Abstract. New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous Published by Copernicus Publications. M. Stengel et al.: Cloud_cci datasetsuncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.

Aim Mediterranean terrestrial ecosystems serve as reference laboratories for the investigation of global change because of their transitional climate, the high spatiotemporal variability of their environmental conditions, a rich and unique biodiversity and a wide range of socio-economic conditions. As scientific development and environmental pressures increase, it is increasingly necessary to evaluate recent progress and to challenge research priorities in the face of global change. Location Mediterranean terrestrial ecosystems.Methods This article revisits the research priorities proposed in a 1998 assessment.Results A new set of research priorities is proposed: (1) to establish the role of the landscape mosaic on fire-spread; (2) to further research the combined effect of different drivers on pest expansion; (3) to address the interaction between drivers of global change and recent forest management practices; (4) to obtain more realistic information on the impacts of global change and ecosystem services; (5) to assess forest mortality events associated with climatic extremes; (6) to focus global change research on identifying and managing vulnerable areas; (7) to use the functional traits concept to study resilience after disturbance; (8) to study the relationship between genotypic and phenotypic diversity as a source of forest resilience; (9) to understand the balance between C storage and water resources; (10) to analyse the interplay between landscape-scale processes and biodiversity conservation; (11) to refine models by including interactions between drivers and socio-economic contexts; (12) to understand forest-atmosphere feedbacks; (13) to represent key mechanisms linking plant hydraulics with landscape hydrology.Main conclusions (1) The interactive nature of different global change drivers remains poorly understood. (2) There is a critical need for the rapid development of regional-and global-scale models that are more tightly connected with largescale experiments, data networks and management practice. (3) More attention should be directed to drought-related forest decline and the current relevance of historical land use.

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Abstract. We present version 3 of the Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset, which contains a comprehensive set of cloud and radiative flux properties on a global scale covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by the afternoon (post meridiem – PM) satellites of the National Oceanic and Atmospheric Administration (NOAA) Polar Operational Environmental Satellite (POES) missions. The cloud properties in version 3 are of improved quality compared with the precursor dataset version 2, providing better global quality scores for cloud detection, cloud phase and ice water path based on validation results against A-Train sensors. Furthermore, the parameter set was extended by a suite of broadband radiative flux properties. They were calculated by combining the retrieved cloud properties with thermodynamic profiles from reanalysis and surface properties. The flux properties comprise upwelling and downwelling and shortwave and longwave broadband fluxes at the surface (bottom of atmosphere – BOA) and top of atmosphere (TOA). All fluxes were determined at the AVHRR pixel level for all-sky and clear-sky conditions, which will particularly facilitate the assessment of the cloud radiative effect at the BOA and TOA in future studies. Validation of the BOA downwelling fluxes against the Baseline Surface Radiation Network (BSRN) shows a very good agreement. This is supported by comparisons of multi-annual mean maps with NASA's Clouds and the Earth's Radiant Energy System (CERES) products for all fluxes at the BOA and TOA. The Cloud_cci AVHRR-PM version 3 (Cloud_cci AVHRR-PMv3) dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them. For the presented Cloud_cci AVHRR-PMv3 dataset a digital object identifier has been issued: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003 (Stengel et al., 2019).

Abstract. The Community Cloud retrieval for Climate (CC4CL) is a cloud property retrieval system for satellite-based multispectral imagers and is an important component of the Cloud Climate Change Initiative (Cloud_cci) project. In this paper we discuss the optimal estimation retrieval of cloud optical thickness, effective radius and cloud top pressure based on the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm. Key to this method is the forward model, which includes the clear-sky model, the liquid water and ice cloud models, the surface model including a bidirectional reflectance distribution function (BRDF), and the "fast" radiative transfer solution (which includes a multiple scattering treatment). All of these components and their assumptions and limitations will be discussed in detail. The forward model provides the accuracy appropriate for our retrieval method. The errors are comparable to the instrument noise for cloud optical thicknesses greater than 10. At optical thicknesses less than 10 modeling errors become more significant. The retrieval method is then presented describing optimal estimation in general, the nonlinear inversion method employed, measurement and a priori inputs, the propagation of input uncertainties and the calculation of subsidiary quantities that are derived from the retrieval results. An evaluation of the retrieval was performed using measurements simulated with noise levels appropriate for the MODIS instrument. Results show errors less than 10 % for cloud optical thicknesses greater than 10. Results for clouds of optical thicknesses less than 10 have errors up to 20 %.

Abstract. We present here the key features of the Community Cloud retrieval for CLimate (CC4CL) processing algorithm. We focus on the novel features of the framework: the optimal estimation approach in general, explicit uncertainty quantification through rigorous propagation of all known error sources into the final product, and the consistency of our long-term, multi-platform time series provided at various resolutions, from 0.5 to 0.02 • .By describing all key input data and processing steps, we aim to inform the user about important features of this new retrieval framework and its potential applicability to climate studies. We provide an overview of the retrieved and derived output variables. These are analysed for four, partly very challenging, scenes collocated with CALIOP (CloudAerosol lidar with Orthogonal Polarization) observations in the high latitudes and over the Gulf of Guinea-West Africa.The results show that CC4CL provides very realistic estimates of cloud top height and cover for optically thick clouds but, where optically thin clouds overlap, returns a height between the two layers. CC4CL is a unique, coherent, multiinstrument cloud property retrieval framework applicable to passive sensor data of several EO missions. Through its flexibility, CC4CL offers the opportunity for combining a variety of historic and current EO missions into one dataset, which, compared to single sensor retrievals, is improved in terms of accuracy and temporal sampling.

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Abstract. An evaluation of the ERA-Interim clouds using satellite observations is presented. To facilitate such an evaluation in a proper way, a simplified satellite simulator has been developed and applied to 6-hourly ERA-Interim reanalysis data covering the period of 1982 to 2014. The simulator converts modelled cloud fields, for example those of the ERA-Interim reanalysis, to simulated cloud fields by accounting for specific characteristics of passive imaging satellite sensors such as the Advanced Very High Resolution Radiometer (AVHRR), which form the basis of many long-term observational datasets of cloud properties. It is attempted to keep the simulated cloud fields close to the original modelled cloud fields to allow a quality assessment of the latter based on comparisons of the simulated clouds fields with the observations. Applying the simulator to ERA-Interim data, this study firstly focuses on the spatial distribution and frequency of clouds (total cloud fraction) and on their vertical position, using cloud-top pressure to express the cloud fraction of high-level, mid-level and low-level clouds. Furthermore, the cloud-top thermodynamic phase is investigated. All comparisons incorporate knowledge of systematic uncertainties in the satellite observations and are further stratified by accounting for the limited sensitivity of the observations to clouds with very low cloud optical thickness (COT). The comparisons show that ERA-Interim cloud fraction is generally too low nearly everywhere on the globe except in the polar regions. This underestimation is caused by a lack of mid-level and/or low-level clouds, for which the comparisons only show a minor sensitivity to the cloud optical thickness thresholds applied. The amount of ERA-Interim high-level clouds, being higher than in the observations, agrees with the observations within their estimated uncertainties. Removing the optically very thin clouds (COT <0.15) from the model fields improves the agreement with the observations for high-level cloud fraction locally (e.g. in the tropics), while for the mid-latitude regions, the best agreement for high-level cloud fraction is found when removing all clouds with COT <1.0. Comparisons of the cloud thermodynamic phase at the cloud top reveal a too high relative ice cloud frequency in ERA-Interim, being most pronounced in the higher latitudes. Indications are found that this is due to the suppression of liquid cloud occurrence for temperatures below −23 ∘C in ERA-Interim. The application of this simulator facilitates a more effective use of passive satellite observations of clouds in the evaluation of modelled cloudiness, for example in reanalyses.