The PM2.5 data from December 2013 to February 2020 used in this study are obtained from the Chinese Ministry of Ecology and Environment website (http://220.127.116.11:20035/). A longer record of PM2.5 dataset over 2009–2020 from US Embassy in Beijing is also used (https://www.airnow.gov/international/us-embassies-and-consulates/#China$Beijing). This dataset has been widely used in previous studies and is reported to well represent PM2.5 variation in BTH 1, 22, 31, 32, 33. The daily average PM2.5 is processed and quality controlled following a previous study 2. The cities with continuous PM2.5 observations since 2013 are displayed in Supplementary Fig. 1a. Daily meteorology fields, including geopotential heights and winds at different pressure levels and mean sea level pressure, are from the fifth-generation reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) with a resolution of 2.5°×2.5° downloaded from the Copernicus Climate Change Service (C3S, 2017).
The observed sea surface temperatures (SSTs) from the Hadley Centre are downloaded from https://www.metoffice.gov.uk/hadobs/hadisst/ 34. The anomaly of a parameter on a specific day is calculated relative to the daily climatology spanning 40 years (1979–2018). The EA/WR pattern is defined as the second leading rotated empirical orthogonal function mode of the 500 hPa geopotential height in the region 15°–85°N, 70°W–140°E 23, 35, and its climate impact extends from eastern North America to Eurasia through wave train propagation. The Victoria Mode is defined as the second leading mode of the SST in the North Pacific Ocean (20°–61°N, 100°E–80°W).
Classification of weather patterns during SPPDs
We identify the typical weather patterns during DJF during 2013–2019 by using obliquely rotated principal component analysis in T-mode (T-PCA), which is commonly used to classify circulation patterns 36. This method has also been employed to investigate the circulation patterns that are conducive to particulate pollution in North China 2, 37 and the Yangtze River Delta 38. In this study, we use the T-PCA method in the Cost733class software package (http://cost733.met.no) to identify typical circulation patterns during DJF in BTH from 2013 to 2019. More details of the T-PCA procedure in cost733class can be found in the literatures 2, 39. The key input meteorological parameters for the classification of weather patterns are identified by linear regression analyses between daily mean meteorological variables and the daily average PM2.5 concentrations of the 10 cities. Three key meteorological parameters are identified: U200, Z500 and V850. We also test other associated variables reported by previous studies. As shown in Supplementary Fig. 2, the regression coefficients of the temperature inversion display nearly the same pattern as those of Z500 (Supplementary Fig. 2b). The regions with maximum variability of the near-surface relative humidity (RH1000, Supplementary Fig. 2d) are too regional compared to U200, Z500 and V850, leading to little influence on the classification results.
The classification performance is evaluated by the explained variation and pseudo-F values (Supplementary Fig. 4a). Seven weather patterns are classified, among which two conducive weather pattern types are identified. Although six classifications results in better performance from a meteorological perspective, seven types can better distinguish the CWPs (Supplementary Figs. 4b-e). After selecting the appropriate classification, historical daily DJF weather samples from 1979 to 2019 are assigned to the corresponding CWPs with the smallest Euclidean distance, which is also performed in the cost733class software 40.
Wave activity fluxes
The dynamic mechanism by which a climate factor (or pattern) leads to CWPs for the formation of SPPDs is usually a teleconnection, which can be measured by wave activities 41. In this study, the horizontal wave train flux 42 is calculated by using meteorological variables (e.g., geopotential height, air density, and pressure level) to display the stream function (wave energy pattern) and intensity and direction of wave propagation. Based on the horizontal wave train flux, we can diagnose the source and propagation direction of stationary waves that lead to CWPs for the formation of SPPDs. This approach has been widely used to examine the relationship between climate factors and circulation patterns for haze pollution in China 14, 15, 18.
The surface PM2.5 observations from the Chinese Ministry of Ecology and Environment can be obtained from http://18.104.22.168:20035/ and https://quotsoft.net/air/. The surface PM2.5 observations for US Embassy in Beijing is download from https://www.airnow.gov/international/us-embassies-and-consulates/#China$Beijing. The ERA5 reanalysis data is available from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels?tab=form. The observed sea surface temperatures from the Hadley Centre are downloaded from https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html.
The Cost733class software is open source (http://cost733.met.no/).
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