ALBERT

Description: Forest models are being increasingly used to study ecosystem functioning, through the reproduction of carbon fluxes and productivity in very different forests all over the world. Over the last two decades, the need for simple and "easy to use" models for practical applications, characterized by few parameters and equations, has become clear, and some have been developed for this purpose. These models aim to represent the main drivers underlying forest ecosystem processes while being applicable to the widest possible range of forest ecosystems. Recently, it has also become clear that model performance should not be assessed only in terms of accuracy of estimations and predictions, but also in terms of estimates of model uncertainties. Therefore, the Bayesian approach has increasingly been applied to calibrate forest models, with the aim of estimating the uncertainty of their results, and of comparing their performances. Some forest models, considered to be user-friendly, rely on a multiplicative or quasi-multiplicative mathematical structure, which is known to cause problems during the calibration process, mainly due to high correlations between parameters. In a Bayesian framework using a Markov Chain Monte Carlo sampling this is likely to impair the reaching of a proper convergence of the chains and the sampling from the correct posterior distribution. Here we show two methods to reach proper convergence when using a forest model with a multiplicative structure, applying different algorithms with different number of iterations during the Markov Chain Monte Carlo or a two-steps calibration. The results showed that recently proposed algorithms for adaptive calibration do not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest model used here. Moreover, the calibration remains time consuming and mathematically difficult, so advantages of using a fast and user-friendly model can be lost due to the calibration process that is needed to obtain reliable results.

Description: We present a multi-year database of atmospheric fields of the upper troposphere, stratosphere and lower mesosphere retrieved from satellite measurements adopting a 2-dimensional tomographic approach. The full mission of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument, on board the European Space Agency ENVISAT satellite, is analyzed with the Geofit Multi-Target Retrieval (GMTR) system to obtain the MIPAS2D database with atmospheric fields of pressure, temperature and volume mixing ratio of MIPAS main targets H2O, O3, HNO3, CH4, N2O, and NO2. The database covers both the MIPAS nominal observation mode measured at Full Resolution (FR) from July 2002 to March 2004 and the nominal observation mode of the new configuration, measured at Optimized Resolution (OR) and introduced in 2005. Further to the main targets, minor species N2O5, ClONO2, COF2, CFC-11, and CFC-12 for the FR mission only have been included in MIPAS2D to enhance its applicability in studies of stratospheric chemistry. The database is continuously updated with the analysis of the ongoing measurements that are planned to last until the end of 2013 and extended to other targets. The GMTR algorithm is operated on a fixed vertical grid coincident with the tangent altitudes of the FR nominal mode, spanning the altitude range from 6 to 68 km. In the horizontal domain, FR measurements are retrieved on both the observational grid and an equispaced 5 latitudinal-degrees grid which is made possible by the 2-dimensional retrieval algorithm. The analysis of MIPAS OR observations is operated on the same altitude-latitude fixed retrieval grid used for the FR measurements. This choice provides a database with a homogeneous altitude and latitude grid, over the whole globe, covering to date about seven years of measurements. The equispaced latitude grid provides a new and convenient layout for the much needed synergetic studies of data from various instrumental and modeling sources. MIPAS2D is available to the scientific community through the two web sites http://www.mbf.fci.unibo.it/mipas2d.html, and http://www.isac.cnr.it/~rss/mipas2d.htm