External forcings and predictability in Lorenz model: An analysis via neural network modelling

What’s about predictability in future climate scenarios? At present, we have no answer to this question in realistic climate models, due to the need of a difficult and time-consuming analysis. So, in the present paper an investigation of this situation has been performed through low-dimensional models, by considering unforced and forced Lorenz systems as toy-models. By coupling dynamical and neural network analyses, some clear results are achieved: for instance, an increase of mean predictability in forced situations (which simply mimic the actual increase of anthropogenic forcings in the real system) is discovered. In particular, the application of neural network modelling to this problem supplies us with some “surplus” information and opens new prospects as far as the operational assessment of predictability is concerned

Perception and risk of Covid-19 and climate change: Investigating analogies in a common framework

This paper investigates analogies in the dynamics of Covid-19 pandemic and climate change. A comparison of their common features (such as nonlinearity and inertia) and differences helps us to achieve a correct scientific perception of both situations, increasing the chances of actions for their solutions. Besides, applying to both the risk equation provides different angles to analyse them, something that may result useful especially at the policy level. It shows that not only short-term interventions are needed, but also long-term strategies involving some structural changes. More specifically, it also shows that, even if climate change is probably more critical and long-lasting than the Covid-19 crisis, we still have, at least currently, more options for reducing its related risk

Evidence of changes in diffusive properties over Italy during the period November 2006-April 2007: A case study

Extended winter 2006-2007 and April 2007 have been characterised by strong positive anomalies in temperature and pressure fields over Italy and part of the European continent. In this framework, first evidence of the influence of this situation on boundary layer diffusive properties is shown here. This is achieved by estimations of Pasquill’s classes from surface observations at Rome-Fiumicino meteorological station and an analysis of their frequencies of occurrence vs. a 33-year local diffusive climatology

Eventi estremi in un mondo più caldo

It is now well established that recent global warming (that of the last half century) is unprecedented in the last two thousand years (Neukom et al., 2019) and that it is driven to a very large extent by human actions, in terms of greenhouse gas emissions and deforestation. These results are very robust, as they are derived from the convergence of countless observations and diverse and independent models (Mazzocchi & Pasini, 2017).È ormai assodato come il riscaldamento globale recente (quello dell’ultimo mezzo secolo) non abbia precedenti negli ultimi duemila anni (Neukom et al., 2019) e come esso sia pilotato in grandissima parte dalle azioni umane, in termini di emissioni di gas serra e di deforestazione. Questi risultati sono molto robusti, poiché derivano dalla convergenza di innumerevoli osservazioni e di modelli diversi e indipendenti tra loro (Mazzocchi & Pasini, 2017).È ormai assodato come il riscaldamento globale recente (quello dell’ultimo mezzo secolo) non abbia precedenti negli ultimi duemila anni (Neukom et al., 2019) e come esso sia pilotato in grandissima parte dalle azioni umane, in termini di emissioni di gas serra e di deforestazione. Questi risultati sono molto robusti, poiché derivano dalla convergenza di innumerevoli osservazioni e di modelli diversi e indipendenti tra loro (Mazzocchi & Pasini, 2017)

A neural-network approach to radon short-range forecasting from concentration time series

The relevance of particulate radon progeny measurements for an estimation of the mixing height was recently established. Here, an attempt at a shortrange forecast of radon concentration is presented using a neural-network model applied at a 2-hour based time series. This forecasting activity leads to useful predictions of the mixing height during stability conditions

Neural network modelling for the analysis of forcings/temperatures relationships at different scales in the climate system

Abstract A fully non-linear analysis of forcing influences on temperatures is performed in the climate system by means of neural network modelling. Two case studies are investigated, in order to establish the main factors that drove the temperature behaviour at both global and regional scales in the last 140 years. In particular, our neural network model shows the ability to catch non-linear relationships among these variables and to reconstruct temperature records with a high degree of accuracy. In this framework, we clearly show the need of including anthropogenic inputs for explaining the temperature behaviour at global scale and recognise the role of El Nino southern oscillation for catching the inter-annual variability of temperature data. Furthermore, we analyse the relative influence of global forcing and a regional circulation pattern in determining the winter temperatures in Central England, showing that the North Atlantic oscillation represents the driven element in this case study. Our modelling activity and results can be very useful for simple assessments of relationships in the complex climate system and for identifying the fundamental elements leading to a successful downscaling of atmosphere–ocean general circulation models

Non-linear atmospheric stability indices by neural-network modelling

New atmospheric stability indices have been recently developed for the evaluation of primary pollution and the application results show their ability to grasp the physical features of the boundary layer. They are based on radon progeny measurements and multiple linear correlations with benzene. Here, neural networks are used in order to catch non-linearities in the boundary layer and to build nonlinear indices. Their application to the modelling of benzene behaviour shows better prognostic results if compared with those coming from linear indices

Has natural variability a lagged influence on global temperature? A multi-horizon Granger causality analysis

At present, the role of natural variability in influencing climate behaviour is widely discussed. The generally accepted view is that atmosphere-ocean coupled circulation patterns are able to amplify or reduce temperature increase from interannual to multidecadal time ranges, leaving the principal driving role to anthropogenic forcings. In this framework, the influence of these circulation patterns is considered synchronous with global temperature changes. Here, we would like to investigate if there exists a lagged influence of these indices on temperature. In doing so, an extension of the Granger causality technique, which permits to test both direct and indirect causal influences, is applied. A lagged influence of natural variability is not evident in our analysis, if we except weak influences of some peculiar circulation indices in specific periods

Linear and nonlinear influences of climatic changes on migration flows: a case study for the 'Mediterranean bridge'

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Clarifying the Roles of Greenhouse Gases and ENSO in Recent Global Warming through Their Prediction Performance

Abstract It is well known that natural external forcings and decadal-to-millennial variability drove changes in the climate system throughout the Holocene. Regarding recent times, attribution studies have shown that greenhouse gases (GHGs) determined the trend of temperature (T) in the last half century, while circulation patterns contributed to modify its interannual, decadal, or multidecadal behavior over this period. Here temperature predictions based on vector autoregressive models (VARs) have been used to study the influence of GHGs and El Niño–Southern Oscillation (ENSO) on recent temperature behavior. It is found that in the last decades of steep temperature increase, ENSO shows just a very short-range influence on T, while GHGs are dominant for each forecast horizon. Conversely and quite surprisingly, in the previous quasi-stationary period the influences of GHGs and ENSO are comparable, even at longer range. Therefore, if the recent hiatus in global temperatures should persist into the near future, an enhancement of the role of ENSO can be expected. Finally, the predictive ability of GHGs is more evident in the Southern Hemisphere, where the temperature series is smoother