Clouds, photolysis and regional tropospheric ozone budgets.

We use a three-dimensional chemical transport model to examine the shortwave radiative effects of clouds on the tropospheric ozone budget. In addition to looking at changes in global concentrations as previous studies have done, we examine changes in ozone chemical production and loss caused by clouds and how these vary in different parts of the troposphere. On a global scale, we find that clouds have a modest effect on ozone chemistry, but on a regional scale their role is much more significant, with the size of the response dependent on the region. The largest averaged changes in chemical budgets (±10–14%) are found in the marine troposphere, where cloud optical depths are high. We demonstrate that cloud effects are small on average in the middle troposphere because this is a transition region between reduction and enhancement in photolysis rates. We show that increases in boundary layer ozone due to clouds are driven by large-scale changes in downward ozone transport from higher in the troposphere rather than by decreases in in-situ ozone chemical loss rates. Increases in upper tropospheric ozone are caused by higher production rates due to backscattering of radiation and consequent increases in photolysis rates, mainly J(NO2). The global radiative effect of clouds on isoprene, through decreases of OH in the lower troposphere, is stronger than on ozone. Tropospheric isoprene lifetime increases by 7% when taking clouds into account. We compare the importance of clouds in contributing to uncertainties in the global ozone budget with the role of other radiatively-important factors. The budget is most sensitive to the overhead ozone column, while surface albedo and clouds have smaller effects. However, uncertainty in representing the spatial distribution of clouds may lead to a large sensitivity of the ozone budget components on regional scales

Protons associated with centers of solar activity and their propagation in interplanetary magnetic field regions co-rotating with the sun

Protons associated with centers of solar activity and propagation in interplanetary magnetic field regions co-rotating with su

Interannual variability of tropospheric composition:the influence of changes in emissions, meteorology and clouds

We have run a chemistry transport model (CTM) to systematically examine the drivers of interannual variability of tropospheric composition during 1996-2000. This period was characterised by anomalous meteorological conditions associated with the strong El Nino of 1997-1998 and intense wildfires, which produced a large amount of pollution. On a global scale, changing meteorology (winds, temperatures, humidity and clouds) is found to be the most important factor driving interannual variability of NO2 and ozone on the timescales considered. Changes in stratosphere-troposphere exchange, which are largely driven by meteorological variability, are found to play a particularly important role in driving ozone changes. The strong influence of emissions on NO2 and ozone interannual variability is largely confined to areas where intense biomass burning events occur. For CO, interannual variability is almost solely driven by emission changes, while for OH meteorology dominates, with the radiative influence of clouds being a very strong contributor. Through a simple attribution analysis for 1996-2000 we conclude that changing cloudiness drives 25% of the interannual variability of OH over Europe by affecting shortwave radiation. Over Indonesia this figure is as high as 71%. Changes in cloudiness contribute a small but non-negligible amount (up to 6%) to the interannual variability of ozone over Europe and Indonesia. This suggests that future assessments of trends in tropospheric oxidizing capacity should account for interannual variability in cloudiness, a factor neglected in many previous studies

Automated data pre-processing via meta-learning

The final publication is available at link.springer.comA data mining algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. As a matter of fact, a dataset usually needs to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and nonexperienced users become overwhelmed. We show that this problem can be addressed by an automated approach, leveraging ideas from metalearning. Specifically, we consider a wide range of data pre-processing techniques and a set of data mining algorithms. For each data mining algorithm and selected dataset, we are able to predict the transformations that improve the result of the algorithm on the respective dataset. Our approach will help non-expert users to more effectively identify the transformations appropriate to their applications, and hence to achieve improved results.Peer ReviewedPostprint (published version

Ozone loss derived from balloon-borne tracer measurements in the 1999/2000 Arctic winter

Balloon-borne measurements of CFC11 (from the DIRAC in situ gas chromatograph and the DESCARTES grab sampler), ClO and O3 were made during the 1999/2000 Arctic winter as part of the SOLVE-THESEO 2000 campaign, based in Kiruna (Sweden). Here we present the CFC11 data from nine flights and compare them first with data from other instruments which flew during the campaign and then with the vertical distributions calculated by the SLIMCAT 3D CTM. We calculate ozone loss inside the Arctic vortex between late January and early March using the relation between CFC11 and O3 measured on the flights. The peak ozone loss (~1200ppbv) occurs in the 440-470K region in early March in reasonable agreement with other published empirical estimates. There is also a good agreement between ozone losses derived from three balloon tracer data sets used here. The magnitude and vertical distribution of the loss derived from the measurements is in good agreement with the loss calculated from SLIMCAT over Kiruna for the same days

Potential and limitations of NARX for defect detection in guided wave signals

Previously, a nonlinear autoregressive network with exogenous input (NARX) demonstrated an excellent performance, far outperforming an established method in optimal baseline subtraction, for defect detection in guided wave signals. The principle is to train a NARX network on defect-free guided wave signals to obtain a filter that predicts the next point from the previous points in the signal. The trained network is then applied to new measurement and the output subtracted from the measurement to reveal the presence of defect responses. However, as shown in this paper, the performance of the previous NARX implementation lacks robustness; it is highly dependent on the initialisation of the network and detection performance sometimes improves and then worsens over the course of training. It is shown that this is due to the previous NARX implementation only making predictions one point ahead. Subsequently, it is shown that multi-step prediction using a newly proposed NARX structure creates a more robust training procedure, by enhancing the correlation between the training loss metric and the defect detection performance. The physical significance of the network structure is explored, allowing a simple hyperparameter tuning strategy to be used for determining the optimal structure. The overall detection performance of NARX is also improved by multi-step prediction, and this is demonstrated on defect responses at different times as well as on data from different sensor pairs, revealing the generalisability of this method

Evolution of breeding plumages in birds: A multiple-step pathway to seasonal dichromatism in New World warblers (Aves: Parulidae)

Ecology and Evolution published by John Wiley & Sons Ltd Many species of birds show distinctive seasonal breeding and nonbreeding plumages. A number of hypotheses have been proposed for the evolution of this seasonal dichromatism, specifically related to the idea that birds may experience variable levels of sexual selection relative to natural selection throughout the year. However, these hypotheses have not addressed the selective forces that have shaped molt, the underlying mechanism of plumage change. Here, we examined relationships between life-history variation, the evolution of a seasonal molt, and seasonal plumage dichromatism in the New World warblers (Aves: Parulidae), a family with a remarkable diversity of plumage, molt, and life-history strategies. We used phylogenetic comparative methods and path analysis to understand how and why distinctive breeding and nonbreeding plumages evolve in this family. We found that color change alone poorly explains the evolution of patterns of biannual molt evolution in warblers. Instead, molt evolution is better explained by a combination of other life-history factors, especially migration distance and foraging stratum. We found that the evolution of biannual molt and seasonal dichromatism is decoupled, with a biannual molt appearing earlier on the tree, more dispersed across taxa and body regions, and correlating with separate life-history factors than seasonal dichromatism. This result helps explain the apparent paradox of birds that molt biannually but show breeding plumages that are identical to the nonbreeding plumage. We find support for a two-step process for the evolution of distinctive breeding and nonbreeding plumages: That prealternate molt evolves primarily under selection for feather renewal, with seasonal color change sometimes following later. These results reveal how life-history strategies and a birds\u27 environment act upon multiple and separate feather functions to drive the evolution of feather replacement patterns and bird coloration

Do sophisticated evolutionary algorithms perform better than simple ones?

Evolutionary algorithms (EAs) come in all shapes and sizes. Theoretical investigations focus on simple, bare-bones EAs while applications often use more sophisticated EAs that perform well on the problem at hand. What is often unclear is whether a large degree of algorithm sophistication is necessary, and if so, how much performance is gained by adding complexity to an EA. We address this question by comparing the performance of a wide range of theory-driven EAs, from bare-bones algorithms like the (1+1) EA, a (2+1) GA and simple population-based algorithms to more sophisticated ones like the (1+(λ,λ)) GA and algorithms using fast (heavy-tailed) mutation operators, against sophisticated and highly effective EAs from specific applications. This includes a famous and highly cited Genetic Algorithm for the Multidimensional Knapsack Problem and the Parameterless Population Pyramid for Ising Spin Glasses and MaxSat. While for the Multidimensional Knapsack Problem the sophisticated algorithm performs best, surprisingly, for large Ising and MaxSat instances the simplest algorithm performs best. We also derive conclusions about the usefulness of populations, crossover and fast mutation operators. Empirical results are supported by statistical tests and contrasted against theoretical work in an attempt to link theoretical and empirical results on EAs

Melting during late-stage rifting in Afar is hot and deep

Investigations of a variety of continental rifts and margins worldwide have revealed that a considerable volume of melt can intrude into the crust during continental breakup, modifying its composition and thermal structure. However, it is unclear whether the cause of voluminous melt production at volcanic rifts is primarily increased mantle temperature or plate thinning. Also disputed is the extent to which plate stretching or thinning is uniform or varies with depth with the entire continental lithospheric mantle potentially being removed before plate rupture. Here we show that the extensive magmatism during rifting along the southern Red Sea rift in Afar, a unique region of sub-aerial transition from continental to oceanic rifting, is driven by deep melting of hotter-than-normal asthenosphere. Petrogenetic modelling shows that melts are predominantly generated at depths greater than 80 kilometres, implying the existence of a thick upper thermo-mechanical boundary layer in a rift system approaching the point of plate rupture. Numerical modelling of rift development shows that when breakup occurs at the slow extension rates observed in Afar, the survival of a thick plate is an inevitable consequence of conductive cooling of the lithosphere, even when the underlying asthenosphere is hot. Sustained magmatic activity during rifting in Afar thus requires persistently high mantle temperatures, which would allow melting at high pressure beneath the thick plate. If extensive plate thinning does occur during breakup it must do so abruptly at a late stage, immediately before the formation of the new ocean basin