The modern reanalysis datasets provide not only meteorological variables, but also atmospheric chemical compositions such as tropospheric ozone and aerosol concentration. However, the quality of chemical compositions has been rarely assessed especially over East Asia. To better understand the characteristics of reanalysis datasets on regional scale, the present study evaluates tropospheric ozone derived from seven reanalyses against five independent ozonesonde observations in East Asia. The reanalysis datasets are the ECMWF Reanalysis 5th (ERA5), Monitoring Atmospheric Composition and Climate reanalysis (MACCRA), Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), as well as the NCEP Climate Forecast System Reanalysis (CFSR), NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), Japanese 55-year Reanalysis (JRA-55), and updated Tropospheric Chemistry Reanalysis (TCR-2). It turns out that MACCRA, CAMSRA, and TCR-2, which incorporate chemical transport model, depict most reasonable spatio-temporal variability of tropospheric ozone in East Asia. The MACC exhibits a better quality with relatively small mean biases of 6.4 ± 1.3% in tropospheric column ozone than biases of 7.8 ± 2.7% and 7.8 ± 2.8% for CAMSRA and TCR-2. The CAMSRA further shows a significant monthly correlation with the observation of up to 0.7 at 850 hPa. Among the seven reanalyses, MACC, CAMSRA, and TCR-2 are suitable for local tropospheric ozone study on seasonal to inter-annual time scales. However, none of the seven reanalysis datasets reproduce the observed trend of tropospheric ozone. This result suggests that even the latest datasets are inadequate for the long-term ozone change study.
This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model's prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4-to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.
Understanding air pollution in East Asia is of great importance given its high population density and serious air pollution problems during winter. Here, we show that the day-to-day variability of East Asia air pollution, during the recent 21-year winters, is remotely influenced by the Madden–Julian Oscillation (MJO), a dominant mode of subseasonal variability in the tropics. In particular, the concentration of particulate matter with aerodynamic diameter less than 10 micron (PM10) becomes significantly high when the tropical convections are suppressed over the Indian Ocean (MJO phase 5–6), and becomes significantly low when those convections are enhanced (MJO phase 1–2). The station-averaged PM10 difference between these two MJO phases reaches up to 15% of daily PM10 variability, indicating that MJO is partly responsible for wintertime PM10 variability in East Asia. This finding helps to better understanding the wintertime PM10 variability in East Asia and monitoring high PM10 days.
Given the high population density and serious air pollution problems, understanding and predicting air pollution in East Asia are of great importance. Here, we show that the day-to-day variability of East Asia air pollution in winter is remotely controlled by the convection over the tropical Indian Ocean and western Pacific, the so-called Madden–Julian oscillation (MJO), through its extratropical teleconnections. In particular, the concentration of particulate matter with aerodynamic diameter less than 10 micron (PM10) becomes significantly high when the tropical convection is suppressed over the Indian Ocean (MJO phases 5–6). In contrast, PM10 concentration becomes significantly low when the convection is enhanced there (MJO phase 1–2). The station-averaged PM10 difference between the two MJO phases reaches up to 47% of the daily PM10 variability, indicating that the MJO is a primary source of wintertime subseasonal variability of East Asia PM10 concentration. We also show that PM10 anomaly typically lags the tropical convection by one to two weeks. This opens a new window of opportunity for subseasonal PM10 prediction in East Asia.
This study explores the 6-month lead prediction skill of stratospheric temperature and circulations in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment over the period of 1996~2009. Both the tropical and extratropical circulations are considered by analyzing the Quasi-Biennial Oscillation (QBO) and Northern Hemisphere Polar Vortex (NHPV). Their prediction skills are quantitatively evaluated by computing the Anomaly Correlation Coefficient (ACC) and Mean Squared Skill Score (MSSS), and compared with those of El Nino-Southern Oscillation (ENSO) and Arctic Oscillation (AO). Stratospheric temperature is generally better predicted than tropospheric temperature. Such improved prediction skill, however, rapidly disappears in a month, and a reliable prediction skill is observed only in the tropics, indicating a higher prediction skill in the tropics than in the extratropics. Consistent with this finding, QBO is well predicted more than 6 months in advance. Its prediction skill is significant in all seasons although a relatively low prediction skill appears in the spring when QBO phase transition often takes place. This seasonality is qualitatively similar to the spring barrier of ENSO prediction skill. In contrast, NHPV exhibits no prediction skill beyond one month as in AO prediction skill. In terms of MSSS, both QBO and NHPV are better predicted than their counterparts in the troposphere, i.e., ENSO and AO, indicating that the GloSea5 has a higher prediction skill in the stratosphere than in the troposphere.
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