299 research outputs found
Nonlinear projective filtering in a data stream
We introduce a modified algorithm to perform nonlinear filtering of a time
series by locally linear phase space projections. Unlike previous
implementations, the algorithm can be used not only for a posteriori processing
but includes the possibility to perform real time filtering in a data stream.
The data base that represents the phase space structure generated by the data
is updated dynamically. This also allows filtering of non-stationary signals
and dynamic parameter adjustment. We discuss exemplary applications, including
the real time extraction of the fetal electrocardiogram from abdominal
recordings.Comment: 8 page
Attribution of human-induced dynamical and thermodynamical contributions in extreme weather events
This is the final version. Available on open access from IOP Publishing via the DOI in this recordWe present a new method that allows a separation of the attribution of human influence in extreme events into changes in atmospheric flows and changes in other processes. Assuming two data sets of model simulations or observations representing a natural, or 'counter-factual' climate, and the actual, or 'factual' climate, we show how flow analogs used across data sets can provide quantitative estimates of each contribution to the changes in probabilities of extreme events. We apply this method to the extreme January precipitation amounts in Southern UK such as were observed in the winter of 2013/2014. Using large ensembles of an atmospheric model forced by factual and counterfactual sea surface temperatures, we demonstrate that about a third of the increase in January precipitation amounts can be attributed to changes in weather circulation patterns and two thirds of the increase to thermodynamic changes. This method can be generalized to many classes of events and regions and provides, in the above case study, similar results to those obtained in Schaller et al (2016 Nat. Clim. Change 6 627-34) who used a simple circulation index, describing only a local feature of the circulation, as in other methods using circulation indices (van Ulden and van Oldenborgh 2006 Atmos. Chem. Phys. 6 863-81).European Union FP7French Ministry of EcologyEuropean Research Council (ERC
Dimension reduction for systems with slow relaxation
We develop reduced, stochastic models for high dimensional, dissipative
dynamical systems that relax very slowly to equilibrium and can encode long
term memory. We present a variety of empirical and first principles approaches
for model reduction, and build a mathematical framework for analyzing the
reduced models. We introduce the notions of universal and asymptotic filters to
characterize `optimal' model reductions for sloppy linear models. We illustrate
our methods by applying them to the practically important problem of modeling
evaporation in oil spills.Comment: 48 Pages, 13 figures. Paper dedicated to the memory of Leo Kadanof
Human influence on growing-period frosts like the early April 2021 in Central France
International audienceAbstract. In early April 2021 several days of harsh frost affected central Europe. This led to very severe damage in grapevine and fruit trees in France, in regions where young leaves had already unfolded due to unusually warm temperatures in the preceding month (March 2021). We analysed with observations and 172 climate model simulations how human-induced climate change affected this event over central France, where many vineyards are located. We found that, without human-caused climate change, such temperatures in April or later in spring would have been even lower by 1.2ââC (0.75 to 1.7ââC). However, climate change also caused an earlier occurrence of bud burst that we characterized in this study by a growing degree day index value. This shift leaves young leaves exposed to more winter-like conditions with lower minimum temperatures and longer nights, an effect that overcompensates the warming effect. Extreme cold temperatures occurring after the start of the growing season such as those of April 2021 are now 2ââC colder (0.5 to 3.3ââC) than in preindustrial conditions, according to observations. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change with each of the five climate model ensembles used here simulating a cooling of growing-period annual temperature minima of 0.41ââC (0.22 to 0.60ââC) since preindustrial conditions. The 2021 growing-period frost event has become 50â% more likely (10â%â110â%). Models accurately simulate the observed warming in extreme lowest spring temperatures but underestimate the observed trends in growing-period frost intensities, a fact that yet remains to be explained. Model ensembles all simulate a further intensification of yearly minimum temperatures occurring in the growing period for future decades and a significant probability increase for such events of about 30â% (20â%â40â%) in a climate with global warming of 2ââC
Evidence of Deterministic Components in the Apparent Randomness of GRBs: Clues of a Chaotic Dynamic
Prompt Îł-ray emissions from gamma-ray bursts (GRBs) exhibit a vast range of extremely complex temporal structures with a typical variability time-scale significantly short â as fast as milliseconds. This work aims to investigate the apparent randomness of the GRB time profiles making extensive use of nonlinear techniques combining the advanced spectral method of the Singular Spectrum Analysis (SSA) with the classical tools provided by the Chaos Theory. Despite their morphological complexity, we detect evidence of a non stochastic short-term variability during the overall burst duration â seemingly consistent with a chaotic behavior. The phase space portrait of such variability shows the existence of a well-defined strange attractor underlying the erratic prompt emission structures. This scenario can shed new light on the ultra-relativistic processes believed to take place in GRB explosions and usually associated with the birth of a fast-spinning magnetar or accretion of matter onto a newly formed black hole
Sensitivity of the Northern Hemisphere blocking frequency to the detection index
This work has been supported in part by the European Commission in the framework of the Environment and Climate Research Programme (MERCURE, ENV4-CT97-0485 for FJDR) and by the Spanish CICYT CLI97-0558 grant
Investigating the topology of interacting networks - Theory and application to coupled climate subnetworks
Network theory provides various tools for investigating the structural or
functional topology of many complex systems found in nature, technology and
society. Nevertheless, it has recently been realised that a considerable number
of systems of interest should be treated, more appropriately, as interacting
networks or networks of networks. Here we introduce a novel graph-theoretical
framework for studying the interaction structure between subnetworks embedded
within a complex network of networks. This framework allows us to quantify the
structural role of single vertices or whole subnetworks with respect to the
interaction of a pair of subnetworks on local, mesoscopic and global
topological scales.
Climate networks have recently been shown to be a powerful tool for the
analysis of climatological data. Applying the general framework for studying
interacting networks, we introduce coupled climate subnetworks to represent and
investigate the topology of statistical relationships between the fields of
distinct climatological variables. Using coupled climate subnetworks to
investigate the terrestrial atmosphere's three-dimensional geopotential height
field uncovers known as well as interesting novel features of the atmosphere's
vertical stratification and general circulation. Specifically, the new measure
"cross-betweenness" identifies regions which are particularly important for
mediating vertical wind field interactions. The promising results obtained by
following the coupled climate subnetwork approach present a first step towards
an improved understanding of the Earth system and its complex interacting
components from a network perspective
Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea
Observing, modelling and understanding the climate-scale variability of the deep water formation (DWF) in the North-Western Mediterranean Sea remains today very challenging. In this study, we first characterize the interannual variability of this phenomenon by a thorough reanalysis of observations in order to establish reference time series. These quantitative indicators include 31 observed years for the yearly maximum mixed layer depth over the period 1980â2013 and a detailed multi-indicator description of the period 2007â2013. Then a 1980â2013 hindcast simulation is performed with a fully-coupled regional climate system model including the high-resolution representation of the regional atmosphere, ocean, land-surface and rivers. The simulation reproduces quantitatively well the mean behaviour and the large interannual variability of the DWF phenomenon. The model shows convection deeper than 1000 m in 2/3 of the modelled winters, a mean DWF rate equal to 0.35 Sv with maximum values of 1.7 (resp. 1.6) Sv in 2013 (resp. 2005). Using the model results, the winter-integrated buoyancy loss over the Gulf of Lions is identified as the primary driving factor of the DWF interannual variability and explains, alone, around 50 % of its variance. It is itself explained by the occurrence of few stormy days during winter. At daily scale, the Atlantic ridge weather regime is identified as favourable to strong buoyancy losses and therefore DWF, whereas the positive phase of the North Atlantic oscillation is unfavourable. The driving role of the vertical stratification in autumn, a measure of the water column inhibition to mixing, has also been analyzed. Combining both driving factors allows to explain more than 70 % of the interannual variance of the phenomenon and in particular the occurrence of the five strongest convective years of the model (1981, 1999, 2005, 2009, 2013). The model simulates qualitatively well the trends in the deep waters (warming, saltening, increase in the dense water volume, increase in the bottom water density) despite an underestimation of the salinity and density trends. These deep trends come from a heat and salt accumulation during the 1980s and the 1990s in the surface and intermediate layers of the Gulf of Lions before being transferred stepwise towards the deep layers when very convective years occur in 1999 and later. The salinity increase in the near Atlantic Ocean surface layers seems to be the external forcing that finally leads to these deep trends. In the future, our results may allow to better understand the behaviour of the DWF phenomenon in Mediterranean Sea simulations in hindcast, forecast, reanalysis or future climate change scenario modes. The robustness of the obtained results must be however confirmed in multi-model studies
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