V. Pavan

Is the North Atlantic Oscillation a random walk

The North Atlantic Oscillation (NAO) is a major mode of large-scale climate variability which contains a broad spectrum of variations. There are substantial contributions from short-term 2–5 year variations, which have clearly marked teleconnections. Decadal trends are also apparent in the historical record of the NAO and may be due to either stochastic or deterministic processes. Evidence is presented that suggests the NAO exhibits ‘long-range’ dependence having winter values residually correlated over many years. Several simple stochastic models have been used to fit the NAO SLP (sea-level pressure) wintertime index over the period 1864–1998, and their performance at predicting the following year has been assessed. Long-range fractionally integrated noise provides a better fit than does either stationary red noise or a non-stationary random walk. Copyright

Climate of the Mediterranean: synoptic patterns, temperature, precipitation, winds and their extremes

This chapter considers a set of issues related to the synoptic climatology of the Mediterranean region (MR). The main Northern Hemisphere teleconnections affecting the MR and their role on temperature, precipitation, and atmospheric cyclones are described. The characteristics of the cyclones in the MR are presented. The role of teleconnections and atmospheric regimes on temperature and precipitation is discussed. The content includes extremes of temperature, precipitation, wind, and storminess (considering also marine aspects such as waves and storm surges).

Monitoring and Forecasting Drought on a Regional Scale: Emilia-Romagna Region

This chapter presents an overview of the occurrences and effects of droughts through a study of the Standard Precipitation Index (SPI) in the Emilia-Romagna region, which is located in the northern-central part of the Italian peninsula. The link between this index and large-scale atmospheric circulation was investigated and the SPI index was also used to predict drought. The study describes the development of a method of forecasting SPI index based on an earlier Interregional project (SEDEMED), involving a statistical downscaling scheme model using as input the large-scale seasonal forecasts obtained from Atmospheric Global Circulation Models. The downscaling scheme, which has already been used with relatively good results to predict surface parameters of temperature and precipitation, is applied to the SPI index, providing a statistical regionalization of this indicator

Statistically downscaled climate change projections of surface temperature over Northern Italy for the periods 2021–2050 and 2070–2099

Future changes of seasonal minimum and maximum temperature over Northern Italy are assessed for the periods 2021–2050 and 2070–2099 against 1961–1990. A statistical downscaling technique, applied to the ENSEMBLES-Stream1 and CIRCE global simulations (A1B scenario), is used to reach this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The set-up of the statistical scheme is done using large-scale fields (predictors) derived from ERA40 reanalysis and seasonal mean minimum and maximum temperature (predictands) derived from observational data at around 75 stations, distributed over Northern Italy, over the period 1960–2002. A similar technique is also applied to the number of frost days and ice days at a reduced number of stations in order to construct projections on change of the selected extreme temperature indices for the two future periods. The evaluation of future projections for these extreme indices is relevant due to its impacts on transports, health, and agriculture. The downscaling scheme constructed using observed data is then applied to large-scale fields simulated by global models (A1B scenario), in order to construct scenarios on future change of seasonal temperature, mean and extreme indices, at local scale. The significance of changes is tested from the statistical point of view. The results show that significant increases could be expected to occur under scenario conditions in both minimum and maximum temperature, associated with a decrease in the number of frost and ice days in both periods and more intense to the end of the century.

Downscaling of DEMETER winter seasonal hindcasts over Northern Italy

A novel method is applied in order to obtain winter predictions over Northern Italy using state-of-the-art multi-model seasonal ensemble hindcasts. The method consists of several stages. In the first stage, the best predictions are computed for a group of eight indices of large-scale circulation variability using the multi-model ensemble data set. The predictions are multiple linear regressions of single-model ensemble mean hindcasts produced within the European project DEMETER using six different coupled models. The regression is obtained using the method of the best linear unbiased estimate (BLUE). In the second stage, a standard statistical downscaling technique of the ‘perfect prog’ kind is applied in order to predict a group of 12 surface predictands starting from a group of predictors selected between the large-scale indices identified during the first stage. The selection of the predictands is carried out empirically, using those which lead to the best final prediction, while the regression coefficients are defined using observational data only, as in a ‘perfect prog’ downscaling technique. All steps of the prediction computation up to this point are performed in cross-validation mode. Finally, the full high-resolution surface winter predictions are reconstructed using an adequate selection of the forecasted predictands. The predictions obtained have a much higher detail than the DEMETER direct model output predictions and, in parts of the domain, they are characterized by substantially significant skill. The improvement of the skill with respect to single-model ensembles is due to the use of the BLUE technique, while the statistical downscaling allows us to increase significantly the detail of the prediction. The study includes a discussion on the sensitivity of the results to both the period in years and the number of models used to produce the forecasts, and a comparison with the results obtained using a simple multi-model forecast in which all models are given the same weight.

High resolution climate precipitation analysis for north-central Italy, 1961–2015

Observational daily precipitation data from a group of 1762 stations over north-central Italy and adjacent areas are used to produce a high resolution daily gridded precipitation analysis covering the period from 1961 to 2015. Input data are checked for quality, time consistency, synchronicity and statistical homogeneity and the final result has been used to describe the spatial and temporal variability of precipitation over the area. Data are interpolated using a modified Shepard scheme and the interpolation errors are compatible with those presented in Isotta et al. (Int J Climatol 34(5):1657–1675, 2014). The analysis is compared with other similar products available over the area considered, and differences and similarities are described, taking into account the impacts of different spatial resolution and time coverage. The data set is used to describe local climate with respect to precipitation, including mean values and seasonality, by using a group of climate annual and seasonal indices: cumulated precipitation, maximum number of consecutive dry days, frequency of wet days, mean precipitation intensity and 50th and 90th percentile of daily precipitation over a season. The linear trends over the full period of these indices are described and compared. It is shown that although the time series of area average total annual precipitation over north-central Italy does not show significant linear trends, these are present locally. In particular, significant negative trends of annual total precipitation are found in central Italy and in the inner part of northern plains, while significant positive linear trends are present in several areas over the Alps and over the Liguria coast. The seasons most affected by changes in precipitation are summer and autumn, which, in most areas, are the driest and wettest seasons. In summer, significant positive trends in total precipitation have been found in areas close to the northern national borders, while significant negative trends are located elsewhere. The number of wet days is significantly decreasing over most of the domain, but the 90th percentile of precipitation is significantly increasing over most of the Alpine area and northern Po Valley. Over the southern part of the Po Valley and central Italy summer precipitation is significantly becoming less frequent and, generally, less intense. In autumn, total precipitation is characterised by significant positive trends over large areas in Northern Italy and by significant negative trends in inner areas of the Central Apennines. The trend patterns present great similarities with those of the 90th percentile of daily precipitation for the same season. The maximum length of dry spell is significantly decreasing in autumn over most areas, including central Italy, while the number of wet days presents negative but mostly non significant trends over the whole domain.