Response of Pacific-sector Antarctic ice shelves to the El Niño/Southern Oscillation.

Satellite observations over the past two decades have revealed increasing loss of grounded ice in West Antarctica, associated with floating ice shelves that have been thinning. Thinning reduces an ice-shelf's ability to restrain grounded-ice discharge, yet our understanding of the climate processes that drive mass changes is limited. Here, we use ice-shelf height data from four satellite altimeter missions (1994-2017) to show a direct link between ice-shelf-height variability in the Antarctic Pacific sector and changes in regional atmospheric circulation driven by the El Niño-Southern Oscillation. This link is strongest from Dotson to Ross ice shelves and weaker elsewhere. During intense El Niño years, height increase by accumulation exceeds the height decrease by basal melting, but net ice-shelf mass declines as basal ice loss exceeds lower-density snow gain. Our results demonstrate a substantial response of Amundsen Sea ice shelves to global and regional climate variability, with rates of change in height and mass on interannual timescales that can be comparable to the longer-term trend, and with mass changes from surface accumulation offsetting a significant fraction of the changes in basal melting. This implies that ice-shelf height and mass variability will increase as interannual atmospheric variability increases in a warming climate

Current Approaches to Seasonal to Interannual Climate Predictions

This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described

Nonlinear Dimensionality Reduction Methods in Climate Data Analysis

Linear dimensionality reduction techniques, notably principal component analysis, are widely used in climate data analysis as a means to aid in the interpretation of datasets of high dimensionality. These linear methods may not be appropriate for the analysis of data arising from nonlinear processes occurring in the climate system. Numerous techniques for nonlinear dimensionality reduction have been developed recently that may provide a potentially useful tool for the identification of low-dimensional manifolds in climate data sets arising from nonlinear dynamics. In this thesis I apply three such techniques to the study of El Nino/Southern Oscillation variability in tropical Pacific sea surface temperatures and thermocline depth, comparing observational data with simulations from coupled atmosphere-ocean general circulation models from the CMIP3 multi-model ensemble. The three methods used here are a nonlinear principal component analysis (NLPCA) approach based on neural networks, the Isomap isometric mapping algorithm, and Hessian locally linear embedding. I use these three methods to examine El Nino variability in the different data sets and assess the suitability of these nonlinear dimensionality reduction approaches for climate data analysis. I conclude that although, for the application presented here, analysis using NLPCA, Isomap and Hessian locally linear embedding does not provide additional information beyond that already provided by principal component analysis, these methods are effective tools for exploratory data analysis.Comment: 273 pages, 76 figures; University of Bristol Ph.D. thesis; version with high-resolution figures available from http://www.skybluetrades.net/thesis/ian-ross-thesis.pdf (52Mb download

An error indicator-based adaptive reduced order model for nonlinear structural mechanics -- application to high-pressure turbine blades

The industrial application motivating this work is the fatigue computation of aircraft engines' high-pressure turbine blades. The material model involves nonlinear elastoviscoplastic behavior laws, for which the parameters depend on the temperature. For this application, the temperature loading is not accurately known and can reach values relatively close to the creep temperature: important nonlinear effects occur and the solution strongly depends on the used thermal loading. We consider a nonlinear reduced order model able to compute, in the exploitation phase, the behavior of the blade for a new temperature field loading. The sensitivity of the solution to the temperature makes {the classical unenriched proper orthogonal decomposition method} fail. In this work, we propose a new error indicator, quantifying the error made by the reduced order model in computational complexity independent of the size of the high-fidelity reference model. In our framework, when the {error indicator} becomes larger than a given tolerance, the reduced order model is updated using one time step solution of the high-fidelity reference model. The approach is illustrated on a series of academic test cases and applied on a setting of industrial complexity involving 5 million degrees of freedom, where the whole procedure is computed in parallel with distributed memory

Isogeometric analysis-based reduced order modelling for incompressible linear viscous flows in parametrized shapes

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Hand in Hand Tropical Cyclones and Climate Change: Investigating the Response of Tropical Cyclones to the Warming World

What are the primary factors governing Tropical Cyclone Potential Intensity (TCPI) and how does the TCPI vary with the change in CO2 concentration are the two fundamental questions we investigated here. In the first part, a strong spatial correlation between the TCPI and the ocean temperature underneath was used to develop a statistical model to quantify the TCPI over the remote regions where the tropical cyclone related observations are difficult to acquire. The model revealed an overall increase in the TCPI when the atmospheric CO2 concentration was doubled. Finally, the study examines the TCPI’s sensitivity on the ocean temperature (at the spatial scales). Two independent models (HADCM3 from Met Office, UK and GFDL-CM3 from GFDL, NOAA, USA) on an average reveals an increase in the TCPI between 8 to 10 m/s per unit increase in the ocean temperature (in degree C). The key finding to emerge from this study is that the increase in the TCPI responds comparatively weakly to the increasing ocean temperature when CO2 amount is increased. We call this observation as, “the sensitivity saturation effect”. According to our findings, the TCPI responds weakly (become less sensitive) to the ocean temperature on doubling the CO2 concentration. This effect was observed in all the ocean basins and in both the considered climate models. Though the TCPI show a rise in increasing the CO2 concentration but, its response to the SST decreases. This observation leads to a set of next level questions for instance, will there be a sensitivity saturation effect, analogous to the well-known “Band Saturation effect”, on increasing the CO2 levels and if it does, will the TCPI’s sensitivity plateau? If it plateaus, at what cut-off CO2 levels would that happen? These emerging questions open up a new area of investigation for the climatologists and the enthusiasts in the related fields. In this manner, this part of the research provides a framework for the future exploration of the subject.UKIERI Fellowshi