322,877 research outputs found
Is the Atlantic a Source for Decadal Predictability of Sea-Level Rise in Venice?
No full textSea-level rise is one of the most critical consequences of global warming, with potentially vast impacts on coastal environments and societies. Sea-level changes are spatially and temporally heterogeneous on multiannual-to-multidecadal timescales. Here, we demonstrate that the observed rate of winter sea-level rise in the Italian city of Venice contains significant multidecadal fluctuations, including interdecadal periods of near-zero trend. Previous literature established a connection between the local sea-level trend in Venice and over the broad subpolar and eastern North Atlantic. We demonstrate that for multidecadal variations in sea-level trend such connection holds only since the mid-20th Century. Such multidecadal sea-level fluctuations relate to North Atlantic sea-surface temperature changes described by the Atlantic multidecadal variability, or AMV. The link is explained by combined effect of AMV-linked steric variations in the North Atlantic propagating in the Mediterranean Sea, and large-scale atmospheric circulation anomalies over the North Atlantic with a local effect on sea level in Venice. We discuss the implications of such variability for near-term predictability of winter sea-level changes in Venice. Combining available sea-level projections for Venice with a scenario of imminent AMV cooling yields a slowdown in the rate of sea-level rise in Venice, with the possibility of mean values remaining even roughly constant in the next two decades as AMV effects contrast the expected long-term sea-level rise. Acknowledging, understanding, and communicating this multidecadal variability in local sea-level rise is crucial for management and protection of this world-class historical site.Plain Language Summary Environmental and socioeconomic impacts of sea-level rise are one of the major concerns of global warming. Here, we consider the case of the Italian city of Venice, one of the iconic locations for the potentially dramatic effects of sea-level rise. We show that the sea-level evolution in Venice during the past similar to 150 years contains strong multidecadal fluctuations, so that periods of more than two decades when there is little or no trend occurred even in the recent past. We link these fluctuations with sea-level and climatic variations in the North Atlantic. In particular, we focus on the phenomenon known as Atlantic multidecadal variability, or AMV, which describes the alternation over multidecadal periods of warm and cold phases of the North Atlantic surface. Our results indicate that warm AMV phases are linked to faster sea-level rise in Venice and vice versa. Accordingly, we build sea-level rise scenarios for Venice until 2035 by considering an imminent AMV cooling as suggested by recent studies. The scenarios yield a temporary slowdown of sea-level rise as the AMV contrasts the effects of global warming. This sea-level variability can strongly impact on the management of protective measures against flooding currently operative in Venice
Spatio-Temporal Evolution and North–South Asymmetry of Quasi-Biennial Oscillations in the Coronal Fe xiv Emission
No full textIn this work, we apply multichannel singular spectrum analysis (MSSA), a data-adaptive, multivariate, non-parametric technique that simultaneously exploits the spatial and temporal correlations of the input data to extract common modes of variability, to investigate the intermediate quasi-periodicities of the Fe xiv green coronal emission line at 530.3 nm for the period between 1944 and 2008. Our analysis reveals several significant mid-term periodicities in a range from about one to four years that are consistent with the so-called quasi-biennial oscillations (QBOs), which have been detected by several authors using different data sets and analysis methods. These QBOs display amplitudes varying significantly with time and latitude over the six solar cycles (18 to 23) covered by this study. A clear North–South asymmetry is detected both in their intensity and period distribution, with a net predominance of spectral power in the active-region belt of the northern hemisphere. On the other hand, while the QBOs with periods ≳ 1.7 years are particularly intense around the polar regions and therefore related to the global magnetic field, the ones with shorter periods are mainly generated at mid-latitudes, in correspondence with the emergence of active regions. Our findings indicate that the North–South asymmetry manifested in the uneven latitudinal distribution of QBOs is a fundamental, albeit puzzling, characteristic of solar activity
Atlantic origin of asynchronous European interdecadal hydroclimate variability
No full textDischarge time series of major large-catchment European rivers are known to display significant decadal and interdecadal fluctuations. However, the hydroclimate variability causing such fluctuations remains poorly understood, particularly due to a lack of a spatio-temporal integrated assessment. Here, we demonstrate for the first time that European hydroclimate variability is dominated by a meridional delayed oscillation characterized by a lag of approximately 5 years in interdecadal discharge fluctuations of continental (northern) European rivers with respect to those of Euro-Mediterranean (southern) rivers. We demonstrate a connection of this coherent signal with the large-scale atmospheric circulation over the North Atlantic, and suggest a hitherto unexplored multiannual atmosphere-ocean mechanism in the subpolar North Atlantic at its root
Mid-XIX Century Estuary SST Time Series Recorded in the Venice Lagoon
No full textSea surface temperature (SST) is of paramount importance for comprehending ocean dynamics and hence the Earth’s climate system. Accordingly, it is also the most measured oceanographic parameter. However, until the end of the XIX century, no continuous time series of SST seems to exist, with most of the available data deriving from measurements on ships. Here, we present a continuous digitalized record of surface water measurements originally written in a book published in 1853. The measurements were retrieved thrice daily in the Venice lagoon, in the northeastern part of the Italian peninsula, from June to August 1851 and 1852. To the best of our knowledge, these data constitute the oldest time series of the entire world ocean. The measurements were performed by immersing a Réaumur thermometer a few meters deep in the lagoon water at 8 a.m., 12 p.m., and 8 p.m. Despite several limitations affecting these data (e.g., lacking information regarding the exact water depth where measurements were performed and instrumental metadata), they are of utmost significance, as they put many decades backward the date of the development of a fundamental aspect of oceanographic observations. Moreover, the data were collected close to the Punta della Salute site, where actual sea water temperature measurements have been performed since 2002. Therefore, a unique comparison between surface water temperatures within the Lagoon of Venice across three centuries is possible
O VI 1032 Å intensity and Doppler shift oscillations above a coronal hole: Magnetosonic waves or quasi-periodic upflows?
No full textOn 1996 December 19, the Ultraviolet Coronagraph Spectrometer (UVCS) on board the Solar
and Heliospheric Observatory (SOHO) conducted a special high-cadence sit-and-stare
observation in the O v
A Complete 60‐Year Catalog of Wind Events in the German Bight (North Sea) Derived From ERA5 Reanalysis Data
No full textThe German Bight is a shallow area in the southeastern North Sea. Atmospheric forcing, particularly wind stress, plays an essential role in the sea circulation dynamics in the area as a source of momentum and consequent driver of the variability on seasonal to interannual timescales. Westerly and SouthWesterly winds constitute the mean state of this forcing over the North Sea due to the persistent pressure gradients between the Icelandic Low and the Azores High. Consequently, the transport in the North Sea is primarily cyclonic. Nevertheless, the presence of land influences wind stress in the coastal regions (in terms of both intensity and direction). Moreover, distant locations respond differently to the action of the atmospheric pressure centers. Therefore, studies characterizing wind statistics in specific regions are mandatory for understanding and numerically simulating the sea circulation patterns in such areas. We present a detailed analysis of wind patterns in the German Bight, specifically in the Marine Protected Areas and Helgoland Island, using ERA5 reanalysis atmospheric data. We define and catalog area-specific “events” according to their typical duration and magnitude and analyze their seasonality and interannual variability. We investigate the most recurrent locations and intensities of the high- and low-pressure dipoles causing specific wind patterns over the German Bight during the different periods of the year. We show how, besides Westerly and SouthWesterly winds, NorthWesterly flows are a recurrent pattern in the area; winds from the East are less frequent but can be extremely persistent over the same site in the spring
Long-term evolution of the heliospheric magnetic field inferred from cosmogenic
No full textTypical reconstructions of historic heliospheric magnetic field (HMF) BHMF are based on the analysis of the sunspot activity, geomagnetic data or on measurement of cosmogenic isotopes stored in terrestrial reservoirs like trees (14C) and ice cores (10Be). The various reconstructions of BHMF are however discordant both in strength and trend. Cosmogenic isotopes, which are produced by galactic cosmic rays impacting on meteoroids and whose production rate is modulated by the varying HMF convected outward by the solar wind, may offer an alternative tool for the investigation of the HMF in the past centuries. In this work, we aim to evaluate the long-term evolution of BHMF over a period covering the past twenty-two solar cycles by using measurements of the cosmogenic 44Ti activity (τ1∕2 = 59.2 ± 0.6 yr) measured in 20 meteorites which fell between 1766 and 2001. Within the given uncertainties, our result is compatible with a HMF increase from
4.87^{+0.24}_{-0.30} nT in 1766 to
6.83^{+0.13}_{-0.11} nT in 2001, thus implying an overall average increment of
1.96^{+0.43}_{-0.35} nT over 235 years since 1766 reflecting the modern Grand maximum. The BHMF trend thus obtained is then compared with the most recent reconstructions of the near-Earth HMF strength based on geomagnetic, sunspot number, and cosmogenic isotope data
Why the 2022 Po River drought is the worst in the past two centuries
No full textThe causes of recent hydrological droughts and their future evolution under a changing climate are still poorly
understood. Banking on a 216-year river flow time series at the Po River outlet, we show that the 2022 hydrological
drought is the worst event (30% lower than the second worst, with a six-century return period), part of an
increasing trend in severe drought occurrence. The decline in summer river flows (−4.14 cubic meters per
second per year), which is more relevant than the precipitation decline, is attributed to a combination of
changes in the precipitation regime, resulting in a decline of snow fraction (−0.6% per year) and snowmelt
(−0.18 millimeters per day per year), and to increasing evaporation rate (+0.013 cubic kilometers per year)
and irrigated areas (100% increment from 1900). Our study presents a compelling case where the hydrological
impact of climate change is exacerbated by local changes in hydrologic seasonality and water use
Forecasting the solar cycle 25 using a multistep Bayesian neural network
No full textABSTRACT
Predicting the solar activity of upcoming cycles is crucial nowadays to anticipate potentially adverse space weather effects on the Earth’s environment produced by coronal transients and traveling interplanetary disturbances. The latest advances in deep learning techniques provide new paradigms to obtain effective prediction models that allow to forecast in detail the evolution of cosmogeophysical time series. Because of the underlying complexity of the dynamo mechanism in the solar interior that is at the origin of the solar cycle phenomenon, the predictions offered by state-of-the-art machine learning algorithms represent valuable tools for our understanding of the cycle progression. As a plus, Bayesian deep learning is particularly compelling thanks to recent advances in the field that provide improvements in both accuracy and uncertainty quantification compared to classical techniques. In this work, a deep learning long short-term memory model is employed to predict the complete profile of Solar Cycle 25, thus forecasting also the advent of the next solar minimum. A rigorous uncertainty estimation of the predicted sunspot number is obtained by applying a Bayesian approach. Two different model validation techniques, namely the Train-Test split and the time series k-fold cross-validation, have been implemented and compared, giving compatible results. The forecasted peak amplitude is lower than that of the preceding cycle. Solar Cycle 25 will last 10.6 ± 0.7 yr, reaching its maximum in the middle of the year 2024. The next solar minimum is predicted in 2030 and will be as deep as the previous one.</jats:p
Robust decadal hydroclimate predictions for northern Italy based on a twofold statistical approach
No full textThe Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions
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