Publisher: Oxford University Press (Total: 369 journals)
Dynamics and Statistics of the Climate System
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Open Access journal
ISSN (Online) 2059-6987
Published by Oxford University Press [369 journals]
- Characterising the changing behaviour of heatwaves with climate change
Authors: Winter H; Brown S, Tawn J.
Abstract: AbstractUnderstanding the impact of future heatwaves and the development of effective adaptation strategies requires knowledge of both the changes in heatwave temperatures and their durations. We develop a framework, utilising extreme value theory, which allows for the effect of a covariate on both the marginal quantiles and the temporal dependence structure of daily maximum temperatures enabling the changes in heatwave temperatures (marginal effects) to be identified separately from duration changes (dependence effects). To characterize future heatwave changes we use global mean temperature anomalies as a covariate to provide the metric for climate change. Future daily maximum temperatures and global mean temperature changes are provided by 13 general circulation models (GCMs) from the CMIP5 archive forced with predicted future emissions of radiative forcing agents from the RCP8.5 scenario. For Orleans, central France, we find that for all GCMs temporal dependence is unaffected by greenhouse gas induced climate change indicating that durations of heatwaves that exceed time varying high thresholds (i.e. the 1-year level) will not change in the future. However, all GCMs project significant changes in the temperature margins with events similar to the 2003 European heatwave increasing by 1.3°C to 2.7°C and (8.0°C to 18.7°C) for a 1°C (5°C) increase in global temperature. Collectively our results indicate there could be a significant increase in heatwave risk as the world warms with heatwaves increasing in temperature significantly faster than the global mean and local average temperatures.
Issue No: Vol. 1, No. 1 (2017)
- State dependence of climate sensitivity: attractor constraints and
Authors: von der Heydt A; Ashwin P.
Abstract: AbstractEquilibrium climate sensitivity is a key predictor of climate change. However, it is not very well constrained, either by climate models or by observational data. The reasons for this include strong internal variability and forcing on many timescales. In practice, this means that the ‘equilibrium’ will only be relative to fixing the slow feedback processes before comparing palaeoclimate sensitivity estimates with estimates from model simulations. In addition, information from the late Pleistocene ice age cycles indicates that the climate cycles between cold and warm regimes, and the climate sensitivity varies considerably between regime because of fast feedback processes changing relative strength and timescales over one cycle. In this paper, we consider climate sensitivity for quite general climate dynamics. Using a conceptual Earth system model of Gildor and Tziperman (A sea ice climate switch mechanism for the 100-kyr glacial cycles. J Geophys Res 2001; 106: 9117–33) (with Milankovich forcing and dynamical ocean biogeochemistry), we explore various ways of quantifying the state dependence of climate sensitivity from unperturbed and perturbed model time series. Even without considering any perturbation, we suggest that climate sensitivity can be usefully thought of as a distribution that quantifies variability within the ‘climate attractor’. On the ‘climate attractor’, there is a strong dependence on climate state or more specifically on the ‘climate regime’ where fast processes are approximately in equilibrium. We also consider perturbations by instantaneous doubling of CO2 and similarly find a strong dependence on the climate state using our approach.
- A numerical framework to understand transitions in high-dimensional
stochastic dynamical systems
Authors: Dijkstra H; Tantet A, Viebahn J, et al.
Abstract: AbstractDynamical systems methodology is a mature complementary approach to forward simulation which can be used to investigate many aspects of climate dynamics. With this paper, a review is given on the methods to analyse deterministic and stochastic climate models and show that these are not restricted to low-dimensional toy models, but that they can be applied to models formulated by stochastic partial differential equations. We sketch the numerical implementation of these methods and illustrate these by showing results for two canonical problems in climate dynamics.
- Beyond bifurcation: using complex models to understand and predict abrupt
Authors: Bathiany S; Dijkstra H, Crucifix M, et al.
Abstract: AbstractResearch on the possibility of future abrupt climate change has been popularized under the term ‘tipping points’ and has often been motivated by using simple, low-dimensional concepts. These include the iconic fold bifurcation, where abrupt change occurs when a stable equilibrium is lost, and early warning signals of such a destabilization that can be derived based on a simple stochastic model approach. In this paper, we review the challenges and limitations that are associated with this view, and we discuss promising research paths to explore the causes and the likelihood of abrupt changes in future climate.We focus on several climate system components and ecosystems that have been proposed as candidates for tipping points, with an emphasis on ice sheets, the Atlantic Ocean circulation, vegetation in North Africa and Arctic sea ice. In most example cases, multiple equilibria found in simple models do not appear in complex models or become more difficult to find, while the potential for abrupt change still remains. We also discuss how the low-dimensional logic of current methods to detect and interpret the existence of multiple equilibria can fail in complex models. Moreover, we highlight promising methods to detect abrupt shifts and to obtain information about the mechanisms behind them. These methods include linear approaches such as statistical stability indicators and radiative feedback analysis as well as non-linear approaches to detect dynamical transitions and infer the causality behind events.Given the huge complexity of comprehensive process-based climate models and the non-linearity and regional peculiarities of the processes involved, the uncertainties associated with the possible future occurrence of abrupt shifts are large and not well quantified. We highlight the potential of data mining approaches to tackle this problem and finally discuss how the scientific community can collaborate to make efficient progress in understanding abrupt climate shifts.
- Has natural variability a lagged influence on global temperature? A
multi-horizon Granger causality analysis
Authors: Attanasio A; Pasini A, Triacca U.
Abstract: AbstractAt 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.
- The vertical structure of ocean eddies
Authors: de La Lama M; LaCasce J, Fuhr H.
Abstract: AbstractVelocity data from 81 globally distributed current meters are used to characterize the vertical structure of ocean current fluctuations. The primary empirical orthogonal functions from most of the moorings are similar, decreasing monotonically with depth to a value near zero at the bottom. This contrasts with the standard barotropic (BT) and baroclinic (BC) modes, which have flow at the bottom. However, the structure is very similar to the first baroclinic (BC1) mode with zero horizontal flow at the bottom, as is appropriate over a rough or steeply sloping bottom. This mode captures a greater fraction of the observed variance than the standard flat bottom BC1 mode. Also, an analytical approximation of the first mode, obtained assuming exponential stratification, is as accurate as the numerically generated BC mode. This suggests a simple way to project surface velocities into the interior.
Authors: Jones C.
Abstract: We are living with a climate system undergoing significant changes. Scientists have established a critical mass of facts and have quantified them to a degree sufficient to support international action to mitigate against drastic change and adapt to committed climate shifts. The primary example being the relation between increased atmospheric carbon dioxide concentrations and the extent of warming in the future. But the climate system in its entirety is a highly complex system and mysteries abound as to how its internal mechanisms work and interact with each other.