Monthly means of the IST were calculated from the daily averages. In order to quantify the katabatic warm signatures, an anomaly was computed as the temperature difference between a region affected by the katabatic wind and a reference region, where no or only weak warming effects due to katabatic winds or surges are present.

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Monthly means of the IST were calculated from the daily averages. In order to quantify the katabatic warm signatures, an anomaly was computed as the temperature difference between a region affected by the katabatic wind and a reference region, where no or only weak warming effects due to Remote Sens. 2019, 11, x FOR PEER REVIEW 5 of 17 katabatic winds or surges are present. TheThe dailydaily anomalyanomaly forfor eacheach pixel was calculated based on the didifferencefference betweenbetween thethe dailydaily valuevalue andand thethe monthlymonthly meanmean surface temperature in the reference region and then averaged as monthly, seasonalseasonal oror overalloverall meansmeans (for(for detailsdetails seesee SectionSection3 3).). 2.2. Model Data 2.2. Model Data The non-hydrostatic regional climate model COSMO-CLM (CCLM, [27]) was used to produce a The non-hydrostatic regional climate model COSMO-CLM (CCLM, [27]) was used to produce a high-resolution atmospheric data set for the years 2002 to 2016. The model is nested in ERA-Interim high-resolution atmospheric data set for the years 2002 to 2016. The model is nested in ERA-Interim data [28] and uses high-resolution sea-ice concentration data [29] on a daily basis to keep the hindcast data [28] and uses high-resolution sea-ice concentration data [29] on a daily basis to keep the hindcast close to reality. The model was adapted to the polar regions by the implementation of a thermodynamic close to reality. The model was adapted to the polar regions by the implementation of a sea-ice model [30–32]. The model was run for the Weddell Sea region with resolutions of 15 and 5 km thermodynamic sea-ice model [30–32]. The model was run for the Weddell Sea region with using a similar set-up as in [15]. Data is available every hour. Data of the model with 5 km resolution resolutions of 15 and 5 km using a similar set-up as in [15]. Data is available every hour. Data of the are used in this study. model with 5 km resolution are used in this study. 3. Results 3. Results 3.1. Overview 3.1. Overview Figure5 shows the mean cloud-free surface temperature for all winters. For this composite, a total of aboutFigure 460,000 5 shows swaths the havemean beencloud-free processed. surface A temper lot of detailsature for can all be winters. seen, e.g., For thethis cold composite, signal of a icebergtotal of A23Aabout in460,000 the Weddell swaths Sea have north been of processed. Berkner Island, A lot warm of details signals can in be regions seen, e.g., of reoccurring the cold signal coastal of polynyasiceberg A23A (such in as the in TerraWeddell Nova Sea Bay) north and of warm Berkner katabatic Island, signals warm (warm signals thermal in regions belt [of13 ])reoccurring almost in allcoastal coastal polynyas regions. (such In the as following, in Terra Nova we will Bay) focus and on warm the regions katabatic of thesignals Ross (warm Ice Shelf thermal (Figure belt1c) and[13]) thealmost eastern in all Weddell coastal regions. Sea (Figure In the1b). following, we will focus on the regions of the Ross Ice Shelf (Figure 1c) and the eastern Weddell Sea (Figure 1b).

FigureFigure 5.5. MeanMean cloud-freecloud-free surfacesurface temperaturetemperature forfor winterwinter (April–September)(April–September) 20022002 toto 20172017 (isolines(isolines= = topographytopography everyevery 500500 m,m, temperaturestemperatures outsideoutside thethe rangerange inin grey).grey).

3.2. Ross Ice shelf (RIS) The mean surface temperature anomaly for the RIS for all winters is shown in Figure 6. The method for computing the anomalies is described in Section 2, and the reference area is marked in Figure 6.

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3.2. Ross Ice shelf (RIS) The mean surface temperature anomaly for the RIS for all winters is shown in Figure6. The method for computing the anomalies is described in Section2, and the reference area is marked in

FigureRemote Sens.6. 2019, 11, x FOR PEER REVIEW 6 of 17 The positions of strong warm anomalies agree with the outlets of the main glaciers, particularly with theThe Byrdpositions Glacier of strong and the warm Nimrod anomalies Glacier agree (see Figurewith the1c). outlets The katabatic of the main warm glaciers, signal particularly associated withwith thethe outflowByrd Glacier of these and regions the Nimrod extends Glacier more than (see 100Figure km over1c). The the katabatic ice shelf, andwarm for signal the Byrd associated Glacier itwith even the extends outflow to of thethese front regions of the extends RIS. This more may than also 100 reflectkm over the the fact ice thatshelf, under and for suitable the Byrd synoptic Glacier conditions,it even extends katabatic to the surges front are of foundthe RIS. as flowsThis may parallel also to reflect the Transantarctic the fact that Mountains, under suitable which synoptic are fed alsoconditions, by the katabatic katabatic outflow surges ofare both found the Byrdas flows Glacier parallel and Nimrod to the Transantarctic Glacier [12]. Mountains, which are fed also

FigureFigure 6.6. MeanMean surfacesurface temperaturetemperature anomalyanomaly forfor thethe RISRIS forfor winterwinter (April–September)(April–September) 20022002 toto 20172017 (isolines(isolines= = topographytopography every every 500 500 m). m). The The reference reference area area is markedis marked by by the the box box over over the icethe shelf,ice shelf, the exitthe areasexit areas for both for both the Byrd the Byrd and Nimrodand Nimrod glaciers glacie (seers Figure(see Figure1c) are 1c) marked are marked by crosses. by crosses.

TheThe surfacesurface temperaturetemperature anomalyanomaly distributiondistribution for all winterswinters forfor thethe exitexit regionsregions ofof thethe ByrdByrd GlacierGlacier andand thethe NimrodNimrod GlacierGlacier isis presentedpresented inin FigureFigure7 7.. ForFor thethe ByrdByrd Glacier,Glacier, the the highest highest frequencies frequencies areare foundfound forfor anomaliesanomalies betweenbetween 8–15K,8–15K, butbut 20K20K areare alsoalso exceeded.exceeded. For the Nimrod Glacier, thethe peakpeak isis atat approximately the the same same values values,, but but the the distribution distribution is isbroader broader and and values values of more of more than than 25K 25K are arefound found as well. as well. Anomalies Anomalies exceeding exceeding 5K 5K are are found found on on 146 146 and and 123 123 days days per per winter winter for Byrd andand NimrodNimrod Glacier,Glacier, respectively.respectively. ThisThis correspondscorresponds toto 80%80% ofof allall daysdays andand 82%82% ofof observedobserved daysdays forfor thethe ByrdByrd GlacierGlacier andand 67%67% ofof allall daysdays andand 72%72% ofof observedobserved daysdays forfor thethe NimrodNimrod Glacier.Glacier. TheThe statisticsstatistics forfor didifferentfferent monthsmonths areare shownshown inin FigureFigure8 8.. Median Median values values for for Byrd Byrd Glacier Glacier increase increase from from around around 8K 8K in in AprilApril/May/May to to 10K 10K in in July, July, and and stay stay relatively relatively constant constant through through August August and and September. September. The highestThe highest 10% of10% the of anomalies the anomalies exceed exceed 18K in18K winter. in winter. The same The same behavior behavior can be can seen be forseen Nimrod for Nimrod Glacier, Glacier, but with but anomalieswith anomalies being weakerbeing weaker by around by around 2K, and 2K, with and a larger with spreada larger of spread values. of The values. interannual The interannual variability ofvariability the anomalies of the isanomalies relatively is small relatively (see supplement small (see supplement Figure S1). Figure S1).

3.3. Eastern Weddell Sea

3.3.1. IST Climatology The mean surface temperature anomaly for the Eastern Weddell Sea for all winters is shown in Figure9. The reference area is in a region of the Brunt Ice Shelf (BIS) that is generally not a ffected by Remote Sens. 2019, 11, 1539 7 of 17 katabatic surges, which mainly occur in the region of the Stancomb-Wills Glacier (SWG, see Figure1b). A strong warm anomaly can be seen at the end of the slopes of Coats Land and the BIS. Unlike the signatures found for the RIS, katabatic surges (see Figure3) are not detected as a signal in the mean anomaly. The reference area for the surge anomaly was chosen on the BIS close to the slope, being in the region of expected surges (see Figure3). Besides the warm signals of katabatic winds over the slopes and katabatic surges of the ice shelves, a lot of small-scale structures can be seen over the ice sheet (Figure9). While the small-scale warm anomaly at the Goldsmith Glacier in the western part of FIS (see Figure1b) can be attributed to the convergence of the katabatic wind at the steep southern slope of this glacier valley, other features seem to be related to small structures in the surface elevation ofRemote the Sens. ice sheet 2019,, 11 (see,, xx FORFOR supplement PEERPEER REVIEWREVIEW Figure S2). 7 of 17

(a) (b)

Figure 7.7. Surface temperature anomaly distribution (1 (1KK bins) for winterwinter (April–September)(April–September) 2002 to 2017 for the exit regions of a) Byrd Glacier and b) Nimrod Nimrod Glacier Glacier (marked(marked byby crossescrosses inin FigureFigure 66).6).). The red line marks a value of +5°C. TheThe redred lineline marksmarks aa valuevalue ofof ++5°C.5◦C.

(a) (b)

Figure 8.8. Box-whiskerBox-whisker plot plot for for the the surf surfaceace temperature anomaly for di fferentfferent months for winterwinter (April–September)(April–September) 20022002 toto 20172017 forfor ByrdByrd GlacierGlacier ((aa)) andand NimrodNimrod GlacierGlacier ((bb).). TheThe median,median, thethe 2525 andand 75% percentiles areare plottedplotted asas thethe box,box, thethe 1010 andand 90%90% percentilespercentiles areare plottedplotted asas errorerror bars.bars.

3.3. EasternThe anomaly Weddell statistics Sea for the slope area (Figure 10a) for all winters show the highest frequencies found between 8–15K, which is comparable to the RIS. In extreme cases values of more than 20K are3.3.1. found. IST Climatology Only few cases show negative values. For the surge region, a high amount of negative values is found. The positive peak is at lower values (6–7K), but values of more than 20K are found as The mean surface temperature anomaly for the Eastern Weddell Sea for all winters is shown in Figure 9. The reference area is in a region of the Brunt Ice Shelf (BIS) that is generally not affected by katabatic surges, which mainly occur in the region of the Stancomb-Wills Glacier (SWG, see Figure 1b). A strong warm anomaly can be seen at the end of the slopes of Coats Land and the BIS. Unlike the signatures found for the RIS, katabatic surges (see Figure 3) are not detected as a signal in the mean anomaly. The reference area for the surge anomaly was chosen on the BIS close to the slope, being in the region of expected surges (see Figure 3). Besides the warm signals of katabatic winds over the slopes and katabatic surges of the ice shelves, a lot of small-scale structures can be seen over the ice sheet (Figure 9). While the small-scale warm anomaly at the Goldsmith Glacier in the western part of FIS (see Figure 1b) can be attributed to the convergence of the katabatic wind at the steep southern slope of this glacier valley, other features seem to be related to small structures in the surface elevation of the ice sheet (see supplement Figure S2).

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Remote Sens. 2019, 11, 1539 8 of 17 well. Anomalies exceeding 5K are found on 76 and 72 days per winter for the slope and surge area, respectively. This corresponds to 42% of all days and 80% of observed days for the slope area and 39% of all days and 43% of observed days for the surge area. The statistics for different months are shown in Figure 11. The highest anomalies for the slope area occur in September, but the differences between different months are relatively small. For the surge area, median values reach only 2–3K, and the 25% percentile is negative for all months. 10% of the anomalies exceed 11–13K, indicating that strong katabatic surges occur less frequently than for the RIS. As for the RIS, the interannual variability of the anomalies is relatively small, only for 2013 a median value close to zero is found for the surge area (see supplement Figure S3). Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 17

Figure 9. As Figure 6, but for the eastern Weddell Sea. The green box marks the slope area, the blue box marks the surge area, the black box marks the reference area.

The anomaly statistics for the slope area (Figure 10a) for all winters show the highest frequencies found between 8–15K, which is comparable to the RIS. In extreme cases values of more than 20K are found. Only few cases show negative values. For the surge region, a high amount of negative values is found. The positive peak is at lower values (6–7K), but values of more than 20K are found as well. Anomalies exceeding 5K are found on 76 and 72 days per winter for the slope and surge area, respectively. This corresponds to 42% of all days and 80% of observed days for the slope area and 39% of all days and 43% of observed days for the surge area. The statistics for different months are shown in Figure 11. The highest anomalies for the slope area occur in September, but the differences between different months are relatively small. For the surge area, median values reach only 2–3K, and the 25% percentile is negative for all months. 10% of the anomalies exceed 11–13K, indicating that strong katabatic surges occur less frequently than for the RIS. As for the RIS, the interannual Figure 9. As Figure 6, but for the eastern Weddell Sea. The green box marks the slope area, the blue variabilityFigure of 9. theAs Figureanomalies6, but is for relatively the eastern small, Weddell only Sea. for The2013 green a median box marks value the close slope to area, zero the is found blue for box marks the surge area, the black box marks the reference area. the surgebox marks area the(see surge supplement area, the blackFigure box S3). marks the reference area. The anomaly statistics for the slope area (Figure 10a) for all winters show the highest frequencies found between 8–15K, which is comparable to the RIS. In extreme cases values of more than 20K are found. Only few cases show negative values. For the surge region, a high amount of negative values is found. The positive peak is at lower values (6–7K), but values of more than 20K are found as well. Anomalies exceeding 5K are found on 76 and 72 days per winter for the slope and surge area, respectively. This corresponds to 42% of all days and 80% of observed days for the slope area and 39% of all days and 43% of observed days for the surge area. The statistics for different months are shown in Figure 11. The highest anomalies for the slope area occur in September, but the differences between different months are relatively small. For the surge area, median values reach only 2–3K, and the 25% percentile is negative for all months. 10% of the anomalies exceed 11–13K, indicating that strong katabatic surges occur less frequently than for the RIS. As for the RIS, the interannual variability of the anomalies is relatively small, only for 2013 a median value close to zero is found for the surge area (see supplement Figure S3). (a) (b)

Figure 10. AsAs Figure Figure 77,, butbut forfor thethe easterneastern WeddellWeddell SeaSea forfor ((a) the slopeslope areaarea andand ((b)) thethe surgesurge area.area.

(a) (b)

Figure 10. As Figure 7, but for the eastern Weddell Sea for (a) the slope area and (b) the surge area.

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(a) (b)

Figure 11. Box-whisker plot plot for for the surface temperatur temperaturee anomaly for different different months for winter (April–September)(April–September) 2002 to 2017 for the eastern Weddell SeaSea forfor thethe slopeslope ((a)) andand surgesurge areasareas ((bb).). The median, the 25 and 75% percentiles are plotted as the box, the 10 and 90% percentiles are plotted as error bars.

3.3.2. IST Relation to CCLM-simulated Wind In order to study the relation of the katabatic warming signal to the wind, we firstfirst compare the MODIS IST to CCLM-simulated wind andand IST fieldsfields for a case study. Figure 12 shows the daily series of these fieldsfields for 30 April–2 May 2014. This case study demonstrates a katabatic wind situation with a duration of five five days days (29 (29 April–3 April–3 May May 2014). 2014). On On 30 30 April, April, a pronounced a pronounced warm warm belt belt structure structure can can be beseen seen in inthe the MODIS MODIS IST IST over over the the slopes slopes of of Coats Coats Land Land extending extending from from the the Filchner Filchner Ice Ice Shelf Shelf to the Stancomb-Wills Glacier.Glacier. Temperatures Temperatures over over the the Brunt Brunt Ice ShelfIce Shelf (BIS) (BIS) and Riiser-Larsenand Riiser-Larsen Ice Shelf Ice (RLIS)Shelf are(RLIS) significantly are significantly colder (temperaturescolder (temperatures 10–20K 10 lower).–20K lower). The higher The ISThigher values IST overvalues the over BIS indicatethe BIS theindicate starting the katabaticstarting katabatic surge, which surge, is which well developedis well developed on the nexton the two next days. two On days. 1 May, On 1 the May, warm the temperatureswarm temperatures associated associated with the with katabatic the katabatic surge reach surge the reach edge the of theedge BIS. of Onthe 2BIS. May, On the 2 katabaticMay, the surgekatabatic shifts surge to the shifts north to andthe travelsnorth and over travels a distance over ofa aboutdistance 250 of km about across 250 the km BIS. across The dailythe BIS. data The of thedaily CCLM data of model the CCLM with 5model km resolution with 5 km can resolution reproduce can the reproduce observed the temperature observed temperature patterns (Figure patterns 12, right(Figure column). 12, right The column). warm The belt warm structure belt structure over the slopesover the is slopes associated is associated with strong with katabaticstrong katabatic winds. Thewinds. katabatic The katabatic surge develops surge develops with the with increasing the incr influenceeasing influence of a synoptic of a synoptic cyclone cyclone over the over eastern the Weddelleastern Weddell Sea. The Sea. synoptic The synoptic support support leads to leads the extent to the of extent the katabatic of the katabatic flow over flow the over BIS the being BIS almost being inalmost geostrophic in geostrophic balance. balance. On 3 May, On a 3 cyclone May, a over cyclone the central over the Weddell central Sea Weddell approaches Sea theapproaches region from the theregion west. from This the leads west. to aThis northeasterly leads to a wind northeasterly along the wind Luitpold along Coast the andLuitpold ends theCoast katabatic and ends surge. the katabaticThe goodsurge. agreement between IST from MODIS and CCLM allows one to study the relation of katabatic warm signals and the wind speed. Figure 13 shows the daily IST values for the slope and surge areas as well as the CCLM wind speed for April and May 2014. The high peaks in the IST 1 and wind speed values (up to 20 ms− ) at the end of April correspond to the case study discussed in Figure 12. A similar signal is present on 16 April. The IST changes are the largest for the surge region over the ice shelf. It is also obvious that there are more data gaps over the slope, which results also from the fact that two different data sets are used (see Section 2.1).

Remote Sens. 2019, 11, 1539 10 of 17 Remote Sens. 2019, 11, x FOR PEER REVIEW 10 of 17

30 April 2014. 30 April 2014.

1 May 2014 1 May 2014

2 May 2014 2 May 2014

Figure 12. Daily composite of MODIS surface temperature (left column) and simulated CCLM daily averages of surface temperature, MSLP (isolines) and 10m-wind vectors (rig (rightht column) for 30 April 2014 (upper row), 1 May 2014 (middle row) and 2 May 20142014 (lower(lower row).row).

Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 17 Remote Sens. 2019, 11, x FOR PEER REVIEW 11 of 17

TheThe goodgood agreementagreement between between IST IST from from MODIS MODIS and and CCLM CCLM allows allows one one to to study study the the relation relation of of katabatickatabatic warmwarm signals signals and and the the wind wind speed. speed. Figure Figure 13 13 shows shows the the daily daily IST IST values values for for the the slope slope and and surge areas as well as the CCLM wind speed for April and May 2014. The high peaks in the IST and surge areas as well as the CCLM wind speed for April and May 2014. The high peaks in the IST and wind speed values (up to 20 ms−1) at the end of April correspond to the case study discussed in Figure wind speed values (up to 20 ms−1) at the end of April correspond to the case study discussed in Figure 12. A similar signal is present on 16 April. The IST changes are the largest for the surge region over Remote12. A Sens.similar2019 signal11 is present on 16 April. The IST changes are the largest for the surge region over the ice shelf. It, is ,also 1539 obvious that there are more data gaps over the slope, which results also from11 of 17 the ice shelf. It is also obvious that there are more data gaps over the slope, which results also from the fact that two different data sets are used (see Section 2.1). the fact that two different data sets are used (see Section 2.1).

(a) (b) (a) (b) FigureFigure 13. 13. TimeTime series series of of daily daily averages averages of of MODIS MODIS ice ice surface surface temperature temperature (IST) (IST) (blue) (blue) and and CCLM CCLM 10 10 m-windm-windFigure 13. speed speed Time (red) (red) series for for ofthe the daily slope slope averages (a (a) )and and surgeof surge MODIS areas areas ice (b (b) surface )for for April April temperature and and May May 2014. 2014.(IST) (blue) and CCLM 10 m-wind speed (red) for the slope (a) and surge areas (b) for April and May 2014. AA comparison comparison of simulated IST from CCLM with the MODIS IST (both as daily values) is shownshown in inFigure FigureA 14comparison 14for for the the surge surge of simulated region. region. For ForIST April April from and andCCLM May May with 2014, 2014, the a verya MODISvery good good IST correlation correlation (both as (rdaily (r²2 of of 66%)values) 66%) is is foundis found shown for forthein Figure the two two data 14 data for sets thesets using surge using a linearregion. a linear regression For regression April (Table and (Table May1). In 201). the14, In average,athe very average, good CCLM correlationCCLM IST areIST too(r²are of warmtoo 66%) warm by is found 3–5K. by 3–5K.Thisfor the warm This two warm biasdata is setsbias also usingis present also a present linear for all regression for winters all winters 2002–2016, (Table 2002–2016, 1). In while the while average, the correlation the correlationCCLM is IST only isare only slightly too slightly warm lower. by lower.For3–5K. the ThisFor slope warmthe and slope referencebias and is also reference areas, present the areas, correlationfor all the winters corre is slightly 2002–2016,lation largeris slightly while (r2 of 71%thelarger correlation and (r² 67%, of 71% respectively) is onlyand slightly67%, for respectively)alllower. winters For 2002–2016the for slopeall winters (seeandsupplement 2002–2016reference (sareas, Figureee supplement the S4 andcorre Table lationFigure S1). is S4 slightly and Table larger S1). (r² of 71% and 67%, respectively) for all winters 2002–2016 (see supplement Figure S4 and Table S1).

(a) (b) Figure 14. IST from MODIS(a and) CCLM for the surge area for April and May(b )2014 (a) and for winters 2002–2016Figure 14. ( ISTb). The from blue MODIS line indicates and CCLM the forlinear the fit. surge area forfor April andand MayMay 20142014 ((a)) andand forfor winterswinters 2002–2016 ((b).). The blue line indicates the linear fit.fit.

The relation between CCLM wind and IST for the surge area is shown in Figure 15. Since the IST of the reference area is also sensitive to wind, the differences between the surge area and reference area were chosen for daily values (only days with positive differences are accounted for). For April and May 2014, a significant linear relation is found (p < 0.01, r2 = 0.72, Table1). When using only data with wind directions corresponding to a downslope wind (directions 2 90–180◦), the coefficient of determination is even improved (r = 0.83). For all of the winters of 2002–2016, still a reasonable r2 is found (r2 = 0.44 and r2 = 0.35 for the downslope and all directions, respectively, Figure 15b). For the slope region, the correlation is reasonable for April and May 2014 Remote Sens. 2019, 11, x FOR PEER REVIEW 12 of 17

The relation between CCLM wind and IST for the surge area is shown in Figure 15. Since the IST of the reference area is also sensitive to wind, the differences between the surge area and reference area were chosen for daily values (only days with positive differences are accounted for). For April and May 2014, a significant linear relation is found (p < 0.01, r² = 0.72, Table 1). When using only data with wind directions corresponding to a downslope wind (directions 90– Remote180°), Sens.the coefficient2019, 11, 1539 of determination is even improved (r² = 0.83). For all of the winters of 2002–2016,12 of 17 still a reasonable r² is found (r² = 0.44 and r² = 0.35 for the downslope and all directions, respectively, Figure 15b). For the slope region, the correlation is reasonable for April and May 2014 (r² = 0.41), but (r2 = 0.41), but the explained variance is only 0.25 to 0.29 for the whole period (see supplement Figure S5 the explained variance is only 0.25 to 0.29 for the whole period (see supplement Figure S5 and Table and Table S1). S1).

(a) (b)

Figure 15. ISTIST difference difference between surge area and refere referencence area and corresponding CCLM 10 m-wind speed didifferencefference forfor ((aa)) AprilApril andand MayMay 20142014 forfor allall daysdays (red(red symbols)symbols) and days with the downslope wind (black symbols)symbols) andand ((bb)) winterswinters 2002–20162002–2016 forfor thethe daysdays withwith thethe downslopedownslope wind.wind. The blue line indicates thethe linearlinear fit.fit.

The period of April and May 2014 is an exceptionally long period with almost no clouds in the eastern WeddellWeddell Sea,Sea, which which reduces reduces potential potential errors errors in in the the daily daily IST IST product. product. But But other other factors, factors, such such as theas the presence presence of drifting of drifting snow, snow, could could potentially potentia decreaselly decrease the correlation the correlation between between wind and wind IST and [33 ,IST34]. However,[33,34]. However, the largest the influencelargest influe on thence relation on the relation between between IST and IST wind and is wind given is bygiven the by processes the processes in the atmosphericin the atmospheric boundary boundary layer. Itlayer. is also It is obvious also obviou froms Figure from Figure 13 that 13 the that IST the response IST response to the to wind the iswind not linear,is not linear, since a since large a temperature large temperature change canchange be associated can be associated with a large with change a large in change wind speedin wind (30 speed April 2014),(30 April but also2014), with but a also relatively with smalla relatively change sm inall wind change speed in (12 wind May speed 2014). (12 These May ambiguities 2014). These are confirmedambiguities by are the confirmed results of theby the simulations results of for the the simulations relation between for the windrelation and between IST, selecting wind theand same IST, daysselecting as for the the same MODIS days comparison as for the for MODIS 2002–2016 comp (seearison supplement for 2002–2016 Figure S6, (see Figure supplement S7 and Table Figure S1). ForS6, theFigure surge S7 area,and theTable model-based S1). For the correlation surge area, is slightly the mo higherdel-based than correlation for the MODIS is slightly results higher (r2 = 0.51), than and for forthe theMODIS slope results area the (r² correlation = 0.51), and is for comparable the slope area to the the MODIS correlation results is (rcomparable2 = 0.38 for to downslope the MODIS winds). results (r² = 0.38 for downslope winds). Table 1. Statistics for the linear regressions (y = a + bx) of the different Figures (n = number of values, a = interception, b = slope, with dimensions). Wind is always from CCLM. All regressions are significant with a p value less than 0.01.

Figure x y r r2 RMSE n b a

14a IST MODIS IST CCLM 0.81 0.66 4.06 58 0.92 2.01 ◦C

14b IST MODIS IST CCLM 0.80 0.64 5.92 2347 1.00 5.85 ◦C 15a (all data) IST diff. MODIS wind diff. 0.85 0.72 1.79 42 0.52 m/(sK) 0.23 m/s

15a (downslope) IST diff. MODIS wind diff. 0.91 0.83 1.61 19 0.55 m/(sK) 0.51 m/s 15b (all data) IST diff. MODIS wind diff. 0.59 0.35 1.54 1516 0.32 m/(sK) 1.32 m/s 15b (downslope) IST diff. MODIS wind diff. 0.66 0.44 1.88 517 0.37 m/(sK) 1.13 m/s 16a T2m-IST CCLM wind 0.82 0.68 0.97 1925 -0.44 m/(sK) 7.34 m/s 16b T2m-IST CCLM wind 0.77 0.59 1.56 764 -0.72 m/(sK) 9.05 m/s Remote Sens. 2019, 11, x FOR PEER REVIEW 13 of 17

Table 1. Statistics for the linear regressions (y = a + bx) of the different Figures (n = number of values, a = interception, b = slope, with dimensions). Wind is always from CCLM. All regressions are significant with a p value less than 0.01.

Figure x y r r2 RMSE n b a 14a IST MODIS IST CCLM 0.81 0.66 4.06 58 0.92 2.01 °C 14b IST MODIS IST CCLM 0.80 0.64 5.92 2347 1.00 5.85 °C 15a (all data) IST diff. MODIS wind diff. 0.85 0.72 1.79 42 0.52 m/(sK) 0.23 m/s 15a (downslope) IST diff. MODIS wind diff. 0.91 0.83 1.61 19 0.55 m/(sK) 0.51 m/s 15b (all data) IST diff. MODIS wind diff. 0.59 0.35 1.54 1516 0.32 m/(sK) 1.32 m/s 15b (downslope) IST diff. MODIS wind diff. 0.66 0.44 1.88 517 0.37 m/(sK) 1.13 m/s 16a T2m-IST CCLM wind 0.82 0.68 0.97 1925 -0.44 m/(sK) 7.34 m/s Remote Sens. 2019, 11, 1539 13 of 17 16b T2m-IST CCLM wind 0.77 0.59 1.56 764 -0.72 m/(sK) 9.05 m/s

TheThe ISTIST isis aa result result of of the the energy energy balance balance terms terms at at the the snow snow surface surface and and their their interactions interactions with with the lowerthe lower boundary boundary layer. layer. For example, For example, the IST the (as IST well (as as well the near-surfaceas the near-surface temperature) temperature) reacts on reacts changes on ofchanges the incoming of the incoming longwave longwave radiation onradiation a short on time a short scale [time35]. Thescale sensible [35]. The heat sensible flux is heat a function flux is of a thefunction near-surface of the near-surface stability and stability the wind and speed. the wind The atmosphericspeed. The atmospheric boundary layer boundary over polar layer ice over surfaces polar isice generally surfaces stablyis generally stratified stably for cloud-free stratified conditions,for cloud-free and conditions, an increase and in wind an increase speed leads in wind to a higherspeed sensibleleads to heata higher flux sensible towards heat the surfaceflux towards and a the decrease surface of and the near-surfacea decrease of stability the near-surface due to enhanced stability generationdue to enhanced of turbulence generation by wind of tu shearrbulence [7]. by The wind CCLM shear simulations [7]. The CCLM can be simulations used to further can explore be used the to processfurther ofexplore the change the process of the surfaceof the change inversion of duethe su torface the wind. inversion Figure due 16 toshows the wind. the di ffFigureerence 16 between shows thethe temperaturedifference between at 2 m (T2m) the temperature and the IST at for 2 allm CCLM(T2m) and grid the points IST over for all ice CCLM shelves grid and points over the over lower ice slopeshelves area and (50–500 over m)the for lower the easternslope area Weddell (50–500 Sea form) 2for May the 2014 eastern (cf.Figure Weddell 12). Sea There for is 2 aMay clear 2014 relation (cf. betweenFigure 12). the There near-surface is a clear stability relation (T2m-IST) between the and near-surface the wind speed stability for wind(T2m-IST) speeds and lower the wind than 6speed m/s, butfor wind for high speeds wind lower speeds than the 6 m/s, scatter but is for large. high For wind T2m-IST speeds values the scatter exceeding is large. 3K For a high T2m-IST correlation values betweenexceeding near-surface 3K a high stabilitycorrelation and between wind speed near-surfa is foundce forstability the ice and shelf wind and forspeed the slopeis found (r2 =for0.68 the and ice rshelf2 = 0.59, and respectively).for the slope The(r² = near-surface 0.68 and r² stability= 0.59, respectively). is a better measure The near-surface for the sensitivity stability to is the a better wind thanmeasure the ISTfor itself,the sensitivity since it is to less the sensitive wind than to the the spatial IST itself, gradients since init is temperature less sensitive (see to supplement the spatial Figuregradients S8). in However, temperature only (see IST issupplement measurable Figure by MODIS, S8). However, which underlines only IST the is valuemeasurable of high-resolution by MODIS, simulationswhich underlines of the Antarcticthe value boundary of high-resolution layer in understanding simulations theof wind-inducedthe Antarctic warming.boundary layer in understanding the wind-induced warming.

(a) (b)

FigureFigure 16.16. CCLMCCLM (T2m-IST) didifferencefference as aa functionfunction of windwind speed as for the dailydaily meansmeans for 22 MayMay 20142014 ((aa)) forfor thethe iceice shelfshelf areasareas ofof BISBIS andand Riiser-LarsenRiiser-Larsen (RLIS)(RLIS) andand ((bb)) forfor thethe slopeslope areaarea forfor thethe heightheight rangerange 50–50050–500 mm (temperatures(temperatures height-correctedheight-corrected withwith anan adiabaticadiabatic lapselapse rate).rate). TheThe blueblue lineline indicatesindicates thethe linearlinear fitfit forfor (T2m-IST)(T2m-IST) >> 3K.

4. Discussion Warm signatures in the surface temperature related to katabatic winds and katabatic surges in the Antarctic have been reported in a number of studies previously. [13] show that the surface temperature field derived from Advanced Very High Resolution Radiometer (AVHRR) infrared imagery for the years 1993 and 1995 (a total of 53 images) typically show a warm thermal belt over the slopes of Coats Land in the Weddell Sea. This implies that the katabatic flow does not reach the Brunt Ice Shelf (BIS), where the surface is colder, and a strong surface inversion is present. The temperature difference between the slope and BIS is at its largest in the winter (March-August) with values of 11–15K. Our results agree with this study, as the warm belt is clearly seen in Figure9. The distribution of temperature anomalies (Figure 10a) cover a much broader range than shown in [13], because the Remote Sens. 2019, 11, 1539 14 of 17 present study is based on data for 15 years, and includes more than 1400 days. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. For the RIS, the classical study of [10] evaluates warm IST signals associated with katabatic surges using daily Defense Meteorological Satellite Program (DMSP) infrared imagery for the winters 1984 and 1985 (April–October). Clear-sky images covering the north-western RIS were obtained on 32% of the days. The exit of Byrd Glacier was found to be the most prominent feature of warm signatures (87% occurrence frequency), while Nimrod Glacier had only a 3% occurrence frequency of warm signatures. In our long-term study, we find only small differences in warm anomalies for these two glacier regions. Anomalies exceeding 5K are found on 82% of observed days for the Byrd Glacier and for 72% of observed days for the Nimrod Glacier. The reason for the warm katabatic signatures is that the strong turbulence in katabatic winds [7] weakens the surface inversion compared to the weak-wind surroundings over the ice shelves (see Figure2). In accordance with previous studies, e.g., [ 10,11,13,16], it was therefore the working hypothesis of the present paper that warm signatures are related to the wind speed or wind speed difference between areas influenced by katabatic flows (slope and surge areas) and undisturbed areas of the ice shelves. This relation between wind and IST anomalies could be demonstrated for case studies (Figure 12), but for the climatology of 15 winters, the relation between wind (from a high-resolution (5 km) regional climate model) and IST (from MODIS) is less well defined (correlation of 0.66 for the surge region and 0.54 for the slope). Since the comparison of IST from MODIS and IST from the simulations showed a very good agreement, the relation of wind and IST was further examined using only model data. Using the model-based near-surface stability (difference between temperature at 2 m and the surface), a high correlation between near-surface stability and wind speed is found for the ice shelf and for the slope for wind speeds less than about 6 m/s. For higher wind speeds (or T2m-IST values smaller than 3K) the scatter is large. Thus there is a strong non-linear behavior of the response of the near-surface stability to the wind. For the high wind speeds which are generally associated with the katabatic winds over the slopes, wind speed changes have almost no effect on the near-surface stability. A similar behavior was found by [8] investigating inversions over the Antarctic plateau for the period 1994–2003. For the strongest inversions (90% percentile), a linear decrease in inversion strength is found with increasing wind for wind speeds up to 8 m/s. In summary, the near-surface stability is found to be a better measure for the response to the wind than the IST itself, since it is less sensitive to the spatial gradients in temperature. The change of the boundary layer temperature structure is the primary signal of the wind response, but it has also feedbacks on the wind, the surface energy balance and the structure of the surface. Katabatic winds are a result of a stable boundary layer over sloped terrain, and the inversion strength influences strongly the wind intensity [36]. On the other hand the turbulent mixing reduces the inversion strength. Inversion strength and inversion height over the Antarctic slopes are much weaker/lower compared to the Antarctic plateau [37,38]. IST signatures in the form of streaks have been considered as an indicator of katabatic flows by [11,14]. For Greenland, these katabatic streaks are linked to topographic structures [14]. For the present study, streaks are not found in the long-term mean for the Antarctic. Streaks are present for individual katabatic wind cases (see Figure3), but they are smoothed out evenly by the daily composites (see Figure 12). However, small-scale IST anomaly structures are present in the climatology that are related to the channeling of the katabatic wind in valleys, but also by structures in the surface elevation of the ice sheet (see supplement Figure S2). Wind-induced warming is relevant for the surface energy balance, the snow structure and the surface melting over the Antarctic ice shelves. For the winter period studied in this paper, melting is very unlikely for the areas investigated. But similar warming effects are also present in the summer season, and the relation to melting is part of the study of [39]. They show that katabatic winds during summer lead to a wind-induced warming of >3K and a doubling of the surface melt in the grounding Remote Sens. 2019, 11, 1539 15 of 17 zone. In addition, these winds cause areas with lower surface albedo (e.g., blue ice), which enhances melting. This preconditioning of the surface takes place also during winter, since the relatively warm (and dry) katabatic surges are associated with high latent heat fluxes. The blue ice areas at the exit region of the Stancomb-Wills Glacier on the BIS (see Figure1b and Figure S2 in the supplement) indicate these effects of the katabatic surges. The present study has the focus on the Ross Sea and Weddell Sea areas of the Antarctic. Future research directions will be the expansion of these studies to other areas using a combination of satellite and model data.

5. Conclusions MODIS ice surface temperature (IST) data are used for the detection of warm signatures over the Antarctic for the winter periods 2002–2017. In addition, high-resolution (5 km) regional climate model data is used for 2002 to 2016. For the first time, we present a climatology of wind-induced IST anomalies for the Ross Ice Shelf and the eastern Weddell Sea. The IST anomaly distributions show maxima around 10–15K for the slopes, but values of more than 25K are also found. Katabatic surges represent a strong climatological warming signal, which has impacts on the surface energy balance and the surface characteristics (such as the melting and the formation of blue ice areas). The IST response to the wind is not linear, and is a result of all energy balance terms of the snow surface and their interaction with the lower boundary layer. While MODIS yields only the IST, the synergy with atmospheric model data allows for a deeper analysis. Using the model-based near-surface stability, a high correlation between near-surface stability and wind speed is found for the ice shelf and for the slope for wind speeds less than about 6 m/s.

Supplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/11/13/1539/s1. Figure S1: Box-whisker plot for the IST anomaly for different years for winters 2002 to 2017 for Byrd Glacier (a) and Nimrod Glacier (b). Figure S2: Subarea of Figure9 for a) the IST anomaly for winter seasons 2002 – 2017 and b) the Cryosat-2 topography and hillshade. Figure S3: as Figure S1, but for the eastern Weddell Sea for the slope (a) and surge areas (b). Figure S4: IST from MODIS and CCLM for the (a) slope area and (b) reference area for winters 2002-2016. Figure S5: IST difference between slope area and reference area and wind speed difference for winters 2002-2016. Figure S6: CCLM IST difference between surge (a)/slope area (b) and reference area and wind speed difference for April and May 2014. Figure S7: CCLM IST difference between (a) surge and (b) slope area and reference area and wind speed difference for winters 2002–2016. Figure S8: CCLM IST as a function of wind speed as for the daily means for 2 May 2014 (a) for the ice shelf areas of BIS and RLIS and (b) for the slope area. Table S1: Statistics for the linear regressions. Author Contributions: Conceptualization, G.H.; Methodology, G.H., L.G., S.W.; Software, L. Glaw, S.W.; Formal Analysis, L.G.; Data Curation, L.G., S.W.; Writing-Original Draft Preparation, G.H.; Writing-Review & Editing, G.H., L.G., S.W.; Visualization, G.H., L.G.; Supervision, G.H.; Project Administration, G.H.; Funding Acquisition, G.H., S.W. Funding: This research was partly funded by the Deutsche Forschungsgemeinschaft under grant numbers HE 2740/19, HE 2740/22 and WI 3314/3 in the framework of the priority programme SPP1158 “Antarctic Research with comparative investigations in Arctic ice areas”. The publication was funded by the Open Access Fund of the University of Trier and the German Research Foundation (DFG) within the Open Access Publishing funding programme. Acknowledgments: We thank Rolf Zentek for making the CCLM data available. MODIS data were obtained from NSIDC. The Quantarctica project (quantarctica.npolar.no) is also acknowledged. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

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