This study presents a new global gridded dataset of bioclimatic indicators at 0.5° by 0.5° resolution for historical and future conditions. The dataset, called CMCC-BioClimInd, provides a set of 35 bioclimatic indices, expressed as mean values over each time interval, derived from post-processing both climate reanalysis for historical period (1960–1999) and an ensemble of 11 bias corrected CMIP5 simulations under two greenhouse gas concentration scenarios for future climate projections along two periods (2040–2079 and 2060–2099). This new dataset complements the availability of spatialized bioclimatic information, crucial aspect in many ecological and environmental wide scale applications and for several disciplines, including forestry, biodiversity conservation, plant and landscape ecology. The data of individual indicators are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format.

This work tackles the problem of the automated detection of the atmospheric boundary layer (BL) height h, from aerosol lidar/ceilometer observations. A new method, the Bayesian selective method (BSM), is presented. It implements a Bayesian statistical inference procedure which combines in a statistically optimal way different sources of information. Firstly, atmospheric stratification boundaries are located from discontinuities in the ceilometer backscattered signal. The BSM then identifies the discontinuity edge that has the highest probability to effectively mark the BL height. Information from the contemporaneous physical boundary layer model simulations and a climatological dataset of BL height evolution are combined in the assimilation framework to assist this choice.The BSM algorithm has been tested for 4 months of continuous ceilometer measurements collected during the BASE:ALFA project, and is shown to realistically diagnose the BL depth evolution in many different weather conditions. A standard one‐dimensional processing of the ceilometer signal without the a priori support of the dynamical and climatological BL models often fails to correctly detect h, with the greatest inaccuracies occurring at night‐time when residual layers can generate very strong signals, which are then classified by an automated application of the gradient or of the wavelet analysis as the most probable BL height. The BSM approach instead carries information on the low climatological probability to find elevated BL depths at night and penalizes the selection of these points. Moreover, this method is able to correctly convey information along the temporal dimension, thus filling data gaps using earlier and subsequent ceilometer information for the retrieval. Copyright © 2011 Royal Meteorological Society

The LIDAR (laser radar) is an active remote sensing technique, which allows for the altitude-resolved observation of several atmospheric constituents. A typical application is the measurement of the vertically resolved aerosol optical properties. By using aerosol particles as a marker, continuous determination of the mixing layer height (MLH) can also be obtained by LIDAR. Some examples of aerosol extinction coefficient profiles and MLH extracted from a 1-year LIDAR data set collected in Milan (Italy) are discussed and validated against in situ data (from a balloon-borne optical particle counter). Finally a comparison of the observation-based MLH with relevant numerical simulations (mesoscale model MM5) is provided.