No abstract available

[en] The awareness of the importance of data quality and homogeneity issues in the correct detection of climate change has increased rapidly in the last few years. Most of the contributions have been addressed to upper air data, however errors and inhomogeneities also concern surface ones. At surface level it is often assumed that such inhomogeneities have random distribution and that, considering a sufficiently large number of series, average records with negligible bias can be obtained. This assumption is likely to be correct if global or hemispheric averages are considered, but it may not be correct at a regional scale. The aim of the work is a rigorous reconstruction of the Italian climate for the last centuries (the longest series start in the late 1700s), with particular attention to the identification of spurious non-climatic signals introduced by changing instruments and methods in the measurement procedures. A data set of 111 precipitation series, 48 minimum and maximum temperature series and 67 mean temperature series was set up, together with the information about the station history (metadata). The records were subjected to a detailed quality control and homogenisation procedure that was extensively supported by a large metadata availability. The series were grouped by means of Principal Component Analysis and regional average records were obtained and analysed for trends. Trend analysis was performed on seasonal and annual basis by means of the progressive Mann-Kendall statistics and the progressive analysis of the linear regression coefficients. A comparison between the homogenized and the original series and the preliminary results of the analysis are presented. Particular emphasis is given to stress the importance of data homogenisation in the correct detection of long-term trends

[en] Venice risks to be submerged as a consequence of two problems: local land subsidence and sea level rise due to global warming. They both contribute to what is referred as Apparent Sea Level Rise (ASLR). Flooding Tides (locally: Acqua Alta) submerge Venice with an exponentially increasing frequency. The Acqua Alta is generated by a number of factors, the main of them being the Sirocco wind blowing over the Adriatic Sea, that ultimately displaces waters towards Venice. These extreme events have been investigated by using the documentary description of past floods, accurately reported over the last millennium, and tide gauge records for the recent period. A fundamental problem is to know the trend of the ASLR, possibly distinguishing between land subsidence and sea level components. Instrumental data go back to 1872 and a key point is to extend our knowledge back in time. Long-term ASLR has been investigated with the help of a biological indicator, the height of the green belt of the algae that live in the tidal range and whose upper front shows the average high tide level. Fortunately, in the first half of the 18. century, this indicator was accurately drawn by the famous painter Antonio Canaletto (1697-1768) and his pupils, mainly Bernardo Bellotto (1722-1780), in their photographic paintings made with an optical 'camera obscura'. It has been possible to compare the tidal level, as it was in the 1700s and today. After careful spot investigation and minor corrections for some changes to the hydrological system occurred in the meantime, the bulk submersion of Venice estimated from the paintings is 61±11 cm with average yearly trend 1.9 mm y^-1

[en] This study examines the subset climate model ensemble size required to reproduce certain statistical characteristics from a full ensemble. The ensemble characteristics examined are the root mean square error, the ensemble mean and standard deviation. Subset ensembles are created using measures that consider the simulation performance alone or include a measure of simulation independence relative to other ensemble members. It is found that the independence measure is able to identify smaller subset ensembles that retain the desired full ensemble characteristics than either of the performance based measures. It is suggested that model independence be considered when choosing ensemble subsets or creating new ensembles. (letter)

[en] Analyses of observations show correlations between the mean state of indices representing either the North Atlantic Oscillation (NAO) or the Arctic Oscillation (AO, also called Northern Annular Mode) with various external forcings. These include volcanic eruptions, solar variability, greenhouse gas levels and stratospheric ozone depletion. Climate model simulations have been able to reproduce many aspects of these correlations over a variety of time scales ranging from interannual to century scale. This has allowed some insight to be gained into how external forcing modulate these intrinsic variability patterns. I review current understanding derived from comparisons of a range of models with observations to highlight areas of commonality as well as remaining uncertainties. Contrasts between the Northern and Southern Hemispheres suggest that much of the response to external forcing occurs via wave-driven processes. Comparison of the response to forcing at different levels in the atmosphere indicate a sizeable role for both stratospheric and surface-level perturbations. Implications for forcing of changes in Mediterranean climate are presented for pre-industrial times during the last millennium, for the twentieth century, and for the potential future

[en] We describe briefly here the main mechanisms and time scales involved in natural and anthropogenic climate variability, based on quantitative paleo-climatic reconstructions from natural archives and climate model simulations: the large glacial-interglacial cycles of the last million years (the Quaternary), lasting typically a hundred thousand years, triggered by changes in the solar radiation received by the Earth due to its position around the Sun; the century-long climatic changes occurring during last glacial period and triggered by recurrent iceberg discharges of the large northern hemisphere ice caps, massive freshwater flux to the north Atlantic, and changes in the ocean heat transport. We show the strong coupling between past climatic changes and global biogeochemical cycles, namely here atmospheric greenhouse gases. We also discuss the decadal climatic fluctuations during the last thousand years, showing an unprecedented warming attributed to the anthropogenic greenhouse gas emissions. We show the range of atmospheric greenhouse concentrations forecasted for the end of the 21. century and the climate model predictions for global temperature changes during the 21. century. We also discuss the possible climatic changes at longer time scales involving the possibility of north Atlantic heat transport collapse (possibility of abrupt climate change), and the duration of the current interglacial period. (author)

[en] To date the Intergovernmental Panel on Climate Change (IPCC) has concerned itself with gathering a state of the art review of the science of climate change. While significant progress has been made in enhancing our integrated understanding of the climate system and the dynamics of the social systems that produce an array of potential greenhouse gases, it is also clear from the panel's reports how far the science community is from being able to present a dynamic and synoptic view of the climate system as a whole. Clear evidence of these complexities and uncertainties inherent in the climate system is evident in efforts aimed at designing robust policy interventions. In this paper, we argue that the adaptive management framework in ecosystem management may be a useful model for guiding how the IPCC can continue to be relevant both as a scientific establishment and as a policy-relevant scientific endeavor

[en] Data from a 500-year preindustrial control run of climate model INMCM4 show distinct climate variability in the Arctic and North Atlantic with a period of 35–50 years. The variability can be seen as anomalies of upper ocean density that appear in the Arctic and propagate to the North Atlantic. The density gradient in a northeast–southwest direction alternates with the density gradient in a northwest–southeast direction. A positive density anomaly in the Arctic is associated with a positive salinity anomaly, a positive surface temperature anomaly and a reduction of sea ice in the Barents and Kara Seas. The nature of the variability is a vertical advection of density by thermal currents similar to that proposed in Dijkstra et al (2008 Phil. Trans. R. Soc. A 366). The cycle of model variability shows that after a negative anomaly of density in the northwest Atlantic, one should expect warming in the Arctic in 5–10 years. The ensemble of decadal predictions with climate model INMCM4 starting from 1995 shows that warming in the western Arctic and especially in the Barents Sea observed in 1996–2010 can be reproduced by eight of ten ensemble members. Arctic climate predictability in this case is associated with a proposed mechanism of a 35–50 year North Atlantic–Arctic oscillation. (letter)

[en] Tropical precipitation and the character of its adjustment in response to climate warming have been examined in an ensemble of climate models. Partitioning the 500 hPa pressure velocity, ω, into four basic dynamical regimes reveals that areas which exhibit a reversal of ω from descent to ascent make a disproportionately large contribution to the total precipitation change. The four regimes' occurrences are remarkably consistent across the ten models considered but the inter-model spread of some of the precipitation changes is very large. This large variation is, however, primarily due to two of the models, IPSL and CCSM3. A further separation into 'dynamic' and 'thermodynamic' changes confirms that the inter-model spread in precipitation is related to variations in the dynamical responses of the models. The reliability of models for climate change studies can to some extent be gauged by their ability to represent present day climate variability. An example, using interannual variability, is presented for the Hadley Centre model, HadGEM1. This highlights potential strengths and weaknesses of the model regarding simulation of the relationships between precipitation, surface temperature, and the large-scale circulation.

No abstract available