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Understanding how historical climate and land cover changes have affected tropospheric ozone in East Asia would help constrain the large uncertainties associated with future East Asian air quality projections. We perform a series of simulations using a global chemical transport model driven by assimilated meteorological data and a suite of land cover and land use data to examine the public health effects associated with changes in climate, land cover, land use, and anthropogenic emissions between the 5-year periods 1981-1985 and 2007-2011 in East Asia. We find that between these two periods land cover change alone could lead to a decrease in summertime surface ozone by up to 4 ppbv in East Asia and ∼ 2000 fewer ozone-related premature deaths per year, driven mostly by enhanced dry deposition resulting from climate-and CO 2 -induced increase in vegetation density, which more than offsets the effect of reduced isoprene emission arising from cropland expansion. Climate change alone could lead to an increase in summertime ozone by 2-10 ppbv in most regions of East Asia and ∼ 6000 more premature deaths annually, mostly attributable to warming. The combined impacts (−2 to +12 ppbv) show that while the effect of climate change is more pronounced, land cover change could offset part of the climate effect and lead to a previously unknown public health benefit. While the changes in anthropogenic emissions remain the largest contributor to deteriorating ozone air quality in East Asia over the past 30 years, we show that climate change and land cover changes could lead to a substantial modification of ozone levels, and thus should come into consideration when formulating future air quality management strategies. We also show that the sensitivity of surface ozone to land cover change is more dependent on dry deposition than on isoprene emission in most of East Asia, leading to ozone responses that are quite distinct from that in North America, where most ozonevegetation sensitivity studies to date have been conducted.Published by Copernicus Publications on behalf of the European Geosciences Union.

Abstract.We have developed a novel framework ("TanTracker") for assimilating observations of atmospheric CO 2 concentrations, based on the POD-based (proper orthogonal decomposition) ensemble four-dimensional variational data assimilation method (PODEn4DVar). The high flexibility and the high computational efficiency of the PODEn4DVar approach allow us to include both the atmospheric CO 2 concentrations and the surface CO 2 fluxes as part of the large state vector to be simultaneously estimated from assimilation of atmospheric CO 2 observations. Compared to most modern top-down flux inversion approaches, where only surface fluxes are considered as control variables, one major advantage of our joint data assimilation system is that, in principle, no assumption on perfect transport models is needed. In addition, the possibility for Tan-Tracker to use a complete dynamic model to consistently describe the time evolution of CO 2 surface fluxes (CFs) and the atmospheric CO 2 concentrations represents a better use of observation information for recycling the analyses at each assimilation step in order to improve the forecasts for the following assimilations. An experimental Tan-Tracker system has been built based on a complete augmented dynamical model, where (1) the surface atmosphere CO 2 exchanges are prescribed by using a persistent forecasting model for the scaling factors of the first-guess net CO 2 surface fluxes and (2) the atmospheric CO 2 transport is simulated by using the GEOSChem three-dimensional global chemistry transport model. Observing system simulation experiments (OSSEs) for assimilating synthetic in situ observations of surface CO 2 concentrations are carefully designed to evaluate the effectiveness of the Tan-Tracker system. In particular, detailed comparisons are made with its simplified version (referred to as TT-S) with only CFs taken as the prognostic variables. It is found that our Tan-Tracker system is capable of outperforming TT-S with higher assimilation precision for both CO 2 concentrations and CO 2 fluxes, mainly due to the simultaneous estimation of CO 2 concentrations and CFs in our TanTracker data assimilation system. A experiment for assimilating the real dry-air column CO 2 retrievals (XCO 2 ) from the Japanese Greenhouse Gases Observation Satellite (GOSAT) further demonstrates its potential wide applications.