Aerosols / Clouds 2

Measuring the flow of radiant energy through the Earth’s climate system is essential to understanding the forcers of global climate change. A broadband radiative flux product has been developed jointly by the University of Oxford and RAL Space under the ESA Climate Change Initiative (CCI) programme to quantify the global cloud radiative effect (CRE) and how CRE has changed over 17 years of (A)ATSR (Advanced Along Track Scanning Radiometer) satellite observations. CRE is a key climate variable that is a measure of the effect of clouds on the reflected solar broadband flux relative to the clear-sky flux.

Fluxes are computed in the post-processing step of ORAC (Optimal Retrieval of Aerosol and Cloud) using BUGSrad (Stephens et al. 2001). BUGSrad is based on the two-stream approximation and correlated-k distriubition methods of atmospheric radiative transfer over 18 spectral bands (i.e., 6 shortwave and 12 longwave). The ORAC scheme, developed under aerosol CCI and cloud CCI programmes, ensures a radiatively consistent set of cloud and aerosol retrieved properties because it performs the retrieval using all available instrument channels simultaneously.  Cloud and aerosol retrievals are then ingested into BUGSrad to compute both shortwave and longwave radiative fluxes for the top and bottom of atmosphere for cloud and clear sky conditions. This presentation will provide an overview of the new product and the first assessment of CRE using (A)ATSR satellite series.

An important step during the retrieval of atmospheric trace gases is the determination of the presence or absence of clouds and the characterization of their extent, usually expressed as a cloud fraction, because clouds have a significant influence on the accuracy of the trace gas retrieval. In contrast to many cloud detection algorithms working on sounders or on imagers in the infra-red or micrometer range, the Optical Cloud Recognition Algorithm (OCRA) is developed for sensors operating in the UV/VIS part of the spectrum, as covered by e.g. Sentinel-4, -5 or -5P. By design, OCRA can be applied to both, broad band and narrow optical wavelength ranges. Due to its RGB color space approach, a basic requirement for OCRA to be applied is that there is information available from the blue, green and red parts of the optical spectral range. The basic idea behind OCRA is to split the measurement of a scene into contributions of clouds and a cloud-free background, i.e. the reflectance in the absence of clouds.

OCRA uses the general assumption that clouds have a higher reflectivity than the surrounding ground in all optical wavelengths. In the optical part, the cloud reflectivity is almost wavelength-independent and therefore clouds appear “white” in the normalized RGB color space since all three components are represented with the same amount. The scene which is furthest away from the “white” situation is the one where we expect the least possible amount of cloud contamination. Joining all these scenes on a global grid then provides the cloud-free background map, which is usually based on several years of data. The comparison of a measured reflectance with the corresponding predetermined cloud free reflectance can then be used to derive a radiometric cloud fraction for this given scene.

With respect to earlier versions, OCRA now also includes degradation corrections for the reflectances as well as corrections for viewing zenith angle dependencies and latitudinal and seasonal dependencies.

OCRA is well established for polar orbiting sensors like GOME, SCIAMACHY and GOME-2. An adaptation of OCRA to the polar orbiting Sentinel-5 and -5P is more or less straightforward, while it is more challenging to adapt to the Sentinel-4 geostationary geometry since there is no OCRA heritage for this type of orbital geometry.

Clouds and aerosols remain two of the largest uncertainties in the interpretation of the changing climate. A 17 year record of cloud and aerosol properties has been produced from the ATSR (Along Track Scanning Radiometer) series of instruments using the ORAC (Optimal retrieval of Aerosol and Cloud)/CC4CL (Community Cloud code for Climate) algorithm within the ESA cloud and aerosol CCI (Climate Change Initiative) program.  The ATSR series of instruments is well calibrated with  stable orbits which makes it well suited for analysing the climate. The data has been produced using similar optimal estimation algorithms to maximise consistency in  the  products with respect to  technique, uncertainty, radiative properties and cloud and aerosol identification. I will describe the products produced and the associated uncertainty and review how successful we have been in developing a consistent cloud and aerosol record and why this is important for climate records. I will show the latest analysis of climate trends from the aerosol and cloud records and comment on the significance and correlation with phenomena such as ENSO and examine the potential to evaluate aerosol and cloud interactions over a long time period. Finally I will touch on future developments and application to SLSTR.

In 2010 the ESA Climate Change Initiative (CCI) Cloud project was started along with 12 other CCI projects covering atmospheric, oceanic and terrestrial ECV data products. The main goal is the generation of satellite-based climate data records that meet the challenging requirements of the Global Climate Observing System (GCOS). The objective target within the ESA Cloud_cci project is the investigation of synergetic capabilities of past, existing and upcoming European and American satellite missions and thus, the generation of long-term coherent cloud property datasets. The cloud properties considered are cloud mask, cloud top estimates, cloud optical thickness, cloud effective radius and post processed parameters such as cloud liquid and ice water path.

 The ESA’s Climate Change Initiative is reprocessing and reassessing over 40 years of multi-sensor satellite records to generate consistent, traceable, long-term datasets of “essential climate variables” for the climate modeling and research communities. The Global Climate Observing System (GCOS) has set out requirements for satellite data to meet the needs of climate science, designating key variables that are currently feasible for observation and important to the United Nations Framework Convention on Climate Change (UNFCCC) as “essential climate variables” (ECVs). According to GCOS specifications, ECV data products are to be designed to provide information about the state of the global climate system and to enable long-term climate monitoring. However, ECV data are applied for various purposes, each of which has its own requirements for temporal resolution. At the same time, products must be made consistent when different instruments contribute observations toward an ECV. The ESA CCI intends to meet these challenging requirements of the climate community. Hereby, it is crucial to have a transparent, traceable, and sustainable process during algorithm development and dataset production. CCI foci are to provide products for climate modelers, data records for budget closure studies, and the provision of uniformly processed datasets. In this context, the ESA Cloud_cci project abides to and contributes scientific advancements in all of the aforementioned aspects.

Here, we will examine how the newly produced ESA Cloud_cci dataset fulfills the expected product improvements and GCOS requirements as mentioned above in terms of (1) realizing a combination of both European/ESA and U.S. instruments, (2) implementing an optimal estimation retrieval scheme applied to different instruments to produce a long time series, (3) providing improved spectral consistency using a five AVHRR-heritage channels, (4) calibrating level-1 data with advanced sensors, and (5) producing a 1982-2014 long term dataset.

Clouds scatter solar radiation back into space as well as preventing terrestrial radiation from leaving the atmosphere. For global climate changes cloud top height, cloud coverage, cloud optical thickness and cloud droplet size are important properties. For the prediction of climate change through models a global knowledge of cloud properties is required.

The cloud top pressure can be estimated with MERIS (Medium Resolution Imaging Spectrometer) and OLCI (Ocean Land Color Instrument on-board ESA's Sentinel-3 satellite) utilizing measurements in the O₂ A-band at 760nm.

The O₂ A-band method is based on the ratio of measured radiance in an absorption channel and a window channel. The procedure was already successfully applied to MERIS observations and is part of the standard L2 products. In contrast to MERIS, OLCI has three channels within the O₂ absorption band: channel 12 (761.25nm), channel 13 (764.375nm) and channel 14 (773,75nm).

On the basis of radiative transfer calculations, using the Matrix Operator Method (MOMO), sensitivity studies will be presented in order to quantify the additional information about cloud properties available from the additional channels. However recent studies have already shown, that the cloud top pressure retrieval issensitive towards the assumed vertical profile of cloud extinction. At the same time the retrievable information about the vertical structure of clouds with passive imagers is limited.

In preparation for the Sentinel-3 we introduce a procedure to estimate the vertical profile of cloud extinction through a cloud classification by spatial heterogeneity. Therefore, the grey level co-occurence matrix (glcm) is retrieved for a sub-image of appropriate size. The glcm is then the basis to calculate textural parameters as heterogeneity, contrast, dissimilarity and energy for the central pixel of the sub-image. A random forest classifier uses the texture measures as basis to assign a cloud class for each individual pixel. For each cloud class an average vertical extinction profile is retrieved from CloudSat CPR measurements. The procedure is developed and tested with MODIS Aqua Level 1B calibrated radiances for bands 1,17 and 32. A global selection of test cases was chosen to cover cases from oceanic surfaces over vegetation as well as highly reflecting ground such as snow/ice and deserts.

This procedure is developed and will be applied in the framework of the ESA Cloud CCI project.