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  • View in gallery

    The topographic map (shading; m) and locations of the 46 lakes (red circles) that cover more than 100 km2 over the Tibetan Plateau.

  • View in gallery

    Linear trend (days yr−1) of the (a) freeze-up date, (b) break-up date, and (c) ice duration days during 2002–15. A circle with a cross means the change is significant at the 95% confidence level.

  • View in gallery

    Plots of the correlation between ice duration (ID) and freeze-up date (FU), as well as the break-up (BU) date of a single lake over the Tibetan Plateau. The numbers on the x axis correspond to the lake numbers in Fig. 1. The black dashed line means the threshold line with the correlation is significant at the 95% confidence level.

  • View in gallery

    The spatial distribution maps of the Pearson correlation between the winter NAO index and the (a) break-up date and (b) ice duration days. The orange, red, and purple circles mean the correlation is significant at the 90%, 95%, and 99% confidence levels, respectively, and the gray indicates that the significance test has not been passed. The plus means a positive correlation, and the box represents the region of the southern TP that was analyzed in this study.

  • View in gallery

    Scatterplots of the boreal winter NAO index with the annual average (a) break-up date and (b) ice duration days over all the lakes from the southern TP for each year. The “cc” represents the Pearson correlation coefficient.

  • View in gallery

    Plots of the average (a) break-up date and (c) ice duration days of the lakes over the southern Tibetan Plateau during 2002–15 (the black straight lines represent the average break-up date and ice duration days in the southern TP), as well as the regional average variations in the (b) break-up date and (d) ice duration days. The black straight line represents the linear trend that was calculated using the least squares method.

  • View in gallery

    Scatterplots of the annual average (a) break-up dates of 18 lakes with the average surface air temperature for April in the southern Tibetan Plateau, and (b) ice duration with the regional average JFM surface air temperature. The “cc” represents the Pearson correlation coefficient.

  • View in gallery

    Composite difference of winter mean (JFM) (a) geopotential height at 500 hPa, (b) the winds at 500 hPa, and (c) zonal wind at 200 hPa between the three longest duration events and the three shortest duration events. The ice duration index is calculated by the average of 18 lakes in the southern TP from 2002 to 2015. The black dots in (a) and (c) and the green color in (b) indicate that the anomalies are significant at the 90% confidence level from a Student’s t test.

  • View in gallery

    Composite difference of winter mean (JFM) vertically integrated water vapor transport vectors between three long duration events and three short duration events. The green color indicates the anomalies are significant at the 90% confidence level from a Student’s t test.

  • View in gallery

    Anomalous JFM wave activity flux (m2 s−2) for (a) five positive NAO years and (b) five negative NAO years from 1990 to 2015. Geostrophic streamfunction anomalies are shown as colors (106 m2 s−1).

  • View in gallery

    Composite difference of JFM zonal wind at 300 hPa for (a) five positive NAO years, (b) five negative NAO years from 1990 to 2015 and the climatological zonal winds from 1990 to 2015, and (c) the composite difference of zonal wind at 300 hPa for five positive NAO years and five negative years. The contour lines represent the climatological zonal wind from 1990 to 2015. The black dots indicate that the anomalies are significant at the 90% confidence level from a Student’s t test.

  • View in gallery

    Composite difference of winter mean (JFM) (a) geopotential height at 500 hPa, (b) the winds at 500 hPa, and (c) zonal wind at 200 hPa between five positive NAO years and five negative NAO years from 1990 to 2015. The black dots in (a) and (c) and the green color in (b) indicate that the anomalies are significant at the 90% confidence level from a Student’s t test.

  • View in gallery

    The correlation maps between the JFM vertically integrated water vapor and DJF NAO index from 2002 to 2015. The green color indicates the anomalies are significant at the 90% confidence level from a Student’s t test.

  • View in gallery

    Time series for the average temperature (red curve) and average precipitation (blue curve) of Shiquanhe, Gerze, Amdo, Nagchu, Burang, Bangoin, Dangxiong, and Xainza in the southern Tibetan Plateau.

  • View in gallery

    The correlation maps of the February–April (a) net longwave radiation fluxes (W m−2) and (b) net shortwave radiation fluxes (×−1; W m−2), with the average ice duration of 18 lakes in the southern Tibetan Plateau. The black dots mean the correlations are significant at the 90% confidence level from a Student’s t test, and the box represents the study area.

  • View in gallery

    As in Fig. 1, but for the correlation with the DJF NAO index.

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The Impact of the NAO on the Delayed Break-Up Date of Lake Ice over the Southern Tibetan Plateau

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  • 1 Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • 2 Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University for Information Science and Technology, Nanjing, China
  • 3 Digital Earth Laboratory, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
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ABSTRACT

The changing characteristics of lake ice phenology over the Tibetan Plateau (TP) are investigated using historical satellite retrieved datasets during 2002–15 in this study. The results indicate that the freezing process mainly starts in December, and the ice melting process generally occurs in April for most lakes. However, the changes in lake ice phenology have varied depending on the location in recent years, with delayed break-up dates and prolonged ice durations in the southern TP, but no consistent changes have occurred in the lakes in the northern TP. Further analysis presents a close connection between the variation in the lake ice break-up date/ice duration over the southern TP and the winter North Atlantic Oscillation (NAO). The positive NAO generally excites an anomalous wave activity that propagates southward from the North Atlantic to North Africa and, in turn, strengthens the African–Asian jet stream at its entrance. Because of the blocking effect of the TP, the enhanced westerly jet can be divided into two branches and the south branch flow can deepen the India–Myanmar trough, which further strengthens the anomalous cyclonic circulation and water vapor transport. Therefore, the increased water vapor transport from the northern Indian Ocean to the southern region of the TP can increase the snowfall over this region. The increased snow cover over the lake acts as an insulating layer and lowers the lake surface temperature in the following spring by means of snow–ice feedback activity, resulting in a delayed ice break-up date and the increased ice duration of the lakes over the southern TP in recent years.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chen Huopo, chenhuopo@mail.iap.ac.cn

ABSTRACT

The changing characteristics of lake ice phenology over the Tibetan Plateau (TP) are investigated using historical satellite retrieved datasets during 2002–15 in this study. The results indicate that the freezing process mainly starts in December, and the ice melting process generally occurs in April for most lakes. However, the changes in lake ice phenology have varied depending on the location in recent years, with delayed break-up dates and prolonged ice durations in the southern TP, but no consistent changes have occurred in the lakes in the northern TP. Further analysis presents a close connection between the variation in the lake ice break-up date/ice duration over the southern TP and the winter North Atlantic Oscillation (NAO). The positive NAO generally excites an anomalous wave activity that propagates southward from the North Atlantic to North Africa and, in turn, strengthens the African–Asian jet stream at its entrance. Because of the blocking effect of the TP, the enhanced westerly jet can be divided into two branches and the south branch flow can deepen the India–Myanmar trough, which further strengthens the anomalous cyclonic circulation and water vapor transport. Therefore, the increased water vapor transport from the northern Indian Ocean to the southern region of the TP can increase the snowfall over this region. The increased snow cover over the lake acts as an insulating layer and lowers the lake surface temperature in the following spring by means of snow–ice feedback activity, resulting in a delayed ice break-up date and the increased ice duration of the lakes over the southern TP in recent years.

© 2018 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Chen Huopo, chenhuopo@mail.iap.ac.cn

1. Introduction

The Tibetan Plateau (TP) was identified as one of the most sensitive regions to climate change around the world (Liu and Chen 2000). However, its sparse meteorological stations, steep terrain, and difficult accessibility have hindered further studies of how the TP responds to global climate change. Remote satellite records have thus become an effective tool to evaluate the impact of climate change over this region (Cai et al. 2017; Kropáček et al. 2013; Yao et al. 2012). Variations in the climate in this area are reflected by several phenomena, such as snow depth (Xin et al. 2010; Xu et al. 2017; You et al. 2011), changes in the glacier extent (Yao et al. 2012), and lake ice (Cai et al. 2017; Yao et al. 2016). The changes in lake ice phenology are of great significance to the local hydrometeorology, freshwater ecosystems, regional climate, and downstream environment (Bai et al. 2012; Takaya and Nakamura 2001). Variations in the lake ice phenology such as break-up, freeze-up, and ice duration are also clear and direct indicators of climate conditions and the physical characteristics of lakes (Latifovic and Pouliot 2007). The physical characteristics of lakes, including the magnitude of evaporation, the timing of stratification, and the productivity of aquatic ecosystems, are closely associated with the lake ice dynamics that control the seasonal heat budget of lake systems (Latifovic and Pouliot 2007). Recent studies have revealed that deep lakes, such as Nam Co over the TP, can supply a great amount of heat energy to the atmosphere in postmonsoon periods and have a significant effect on the snowfall on the downwind shore during winter (Haginoya et al. 2009; Kropáček et al. 2013). Some studies have also suggested that lake ice phenology is a good proxy for climate variability and change, and in some cases, the lake ice phenology can be a more robust measure than temperature (Livingstone 1997; Palecki and Barry 1986; Robertson et al. 1992; Wang and Sun 2009).

The North Atlantic Oscillation (NAO) is the dominant pattern of atmospheric low-frequency variability over the North Atlantic/Northern Hemisphere at an interannual time scale (Barnston and Livezey 1987; Wallace and Gutzler 1981). Some studies have suggested that the influences of the NAO downstream extension on the East Asian winter climate are related to NAO-induced upper-level convergence anomalies over the Mediterranean region, which can stimulate quasi-stationary Rossby waves along the North African–Asian jet waveguide (Watanabe 2004; Zuo et al. 2016). Atmospheric teleconnection may allow the NAO to exert a significant downstream impact on the East Asian winter climate (Ding and Li 2017; Li and Sun 2015; Sun et al. 2008; Sun and Wang 2012; Tian and Fan 2012; Wan et al. 2017; Xu et al. 2013; Yuan and Sun 2009; Zuo et al. 2016). Xin et al. (2010) presented evidence that changes in the sign of winter NAO are connected to the adjustment of the atmospheric circulation over the Tibetan Plateau. During the positive phase of NAO, the Asian subtropical westerly jet intensifies and the India–Myanmar trough deepens, which lead to favorable environments for snowstorms (Xin et al. 2010).

Regional climate variability exerts an important impact on lake ice phenological characteristics by modulating the heat transfer processes related to the formation and melting of the seasonal ice cover (Kirillin et al. 2012). Lake ice phenology changes have typically been associated with variations in air temperature (Brown and Duguay 2010). Additionally, large-scale atmospheric and oceanic circulation patterns known as teleconnections also play an important role in the variations in regional lake ice. Recent evidence has revealed that different teleconnection patterns have variously contributed to the variations in lake ice cover (Ghanbari et al. 2009). For example, the Arctic Oscillation/North Atlantic Oscillation (AO/NAO) presented a significantly negative relationship with the interannual variation in the Great Lakes ice cover (Wang et al. 2012), and an intimate connection can also be observed between NAO and the variation in European lake ice (George 2007; Sánchez-López et al. 2015).

A clear relationship between the lake ice phenology and teleconnection patterns, such as NAO, has been revealed by early studies. However, the issue related to how the teleconnection pattern impacts the variation in lake ice phenology is still unclear. Additionally, these early studies on lake ice phenology are mainly concentrated over Europe and North America and less concern can be seen in China. Because of the lack of site-measured lake ice phenology dataset on the TP, the related cryosphere research has mainly focused on glaciers, snowpacks, and permafrost (Yao et al. 2012; Xu et al. 2017), but there are few studies on the lake ice phenology, especially on the possible mechanism from the impact of large-scale atmospheric circulation anomalies.

Our study thus aims to explore the mechanism of the variations in the lake ice phenology over the TP related to large-scale atmospheric circulation anomalies. This paper is arranged as follows. First, section 2 presents a brief description of the data and methods. In section 3, the main results are provided, including an analysis of the lake ice phenological characteristics and explanation of the impact from NAO. Finally, a simple discussion and the conclusions are presented in section 4.

2. Data and methods

a. Dataset

The precipitous geographical characteristics across the TP make it difficult to access this region and thus make it impossible to collect data directly. An alternative and effective way to acquire data is to retrieve the freeze–thaw parameters of the lake ice from remote satellite records. Recently, the Science Data Bank (http://www.sciencedb.cn/dataSet/handle/371) released a set of lake ice phenology data spanning from 2002 to 2016 that includes 46 lakes (Fig. 1) covering more than 100 km2 over the TP (Qiu et al. 2017a). This dataset was originally derived from the Advanced Microwave Scanning Radiometer for Earth Observing System (EOS) (AMSR-E), Advanced Micrometer Scanning Radiometer 2 (AMSR2), and Microwave Radiation Imager (MWRI). As indicated by Qiu et al. (2017b), the satellite-borne microwave remote sensing can realize the rapid determination of lake ice thaw and freezing caused by the great difference in ice and water emissivity at 18.7-GHz polarizations. Based on the proportion rate of lake and land area within the microwave radiometer pixels, the brightness temperature of the lake surface is calculated by way of linear and dynamic decomposition from the footprint measurement of the radiometer. And then, the lake freeze–thaw dates, like freeze start, freeze completion, ablation start, and ablation completion dates, can be determined by the turning points of periodic brightness temperature curves. In particular, this unmixing methodology can lead to subpixel lake ice phenology detection, and this is the largest number ever for the lake ice monitoring in the TP according to the all-weather capability of microwave remote sensing. Furthermore, three different sizes of lakes, Dagze Co, Kusai Lake, and Hoh Xil Lake, which were distributed in different regions of TP, were selected to validate the accuracy of retrieved lake freeze–thaw parameters, compared with the cloudless Moderate Resolution Imaging Spectroradiometer (MODIS) optical snow products. The results show that lake ice freeze–thaw parameters extracted from AMSR-E and the cloudless MODIS products present high correlation coefficients, 0.968 and 0.987, respectively. It is noteworthy that the lake ice phenology dataset was absent from October 2011 to July 2012 because the AMSR-E sensor stopped working on 4 October 2011 because of a problem with its antenna.

Fig. 1.
Fig. 1.

The topographic map (shading; m) and locations of the 46 lakes (red circles) that cover more than 100 km2 over the Tibetan Plateau.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

As indicated by the National Snow and Ice Data Center (NSIDC), the freeze date is defined as the first date on which the water body was observed to start to freeze, and the break-up date is defined as the date of observed ice-free conditions before the summer open water phase. The ice duration is thus the number of days that a lake body is covered with ice. Therefore, we selected the freeze start date/ablation completion date as freeze date/break-up date. For the convenience of calculation, we converted the Gregorian calendar date to the number of days in a year. Note that the boreal winter in this paper is defined as the months of January–March (JFM).

In this study, we use the monthly mean data from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay et al. 1996), with the configuration of 2.5° × 2.5° horizontal resolution and 17 vertical pressure levels extending from 1000 to 10 hPa. The variables including air temperature, zonal and meridional wind, geopotential height, and specific humidity would be used for the analyses in this study, as well as the net shortwave and longwave radiation flux. Early studies have suggested that the NCEP–NCAR reanalysis data can reflect the changes of the atmospheric circulation anomalies and temperature from the synoptic perspective around the TP (Xie et al. 2007).

In addition, the site-observed daily precipitation and temperature datasets from 2002 to 2015 over China used in this study were provided by the National Climate Center, China Meteorological Administration. The monthly standardized NAO index was obtained from the National Oceanic and Atmospheric Administration Climate Prediction Center (https://www.esrl.noaa.gov/psd/data/climateindices).

Seasonal means are constructed from the monthly means by averaging the data of JFM, which results in data fields for 12 boreal winters (2002–14).

b. Methods

This study mainly adopted Pearson correlation and regression analysis to examine variations in lake ice phenology over the TP related to atmospheric circulation fields. The statistical significance of regression and correlation coefficients was assessed using the Student’s t test. To examine the NAO-induced atmospheric circulation anomalies, we also calculated the three-dimensional stationary wave activity flux to demonstrate its propagation based on the method proposed by Takaya and Nakamura (2001):
e1
Here, denotes the streamfunction; f is the Coriolis parameter; U is the horizontal wind velocity; and is the buoyancy frequency squared, where θ denotes the potential temperature, P equals pressure divided by 1000 hPa, is the gas constant of dry air, H is the constant scale height, and κ is defined as normalized by the specific heat of air for constant pressure. The overbars and primes denote the JFM mean and monthly disturbances. Furthermore, in this study, we put more attention on the three-dimensional stationary wave activity flux propagation. So it is worth noticing that Eq. (1) overlooks the term CuM, which represents the effects of the phase propagation in the original equation proposed by Takaya and Nakamura (2001), and this approximation employed here is widely used in other studies (e.g., Hu et al. 2017).

3. Results

a. Change characteristics of lake ice phenology over the TP

A recent study has presented evidence that a later freeze, earlier break-up, and shorter ice duration occurred around the world using long-term ice phenology data for a total of 75 lakes in the Northern Hemisphere, which corresponds to global warming trends (Benson et al. 2012). A similar phenomenon in freezing process can also be seen over the TP. Figure 2 shows the spatial distribution of the linear trends of the lake ice phenology over the TP during 2002–15, including the lake ice freeze-up date, break-up date, and ice duration. It is obvious that there are delayed freeze-up dates for most lakes across the TP, which is indicative of warming temperatures in line with global trends. Normally, the break-up dates of the lakes over the TP should be shifted to an earlier date resulting from the warming. However, we have found that the thaw times of the ice for most lakes over the TP are also delayed, which directly results in the increase in ice duration for most lakes. Some studies have also indicated that there are slightly longer lake ice durations in part of Canada in the recent years (Du et al. 2017; Murfitt and Brown 2017). This result is further validated by the correlation analyses, with positive correlations observed between the ice break-up time and ice duration but negative correlations observed between the freeze-up date and ice duration (Fig. 3). The main purpose of this study is thus to explore the possible mechanisms of changes in both the lake ice break-up date and duration over the TP.

Fig. 2.
Fig. 2.

Linear trend (days yr−1) of the (a) freeze-up date, (b) break-up date, and (c) ice duration days during 2002–15. A circle with a cross means the change is significant at the 95% confidence level.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 3.
Fig. 3.

Plots of the correlation between ice duration (ID) and freeze-up date (FU), as well as the break-up (BU) date of a single lake over the Tibetan Plateau. The numbers on the x axis correspond to the lake numbers in Fig. 1. The black dashed line means the threshold line with the correlation is significant at the 95% confidence level.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

The variations in the local meteorological conditions, including surface air temperature and precipitation, can greatly impact the lake ice development (Sánchez-López et al. 2015). We have also investigated the relationship between lake ice parameters over the TP and the related site-observed surface air temperature and precipitation from the adjacent meteorological stations (table not shown). It is noteworthy that there are many lakes that are located far from the surrounding meteorological sites, and only a few lakes are within 1° range in the TP. The results show that there is a relatively weak relationship between the freeze-up time and the surface air temperature and precipitation in the early winter. This is because the freezing process is primarily dependent on the lake hydrological characteristics, which might contradict the general knowledge that a higher preceding winter temperature and more rainfall would postpone the freeze date. However, relatively higher correlations are observed between the break-up date and surface air temperature/precipitation during the JFM period in the southern TP prior to the complete thawing of the ice for some lakes, as well as the ice duration. For example, some lakes over the southern TP like BamCo, NamCo, Dung Co, Pung Co, Langa Co, and Selin Co show a high correlation with the site-observed surface air temperature and precipitation in JFM, with Pearson correlation coefficients ranging from 0.435 to 0.736 and from 0.521 to 0.746 for temperature and precipitation, respectively. And in the following, we will only look at the ice duration and break-up date as freeze-up date is largely controlled by internal factors.

Some studies have documented that the variation in the NAO has strongly impacted the climate variability over East Asia (Ding and Li 2017; Li and Sun 2015; Sun et al. 2008; Sun and Wang 2012; Wan et al. 2017; Yuan and Sun 2009; Zuo et al. 2016). Thus, the relationships between the winter NAO index and the break-up date as well as the ice duration over the TP are evaluated first (Fig. 4). Obviously, a positive correlation is observed across most lakes over the TP for the variations in both the ice break-up date and duration. However, the significant correlations between the NAO and lake ice phenology are mainly concentrated over the southern region of the TP, and there is almost no significant correlation observed over the northern region. This implies that the impact of NAO on the lake ice phenology may only be limited over the southern TP. Figure 5 presents the scatterplots of the winter NAO with the ice break-up date as well as the ice duration for the lakes located over the southern TP. Clearly, high correlations are observed between the NAO and the regional averaged values of the lake ice phenology over the southern TP. The explanation of the impact of the NAO would be thus limited by the variations in the lake ice phenology over the southern TP.

Fig. 4.
Fig. 4.

The spatial distribution maps of the Pearson correlation between the winter NAO index and the (a) break-up date and (b) ice duration days. The orange, red, and purple circles mean the correlation is significant at the 90%, 95%, and 99% confidence levels, respectively, and the gray indicates that the significance test has not been passed. The plus means a positive correlation, and the box represents the region of the southern TP that was analyzed in this study.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 5.
Fig. 5.

Scatterplots of the boreal winter NAO index with the annual average (a) break-up date and (b) ice duration days over all the lakes from the southern TP for each year. The “cc” represents the Pearson correlation coefficient.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Our statistical results further show that the ice formation in the southern TP generally starts on 5 December (figure not shown), and the ice break-up occurs on 23 April (Fig. 6), resulting in 139 days of lake ice cover. To measure the characteristic changes in the lake ice phenology in the southern TP, we calculated the average ice date of 18 lakes as a break-up index and an ice duration index. The average ice duration presents a clear increase in recent years. Furthermore, the variation in the ice duration presents a significant negative correlation with the regional JFM surface air temperature, while a relatively weak relationship is observed for the ice break-up date (Fig. 7). The surface air temperature data used here are obtained from the site observations over the southern TP including the meteorological stations of Shiquanhe, Gerze, Amdo, Nagchu, Burang, Bangoin, Dangxiong, and Xainza. In the following, the possible physical processes of the impact of the NAO on the local climate anomalies over the southern TP that are related to the variation in the lake ice phenology will be explored and discussed.

Fig. 6.
Fig. 6.

Plots of the average (a) break-up date and (c) ice duration days of the lakes over the southern Tibetan Plateau during 2002–15 (the black straight lines represent the average break-up date and ice duration days in the southern TP), as well as the regional average variations in the (b) break-up date and (d) ice duration days. The black straight line represents the linear trend that was calculated using the least squares method.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 7.
Fig. 7.

Scatterplots of the annual average (a) break-up dates of 18 lakes with the average surface air temperature for April in the southern Tibetan Plateau, and (b) ice duration with the regional average JFM surface air temperature. The “cc” represents the Pearson correlation coefficient.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

b. Impact of the NAO on lake ice phenology

To explore the local atmospheric circulation anomalies that are associated with the variations in the lake ice phenology over the TP, the composite difference of winter mean (JFM) atmospheric circulation anomalies between the three longest duration events and the three shortest duration events from 2002 to 2015 are analyzed here. The identified three longest events include the winters of 2012, 2013, and 2014, and the three shortest events include the winters of 2003, 2008, and 2009. As indicated in Fig. 8, negative geopotential height anomalies can be seen in the TP at 500 hPa. Additionally, the northerly anomalies over western Asia and the Arabian Sea and the southerly anomalies over the Bay of Bengal and the TP suggest the India–Myanmar trough has deepened. The result of the composite analysis also suggests a much stronger Asian jet occurs over the TP for years with prolonged ice duration. Figure 9 shows the composite difference of winter mean (JFM) vertically integrated water vapor fluxes from the surface to 300 hPa between the three longest duration events and the three shortest duration events. Clearly, significant southerly anomalies occur over the southern TP, implying that more water vapor is brought to the southern TP from the low latitudes, which increases the snowfall over this region.

Fig. 8.
Fig. 8.

Composite difference of winter mean (JFM) (a) geopotential height at 500 hPa, (b) the winds at 500 hPa, and (c) zonal wind at 200 hPa between the three longest duration events and the three shortest duration events. The ice duration index is calculated by the average of 18 lakes in the southern TP from 2002 to 2015. The black dots in (a) and (c) and the green color in (b) indicate that the anomalies are significant at the 90% confidence level from a Student’s t test.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 9.
Fig. 9.

Composite difference of winter mean (JFM) vertically integrated water vapor transport vectors between three long duration events and three short duration events. The green color indicates the anomalies are significant at the 90% confidence level from a Student’s t test.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

As mentioned above, there is a significant statistical relationship between NAO and lake ice phenology in the southern TP. This raises the question of how NAO is connected to the change in atmospheric circulation there, which further impacts the variations in lake ice phenology. Some studies have indicated that a Rossby wave train emanates from the North Atlantic and propagates southeast and reaches the Asian jet associated with NAO (Watanabe 2004; Zuo et al. 2016). Branstator (2002) has also emphasized the effects of the South Asian jet waveguide on downstream disturbance trapping and the energy dispersion, and the wave activity can be traced back to the North Atlantic. To clarify the dynamical impact of NAO, we investigated the NAO-associated wave activity flux as defined by Takaya and Nakamura (2001). Figure 10 presents the composite maps of the JFM wave activity flux for five positive NAO years and five negative NAO years from 1990 to 2015. Here the five largest positive NAO years are identified as 1993, 1994, 1999, 2011, and 2014, and the five smallest negative NAO years are identified as 1995, 1997, 2008, 2009, and 2010. Clearly, there are two wave activity flux propagation pathways over the Eurasian region during the positive NAO, and both can be traced back to the North Atlantic. The north path propagates from north Europe eastward along the belt of 50°–60°N, and the other extends from the North Atlantic to North Africa and then spreads eastward to the Middle East, TP, and East Asia. It is clear that there is an anomalous wave train along the southern wave propagating path at 300 hPa. Furthermore, the zonal wind at the entrance of the North Africa jet stream over the high level is enhanced by this anomalous wave activity (Fig. 11). The Rossby wave energy from the high latitude dispersed into the subtropical region and then strengthened the regional wave activity flux and vorticity anomalies, which finally resulted in the intensification of the African–Asian jet. Early studies (e.g., Yang et al. 2004) have suggested that the westerly jet propagation could be hindered because of the complex topography of TP, which increased the persistence of the low-temperature anomalies, and this signal would extend from winter to spring. Therefore, the strengthened Africa–Asian jet can exert its impact on the variations in the lake ice phenology over the southern TP via changing the atmospheric circulation here. When the NAO is in the negative phase, the strong wave train activity can still be seen along the south pathway, but some differences existed for the propagating direction when compared to the positive phase. During the negative NAO, the wave propagation extends from the North Atlantic to North Africa by taking the southeastward track. The asymmetrical wave propagation direction depending on the phase of NAO has also been noted by previous studies (Gong et al. 2014; Sung et al. 2010). To some extent, the above analysis implies that the relationship between the NAO and the lake ice phenology in the southern TP may be explained via the bridge effect of the African–Asian jet stream.

Fig. 10.
Fig. 10.

Anomalous JFM wave activity flux (m2 s−2) for (a) five positive NAO years and (b) five negative NAO years from 1990 to 2015. Geostrophic streamfunction anomalies are shown as colors (106 m2 s−1).

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 11.
Fig. 11.

Composite difference of JFM zonal wind at 300 hPa for (a) five positive NAO years, (b) five negative NAO years from 1990 to 2015 and the climatological zonal winds from 1990 to 2015, and (c) the composite difference of zonal wind at 300 hPa for five positive NAO years and five negative years. The contour lines represent the climatological zonal wind from 1990 to 2015. The black dots indicate that the anomalies are significant at the 90% confidence level from a Student’s t test.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Figure 12 shows the composite difference of winter mean (JFM) atmospheric circulation anomalies between five positive NAO years and five negative NAO years from 1990 to 2015. It is found that there is an arcuate wave train extending from the North Atlantic to the TP at 500 hPa, which is similar to the wave pattern in Fig. 10 during the NAO positive phase. The results suggest that the NAO can induce negative geopotential height anomalies in the India–Myanmar region through the effects of the Asian jet waveguide on the downstream energy dispersion. Clearly, a cyclonic anomaly can be seen at the 500-hPa wind fields around the TP, which further verifies the wave train emanating from the North Atlantic to the TP, implying an anomalous strengthened southerly wind in the southern region of the TP. In addition, the positive geopotential height anomalies in western Europe and the negative geopotential height anomalies in North Africa and western Asia are favorable to the cold air intrusion from east-central Europe to the Middle East and Southeastern Asia, resulting in a stronger African–Asian jet stream. This result correlates well with the work by Yang et al. (2004).

Fig. 12.
Fig. 12.

Composite difference of winter mean (JFM) (a) geopotential height at 500 hPa, (b) the winds at 500 hPa, and (c) zonal wind at 200 hPa between five positive NAO years and five negative NAO years from 1990 to 2015. The black dots in (a) and (c) and the green color in (b) indicate that the anomalies are significant at the 90% confidence level from a Student’s t test.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

During the NAO positive phase, the strengthened westerly jet stream and the negative geopotential height anomalies deepen the India–Myanmar trough. Thus, the enhanced southerly anomalies can bring more water vapor and contribute to the winter snowfall in the southern TP. Figure 13 presents the vertically integrated water vapor fluxes from the surface to 300 hPa associated with boreal DJF NAO index. Generally, the enhanced water vapor transport in the southern TP corresponds exactly to the NAO-induced atmospheric circulation anomalies. These results suggest that the NAO affects the winter atmospheric circulations over the TP through the bridge role of the African–Asian jet stream, which further results in increased snowfall in the southern TP.

Fig. 13.
Fig. 13.

The correlation maps between the JFM vertically integrated water vapor and DJF NAO index from 2002 to 2015. The green color indicates the anomalies are significant at the 90% confidence level from a Student’s t test.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

According to the above analysis, the snow accumulation over the lake is an important link in establishing the relationship between the ice phenology and the associated atmospheric circulation anomalies. Snow cover over the lake ice, which acts as an insulating layer, is highly significant to lake ice thawing in the land process. More snow cover may decrease the surface temperature by reducing the net solar radiation at the lake surface because of the increasing reflectance. In addition, the increased snow accumulation in winter and spring can further lower the surface temperature around the TP lakes since snow melting needs enough energy consumed. Thus, the snow–albedo feedback activity largely contributes to the late ice break-up date and the prolonged lake ice duration.

Figure 14 shows the observed linear trends of the regional average temperature and precipitation over the southern TP during 2003–15 in boreal JFM. The results indicate that the precipitation (snowfall in this season over the TP) presents a substantial increasing trend, while the surface air temperature shows a decreasing trend, suggesting increasing snow cover over the lakes in recent years. Figure 15 shows the correlation distributions of the net longwave radiation and the net shortwave radiation with the lake ice duration over the southern TP. Both negative longwave and shortwave radiation anomalies are clear in most of the TP region, implying reduced radiation fluxes when the lake ice duration is prolonged. This result shows good agreement with that of the previous analysis that the increased surface reflectance can reduce the solar radiation as a result of the increased snowfall in the southern TP. Furthermore, the possible impact of wintertime NAO on the late winter and early spring radiation anomalies over the TP was also analyzed (Fig. 16). During the positive phase of NAO, the clearly negative longwave and shortwave radiation anomalies are also observed over the TP. The spatial distributions of the radiation anomalies resemble those shown in Fig. 15, implying that winter NAO highly impacts the following spring radiation conditions. These results suggest that increased snowfall in the winter contributes to prolonged snow cover over the lakes from winter to the following spring and consequently results in a lower lake surface temperature because of the decline in the solar shortwave radiation received in the following spring. Thus, lower temperatures in the spring can postpone the lake ice melting date and prolong the ice duration in the southern TP.

Fig. 14.
Fig. 14.

Time series for the average temperature (red curve) and average precipitation (blue curve) of Shiquanhe, Gerze, Amdo, Nagchu, Burang, Bangoin, Dangxiong, and Xainza in the southern Tibetan Plateau.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 15.
Fig. 15.

The correlation maps of the February–April (a) net longwave radiation fluxes (W m−2) and (b) net shortwave radiation fluxes (×−1; W m−2), with the average ice duration of 18 lakes in the southern Tibetan Plateau. The black dots mean the correlations are significant at the 90% confidence level from a Student’s t test, and the box represents the study area.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

Fig. 16.
Fig. 16.

As in Fig. 1, but for the correlation with the DJF NAO index.

Citation: Journal of Climate 31, 22; 10.1175/JCLI-D-18-0197.1

4. Conclusions and discussion

The Tibetan Plateau is one of the most sensitive regions to climate change around the world and highly impacts the regional and global climates (Duan et al. 2012). Lake ice phenology has been a good proxy for climate monitoring and is important for limnology and ecology. Furthermore, lake ice phenology also has potential implications for understanding the sedimentary record (Todd and Mackay 2003). In the present study, the changing characteristics of the lake ice phenology are explored over the TP from 2002 to 2015 using datasets retrieved from satellite profiles. The results indicate that the freezing process of most lakes over the southern TP generally starts in December and ice melting occurs in April. However, the changing characteristics of lake ice phenology have varied based on location in recent years, with delayed break-up dates and prolonged ice durations in the southern TP but nonuniform changes in the northern region of the TP. Further analysis suggests that the variations in break-up and ice duration are closely linked to the surface air temperature and precipitation in winter.

The possible impact of the NAO on the variations in the lake ice break-up date and ice duration over the southern TP are also evaluated in this study. The results reveal that the NAO highly impacts the lake ice melting date and ice duration over this region. The NAO-induced atmospheric circulation anomalies are quite favorable to the delay of the lake ice break-up date and the prolonged ice duration. The positive NAO generally excites an anomalous wave activity that propagates southward from the North Atlantic to North Africa and, in turn, strengthens the African–Asian jet stream at its entrance. Then, because of the blocking effect of TP, the enhanced westerly jet can be divided into two branches and the south branch flow can deepen the India–Myanmar trough, which further strengthens the anomalous cyclonic circulation and water vapor transport. Therefore, the increased water vapor transport from the northern Indian Ocean to the southern region of the TP can provide a beneficial environment for snowfall over this region. The increased snow cover over the lake acts as an insulating layer and lowers the lake surface air temperature in the following spring by means of snow–ice feedback, resulting in a delayed ice break-up date and a prolonged ice duration for these lakes over the southern TP in recent years.

Compared with earlier studies (Cai et al. 2017; Haginoya et al. 2009), some new insights can be derived from this study. First, the clear impact process of the NAO on the lake ice phenology over the TP has been explored in this study, unlike earlier studies that were only limited to the statistical relationship analysis. Second, the NAO generally occurs prior to the lake ice melting process over the TP and can thus be considered as a potential predictor for the variations in lake ice phenology over the southern TP.

It is unclear how the changes in lake ice phenology in the northern TP respond to climate change. The nonuniform changes in the lake ice phenology and associated climate dynamic mechanism also lack an explanation. Some other climatic dynamic processes may contribute to the variations in the lake ice phenology in the northern TP. For example, earlier studies indicated that the boreal winter Antarctic Oscillation highly impacts the East Asia summer monsoon and climates (Fan and Wang 2004; Gao et al. 2013; Wang and Fan 2006; Zhu 2013), and the autumn SST anomalies over the South Pacific are closely related to the variability in the winter precipitation over East Asia (Ao and Sun 2016; Sun et al. 2009). Additionally, Shaman and Tziperman (2005) revealed that the wintertime ENSO conditions contributed to increased Tibetan Plateau snowfall. However, the possible linkages between these phenomena and the variations in the lake ice phenology over the TP have not been revealed thus far. More studies are thus necessary to explore the possible effects from both local and teleconnection phenomena on the lake ice phenology over the TP for a better understanding of variations in lake ice and to improve the ability to predict lake ice phenology in the future.

Acknowledgments

This research was jointly supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA19070201), the National Natural Science Foundation of China (Grant 41421004), and the CAS-PKU Joint Research Program.

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