*Article* **Seasonal and Interannual Variation Characteristics of Low-Cloud Fraction in Different North Pacific Regions**

#### **Qian Wang 1,2, Haiming Xu 1,2,\*, Leying Zhang <sup>3</sup> and Jiechun Deng 1,2**


Received: 18 February 2019; Accepted: 2 March 2019; Published: 7 March 2019

**Abstract:** In this study, we use the long-term satellite data to investigate seasonal and interannual variation of low-cloud fraction (LCF) and the associated controlling factors over the eastern and western North Pacific. On the seasonal time scale, the enhanced LCF over the eastern North Pacific in summer is actively coupled with strong estimated inversion strength (EIS) and 700-hPa relative humidity, and the LCF over the western North Pacific in winter is large and mainly caused by increased sensible heat flux and tropospheric low-level cold advection. On the interannual time scale, the increased LCF over the eastern North Pacific in summer is associated with increased EIS and decreased sea surface temperatures, in which the El Niño plays an important role; the enhanced LCF over the western North Pacific in spring and winter has a positive correlation with enhanced sensible heat flux (SHF) and tropospheric low-level cold advection, which can be partly explained by the subpolar frontal zone (SPFZ) intensity.

**Keywords:** low-level clouds; North Pacific; seasonal variation; interannual variation

#### **1. Introduction**

Low-level clouds play an important role in the global radiation balance, which includes longwave radiation emission, as well as absorption and reflection of solar shortwave radiation [1]. Garrett and Zhao found that where thin water clouds and pollution are coincident, there is an increase in cloud longwave emissivity resulting from elevated haze levels. This results in an estimated surface warming under cloudy skies [2]. They also found that in Alaska, the cloud radiative impact on the surface is a net warming effect between October and May and a net cooling in summer. During episodes of high surface haze aerosol concentrations and cloudy skies, both the net warming and net cooling are amplified. Thus the low cloud has an important influence on global climate change [3]. A small change in fractional coverage of low-level clouds can exert significant influences on weather and climate [4]. For example, the marine stratocumulus has a potential positive feedback to global warming [5]. However, since the formation of low-level clouds is governed by small-scale turbulent processes, the associated controlling factors of low-cloud fraction (LCF) are complex. Although Ma et al. has found a prognostic method of cloud-cover calculation (PROGCS) which has significant advantage over the conventional diagnostic one, the complex controlling factors still are the main sources of the uncertainty in state-of-the-art models [6]. For example, Fan et al. found that the aerosol errors

have a certain contribution to cloud fraction biases in Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations [7]. As a result, the variation of LCF has been poorly simulated in present climate projections [8,9]. Thus, it is very important to investigate the controlling factors of LCF for climate research [10,11].

Low-level clouds are frequently observed over the cool oceans where deep convection is unlikely to occur. Due to their significant potential impacts on the Earth's energy balance, low-level clouds have been intensively investigated at various time scales [12]. At the seasonal time scale, it is well known that the LCF is positively related to the inversion strength of lower-tropospheric temperature. In previous studies, the estimated inversion strength (EIS) was defined as a refinement of lower-tropospheric stability (LTS), and there is a linear relationship between LCF and EIS over the subtropical and mid-latitude oceans [13]. In fact, the EIS can only explain the seasonality of LCF over the eastern area of an ocean basin, which is located east of the western Pacific subtropical high that accompanies persistent mid-tropospheric subsidence and equatorward surface winds [14,15]. Besides the subtropical and mid-latitude oceans, large LCF also appears over the high-latitude and subpolar oceans. In these regions, one of the important factors that affect LCF is a prominent ocean front [16]. Over the ocean front, the sea surface temperature (SST) anomalies are controlled by surface temperature advection, resulting in a source of sensible heat flux (SHF) anomalies [17]. The upward SHF destabilizes the surface layer and facilitates shallow convection in the boundary layer to further increase LCF. In addition, the decreased relative humidity (RH) at 700-hPa acts to reduce cloudiness [18,19]. Besides, the seasonal variation of low clouds is also affected by aerosol and haze [20,21].

At the interannual time scale, the variation of LCF is considered to be associated with different environmental fields [22]. Previous studies focused on the variation of LCF and its relationship with SST anomalies (SSTA). Norris and Leovy [23] showed that LCF is negatively correlated with the SSTA in the eastern subtropical oceans, especially during summer [24]. They further noted that surface cold advection may play an important role in the interannual variation of LCF. The summertime interannual variation of LCF over the North Pacific is the largest in the central and western regions along 35◦ N and in the eastern region near 15◦ N. The LCF over these two regions are in good relationship with local SST and sea-level pressure (SLP) field [25]. Over the North Atlantic, the North Atlantic subtropical high (NASH) also plays an important role in the interannual variation of summertime LCF. A stronger NASH is often accompanied by increased LCF and cooler SSTs along the southeast of the NASH. The northeasterly surface wind anomalies associated with an intensified NASH tend to induce colder advection and stronger coastal upwelling in the LCF region, acting to decrease surface temperature. Meanwhile, the anomalous warm advection associated with the easterly wind anomalies from Africa leads to a warming at 700 hPa over the LCF region. Such warming and surface cooling increase atmospheric static stability, favoring the growth of LCF. The anomalous diabatic cooling associated with the growth of LCF dynamically excites an anomalous anticyclone to its north and enhances the NASH in turn. Besides the subtropical high, the El Niño-Southern Oscillation (ENSO) is a primary variability of interannual time scale in the Pacific Ocean, which has a positive relationship with summertime-enhanced LCF over the southeastern North Pacific [26].

The seasonal and interannual variations of LCF have been investigated in many studies. However, due to limited observation data, the data range used in previous studies is very short. In addition, previous studies showed that the EIS can only explain the variation of LCF over the eastern side of an ocean basin, while it is weakly related to the LCF over the western side [12]. Thus, different controlling factors of LCF between the eastern and western sides need to be studied. In this study, we use long-term satellite data to explore seasonal and interannual variations of low-level clouds over the North Pacific, where the associated controlling factors exhibit significant differences in the eastern and western regions.

The rest of this paper is organized as follows. In Section 2, we introduce the data and methods used in this study. In Section 3, we investigate the seasonal distribution of LCF over the North Pacific and its associated controlling factors. Multiple linear regression model analysis is used. Interannual variation of LCF is also explored in Section 3 and a conclusion is given in Section 4.

#### **2. Data and Methods**

#### *2.1. Data*

The cloud data used in this study are the collection 06 Level-3 monthly cloud product of the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a horizontal resolution of 1◦ × 1◦, and cover the period from January 2003 to December 2015 [27,28]. It is noteworthy that time representation errors exist for cloud fraction observed by MODIS, since it only observes clouds twice a day. The correlation coefficient between MODIS monthly cloud fraction (CF) and continuous day-and-night radar/lidar CF is 0.97. This small error will not affect our results [21,29]. MODIS cloud data include cloud fraction and cloud top pressure. Clouds with top pressure higher than 700 hPa are considered as low-level clouds [30]. Since the MODIS instruments cannot detect low-level clouds that are overlapped with mid- and high-level clouds, the random overlap assumption is used to reduce the influence of mid- and high-level clouds [31]. It is a reasonable assumption outside the areas of deep convection and landmass [32,33].

The meteorological fields used in this study are ERA-Interim global atmospheric reanalysis at 1◦ × 1◦ grid from European Centre for Medium-Range Weather Forecasts (ECMWF) [34], including 700-hPa subsidence (W), 700-hPa RH, 700-hPa potential height (Z), 2-m surface air temperature (SAT), dew point temperature (Td), 10-m surface wind, and SLP. All variables used cover the period from January 2003 to December 2015.

In addition, the SST at 1◦ × 1◦ grid from the Hadley Center [35], the SHF at 1◦ × 1◦ grid from the Woods Hole Oceanographic Institution (WHOI), and the Niño-3.4 index provided by the National Oceanic and Atmospheric Administration (NOAA) are used in this study [36,37]. For consistency, all variables used cover the period from January 2003 to December 2015.

#### *2.2. Methods*

The EIS defined by Wood and Bretherton [13] is used as a measure of inversion layer strength at the top of the boundary layer:

$$EIS = \left(\theta\_{700} - \theta\_{sfc}\right) - \gamma\_m^{850} (Z\_{700} - Z\_{LCL}),\tag{1}$$

where *θ*<sup>700</sup> and *θ*sfc are the potential temperatures at 700 hPa and surface, respectively. *Z*<sup>700</sup> is the 700-hPa height, *Z*LCL is the lifting condensation level, and *γ*<sup>850</sup> *<sup>m</sup>* is the 850-hPa moist adiabatic lapse rate. *Z*LCL is calculated by using *SAT* and *T*d:

$$Z\_{\rm LCL} = 123 \times (SAT - T\_{\rm d}) \tag{2}$$

The near-surface temperature advection (advT) is calculated by using −*Vsfc*·∇SST, where *Vsfc* represents surface zonal and meridional winds, and ∇SST are the zonal and meridional SST gradients [38–40].

We also define the subpolar frontal zone (SPFZ) to measure SST gradient strength in the subpolar North Pacific [41]. The SPFZ intensity index (Iint) is defined as the meridional SST gradient (−*∂*SST/*∂*y) averaged over the climatological SPFZ area (145◦–170◦ E, 35◦–47◦ N).

To quantify the relative importance of the associated controlling factors in seasonal variation of LCF, LCF dependence on these factors is derived using multiple linear regression. Although the multiple linear regression method cannot perfectly extract the impact of individual large-scale forcing, the derived local dependence is useful for quantifying their local controls on LCF [5].

In this study, spring refers to the period of March, April and May; summer refers to the period of June, July, August; autumn refers to the period of September, October, November; winter refers to the period of December, next January, next February.

#### **3. Results**

#### *3.1. Climatological Distribution of LCF*

Figure 1a,b displays the distributions of climatological LCF over the North Pacific in summer and winter, respectively. Winter is defined from December to the following February, and summer is defined from June to August. The LCF over the North Pacific is zonally inhomogeneous in both winter and summer, and exhibits obvious seasonal difference. In summer, the LCF over the eastern North Pacific is larger than that over the western North Pacific, with a local maximum around 20◦ N (Figure 1a). In contrast, the LCF maximum in winter appears over the western North Pacific (Figure 1b). Note that the LCF over the Bering Sea is large in both summer and winter.

**Figure 1.** Climatological low-cloud fraction (LCF; shading; units: %) and surface potential temperature (contour interval: 2 K) in summer (**a**) and in winter (**b**); (**c**,**d**) are the same as (**a**,**b**), but for estimated inversion strength (EIS; shading; units: K) and 700-hPa potential temperature (contour interval: 4 K).

Previous studies indicated that the climatological distribution of LCF and the seasonality of LCF can be well explained by the EIS, whose enhancement acts to increase LCF [42,43]. Atmospheric circulation also make contribution to the variation of LCF, such as subtropical high, Hadley–Walker circulation, and mesoscale waves [12,44]. However, EIS was proved to be the dominating factor in the seasonal variation of the LCF in previous studies [12,31]. Thus, EIS is focused on in our study. Hence, the climatological EIS defined in Equation (1) is shown in Figure 1c,d for summer and winter, respectively. Across the summertime subtropical basin (Figure 1c), the EIS is maximal off the west coast of North America around 125◦ W, in good correspondence with the spatial pattern of LCF (Figure 1a). Compared to the summertime situation, the EIS in winter exhibits a zonal minimum (negative center) distribution over the mid-latitude western North Pacific (Figure 1d), where a maximum LCF dominates. This is in contrast to the well-known liner relationship between EIS and LCF [13]. The enhancement of EIS could maintain a strong temperature inversion at the top of the boundary layer, inhibiting cloud-top entrainment of dry air, further contributing to LCF increase. Overall, the LCF over the eastern North Pacific is positively associated with the EIS, while the relationship between LCF and EIS over the western North Pacific is negative. Thus, the EIS alone is not sufficient to explain the observed LCF. Next, we will discuss the possible factors dominating the seasonal cycles of LCF over the eastern and western North Pacific, respectively.

#### *3.2. Seasonal Cycle of LCF*

According to previous studies, the EIS, cool advT, SHF, 700-hPa subsidence, and 700-hPa RH are the main factors affecting the formation of low-level clouds over the oceans. Overall, the enhancements of these factors contribute to LCF increase [45]. The enhancement of EIS could maintain a strong temperature inversion at the top of the boundary layer, inhibiting cloud-top entrainment of dry air, further contributing to LCF increase. The cool advT could expand the difference value between SST and SAT, further increasing SHF. The increased SHF could destabilize the surface layer, and thereby facilitate shallow convection in the boundary layer, to further increase LCF. The enhanced 700-hPa W acts to warm the mid-troposphere, inhibiting cloud-top entrainment of dry air, further increasing LCF. The 700-hPa RH could contribute to the increase of LCF by increasing the water vapor content in the air. To discuss the seasonality of LCF and its distribution over the eastern and western North Pacific, longitude-time sections of climatological LCF and the associated controlling factors along 25◦ N and 45◦ N are shown in Figures 2 and 3, respectively.

**Figure 2.** Time-longitude section of climatological (**a**) LCF (shading; units: %), (**b**) EIS (shading; units: K), (**c**) near-surface temperature advection (advT; shading; units: K/day), (**d**) 700-hPa relative humidity (RH; shading; units: %), (**e**) 700-hPa subsidence (W; shading; units: m/s), and (**f**) sensible heat flux (SHF; shading; units: W/m2) along 25◦ N.

**Figure 3.** Time-longitude section of climatological (**a**) LCF (shading; units: %), (**b**) EIS (shading; units: K), (**c**) near-surface advT (shading; units: K/day), (**d**) 700-hPa RH (shading; units: %), (**e**) 700-hPa W (shading; units: m/s), and (**f**) SHF (shading; units: W/m2) along 45◦ N.

\$UHD These figures reveal complex relationships of LCF with its controlling factors in the course of seasonal cycles over the North Pacific. At 25◦ N (Figure 2a), LCF is larger in summer than in winter over the eastern subtropics (115◦–135◦ W), which is consistent with the winter-summer difference of the EIS shown in Figure 2b. As evident in Figure 2c,d, the distributions of cold temperature advection and 700-hPa RH are also in good accordance with LCF. However, 700-hPa W and SHF are relatively weaker in summer, which is in contrary to the seasonality of LCF (Figure 2e,f). Therefore, the EIS, cold advection, and 700-hPa RH have great contributions to the enhancement of LCF over the eastern North Pacific in summer. In summer, LCF prevails over the eastern portion of the subtropical North Pacific, which is located east of the surface subtropical high that accompanies persistent mid-troposphere subsidence and equatorward surface winds. The equatorward winds induce coastal upwelling (west coast of Mexico), upper-ocean mixing, and surface evaporation, acting to maintain relatively low SST. Meanwhile, the mid-troposphere subsidence associated with the subtropical high acts to warm the mid-troposphere. The combination of cool SST and warm mid-troposphere maintains a strong temperature inversion at the top of the boundary layer, inhibiting cloud-top entrainment of dry air, further increasing LCF. Thus, the EIS can explain the summertime enhancement of LCF to a certain extent. Note that the EIS reaches its maximum in spring, while the maximum of LCF is in summer (Figure 2a,b). This may be due to both EIS and RH being large in summer, while only the EIS is large in spring. Thus, LCF reaches its maximum in summer, resulting from the combined contribution of EIS and RH, rather than in spring, when only the EIS is the strongest, suggesting the essential influence of RH on the seasonal variability of LCF over the eastern North Pacific. RH may lead to a time-lag

correlation between EIS and LCF in the subtropics. The enhanced RH indicates an increase in vapor concentration in summer, which provides positive condition to the formation of low-level clouds in summer, despite the EIS being maximum in spring [46].

Figure 3 is the same as Figure 2, except along 45◦ N. Over the western North Pacific (145◦–160◦ E), the EIS is larger in summer than in winter (Figure 3b), while LCF is larger in winter than in summer (Figure 3a). This is in contrast to the well-known liner relationship between EIS and LCF [47]. Meanwhile, cold advection, 700-hPa RH, 700-hPa W, and SHF are enhanced in winter over the western portion (Figure 3c–f), which is in accordance with the distribution of LCF (Figure 3a). Therefore, the wintertime enhanced LCF over the western North Pacific may be due to the enhancement of cold advection, SHF, 700-hPa W, and 700-hPa RH. On the one hand, the cold advection in winter over the western region destabilizes the surface layer, increasing the difference between SST and SAT, further resulting in large upward SHF. The wintertime enhancement of upward SHF facilitates shallow convection in the boundary layer, and further increases LCF. On the other hand, the enhanced storm track activity also contributes to the wintertime enhancement of SHF over the western North Pacific (not shown). In this area, the wintertime enhanced 700-hPa W acts to warm the mid-troposphere, inhibiting cloud-top entrainment of dry air to further increase LCF to a certain extent [20]. Moreover, similar to the situation over the eastern North Pacific, the wintertime enhancement of 700-hPa RH may also have positive effects on enhancing LCF.

As shown in the preceding section, the factors favoring increased LCF are different over the eastern and western North Pacific regions. One may question the relative importance of contributions from the EIS, advT, SHF, 700-hPa W, and 700-hPa RH to the enhancement of LCF. To further quantify their relative contributions, we reconstruct LCF using a multiple linear regression model. In this study, the regression model is constructed from climatological LCF and the factors over the eastern (115◦–136◦ W, 15◦–28◦ N) and western (140◦–155◦ E, 47◦–60◦ N) North Pacific, respectively. To describe the relative importance of cloud controlling factors, the annual mean has been removed. The regression slope of LCF variation against each predictor is given in Table 1.


**Table 1.** Regression slope for each predictor (EIS: estimated inversion strength; advT: surface temperature advection; SHF: sensible heat flux; W: 700-hPa subsidence; RH: 700-hPa relative humidity).

Figures 4 and 5 show the longitude-time distributions of the predicted climatological seasonal cycles# of LCF along 25◦ N and 45◦ N, respectively. Besides, the corresponding LCF predicted by the multiple linear regression model is also shown in Figures 4b and 5b, named "Total". At 25◦ N, the multiple linear regression model explains 71% of the total variance of LCF regionality and its seasonal cycle, whereas the root mean square error (RMSE) between observed and predicted LCF is 8%. The model reproduces the summertime LCF maximum from July to September over the eastern subtropics (Figure 4a,b). The reconstruction indicates that the EIS (Figure 4c) and RH (Figure 4e) make the greatest contributions to the summertime enhancement of LCF, and the contribution from cold advection is also important (Figure 4d). In contrast, the 700-hPa W and SHF act to reduce LCF in summer (Figure 4f,g). *Atmosphere* **2019**, *10*, 126

**Figure 4.** (**a**) Same as Figure 2a, except that the annual-mean LCF (%) has been removed. (**b**) Same as (**a**), except for the corresponding LCF (%) predicted by the multiple linear regression model. The correlation and root mean square error (RMSE) between (**a**,**b**) are shown below (**a**). (**c**–**g**) Same as (**b**), except for individual contributions from EIS, advT, 700-hPa RH, 700-hPa subsidence, and SHF to (**b**), respectively.

**Figure 5.** (**a**) Same as Figure 3a, except that the annual-mean LCF (%) has been removed. (**b**) Same as (**a**), except for the corresponding LCF (%) predicted by the multiple linear regression model. The correlation and root mean square error (RMSE) between (**a**,**b**) are shown below (**a**). (**c**–**g**) Same as (**b**), except for individual contributions from EIS, advT, 700-hPa RH, 700-hPa subsidence, and SHF to (**b**), respectively.

At 45◦ N, the reconstructed LCF can also reproduce LCF well. In this area, the reconstruction explains 68% of the total variance of LCF, and the RMSE between observed and predicted LCF is 9% (Figure 5a,b). The wintertime LCF enhancement over the western North Pacific is mostly attributable to enhanced cold advection and SHF (Figure 5d,g). Nevertheless, the EIS in this area acts to suppress the wintertime enhancement of LCF (Figure 5c), and the direct impacts of 700-hPa RH and W are negligible (Figure 5e,f).

Overall, the dominating factors associated with the seasonal cycle of LCF are different over the eastern and western North Pacific regions. Over the eastern North Pacific, the EIS dominates the enhancement of LCF in summer, together with the 700-hPa RH. Over the western North Pacific, the enhancement of LCF in winter is mostly due to enhanced SHF and cold advection.

#### *3.3. Association with Meteorological Parameters*

In this subsection, we examine the interannual variability of EIS, 700-hPa W, advT, SHF, and SST to investigate the possible factors associated with the interannual variability of LCF over eastern and western North Pacific regions, respectively. We calculated the interannual variance of LCF in different seasons. We first choose three regions over the eastern North Pacific where the variances are large. However, in order to investigate the different factors of LCF between eastern and western North Pacific, we also choose three regions over the western North Pacific where the variances of LCF are also large (Figure 6). The six different regions are defined as follows: the Okhotsk Sea (OS; 140◦–155◦ E, 47◦–60◦ N), the Kuroshio Extension (KE; 142◦–180◦ E, 37◦–44◦ N), the south basin of Japan (SJ; 124◦–145◦ E, 25◦–31◦ N), the center of the North Pacific (CE; 136◦–165◦ W, 26◦–37◦ N), the southeastern North Pacific (SE; 115◦–136◦ W, 15◦–28◦ N), and the northeastern North Pacific (NE; 124◦–148◦ W, 26◦–43◦ N).

**Figure 6.** Interannual LCF variance (shading; units: %2) in (**a**) spring, (**b**) summer, (**c**) autumn, and (**d**) winter. Thick solid rectangles designate different regions discussed in Section 4.

First, we calculated the correlation coefficients between regional-mean LCF and corresponding meteorological variables for each region. Table 2 lists these correlation coefficients. For the correlation coefficients of different regions and seasons, we use the method of significance test of correlation coefficients [48]. According to the significance test table of correlation coefficient, we can see that when the degree of freedom *n* = 11, the correlation coefficient (COR) which is larger than 0.553 (COR < −0.553) exceeds a 95% confidence level; the COR which is less than 0.476 (−0.476 < COR < 0) cannot exceed a 90% confidence level.

**Table 2.** Correlation coefficients of regionally-averaged interannual anomalies of various meteorological parameters with those of LCF. The bold type exceed a 95% confidence level, while the parentheses indicate that the correlation coefficient is not statistically significant. (SE: the southeastern North Pacific; NE: the northeastern North Pacific; CE: the center of the North Pacific; OS: the Okhotsk Sea; KE: the Kuroshio Extension; SJ: the south basin of Japan).


Over the eastern portions (SE, NE, and CE), the LCF is positively correlated with the EIS and negatively correlated with the SST, especially in summer and winter; the 700-hpa W also plays a positive role in the interannual variation of LCF over the eastern portions. In NE and CE, there is a positive correlation between LCF and cold advection. Over the eastern portions, the correlation between LCF and SHF is not obvious.

In contrast to the eastern regions, the correlations between LCF and meteorological parameters over the western regions (OS, KE, and SJ) present radically different features. Over the western regions, the LCF is positively connected with cold advection and SHF, especially in winter and spring. Note that there is no relationship between LCF and EIS, or between LCF and SST. Over the western regions, the 700-hPa W also has a positive correlation with LCF, similar to that in the eastern regions.

These correlation coefficients reflect regional contrast at the interannual time scale. The LCF over the eastern North Pacific is positively correlated with both EIS and SST; the LCF over the western regions coincides well with SHF and cold advection. The 700-hPa W has positive relation with LCF over both western and eastern regions.

#### *3.4. Association with El Niño and SPFZ*

What causes the characteristic interannual variations in SST, EIS, advT, and SHF, which are closely linked with the interannual variation of LCF? In this subsection, we first discuss the possible relationship of LCF with the El Niño, which is the primary variability in the North Pacific at the interannual time scale. Figure 7 shows the interannual anomalies of LCF, SST, and EIS over the North Pacific regressed onto the synchronization Niño-3.4 index in different seasons. Usually, the El Niño occurs in the eastern equatorial Pacific. Warm SST anomalies simultaneously occur in the surrounding regions (Figure 7a–d), which are well-known as the typical SST anomaly distribution associated with the El Niño [49,50]. Meanwhile, the warm SST benefits warm surface potential temperature, corresponding to the overlying negative EIS. This is consistent with the negative correlation between EIS and El Niño over the eastern North Pacific. The negative EIS over the eastern North Pacific favors negative LCF. Thus, El Niño-related SSTs have negative feedback on the LCF over the eastern North Pacific, in which the EIS plays an important role. This is consistent with the results of previous studies [51].

**Figure 7.** Coefficients of interannual LCF (shading; units: %), SST (purple contour; units: K), and EIS (black contour; units: K) onto the synchronization Niño-3.4 index for (**a**) spring, (**b**) summer, (**c**) autumn, and (**d**) winter. Stippling indicates the 90% confidence level using the t test. Contours indicate statistically significant positive (solid) and negative (dashed) differences.

The LCF over the western North Pacific does not have a clear relationship with the El Niño. This begs the question: Is there any possible link with other climate modes? From Table 2, we can see that the LCF over the western North Pacific is positively related to cold advection and SHF. Thus, in Figure 8, we show the maps of LCF, advT, and SHF anomalies regressed onto the normalized subpolar frontal zone (SPFZ) index, similar to Figure 7. The interannual LCF anomalies are positively correlated with the intensity of the SPFZ over the western North Pacific (140◦–170◦ W, 40◦–60◦ N), especially in winter and spring (Figure 8a,d). At the same time, both cold advection and SHF present positive correlations with the SPFZ (Figure 8a,d). In spring and winter, the enhancement of the SPFZ over the western North Pacific can strengthen the cold advection through northerly wind, rendering SAT lower than the SST underneath, further resulting in large upward SHF [52]. The wintertime enhancement of SHF destabilizes the surface layer and facilitates shallow convection in the boundary layer, thus increasing the convective LCF. This implies that the SPFZ-related cold advection has a positive effect on the enhanced LCF by interacting with the SHF over the western North Pacific.

**Figure 8.** Regression coefficients of interannual LCF (shading; units: %), advT (purple contour; units: K/day), and SHF (black contour; units: W/m2) onto the SPFZ index for (**a**) spring, (**b**) summer, (**c**) autumn, and (**d**) winter. Stippling indicates the 90% confidence level using the t test. Contours indicate statistically significant positive (solid) and negative (dashed) differences at the 90% confidence level based on the t test.

Overall, the interannual variation of LCF is different over the eastern and western North Pacific regions, due to different controlling factors and large-scale atmospheric processes. The increased LCF over the eastern North Pacific is associated with increased EIS and decreased SST, especially in summer. Our regression analysis signifies the El Niño contribution. Over the western North Pacific, the springtime and wintertime enhanced LCF has a positive correlation with enhanced SHF and cold advection, which can be partly explained by SPFZ intensity.

#### **4. Discussion and Concluding**

In this study, we investigate the seasonal and interannual variability of LCF and associated controlling factors over the eastern and western North Pacific. On the seasonal time scale, LCF shows obvious regional contrast. Over the eastern North Pacific, the EIS dominates the enhancement of LCF in summer, together with the 700-hPa RH. Over the western North Pacific, the enhancement of LCF in winter is mostly due to enhanced SHF and surface cold advection.

In terms of interannual variation, the increased LCF over the eastern North Pacific is associated with increased EIS and decreased SSTs, especially in summer. Our regression analysis indicates that El Niño contributes most. Over the western North Pacific, the springtime and wintertime enhanced LCF has a positive correlation with enhanced SHF and cold advection, which can be partly explained by SPFZ intensity.

This paper only discusses the factors that influence LCF variability in previous work. However, other factors, such as ocean or atmospheric circulation, may also play an important role in LCF changes, which need to be further studied in the future. Cloud microphysical processes and aerosol properties are also important for the formation of low-level clouds [53]. For example, oceanic aerosol productivity plays an important role in determining cloud condensation nuclei. Zhao et al. have found that if liquid water content (LWC) is high and aerosol amount is not too large, both cloud droplet number concentration (N) and effective radius (re) increase with increasing aerosols; if LWC is low or if LWC is high but aerosol amount is too large, cloud N increases but re decreases with increasing aerosols [54]. These aspects will be explored in our future study.

**Author Contributions:** Conceptualization, H.X. Formal analysis, Q.W. Methodology, Q.W., H.X. and L.Z. Software, Q.W. Supervision, H.X. Validation, Q.W. and L.Z. Writing—original draft, Q.W. and L.Z. Writing—review and editing, Q.W., H.X., L.Z. and J.D.

**Funding:** This work was jointly supported by the Natural Science Foundation of China (41575077, 41490643, and 41705054), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). Deng was supported by the Natural Science Foundation of Jiangsu Province (BK20170942), the General Program of Natural Science Research of Jiangsu Province University (17KJB170012), and the China Scholarship Council (CSC).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Diurnal Variations in Surface Wind over the Tibetan Plateau**

#### **Yufei Zhao 1,\*, Jianping Li 2,3, Qiang Zhang 1, Xiaowei Jiang <sup>1</sup> and Aixia Feng <sup>1</sup>**


Received: 26 January 2019; Accepted: 26 February 2019; Published: 2 March 2019

**Abstract:** This study uses hourly surface wind direction and wind speed observations from 53 meteorological stations on the Tibetan Plateau (TP) (70–105◦ E, 25–45◦ N) between 1995 and 2017 to investigate diurnal variations in the surface wind. The results show large diurnal variations in surface wind on the TP. The minimum wind speed occurs in the morning and the maximum in the afternoon. In all four seasons, the prevailing meridional wind is a southerly, and this is typically evident for more than two-thirds of each day. However, in the mornings during December–February and September–November, this southerly wind is replaced by a northerly, but remains southerly in the afternoon. The TP shows remarkable regional characteristics with respect to diurnal variations in wind speed. In the eastern region, the minimum and maximum daily wind speeds occur about 1 h later than in the west. Among the 53 meteorological stations, 79% observed that it took less time for the minimum speed to rise to the maximum speed than for the maximum to drop to the minimum. The blocking effect of the high surrounding terrain causes the diurnal variations seen in the surface winds at the three stations in the Qaidam Basin to differ significantly from those observed at the other stations elsewhere on the plateau. These Qaidam Basin stations recorded their maximum wind speeds around noon, with the minimum at dusk, which is around 1900 LST. The EOF1 (EOF = empirical orthogonal function) of the hourly wind speed on the TP indicates the key daily circulation feature of the region; i.e., the wind speed is high in the afternoon and low in the morning. The EOF2 reflects the regional differences in the diurnal variations of wind speed on the TP; i.e., the eastern region reaches the daily maximum and minimum wind speeds slightly later than the western region.

**Keywords:** Tibetan Plateau; surface wind; diurnal variation

#### **1. Introduction**

Research into diurnal variations in meteorological parameters is important in improving our understanding of weather and climate systems. There has been much research into diurnal variations in global or local meteorological parameters (e.g., precipitation and surface winds). However, the limited amount of available observational data meant that early studies of these diurnal variations focused mainly on tropical areas. Infrared satellite data have been used to investigate the diurnal variations in convective activity using cloud cover, cloud crest brightness temperature, and water vapor (e.g., [1–4]). Studies in tropical regions have shown that the daily cycle of convective activity is strongly dependent on geographical location, and that topography plays a crucial role in the daily cycle of weather patterns [1,5].

The Tibetan Plateau (TP), also known as the third pole, is the world's largest landform and has an average elevation of about 4500 m. The TP has a significant effect on its surroundings through thermal and dynamic processes [6,7]. Wind is an important indicator of atmospheric circulation, and changes in wind speed are an indication of circulation changes caused by natural or anthropogenic processes [8]. The wind is known to be distinctively turbulent and non-stationary. As a consequence, the wind velocity varies rather randomly on many different time scales [9]. Previous studies have examined the surface wind regime on the TP. For example, analysis of daily wind speed data from ground observation stations on the TP has shown seasonal differences in the surface wind, with the wind speed dropping most significantly during March–May [10]. Other research has found that changing wind speed is the most important meteorological control on trends in potential evapotranspiration on the TP [11].

There are limited observational data to study diurnal variations in meteorological elements over the TP. This has led to some studies having to rely on data with a low temporal resolution, or data obtained from indirect observations, to analyze such variations. For example, data with a temporal resolution of 3 h have been used to analyze diurnal variations in precipitation, thunderstorms [12], and surface winds [13]. Other studies have used satellite and radar data to explore the changing weather and climate on the TP (e.g., [14–17]).

Following the rapid deployment of automatic weather stations across China in recent years, hourly observational data are now available. However, these automatic weather stations are typically about 10 years old, so their records are short. Before the establishment of automatic weather stations, various self-recording instruments were widely used for hourly and even minute-by-minute observations, such as self-recording rain gauges and self-registering anemometers, but most of these datasets were recorded on paper, which is more difficult to collate and analyze than digital data, thereby restricting the application of these valuable data. In 2017, the China Meteorological Administration (CMA), after nearly 10 years of data processing work that included integrating the surface wind data observed by automatic weather stations, established the first hourly wind series from 2400 Chinese stations covering the period since 1951. The CMA also developed suitable quality control procedures, based on the characteristics of hourly wind data, to check the accuracy and completeness of the hour-by-hour wind series, and so established the Hourly Surface Wind dataset (HSW dataset) for mainland China [18].

The reliability of various reanalysis data is relatively low (e.g., [19,20]). Observational data recorded at meteorological stations are fundamental to the data processing and analysis that underpins climate research. The direction and speed of surface wind are major elements of China's meteorological observational dataset. However, few studies have used the long sequence of hourly wind data observed by the stations to analyze diurnal variations in surface winds on the TP. Accordingly, this study uses hourly wind direction and speed data from the TP in the HSW dataset to analyze diurnal variations in surface winds on the TP.

#### **2. Data and Methodology**

We used the HSW dataset from mainland China, which was developed, collated, and quality controlled by the National Meteorological Information Center (NMIC) of the CMA (Zhao et al., 2017 [18]). In this study, the TP region is defined as that bounded by 70–105◦ E and 25–45◦ N. Our study area in west China encompasses Qinghai Province, Tibet, and parts of neighboring Xinjiang, Gansu, Yunnan, and Sichuan provinces. To avoid biases introduced by missing data, we limited our analysis to the 53 stations on the TP that provide a complete hourly wind speed series from 1995 to 2017 and are located at altitudes above 2000 m (figure omitted).

In China, the standard time (Beijing time) is UTC + 8, and this is the time used for meteorological observations. However, because of its vast size, China is divided into five time zones, and the TP alone spans three time zones (UTC + 5 to UTC + 7). Therefore, using LST to analyze diurnal variations on the TP avoids the difference in diurnal variations in wind speed caused by the gap between LST and Beijing time. In the diurnal cycle of surface wind speed at each station, the hour when the wind speed reached its maximum is defined as the 'max-hour', and the hour when the wind speed reached its minimum is defined as the 'min-hour'. The difference between the maximum and minimum is the daily range of wind speed. The 'max-dir' is the wind direction in the hour when the wind speed reached its daily maximum, and the 'min-dir' is the wind direction in the hour when the wind speed reached its daily minimum. For our analysis, we divided the year into four equal periods: December–February (DJF), March–May (MAM), June–August (JJA), and September–November (SON).

#### **3. Diurnal Variations in Wind Speed**

Figure 1 shows the seasonal and annual mean wind speed, variances of wind speed, zonal wind speed, and meridional wind speed on the TP. The maximum wind speed occurs during MAM, with the average wind speeds at all hours being higher than those of the other three seasons. MAM also sees the largest range in wind speed of up to 2.7 m·s<sup>−</sup>1, whereas JJA sees the smallest range of 1.8 m·s<sup>−</sup>1. The minimum wind speeds on the TP in DJF, MAM, JJA, and SON were recorded at 0800, 0600, 0600, and 0600 LST, respectively, and the maximum wind speeds at 1500, 1600, 1600, and 1500 LST, respectively. It is suggested that increased downward turbulent mixing of momentum during the day could be one of the main causes for the early afternoon maximum of surface wind speed [13]. In DJF, the wind speed takes the shortest time to rise from the daily minimum to the daily maximum, of up to 7 h, meaning it takes 17 h to drop from the maximum to the minimum. In MAM and JJA, it takes 10 h for the wind speed to rise from the lowest in the morning to the highest in the afternoon, compared with 9 h in SON. Previous research has shown that in eastern China, wind speed drops to its minimum at 0500 LST and rises to its maximum at 1500 LST each day [21], with a variation of 1.2 m·s<sup>−</sup>1. On the TP, however, the wind speed reaches its minimum at 0600 LST and maximum at 1500 LST, with an annual average diurnal variation of 2.2 m·s<sup>−</sup>1. Therefore, the minimum wind speed in the daily cycle of wind speed on the TP arrives 1 h, on average, later than in the eastern part of China, whereas the maximum wind speed arrives at the same time. The amplitude of the variation in the former is nearly twice that in the latter.

The change in wind speed is large when the wind speed is high (Figure 1a,b). In addition, although in the daily cycle of wind speed the hourly wind speeds during SON are lower than those during MAM, the changes in SON wind speed are larger than those in MAM between 1200 and 1500 LST. The maximum hourly wind speed variation during MAM and SON arrives 1 h earlier than the maximum hourly wind speeds.

In the zonal wind diagram (Figure 1c), the prevailing wind alternates between an easterly and a westerly in JJA and SON. In most cases, a westerly wind prevails in JJA, and the westerly component reaches a maximum at 2000 LST of only up to 0.52 m·s−<sup>1</sup> which is much smaller than the maxima in other seasons. The zonal wind speed is compensated in averaging on multiple station data, not weakened at each station (figure omitted). The effect of large terrain on airflow movement is mainly manifested in blocking and diverting. On the one hand, these effects of mountain ranges on airflow will lead to asymmetric distribution of surface pressure on windward and leeward slopes, which usually results in a gradient of pressure from the windward slope to the leeward slope. On the other hand, the southwest monsoon from the Indian Ocean can affect southeastern Tibet and southwestern Sichuan province in JJA. Therefore, the zonal wind at some of the 53 stations is westerly in JJA (Figure 1), and the weaker westerly wind will appear in most hours of a day by being compensated in averaging on multiple stations which are not affected by the southwest wind. An east wind prevails during SON, with the zonal wind speeds all above 0.5 m·s−<sup>1</sup> between 1200 and 1700 LST, reaching a maximum at 1400 LST of up to 1.1 m·s<sup>−</sup>1. During DJF and MAM, the average zonal wind for all hours on the TP is an easterly. During DJF, the zonal wind speeds between 1200 and 1700 LST exceed 1.0 m·s<sup>−</sup>1, reaching a maximum at 1500 LST of up to 2.1 m·s−1. During MAM, the zonal wind speeds between 1100 and 1700 LST exceed 1.0 m·s<sup>−</sup>1, reaching a maximum at 1400 LST of up to 1.6 m·s<sup>−</sup>1.

The diurnal variations in the average meridional wind speed during each season and the annual mean can be divided into two groups (Figure 1d): 2000–0800 and 0800–2000 LST. Overall, the prevailing meridional wind in each season is a southerly. To be specific, a southerly and northerly wind alternate during DJF and SON, with the southerly being replaced by a northerly at about 0800–0900 LST, returning to a southerly at 1500–1600 LST. Over the 24 h of each day, the north wind prevails for about 7 h in DJF and SON, while the south wind prevails for the remaining 17 h. During MAM and JJA, the south wind is replaced by a weak northerly for only the 2–3 h before noon, and the southerly prevails for the remaining 21–22 h of each day.

**Figure 1.** Diurnal variations in seasonal and annual mean wind speed (**a**), variances of wind speed (**b**), zonal wind speed (**c**), and meridional wind speed (**d**) averaged over the TP.

The max-hour and min-hour in the average diurnal wind speed cycle for each station on the TP are shown in Figure 2. The eastern region typically experiences the maximum wind speed about 1 h later than the western region. Among the 53 stations, 10, 15, and 15 stations reach their peak values at 1400, 1500, and 1600 LST, respectively, indicating that most stations (57%) reach the maximum wind speed between 1500 and 1600 LST each day. In contrast to most other stations, Xiaozaohuo (93.2◦ E, 36.9◦ N) in Qinghai Province reaches its peak wind speed at 0900 LST, the earliest among the stations, Nuomuhong (96.5◦ E, 36.4◦ N) in Qinghai Province reaches its peak at 1100 LST, while Huajialing (105.0◦ E, 35.4◦ N) in Gansu Province and Jianzha (102.0◦ E, 35.9◦ N) in Qinghai Province reach their peaks at 2100 and 1900 LST, respectively.

Similarly, according to Figure 2b, the eastern region attains a daily minimum wind speed about 1 h later than the western region. Thirty six stations (68% of the total) reach the daily valley at 0600–0700 LST. The Xiaozaohuo, Delingha (97.4◦ E, 37.4◦ N), and Nuomuhong stations (all in Qinghai Province) differ from the other stations, reaching their daily minimum wind speed at 1900 LST. Anduo (91.1◦ E, 32.4◦ N) in Tibet and Tianjun (99.0◦ E, 37.3◦ N) in Qinghai Province reach their daily minimum wind speed at 0100 LST.

**Figure 2.** Timing of daily maximum (**a**) and minimum (**b**) surface wind speeds (vectors according to key at right, LST). Shading denotes elevation (m). The insets show histograms of the peak (**a**) and valley (**b**) hours.

The time interval between the min-hour and max-hour for the stations on the TP varies between 6 h (3 stations) and 19 h (2 stations). A total of 30 stations take 8–10 h to see the wind speed rise from the minimum to the maximum, and 42 stations need less than 12 h (Table 1). That is, 79% of the stations take a shorter time to see the wind speed rise from the minimum to the maximum than to see the wind speed drop from the maximum to the minimum. It takes 12 h for three stations to see their wind speed rise from the lowest to the highest, so 6% of the stations take an equal time to see the wind speed go from the lowest to the highest and from the highest to the lowest. A total of eight stations take more than 12 h to see their wind speed rise from the lowest to the highest, so 15% of the stations take longer to see their wind speed rise from the lowest to the highest than to see the wind speed drop from the highest to the lowest.

**Table 1.** Frequency distribution of the time (hours) required from the lowest to the highest daily wind speeds among the stations.


#### **4. Diurnal Variations in Surface Wind Directions and Diurnal Range of Wind Speed**

Figure 3 and Table 2 show that the peak wind directions at each station differ, being northeasterly, southeasterly, northwesterly, and min-hour southwesterly for 24, 22, 4, and 3 stations, respectively. For 87% of the stations, the max-dir is easterly and for 13% it is westerly (Figure 3). For 55% of the stations, the min-dir is easterly. In the min-hour, 10, 19, 15, and 9 stations recorded northeasterly, southeasterly, northwesterly, and southwesterly winds, respectively. For 74% of the stations, the average wind

direction is easterly, and 17, 22, 6, and 8 stations have an average wind direction of northeast, southeast, northwest, and southwest, respectively.

**Figure 3.** Surface wind direction and speed at the max-hour for each station.

Next, we consider the daily range of wind speed at the stations. Figure 4a,b shows the daily range of annual average wind speed and wind speed variance for all stations. The variation in hourly wind speed is greater in the areas with a larger daily range. Tuotuohe (92.6◦ E, 34.0◦ N) station in Qinghai Province has the largest daily range of wind speed, reaching 4.2 m·s<sup>−</sup>1, and Delingha station in Qinghai Province in the northerly part of the TP has the smallest daily range of wind speed, at about 0.7 m·s<sup>−</sup>1. The average daily range of wind speed across the TP is 2.4 m·s<sup>−</sup>1. The daily range of wind speed for all stations on the TP varies significantly from one season to another (Figure 4c–f). The high-value zone with a daily range over 3.6 m·s−<sup>1</sup> moves northwestward from DJF to MAM. From MAM to JJA, the high-value zone continues to move northwestward. The spatial distribution of the wind speed daily range in SON is similar to that in DJF, but with a smaller daily range.

**Figure 4.** Daily range of annual average wind speed (**a**) (m·s−1), hourly wind speed variance (**b**), and daily range of wind speed (m·s−1) in December–February (DJF) (**c**), March–May (MAM) (**d**), June–August (JJA) (**e**), and September–November (SON) (**f**).


**Table 2.** The numbers of stations which recorded northeasterly, southeasterly, northwesterly, and southwesterly winds in max-dir, min-dir, and average wind direction.

#### **5. Diurnal Cycle of Surface Winds in Different Zones**

As mentioned above, the diurnal cycles of surface winds recorded in Xiaozaohuo, Delingha, and Nuomuhong are strikingly different from the other zones of the TP, and these three stations are all located in the Qaidam Basin. In this section, we analyze these diurnal variations by treating the three stations as a 'basin area' and the remaining 50 stations as a 'plateau area'. Figure 5 shows the annual average diurnal wind speed variations for the basin area and the plateau area. The diurnal wind speed variations of the two areas are obviously different. The wind speed in the basin area features a broad peak that reaches a maximum either side of noon and with a minimum at 1900 LST. In contrast, the plateau area sees a maximum wind speed at 1500 LST and a minimum wind speed at 0600 LST. At this time (0600 LST), the wind speed in the basin area is not at its minimum, but shows a small reduction compared with the wind speeds in the adjacent periods. Yu et al. (2009) [21] found a similar reversed day–night phase in the surface wind speed between mountain regions and plain regions of China. Based on a single year of wind data from television towers, Crawford and Hudson (1973) [22] concluded that the wind speed of the lower (higher) layers reached a minimum around midnight (noon) and a maximum in the afternoon (midnight). Their research target was diurnal wind speed variations on large plains and high mountain stations. In the present study, most of the stations from which data were collected on the TP are located in large high-altitude terrain, other than the stations at Xiaozaohuo, Delingha, and Nuomuhong, which are located in the Qaidam Basin at lower altitudes than the surrounding stations. Consequently, we suggest that the clear differences in the diurnal variations of the surface winds at these three stations are caused by the blocking effect of the surrounding mountainous terrain.

**Figure 5.** (**a**) Diurnal variations in annual mean wind speed averaged over the plateau area (blue line) and the basin area (red line). (**b**) Diurnal variations in seasonal mean wind speed averaged over the basin area.

In MAM, DJF, and SON the Qaidam Basin shows similar diurnal variations in wind speed to the annual mean (Figure 5b). The difference is that the wind speeds in MAM (SON and DJF) are higher (lower) than the annual mean wind speed. The diurnal variation in surface wind speed during JJA is different to that in the other three seasons. In JJA, the wind speed gradually decreases from 0800 to 1900 LST and reaches a daily minimum at 1900 LST. In the other three seasons, however, the diurnal variations in wind speed feature a gradual rise (in MAM) or continuous high speeds (in SON and DJF) from 0800 LST to about 1400 LST. In JJA afternoons on the TP the prevailing wind is a southwesterly. In the afternoons of the other seasons, however, the prevailing wind is a southeasterly (Figure 1c,d). As the TP is higher in the northwest and lower in the southeast, and the Qaidam Basin is aligned northwest–southeast, the blocking effect of the surrounding terrain on the Qaidam Basin is more pronounced in JJA, when a southwest wind prevails, than in the other seasons when a southeasterly prevails. In other words, because the dominant wind direction is aligned with the basin except in JJA, the blocking effect would not play a major role in DJF, MAM, and SON like JJA.

We applied empirical orthogonal function (EOF) analysis to the hourly wind speed data of the TP (51 stations, excluding two remote stations in Xinjiang Province). The hourly wind speed series from each station was standardized prior to the EOF decomposition. The first and second leading EOF (EOF1 and EOF2) passed the North test [23]. EOF1 and EOF2 account for 83% and 13% of the total variance, respectively. EOF1 shows mainly positive values, with weakly negatives in the Qaidam Basin (Figure 6a). The time coefficient curve of EOF1 (Figure 6c) is similar to the series in Figure 1a, with both featuring a single peak. The peak in diurnal wind speed occurs between 1400 and 1500 LST, and the wind speed at night tends to decrease slowly. EOF1 reflects mainly the diurnal variation of wind speed in most areas of the TP, and the diurnal variations that differ between the Qaidam Basin and the much larger plateau area. EOF2 (Figure 6b) varies between positive and negative values with an east–west dipole pattern, while the corresponding time coefficient features a single peak (Figure 6d). EOF2 is negative in the Qilian Mountains in Gansu Province, eastern Qinghai Province, western Sichuan Province, and southeast Tibet (the eastern region), and is positive in mid-west Qinghai Province, eastern Sichuan Province, and central Tibet (the western region). This suggests that EOF2 indicates that the eastern region reaches the peak and valley of diurnal wind speed slightly later than the western region, which is consistent with the conclusion drawn above. The branching effect of the TP on airflow makes the surface wind of the eastern edge of the TP different from that of the western part of the TP. The northerly airflow generated by the circulation of the plateau forms a "leeward wake zone" on the eastern edge of the plateau, and the anticyclone vortex is very strong, while westerly winds prevail in the western part of the plateau [24]. The difference of atmospheric circulation pattern will inevitably lead to the difference of surface wind diurnal variation between the eastern and western regions.

**Figure 6.** (**a**) The first leading empirical orthogonal function (EOF1) pattern of climatological hourly wind speeds on the Tibetan Plateau (TP). (**b**) As for (**a**), but for the second leading empirical orthogonal function (EOF2). (**c**) Time series corresponding to EOF1. (**d**) Time series corresponding to EOF2.

#### **6. Summary and Discussion**

The diurnal cycle of surface wind speed on the TP was analyzed using hourly wind observation data from 53 meteorological stations with 23 years (1995–2017) of complete data. Our results reveal some novel spatial and temporal characteristics. The main conclusions are summarized as follows.


**Author Contributions:** Conceptualization, Y.Z. and J.L.; Data curation, Y.Z. and Q.Z.; Formal analysis, Y.Z.; Funding acquisition, Y.Z.; Investigation, Q.Z.; Methodology, J.L.; Project administration, Q.Z. and X.J.; Resources, Y.Z.; Software, X.J. and A.F.; Supervision, J.L.; Writing—Original Draft, Y.Z. and X.J.; Writing—Review & Editing, Y.Z. and J.L.

**Funding:** This research received no external funding.

**Acknowledgments:** This work was supported by the Chinese National Natural Science Foundation 'Development of Data Sharing Platform of the Tibetan Plateau's Multi-Source Land-Atmosphere System Information' under grant number 91637313 and the National Natural Science Foundation of China (NSFC) Project (41530424).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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