AOD trends during 2001–2010 from observations and model simulations

Pozzer, Andrea; Meij, Alexander de; Yoon, Jongmin; Tost, H.; Georgoulias, Aristeidis K.; Astitha, Marina

doi:10.5194/acp-15-5521-2015

Cited by 159 publications

(153 citation statements)

References 83 publications

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“…Even in the simplest version, prescribed (offline) dust emissions can produce acceptable results for the global aerosol distribution and variability due to the importance of atmospheric transport (Pozzer et al, 2015;Pringle et al, 2010b). Improved agreement with observations is generally achieved with online emission schemes which consider actual meteorological conditions, most importantly the surface friction velocity and the wind speed close to the surface.…”

Section: K Klingmüller Et Al: Revised Mineral Dust Emissions In Emacmentioning

confidence: 99%

Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

et al. 2018

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Abstract. To improve the aeolian dust budget calculations with the global ECHAM/MESSy atmospheric chemistryclimate model (EMAC), which combines the Modular Earth Submodel System (MESSy) with the ECMWF/Hamburg (ECHAM) climate model developed at the Max Planck Institute for Meteorology in Hamburg based on a weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF), we have implemented new input data and updates of the emission scheme.The data set comprises land cover classification, vegetation, clay fraction and topography. It is based on up-todate observations, which are crucial to account for the rapid changes of deserts and semi-arid regions in recent decades. The new Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover and vegetation data are time dependent, and the effect of long-term trends and variability of the relevant parameters is therefore considered by the emission scheme. All input data have a spatial resolution of at least 0.1 • compared to 1 • in the previous version, equipping the model for high-resolution simulations.We validate the updates by comparing the aerosol optical depth (AOD) at 550 nm wavelength from a 1-year simulation at T106 (about 1.1 • ) resolution with Aerosol Robotic Network (AERONET) and MODIS observations, the 10 µm dust AOD (DAOD) with Infrared Atmospheric Sounding Interferometer (IASI) retrievals, and dust concentration and deposition results with observations from the Aerosol Comparisons between Observations and Models (AeroCom) dust benchmark data set. The update significantly improves agreement with the observations and is therefore recommended to be used in future simulations.

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Section: K Klingmüller Et Al: Revised Mineral Dust Emissions In Emacmentioning

confidence: 99%

Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

et al. 2018

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“…This behavior with pivot points in 2006 and 2011 is in line with the reduction of emissions of aerosol precursor gases, such as SO 2 and NO 2 (van der A et al, 2017), possibly together with large-scale climate variability as discussed above. The increase/decrease of anthropogenic particles is expected to increase/decrease the water uptake from the aerosols and hence the recorded AODs (Pozzer et al, 2015).…”

Section: Seasonal Variationmentioning

confidence: 99%

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

et al. 2018

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Abstract. The retrieval of aerosol properties from satellite observations provides their spatial distribution over a wide area in cloud-free conditions. As such, they complement ground-based measurements by providing information over sparsely instrumented areas, albeit that significant differences may exist in both the type of information obtained and the temporal information from satellite and ground-based observations. In this paper, information from different types of satellite-based instruments is used to provide a 3-D climatology of aerosol properties over mainland China, i.e., vertical profiles of extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), a lidar flying aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the columnintegrated extinction (aerosol optical depth -AOD) available from three radiometers: the European Space Agency (ESA)'s Along-Track Scanning Radiometer version 2 (ATSR-2), Advanced Along-Track Scanning Radiometer (AATSR) (together referred to as ATSR) and NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite, together spanning the period 1995-2015. AOD data are retrieved from ATSR using the ATSR dual view (ADV) v2.31 algorithm, while for MODIS Collection 6 (C6) the AOD data set is used that was obtained from merging the AODs obtained from the dark target (DT) and deep blue (DB) algorithms, further referred to as the DTDB merged AOD product. These data sets are validated and differences are compared using Aerosol Robotic Network (AERONET) version 2 L2.0 AOD data as reference. The results show that, over China, ATSR slightly underestimates the AOD and MODIS slightly overestimates the AOD. Consequently, ATSR AOD is overall lower than that from MODIS, and the difference increases with increasing AOD. The comparison also shows that neither of the ATSR and MODIS AOD data sets is better than the other one everywhere. However, ATSR ADV has limitations over bright surfaces which the MODIS DB was designed for. To allow for comparison of MODIS C6 results with previous analyses where MODIS Collection 5.1 (C5.1) data were used, also the difference between the C6 and C5.1 merged DTDB data sets from MODIS/Terra over China is briefly discussed.The AOD data sets show strong seasonal differences and the seasonal features vary with latitude and longitude across China. Two-decadal AOD time series, averaged over all of mainland China, are presented and briefly discussed. Using the 17 years of ATSR data as the basis and MODIS/Terra to follow the temporal evolution in recent years when the environmental satellite Envisat was lost requires a compariPublished by Copernicus Publications on behalf of the European Geosciences Union.

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“…Not only fossil fuels but also changes in land use and agricultural emissions contribute to aerosol trends. While the anthropogenic emission changes mask possible climate effects influencing aerosol concentrations, in other regions like North Africa and Middle East, aerosol variations can be attributed to meteorological modifications of natural aerosol emissions [9]. The variability of aerosols from natural sources will become increasingly relevant for the aerosol mixture in regions where anthropogenic aerosol concentrations decrease.…”

Section: Introductionmentioning

confidence: 99%

Climate Feedback on Aerosol Emission and Atmospheric Concentrations

Tegen

Schepanski

2018

Curr Clim Change Rep

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Purpose of Review Climate factors may considerably impact on natural aerosol emissions and atmospheric distributions. The interdependencies of processes within the aerosol-climate system may thus cause climate feedbacks that need to be understood. Recent findings on various major climate impacts on aerosol distributions are summarized in this review. Recent Findings While generally atmospheric aerosol distributions are influenced by changes in precipitation, atmospheric mixing, and ventilation due to circulation changes, emissions from natural aerosol sources strongly depend on climate factors like wind speed, temperature, and vegetation. Aerosol sources affected by climate are desert sources of mineral dust, marine aerosol sources, and vegetation sources of biomass burning aerosol and biogenic volatile organic gases that are precursors for secondary aerosol formation. Different climate impacts on aerosol distributions may offset each other. Summary In regions where anthropogenic aerosol loads decrease, the impacts of climate on natural aerosol variabilities will increase. Detailed knowledge of processes controlling aerosol concentrations is required for credible future projections of aerosol distributions.

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AOD trends during 2001–2010 from observations and model simulations

Cited by 159 publications

References 83 publications

Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

Revised mineral dust emissions in the atmospheric chemistry–climate model EMAC (MESSy 2.52 DU_Astitha1 KKDU2017 patch)

Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns

Climate Feedback on Aerosol Emission and Atmospheric Concentrations

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