| 1. |
- De Luca, Paolo, et al.
(författare)
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Compound warm-dry arid cold-wet events over the Mediterranean
- 2020
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Ingår i: Earth System Dynamics. - : Copernicus GmbH. - 2190-4979 .- 2190-4987. ; 11:3, s. 793-805
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Tidskriftsartikel (refereegranskat)abstract
- The Mediterranean (MED) Basin is a climate change hotspot that has seen drying and a pronounced increase in heatwaves over the last century. At the same time, it is experiencing increased heavy precipitation during wintertime cold spells. Understanding and quantifying the risks from compound events over the MED is paramount for present and future disaster risk reduction measures. Here, we apply a novel method to study compound events based on dynamical systems theory and analyse compound temperature and precipitation events over the MED from 1979 to 2018. The dynamical systems analysis quantifies the strength of the coupling between different atmospheric variables over the MED. Further, we consider compound warm-dry anomalies in summer and cold-wet anomalies in winter. Our results show that these warm-dry and cold-wet compound days are associated with large values of the temperature-precipitation coupling parameter of the dynamical systems analysis. This indicates that there is a strong interaction between temperature and precipitation during compound events. In winter, we find no significant trend in the coupling between temperature and precipitation. However in summer, we find a significant upward trend which is likely driven by a stronger coupling during warm and dry days. Thermodynamic processes associated with long-term MED warming can best explain the trend, which intensifies compound warm-dry events.
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| 2. |
- Faranda, Davide, et al.
(författare)
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Diagnosing concurrent drivers of weather extremes : application to warm and cold days in North America
- 2020
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Ingår i: Climate Dynamics. - : Springer Science and Business Media LLC. - 0930-7575 .- 1432-0894. ; 54:3-4, s. 2187-2201
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Tidskriftsartikel (refereegranskat)abstract
- A fundamental challenge in climate science is decomposing the concurrent drivers of weather extremes in observations. Achieving this can provide insights into the drivers of individual extreme events as well as into possible future changes in extreme event frequencies under greenhouse forcing. In the present work, we exploit recent results from dynamical systems theory to study the co-variation and recurrence statistics of different atmospheric variables. Specifically, we present a methodology to quantify the recurrences of bivariate variables and the coupling between distinct univariate variables in terms of their joint recurrences. The coupling is defined by a parameter which varies according to the chosen variables, season, and domain and can be understood in terms of the underlying physics of the atmosphere. For suitably chosen variables, this approach enables to decompose the different drivers of weather extremes. Here, we compute the above metrics for near-surface temperature and sea level pressure, and use them to study warm or cold days over North America. We first identify states where temperature is strongly or weakly coupled to the large-scale atmospheric circulation, and then elucidate the interplay between coupling and the occurrence of temperature extremes.
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| 3. |
- Pons, Flavio Maria Emanuele, et al.
(författare)
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Sampling Hyperspheres via Extreme Value Theory : Implications for Measuring Attractor Dimensions
- 2020
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Ingår i: Journal of statistical physics. - : Springer Science and Business Media LLC. - 0022-4715 .- 1572-9613. ; 179, s. 1698-1717
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Tidskriftsartikel (refereegranskat)abstract
- The attractor Hausdorff dimension is an important quantity bridging information theory and dynamical systems, as it is related to the number of effective degrees of freedom of the underlying dynamical system. By using the link between extreme value theory and Poincare recurrences, it is possible to estimate this quantity from time series of high-dimensional systems without embedding the data. In general d <= n, where n is the dimension of the full phase-space, as the dynamics freezes some of the available degrees of freedom. This is equivalent to constraining trajectories on a compact object in phase space, namely the attractor. Information theory shows that the equality d = n holds for random systems. However, applying extreme value theory, we show that this result cannot be recovered and that d < n. We attribute this effect to the curse of dimensionality, and in particular to the phenomenon of concentration of the norm observed in high-dimensional systems. We derive a theoretical expression for d(n) for Gaussian random vectors, and we show numerically that similar curse of dimensionality effects are found for random systems characterized by non-Gaussian distributions. Finally, we show that the effect of the curse of dimensionality can be quantified using the extreme value theory, thus enabling to retrieve the degree of nonrandomness of a system. We provide examples issued from real-world climate and financial datasets.
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