Uncertainties in European summer precipitation changes: role of large scale circulation

Climate models suggest that anthropogenic emissions are likely to induce an important drying during summer over most of Europe in the late 21st century. However, the amplitude of the associated decrease in precipitation strongly varies among the different climate models. In order to reduce this spread, it is first necessary to identify its causes and the associated physical mechanisms. Consequently, the focus of this paper is to better estimate the role of large scale circulation (LSC) in precipitation changes over Europe using a multi-model framework and then to characterize the LSC changes using the weather regime paradigm. We show that LSC changes directly lead to a decrease of precipitation over northwestern Europe. This circulation-driven decrease in rainfall is mainly linked to an increase (decrease) of the occurrence of positive (negative) phase of the North Atlantic Oscillation regime. LSC is also responsible for a significant part of the models spread in precipitation changes over these regions. Over southern Europe, the role of LSC changes on multi-model mean precipitation changes is generally weak. We also show that the precipitation anomalies directly induced by LSC modifications seem to be further amplified through local feedbacks.     

Inertial instability of large Rossby number horizontal shear flows in a thin homogeneous layer

A horizontal shear flow having a Rossby number, Ro, greater than unity on a rotating plane can become unstable when its shear value is less than −f, the Coriolis frequency. In this paper, this instability is investigated for an O(10 km) submesoscale, sinusoidal shear flow in a thin homogeneous fluid layer as in an oceanic mixed layer or a shallow sea. The most unstable mode is shown by a linear analysis to occur in a narrow localized region centered around the maximum anticyclonic current shear. However, nonlinear numerical calculations show that the instability can grow to encompass both unstable and stable regions of the current. A consequence of this finite-amplitude evolution is the formation of surface convergence/shear fronts. The possibility that inertial instability mechanism is a source of some surface convergence/shear features seen in remote sensing images of the sea surface is discussed. A comparison is made with the shear-flow instability that can occur concurrently in a sinusoidal shear current, and inertial instability is shown to be the dominant instability mechanism in the immediate range above Ro=2.     

Inversion for seismic anisotropy using genetic algorithms1

A general inversion scheme based on a genetic algorithm is developed to invert seismic observations for anisotropic parameters. The technique is applied to the inversion of shear-wave observations from two azimuthal VSP data sets from the Conoco test site in Oklahoma. Horizontal polarizations and time-delays are inverted for hexagonal and orthorhombic symmetries. The model solutions are consistent with previous studies using trial and error matching of full waveform synthetics. The shear-wave splitting observations suggest the presence of a shear-wave line singularity and are consistent with a dipping fracture system which is known to exist at the test site. Application of the inversion scheme prior to full waveform modelling demonstrates that a considerable saving in time is possible whilst retaining the same degree of accuracy.     

A multi-model ensemble of downscaled spatial climate change scenarios for the Dommel catchment, Western Europe

Regional or local scale hydrological impact studies require high resolution climate change scenarios which should incorporate some assessment of uncertainties in future climate projections. This paper describes a method used to produce a multi-model ensemble of multivariate weather simulations including spatial–temporal rainfall scenarios and single-site temperature and potential evapotranspiration scenarios for hydrological impact assessment in the Dommel catchment (1,350 km²) in The Netherlands and Belgium. A multi-site stochastic rainfall model combined with a rainfall conditioned weather generator have been used for the first time with the change factor approach to downscale projections of change derived from eight Regional Climate Model (RCM) experiments for the SRES A2 emission scenario for the period 2071–2100. For winter, all downscaled scenarios show an increase in mean daily precipitation (catchment average change of +9% to +40%) and typically an increase in the proportion of wet days, while for summer a decrease in mean daily precipitation (−16% to −57%) and proportion of wet days is projected. The range of projected mean temperature is 7.7°C to 9.1°C for winter and 19.9°C to 23.3°C for summer, relative to means for the control period (1961–1990) of 3.8°C and 16.8°C, respectively. Mean annual potential evapotranspiration is projected to increase by between +17% and +36%. The magnitude and seasonal distribution of changes in the downscaled climate change projections are strongly influenced by the General Circulation Model (GCM) providing boundary conditions for the RCM experiments. Therefore, a multi-model ensemble of climate change scenarios based on different RCMs and GCMs provides more robust estimates of precipitation, temperature and evapotranspiration for hydrological impact assessments, at both regional and local scale.     

Local climatic drivers of changes in phenology at a boreal-temperate ecotone in eastern North America

Ecosystems in biogeographical transition zones, or ecotones, tend to be highly sensitive to climate and can provide early indications of future change. To evaluate recent climatic changes and their impacts in a boreal-temperate ecotone in eastern North America, we analyzed ice phenology records (1975?C2007) for five lakes in the Adirondack Mountains of northern New York State. We observed rapidly decreasing trends of up to 21?days less ice cover, mostly due to later freeze-up and partially due to earlier break-up. To evaluate the local drivers of these lake ice changes, we modeled ice phenology based on local climate data, derived climatic predictors from the models, and evaluated trends in those predictors to determine which were responsible for observed changes in lake ice. November and December temperature and snow depth consistently predicted ice-in, and recent trends of warming and decreasing snow during these months were consistent with later ice formation. March and April temperature and snow depth consistently predicted ice-out, but the absence of trends in snow depth during these months, despite concurrent warming, resulted in much weaker trends for ice-out. Recent rates of warming in the Adirondacks are among the highest regionally, although with a different seasonality of changes (early winter > late winter) that is consistent with other lake ice records in the surrounding area. Projected future declines in snow cover could create positive feedbacks and accelerate current rates of ice loss due to warming. Climate sensitivity was greatest for the larger lakes in our study, including Wolf Lake, considered one of the most ecologically intact ??wilderness lakes?? in eastern North America. Our study provides further evidence of climate sensitivity of the boreal-temperate ecotone of eastern North America and points to emergent conservation challenges posed by climate change in legally protected yet vulnerable landscapes like the Adirondack Park.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

Triggering of El Niño by westerly wind events in a coupled general circulation model

Two ten-members ensemble experiments using a coupled ocean-atmosphere general circulation model are performed to study the dynamical response to a strong westerly wind event (WWE) when the tropical Pacific has initial conditions favourable to the development of a warm event. In the reference ensemble (CREF), no wind perturbation is introduced, whereas a strong westerly wind event anomaly is introduced in boreal winter over the western Pacific in the perturbed ensemble (CWWE). Our results demonstrate that an intense WWE is capable of establishing the conditions under which a strong El Niño event can occur. First, it generates a strong downwelling Kelvin wave that generates a positive sea surface temperature (SST) anomaly in the central-eastern Pacific amplified through a coupled ocean-atmosphere interaction. This anomaly can be as large as 2.5°C 60 days after the WWE. Secondly, this WWE also initiates an eastward displacement of the warm-pool that promotes the occurrence of subsequent WWEs in the following months. These events reinforce the initial warming through the generation of additional Kelvin waves and generate intense surface jets at the eastern edge of the warm-pool that act to further shift warm waters eastward. The use of a ten-members ensemble however reveals substantial differences in the coupled response to a WWE. Whereas four members of CWWE ensemble develop into intense El Niño warming as described above, four others display a moderate warming and two remains in neutral conditions. This diversity between the members appears to be due to the internal atmospheric variability during and following the inserted WWE. In the four moderate warm cases, the warm-pool is initially shifted eastward following the inserted WWE, but the subsequent weak WWE activity (when compared to the strong warming cases) prevents to further shift the warm-pool eastwards. The seasonal strengthening of trade winds in June–July can therefore act to shift warm waters back into the western Pacific, reducing the central-eastern Pacific warming. This strong sensitivity of the coupled response to WWEs may therefore limit the predictability of El Niño events, as the high frequency wind variability over the warm pool region remains largely unpredictable even at short time lead.     

Internal variability in a regional climate model over West Africa

Sensitivity studies with regional climate models are often performed on the basis of a few simulations for which the difference is analysed and the statistical significance is often taken for granted. In this study we present some simple measures of the confidence limits for these types of experiments by analysing the internal variability of a regional climate model run over West Africa. Two 1-year long simulations, differing only in their initial conditions, are compared. The difference between the two runs gives a measure of the internal variability of the model and an indication of which timescales are reliable for analysis. The results are analysed for a range of timescales and spatial scales, and quantitative measures of the confidence limits for regional model simulations are diagnosed for a selection of study areas for rainfall, low level temperature and wind. As the averaging period or spatial scale is increased, the signal due to internal variability gets smaller and confidence in the simulations increases. This occurs more rapidly for variations in precipitation, which appear essentially random, than for dynamical variables, which show some organisation on larger scales.     

Building comparable global change vulnerability assessments: The vulnerability scoping diagram

Advancing vulnerability science depends in part on identifying common themes from multiple, independent vulnerability assessments. Such insights are difficult to produce when the assessments use dissimilar, often qualitative, measures. The Vulnerability Scoping Diagram is presented to facilitate the comparison of assessments with dissimilar measures. The diagram is illustrated with recent research on drought vulnerabilities, showing that common insights into vulnerability may emerge if independent research teams use a common structure for organizing information about exposure, sensitivity and adaptive capacity—even if the underlying measures differ between assessments. Broadly adopting this technique, which is grounded in the “Eight Steps” methodological protocol [Schröter, D., Polsky, C., Patt, A., 2005. Assessing vulnerabilities to the effects of global change: an eight step approach. Mitigation and Adaptation Strategies for Global Change 10(4), 573–595], will enable a vulnerability meta-analysis, the lessons from which may permit places to identify helpful adaptation or mitigation options without first having to conduct their own vulnerability assessments.