Lars Klüser

Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci)

Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).

Vertical Profiling of Volcanic Ash from the 2011 Puyehue Cordón Caulle Eruption Using IASI

Volcanic ash is emitted by most eruptions, sometimes reaching the stratosphere. In addition to its climate effect, ash may have a significant impact on civilian flights. Currently, the horizontal distribution of ash aerosols is quite extensively studied, but not its vertical profile, while of high importance for both applications mentioned. Here, we study the sensitivity of the thermal infrared spectral range to the altitude distribution of volcanic ash, based on similar work that was undertaken on mineral dust. We use measurements by the Infrared Atmospheric Sounding Interferometer (IASI) instruments onboard the MetOp satellite series. The retrieval method that we develop for the ash vertical profile is based on the optimal estimation formalism. This method is applied to study the eruption of the Chilean volcano Puyehue, which started on the 4th of June 2011. The retrieved profiles agree reasonably well with Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP) measurements, and our results generally agree with literature studies of the same eruption. The retrieval strategy presented here therefore is very promising for improving our knowledge of the vertical distribution of volcanic ash and obtaining a global 3D ash distribution twice a day. Future improvements of our retrieval strategy are also discussed.

Benefits and limitations of the synergistic aerosol retrieval SYNAER

Air pollution by solid and liquid aerosol particles suspended in the air is one of the major concerns in developed countries because of potential health impact of increasing numbers of nano-particles in particular from diesel engines (see, e.g., [2002] and [2004]), as well as in developing countries with their high particle concentrations in the air. Furthermore, windblown dust can also act as carrier for long-range transport of diseases, e.g., from the Sahara to the Caribbean or Western Europe [Pohl, 2003], or even around the globe [Prospero et al., 2002]. Also well known in principle are direct (by reflecting light back to space) and several indirect (e.g., by acting as cloud condensation nuclei) climate effects of aerosols, although large uncertainties exist in the exact values of the forcing [IPCC, 2007]. Finally, the highly variable atmospheric aerosol load has a major impact on satellite observations of the Earth’s surface that need to be atmospherically corrected for quantitative analysis and on the solar irradiance which is exploited in solar energy applications (aerosols are the determining factor in clear-sky conditions). In all these cases an estimation of the type of aerosols is required for an accurate quantitative assessment. For example, [2002] point out, that the absorption behavior of particles (mainly soot and minerals) needs to be known in order to assess their total direct and indirect climate effects. This is because strongly absorbing particles can regionally reverse the sign of the aerosol direct forcing from cooling to heating or suppress cloud formation. Therefore, attempts have been made to extend satellite aerosol retrieval beyond observation of the spatial-temporal distribution patterns to estimate the type of aerosols.

Quantifying Uncertainty in Satellite-Retrieved Land Surface Temperature from Cloud Detection Errors

Clouds remain one of the largest sources of uncertainty in remote sensing of surface temperature in the infrared, but this uncertainty has not generally been quantified. We present a new approach to do so, applied here to the Advanced Along-Track Scanning Radiometer (AATSR). We use an ensemble of cloud masks based on independent methodologies to investigate the magnitude of cloud detection uncertainties in area-average Land Surface Temperature (LST) retrieval. We find that at a grid resolution of 625 km 2 (commensurate with a 0.25 ∘ grid size at the tropics), cloud detection uncertainties are positively correlated with cloud-cover fraction in the cell and are larger during the day than at night. Daytime cloud detection uncertainties range between 2.5 K for clear-sky fractions of 10–20% and 1.03 K for clear-sky fractions of 90–100%. Corresponding night-time uncertainties are 1.6 K and 0.38 K, respectively. Cloud detection uncertainty shows a weaker positive correlation with the number of biomes present within a grid cell, used as a measure of heterogeneity in the background against which the cloud detection must operate (e.g., surface temperature, emissivity and reflectance). Uncertainty due to cloud detection errors is strongly dependent on the dominant land cover classification. We find cloud detection uncertainties of a magnitude of 1.95 K over permanent snow and ice, 1.2 K over open forest, 0.9–1 K over bare soils and 0.09 K over mosaic cropland, for a standardised clear-sky fraction of 74.2%. As the uncertainties arising from cloud detection errors are of a significant magnitude for many surface types and spatially heterogeneous where land classification varies rapidly, LST data producers are encouraged to quantify cloud-related uncertainties in gridded products.

Large-Scale Analysis of Relationships between Mineral Dust, Ice Cloud Properties, and Precipitation from Satellite Observations Using a Bayesian Approach: Theoretical Basis and First Results for the Tropical Atlantic Ocean

Mineral dust and ice cloud observations from the Infrared Atmospheric Sounding Interferometer (IASI) are used to assess the relationships between desert dust aerosols and ice clouds over the tropical Atlantic Ocean during the hurricane season 2008. Cloud property histograms are first adjusted for varying cloud top temperature or ice water path distributions with a Bayesian approach to account for meteorological constraints on the cloud variables. Then, histogram differences between dust load classes are used to describe the impact of dust load on cloud property statistics. The analysis of the histogram differences shows that ice crystal sizes are reduced with increasing aerosol load and ice cloud optical depth and ice water path are increased. The distributions of all three variables broaden and get less skewed in dusty environments. For ice crystal size the significant bimodality is reduced and the order of peaks is reversed. Moreover, it is shown that not only are distributions of ice cloud variables simply shifted linearly but also variance, skewness, and complexity of the cloud variable distributions are significantly affected. This implies that the whole cloud variable distributions have to be considered for indirect aerosol effects in any application for climate modelling.

On the Information Content of Hyperspectral Infrared Observations with Respect to Mineral Dust

Desert dust is characterized by strong silicate absorption bands located within the atmospheric window region in the terrestrial infrared (TIR) between 8 µm and 12 µm. These absorption bands and the corresponding optical properties (extinction efficiency, single scattering albedo, scattering phase function) have very specific spectral shapes for different silicate minerals, modulated by the particle size and shape. The asphericity of desert dust particles strongly affects the absorption band characteristics, for example due to surface wave modes for small particles. The use of the correct particle shape model significantly increases the spectral correlation between simulated dust optical properties for typical minerals and corresponding laboratory measurements for single minerals as well as for bulk dust from desert samples. The presence of absorption peaks and the spectral shape of the extinction signal carry dust information, which can be exploited for remote sensing purposes. With hyperspectral infrared methods it is thus possible to infer information beyond dust optical depth, that is to acquire information about dust particle size, composition and also vertical information. Examples of such information are shown for the Infrared Mineral Aerosol Retrieval Scheme (IMARS) which has been developed for the Infrared Atmospheric Sounding Interferometer (IASI) on board the European Metop satellite series. Another strong advantage of the hyperspectral signal from satellite instruments is the capability to minimize the influence of disturbing gas absorption lines within these bands. The probabilistic IMARS approach also directly provides the number of independent signals (variables) for each observation. For desert dust this number typically ranges from 2.5 to 4.0 depending on the characteristics of the observed dust plume. Consequently a lot more information beyond Aerosol Optical Depth (AOD) can be retrieved from these measurements.