Figure 2 and Fig. 3 show the simulated precipitation and atmospheric circulation responses, respectively. Based on the MME of the 5 selected CGCMs (see Section 2) participating in COVID-MIP, the precipitation generally increases over continental East Asian monsoon region (Fig. 2a). The simulated change in the atmospheric circulation at 850 hPa is characterized by decreased He over the Asian continent and increased He over the western North Pacific (WNP), associated with anomalous anticyclone over the subtropical WNP and southerly wind over the East Asian monsoon region (Fig. 3a), suggesting an enhancement of the WNP subtropical high (WNPSH) and the southerly monsoon circulation in the lower troposphere. The change in the atmospheric circulation at 200 hPa is characterized by increased He and anticyclone anomaly over the Asian continent with a center near the Tibetan Plateau (Fig. 3b), suggesting an enhancement of the South Asian High (SAH) in the upper troposphere.
The MME-simulated change in atmospheric circulation is featured by wave train pattern (Fig. 3a,b), which explains the noisy pattern of the simulated change in precipitation (Fig. 2a). In addition, the simulated changes differ a lot among the individual models (Supplementary Figs. S1 and S2), possibly suggesting that the number of models/realizations may not be large enough to effectively suppress the internal variability and stochastic model error. Therefore, the 30-year averaged difference between piClim-control and piClim-aer experiments based on the MME of 16 AGCMs is also shown in Fig. 2 and Fig. 3, since it is less affected by the internal variability and stochastic model error because of stronger forcing (removal of all anthropogenic aerosols), larger number of models (16 models), and less effect from the SST variability (exactly fixed SST in the AGCM simulation).
The large-scale pattern for the AGCM-simulated response is consistent with the CGCMs participating in COVID-MIP, and it more clearly shows increased precipitation over the entire East Asian monsoon region (Fig. 2b), enhanced WNPSH and the southerly monsoon circulation over East Asia (Fig. 3c), and enhanced SAH (Fig. 3d). The response based on the AGCM simulation is more robust, and the magnitude is greater than the CGCMs participating in COVID-MIP. Therefore, the AGCM simulation confirms an enhanced EASM as a fast response to reduced aerosols, which is directly caused by the processes within the atmosphere instead of the aerosol-induced SST change. The enhancement of EASM due to emission reduction could be interpreted as a reversal of the decadal weakening of EASM forced by increased aerosol concentration (Song et al. 2014; Li et al. 2015, 2018; Tian et al. 2018a). Previous studies argued that the EASM response to absorbing and scattering aerosols are different, and the simulated enhancement of EASM during COVID-19 pandemic here is consistent with a fast response to reduced scattering aerosol concentration (Wang et al. 2017; Mu and Wang 2021).
The magnitude of responses of EASM are measured by some indices in Table 1. The EASM precipitation index (PEA index), defined as the regional averaged precipitation within the entire EASM region (enclosed by the purple contour in Fig. 2), increases by 2.0% based on the 5 selected CGCMs participating in COVID-MIP. The southerly wind at 850 hPa averaged within 20°-50°N, 110°-130°E (V850 index) increases by 0.11 m/s. The WNPSH intensity index (regional averaged 850 hPa He over 15°-40°N, 130°E-160°W) increases by 0.74 m, and the SAH intensity index (regional averaged 200 hPa He over 15°-40°N, 60°-120°E) increases by 1.7 m (Table 1). In all, the signs of the response in the above precipitation and circulation indices are all positive and agreed by at least 4 of the 5 models, suggesting a robust enhancement of EASM after the COVID-19 outbreak. Similarly, the responses of the above indices to the removal of all anthropogenic aerosols based on the AGCMs are also positive (second row in Table 1), and agreed by at least 12 of the 16 models, which confirms an enhanced EASM as a fast response to the reduced anthropogenic aerosols.
The magnitudes of interannual variability for the above indices are also evaluated (3rd row in Table 1), based on the MME of the interannual standard deviation of the indices in the piControl experiment performed by the five selected CGCMs. Overall, the amplitudes of the response in these indices to COVID-19 are smaller than the interannual standard deviations, and only reach about 1/3 of the amplitudes for interannual variability. The ratio of the forced response divided by the standard deviation of interannual variability ranges from about 10–40% at most of the grid points over East Asia and WNP, for both precipitation and atmospheric circulation variables (Supplementary Fig. S3).
The year-by-year evolution of the above four indices within the five years after COVID-19 outbreak, based on the MME of the five selected CGCMs, generally shows a downward tendency despite of evident interannual variation (thick black curve in Fig. 4). This is possibly because of the gradual rebound of the aerosol precursor emission according to the experimental design or the slow response with an SST adjustment. Indeed, it is suggested that the fast response determines the total EASM response to increased aerosol concentration in recent decades, but the slow response associated with aerosol-induced SST change partially dampens the fast response (Wang et al. 2017; Li et al. 2018; Mu and Wang 2021). Our result under the case of decreased aerosol concentration is consistent with the above studies in terms of the critical role of fast response, and suggests that the climatic effect of COVID-19 on EASM dampens quickly after the rebound of aerosol emissions, like the time scale of the effect of volcanic eruptions on monsoon (Man et al. 2014; Liu et al. 2016).