• <tr id="yyy80"></tr>
  • <sup id="yyy80"></sup>
  • <tfoot id="yyy80"><noscript id="yyy80"></noscript></tfoot>
  • 99热精品在线国产_美女午夜性视频免费_国产精品国产高清国产av_av欧美777_自拍偷自拍亚洲精品老妇_亚洲熟女精品中文字幕_www日本黄色视频网_国产精品野战在线观看 ?

    Evaluation of Arctic Sea Ice Drift and its Relationship with Near-surface Wind and Ocean Current in Nine CMIP6 Models from China

    2022-04-02 05:29:02XiaoyongYUChengyanLIUXiaocunWANGJianCAOJihaiDONGandYuLIU
    Advances in Atmospheric Sciences 2022年6期
    關(guān)鍵詞:冷啟動(dòng)陰極燃料電池

    Xiaoyong YU, Chengyan LIU, Xiaocun WANG, Jian CAO, Jihai DONG, and Yu LIU

    1Binjiang College, Nanjing University of Information Science and Technology, Wuxi 214105, China

    2Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

    3School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    4School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China

    5Marine Science and Technology College, Zhejiang Ocean University, Zhoushan 316022, China

    ABSTRACT The simulated Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current during 1979-2014 in nine models from China that participated in the sixth phase of the Coupled Model Intercomparison Project(CMIP6) are examined by comparison with observational and reanalysis datasets. Most of the models reasonably represent the Beaufort Gyre (BG) and Transpolar Drift Stream (TDS) in the spatial patterns of their long-term mean sea ice drift,while the detailed location, extent, and strength of the BG and TDS vary among the models. About two-thirds of the models agree with the observation/reanalysis in the sense that the sea ice drift pattern is consistent with the near-surface wind pattern. About the same proportion of models shows that the sea ice drift pattern is consistent with the surface ocean current pattern. In the observation/reanalysis, however, the sea ice drift pattern does not match well with the surface ocean current pattern. All nine models missed the observational widespread sea ice drift speed acceleration across the Arctic. For the Arctic basin-wide spatial average, five of the nine models overestimate the Arctic long-term (1979-2014) mean sea ice drift speed in all months. Only FGOALS-g3 captures a significant sea ice drift speed increase from 1979 to 2014 both in spring and autumn. The increases are weaker than those in the observation. This evaluation helps assess the performance of the Arctic sea ice drift simulations in these CMIP6 models from China.Key words: Arctic sea ice, sea ice drift, CMIP6, model evaluation

    1. Introduction

    Arctic sea ice is a vital component of the Earth’s climate system (de Vernal et al., 2020). In addition to the coverage and thickness, the drift of Arctic sea ice is also of substantial research interest because of its important roles in Arctic climate, such as regulating ice mass distribution and atmosphere-ocean energy exchange (Kwok et al., 2013). The drift of Arctic sea ice has two large-scale patterns: the Beaufort Gyre (BG) and the Transpolar Drift Stream (TDS)(e.g., Colony and Thorndike, 1984). Arctic sea ice drift also exhibits significant seasonality, with maximum speed in September/October and minimum speed in March/April(Rampal et al., 2009; Olason and Notz, 2014). As Arctic sea ice extent has decreased rapidly in recent decades (e.g., Stroeve et al., 2012; Serreze and Stroeve, 2015), the sea ice tends to move faster (Rampal et al., 2009; Spreen et al.,2011; Kwok et al., 2013; Zhang et al., 2021). Although it is clear that Arctic sea ice moves in response to atmospheric forcing (e.g., Vihma et al., 2012; Lei et al., 2019; Zhang et al.,2021), Rampal et al. (2009) found that the substantial increase in the Arctic spatially averaged sea ice drift speed(+17% per decade for winter and +8.5% for summer, based on buoy observation) from 1979 to 2007 is more likely caused by decreased sea ice mechanical strength instead of increased atmospheric forcing. Vihma et al. (2012) confirmed the finding in Rampal et al. (2009) that atmospheric forcing cannot explain the increasing trend of Arctic spatially averaged sea ice drift speed during a similar time(1989-2009). The changes in Arctic sea ice drift and its driver are not regionally uniform. Based on satellite data,Spreen et al. (2011) showed that the winter Arctic sea ice drift speed trend for the period 1992-2009 varies between-4% and 16% per decade depending on the location. Increasing wind speed may explain part of the observed increase in drift speeds in the Central Arctic, but thinning of the sea ice is a more likely cause of sea ice drift acceleration in other regions (Spreen et al., 2011). Kwok et al. (2013) showed the BG and the TDS were enhanced during 1982-2009, especially during the last decade. Based on a longer period(1980-2013) and focused over the Canadian Basin, Petty et al. (2016) also showed the strengthening of the BG and proposed several mechanisms to explain the change, such as ice strength reduction (which is caused by declines in ice thickness and concentration), changes to the ice morphology, the atmospheric boundary layer stability, and/or geostrophic currents.

    For information on how Arctic sea ice will change in the future, we rely on predictions and projections from climate models. Therefore, it is vital to know whether climate models can properly capture observed historical sea ice drift and its dependency on atmospheric and oceanic forcing and sea ice conditions. Rampal et al. (2011) examined Arctic sea ice simulation in the models that participated in the third phase of the Coupled Model Intercomparison Project(CMIP3) and showed that these models failed to capture the observed seasonal cycle and the acceleration of Arctic sea ice drift in recent decades. Based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5) models,Tandon et al. (2018) found that only a few models captured the observed seasonal cycle of sea ice drift speed. Among the state-of-art global climate models that participated in the sixth phase of the Coupled Model Intercomparison Project(CMIP6), 13 are from China (Zhou et al., 2019). Their performance on simulating Arctic sea ice concentration, area,extent, thickness, volume, and mass budget and snow depth on the ice under modern climate conditions is examined in recent multi-model (Davy and Outten, 2020; Notz et al.,2020; Shu et al., 2020; Smith et al., 2020; ?rthun et al.,2021; Chen et al., 2021; Keen et al., 2021; Long et al.,2021; Shen et al., 2021) and single model (Guo et al.,2020a; Wang et al., 2020; Ren et al., 2021; Rong et al.,2021) evaluation studies. Notz et al. (2020) and Long et al.(2021) showed that BCC-CSM2-MR, FGOALS-f3-L, and FIO-ESM-2-0 are able to simultaneously simulate a plausible amount of Arctic sea-ice loss and a plausible change in global mean temperature over time. In addition, Long et al.(2021) found that CAMS-CSM1-0 largely underestimates the Arctic sea ice extent decline, and BCC-CSM2-MR,CASM-CSM1-0, and FGOALS-f3-L obviously overestimate the climatological sea ice concentration over the Barents Sea and East Greenland Sea. Shen et al. (2021) and Rong et al. (2021) confirmed the underestimation of the Arctic sea ice extent decline in CAMS-CSM1-0. Guo et al.(2020a) confirmed the overestimated climatological sea ice concentration over the Barents Sea and East Greenland Sea in FGOALS-f3-L. Ren et al. (2021) and Wang et al. (2020)pointed out that the underestimation of climatological sea ice concentration over the Barents Sea and East Greenland Sea in BCC-CSM2-MR may be caused by the underestimated surface net radiation and heat transport from the Atlantic Ocean. Smith et al. (2020) reported that BCCCSM2-MR and BCC-ESM1 overestimated the Arctic sea ice melt-period and underestimated the freeze-up and closing period. Cao et al. (2018) demonstrated that NESM3 can represent the modern Arctic climate well, while a cold bias exists over the Barents Sea. However, the performance of these models in simulating the Arctic sea ice drift is unknown so far. Therefore, this study aims to extend the current evaluation studies by providing evaluation of Arctic sea ice drift and its relationship with near-surface wind and surface ocean current in the historical runs of the CMIP6 models from China. This paper is organized in the following way: section 2 describes the model characteristics and the observational data as well as the analysis methods used; section 3 presents the evaluation of spatial patterns in simulated Arctic sea ice drift climatology and trends and the relationship of these patterns with those in Arctic near-surface wind and surface ocean current; evaluation of the seasonal evolution and trend in the simulated Arctic basin-wide mean sea ice drift speed, near-surface wind speed, and surface ocean current is given in section 4; section 5 presents the summary and conclusions.

    2. Data and method

    2.1. Model data

    We evaluate nine coupled models (BCC-CSM2-MR,BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM,FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, and NESM3)from China that participated in the CMIP6. We only investigate 9 of 13 models from China because the other 4 models(BCC-CSM2-HR, BNU-ESM-1-1, FGOALS-f3-H, and TaiESM1) have not provided the sea ice drift vector in their CMIP6 historical experiments on the ESGF CMIP6 data distribution website yet. Table 1 shows that only two different sea ice models are used in these coupled models: Sea Ice Simulator (SIS) and Los Alamos sea ice model (CICE). BCCCSM2-MR and BCC-ESM1 use the SIS, and the other seven coupled models use the CICE. More detailed information about these coupled models can be found in Table 1.The monthly sea ice drift, near-surface wind, and surface ocean current vectors from the CMIP6 historical experiments of these nine models are selected for evaluation in this study. As the near-surface wind vector (CMIP6 standard name uas, vas) in CIESM, FGOALS-g3, and FIO-ESM-2-0 are not provided, the wind vector at 1000 hPa is used instead in these three models. The data from the first ensemble member of each model and for the period of 1979-2014 (36 years) is used. We focus the evaluation on spring and autumn when sea ice usually reaches the maximum and minimum extent, respectively.

    Table 1. Characteristics of the nine CMIP6 models from China.

    2.2. Datasets for evaluation

    For the evaluation of the simulated Arctic sea ice drift,the NSIDC-0116 Polar Pathfinder (referred to as NSIDC Pathfinder hereafter) daily sea ice motion vectors(https://nsidc.org/data/NSIDC-0116/versions/3) are used.This dataset provides daily sea ice motion vectors derived from a wide variety of sensors in both gridded and non-gridded (raw) files. We selected the daily sea ice motion vector that was projected on the 25 km EASE-grid and merged observations from a variety of sensors over the Northern Hemisphere. As we use the monthly sea ice drift vectors to obtain the sea ice drift magnitude and direction in the nine CMIP6 models, the monthly sea ice drift vector components in NSIDC Pathfinder are calculated accordingly to assess the observational sea ice drift magnitude and direction. Previous evaluation studies (Sumata et al., 2014, 2015; Gui et al.,2020) show that the bias of NSIDC Pathfinder sea ice drift speed in summer is larger than that in winter, and the summer Arctic average sea ice drift speed is obviously underestimated. These studies also show that the larger the sea ice drift speed or lower the sea ice concentration, the larger the absolute error in sea ice drift. More detail about this dataset can be found in Tschudi et al. (2016). The NSIDC Pathfinder is used in this study because only this product provides the full-season, long-term (1979-2014) sea icedrift data over the whole Arctic so far. This advantage enables us to evaluate the climatology and trend of Arctic sea ice drift in the models during different seasons.

    For the near-surface wind, the monthly 10-m wind from ERA-Interim (referred to as ERA-I hereafter) with 1° × 1° horizontal resolution is used. Compared with the daily average 10-m wind speed from the North Pole drifting ice stations of the former Soviet Union, Lindsay et al.(2014) found that the monthly mean bias of daily averaged 10-m wind speed in ERA-Interim is mostly less than 0.5 m s-1.Besides, the ERA-Interim wind speed has the best correlation (higher than 0.85) with the observation among six atmospheric reanalysis products (Lindsay et al., 2014). More information about this dataset is given by Berrisford et al.(2011). For the surface ocean current, we use the monthly surface ocean current from Ocean Reanalysis System 4(ORAS4) with 1° × 1° horizontal resolution. Detailed information about ORSA4 is given by Balmaseda et al. (2013). Caution is necessary when using these two reanalysis products because of the sparse observation over the Arctic Ocean.

    2.3. Method

    2.3.1. Spatial average

    For the calculations of Arctic basin-wide mean sea ice drift, near-surface wind, and surface ocean current, we follow the method of Olason and Notz (2014) and Docquier et al. (2017) by using the Scientific Ice Expeditions (SCICEX)box (Rothrock et al., 2008) as the domain for the spatial average. The domain of the SCICEX box is shown in Fig. 1a.

    2.3.2. Trends

    Since sea ice drift, near-surface wind, and surface ocean current are vectors, the changes of sea ice drift, nearsurface wind, and surface ocean current could happen in their magnitude or direction, or both. Therefore, we calculate not only the trends of the sea ice drift, near-surface wind, and surface ocean current magnitude, but also the trends of their vector components when we evaluate the spatial patterns of the sea ice drift, near-surface wind, and surface ocean current changes over the Arctic. The trends of their vector components are then used to compose a trend vector that shows the direction of their change. For the evaluation of the Arctic basin-wide mean sea ice drift change, we only calculate the trend of the sea ice drift magnitude. The two-tailed Student’s t-test is used to perform the significance test for the trend. A trend with a confidence level equal to or higher than 95% is considered significant.

    3. Spatial patterns of Arctic sea-ice drift,near-surface wind, and ocean current

    3.1. Spatial patterns of Arctic sea ice drift

    The spatial patterns of spring (March-April-May;MAM) long-term (1979-2014) mean sea ice drift direction(vector) and speed (shading) in the observation and models are shown in Fig. 1. In the NSIDC Pathfinder, the spring sea ice drift pattern is characterized by a typical BG, in which sea ice moves anticyclonically over the Amerasian basin,and a typical TDS, in which there is sea ice drift from the Siberian coast all the way to the Fram Strait (Fig. 1a). All nine models capture the BG and TDS in the spring sea ice drift pattern except for NESM3, in which there are three small anticyclonic vortices aligned together instead of a BG in the sea ice drift field over the Amerasian basin. This distinct sea ice drift pattern in NESM3 (Fig. 1j) is linked to the ocean current beneath the sea ice (see section 3.2). The exact extent, location, and strength of BG and TDS vary among the models. The BG and TDS in CAMS-CSM1-0,CIESM, FGOALS-f3-L, and FIO-ESM-2-0 are close to those in the observation. In BCC-CSM2-MR and FGOALSg3, however, the simulated BG and TDS are different from those in the observation. The BG extent in BCC-CSM2-MR is smaller than that in the observation. The TDS is curved instead of straight, as in the observation. Consequently, in BCC-CSM2-MR, the sea ice over the Siberian coast first drifts toward the Canadian Archipelago and north of Greenland and then turns to drift toward the Fram Strait and the water between Svalbard and the Franz Josef Land (Fig. 1b).The cyclonic near-surface wind centered near the Barents/Kara Sea may be the driver of the curved sea ice drift (see section 3.2 for detail). The BG in FGOALS-g3 is much smaller and weaker than that in the observation. The simulated TDS starts not just from the Siberian coast, but also from the Beaufort Sea. This makes the TDS in FGOALS-g3 much wider than that in the observation. Also,the simulated TDS is weaker and its axis is tilted more eastward compared to that in the observation. In BCC-ESM1,the bias of the simulated TDS is very close to that in BCCCSM2-MR. This could be because both BCC-ESM1 and BCC-CSM2-MR used the same sea ice model. In CASESM2-0, the BG and TDS are both interrupted by the data void at the North Pole (Fig. 1e). Near there, the sea ice drifts around the data void. This is because the sea ice grid in CAS-ESM2-0 filtered the data near the North Pole, so the North Pole acts as an artificial island for the sea ice (Sun and Zhou, 2010; Xu et al., 2013). The simulated TDS in NESM3 is narrower than that in the observation.

    In autumn, the observational extent of the BG is smaller and its shape is more asymmetrical compared to that in spring (Fig. 2a). Meanwhile, the TDS is curved instead of straight. Of the nine models, two of them (CAS-ESM2-0 and FGOALS-g3) show BG extents similar to the BG extent in the observation; five of them (BCC-CSM2-MR, BCCESM1, CAMS-CSM1-0, FGOALS-f3-L, and FIO-ESM-2-0) simulate a larger and stronger BG than that in the observation; two of them (NESM3 and CIESM) do not capture the BG. For TDS simulation, four of the nine models (BCCCSM2-MR, BCC-ESM1, CAS-ESM2-0, and FGOALS-g3)simulate a curved TDS. However, the curved TDS in CASESM2-0 is caused by the “artificial island” near the North Pole, and the direction of the curve is different from that in the observation. Another four models (CAMS-CSM1-0,FGOALS-f3-L, FIO-ESM-2-0, and NESM3) simulate a straight TDS. In CIESM, a reversed TDS is found. For the Arctic sea ice drift pattern shift from spring to autumn, none of the models capture the shrinking of the BG, and only FGOALS-g3 captures the shift of straight a TDS to a curved TDS. Caution is needed when interpreting the NSIDC Pathfinder sea ice drift speed in autumn because large areas of marginal ice zone exist in autumn and the sea ice drift speed uncertainty over the marginal ice zone is large. Previous studies (e.g., Stroeve et al., 2011; Zhang et al., 2021)have shown that the negative phase of Arctic Oscillation(AO) in winter is associated with a stronger BG. Therefore,the BG strength and range differences among the models may be linked to their differences in AO strength and range.

    Fig. 1. Spatial pattern of the spring (MAM) long-term (1979-2014) mean sea ice drift direction (vector) and speed (shading)in NSIDC Polar Pathfinder and nine CMIP6 models (BCC-CSM2-MR, BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0,CIESM, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, and NESM3) from China. The SCICEX domain is marked as the red box.

    Fig. 2. Same as that in Fig.1, but for autumn (SON).

    3.2. Relationship among the spatial patterns of Arctic sea ice drift, near-surface wind, and surface ocean current

    Figures 3a0, b0, and c0 show that the large-scale pattern of the spring sea ice drift over the Arctic in NSIDC Pathfinder is in good agreement with the near-surface wind pattern in ERA-I. Both in the NSIDC Pathfinder sea ice drift and ERA-I near-surface wind, there is an anticyclonic circulation over the Amerasian basin and straight flow from the Siberian coast to the Fram Strait and north of Greenland.In the ORAS4 surface ocean current, however, the extent of the anticyclonic circulation is obviously smaller than that in the sea ice drift field.

    Of the nine models, four of them (CAMS-CSM1-0,CIESM, FGOALS-f3-L, and FIO-ESM-2-0) show that the spatial patterns of long-term mean sea ice drift, near-surface wind, and surface ocean current vectors in spring are very similar with each other despite there being some displacements in their anticyclonic centers over the Canadian Basin (Fig. 3). Three of the nine models (BCC-ESM1,BCC-CSM2-MR, and CAS-ESM2-0) also show good agreement between the sea ice drift and near-surface wind patterns, but the agreement between their sea ice drift and surface ocean current patterns is poor. In these two models, the large-scale anticyclonic circulation in sea ice drift is mainly confined in the Amerasian Basin. In contrast, the anticyclonic circulation in surface ocean current almost encloses the whole Arctic Ocean. Two of the nine models(FGOALS-g3 and NESM3) show that the sea ice drift pattern does not match well with the near-surface wind pattern.In FGOALS-g3, a BG appears over the Canadian Basin in the sea ice drift. However, no similar circulation is found over the same area in near-surface wind. Additionally, a cyclonic circulation over the central Arctic appears in near-surface wind while no similar pattern is found in sea ice drift accordingly. In NESM3, the single anticyclonic circulation over the Amerasian Basin in near-surface wind is clearly different from the three small anticyclonic vortices aligned together in the same area in sea ice drift. In contrast, the above sea ice drift pattern in NESM3 matches well with the surface ocean current pattern. Since the corresponding surface ocean current magnitude is much larger than the sea ice drift magnitude, the distinct sea ice drift pattern over the Amerasian Basin in NESM3 is likely driven by the surface ocean current.

    Figure 4 shows that although the sea ice drift, near-surface wind, and surface ocean current patterns in autumn(September-October-November; SON) are different from those in spring in the observation/reanalysis, the spatial pattern among these three variables is very similar. The pattern agreement among the sea ice drift, near-surface wind,and surface ocean current in autumn is also very similar to that in spring in each model except for CIESM. In CIESM,the sea ice drift pattern in autumn is no longer in good agreement with the near-surface wind and surface ocean current patterns.

    3.3. Relationship among the trend patterns of Arctic sea ice drift, surface ocean current, near-surface wind

    The trends in spring sea ice drift, near-surface wind,and surface ocean current magnitude (indicated by the color shadings) and their vector components (indicated by the arrows) are shown in Fig. 5. The latter shows the direction of the change in sea ice drift, near-surface wind, and surface ocean current. The areas that the confidence level of the magnitude trend is less than 95% are masked out. The spring sea ice drift speed significantly increased over most of the Arctic during 1979-2014 in the NSIDC Pathfinder(Fig. 5a0). This is consistent with the trend found in Zhang et al. (2021), which also calculated based on NSIDC Pathfinder. The trend vector (indicated by the arrows) also shows that both the BG and TDS are enhanced in the NSIDC Pathfinder (For BG, the trend is about 0.8-1.2 cm s-1(10 yr)-1near Alaska/Canada coast; for TDS, the trend is about 1.6-2.0 cm s-1(10 yr)-1near the Fram Strait). These observational sea ice drift speed increases seem not to be wind-driven because no significant near-surface wind speed changes are found over the corresponding areas in ERA-I.Only a small area of sea ice drift speed decrease over the Siberian coast is matched with the decrease of near-surface wind speed (Fig. 5b0). The observed sea ice drift speed increases are, at most, weakly link to surface ocean current speed changes because the surface ocean current speed in ORSA4 only changes significantly over some narrow, bandshaped areas over the Arctic (Fig. 5c0). Therefore, the observed Arctic sea ice drift acceleration during 1979-2014 is more likely caused by the increased response of the sea ice drift to the wind. This is supported by the fact that the wind factor (the sea ice drift speed from NSIDC Pathfinder divided by the near-surface wind speed from ERA-I) in the observation/reanalysis increases significantly over the sea ice drift speed acceleration areas (Fig. S1 in the electronic supplementary material).

    第一階段:在燃料電池冷啟動(dòng)開(kāi)始階段沒(méi)有冰形成,首先在電池陰極產(chǎn)生水,隨著反應(yīng)的進(jìn)行,陰極側(cè)含水量逐漸升至飽和狀態(tài)。

    Fig. 3. (Continued).

    Fig. 3. Spatial pattern of the spring (MAM) long-term (1979-2014) mean direction (vector) and speed (shading) of sea ice drift (left), near-surface wind (middle), and surface ocean current (right) in the observation/reanalysis (NSIDC Polar Pathfinder for sea ice drift, ERA-Interim for near-surface wind, and ORAS4 for upper layer ocean current) and nine CMIP6 models (BCC-CSM2-MR, BCCESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, and NESM3) from China.

    Fig. 4. (Continued).

    Fig. 4. Same as that in Fig. 3, but for autumn (SON).

    Fig. 5. (Continued).

    Fig. 5. The trend of spring (MAM) sea ice drift (left), near-surface wind (middle), and surface ocean current (right) in the observation/reanalysis (NSIDC Polar Pathfinder for sea ice drift speed, ERA-Interim for near-surface wind speed, and ORAS4 for upper layer ocean current) and nine CMIP6 models (BCC-CSM2-MR, BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM,FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0, and NESM3) from China for the period of 1979-2014. Colors and arrows represent the trend in the magnitude and vector components of sea ice drift, near-surface wind, and surface ocean current, respectively. Areas where the confidence level of the magnitude trend is less than 95% are masked out.

    There are three models (FGOALS-f3-L, FGOALS-g3,and NESM3) that partly capture the spring sea ice drift speed acceleration over the Arctic (Fig. 5). In FGOALS-f3-L, the significant sea ice drift speed increase only appears over the north of the Beaufort Sea, part of the central Arctic,the Baffin Bay, and the Davis Strait (Fig. 5a6). These changes may be driven by the wind speed acceleration because significant near-surface wind speed increases are associated with them. The sea ice drift speed increases over the central Arctic are also associated with the surface ocean current speed increases. In FGOALS-g3, the significant sea ice drift speed increase appears approximately over the area between 120°W and 110°E (Fig. 5a7). These increases are unlikely to be wind-driven because no significant near-surface wind speed changes are associated with them. Over the north of the Beaufort Sea, however, there are significant surface ocean current speed increases associated with the sea ice drift speed increases. In NESM3, the significant sea ice drift speed increases appear mainly over the Laptev Sea,Kara Sea, Barents Sea, Fram Strait, and part of the central Arctic (Fig. 5a9). These changes are not wind-driven except over the north and south of the Fram Strait and the west of the Barents Sea, where significant near-surface wind speed increases appear. Additionally, there are some areas of significant sea ice drift speed decreases over the Canadian Basin in NESM3, and they are associated with the strong significant surface ocean current speed decreases over the same area. In the other six models (BCC-CSM2-MR, BCCESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM, and FIOESM-2-0), there are only a few scattered areas of significant sea ice drift speed, near-surface wind speed, and surface ocean current speed changes, and their locations are not matched well (Fig. 5).

    In autumn, the sea ice drift trends and their relationship with the near-surface wind speed and surface ocean current speed trends in the observation/reanalysis data (Figs. 6a0,b0, and c0) are similar to those in spring in the following way: the autumn sea ice drift speed also increases significantly over most of the Arctic, and the BG and TDS are also strengthened during 1979-2014 in the NSIDC Pathfinder.The sea ice drift speed trends in autumn are also not winddriven because no significant near-surface wind speed trends in ERA-I are associated with them. They are only weakly linked with the surface ocean current speed trends.The differences between the autumn and spring sea ice drift trends are in their magnitudes and patterns. The autumn sea ice drift speed trends over the southern Canadian Basin and the Chukchi Sea are much larger than those in spring. The autumn sea ice drift trend vectors over the north of the Laptev Sea are more curved than those in spring.

    Compared to the observation/reanalysis, the autumn sea ice drift speed trends in the nine models are only significant over a small part of the Arctic (Fig. 6). These areas are almost only located outside the central Arctic except in NESM3, in which significant sea ice drift speed trends appear over a few narrow band-shaped areas in the central Arctic. Areas with significant near-surface wind speed and surface ocean current speed trends are also small, and they are rarely co-located with the significant sea ice drift speed trends in the nine models.

    4. Arctic basin-wide mean sea ice drift speed,near-surface wind speed, and surface ocean current speed

    4.1. Climatology of Arctic basin-wide mean sea ice drift speed

    Figure 7 shows that both the simulated magnitude and seasonal evolution of the Arctic basin-wide (the domain is defined by the SCICEX box, which is shown as the red box in Fig. 1a) mean sea ice drift speed vary among the nine models and are different from those in the observation. In NSIDC Pathfinder, the monthly Arctic sea ice drift speed climatology (1979-2014) varies from 2.36 cm s-1(in July) to 4.14 cm s-1(in October) across different months. The ensemble means of the sea ice drift speed from the nine models are overestimated in all the months. Individually, five of the nine models (BCC-CSM2-MR, BCC-ESM1, CAMSCSM1-0, FGOALS-f3-L, and FIO-ESM-2-0) overestimate the climatological sea ice drift speed for all the months. One model (FGOALS-g3) underestimates the sea ice drift speed for all the months. CAS-ESM2-0 overestimates the sea ice drift speed from July to September and underestimates the sea ice drift speed in the other months. NESM3 overestimates the sea ice drift speed from March to October, especially in July (overestimated by 3.38 cm s-1), and underestimates the sea ice drift speed in the other months. CIESM overestimates the sea ice drift speed from December to July and underestimates the sea ice drift speed in the other months.The climatological sea ice drift speed in CIESM is very low from August to October. The September sea ice drift speed is near zero.

    Fig. 6. (Continued).

    Fig. 6. Same as that in Fig. 5, but for autumn (SON).

    Fig. 7. Arctic basin-wide mean sea ice drift speed (cm s-1) in NSIDC Polar Pathfinder (black line) and nine CMIP6 models (BCC-CSM2-MR, BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM, FGOALS-f3-L,FGOALS-g3, FIO-ESM-2-0, and NESM3) from China for the period of 1979-2014. The domain of the spatial mean is the same as the SCICEX domain, which is marked as the red box in Fig. 1a.

    The seasonality of the sea ice drift speed in the model ensemble mean is similar to that in the NSIDC Pathfinder.Individually, however, none of the models reach a minimum in July like the observation does: four reach a minimum in May, two in September, one in January, one in February, and one in August. The simulated sea ice drift speed from three of the models even peaks in July. Another three models peak in October (same with the observation), one in November, one in December, and one in January. The seasonal variability among the 12 months (defined as the standard deviation of the climatological sea ice drift speed in 12 months) is 0.53 cm s-1in the NSIDC Pathfinder. In the nine models, the sea ice drift speed seasonal variabilities in BCC-CSM2-MR (0.54 cm s-1), CAMS-CSM1-0(0.57 cm s-1), and FGOALS-g3 (0.50 cm s-1) are close to that in the NSIDC Pathfinder. The variabilities in BCC-ESM1(0.84 cm s-1), CAS-ESM2-0 (0.71 cm s-1), CIESM(2.21 cm s-1), FIO-ESM-2-0 (0.81 cm s-1), and NESM3(1.29 cm s-1) are obviously larger than that in the NSIDC Pathfinder. In particular, the sea ice drift speed seasonal variabilities in CIESM and NESM3 are about 2.4 and 4.2 times that in the NSIDC Pathfinder, respectively. The variability in FGOAL-f3-L (0.34 cm s-1) is smaller than that in the NSIDC Pathfinder.

    In order to understand which range of sea ice drift speed was the main cause of the bias in the Arctic mean sea ice drift speed in these nine models, we present the probability distribution of the Arctic sea ice drift in the models against that in the NSIDC Pathfinder (Figs. 8 and 9). Figure 8 shows that six models (BCC-CSM2-MR, BCC-ESM1,CAMS-CSM1-0, CIESM, FGOALS-f3-L, and FIO-ESM-2-0) overestimate the mean sea ice drift speed in MAM because they overestimate the frequency of the high-speed component and underestimate the frequency of the lowspeed component. The threshold between the overestimation and underestimation for these models ranges from 3.2 cm s-1(BCC-CSM2-MR) to 4.0 cm s-1(BCC-ESM1). The MAM sea ice drift speed distribution in NESM3 is close to the observation, with a slight overestimation of sea ice drift speed between 2.0 cm s-1and 4.9 cm s-1. Two models(CAS-ESM2-0 and FGOALS-g3) underestimate the mean sea ice drift speed because they overestimate the frequency of the low-speed component and underestimate the frequency of the high-speed component. The threshold between the overestimation and underestimation for CASESM2-0 and FGOALS-g3 is 2.6 cm s-1and 1.7 cm s-1,respectively.

    In SON, seven models (BCC-CSM2-MR, BCC-ESM1,CAMS-CSM1-0, CAS-ESM2-0, FGOALS-f3-L, FIO-ESM-2-0, and NESM3) overestimate the mean sea ice drift speed because they overestimate the frequency of the high-speed component and underestimate the frequency of the lowspeed component (Fig. 9). The threshold between the overestimation and underestimation for these models ranges from 3.0 cm s-1(CAS-ESM2-0) to 4.4 cm s-1(CAMS-CSM1-0).Two models (CIESM and FGOALS-g3) underestimate the mean sea ice drift speed because they overestimate the frequency of the low-speed component and underestimate the frequency of the high-speed component. The threshold between the overestimation and underestimation for CIESM and FGOALS-g3 is 2.4 cm s-1and 2.3 cm s-1, respectively.

    4.2. Relationship among the climatology of Arctic basinwide mean sea ice drift speed, surface ocean current speed, and near-surface wind speed

    The seasonal evolution of the Arctic basin-wide mean sea ice drift speed, surface ocean current speed, and near-surface wind speed are shown in Fig. 10. There is no clear relation between the seasonal variations of the sea ice drift speed and near-surface wind speed in the observation/reanalysis data. In contrast, the seasonal variation of the sea ice drift speed agrees with that of the surface ocean current speed. Also, the seasonal variation of the near-surface wind speed agrees with that of the surface ocean current speed.

    Fig. 8. The frequency distribution of the spring (MAM) Arctic sea ice drift speed in nine CMIP6 models (BCCCSM2-MR, BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0,and NESM3) for the period of 1979-2014 against that in the NSIDC Polar Pathfinder (blue line). The domain of probability distribution calculation is the same as the SCICEX domain, which is marked as the red box in Fig. 1a.

    Fig. 9. Same as that in Fig. 8, but for autumn (SON).

    4.3. Trend of Arctic basin-wide mean sea ice drift speed

    Figures 11 and 12 show the time series and linear trends of the Artic basin-wide mean sea ice drift speed in spring and autumn, respectively, during 1979-2014. In the observation, the spring Arctic sea ice drift speed increases significantly with a rate of 0.64 cm s-1(10 yr)-1from 1979 to 2014 (Fig. 11). In the models, however, only FGOALS-g3 shows a significant increase in spring Arctic sea ice drift speed, and the trend is much weaker [0.18 cm s-1(10 yr)-1].NESM3 shows a weak and significant decrease [-0.15 cm s-1(10 yr)-1] in the Arctic sea ice drift speed. For the other seven models, no significant trend in the Arctic sea ice drift speed is detected. In autumn, the observational Arctic sea ice drift speed shows a significant increase with a rate of 0.89 cm s-1(10 yr)-1from 1979 to 2014 (Fig. 12), which is larger than that in spring. Of the models, also only FGOALS-g3 shows a significant Arctic sea ice drift speed increase, with a rate of 0.12 cm s-1(10 yr)-1. No significant Arctic sea ice drift speed trend is found for the other eight models. Zhang et al. (2021) also investigated the linear trends of the Arctic basin-wide mean sea ice drift speed based on the NSIDC Pathfinder product and shows larger trends in spring and autumn than those found in this study.This may be linked to the differences in spatial average domain and time period between our study and Zhang et al.(2021).

    Fig. 10. The seasonal cycle of Arctic basin-wide mean sea ice drift speed (cm s-1, red line), near-surface wind speed(m s-1, green line), and surface ocean current (cm s-1, blue line) in the observation/reanalysis (NSIDC Polar Pathfinder for sea ice drift speed, ERA-Interim for near-surface wind speed, and ORAS4 for upper layer ocean current) and in nine CMIP6 models from China for the period of 1979-2014. The domain of the spatial mean is the same as the SCICEX domain, which is marked as the red box in Fig. 1a.

    Fig. 11. Arctic mean spring (MAM) sea ice drift speed in NSIDC Polar Pathfinder and nine CMIP6 models (BCCCSM2-MR, BCC-ESM1, CAMS-CSM1-0, CAS-ESM2-0, CIESM, FGOALS-f3-L, FGOALS-g3, FIO-ESM-2-0,and NESM3) from China for the period of 1979-2014. The table in the upper left shows the corresponding linear trend of the sea ice drift speed [cm s-1 (10 yr)-1]. Asterisk indicates the confidence level of the trend reaches 95%.The domain of the spatial mean is the same as the SCICEX domain, which is marked as the red box in Fig. 1a.

    Fig. 12. Same as that in Fig. 11, but for autumn (SON).

    5. Summary and conclusions

    We have evaluated the Arctic sea ice drift and its relationship with the near-surface wind and surface ocean current in the historical runs of nine CMIP6 models from China.These models are BCC-CSM2-MR, BCC-ESM1, CAMSCSM1-0, CAS-ESM2-0, CIESM, FGOALS-f3-L,FGOALS-g3, FIO-ESM-2-0, and NESM3. Sea ice drift from the NSIDC Pathfinder product, near-surface wind from ERA-I, and surface ocean current from ORAS4 are used to evaluate the model results for the period of 1979-2014. Both the spatial patterns and the Arctic basinwide mean (averaged over the SCICEX domain) of the sea ice drift, near-surface wind, and surface ocean current are compared. The main conclusions are listed below:

    (1) All nine models capture the Beaufort Gyre (BG)and the Transpolar Drift Stream (TDS) in spring except for NESM3, in which there are three small anticyclonic vortices aligned together instead of a BG over the Amerasian basin. These anticyclonic vortices are likely current-driven.Four of the nine models show similar extent, location, and strength of BG and TDS as that in the observation in spring.In autumn, two of the nine models show a similar BG extent as that in the observation while five of the nine models show a larger BG extent and stronger BG magnitude than that in the observation.

    (2) For the relationship among the spatial patterns of sea ice drift, near-surface wind, and surface ocean current,seven of the nine models agree with the observation/reanalysis in the sense that the spring (MAM) sea ice drift pattern is in good agreement with the near-surface wind pattern. Six of the nine models also show that the sea ice drift pattern is in good agreement with the surface ocean current pattern. However, they are not in good agreement in the observation/reanalysis. In autumn (SON), the relationship among the spatial patterns of sea ice drift, near-surface wind, and surface ocean current is similar to that in spring for all nine models except CIESM, in which the sea ice drift pattern does not match well with near-surface wind in autumn.

    (3) The observation/reanalysis shows that the sea ice drift speed significantly increased over most of the Arctic in spring and autumn from 1979 to 2014. These sea ice drift speed changes are not wind-driven because no significant near-surface wind speed changes are associated with them.Besides, the observational sea ice drift speed changes are only weakly linked with the surface ocean current speed changes. Of the nine models, only FGOALS-f3-L,FGOALS-g3, and NESM3 partly capture the significant spring sea ice drift acceleration over the Arctic. Areas with the significant near-surface wind speed and surface ocean current speed changes are also small and rarely co-located with the sea ice drift speed changes in all nine models except for NESM3.

    (4) Compared with the observation, more than half of the models (five out of nine) overestimate the Arctic basinwide climatological sea ice drift speed in all 12 months during 1979-2014. One model (FGOALS-g3), in contrast, underestimates the sea ice drift speed in all 12 months. The simulated peaks and troughs of the sea ice drift speed seasonal cycle in most of the models do not agree with the observation.

    (5) For the trend of the Arctic basin-wide mean sea ice drift speed from 1979 to 2014, eight of the nine models do not capture the observational significant sea ice drift speed increase in both spring and autumn. Only FGOALS-g3 captures a weak, but significant sea ice drift speed increase in both spring and autumn.

    Since both the BG and TSD patterns of the nine models in the normal mean sea ice drift field (averaged over all the years from 1979-2014) are close to these patterns in the sea ice field averaged over the years with the AO index being less than -1.0 (Figs. S2 and S3 in the electronic supplementary material), the differences in BG and TSD depiction ability of the nine models are associated with their BG and TSD depiction ability in the negative phase of the AO.The missing widespread sea ice drift speed acceleration across the Arctic in the nine models indicates that improvements in the formulation and parameterization of sea ice dynamics are needed in these models, such as the sea ice rheology.

    The uncertainty in NSIDC Pathfinder sea ice drift speed is noteworthy. Based on daily sea ice drift speed, Docquier et al. (2017) showed that the Arctic basin-wide sea ice drift speed seasonal evolution in NSDIC Pathfinder is different from that in the Arctic buoy observation. According to the daily or 12-hourly Arctic buoy observations, sea ice drift speed peaks in September and troughs in March(Olason and Notz, 2014; Docquier et al., 2017; Tandon et al., 2018).

    The source of Arctic sea ice drift is different from the sources of near-surface wind and surface ocean current in our study. These differences may introduce uncertainty in the obtained relationship between the sea ice drift, near-surface wind, and surface ocean current. In order to investigate this uncertainty, we changed the near-surface wind data source from ERA-Interim to NCEP/NCAR Reanalysis(NCEP-R1) because it is one of the sources to calculate the NSIDC Pathfinder sea ice motion. We also changed the surface ocean current source from ORAS4 to Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS)because PIOMAS uses the NCEP-R1 as the atmospheric forcing, including the near-surface wind. After the near-surface and surface ocean current data sources were changed,the relationship between the Arctic sea ice drift and the near-surface wind remained the same (Figs. S4-S8 in the electronic supplementary material). The relationship between the Arctic sea ice drift speed and surface ocean current speed trend is much better after the sources of near-surface wind and surface ocean current were changed (Figs. S7 and S8 in the electronic supplementary material). Therefore, the uncertainty in the relationship between the Arctic sea ice drift and the surface ocean current is large.

    In the future, investigation of the air-ice and ice-ocean drag coefficient differences among the models could be helpful to explain the differences in sea ice drift-wind and sea ice drift-ocean current relationships among the models (Tandon et al., 2018). In addition, the temporal variations of the relationship between sea ice drift speed, near-surface wind,and surface ocean current in the models also need to be investigated in the future as the influence of wind and ocean current on the Arctic sea ice drift change has decadal variability (Spreen et al., 2011; Kwok et al., 2013).

    Acknowledgements.This research is supported by the National Key R&D Program of China (Grant No.2018YFA0605904) and the National Natural Science Foundation of China (Grant No. 41701411).

    Electronic supplementary material:Supplementary material is available in the online version of this article at https://doi.org/10.1007/s00376-021-1153-4.

    猜你喜歡
    冷啟動(dòng)陰極燃料電池
    輕型汽油車(chē)實(shí)際行駛排放試驗(yàn)中冷啟動(dòng)排放的評(píng)估
    基于學(xué)習(xí)興趣的冷啟動(dòng)推薦模型
    客聯(lián)(2021年2期)2021-09-10 07:22:44
    燃料電池題解法分析
    場(chǎng)發(fā)射ZrO/W肖特基式場(chǎng)發(fā)射陰極研究進(jìn)展
    電子制作(2018年12期)2018-08-01 00:47:46
    試駕豐田氫燃料電池車(chē)“MIRAI未來(lái)”后的六個(gè)疑問(wèn)?
    車(chē)迷(2017年12期)2018-01-18 02:16:11
    燃料電池的維護(hù)與保養(yǎng)
    電子制作(2017年10期)2017-04-18 07:23:13
    IT-SOFCs陰極材料Sm0.8La0.2Ba1-xSrxFe2O5+δ的制備與表征
    微生物燃料電池空氣陰極的研究進(jìn)展
    軍事技能“冷啟動(dòng)”式訓(xùn)練理念初探
    非固體電解質(zhì)鉭電容器陰極表面的處理方法
    河南科技(2014年8期)2014-02-27 14:07:47
    性色avwww在线观看| 国产精品久久久久久久久免| 国产老妇伦熟女老妇高清| 性色avwww在线观看| 在线观看美女被高潮喷水网站| 国产成人精品婷婷| 国产欧美日韩综合在线一区二区| 精品人妻偷拍中文字幕| 亚洲在久久综合| 久久久欧美国产精品| 午夜福利视频在线观看免费| 老汉色∧v一级毛片| 最近2019中文字幕mv第一页| 欧美国产精品va在线观看不卡| 亚洲精品久久午夜乱码| 啦啦啦在线观看免费高清www| 国产成人91sexporn| 99香蕉大伊视频| 午夜福利网站1000一区二区三区| 大香蕉久久网| 尾随美女入室| 久久国产精品男人的天堂亚洲| 国产精品久久久久久久久免| 在线免费观看不下载黄p国产| 天天躁日日躁夜夜躁夜夜| 精品第一国产精品| 中文字幕色久视频| 少妇被粗大猛烈的视频| 国产女主播在线喷水免费视频网站| 捣出白浆h1v1| 另类亚洲欧美激情| 国产又爽黄色视频| 新久久久久国产一级毛片| 99久国产av精品国产电影| 日本vs欧美在线观看视频| 免费高清在线观看视频在线观看| 美女高潮到喷水免费观看| 国产xxxxx性猛交| 亚洲国产欧美日韩在线播放| 久久久久国产精品人妻一区二区| 国产精品久久久久成人av| 成人18禁高潮啪啪吃奶动态图| 国产欧美日韩综合在线一区二区| 国产成人精品无人区| 香蕉国产在线看| 日本色播在线视频| 最近最新中文字幕大全免费视频 | 国产男女内射视频| 日日摸夜夜添夜夜爱| 美女国产高潮福利片在线看| 18禁观看日本| www.自偷自拍.com| 国产在线视频一区二区| 亚洲国产精品一区二区三区在线| 1024视频免费在线观看| 国产黄频视频在线观看| 国产精品无大码| 久久99蜜桃精品久久| 国产黄频视频在线观看| 99香蕉大伊视频| 国产极品粉嫩免费观看在线| av福利片在线| 日韩精品有码人妻一区| 99久久中文字幕三级久久日本| 自拍欧美九色日韩亚洲蝌蚪91| 精品一区二区三区四区五区乱码 | 亚洲,一卡二卡三卡| 91在线精品国自产拍蜜月| 成人影院久久| xxxhd国产人妻xxx| 男男h啪啪无遮挡| 高清欧美精品videossex| 最近手机中文字幕大全| 日韩欧美精品免费久久| 毛片一级片免费看久久久久| 久久97久久精品| av国产久精品久网站免费入址| 一区二区三区乱码不卡18| 免费日韩欧美在线观看| 久久精品国产综合久久久| 亚洲国产欧美网| 水蜜桃什么品种好| 国产精品蜜桃在线观看| 999久久久国产精品视频| 极品少妇高潮喷水抽搐| 精品亚洲乱码少妇综合久久| 国产在线一区二区三区精| 亚洲精品中文字幕在线视频| 久热久热在线精品观看| 日韩一区二区三区影片| 日韩一区二区三区影片| 亚洲国产欧美日韩在线播放| 国产一级毛片在线| 三上悠亚av全集在线观看| 乱人伦中国视频| 男女无遮挡免费网站观看| 欧美97在线视频| 波多野结衣av一区二区av| 久久久精品免费免费高清| 亚洲,欧美,日韩| 在线天堂中文资源库| 国产成人精品福利久久| 国产成人午夜福利电影在线观看| 国产精品二区激情视频| 欧美日韩综合久久久久久| 精品福利永久在线观看| 久久精品亚洲av国产电影网| 免费播放大片免费观看视频在线观看| 欧美亚洲 丝袜 人妻 在线| 亚洲激情五月婷婷啪啪| 青草久久国产| 欧美亚洲 丝袜 人妻 在线| 国产成人精品无人区| 亚洲av国产av综合av卡| 日本wwww免费看| 中文精品一卡2卡3卡4更新| 国产高清国产精品国产三级| 日韩成人av中文字幕在线观看| 卡戴珊不雅视频在线播放| 欧美日韩av久久| 一级黄片播放器| 激情视频va一区二区三区| 久久久久久久大尺度免费视频| 在线观看一区二区三区激情| 国产精品国产av在线观看| 国产不卡av网站在线观看| 深夜精品福利| 女性生殖器流出的白浆| 久久国产精品大桥未久av| 久久精品国产亚洲av高清一级| 边亲边吃奶的免费视频| 只有这里有精品99| 熟女av电影| 日韩在线高清观看一区二区三区| 人人妻人人澡人人爽人人夜夜| 在线看a的网站| 丝袜人妻中文字幕| 波多野结衣一区麻豆| 免费不卡的大黄色大毛片视频在线观看| 欧美另类一区| 亚洲,一卡二卡三卡| 成年人午夜在线观看视频| 人妻系列 视频| 久久精品国产亚洲av天美| av视频免费观看在线观看| 欧美+日韩+精品| 色94色欧美一区二区| 精品福利永久在线观看| 亚洲国产av新网站| 国产视频首页在线观看| 日本av手机在线免费观看| 午夜日韩欧美国产| 国产乱来视频区| 伊人久久国产一区二区| 成年女人在线观看亚洲视频| 人人妻人人澡人人爽人人夜夜| 91午夜精品亚洲一区二区三区| 婷婷色综合大香蕉| 国产97色在线日韩免费| 免费久久久久久久精品成人欧美视频| 欧美日韩视频高清一区二区三区二| 免费观看性生交大片5| 一本大道久久a久久精品| 久久人人爽人人片av| 18禁观看日本| 亚洲国产精品999| 国产av精品麻豆| 国产黄色免费在线视频| 观看av在线不卡| 最近最新中文字幕免费大全7| 黑人欧美特级aaaaaa片| 亚洲,一卡二卡三卡| 亚洲国产色片| 丝瓜视频免费看黄片| av视频免费观看在线观看| 丝袜美腿诱惑在线| 国产精品国产三级专区第一集| av又黄又爽大尺度在线免费看| 在线观看免费高清a一片| 欧美日韩精品网址| 在线亚洲精品国产二区图片欧美| 午夜福利,免费看| 亚洲国产精品一区二区三区在线| 欧美亚洲 丝袜 人妻 在线| 成人国产av品久久久| 久久久久久久亚洲中文字幕| 亚洲第一青青草原| 欧美精品一区二区大全| 日本vs欧美在线观看视频| 国产精品一区二区在线不卡| av电影中文网址| 一级黄片播放器| 男人添女人高潮全过程视频| 欧美激情极品国产一区二区三区| 男女啪啪激烈高潮av片| 亚洲国产av影院在线观看| 亚洲欧洲精品一区二区精品久久久 | 性色avwww在线观看| 亚洲精品中文字幕在线视频| 国产精品香港三级国产av潘金莲 | 久久影院123| 美女福利国产在线| 97精品久久久久久久久久精品| 97在线人人人人妻| a级片在线免费高清观看视频| 永久网站在线| 少妇人妻 视频| 久久综合国产亚洲精品| 国产片特级美女逼逼视频| 狠狠精品人妻久久久久久综合| 国产淫语在线视频| 美女高潮到喷水免费观看| 欧美日韩成人在线一区二区| 国产精品免费大片| 性色avwww在线观看| 18禁动态无遮挡网站| 亚洲视频免费观看视频| 亚洲av电影在线进入| 最黄视频免费看| 亚洲国产精品一区三区| 最近2019中文字幕mv第一页| 天天躁狠狠躁夜夜躁狠狠躁| 免费大片黄手机在线观看| 18在线观看网站| 日韩三级伦理在线观看| 国产成人a∨麻豆精品| 亚洲国产精品成人久久小说| 欧美av亚洲av综合av国产av | 亚洲欧美色中文字幕在线| 国产成人精品福利久久| 久久久亚洲精品成人影院| 搡女人真爽免费视频火全软件| 两个人看的免费小视频| 美女国产高潮福利片在线看| 欧美 日韩 精品 国产| 国产成人午夜福利电影在线观看| 成年美女黄网站色视频大全免费| 免费在线观看黄色视频的| 久久精品国产亚洲av高清一级| 亚洲国产精品一区二区三区在线| 永久免费av网站大全| 老鸭窝网址在线观看| 亚洲av.av天堂| 久久久久久久精品精品| 久久人人爽人人片av| 亚洲三级黄色毛片| 日本免费在线观看一区| 日本wwww免费看| 在线观看国产h片| 成年人午夜在线观看视频| 18禁动态无遮挡网站| 99re6热这里在线精品视频| 视频在线观看一区二区三区| 99久久中文字幕三级久久日本| 亚洲,一卡二卡三卡| 日韩一区二区三区影片| 日韩精品免费视频一区二区三区| 久久久久国产网址| 91aial.com中文字幕在线观看| 亚洲欧洲日产国产| 国产精品久久久久久av不卡| 国产精品国产三级国产专区5o| 久久国产精品男人的天堂亚洲| 亚洲av电影在线进入| 日韩中字成人| 久久综合国产亚洲精品| 欧美精品亚洲一区二区| 国产日韩欧美在线精品| 久久久久久久久久久免费av| 亚洲色图 男人天堂 中文字幕| 91久久精品国产一区二区三区| a级毛片黄视频| 美女中出高潮动态图| 国产av精品麻豆| 七月丁香在线播放| 色视频在线一区二区三区| 国产精品国产三级国产专区5o| 成年动漫av网址| 日本-黄色视频高清免费观看| 日韩一卡2卡3卡4卡2021年| 91在线精品国自产拍蜜月| 麻豆精品久久久久久蜜桃| 天天躁狠狠躁夜夜躁狠狠躁| 亚洲经典国产精华液单| av免费观看日本| 久久综合国产亚洲精品| 妹子高潮喷水视频| 91成人精品电影| 色网站视频免费| 日本av免费视频播放| 一级毛片电影观看| 九色亚洲精品在线播放| 1024香蕉在线观看| 两性夫妻黄色片| 日韩成人av中文字幕在线观看| av在线app专区| 90打野战视频偷拍视频| 少妇的丰满在线观看| 国产精品久久久久成人av| 国产乱人偷精品视频| 亚洲成人av在线免费| 美女国产视频在线观看| 国语对白做爰xxxⅹ性视频网站| 日韩制服丝袜自拍偷拍| 色94色欧美一区二区| 亚洲熟女精品中文字幕| 青青草视频在线视频观看| 黄网站色视频无遮挡免费观看| 欧美激情 高清一区二区三区| 久久99精品国语久久久| 丰满迷人的少妇在线观看| 性色av一级| 人人妻人人澡人人爽人人夜夜| 99热网站在线观看| 久久韩国三级中文字幕| 日本91视频免费播放| 高清黄色对白视频在线免费看| 国产精品欧美亚洲77777| 在线免费观看不下载黄p国产| 久久鲁丝午夜福利片| 久久久久精品久久久久真实原创| 欧美人与性动交α欧美软件| 免费少妇av软件| 你懂的网址亚洲精品在线观看| 可以免费在线观看a视频的电影网站 | 男的添女的下面高潮视频| 大片电影免费在线观看免费| 欧美国产精品va在线观看不卡| 新久久久久国产一级毛片| 99精国产麻豆久久婷婷| 一级毛片 在线播放| 水蜜桃什么品种好| 久久精品aⅴ一区二区三区四区 | 欧美日韩亚洲国产一区二区在线观看 | 欧美人与性动交α欧美精品济南到 | 精品少妇久久久久久888优播| 一边摸一边做爽爽视频免费| 成年人免费黄色播放视频| 亚洲欧美成人综合另类久久久| 在现免费观看毛片| 亚洲欧美色中文字幕在线| 国产精品 国内视频| 啦啦啦在线观看免费高清www| 777久久人妻少妇嫩草av网站| 久久精品国产亚洲av涩爱| 国产麻豆69| 亚洲欧美中文字幕日韩二区| 中文字幕人妻丝袜一区二区 | 日本wwww免费看| 狂野欧美激情性bbbbbb| 蜜桃在线观看..| 日韩精品有码人妻一区| 中文字幕精品免费在线观看视频| 日本欧美视频一区| 久久久久久久久久久久大奶| 亚洲,一卡二卡三卡| 欧美精品高潮呻吟av久久| 国产 一区精品| 一本大道久久a久久精品| 一本一本久久a久久精品综合妖精 国产伦在线观看视频一区 | 免费av中文字幕在线| 国产精品嫩草影院av在线观看| 欧美变态另类bdsm刘玥| 久久99精品国语久久久| 久久精品久久精品一区二区三区| 丝瓜视频免费看黄片| 男女下面插进去视频免费观看| 国产精品一区二区在线不卡| 免费黄网站久久成人精品| 亚洲av欧美aⅴ国产| 久久青草综合色| tube8黄色片| 久久精品亚洲av国产电影网| 国产精品久久久久久av不卡| 在线观看一区二区三区激情| 91午夜精品亚洲一区二区三区| 校园人妻丝袜中文字幕| 国产野战对白在线观看| 美女大奶头黄色视频| 啦啦啦中文免费视频观看日本| 十八禁高潮呻吟视频| 国产女主播在线喷水免费视频网站| 人人妻人人添人人爽欧美一区卜| 天天躁狠狠躁夜夜躁狠狠躁| 母亲3免费完整高清在线观看 | 亚洲国产最新在线播放| 国产色婷婷99| 久久精品亚洲av国产电影网| 中文字幕av电影在线播放| 在线观看人妻少妇| 电影成人av| 搡老乐熟女国产| 中文字幕制服av| 免费女性裸体啪啪无遮挡网站| 青春草亚洲视频在线观看| 国产精品蜜桃在线观看| 亚洲欧美一区二区三区黑人 | 在线观看免费视频网站a站| 91aial.com中文字幕在线观看| 国产免费视频播放在线视频| 天天影视国产精品| 亚洲美女黄色视频免费看| av不卡在线播放| 亚洲成av片中文字幕在线观看 | 日日撸夜夜添| 麻豆乱淫一区二区| 90打野战视频偷拍视频| 男女边吃奶边做爰视频| 女人被躁到高潮嗷嗷叫费观| 免费观看在线日韩| 国产亚洲最大av| 飞空精品影院首页| 99精国产麻豆久久婷婷| av又黄又爽大尺度在线免费看| 国产亚洲一区二区精品| 最近中文字幕2019免费版| 精品一区在线观看国产| 香蕉精品网在线| 国产免费福利视频在线观看| 国产高清国产精品国产三级| 亚洲伊人色综图| 超色免费av| 国产片特级美女逼逼视频| 女性生殖器流出的白浆| 国产激情久久老熟女| 国产精品蜜桃在线观看| 麻豆av在线久日| 日日爽夜夜爽网站| 80岁老熟妇乱子伦牲交| 日韩精品有码人妻一区| 亚洲欧美一区二区三区国产| av有码第一页| 亚洲精品乱久久久久久| 激情视频va一区二区三区| freevideosex欧美| 18在线观看网站| 综合色丁香网| 国产亚洲一区二区精品| 欧美日韩综合久久久久久| 中文字幕最新亚洲高清| 精品99又大又爽又粗少妇毛片| 午夜日本视频在线| 国产在视频线精品| 成年人免费黄色播放视频| 日韩av免费高清视频| 男人舔女人的私密视频| 交换朋友夫妻互换小说| 1024香蕉在线观看| 国产一区二区在线观看av| 侵犯人妻中文字幕一二三四区| 久久久国产精品麻豆| 黄片播放在线免费| 午夜福利乱码中文字幕| 欧美bdsm另类| 欧美精品亚洲一区二区| 亚洲国产精品一区二区三区在线| 美女xxoo啪啪120秒动态图| av在线app专区| 波多野结衣一区麻豆| 久久久久精品性色| 五月开心婷婷网| √禁漫天堂资源中文www| 欧美日韩亚洲高清精品| 久久免费观看电影| 精品国产国语对白av| 80岁老熟妇乱子伦牲交| 少妇人妻久久综合中文| a级毛片黄视频| 久久久国产精品麻豆| 久久热在线av| 在线观看一区二区三区激情| 亚洲天堂av无毛| 国产成人av激情在线播放| 久久精品国产a三级三级三级| 边亲边吃奶的免费视频| 春色校园在线视频观看| 99re6热这里在线精品视频| 精品久久蜜臀av无| 亚洲图色成人| 超色免费av| 久久久久人妻精品一区果冻| 男女无遮挡免费网站观看| 菩萨蛮人人尽说江南好唐韦庄| 日韩中文字幕欧美一区二区 | 免费观看无遮挡的男女| 亚洲精品一二三| 日韩大片免费观看网站| 欧美国产精品一级二级三级| 女的被弄到高潮叫床怎么办| a级毛片在线看网站| 老司机亚洲免费影院| 欧美日本中文国产一区发布| 欧美少妇被猛烈插入视频| 巨乳人妻的诱惑在线观看| 一区二区av电影网| 国产亚洲一区二区精品| 寂寞人妻少妇视频99o| 国产精品久久久久久av不卡| 欧美激情极品国产一区二区三区| 国产人伦9x9x在线观看 | 成人国产麻豆网| 久久久久网色| 国产精品一区二区在线观看99| 激情五月婷婷亚洲| 国产精品久久久av美女十八| 久久精品久久久久久噜噜老黄| 久久人人爽人人片av| 肉色欧美久久久久久久蜜桃| 午夜91福利影院| 伦理电影大哥的女人| 一级片'在线观看视频| 亚洲国产精品国产精品| 中文字幕人妻熟女乱码| 一边摸一边做爽爽视频免费| 日韩一本色道免费dvd| 中文字幕精品免费在线观看视频| 黑丝袜美女国产一区| 啦啦啦视频在线资源免费观看| 国产精品二区激情视频| av天堂久久9| 久热这里只有精品99| 久久久久视频综合| 久久久久久久国产电影| 国产精品成人在线| 国产乱来视频区| 男女啪啪激烈高潮av片| 欧美人与性动交α欧美软件| 你懂的网址亚洲精品在线观看| av卡一久久| 久久鲁丝午夜福利片| 国产精品女同一区二区软件| 热99久久久久精品小说推荐| 亚洲欧洲日产国产| videos熟女内射| 少妇人妻 视频| 亚洲图色成人| 爱豆传媒免费全集在线观看| 在线观看一区二区三区激情| 自拍欧美九色日韩亚洲蝌蚪91| 国产又色又爽无遮挡免| 欧美日韩视频高清一区二区三区二| 日本黄色日本黄色录像| 国产片内射在线| 国产av一区二区精品久久| 大片电影免费在线观看免费| 一区二区日韩欧美中文字幕| av又黄又爽大尺度在线免费看| 精品亚洲成国产av| av片东京热男人的天堂| 亚洲少妇的诱惑av| 亚洲美女视频黄频| 午夜精品国产一区二区电影| 成人黄色视频免费在线看| 激情视频va一区二区三区| 丝袜人妻中文字幕| 少妇人妻精品综合一区二区| 91午夜精品亚洲一区二区三区| 水蜜桃什么品种好| 欧美精品一区二区大全| 97人妻天天添夜夜摸| 纵有疾风起免费观看全集完整版| 超碰成人久久| 高清黄色对白视频在线免费看| 女性被躁到高潮视频| 伊人亚洲综合成人网| 中文乱码字字幕精品一区二区三区| 亚洲欧美精品自产自拍| 99久国产av精品国产电影| 你懂的网址亚洲精品在线观看| 国产在视频线精品| 纵有疾风起免费观看全集完整版| 热99国产精品久久久久久7| 满18在线观看网站| 久久久久精品性色| 伊人亚洲综合成人网| 国产精品国产三级国产专区5o| 国产黄色免费在线视频| 亚洲精品第二区| 汤姆久久久久久久影院中文字幕| 韩国av在线不卡| 1024视频免费在线观看| 热re99久久精品国产66热6| 两性夫妻黄色片| 香蕉精品网在线| 搡女人真爽免费视频火全软件| 久久精品国产鲁丝片午夜精品| 性少妇av在线| 亚洲国产毛片av蜜桃av| 又粗又硬又长又爽又黄的视频| 80岁老熟妇乱子伦牲交| 天堂8中文在线网| 黄色配什么色好看| 亚洲国产欧美网| 叶爱在线成人免费视频播放| 精品久久久精品久久久| 在线看a的网站| 丝袜脚勾引网站| 天天操日日干夜夜撸| 国产精品久久久久久精品电影小说| 男女边摸边吃奶| 精品少妇一区二区三区视频日本电影 | 午夜福利视频精品| 成人亚洲精品一区在线观看| 午夜福利影视在线免费观看| 永久网站在线| 水蜜桃什么品种好| 91国产中文字幕| 丝瓜视频免费看黄片| 日韩大片免费观看网站| 秋霞伦理黄片| 丰满迷人的少妇在线观看| 不卡视频在线观看欧美| 精品国产国语对白av| 在线看a的网站| 少妇人妻精品综合一区二区|