An Investigation of Ice Surface Albedo and Its Influence on the High

remote sensing

Article An Investigation of Ice Surface Albedo and Its Inﬂuence on the High-Altitude Lakes of the Tibetan Plateau

Jiahe Lang 1,2 ID , Shihua Lyu 3,4, Zhaoguo Li 1,*, Yaoming Ma 2,5,6 and Dongsheng Su 1,2

1 Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China; [email protected] (J.L.); [email protected] (D.S.) 2 University of Chinese Academy of Sciences, Beijing 100049, China; [email protected] 3 Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China; [email protected] 4 Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China 5 Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China 6 CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, Beijing 100101, China * Correspondence: [email protected]; Tel.: +86-0931-4967-239

Received: 22 November 2017; Accepted: 30 January 2018; Published: 1 February 2018

Abstract: Most high-altitude lakes are more sensitive to global warming than the regional atmosphere. However, most existing climate models produce unrealistic surface temperatures on the Tibetan Plateau (TP) lakes, and few studies have focused on the influence of ice surface albedo on high-altitude lakes. Based on field albedo measurements, moderate resolution imaging spectrometer (MODIS) albedo products and numerical simulation, this study evaluates the ice albedo parameterization schemes in existing lake models and investigates the characteristics of the ice surface albedo in six typical TP lakes, as well as the influence of ice albedo error in the FLake model. Compared with observations, several ice albedo schemes all clearly overestimate the lake ice albedo by 0.26 to 0.66, while the average bias of MODIS albedo products is only 0.07. The MODIS-observed albedo of a snow-covered lake varies with the snow proportion, and the lake surface albedo in a snow-free state is approximately 0.15 during the frozen period. The MODIS-observed ice surface (snow-free) albedos are concentrated within the ranges of 0.14–0.16, 0.08–0.10 and 0.10–0.12 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake, respectively. The simulated lake surface temperature is sensitive to variations in lake ice albedo especially in the spring and winter.

Keywords: surface albedo; snow and ice; MODIS; lake model; parameterization scheme

1. Introduction The Tibetan Plateau (TP) contains numerous lakes with a total area of 4.7 × 104 km2 [1], and approximately 389 lakes are larger than 10 km2 [2]. Over the past 82,000 years, there has been a good correlation between the shrinking of lakes in the TP and the weakening of the Asian monsoon [3]. In a global warming context, lake areas and water levels generally show an increasing trend, because of the increases in precipitation and glacier melt water that have occurred in the TP, while the lake ice-cover duration shows an overall decline [4–7]. Land surface temperature (LST) is the key factor regulating the exchange of energy and water in the air–lake interface [8]. In the TP, a few studies have shown that the LST rises even faster than regional air temperature in summer [9]. These lakes are located on the

Remote Sens. 2018, 10, 218; doi:10.3390/rs10020218 www.mdpi.com/journal/remotesensing Remote Sens. 2018, 10, 218 2 of 17 third pole of the Earth, where the cold climate leads to a long frozen period (four to six months) for the TP lakes [10]. Recent studies have shown that shortening of the frozen period can lead to the earlier establishment of the summer thermocline in large and deep lakes, which accelerates the warming of the upper lake water [11–13]. Ice cover can increase the lake surface albedo and attenuate absorbed solar radiation, and thus strongly alters the significance of sensible and latent fluxes exchange as well as evaporation [14]. The freezing date of a lake is significantly affected by the lake’s heat storage, while the melting of lake ice is mainly determined by solar radiation [15], particularly the solar radiation absorbed by the ice and the water. The albedo is one of the main parameters in climate models [16] and regulates the surface energy budget. Previous studies have shown that lake ice and snow albedos generally range from 0.10 to 0.60 and from 0.50 to 0.95 [17–23]. Based on MODIS albedo products (MCD43A3, MOD10A1/MYD10A1) and field observation data, researchers investigated the ice surface albedo of Malcolm Ramsay Lake in Canada (with little snow cover), and found that it was greater than 0.6; moreover, the error of MCD43A3 product was smallest [24]. However, lake ice albedo less than 0.2 has only been reported by a handful of studies [17,25]. The huge spread in lake ice albedo values reported by different studies implies that an accurate ice albedo parameterization is necessary for weather and climate modelling. Lake ice albedo is related to a number of factors, such as solar zenith angle [26,27], ice type (white ice, snow ice, blue ice, etc.) [28], bubble content, ice thickness [29,30], the presence of impurities [20] and cloud cover [27]. There are three types of ice surface albedo schemes in lake models and coupled climate models. The first type is a specified constant, such as that used in the weather research and forecasting model (WRF). The second type is a function of ice surface temperature, like that used in the FLake model. The third type is more complex and takes into account snowfall, snow/ice thickness or other factors. The Canadian lake ice model (CLIMo) uses this type of parameterization scheme. Most ice albedo parameterization schemes in lake models are developed based on the early observations on high-altitude sea or lakes of the Arctic, and a few schemes are even derived from sea ice models. The ice is very thick and is often covered with snow in Arctic, which is quite different from that in the TP. The original observation data employed by the temperature-dependent albedo schemes, temporally averaged quantities as daily means or monthly means [31], are very discrete. The correlation between ice surface temperature and ice albedo is very low. It is impossible to characterize the effect of rapidly changing ice temperatures (like diurnal variations) on ice albedo. These parameterizations are not suitable for lake ice on the TP [32,33]. In northern Europe and North America, the climate is relatively humid, and the lake surface is often covered with snow during the frozen period. The impact of ice albedo error on lake simulation is largely offset by the snow cover. However, little precipitation falls over the arid and semi-arid TP during the frozen period, and there is often little or even no snow cover on the ice surface. Inaccurate estimation of ice albedo may lead to an obvious bias in the LST simulation in the coming year, subsequently affecting the regional climate modelling [24,31]. Mallard et al. [34] significantly improved the simulation of Great Lakes temperatures and ice cover through progressing the ice albedo parameterizations. However, little attention has been paid to the ice albedo of TP lakes. Only a few studies have analyzed the changes in the freezing date of Nam Co Lake [35], as well as lake ice structure [36], thermal conductivity [37] and ice melting [38] in a small TP thermokarst lake. Although the observation of lake ice albedo is quite scarce in the TP, the MODIS albedo products, which have a relatively small error, can help us to obtain an in-depth understanding of the ice albedo characteristics of a large lake. In this study, the ice surface albedo parameterization schemes in existing models and the observation data are used to investigate the bias of ice albedo values in the model. The long-term MODIS albedo products are employed to analyze the characteristics of the surface albedo on TP lakes during the frozen period, and the FLake model is used to illustrate the effects of the inaccurate albedo parameterizations of the model. Remote Sens. 2018, 10, x FOR PEER REVIEW 3 of 15 Remote Sens. 2018, 10, 218 3 of 17 2. Study Area, Data and Method 2. Study Area, Data and Method 2.1. Study Area and In-Situ Measurements 2.1. Study Area and In-Situ Measurements Six typical lakes (Figure 1) with different surface areas (184.9–4254.9 km2), altitudes (3260–4990 m), depths andSix typicalgeographic lakes (Figurelocations,1) with are different selected surface to represent areas (184.9–4254.9 TP lakes (Table km 2), 1). altitudes Aksai (3260–4990Chin Lake, m), Nam Co Lakedepths and and Ngoring geographic Lake locations, will be analyzed are selected as tokey represent targets TPin the lakes following (Table1). sections. Aksai Chin Aksai Lake, Chin Nam Lake, the Cohighest Lake (4848 and Ngoring m AMSL) Lake and will smallest be analyzed lakeas (184.9 key targetskm2) of in the the followingsix lakes, sections.is still large Aksai enough Chin to 2 accommodateLake, the highest many (4848 MODIS m AMSL) 500-m and resolution smallest lakepixels. (184.9 Nam km Co) of Lake the sixis the lakes, third-largest is still large saline enough lake to in accommodate many MODIS 500-m resolution pixels. Nam Co Lake is the third-largest saline lake in the TP and the highest large lake on the earth. Ngoring Lake (4274 m AMSL), with a mean depth of the TP and the highest large lake on the earth. Ngoring Lake (4274 m AMSL), with a mean depth of 17 m and a surface area of 610 km2, is the highest large freshwater lake in China, and it is located in 17 m and a surface area of 610 km2, is the highest large freshwater lake in China, and it is located in the theeastern eastern TP. TP.

FigureFigure 1. 1.LocationsLocations of ofthe the six six selected selected lakes. lakes. The The triangle representsrepresents Ngoring Ngoring Lake Lake where where the the observationsobservations were were carried carried out. out.

TableTable 1. 1.SurfaceSurface area area and and surface surface altitudealtitude of of the the six six selected selected lakes. lakes.

Lake Names Aksai ChinAksai Lake ChinNam Co LakeNamJ Coingyu LakeJingyu Zhari NamcoZhari Lake NgoringNgoring Lake QinghaiQinghai Lake Lake Names Surface area (km2) 184.9 Lake 2040.9 Lake 283.7 Lake Namco 996.9 Lake 629.8Lake Lake 4254.9 AltitudeSurface (m) area (km2) 4848 184.9 4718 2040.9 4708 283.7 4613 996.9 4272 629.8 4254.9 3260 Altitude (m) 4848 4718 4708 4613 4272 3260 From 3 to 6 January 2014, a short-term field experiment aimed at measuring the ice surface radiationFrom balance 3 to was 6 January carried 2014, out aover short-term the west ﬁeld of experimentNgoring Lake aimed (Figure at measuring 2a). The thefield ice observation surface wasradiation forced balanceto terminate was carried on 6 out January over the due west to of a Ngoring huge Lakeice crack (Figure between2a). The ﬁeldthe observationlakeshore and was the observationforced to terminatesite (34.92°N, on 6 January97.58°E). due From to a huge10 to ice 18 crack February between 2017, the an lakeshore in-situ andobservation the observation and another site mobile(34.92 observation◦N, 97.58◦E). of From the 10ice to albedo 18 February were 2017, performed an in-situ over observation the west and Ngoring another Lake mobile (Figure observation 2b). The maximumof the ice observation albedo were range performed is approximately over the west Ngoring630 m in Lake the (Figuremeridional2b). The direction, maximum and observation 960 m in the latitudinalrange is direction. approximately In this 630 mexperiment, in the meridional we obtained direction, 529 and groups 960 m of in thedata latitudinal from mobile direction. albedo observationIn this experiment, within 6 days we obtained (10:00–12:00 529 groups local time of data of every from day), mobile and albedo diurnal observation measurements within 6of days a week from(10:00–12:00 in-situ observation. local time of During every day), the observation and diurnal measurementsperiod, patches of aof week bare from ice and in-situ snow observation. cover were intermixedDuring theacross observation the lake period,surface, patches but the of bare bare ic icee was and still snow dominant. cover were The intermixed instrument across used the for lake these surface, but the bare ice was still dominant. The instrument used for these two observations is two observations is Kipp & Zonen 4-Component Net Radiometer (CNR4). The pyranometers and Kipp & Zonen 4-Component Net Radiometer (CNR4). The pyranometers and pyrgeometers measure pyrgeometers measure shortwave (300–2800 nm) and long-wave infrared (4500–42,000 nm) radiation, shortwave (300–2800 nm) and long-wave infrared (4500–42,000 nm) radiation, respectively. The in-situ respectively. The in-situ observation, at height of 1.20 m, measured once per minute and output a observation, at height of 1.20 m, measured once per minute and output a group of average data every group30 min,of average from 8:00 data to 16:30.every The30 min, mobile from observation, 8:00 to 16:30. at height The mobile of 1.60 m,observation, measured onceat height per 10 of s and1.60 m, measured once per 10 s and output a group of average data every 60 s, from 11:30 to 13:30. The mobile albedo observation had a built-in global positioning system (GPS) sensor, which can record the instrument position in real time. The albedo α measured by pyranometers over the lake ice surface is ↑ obtained from the ratio = , where ↑ and ↓ are the measured upward and downward ↓

Remote Sens. 2018, 10, 218 4 of 17 output a group of average data every 60 s, from 11:30 to 13:30. The mobile albedo observation had a built-in global positioning system (GPS) sensor, which can record the instrument position in real time. S The albedo α measured by pyranometers over the lake ice surface is obtained from the ratio α = W↑ , Remote Sens. 2018, 10, x FOR PEER REVIEW 4 SofW↓ 15 where SW↑ and SW↓ are the measured upward and downward shortwave irradiances, respectively. Theshortwave observed irradiances, long-wave respectively. radiation isused The toobserved calculate long-wave the ice surface radiation temperature is used accordingto calculate to thethe lawice ofsurface Stefan–Boltzmann temperature according Equation (1).to the law of Stefan–Boltzmann Equation (1). = ()−ε +εσ 4 4 RlupRlup= (11− ε)RlRldwdw + εσT0T 0 (1)(1)

−−8 −22 −4−4 Here, σσ (=5.67(= 5.67× ×10 10 W·∙m ∙K·K) is) isthe the Stefan–Boltzmann Stefan–Boltzmann constant; constant; RlRldwdw andand RlRlupup areare downward and upward long-wave radiations, and the iceice surfacesurface emissivityemissivity εε isis 0.99.0.99. T0 is the ice surfacesurface temperature. TheThe observed observed surface surfac temperaturee temperature will will be used be used in the in ice the albedo ice albedo parameterization parameterization scheme toscheme obtain to the obtain model-derived the model-derived albedo. albedo.

(a) (b)

Figure 2. Observation platform in 2014 (a) and 2017 ((b)) onon NgoringNgoring Lake.Lake. In 2017, both of the in-situin-situ and mobile observation platforms areare presented.presented.

2.2. Data 2.2. Data

2.2.1. MODIS Albedo Products The MCD43A3 V005 16-day albedo product, which combines albedo retrievals from the Terra and AquaAqua satellites, satellites, is is used used to analyzeto analyze the characteristicsthe characteristics of the of surface the surface albedo onalbedo TP lakes on TP (October lakes 2012(October to June 2012 2013, to June October 2013, 2013 October to June 2013 2014, to June and 2014, October and 2014October to June2014 2015).to June It 2015). includes It includes albedo measurementsalbedo measurements at a 500-m at a spatial 500-m resolution spatial resolution that is updated that is updated every 8 days every [39 8]. days The pixels[39]. The that pixels cover that the sixcover lakes the are six carefullylakes are selectedcarefully (two selected pixels (two within pixels the within lake boundary the lake boundary are removed) are removed) to ensure to that ensure land contaminationthat land contamination is not an issue. is not The anMCD43A issue. The product MCD43A used product in this studyused isin thethis White study Sky is the Albedo White (WSA) Sky forAlbedo the shortwave (WSA) for bandthe shortwave (459–2155 band nm) product.(459–2155 Svacina nm) product. et al. [24 Svacina] found et that al. [24] the snowfound and that ice the albedo snow obtainedand ice albedo from MCD43A3obtained from had aMCD43A3 smaller error had than a smaller MOD10A1 error andthan MYD10A1 MOD10A1 daily and albedoMYD10A1 products, daily comparedalbedo products, with in-situ compared observations. with in-situ MCD43A2 observations. is the quality MCD43A2 assurance is the quality product assurance for MCD43A3, product and for it recordsMCD43A3, whether and it the records pixel haswhether the snow-cover. the pixel has This the information snow-cover. will This be usedinformation to calculate will thebe used ratio ofto thecalculate snow-covered the ratio toof snow-freethe snow-covered pixels on to the snow-f lakeree surface. pixels The on the MCD43A lake surface. V005 productThe MCD43A used in V005 this studyproduct updates used in the this value study every updates 8 days. the value Daily every MCD43A 8 days. V006 Daily products MCD43A have V006 been products officially have released been andofficially it is usedreleased to make and ait comparisonis used to make with a the comparison mobile observation with the mobile results. observation Daily MCD43A results. product Daily isMCD43A also a 16-day product product is also a that 16-day retrieves product each that day retrieves and utilizes each day all and high-quality, utilizes all multi-date,high-quality, multi-angle, multi-date, cloud-freemulti-angle, surface cloud-free reflectance surface from reflectance a rolling 16-dayfrom a periodrolling in16-day which period the day in of which interest the is day defined of interest as the centreis defined date as [2 ,the40]. centre A brief date discussion [2,40]. A will brief be presenteddiscussion at will the be end presented of this study at the to revealend of thethis difference study to betweenreveal the MCD43A-V005 difference between and MCD43A-V006 MCD43A-V005 (from and MCD43A-V006 October 2013 to (from June 2014).October 2013 to June 2014). The MOD10A1 product is used to consider percentage of snow-covered pixels of Aksai Chin Lake (1 to 16 January 2014), which includes the snow-covered albedo, snow cover proportion, quality information and other metadata at a spatial resolution of 500 m [31,41].

2.2.2. MODIS Surface Temperature Product The MODIS land surface temperature product (MOD11A1) is used to evaluate the long-term simulated results in this study which covers 2012–2016 with a spatial resolution of 1 km. Many studies have shown that the MODIS land surface temperature product has good accuracy [9,42,43].

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The MOD10A1 product is used to consider percentage of snow-covered pixels of Aksai Chin Lake (1 to 16 January 2014), which includes the snow-covered albedo, snow cover proportion, quality information and other metadata at a spatial resolution of 500 m [31,41].

2.3. Lake Ice Albedo Parameterizations and the FLake Model Many lake ice albedo parameterizations have been developed, and these schemes have varying degrees of complexity. The simplest scheme applies two or more constant values of albedo for different ice types. Some schemes (e.g., that used in FLake) add a dependence on temperature to account for the combined effects of LST. More sophisticated schemes (e.g., that used in CLIMo) also include the dependence of albedo on ice and snow thickness. The ice albedo scheme in FLake is derived from a thermodynamic sea ice model [44]. The ice surface albedo (α) in the FLake model is calculated as: h i α = αmax − (αmax − αmin) × exp −95.6 × Tf 0 − Ti /Tf 0 (2) where αmax is set to 0.6, referring to the albedo of white ice or snow ice. The blue ice albedo (αmin) is equal to 0.1. Tf0 is the freezing temperature (273.15 K), Ti is the ice surface temperature. As the ice surface temperature (Ti) approaches the freezing temperature (Tf0), the albedo approaches 0.10. In the WRF model, the lake ice albedo is set to 0.6. The community land model (CLM) is the land surface model for the community earth system model (CESM). The lake model of CLM, denoted the lake, ice, snow, and sediment simulator (LISSS), is derived from Subin et al. [45] with one lake modelled in each grid cell. The CLM model distinguishes the lake ice albedo in different wavelength range of visible light and near infrared light. For frozen lakes without individually resolved snow layers, the albedo at cold temperatures αmax is 0.60 for visible radiation (CLM-max) and 0.40 for near infrared radiation (CLM-min). Therefore, the albedo parameterization scheme in CLM is similar to that used in FLake, evolving from Equation (2). The 1-D FLake model is designed to simulate lake water temperature profiles and surface heat fluxes [46,47]. The FLake model is suggested to be applicable for freshwater lakes with depths of less than 60 m. Furthermore, a lake-ice layer, snow layer, and lake sediment layer are considered. Previous studies have shown that the FLake has been widely used and has a good performance [48–50]. In this study, we employed the FLake model to evaluate the influence of the ice albedo on lake simulation in Ngoring Lake. The simulation experiments begin in July 2011 and end in December 2016. The lake depth is set to be 25 m. The forcing data are derived from the observation in Ngoring Lake which is come from the Automatic weather station (AWS) built on the edge of the lake next to the observation area. The main input variables include the surface temperature, relative humidity, wind speed, air pressure, down short-wave and longwave radiations, precipitation from 1 July 2011 to 1 January 2017. The simulations begin in July 2011 instead of January 2012 for model spin-up.

3. Results

3.1. Albedo from MODIS, Observation and Parameterization-Drived The four parameterization schemes (CLM4.5, FLake, WRF, and WRF-FLake) are evaluated against the in-situ observation collected in Ngoring Lake in 2014 and 2017 (Figure3). The observed surface temperature is employed by these schemes to obtain the ice surface albedo. Of these schemes, the WRF-FLake model shows the maximum bias, followed by WRF and FLake. The albedo values derived from CLM4.5, especially CLM4.5_Min, are relatively closer to the observations. The schemes Remote Sens. 2018, 10, 218 6 of 17

in CLM4.5, FLake and WRF-FLake can reproduce the diurnal variation of albedo relatively well, but are signiﬁcantly overestimated. Taking those of 2017 as an example, the model bias for FLake, CLM4.5, WRF and WRF-FLake range from 0.079 to 0.484, 0.177 to 0.485, 0.197 to 0.516 and 0.362 to 0.677, respectively. The maximum biases in four schemes appear at noon (12:00 in local time) which range from 0.26 to 0.66. Moreover, the diurnal pattern of simulated albedo is not a typical U-shaped curve as same as the observation. The lowest albedos from simulation appear at 13:30–15:00, but the observations remain almost unchanged from 11:00 to 15:00. This is because the aforementioned albedo Remote Sens.schemes 2018, 10 strongly, x FOR dependPEER REVIEW on the ice surface temperature, and the latter has an asymmetric diurnal 6 of 15 variation, with a maximum value at 13:30–15:00.

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Figure 3. Lake ice albedos based on the in-situ observations and different albedo parameterization Figure Figure3. Lake 3. iceLake albedos ice albedos based based on on the the in-situ in-situ observationsobservations and and different different albedo albedo parameterization parameterization schemes in Ngoring Lake for 4 and 5 January 2014 (a) and 11 to 16 February 2017 (b) (local time). schemesschemes in Ngoring in Ngoring Lake Lake for for4 and 4 and 5 January5 January 2014 2014 ((aa)) and and 11 11 to to16 16February February 2017 2017(b) (local (b) time).(local time).

The ice albedos from mobile observation in 2017 and snow-free MODIS product are presented The iceThe albedos ice albedos from from mobile mobile observation observation inin 2017 and and snow-free snow-free MODIS MODIS product product are presented are presented in Figure 44.. TheThe mobilemobile observationobservation expandsexpands thethe spatialspatial representationrepresentation ofof groundground observationsobservations andand in Figure 4. The mobile observation expands the spatial representation of ground observations and makes it easier to compare with MODIS product. Co Comparedmpared with the simulate simulatedd values from different makes italbedo easier parameterizations, to compare with the MODIS al albedobedo product.from MODIS Co mpared lies closest with to thethethe in-situin-situ simulate observations.observations.d values fromThe ice different albedo albedosparameterizations, from mobile observation the albedo range from from MODIS 0.068 to lies 0.256, closest and are to centeredcenteredthe in-situ atat 0.105.0.105. observations. The MODIS The ice albedosobservations from mobile range observation from 0.079 to range 0.223, from and are 0.068 centered to 0.256, at 0.175. and This are means centered that the at average 0.105. bias The of MODIS observationsMODIS range albedo from products 0.079 is onlyto 0.223, 0.07. and are centered at 0.175. This means that the average bias of MODIS albedo products is only 0.07.

Figure 4. ColormapColormap scatter scatter plot plot of of mobile mobile observation observation and and moderate moderate resolution resolution imaging spectrometer (MODIS) productproduct valuesvalues ( a()a) and and the the enlarged enlarged view view of theof the mobile mobile observation observation (b) in(b Ngoring) in Ngoring Lake Lake from 11from to 1811 Februaryto 18 February 2017, solid 2017, circles solid representcircles represen MODISt MODIS values in values the ﬁgure, in the and figure, open and circles open represent circles representmobile observation mobile observation (Obs) result; (Obs) Theirresult; average Their average distributions distributions are presented are presented in the in Boxthe Box graph graph (c). (c). Figure 4. Colormap scatter plot of mobile observation and moderate resolution imaging spectrometer (MODIS)3.2. Lake product Surface Albedo values Characteristics (a) and the enlargedduring the viewFrozen of Period the mobileDerived observationfrom MODIS (Observationb) in Ngoring Lake from 11During to 18 mostFebruary of the 2017, study solid period, circles the medianrepresen valuest MODIS of albedo values with in snowthe figure, cover areand lower open than circles represent0.6 (Figures mobile 5a–7a). observation The albedos (Obs) of result; different Their types average of snow distributions are different, are presentedwhich results in the in drasticBox graph changes (c). in the albedo of the snow-covered lake. The surface albedo without snow cover remains relatively constant 3.2. Lakeat Surface approximately Albedo 0.18 Characteristics in Aksai Chin during Lake, 0.15 the inFrozen Ngoring Period Lake Derived and Nam from Co LakeMODIS during Observation the frozen period (Figures 5b–7b). There are two peaks (approximately 0.5) in albedo with snow-cover in Ngoring DuringLake (Figure most 6a),of thewhich study coincide period, with athe high median proportion values of snow-covered of albedo areawith (Figure snow 6c). cover All the are surface lower than 0.6 (Figuresalbedos 5a–7a). covered The with albedos snow in of Nam diff erentCo Lake types are highof snower than are those different, from Aksai which Chin results Lake and in drasticNgoring changes in the albedoLake during of the thesnow-covered same time. During lake. Theperiods surface with exalbedotensive without snow cover, snow the cover albedos remains are higher, relatively and the constant median value can reach 0.6 in Nam Co Lake (Figure 7a,c). In addition, the average albedo of the lake at approximately 0.18 in Aksai Chin Lake, 0.15 in Ngoring Lake and Nam Co Lake during the frozen surface albedo is close to the snow-covered lake surface albedo because the snow cover area is much larger period (Figuresthan the snow-free 5b–7b). areaThere during are twmosto peaksof the study (approximately period. 0.5) in albedo with snow-cover in Ngoring Lake (Figure 6a), which coincide with a high proportion of snow-covered area (Figure 6c). All the surface albedos covered with snow in Nam Co Lake are higher than those from Aksai Chin Lake and Ngoring Lake during the same time. During periods with extensive snow cover, the albedos are higher, and the median value can reach 0.6 in Nam Co Lake (Figure 7a,c). In addition, the average albedo of the lake surface albedo is close to the snow-covered lake surface albedo because the snow cover area is much larger than the snow-free area during most of the study period.

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3.2. Lake Surface Albedo Characteristics during the Frozen Period Derived from MODIS Observation During most of the study period, the median values of albedo with snow cover are lower than 0.6 (Figure5a, Figure6a, Figure7a). The albedos of different types of snow are different, which results in drastic changes in the albedo of the snow-covered lake. The surface albedo without snow cover remains relatively constant at approximately 0.18 in Aksai Chin Lake, 0.15 in Ngoring Lake and Nam Co Lake during the frozen period (Figure5b, Figure6b, Figure7b). There are two peaks (approximately 0.5) in albedo with snow-cover in Ngoring Lake (Figure6a), which coincide with a high proportion of snow-covered area (Figure6c). All the surface albedos covered with snow in Nam Co Lake are higher than those from Aksai Chin Lake and Ngoring Lake during the same time. During periods with extensive snow cover, the albedos are higher, and the median value can reach 0.6 in Nam Co Lake (Figure7a,c). In addition, the average albedo of the lake surface albedo is close to the snow-covered lake surface albedo because the snow cover area is much larger than the snow-free area during most of Remote Sens. 2018, 10, x FOR PEER REVIEW 7 of 15 the study period.

FigureFigure 5. Snow-covered 5. Snow-covered (a) (anda) and snow-free snow-free (b ()b surface) surface albedo albedo (box (box plots) inin AksaiAksai Chin Chin Lake Lake from from 9 November9 November to 17 toMay 17 Mayof the of thefollowing following year year during during three three years. years. TheTheblack black dots dots represent represent the the average average albedoalbedo derived derived from from all allavailable available data data (snow-cove (snow-coveredred and and snow-free).snow-free). TheThe percentages percentages of snow-covered,of snow-covered, snow-freesnow-free and andmissing missing data data are are shown shown in in (c (c).). The The centre centre ofof eacheach box box represents represents the the median median value, value, the the edgesedges of each of each box boxindicate indicate the the 25th 25th and and 75th 75th percen percentiles,tiles, andand thethe whiskers whiskers represent represent the the 5th 5th and and 95th 95th percentiles of the distributions. The ﬁrst line of tick labels in X-axis represents the date and the second percentiles of the distributions. The first line of tick labels in X-axis represents the date and the second line represents the month. line represents the month.

Figure 6. Snow-covered (a) and snow-free (b) surface albedo (box plots) in Ngoring Lake from 9 November to 9 May of the following year for three years. The black dots represent the average albedo derived from all available data (snow-covered and snow-free). The percentages of snow-covered, snow-free and missing data are shown in (c).

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Figure 5. Snow-covered (a) and snow-free (b) surface albedo (box plots) in Aksai Chin Lake from 9 November to 17 May of the following year during three years. The black dots represent the average albedo derived from all available data (snow-covered and snow-free). The percentages of snow-covered, snow-free and missing data are shown in (c). The centre of each box represents the median value, the edges of each box indicate the 25th and 75th percentiles, and the whiskers represent the 5th and 95th percentiles of the distributions. The first line of tick labels in X-axis represents the date and the second Remoteline represents Sens. 2018, 10 the, 218 month. 8 of 17

FigureFigure 6. Snow-covered 6. Snow-covered (a) ( aand) and snow-free snow-free (b (b) )surface surface albedo albedo (box plots)plots) inin NgoringNgoring Lake Lake from from 9 November9 November to 9 toMay 9 May of the of the following following year year for for three three years. years. The blackblack dots dots represent represent the the average average albedo albedo derivedderived from from all all available available data data (snow-covered (snow-covered andand snow-free).snow-free). The The percentages percentages of snow-covered,of snow-covered, Remotesnow-free Sens.snow-free 2018 ,and 10 and, x missing FOR missing PEER data data REVIEW are are shown shown in in ( c). 8 of 15

FigureFigure 7. Snow-covered 7. Snow-covered (a) ( aand) and snow-free snow-free (b (b) )surface surface albedo albedo (box (box plots) inin NamNam CoCo LakeLake from from 27 December27 December to 17 May to 17 of May the of following the following year yearfor three for three years. years. The Theblack black dots dots represent represent the theaverage average albedo derivedalbedo from derived all fromavailable all available data (snow-covered data (snow-covered and and snow-free). snow-free). The The percentagespercentages of of snow-covered, snow-covered, snow-freesnow-free and and missing missing data data are are shown shown in ((cc).).

3.3. Distribution of the Albedo during the Frozen Period Derived from MODIS Observation Table 2 summarizes the three-year average (from 2013 to 2015) of the monthly average albedo for the six lakes from October to June. The albedos of snow-covered lakes range from 0.12 to 0.54, and both of maximum and minimum values appear in Jingyu Lake, which has the longest snow-covered period that extends from late October to early June. When the ice covers more than 95% of the total lake area, this time is defined as the completely frozen period. The completely frozen period of Nam Co Lake in this study matches perfectly with the observations [51], which suggests that this determination method is reliable. Overall, the surface albedos with snow cover are relatively high in Nam Co Lake, Jingyu Lake and Zhari Namco Lake, especially from January to March, which range from 0.31 to 0.54, 0.12 to 0.51 and 0.23 to 0.4, respectively. The surface albedo values with snow-cover range from 0.23 to 0.35, 0.20 to 0.41 and 0.22 to 0.29 in Aksai Chin Lake, Ngoring Lake and Qinghai Lake, respectively. Take February for example, because February is the intermediate stage of the lake frozen period. Snow-free surface albedos of all lakes range from 0.14 to 0.3, which match perfectly with the observation obtained from the Great Lakes by Bolsenga [7] (0.1–0.46). Of these, the ice albedos are relatively small (0.14) in Nam Co Lake and Ngoring Lake (Table 2).

Table 2. Three-year average (from 2013 to 2015) of the monthly average of snow-covered and snow- free surface albedos for six selected lakes. “SD” means standard deviation.

Lake Names Aksai Chin Lake Jingyu Lake Zhari Namco Lake Ngoring Lake Nam Co Lake Qinghai Lake Oct. - 0.12 - - - - Nov. 0.23 0.32 0.23 0.22 - 0.22 Dec. 0.31 0.37 0.29 0.36 - 0.28 Jan. 0.25 0.40 0.40 0.41 0.40 0.29 Feb. 0.26 0.28 0.32 0.20 0.52 0.29 Snow-covered Mar. 0.35 0.44 0.27 0.38 0.54 0.23 Apr. 0.28 0.51 0.24 0.36 0.42 - May. 0.27 0.47 - 0.23 0.31 - Jun. - 0.27 - - - - Mean 0.28 0.35 0.29 0.31 0.44 0.26 SD 0.04 0.11 0.06 0.08 0.08 0.03 Oct. - 0.02 - - - - Snow-free Nov. 0.11 0.14 0.01 0.02 - 0.01 Dec. 0.21 0.21 0.03 0.10 - 0.05

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3.3. Distribution of the Albedo during the Frozen Period Derived from MODIS Observation Table2 summarizes the three-year average (from 2013 to 2015) of the monthly average albedo for the six lakes from October to June. The albedos of snow-covered lakes range from 0.12 to 0.54, and both of maximum and minimum values appear in Jingyu Lake, which has the longest snow-covered period that extends from late October to early June. When the ice covers more than 95% of the total lake area, this time is deﬁned as the completely frozen period. The completely frozen period of Nam Co Lake in this study matches perfectly with the observations [51], which suggests that this determination method is reliable. Overall, the surface albedos with snow cover are relatively high in Nam Co Lake, Jingyu Lake and Zhari Namco Lake, especially from January to March, which range from 0.31 to 0.54, 0.12 to 0.51 and 0.23 to 0.4, respectively. The surface albedo values with snow-cover range from 0.23 to 0.35, 0.20 to 0.41 and 0.22 to 0.29 in Aksai Chin Lake, Ngoring Lake and Qinghai Lake, respectively. Take February for example, because February is the intermediate stage of the lake frozen period. Snow-free surface albedos of all lakes range from 0.14 to 0.3, which match perfectly with the observation obtained from the Great Lakes by Bolsenga [7] (0.1–0.46). Of these, the ice albedos are relatively small (0.14) in Nam Co Lake and Ngoring Lake (Table2).

Table 2. Three-year average (from 2013 to 2015) of the monthly average of snow-covered and snow-free surface albedos for six selected lakes. “SD” means standard deviation.

Aksai Jingyu Zhari Namco Ngoring Nam Co Qinghai Lake Names Chin Lake Lake Lake Lake Lake Lake October - 0.12 - - - - November 0.23 0.32 0.23 0.22 - 0.22 December 0.31 0.37 0.29 0.36 - 0.28 January 0.25 0.40 0.40 0.41 0.40 0.29 February 0.26 0.28 0.32 0.20 0.52 0.29 Snow-covered March 0.35 0.44 0.27 0.38 0.54 0.23 April 0.28 0.51 0.24 0.36 0.42 - May 0.27 0.47 - 0.23 0.31 - June - 0.27 - - - - Mean 0.28 0.35 0.29 0.31 0.44 0.26 SD 0.04 0.11 0.06 0.08 0.08 0.03 October - 0.02 - - - - November 0.11 0.14 0.01 0.02 - 0.01 December 0.21 0.21 0.03 0.10 - 0.05 January 0.22 0.24 0.25 0.16 0.07 0.20 February 0.20 0.20 0.30 0.14 0.14 0.16 March 0.24 0.27 0.20 0.18 0.19 0.06 Snow-free April 0.22 0.34 0.05 0.10 0.14 0.02 May 0.08 0.25 - 0.02 0.05 - June - 0.04 - - - - October - 0.12 - - - - Mean 0.18 0.18 0.14 0.10 0.12 0.08 SD 0.06 0.10 0.11 0.06 0.05 0.07

The probability distributions of the albedo in three lakes are presented in Figure8. The peaks of the snow-free surface albedo are concentrated within the ranges of 0.15–0.20, 0.10–0.15 and 0.10–0.15 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake, respectively, which are close to those of clear ice [7]. When all the data are included, the albedo is still centered around the peak value of 0.20 in Aksai Chin Lake, and the low albedo samples are dominant. Conversely, compared with snow-free surface (Figure8b), there is an obvious offset in the peak of albedo (0.55–0.60) derived from all data in Nam Co Lake (Figure8e), and the low albedo samples only account for a small fraction. In contrast to the previously mentioned two lakes, the kurtosis of the peak signiﬁcantly decreases in Ngoring Lake, and each interval has many samples from 0.10 to 0.65. Remote Sens. 2018, 10, x FOR PEER REVIEW 9 of 15

Jan. 0.22 0.24 0.25 0.16 0.07 0.20 Feb. 0.20 0.20 0.30 0.14 0.14 0.16 Mar. 0.24 0.27 0.20 0.18 0.19 0.06 Apr. 0.22 0.34 0.05 0.10 0.14 0.02 May. 0.08 0.25 - 0.02 0.05 - Jun. - 0.04 - - - - Oct. - 0.12 - - - - Mean 0.18 0.18 0.14 0.10 0.12 0.08 SD 0.06 0.10 0.11 0.06 0.05 0.07

The probability distributions of the albedo in three lakes are presented in Figure 8. The peaks of the snow-free surface albedo are concentrated within the ranges of 0.15–0.20, 0.10–0.15 and 0.10–0.15 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake, respectively, which are close to those of clear ice [7]. When all the data are included, the albedo is still centered around the peak value of 0.20 in Aksai Chin Lake, and the low albedo samples are dominant. Conversely, compared with snow-free surface (Figure 8b), there is an obvious offset in the peak of albedo (0.55–0.60) derived from all data in Nam Co Lake (Figure 8e), and the low albedo samples only account for a small fraction. In contrast to the previously mentioned two lakes, the kurtosis of the peak significantly decreases in Ngoring Lake, andRemote each Sens. interval2018, 10, 218has many samples from 0.10 to 0.65. 10 of 17

Figure 8. FigureProbability 8. Probability distribution distribution of the of the surface surface albedo in in Aksai Aksai Chin Chin Lake Lake (a,d), Nam(a,d Co), Nam Lake (Cob,e) Lake (b,e) and Ngoringand NgoringLake (c Lake,f) during (c,f) during the thecompletely completely frozen frozen period. period.

3.4. Influence of the Albedo Error in the Simulation with the Observation Data Derived from MODIS 3.4. Influence of the Albedo Error in the Simulation with the Observation Data Derived from MODIS The overestimated albedo means that less solar radiation is absorbed by the lake during the The overestimatedfrozen period. As albedo a result, means it can significantly that less affectsolar the radiation timing of iceis absorbed break-up in by models the [lake52–55 ].during the frozen period.Salonen As et al.a [result,56] found it thatcan the significantly changes in solar affe radiationct the inputtiming can of significantly ice brea affectk-up thein meltingmodels [52–55]. date of lake ice in the last two weeks of the frozen period. Van Cleave et al. [57] found that a drop in the Salonen et al. [56] found that the changes in solar radiation input can significantly affect the melting ice duration can increase the water temperature in summer and the evaporation rates of July–August. date of lake iceThe in effects the last of varying two weeks ice albedos of the (from frozen 0.1 to 0.4)period. on LST, Van ice thicknessCleave andet al. the [57] dates found of freezing that a drop in the ice durationand melting can increase in FLake the model water are presentedtemperature in Figure in summer9 and Table and3. Whenthe evaporation the ice albedo rates is set of to July–August. 0.1, The effects0.15, 0.2 of and varying 0.4, the root-mean-square ice albedos (from error 0.1 (RMSE) to 0.4) of LSTon (fromLST, 2012ice thickness to 2016) is 9.70, and 9.78, the 9.78dates and of freezing and melting9.84 in day, FLake respectively. model The are RMSE presented of the FLakein Figure result 9 is and 10.17 Table day. At 3. the When same the time, ice all albedo results are is set to 0.1, well correlated with MODIS observations result with high correlation coefficient which is range from 0.15, 0.2 and0.95 0.4, to 0.96. the The root-mean-square simulated LSTs are sensitiveerror (RMSE) to variations of LST in ice (from albedo 2012 especially to 2016) from Aprilis 9.70, to June9.78, 9.78 and 9.84 day, (Figurerespectively.9). When The the albedo RMSE ranges of the from FLake 0.1 to 0.4,result the LSTis 10.17 can differ day. by At up the to 10same◦C. Thetime, MODIS all results are well correlatedresults almostwith MODIS coincide withobservations the simulated result results with with lowhigh ice correlation albedo in April coefficient and May. However, which is the range from 0.95 to 0.96.MODIS The results simulated are significantly LSTs lowerare sensitive than the simulation to variations values in Junein ice and albedo July when especially the LST rose tofrom April 7 ◦C (Figure 10). It may be as the cool skin effect caused by wind speed after the lake ice is melted [58]. to June (FigureIn a word, 9). when When the icethe albedo albedo is close ranges to the mobilefrom observation0.1 to 0.4, resultthe LST (0.108) can and differ MODIS by observation up to 10 °C. The MODIS resultsresult (from almost 0.1 to coincide 0.15) in Ngoring with Lake,the simulated the simulation results results with are more low consistent ice albedo with thein MODISApril and May. observation results in melting period of Ngoring Lake (Figure9).

Remote Sens. 2018, 10, 218 11 of 17

Table3 summarizes the effect of the ice albedo on lake ice phenology. The increasing albedo can consistently postpone the time of ice break-up in the simulations. When the albedo ranges from 0.1 to Remote0.4, Sens. the 2018 lake, 10 ice, xmelting FOR PEER date REVIEW will postpone for 10–43 days (2012–2016). When the albedo increases 10 of 15 from 0.1 to 0.2, it postpones for 2–14 days, and when the albedo increases from 0.2 to 0.4, it postpones However,for 10–33 the days. MODIS The results average are ice significantly thickness increases lower th withan the the simulation increase in values albedo. in The June averaged and July LST when thein LST the rose coming to 7 summer°C (Figure (from 10). July It may to August) be as the decreases cool skin with effect the caused increase by of wind albedo speed (from after 0.1 to the 0.4), lake ice exceptis melted 2012–2013. [58]. In Thea word, averaged when LSTs the inice winter albedo (December is close to to the February mobile of observation next year) generally result (0.108) show and a decreasing trend with the increase of albedo, except in 2012–2013. However, the change in freezing MODIS observation result (from 0.1 to 0.15) in Ngoring Lake, the simulation results are more date is irregular in different experiments, because it is also affected by the air temperature in the consistent with the MODIS observation results in melting period of Ngoring Lake (Figure 9). forcing data.

FigureFigure 9. The 9. The land land surface surface temperature temperature (LST) (LST) from from MODI MODISS and thethe simulationssimulations in in Ngoring Ngoring Lake Lake from from 20122012 to to2016; 2016; “FLake” “FLake” means means the simulationsimulation adopts adopts the th defaulte default ice surfaceice surface albedo albedo scheme scheme in FLake, in FLake, and andthe the numbers numbers “0.1, “0.1, 0.15, 0.15, 0.2, 0.2, 0.4” 0.4” mean mean the ice the albedo ice albedo is set tois theset correspondingto the corresponding value in FLakevalue model;in FLake model;Shaded Shaded area indicatesarea indicates April toApril June to (included). June (included).

Table 3. Lake ice phenology from simulated and MODIS observation (freezing date, melting date, ice thickness) and coming summer LST in Ngoring Lake.

Ice Albedo Melting Ice Coming LST in Freezing Date Duration Parameterized Date Thickness Summer LST Winter 0.1 5 December 2012 14 April 2013 0.48 15.11 −9.17 0.15 30 November 2012 18 April 2013 0.56 15.44 −10.24 2012–2013 0.2 8 December 2012 14 April 2013 0.54 15.87 −9.12 0.4 5 December 2012 24 April 2013 0.69 15.68 −10.58 FLake 5 December 2012 12 May 2013 0.74 16.04 −11.56 0.1 17 December 2013 2 April 2014 0.43 15.52 −7.49 0.15 13 December 2013 8 April 2014 0.52 14.86 −8.35 Figure 10. The LST from MODIS and the simulations in Ngoring Lake from March to July 2013 (a) 2013–2014 0.2 3 December 2013 16 April 2014 0.56 14.85 −9.53 and 2014 (b); “FLake”0.4 means 29 the November simulation 2013 adopts 15 May the 2014 default ice 0.66 surface albedo 15.42 scheme in FLake,−11.36 and the numbersFLake “0.1, 0.15, 290.2, November 0.4” mean 2013 the ice28 Aprilalbedo 2014 is set to 0.69the corresponding 14.27 value in −FLake12.58 model. 0.1 2 December 2014 14 April 2015 0.5 14.33 −9.3 0.15 5 December 2014 16 April 2015 0.54 14.31 −9.15 2014–2015 0.2 6 December 2014 20 April 2015 0.57 14.4 −9.27 Table 3 summarizes0.4 the effect 4 December of the 2014ice albedo 18 May on 2015lake ice phenology. 0.66 The 13.99 increasing albedo−10.51 can consistently postponeFLake the time 16 of December ice break-up 2015 in 2 the May simulations. 2015 0.67 When the albedo 14.96 ranges− 9.61from 0.1 to 0.4, the lake ice melting0.1 date 12 will December postpone 2015 for 6 10–43 April 2016 days (2012–2016). 0.53 When 16.97 the albedo− increases9.17 − from 0.1 to 0.2, it postpones0.15 for 92–14 December days, 2015 and when11 April the 2016 albedo increases0.58 from16.97 0.2 to 0.4, it postpones9.61 2015–2016 0.2 8 December 2015 20 April 2016 0.59 16.94 −9.91 for 10–33 days. The average0.4 ice 15 thickness December 2015increases 11 May with 2016 the increase 0.66 in albedo. 15.39 The averaged−9.92 LST in the coming summerFLake (from July 4 Decemberto August) 2015 decreases30 April with 2016 the increase0.73 of albedo 16.87 (from −0.112.93 to 0.4), except 2012–2013. The averaged LSTs in winter (December to February of next year) generally show a decreasing trend with the increase of albedo, except in 2012–2013. However, the change in freezing date is irregular in different experiments, because it is also affected by the air temperature in the forcing data.

Table 3. Lake ice phenology from simulated and MODIS observation (freezing date, melting date, ice thickness) and coming summer LST in Ngoring Lake.

Ice Albedo Melting Ice Coming Summer LST in Freezing Date Duration Parameterized Date Thickness LST Winter 0.1 05 December 2012 14 April 2013 0.48 15.11 −9.17 0.15 30 November 2012 18 April 2013 0.56 15.44 −10.24 2012–2013 0.2 08 December 2012 14 April 2013 0.54 15.87 −9.12 0.4 05 December 2012 24 April 2013 0.69 15.68 −10.58

Remote Sens. 2018, 10, x FOR PEER REVIEW 10 of 15

However, the MODIS results are significantly lower than the simulation values in June and July when the LST rose to 7 °C (Figure 10). It may be as the cool skin effect caused by wind speed after the lake ice is melted [58]. In a word, when the ice albedo is close to the mobile observation result (0.108) and MODIS observation result (from 0.1 to 0.15) in Ngoring Lake, the simulation results are more consistent with the MODIS observation results in melting period of Ngoring Lake (Figure 9).

Figure 9. The land surface temperature (LST) from MODIS and the simulations in Ngoring Lake from 2012 to 2016; “FLake” means the simulation adopts the default ice surface albedo scheme in FLake, Remoteand Sens. the2018 numbers, 10, 218 “0.1, 0.15, 0.2, 0.4” mean the ice albedo is set to the corresponding value in FLake12 of 17 model; Shaded area indicates April to June (included).

Figure 10. TheThe LSTLST fromfrom MODIS MODIS and and the the simulations simulations in in Ngoring Ngoring Lake Lake from from March March to July to July 2013 2013 (a) and (a) 2014and 2014 (b); “FLake”(b); “FLake” means means the simulation the simulation adopts adopts the default the default ice surface ice surface albedo albedo scheme scheme in FLake, in andFLake, the numbersand the numbers “0.1, 0.15, “0.1, 0.2, 0.15, 0.4” mean0.2, 0.4” the mean ice albedo the ice is setalbedo to the is correspondingset to the corresponding value in FLake value model. in FLake model. 4. Discussion Table 3 summarizes the effect of the ice albedo on lake ice phenology. The increasing albedo can consistentlyWe organized postpone field the observations time of ice break-up to obtain in the the actual simulations. albedo When of the the lake albedo ice, which ranges confirmed from 0.1 thatto 0.4, the the parameterization lake ice melting schemedate will seriously postpone overestimated for 10–43 days the (2012–2016). ice albedo When on the the TP albedo lake (0.26–0.66). increases Wefrom proved 0.1 to the0.2, universalityit postpones of for low 2–14 albedo days, (0.1–0.2) and when inTP the lakes albedo through increases the analysisfrom 0.2of toMODIS 0.4, it postpones products infor six 10–33 typical days. TP The lakes. average A few ice albedo thickness observations increases for wi theth Great the increase Lakes and in albedo. high-altitude The averaged lakes have LST been in reportedthe coming [17 ,summer24,25]. However, (from July these to studiesAugust) are decreases rarely associated with the withincrease lake iceof albedo albedo parameterization.(from 0.1 to 0.4), Asexcept previously 2012–2013. stated, The the averaged ice surface LSTs albedo in winter can seriously(December affect to February the simulation of next of year) the lake generally ice-freezing show period,a decreasing and thentrend indirectly with the increase affect the of lake albedo, surface exce temperaturept in 2012–2013. in the However, ice-free the period, change as wellin freezing as the regionaldate is irregular weather andin different climate simulation.experiments, According because toit ouris also simulation affected results, by the when air temperature the albedo changes in the fromforcing 0.1 data. to 0.4, the simulation results can differ by up to ten degrees at the same time, and the lake ice melting date was postponed for 10–43 days (2012–2016). At present, scholars pay less attention to theTable ice duration 3. Lake ice in phenology TP lakes. from Only simulated a few studies and MODIS analyzed observation the changes (freezing in thedate, freezing melting datedate, ofice Nam Co Lakethickness) [34], as and well coming as lake summer ice structure LST in Ngoring [35], thermal Lake. conductivity [36] and ice melting date [37] in a small TP thermokarst lake. Ice Albedo Melting Ice Coming Summer LST in Freezing Date DurationWhat is interestingParameterized is that the significant impactDate of ice albedoThickness on LST continuesLST from mid-AprilWinter to the end of June. Our0.1 further work 05 December will use 2012 the better 14 April remote 2013 sensing 0.48 products, 15.11 calibrated observation−9.17 to improve the parameterization0.15 30 November scheme 2012 of ice albedo,18 April 2013 and make 0.56 it more suitable 15.44 for the TP lakes.−10.24 We 2012–2013 will also focus on more0.2 comprehensive 08 December simulation 2012 14 April using 2013 the atmosphere-lake 0.54 15.87coupling model−9.12 such as 0.4 05 December 2012 24 April 2013 0.69 15.68 −10.58 WRF. Then we will explore how much the different parametrization affects the results of the climate modelling in terms of impact on the main variables. There remain a few uncertainties on MOD43A products. Figure 10 shows the surface albedo and the ratio of snow-covered to snow-free pixels based on the V005 and V006 MCD43A3 products in Aksai Chin Lake during the frozen period. Many models require daily albedos, especially during the ice/snow melting season when the snow/ice albedo changes rapidly [40]. Therefore, the new 16-day MODIS albedo products (V006) that is updated every day have been released, which helps to improve retrievals in high-altitude regions and can better capture conditions associated with snow cover and dormant vegetation [59]. The surface albedo under snow-free conditions in the V005 is almost the same as that in the V006 product (Figure 11a), which means the lake ice albedos used in the previous sections are in line with the results from V006 product. Compared with the V006, the ice albedo with snow cover in the V005 is higher from the 49th day to the 97th day of 2014. However, a paradoxical phenomenon appears from the 1st day to the 16th day of 2014. During this period, the percentage of snow-covered pixels is high, but the surface albedos only hover around 0.2 (Figure 11a). At that time, the normalized difference snow index (NDSI) within each pixel is also quite large, based on the MOD10A1 daily albedo product (Figure 12a). Correspondingly, the MOD10A1 product also presents a relatively small albedo that ranges from 0.1 to 0.3, except on the 6th day of 2014 (Figure 12b). Chen et al. [60] found that the albedo of MODIS MCD43 in the TP was mainly under the “snow-free” state. Therefore, this issue is universal on the TP. There may be some reasons, such as the MODIS product algorithm, which Remote Sens. 2018, 10, 218 13 of 17 lead to low albedos in highly snow-covered pixels during this period. However, the reason needs to be Remote Sens. 2018, 10, x FOR PEER REVIEW 12 of 15 furtherRemote explored Sens. 2018, in10, future x FOR PEER work. REVIEW 12 of 15

Figure 11. Lake surface albedos in Aksai Chin Lake from the 342nd day of 2013 to the 109th day of FigureFigure 11. 11.Lake Lake surface surface albedos albedos in Aksaiin Aksai Chin Chin Lake Lake from from the the 342nd 342nd day day of 2013of 2013 to theto the 109th 109th day day of of 2014, as derived from the V006 and V005 MCD43A3 products (a); and the ratio of snow-covered to 2014,2014, as derivedas derived from from the the V006 V006 and and V005 V005 MCD43A3 MCD43A3 products products (a); (a and); and the the ratio ratio of snow-coveredof snow-covered to to snow-free pixels for the V006 (b) and V005 (c) products. snow-freesnow-free pixels pixels for for the the V006 V006 (b) ( andb) and V005 V005 (c) products.(c) products.

Figure 12. Surface albedo and normalized difference snow index (NDSI) (box plots) in Aksai Chin Figure 12. Surface albedo and normalized difference snow index (NDSI) (box plots) in Aksai Chin Lake LakeFigure (1 to 12. 16 JanuarySurface albedo 2014). (anda) NDSI; normalized (b) Surface difference albedo. snow index (NDSI) (box plots) in Aksai Chin Lake (1 to 16 January 2014). (a) NDSI; (b) Surface albedo. (1 to 16 January 2014). (a) NDSI; (b) Surface albedo.

5. Conclusions5. Conclusions 5. Conclusions UsingUsing the the observational observational data data collected collected in Ngoringin Ngoring Lake Lake in 2014in 2014 and and 2017, 2017, the the ice albedoice albedo Using the observational data collected in Ngoring Lake in 2014 and 2017, the ice albedo parameterizationsparameterizations in existing in existing lake lake models models are evaluatedare evalua inted this in this study. study. The The characteristics characteristics of surface of surface parameterizations in existing lake models are evaluated in this study. The characteristics of surface albedoalbedo in six in typicalsix typical high-altitude high-altitude lakes lakes are investigatedare investigated based based on MODISon MODIS albedo albedo products products covering covering albedo in six typical high-altitude lakes are investigated based on MODIS albedo products covering the lakethe lake ice phenology ice phenology period period from 2013 from to 2015.2013 Further,to 2015. the Further, effect ofthe ice effect albedo of on ice the albedo lake simulation on the lake the lake ice phenology period from 2013 to 2015. Further, the effect of ice albedo on the lake is evaluatedsimulation using is evaluated FLake model. using The FLake results model. can The be summarized results can be as summarized follows. as follows: simulation is evaluated using FLake model. The results can be summarized as follows: ComparedCompared to observations, to observations, the albedothe albedo parameterization parameterization schemes schemes in existing in existing lake lake models models tend tend to to Compared to observations, the albedo parameterization schemes in existing lake models tend to overestimateoverestimate the icethe albedo,ice albedo, with with a bias a bias of 0.26–0.66. of 0.26–0.66. In contrast, In contrast, the albedosthe albedos derived derived from from MODIS MODIS overestimate the ice albedo, with a bias of 0.26–0.66. In contrast, the albedos derived from MODIS coincidecoincide with with the ﬁeld-observedthe field-observed values values well, well, with with only on aly slight a slight overestimation overestimation of 0.07. of 0.07. Different Different from from coincide with the field-observed values well, with only a slight overestimation of 0.07. Different from the typical U-shaped curve presented in the observation, the diurnal variations of simulated albedo the typical U-shaped curve presented in the observation, the diurnal variations of simulated albedo are relatively irregular, and the minimum value lags behind the observation. are relatively irregular, and the minimum value lags behind the observation. The surface albedos of Nam Co Lake, Jingyu Lake and Zhari Namco Lake are relatively high The surface albedos of Nam Co Lake, Jingyu Lake and Zhari Namco Lake are relatively high during the frozen period, ranging from 0.31 to 0.54, 0.12 to 0.51 and 0.23 to 0.4, respectively. The during the frozen period, ranging from 0.31 to 0.54, 0.12 to 0.51 and 0.23 to 0.4, respectively. The

Remote Sens. 2018, 10, 218 14 of 17 the typical U-shaped curve presented in the observation, the diurnal variations of simulated albedo are relatively irregular, and the minimum value lags behind the observation. The surface albedos of Nam Co Lake, Jingyu Lake and Zhari Namco Lake are relatively high during the frozen period, ranging from 0.31 to 0.54, 0.12 to 0.51 and 0.23 to 0.4, respectively. The albedos of Aksai Chin Lake, Ngoring Lake and Qinghai Lake range from 0.23 to 0.35, 0.20 to 0.41 and 0.22 to 0.29, respectively. The albedo variation is closely related to the proportion of snow cover on the lake surface. Snow-free surface albedos range from 0.14 to 0.3 in February. The peaks of ice surface albedos in a snow-free state are concentrated within the ranges 0.14–0.16, 0.08–0.10 and 0.10–0.12 in Aksai Chin Lake, Nam Co Lake and Ngoring Lake during the completely frozen period, which are close to the albedo of clear ice. The lake ice albedo has a signiﬁcant effect on the melting date in spring and the minimum temperature of simulated LST in winter. When the albedos change from 0.1 to 0.4, the lake ice melting date is postponed for 10–43 days (2012–2016). Additionally, the simulation result is closest to the observation when the ice albedo is close to the observation (from 0.1 to 0.2) in the lake model. The averaged LST in winter and summer generally show a decreasing trend with the increase of lake ice albedo (from 0.1 to 0.4).

Acknowledgments: This study is supported by the National Natural Science Foundation of China (Nos. 41605011, 41775016, 41661144043, 91537104, 91637312), the Science and Technology Service Network Initiative of CAREERI (651671001), the Foundation for Excellent Youth Scholars of NIEER, CAS (Y651K51001), the Chinese Academy of Sciences (QYZDJ-SSW-DQC019). We acknowledge the hard work of Shaobo Zhang and Xiaohui Peng in the field observations. We thank American Journal Experts (AJE) English language editing for its assistance with the manuscript preparation. Author Contributions: Shihua Lyu and Yaoming Ma conceived and designed the experiments; Zhaoguo Li, Jiahe Lang, and Dongsheng Su performed the experiments; Zhaoguo Li and Jiahe Lang analyzed the data; Jiahe Lang wrote the main manuscript, and all the authors contributed to write and discuss the paper. Conflicts of Interest: The authors declare no conflict of interest.

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