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Due to the scarcity of observational constraints and the rapidly changing environment in East and Southeast Asia, isoprene emissions predicted by models are expected to bear substantial uncertainties. The aim of this study is to improve upon the existing bottom-up estimates, and to investigate the temporal evolution of the fluxes in Asia over 1979-2012. To this purpose, we calculate the hourly emissions at 0.5& deg; & times;0.5 & deg; resolution using the MEGAN-MOHYCAN model driven by ECMWF ERA-Interim climatology. In order to remedy for known biases identified in previous studies, and to improve the simulation of interannual variability and trends in emissions, this study incorporates (i) changes in land use, including the rapid expansion of oil palms, (ii) meteorological variability according to ERA-Interim, (iii) long-term changes in solar radiation (dimming/brightening) constrained by surface network radiation measurements, and (iv) recent experimental evidence that South Asian tropical forests are much weaker isoprene emitters than previously assumed, and on the other hand, that oil palms have a strong isoprene emission capacity. These effects lead to a significant lowering (factor of 2) in the total isoprene fluxes over the studied domain, and to emission reductions reaching a factor of 3.5 in Southeast Asia. The bottom-up annual isoprene emissions for 2005 are estimated at 7.0, 4.8, 8.3, and 2.9 Tg in China, India, Indonesia and Malaysia, respectively. The isoprene flux anomaly over the whole domain and studied period is found to be strongly correlated with the Oceanic Niño Index ( Combining double low line 0.73), with positive (negative) anomalies related to El Niño (La Niña) years. Changes in temperature and solar radiation are the major drivers of the interannual variability and trends in the emissions, except over semi-arid areas such as northwestern China, Pakistan and Kazakhstan, where soil moisture is by far the main cause of interannual emission changes. In our base simulation, annual positive flux trends of 0.2% and 0.52% throughout the entire period are found in Asia and China, respectively, related to a positive trend in temperature and solar radiation. The impact of oil palm expansion in Indonesia and Malaysia is to enhance the trends over that region, e.g., from 1.17% to 1.5% in 1979-2005 in Malaysia. A negative emission trend is derived in India (ĝ'0.4%), owing to the negative trend in solar radiation data associated with the strong dimming effect likely due to increasing aerosol loadings. The bottom-up emissions are compared to field campaign measurements in Borneo and South China and further evaluated against top-down isoprene emission estimates constrained by GOME-2/MetOp-A formaldehyde columns through 2007-2012. The satellite-based estimates appear to support our assumptions, and confirm the lower emission rate in tropical forests of Indonesia and Malaysia. Additional flux measurements are clearly needed to characterize the spatial variability of emission factors better. Finally, a decreasing trend in the inferred top-down Chinese emissions since 2007 is in line with recorded cooling in China after that year, thus suggesting that the satellite HCHO columns are able to capture climate-induced changes in emissions. © 2014 Author(s).

We use formaldehyde (HCHO) vertical column measurements from the Scanning Imaging Absorption spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI), and a nested-grid version of the GEOS-Chem chemistry transport model, to infer an ensemble of top-down isoprene emission estimates from tropical South America during 2006, using different model configurations and assumptions in the HCHO air-mass factor (AMF) calculation. Scenes affected by biomass burning are removed on a daily basis using fire count observations, and we use the local model sensitivity to identify locations where the impact of spatial smearing is small, though this comprises spatial coverage over the region. We find that the use of the HCHO column data more tightly constrains the ensemble isoprene emission range from 27-61 Tg C to 31-38 Tg C for SCIAMACHY, and 45-104 Tg C to 28-38 Tg C for OMI. Median uncertainties of the top-down emissions are about 60-260% for SCIAMACHY, and 10-90% for OMI. We find that the inferred emissions are most sensitive to uncertainties in cloud fraction and cloud top pressure (differences of ±10%), the a priori isoprene emissions (±20%), and the HCHO vertical column retrieval (±30%). Construction of continuous top-down emission maps generally improves GEOS-Chem's simulation of HCHO columns over the region, with respect to both the SCIAMACHY and OMI data. However, if local time top-down emissions are scaled to monthly mean values, the annual emission inferred from SCIAMACHY are nearly twice those from OMI. This difference cannot be explained by the different sampling of the sensors or uncertainties in the AMF calculation. Key Points Satellite HCHO data constrain range of uncertainty in Amazon isoprene emissions Top-down emissions are sensitive to prior isoprene inventory and HCHO retrieval Top-down emissions from SCIAMACHY are about twice those derived from OMI © 2013. American Geophysical Union. All Rights Reserved.

We use a nested-grid version of the GEOS-Chem chemistry transport model, constrained by isoprene emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN), and the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) bottom-up inventories, to evaluate the impact that surface isoprene emissions have on formaldehyde (HCHO) air-mass factors (AMFs) and vertical column densities (VCDs) over tropical South America during 2006, as observed by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI). Although the large-scale seasonal variability of monthly mean HCHO VCDs is typically unaffected by the choice of bottom-up inventory, large relative differences of up to ±45% in the HCHO VCD can occur for individual regions and months, but typically most VCD differences are of order ±20%. These relative changes are comparable to those produced by other sources of uncertainty in the AMF including aerosols and surface albedo, but less than those from clouds. In a sensitivity test, we find that top-down annual isoprene emissions inferred from SCIAMACHY and OMI HCHO vertical columns can vary by as much as ±30-50% for each instrument respectively, depending on the region studied and the a priori isoprene emissions used. Our analysis suggests that the influence of the a priori isoprene emissions on HCHO AMFs and VCDs is therefore non-negligible and must be carefully considered when inferring top-down isoprene emissions estimates over this, or potentially any other, region. © 2012. American Geophysical Union.

We present an evaluation of a nested high-resolution Goddard Earth Observing System (GEOS)-Chem chemistry transport model simulation of tropospheric chemistry over tropical South America. The model has been constrained with two isoprene emission inventories: (1) the canopy-scale Model of Emissions of Gases and Aerosols from Nature (MEGAN) and (2) a leaf-scale algorithm coupled to the Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model, and the model has been run using two different chemical mechanisms that contain alternative treatments of isoprene photo-oxidation. Large differences of up to 100 Tg C yr-1 exist between the isoprene emissions predicted by each inventory, with MEGAN emissions generally higher. Based on our simulations we estimate that tropical South America (30-85°W, 14°N-25°S) contributes about 15-35% of total global isoprene emissions. We have quantified the model sensitivity to changes in isoprene emissions, chemistry, boundary layer mixing, and soil NOx emissions using ground-based and airborne observations. We find GEOS-Chem has difficulty reproducing several observed chemical species; typically hydroxyl concentrations are underestimated, whilst mixing ratios of isoprene and its oxidation products are overestimated. The magnitude of model formaldehyde (HCHO) columns are most sensitive to the choice of chemical mechanism and isoprene emission inventory. We find GEOS-Chem exhibits a significant positive bias (10-100%) when compared with HCHO columns from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and Ozone Monitoring Instrument (OMI) for the study year 2006. Simulations that use the more detailed chemical mechanism and/or lowest isoprene emissions provide the best agreement to the satellite data, since they result in lower-HCHO columns. Copyright © 2011 by the American Geophysical Union.

Emission of bromine from sea-salt aerosol, frost flowers, ice leads, and snow results in the nearly complete removal of surface ozone during Arctic spring. Regions of enhanced total column BrO observed by satellites have traditionally been associated with these emissions. However, airborne measurements of BrO and O3 within the convective boundary layer (CBL) during the ARCTAS and ARCPAC field campaigns at times bear little relation to enhanced column BrO. We show that the locations of numerous satellite BrO "hotspots" during Arctic spring are consistent with observations of total column ozone and tropopause height, suggesting a stratospheric origin to these regions of elevated BrO. Tropospheric enhancements of BrO large enough to affect the column abundance are also observed, with important contributions originating from above the CBL. Closure of the budget for total column BrO, albeit with significant uncertainty, is achieved by summing observed tropospheric partial columns with calculated stratospheric partial columns provided that natural, short-lived biogenic bromocarbons supply between 5 and 10 ppt of bromine to the Arctic lowermost stratosphere. Proper understanding of bromine and its effects on atmospheric composition requires accurate treatment of geographic variations in column BrO originating from both the stratosphere and troposphere. Copyright 2010 by the American Geophysical Union.

We perform Empirical Orthogonal Function (EOF) analysis on 12 years of global GOME and SCIAMACHY formaldehyde (HCHO) column observations to determine the most significant spatial and temporal HCHO variations. In most regions, we find that HCHO variability is predominantly driven by seasonal variations of biogenie emissions and biomass burning. However, unusually low HCHO columns are consistently observed over the Amazon rainforest during the transition from the wet-to-dry seasons. We use MODIS leaf area and enhanced vegetation indices, to show variations in vegetation are consistent with the observed decrease in HCHO during this period (correlations of 0.69 and 0.67, respectively). Based on this evidence, we suggest isoprene emitting vegetation experience widespread leaf flushing (new leaf growth) prior to the dry season, resulting in a large-scale annual shutdown of Amazonian isoprene emissions. © 2009.

A new one-decade (1997ĝ€"2006) dataset of formaldehyde (HCHO) columns retrieved from GOME and SCIAMACHY is compared with HCHO columns simulated by an updated version of the IMAGES global chemical transport model. This model version includes an optimized chemical scheme with respect to HCHO production, where the short-term and final HCHO yields from pyrogenically emitted non-methane volatile organic compounds (NMVOCs) are estimated from the Master Chemical Mechanism (MCM) and an explicit speciation profile of pyrogenic emissions. The model is driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, whereas biogenic emissions are provided either by the Global Emissions Inventory Activity (GEIA), or by a newly developed inventory based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) algorithms driven by meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). The comparisons focus on tropical ecosystems, North America and China, which experience strong biogenic and biomass burning NMVOC emissions reflected in the enhanced measured HCHO columns. These comparisons aim at testing the ability of the model to reproduce the observed features of the HCHO distribution on the global scale and at providing a first assessment of the performance of the current emission inventories. The high correlation coefficients (r>0.7) between the observed and simulated columns over most regions indicate a good consistency between the model, the implemented inventories and the HCHO dataset. The use of the MEGAN-ECMWF inventory improves the model/data agreement in almost all regions, but biases persist over parts of Africa and Australia. Although neither GFED version is consistent with the data over all regions, a better agreement is achieved over Indonesia and Southern Africa when GFEDv2 is used, but GFEDv1 succeeds better in getting the correct seasonal patterns and intensities of the fire episodes over the Amazon basin, as reflected in the significantly higher correlations calculated in this region. Although the uncertainties in the HCHO retrievals, especially over fire scenes, can be quite large, this study provides a first assessment about whether the improved methodologies and input data implemented in GFEDv2 and MEGAN-ECMWF lead to better results in the comparisons of modelled with observed HCHO column measurements.

Formaldehyde columns retrieved from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography/Chemistry (SCIAMACHY) instrument onboard ENVISAT satellite through 2003 to 2006 are used as top-down constraints to derive updated global biogenic and biomass burning flux estimates for the non-methane volatile organic compounds (NMVOCs) precursors of formaldehyde. Our interest is centered over regions experiencing strong emissions, and hence exhibiting a high signal-to-noise ratio and lower measurement uncertainties. The formaldehyde dataset used in this study has been recently made available to the community and complements the long record of formaldehyde measurements from the Global Ozone Monitoring Experiment (GOME). We use the IMAGESv2 global chemistry-transport model driven by the Global Fire Emissions Database (GFED) version 1 or 2 for biomass burning, and from the newly developed MEGAN-ECMWF isoprene emission database. The adjoint of the model is implemented in a grid-based framework within which emission fluxes are derived at the model resolution, together with a differentiation of the sources in a grid cell. Two inversion studies are conducted using either the GFEDv1 or GFEDv2 as a priori for the pyrogenic fluxes. Although on the global scale the inferred emissions from the two categories exhibit only weak deviations from the corresponding a priori estimates, the regional updates often present large departures from their a priori values. The posterior isoprene emissions over North America, amounting to about 34 Tg C/yr, are estimated to be on average by 25% lower than the a priori over 2003-2006, whereas a strong increase (55%) is deduced over the south African continent, the optimized emission being estimated at 57 Tg C/yr. Over Indonesia the biogenic emissions appear to be overestimated by 20-30%, whereas over Indochina and the Amazon basin during the wet season the a priori inventory captures both the seasonality and the magnitude of the observed columns. Although neither biomass burning inventory seems to be consistent with the data over all regions, pyrogenic estimates inferred from the two inversions are reasonably similar, despite their a priori deviations. A number of sensitivity experiments are conducted in order to assess the impact of uncertainties related to the inversion setup and the chemical mechanism. Whereas changes in the background error covariance matrix have only a limited impact on the posterior fluxes, the use of an alternative isoprene mechanism characterized by lower HCHO yields (the GEOS-Chem mechanism) increases the posterior isoprene source estimate by 11% over northern America, and by up to 40% in tropical regions.

The global emissions of isoprene are calculated at 0.5° resolution for each year between 1995 and 2006, based on the MEGAN (Model of Emissions of Gases and Aerosols from Nature) version 2 model (Guenther et al., 2006) and a detailed multi-layer canopy environment model for the calculation of leaf temperature and visible radiation fluxes. The calculation is driven by meteorological fields - air temperature, cloud cover, downward solar irradiance, windspeed, volumetric soil moisture in 4 soil layers - provided by analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The estimated annual global isoprene emission ranges between 374Tg (in 1996) and 449Tg (in 1998 and 2005), for an average of ca. 410Tg/year over the whole period, i.e. about 30% less than the standard MEGAN estimate (Guenther et al., 2006). This difference is due, to a large extent, to the impact of the soil moisture stress factor, which is found here to decrease the global emissions by more than 20%. In qualitative agreement with past studies, high annual emissions are found to be generally associated with El Niño events. The emission inventory is evaluated against flux measurement campaigns at Harvard forest (Massachussets) and Tapajós in Amazonia, showing that the model can capture quite well the short-term variability of emissions, but that it fails to reproduce the observed seasonal variation at the tropical rainforest site, with largely overestimated wet season fluxes. The comparison of the HCHO vertical columns calculated by a chemistry and transport model (CTM) with HCHO distributions retrieved from space provides useful insights on tropical isoprene emissions. For example, the relatively low emissions calculated over Western Amazonia (compared to the corresponding estimates in the inventory of Guenther et al., 1995) are validated by the excellent agreement found between the CTM and HCHO data over this region. The parameterized impact of the soil moisture stress on isoprene emissions is found to reduce the model/data bias over Australia, but it leads to underestimated emissions near the end of the dry season over subtropical Africa.

We present a systematic comparison of tropospheric NO2 from 17 global atmospheric chemistry models with three state-of-the-art retrievals from the Global Ozone Monitoring Experiment (GOME) for the year 2000. The models used constant anthropogenic emissions from IIASA/EDGAR3.2 and monthly emissions from biomass burning based on the 1997–2002 average carbon emissions from the Global Fire Emissions Database (GFED). Model output is analyzed at 10:30 local time, close to the overpass time of the ERS-2 satellite, and collocated with the measurements to account for sampling biases due to incomplete spatiotemporal coverage of the instrument. We assessed the importance of different contributions to the sampling bias: correlations on seasonal time scale give rise to a positive bias of 30–50% in the retrieved annual means over regions dominated by emissions from biomass burning. Over the industrial regions of the eastern United States, Europe and eastern China the retrieved annual means have a negative bias with significant contributions (between –25% and +10% of the NO2 column) resulting from correlations on time scales from a day to a month. We present global maps of modeled and retrieved annual mean NO2 column densities, together with the corresponding ensemble means and standard deviations for models and retrievals. The spatial correlation between the individual models and retrievals are high, typically in the range 0.81–0.93 after smoothing the data to a common resolution. On average the models underestimate the retrievals in industrial regions, especially over eastern China and over the Highveld region of South Africa, and overestimate the retrievals in regions dominated by biomass burning during the dry season. The discrepancy over South America south of the Amazon disappears when we use the GFED emissions specific to the year 2000. The seasonal cycle is analyzed in detail for eight different continental regions. Over regions dominated by biomass burning, the timing of the seasonal cycle is generally well reproduced by the models. However, over Central Africa south of the Equator the models peak one to two months earlier than the retrievals. We further evaluate a recent proposal to reduce the NOx emission factors for savanna fires by 40% and find that this leads to an improvement of the amplitude of the seasonal cycle over the biomass burning regions of Northern and Central Africa. In these regions the models tend to underestimate the retrievals during the wet season, suggesting that the soil emissions are higher than assumed in the models. In general, the discrepancies between models and retrievals cannot be explained by a priori profile assumptions made in the retrievals, neither by diurnal variations in anthropogenic emissions, which lead to a marginal reduction of the NO2 abundance at 10:30 local time (by 2.5–4.1% over Europe). Overall, there are significant differences among the various models and, in particular, among the three retrievals. The discrepancies among the retrievals (10–50% in the annual mean over polluted regions) indicate that the previously estimated retrieval uncertainties have a large systematic component. Our findings imply that top-down estimations of NOx emissions from satellite retrievals of tropospheric NO2 are strongly dependent on the choice of model and retrieval.