Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS) in the red spectral range

International audienceA new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms, our method analyses weak narrow-band reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric scattering and absorption are automatically corrected. The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the fitting coefficients for the vegetation spectra (indicating the fraction of the observed ground scene covered by vegetation) illustrate the seasonal cycle of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future

Dependence of cloud properties derived from spectrally resolved visible satellite observations on surface temperature

International audienceCloud climate feedback constitutes the most important uncertainty in climate modelling, and currently even its sign is still unknown. In the recently published report of the intergovernmental panel on climate change (IPCC), 6 out of 20 climate models showed a positive and 14 a negative cloud radiative feedback in a doubled CO2 scenario. The radiative budget of clouds has also been investigated by experimental methods, especially by studying the relation of satellite observed broad band shortwave and longwave radiation to sea surface temperature. Here we present a new method for the investigation of the dependence of cloud properties on temperature changes, derived from spectrally resolved satellite observations in the visible spectral range. Our study differs from previous investigations in three important ways: first, we directly extract cloud properties (effective cloud fraction and effective cloud top height) and relate them to surface temperature. Second, we retrieve the cloud altitude from the atmospheric O2 absorption instead from thermal IR radiation. Third, our correlation analysis is performed using 7.5 years of global monthly anomalies (with respect to the average of the same month for all years). For most parts of the globe (except the tropics) we find a negative correlation of effective cloud fraction versus surface-near temperature. In contrast, for the effective cloud top height a positive correlation is found for almost the whole globe. Both findings might serve as an indicator for an overall positive cloud radiative feedback. Another peculiarity of our study is that the cloud-temperature relationships are determined for fixed locations (instead to spatial variations over selected areas) and are based on the "natural" variability over several years (instead the anomaly for a strong El-Nino event). From a detailed comparison to cloud properties from the International Satellite Cloud Climatology Project (ISCCP), in general good agreement is found. However, also systematic differences occurred indicating that our results provide independent and complementary information on cloud properties. Climate models should thus aim to reproduce our findings. Recommendations for the development of a "processor" to convert model results into the cloud sensitive quantities observed by the satellite are given

Satellite monitoring of different vegetation types by differential optical absorption spectroscopy (DOAS) in the red spectral range

A new method for the satellite remote sensing of different types of vegetation and ocean colour is presented. In contrast to existing algorithms relying on the strong change of the reflectivity in the red and near infrared spectral region, our method analyses weak narrow-band (few nm) reflectance structures (i.e. "fingerprint" structures) of vegetation in the red spectral range. It is based on differential optical absorption spectroscopy (DOAS), which is usually applied for the analysis of atmospheric trace gas absorptions. Since the spectra of atmospheric absorption and vegetation reflectance are simultaneously included in the analysis, the effects of atmospheric absorptions are automatically corrected (in contrast to other algorithms). The inclusion of the vegetation spectra also significantly improves the results of the trace gas retrieval. The global maps of the results illustrate the seasonal cycles of different vegetation types. In addition to the vegetation distribution on land, they also show patterns of biological activity in the oceans. Our results indicate that improved sets of vegetation spectra might lead to more accurate and more specific identification of vegetation type in the future

Dependence of cloud fraction and cloud top height on surface temperature derived from spectrally resolved UV/vis satellite observations

International audienceCloud climate feedback constitutes the most important uncertainty in climate modelling, and currently even its sign is still unknown. In the recently published report of the intergovernmental panel on climate change (IPCC), from 20 climate models 6 showed a positive and 14 a negative cloud radiative feedback in a doubled CO2 scenario. The radiative budget of clouds has also been investigated by experimental methods, especially by studying the relation of satellite observed broad band shortwave and longwave radiation to sea surface temperature. Here we present a new method for the investigation of the dependence of cloud properties on temperature changes, derived from spectrally resolved UV/vis satellite observations. Our study differs from previous investigations in three important ways: first, we directly extract cloud properties (amount and altitude) and relate them to surface temperature. Second, we retrieve the cloud altitude from the atmospheric O2 absorption instead from thermal IR radiation. Third, our correlation analysis is performed using 7.5 years of global monthly anomalies (with respect to the average of the same month for all years). For most parts of the globe (except the tropics) we find a negative correlation of cloud fraction versus surface-near temperature. In contrast, for the cloud top height a positive correlation is found for almost the whole globe. Both findings might serve as an indicator for an overall positive cloud climate feedback. Another peculiarity of our study is that the cloud-temperature relationships are determined for fixed locations (instead to spatial variations over selected areas) and are based on the "natural" variability over several years (instead the anomaly for a strong El-Nino event). Thus our results might be especially representative for the extrapolation to long term climate changes. Climate models should aim to reproduce our findings: if substantial differences are found, this might indicate that important details are not yet well captured by these models. If good agreement is found, from the models reliable information on the magnitude and the detail mechanisms of cloud climate feedback could be gained

Satellite measurements of formaldehyde linked to shipping emissions

International shipping is recognized as a pollution source of growing importance, in particular in the remote marine boundary layer. Nitrogen dioxide originating from ship emissions has previously been detected in satellite measurements. This study presents the first satellite measurements of formaldehyde (HCHO) linked to shipping emissions as derived from observations made by the Global Ozone Monitoring Experiment (GOME) instrument. <br><br> We analyzed enhanced HCHO tropospheric columns from shipping emissions over the Indian Ocean between Sri Lanka and Sumatra. This region offers good conditions in term of plume detection with the GOME instrument as all ship tracks follow a single narrow track in the same east-west direction as used for the GOME pixel scanning. The HCHO signal alone is weak but could be clearly seen in the high-pass filtered data. The line of enhanced HCHO in the Indian Ocean as seen in the 7-year composite of cloud free GOME observations clearly coincides with the distinct ship track corridor from Sri Lanka to Indonesia. The observed mean HCHO column enhancement over this shipping route is about 2.0×10<sup>15</sup> molec/cm<sup>2</sup>. <br><br> Compared to the simultaneously observed NO<sub>2</sub> values over the shipping route, those of HCHO are substantially higher; also the HCHO peaks are found at larger distance from the ship routes. These findings indicate that direct emissions of HCHO or degradation of emitted NMHC cannot explain the observed enhanced HCHO values. One possible reason might be increased CH<sub>4</sub> degradation due to enhanced OH concentrations related to the ship emissions, but this source is probably too weak to fully explain the observed values. <br><br> The observed HCHO pattern also agrees qualitatively well with results from the coupled earth system model ECHAM5/MESSy applied to atmospheric chemistry (EMAC). However, the modelled HCHO values over the ship corridor are two times lower than in the GOME high-pass filtered data. This might indicate uncertainties in the satellite data and used emission inventories and/or that the in-plume chemistry taking place in the narrow path of the shipping lanes are not well represented at the rather coarse model resolution

IMPACT OF CLOUDS ON TROPOSPHERIC TRACEGAS RETRIEVALS

ABSTRACT The sensitivity of nadir-viewing satellite observations for tropospheric columns is strongly affected by clouds: Clouds shield the column below, normally resulting in an underestimation of the actual total column. On the other hand, the high albedo of clouds, as well as multiple scattering within the cloud, increase the visibility of trace gases at and above the cloud top. The observed Tropospheric Slant Column Densities (TSCDs) are thus sensitive to the NO 2 profile. Here we analyze the empirical dependency of tropospheric NO 2 slant column densities (TSCDs) on cloud fractions. The observed ratio of TSCDs for cloudfree versus clouded scenes shows strong regional variations; high values are observed over sources, where the NO 2 is close to the ground. Downwind from the sources, the ratio decreases, indicating a modification of the NO 2 profile

Global Monitoring of Atmospheric Trace Gases, Clouds and Aerosols from UV/vis/NIR Satellite Instruments: Currents Status and Near Future Perspectives

A new generation of UV/vis/near‐IR satellite instruments like GOME (since 1995), SCIAMACHY (since 2002), OMI (since 2004), and GOME‐2 (since 2006) allows to measure several important stratospheric and tropospheric trace gases like O_3, NO_2, OClO, HCHO, SO_2, BrO, and H_2O as well as clouds and aerosols from space. Because of its extended spectral range, the SCIAMACHY instrument also allows the retrieval of Greenhouse gases (CO_2, CH_4) and CO in the near IR. Almost all of the tropospheric trace gases are observed by these instruments for the first time. From satellite data it is possible to investigate the temporal and spatial variation. Also different sources can be characterised and quantified. The derived global distributions can serve as input and for the validation of atmospheric models. Here we give an overview on the current status of these new instruments and data products and their recent applications to various atmospheric and oceanic phenomena

Global Monitoring of Atmospheric Trace Gases, Clouds and Aerosols from UV/vis/NIR Satellite Instruments: Currents Status and Near Future Perspectives

A new generation of UV/vis/near‐IR satellite instruments like GOME (since 1995), SCIAMACHY (since 2002), OMI (since 2004), and GOME‐2 (since 2006) allows to measure several important stratospheric and tropospheric trace gases like O_3, NO_2, OClO, HCHO, SO_2, BrO, and H_2O as well as clouds and aerosols from space. Because of its extended spectral range, the SCIAMACHY instrument also allows the retrieval of Greenhouse gases (CO_2, CH_4) and CO in the near IR. Almost all of the tropospheric trace gases are observed by these instruments for the first time. From satellite data it is possible to investigate the temporal and spatial variation. Also different sources can be characterised and quantified. The derived global distributions can serve as input and for the validation of atmospheric models. Here we give an overview on the current status of these new instruments and data products and their recent applications to various atmospheric and oceanic phenomena

The Heidelberg iterative cloud retrieval utilities (HICRU) and its application to GOME data

International audienceInformation about clouds, in particular the accurate identification of cloud free pixels, is crucial for the retrieval of tropospheric vertical column densities from space. The Heidelberg Iterative Cloud Retrieval Utilities (HICRU) retrieve effective cloud fraction using spectra of two instruments designed for trace gas retrievals from space: The Global Ozone Monitoring Experiment (GOME) on the European Remote Sensing Satellite (ERS-2) and the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) on ENVISAT. HICRU applies the widely used threshold method to the so-called Polarization Monitoring Devices (PMDs) with higher spatial resolution compared to the channels used for trace gas retrievals. Cloud retrieval and in particular the identification of cloud free pixels is improved by HICRU through a sophisticated, iterative retrieval of the thresholds which takes their dependency on different instrumental and geometrical parameters into account. The lower thresholds, which represent the surface albedo and strongly affect the results of the algorithm, are retrieved accurately through a four stage classification scheme using image sequence analysis. The design and the results of the algorithm applied to GOME data are described and compared to several other cloud algorithms for GOME. The differences to other cloud algorithms are discussed with respect to the particular characteristics of the algorithms