An analysis of the urbanization contribution to observed terrestrial stilling in the Beijing–Tianjin–Hebei region of China

Daily NSWS observations for 1980–2016 from 223 national stations in the Beijing–Tianjin–Hebei region (36°N–41°N; 114°E–119°E) were used in this study, compiled by the National Meteorological Information Center of China Meteorological Administration. The raw NSWS time series were quality-controlled and homogenized using the RH-tests version 3 software package (Wang and Feng 2010). The RH-test algorithm contains two change-point detection approaches: a penalized maximum t test and a penalized maximum F test. The former test is good for detecting non-climatic changes (e.g. station relocation, changes in the observing system), when a reference series reflecting the regional NSWS change is available; while the latter test is suitable for sparse observing stations, when a reference series is lacking or distant (Wan et al 2010). Given the high station density in the region, we used a penalized maximum t test that accounts for the first-order autocorrelation to detect the inhomogeneities in individual station NSWS time series, which was further validated by detailed metadata information. Then, quantile-matching adjustment method that takes into account high-order inhomogeneities was applied to adjust the abrupt shifts related to non-climatic changes in NSWS time series. We treated a station as missing, if it had more than 1% missing values (i.e. 365 × 37 × 0.01 ≈ 135 d), and it was discarded from the following analyses. A total of 186 stations were selected. Their individual missing values were replaced by their corresponding climatological means for other available values of this specific calendar day across the entire study period.

To consider the uncertainties in the reanalysis products and their potential capacity to reproduce the regional mean NSWS change, we compared observed NSWS time series with those derived from a variety of different-generation reanalysis products. Table S1 presents the details of these reanalysis products. The first- and second-generation reanalysis products used in this study were from the National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) global reanalysis (NCEP-NCAR; Kalnay et al 1996), and the NCEP/Department of Energy (DOE) global reanalysis (NCPE-DOE; Kanamitsu et al 2002), respectively. The third-generation reanalysis datasets were from the European Reanalysis Interim reanalysis (ERA-Interim; Dee et al 2011), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2; Gelaro et al 2017), and the Japanese 55 year reanalysis (JRA-55; Kobayashi et al 2015). The newest generation reanalysis used was ERA-5. We also evaluated three versions of the Twentieth Century Reanalysis, generated by the US National Oceanic and Atmospheric Administration (NOAA; Compo et al 2011). To facilitate comparison, we used a bilinear interpolation algorithm to linearly interpolate the gridded reanalysis monthly mean NSWS time series to the geographical locations of in situ weather stations. The calculation procedure used for the regional mean NSWS time series based on reanalysis was the same as that based on observations.

We used satellite-based temporally varying LUCC observations for China to dynamically identify urban and rural stations; these were generated and provided by the Data Center for Resources and Environmental Sciences of Chinese Academy of Sciences (http://resdc.cn/). This dataset was produced, based on the Landsat Thematic Mapper/Enhanced Thematic Mapper satellite images, with high spatial resolutions of 100 m, for seven representative years: 1980, 1990, 1995, 2000, 2005, 2010 and 2015 (Liu et al 2014). Following the methods proposed by previous studies (Ren et al 2010, Ren and Zhou 2014, Liao et al 2018), we calculated the fraction of urban land cover (i.e. built-up areas in cities, counties and towns) for each year within a 2 km circular buffer region surrounding each station. When the urban land fraction of one station exceeded 33%, we treated it as an urban station. Several studies have used this procedure to select the reference stations in China (e.g. Ren and Zhou 2014, Liao et al 2018). Ren and Zhou (2014) suggested that this classification criterion could account for the different influences on station observations from local anthropogenic activities (e.g. urbanization). Temporally, we divided the whole analysis period into seven consecutive episodes: 1980–1989, 1990–1994, 1995–1999, 2000–2004, 2005–2009, 2010–2014 and 2015–2016. We used the urban land fraction in the starting year of each episode to determine the stations' categories (i.e. urban or rural) dynamically. Results of this dynamic urban–rural station classification are illustrated in figure 1.

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To eliminate the effect of the climatological mean on the construction of the regional mean NSWS time series, we calculated the daily NSWS anomalies relative to the climatological annual cycle (calculated based on the whole study period) for each station. To estimate the contribution of urbanization to the observed NSWS change in urban areas and eliminate statistical biases linked to the uneven distribution of stations, we divided the region into four 2.5° × 2.5° grids. For each grid, we computed the average NSWS time series separately for urban and rural stations for each episode. Then, according to previous studies (e.g. Mann et al 1998, Cohen et al 2014), we constructed regional mean urban and rural NSWS time series for the Beijing–Tianjin–Hebei region by weighting each grid's time series by the cosine of its latitude. We considered their differences in the long-term (i.e. the period with all the years) NSWS trends as the impact of urbanization in urban areas. To reveal the seasonality of NSWS trends and estimate any related urbanization impacts, we calculated both seasonal mean urban and rural NSWS anomalies time series, based on conventional practice, for winter: December–February; spring: March–May; summer: June–August; and autumn: September–November. A detailed description of this procedure can be found in supplementary note 1, which is available online at stacks.iop.org/ERL/15/034062/mmedia.

In addition, we estimated the frequency trends for different categories of windy days and quantified their urbanization impacts. We defined light, gentle, moderate, and strong windy days, as days with wind speeds lower than their historical 25th percentile, higher than their historical 25th percentile but lower than their historical 50th percentile, higher than their historical 50th percentile but lower than their historical 75th percentile, and higher than their historical 75th percentile, respectively. We determined these daily-based percentiles for a specific calendar day by sorting historical (1980–2016) 15 d NSWS samples around this day (7 d before and after; reflecting data for 15 × 37 = 555 d). We estimated trends in the regional mean annual frequencies of different categories of windy days for urban and rural stations separately and treated their differences as the impact of urbanization in urban areas.

We computed observed and reanalysis trends for the regional mean NSWS in urban and rural areas using the non-parametric Theil–Sen's robust linear trend estimator (Theil 1950, Sen 1968) and determined their corresponding 95% confidence intervals based on Hollander and Wolfe (1973). We conducted non-parametric Mann–Kendall tests of the null hypothesis of these trends of each station at the significance level of 0.05 (Mann 1945, Kendall 1975). We also tested the significance of a trend in each kind of regional mean time series and the time series of urban–rural differences at the significance levels of 0.01, 0.05 and 0.1 using the non-parametric Mann–Kendall tests.

An analysis of the long-term linear trends of NSWS indicated a widespread wind stilling over the Beijing–Tianjin–Hebei region over the period 1980–2016 (figure 2(a)). The largest declines exceeding 0.7 m s⁻¹ decade⁻¹ occurred at two stations located within the cities of Beijing and Tianjin. An increase in NSWS was found at a few stations, but of much smaller magnitude. The regional average urban and rural NSWS time series were constructed and their corresponding long-term linear trends were estimated. As figure 2(b) shows, the regional mean annual anomalies of NSWS during 1980–2016 over the urban areas of the Beijing–Tianjin–Hebei region have decreased by 0.93 m s⁻¹. Urbanization should have contributed approximately 8% (0.07 m s⁻¹) to the observed decrease in the regional mean NSWS in urban areas. Thus, the urbanization impact on the observed NSWS change was an order of magnitude less than the terrestrial stilling observed over the last four decades in urbanized areas of this region in China.

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We also analyzed the decadal-scale trends in urban and rural NSWSs and compared their differences. We found large differences in the impacts of urbanization on NSWS change between earlier (1980–1995) and later (1996–2016) periods. The contribution of urbanization to the NSWS trend in urban areas was close to zero (0.01 m s⁻¹ decade⁻¹) during the earlier period. However, urbanization-induced NSWS change in urban areas became profoundly negative (−0.11 m s⁻¹ decade⁻¹) during the later period. This implies that urbanization influences on NSWS change during the later period are primarily responsible for the urbanization-related terrestrial stilling in the urban areas of Beijing–Tianjin–Hebei region. This result is consistent with historical urbanization trends in China over the last four decades, in which earlier and later periods witnessed relatively slow and rapid urbanization processes, respectively (Wu et al 2015). As shown in figure S1(a), the later period also showed greater increases in stations' urban land cover ratios than during the earlier period. The consistent trend of observed NSWS in rural and urban areas during the earlier period could partly reflect increasing forest cover (including woodland, shrubland, sparse-forest and other forest lands) during this period in this region (figure S1(b)), which was linked to policy-driven afforestation, with a ban on deforestation and a return of cultivated lands to forests (Liu et al 2008). Related to rapid urban development, the ratio of forest land cover surrounding in situ weather stations decreased during the later period (figure S1(b)), reinforcing urbanization effects. Figure 2(c) shows the correlation between changes in urban land cover fraction surrounding meteorological stations from 1980 to 2015 and the observed trends in local NSWS over the whole study period. We found that there is only a weak correlation between the changes in urban fraction and the local NSWS trends with a small explained variance (only 4.5%), which further suggests that the impact of urbanization on the observed NSWS changes was minor over the last four decades in the Beijing–Tianjin–Hebei region of China.

We conducted a series of sensitivity tests to validate the robustness of the estimated contribution of urbanization to the observed regional terrestrial stilling in urban areas (table S2). We first constructed the regional mean urban and rural NSWS time series using a static urban–rural classification scheme. We calculated the urban land-cover ratio within a 2 km circular buffer region surrounding each station for 2015, and considered any station having a fraction of urban land cover of more than 33% as urban during the entire study period. As shown in figure S2(a), the contribution of urbanization to the regional mean urban NSWS trend was estimated to be 20% (−0.20 versus −1.01 m s⁻¹). We noted that the magnitude of the urbanization contribution based on the static urban–rural classification was double that based on the dynamic one. As shown in figure S1(b), this difference could be related to forest growth during the earlier period around stations that became urbanized during the later period. Thus, the effect of forest growth on NSWS at earlier rural stations may be incorrectly ascribed to an effect of urbanization by a static classification scheme.

To test whether the estimated influence of urbanization was sensitive to urban fraction thresholds used in our classification, we recalculated the regional average NSWS time series for urban and rural areas by identifying urban stations as those having an urban land-cover ratio of greater than 20% within a 2 km buffer region. As figure S2(b) shows, we found that urbanization now contributed 11% to the regional mean NSWS trend observed in urban areas (−0.11 versus −0.94 m s⁻¹), which is closer to the initial estimate. In turn, we increased the urban fraction threshold to 40% and found that only 9% (−0.09 m s⁻¹ versus −0.97 m s⁻¹) of the observed wind stilling in urban areas could be ascribed to urbanization in this case (figure S2(c)).

Next, we checked the sensitivity of the estimated urbanization effect on the NSWS trend to changes in the geographical scope of the circular buffer region around each station. We calculated the fraction of urban land-cover within a 1 km circular buffer region surrounding each station and considered any station with an urban fraction of more than 33% as urban. In this case, we found that urbanization only explained a decrease of 0.05 m s⁻¹ in NSWS in urban areas, which was an order of magnitude much smaller than the observed change (−0.92 m s⁻¹; figure S2(d)). Then, we expanded the circular buffer region to a radius of 3-km and computed the urban fraction surrounding each station within this wider geographical scope. By increasing the screening standard around urban stations, we found that the contribution of urbanization to the observed wind stilling in urban areas was approximately 17%, which is slightly higher than the initial estimate (figure S2(e)).

Lastly, we explored the robustness of our results related to filling in missing values. We filled in missing values using a spatial biharmonic spline interpolation of the measurements from other stations. In this analysis, we found that only 8% of the observed NSWS trend could be attributed to the impact of urbanization (figure S2(f)). We also examined the temporal distribution of the missing values for each selected station and found that only three stations have the maximum length of continuous missing values more than or equal to one month (31 d). We removed these three stations in the analysis and found that the re-estimated impact of urbanization (without these stations) is close to that with these stations (−0.0214 versus −0.0189 m s⁻¹ decade⁻¹; figure not shown). We also found the estimated impact of urbanization weighting by the cosine is almost the same as those with equal weights (−0.0189 versus −0.0206 m s⁻¹ decade⁻¹; figure not shown), because the weights' difference between north and south grids is quite small (1.0174 versus 0.9826). Therefore, these robustness tests support the finding that the urbanization effect on the NSWS trend was an order of magnitude less than the terrestrial stilling observed in the urbanized areas of the Beijing–Tianjin–Hebei region over the past four decades.

As for the annual mean NSWS, all four seasonal mean NSWSs over the Beijing–Tianjin–Hebei region also exhibited a decline during 1980–2016. The largest decreases in regional mean NSWS occurred in spring, followed by autumn, winter, and summer (figure 3). This result is consistent with the findings of Fu et al 2011 and Chen et al 2013, which also showed that spring witnessed the largest decreases in NSWS in China. Jiang et al (2010b) suggested that the NSWS decline in China was minimal in summer, and that winter experienced the most obvious terrestrial stilling, rather than spring. This may be associated with the difference in analysis periods. Jiang et al (2010b) focused on the period of 1956–2004, while this study concentrated on the last four decades. The recovery of the East Asian winter monsoon from the mid-2000s, as outlined by Wang and Chen (2014), may be responsible for the reduced decline in wintertime NSWSs over the past decade.

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An analysis of urban–rural differences in NSWS trends for each season indicated that the largest urbanization-induced NSWS change occurred in winter, followed by spring, autumn and summer. Here, we propose two hypotheses for the seasonality of urbanization impacts on regional NSWS trends. Given that the influence of large-scale circulation and synoptic systems is much less dominant in summer than in winter, our first hypothesis is that urban breezes, induced by the urban heat island, play a more prominent role in summer in urban areas, negating the barrier effect of urban buildings on NSWS to some extent. Previous analysis based on numerical simulations also found a stronger response of NSWS to urbanization in winter than in summer (Hou et al 2013). Our second hypothesis is that the effect of vegetation growth on NSWS in rural areas is more profound in summer than in winter, because boreal summer (winter) has the highest (lowest) vegetation productivity. A recent study has carried out a month-long simulation using the Weather Research and Forecast model and found that the westerly or northerly wind is decelerated by 0.3–1.5 m s⁻¹ in the western and northwestern Beijing–Tianjin–Hebei region (mostly rural areas) due to the effects of afforestation (Long et al 2018). However, they did not assess the effects of afforestation for all the seasons. To explore the reasons for the seasonality of urbanization impacts on NSWS trends, high-resolution simulations incorporating information on urban developments and seasonal vegetation activities would be needed in further study.

Given that the occurrence of different categories of winds has a great impact on air quality and the installation of wind energy facilities (Shi et al 2019), we analyzed the changes in frequencies of different kinds of windy days and quantified their relationship to urbanization. As shown in figure 4, the frequencies of the days with light and gentle winds have increased over the Beijing–Tianjin–Hebei region during 1980–2016, at the cost of a substantial decrease in the days with strong winds. This indicates that terrestrial stilling over the region was mainly linked to the huge reduction in strong windy days. By comparing the frequency trends for the four kinds of windy days between urban and rural areas, we found that urbanization increased the frequency of the days with light and gentle winds by about 2.0 and 2.6 d decade⁻¹, but reduced the days with strong winds by 4.7 d decade⁻¹ in urban areas. We infer that during strong windy days, the change in NSWS is most likely controlled by large-scale synoptic factors. In these cases, local breezes induced by the urban heat island have a relatively low probability of accelerating air flows within urban areas. In contrast, for days with gentle or light winds, local circulation induced by the urban heat island would play a greater role in NSWS change, increasing the frequency of weaker windy days in urban areas.

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In addition to a comparison of urban and rural stations, some studies also used the OMR method to assess the urbanization impact on regional NSWS changes in China (Li et al 2018, 2019). Given that reanalysis products poorly represent many characteristics of historical NSWS changes in many regions (McVicar et al 2008, Pryor et al 2009, Chen et al 2013) as well as existing problems in the rationale of the OMR method (Trenberth 2004, Wang et al 2013, 2018), it is advisable to view the OMR-estimated urbanization contribution to the observed regional NSWS trend in urban areas with caution. To quantify uncertainties associated with reanalysis and assess the reliability of the OMR method in the estimation of the urbanization influence on urban NSWS change, we carried out a detailed comparison of observed NSWS trends with those derived from multiple reanalysis products.

Our results showed that except for JRA-55, none of the reanalysis products produced a similar NSWS trend to the station observations (figure 5). Comparatively, NSWS trends derived from third-generation reanalysis products (i.e. ERA-Interim, MERRA2, and JRA-55) appeared to be closer to the observations than first- and second-generation reanalysis products (i.e. NCEP-NCAR and NCEP-DOE). This difference is probably related to the continued improvements in the representation of critical physical processes in climate models, major advances in the data assimilation algorithms, and the greater number of observations used to produce third-generation reanalysis products (Dee et al 2011, Kobayashi et al 2015, Gelaro et al 2017). However, the regional mean NSWS trend derived from the newest generation reanalysis (ERA-5) was very small, despite its higher spatial resolution and more advanced assimilation system. Because the three versions of the Twentieth Century Reanalysis generated by NOAA assimilate only surface pressure observations and use monthly sea surface temperature and sea-ice distributions as boundary conditions within their climate models, coupled with an assimilation system comprising an ensemble Kalman Filter (Compo et al 2011), the NSWS trends derived from these reanalysis datasets were much weaker (20CRv3) and even positive (20CRv2, 20CRv2c). These results were valid for both urban and rural stations (figure S3).

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As shown in figure 6, the JRA-55 reanalysis produced a similar NSWS trend to the observations, but exhibited much stronger interannual variability than the observed regional mean NSWS. This could be linked to the JRA-55's intrinsic deficiencies in deriving wind speeds over land. Torralba et al (2017) indicated that the height of the lowest atmospheric level within the JRA-55 climate model and assimilation system tends to be set too high over regions with high vegetation (trees), leading to underestimates of NSWS when interpolating wind speed from the lowest atmospheric level to 10 m altitude.

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Comparisons of the observed and reanalysis NSWS trends suggested that the reanalysis-based estimates of NSWS trends involved much uncertainty, and that the climate models and assimilation systems used to generate these reanalysis data may have some deficiencies, such as the parameterization of surface layer processes. This implies that the OMR-estimated urbanization contribution to NSWS change was not only related to, but may have been irrelevant to, the impact of urbanization and other land-use changes on the NSWS. Thus, regional terrestrial NSWS trends derived from reanalysis and the OMR-estimated urbanization impacts deserve careful examination.