East Asia (China, Japan, Koreas and Mongolia) has been the world’s economic engine over at least the past two decades, exhibiting a rapid increase in fossil fuel emissions of greenhouse gases (GHGs) and has expressed the recent ambition to achieve climate neutrality by mid-century. However, the GHG balance of its terrestrial ecosystems remains poorly constrained. Here, we present a synthesis of the three most important long-lived greenhouse gases (CO2, CH4 and N2O) budgets over East Asia during the decades of 2000s and 2010s, following a dual constraint bottom-up and top-down approach. We estimate that terrestrial ecosystems in East Asia is close to neutrality of GHGs, with a magnitude of between 196.9 ± 527.0 Tg CO2eq yr-1 (the top-down approach) and -20.8 ± 205.5 Tg CO2eq yr-1 (the bottom-up approach) during 2000-2019. This net GHG emission includes a large land CO2 sink (-1251.3 ± 456.9 Tg CO2 yr-1 based on the top-down approach and -1356.1 ± 155.6 Tg CO2 yr-1 based on the bottom-up approach), which is being fully offset by biogenic CH4 and N2O emissions, predominantly coming from the agricultural sector. Emerging data sources and modelling capacities have helped achieve agreement between the top-down and bottom-up approaches to within 20% for all three GHGs, but sizeable uncertainties remain in several flux terms. For example, the reported CO2 flux from land use and land cover change varies from a net source of more than 300 Tg CO2 yr-1 to a net sink of ~-700 Tg CO2 yr-1.

Atmospheric aerosols influence the radiation budget, cloud amount, cloud properties, and surface albedos of sea ice and snow over the Arctic. In spite of their climatic importance, Arctic aerosol contains large uncertainties due to limited observations. This study evaluates the Arctic aerosol variability in three reanalyses, JRAero, CAMSRA, and MERRA2, in terms of the aerosol optical depth (AOD), and its relationship to the atmospheric disturbances on synoptic timescales. The AOD becomes highest in July–August over most of the Arctic regions, except for the North Atlantic and Greenland, where monthly variability is rather small. The three reanalyses show a general consistency in the horizontal distribution and temporal variability of the total AOD in summer. In contrast, the contributions of individual aerosol species to the total AOD are quite different among the reanalyses. Compared with observations, the AOD variability is represented well in all reanalyses in summer with high correlation coefficients, albeit exhibiting errors as large as the average AOD. The composite analysis shows that large aerosol emissions in Northern Eurasia and Alaska and transport by a typical atmospheric circulation pattern contribute to the high aerosol loading events in each area of the Arctic. Meanwhile, the empirical orthogonal function analysis depicts that the first- and second-largest AOD variabilities on the synoptic timescales appear over Northern Eurasia. Our results indicate that these summertime AOD variabilities mainly result from aerosol transportation and deposition due to the atmospheric disturbances on synoptic scales, suggesting an essential role played by Arctic cyclones.

This study statistically evaluated the aerosol impact on the temperature error in the lower-level troposphere in short-range numerical weather prediction (NWP). The Global Ensemble Forecast System version 12 (GEFSv12) reforecast exhibited large temperature errors in high-loading areas (North India, Africa, South America, and China). In 1-day GEFSv12 forecasts, the largest average temperature error occurred in the aerosol optical depth (AOD) peak month, and the daily error distribution corresponded to the daily AOD distribution. Even though the temperature error in the 1-day operational forecasts was smaller than that in the GEFSv12 forecasts, the forecast uncertainties in the operational forecasts were comparable to those in 3-day GEFSv12 forecasts over high-loading areas. The daily temperature errors in all NWP models exhibited a correlation coefficient of ~0.5–0.6 for the AOD over Central Africa and northern South America and ~0.3–0.6 for AOD anomalies over China and northern South America. These results indicated that the yearly aerosol variability contributed 25–36% to errors, and the daily variability contributed 10–36% to temperature errors in 3-day forecasts. Although the correlation was low, aerosol impacts also emerged in North India and Central Africa. Partial correlation and composite analysis suggested that the direct effect mainly influenced temperature forecast errors over northern South America, whereas both direct and indirect effects influenced temperature errors over China. Model intercomparison revealed that operational NWP models could experience common forecast errors associated with aerosols in high-loading areas.