[1] A worldwide compilation of atmospheric total phosphorus (TP) and phosphate (PO 4 ) concentration and deposition flux observations are combined with transport model simulations to derive the global distribution of concentrations and deposition fluxes of TP and PO 4 . Our results suggest that mineral aerosols are the dominant source of TP on a global scale (82%), with primary biogenic particles (12%) and combustion sources (5%) important in nondusty regions. Globally averaged anthropogenic inputs are estimated to be $5 and 15% for TP and PO 4 , respectively, and may contribute as much as 50% to the deposition over the oligotrophic ocean where productivity may be phosphorus-limited. There is a net loss of TP from many (but not all) land ecosystems and a net gain of TP by the oceans (560 Gg P a À1

Desert dust simulations generated by the National Center for Atmospheric Research's Community Climate System Model for the current climate are shown to be consistent with present day satellite and deposition data. The response of the dust cycle to last glacial maximum, preindustrial, modern, and doubled‐carbon dioxide climates is analyzed. Only natural (non‐land use related) dust sources are included in this simulation. Similar to some previous studies, dust production mainly responds to changes in the source areas from vegetation changes, not from winds or soil moisture changes alone. This model simulates a +92%, +33%, and −60% change in dust loading for the last glacial maximum, preindustrial, and doubled‐carbon dioxide climate, respectively, when impacts of carbon dioxide fertilization on vegetation are included in the model. Terrestrial sediment records from the last glacial maximum compiled here indicate a large underestimate of deposition in continental regions, probably due to the lack of simulation of glaciogenic dust sources. In order to include the glaciogenic dust sources as a first approximation, we designate the location of these sources, and infer the size of the sources using an inversion method that best matches the available data. The inclusion of these inferred glaciogenic dust sources increases our dust flux in the last glacial maximum from 2.1 to 3.3 times current deposition.

Atmospheric inputs of iron to the open ocean are hypothesized to modulate ocean biogeochemistry. This review presents an integration of available observations of atmospheric iron and iron deposition, and also covers bioavailable iron distributions. Methods for estimating temporal variability in ocean deposition over the recent past are reviewed. Desert dust iron is estimated to represent 95% of the global atmospheric iron cycle, and combustion sources of iron are responsible for the remaining 5%. Humans may be significantly perturbing desert dust (up to 50%). The sources of bioavailable iron are less well understood than those of iron, partly because we do not know what speciation of the iron is bioavailable. Bioavailable iron can derive from atmospheric processing of relatively insoluble desert dust iron or from direct emissions of soluble iron from combustion sources. These results imply that humans could be substantially impacting iron and bioavailable iron deposition to ocean regions, but there are large uncertainties in our understanding.

[1] Iron is hypothesized to be an important micronutrient for ocean biota, thus modulating carbon dioxide uptake by the ocean biological pump. Studies have assumed that atmospheric deposition of iron to the open ocean is predominantly from mineral aerosols. For the first time we model the source, transport, and deposition of iron from combustion sources. Iron is produced in small quantities during fossil fuel burning, incinerator use, and biomass burning. The sources of combustion iron are concentrated in the industrialized regions and biomass burning regions, largely in the tropics. Model results suggest that combustion iron can represent up to 50% of the total iron deposited, but over open ocean regions it is usually less than 5% of the total iron, with the highest values (<30%) close to the East Asian continent in the North Pacific. For ocean biogeochemistry the bioavailability of the iron is important, and this is often estimated by the fraction which is soluble (Fe(II)). Previous studies have argued that atmospheric processing of the relatively insoluble Fe(III) occurs to make it more soluble (Fe(II)). Modeled estimates of soluble iron amounts based solely on atmospheric processing as simulated here cannot match the variability in daily averaged in situ concentration measurements in Korea, which is located close to both combustion and dust sources. The best match to the observations is that there are substantial direct emissions of soluble iron from combustion processes. If we assume observed soluble Fe/black carbon ratios in Korea are representative of the whole globe, we obtain the result that deposition of soluble iron from combustion contributes 20-100% of the soluble iron deposition over many ocean regions. This implies that more work should be done refining the emissions and deposition of combustion sources of soluble iron globally.

[1] Predicting mineral aerosol distributions is a difficult task due to the episodic nature of the sources and transport. Here we show comparisons between a 22-year simulation of mineral aerosols and satellite and in situ observations. Our results suggest that the model does a good job of predicting atmospheric mineral aerosol distributions, with some discrepancies. In addition, there are differences between our model results and previously published results [e.g., Ginoux et al., 2001]. We conduct several tests of the sensitivity of mineral aerosol simulations to the meteorological data sets and mobilization parameterizations in order to understand the differences. Comparisons between model simulations using National Center for Atmospheric Research/National Center for Environmental Prediction (NCEP/NCAR) and National Aeronautics and Space Administration Data Assimilation Office (NASA DAO) reanalysis data sets show that the model results with the two data sets are fairly consistent but with some important differences. The sensitivity analysis shows that differences between simulated dust near Australia are likely due to differences in both source parameterization and surface winds. Differences over East Asia are dominated by differences in meteorology. The sensitivity analysis also shows that we cannot tell from comparisons with observations whether the cultivation source is active nor eliminate it because of the large uncertainty in meteorology and source parameterization.

[1] We examine the seasonal variation in lower tropospheric nitrogen oxides (NO x = NO + NO 2 ) at northern midlatitudes by evaluating tropospheric NO 2 columns observed from the Ozone Monitoring Instrument (OMI) satellite instrument with surface NO 2 measurements (SouthEastern Aerosol Research and Characterization and Air Quality System) and current bottom-up NO x emission inventories, using a global model of tropospheric chemistry (GEOS-Chem). The standard (SP) and DOMINO (DP) tropospheric NO 2 column products from OMI exhibit broadly similar spatial and seasonal variation, but differ substantially over continental source regions. A comparison of the two OMI tropospheric NO 2 products with in situ surface NO 2 concentrations and bottom-up NO x emissions over the southeast United States indicates that annual mean NO 2 columns from the DP are biased high by 21%-33% and those from the SP are biased high by 27%-43%. The bias in SP columns is highly seasonal, 67%-74% in summer compared with −6% to −1% in winter. Similar seasonal differences exist between top-down and bottom-up NO x emission inventories over North America, Europe, and East Asia. The air mass factor largely explains the observed seasonal difference between the DP and SP, and in turn the seasonal SP bias. We develop a third product (DP_GC) using averaging kernel information from the DP and NO 2 vertical profiles from GEOS-Chem. This product reduces to 5%-21% the annual mean bias over the southeast United States. We use the seasonal variation in the DP_GC to estimate the seasonal variation in the lifetime of lower tropospheric NO x against oxidation to HNO 3 over the eastern United States. The effective NO x lifetime at OMI overpass time (early afternoon) ranges from 7.6 h in summer to 17.8 h in winter, consistent within 3 h of the simulated lifetime. GEOS-Chem calculations reveal that the seasonal variation in OMI NO 2 columns largely reflects gas-phase oxidation of NO 2 in summer with an increasing role for heterogenous chemistry in winter.Citation: Lamsal, L. N., R. V. Martin, A. van Donkelaar, E. A. Celarier, E. J. Bucsela, K. F. Boersma, R. Dirksen, C. Luo, and Y. Wang (2010), Indirect validation of tropospheric nitrogen dioxide retrieved from the OMI satellite instrument: Insight into the seasonal variation of nitrogen oxides at northern midlatitudes,

The effect of atmospheric aerosols and regional haze from air pollution on the yields of rice and winter wheat grown in China is assessed. The assessment is based on estimates of aerosol optical depths over China, the effect of these optical depths on the solar irradiance reaching the earth's surface, and the response of rice and winter wheat grown in Nanjing to the change in solar irradiance. Two sets of aerosol optical depths are presented: one based on a coupled, regional climate͞air quality model simulation and the other inferred from solar radiation measurements made over a 12-year period at meteorological stations in China. The model-estimated optical depths are significantly smaller than those derived from observations, perhaps because of errors in one or both sets of optical depths or because the data from the meteorological stations has been affected by local pollution. Radiative transfer calculations using the smaller, modelestimated aerosol optical depths indicate that the so-called ''direct effect'' of regional haze results in an Ϸ5-30% reduction in the solar irradiance reaching some of China's most productive agricultural regions. Crop-response model simulations suggest an Ϸ1:1 relationship between a percentage increase (decrease) in total surface solar irradiance and a percentage increase (decrease) in the yields of rice and wheat. Collectively, these calculations suggest that regional haze in China is currently depressing optimal yields of Ϸ70% of the crops grown in China by at least 5-30%. Reducing the severity of regional haze in China through air pollution control could potentially result in a significant increase in crop yields and help the nation meet its growing food demands in the coming decades.

[1] Mineral aerosols are important atmospheric constituents owing to their interactions with climate and biogeochemistry. The interannual variability in atmospheric mineral aerosols is evaluated using a model simulation of 1979-2000 and mineral aerosol observations. Overall, the variability in monthly means between different years is not as large as the variability within a month for column amount, surface concentration, and deposition fluxes. The magnitude of the variability predicted in the model varies spatially and appears similar to that seen in the available observations, although the model is not always able to simulate observed high-and low-dust years. The area over which the interannual variability in the observing station data should be representative is estimated in the model simulation and is shown to be regional in extent. However, correlations between modeled surface concentrations at the stations and modeled deposition in the surrounding region is often low, suggesting that the observations of the variability of surface concentrations are difficult to extrapolate to variability in regional deposition fluxes. The correlations between modeled monthly mean optical depth and modeled deposition or mobilization are low to moderate (0.2-0.6) over much of the globe, indicating the difficulty of estimating mobilization or deposition fluxes from satellite retrievals of optical depth. In both the model and observations there are relationships between climate indices (e.g., North Atlantic Oscillation, El Niño, and Pacific Decadal Oscillation) and dust, although a 22-year simulation is not long enough to well characterize this relationship. In this model, simulation of 1979-2000, dust concentration variability appears to be dominated by transport variability and/or transport and source covariance rather than source strength variability.