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IntroductionBoth mean power output (MPO) and the distribution of the available energy over the race, that is, pacing strategy, are critical factors in performance. The purpose of this study was to determine the relative importance of both pacing strategy and MPO to performance.MethodsSix well-trained, regionally competitive cyclists performed four 1500-m ergometer time trials (∼2 min). For each subject, the fastest (Fast) and slowest (Slow) time trials were compared and the relative importance of differences in power output and pacing strategy were determined with an energy flow model.ResultsThe difference in final time between Fast and Slow was 4.0 (2.5) s. Fast was performed with a higher MPO (437.8 (32.3) W vs 411.3 (39.0) W), a higher aerobic peak power (295.3 (36.8) vs 287.5 (34.7) W) and a higher anaerobic peak power (828.8 (145.4) W vs 649.5 (112.2) W) combined with a relatively higher, but not statistically different anaerobic rate constant (0.051 (0.016) vs 0.041 (0.009) W). The changes in MPO (63% anaerobic, 37% aerobic) largely explained the differences in final times. Athletes chose a different pacing strategy that was close to optimal for their physiological condition in both Fast and Slow.ConclusionDifferences in intraindividual performance were mainly caused by differences in MPO. Athletes seemed to be able to effectively adjust their pacing profile based on their “status of the day”.Keywordsmodelling performance, energy expenditure, aerobic, anaerobic, sports.

Recently, in the economics literature, several papers have put forward arguments for using a declining discount rate in social-cost benefit analysis. This paper examines the impact of employing a declining discount rate on the social cost of carbon-the marginal social damage from a ton of emitted carbon. Six declining discounting schemes are implemented in the FUND 2.8 integrated assessment model, including the recent amendments to the Green Book of HM Treasury (Treasury, H.M., 2003. The Greenbook: Appraisal and Evaluation in Central Government. TSO, London). We find that using a declining schedule of discount rates increases the social cost of carbon estimate by as little as 10% or by as much as a factor of 40, depending upon the scenario selected. Although the range of plausible estimates is large, using declining discounting schemes in FUND 2.8 in most cases does not yield values at the £70/tC level suggested by UK DEFRA [Clarkson, R., Deyes, K., 2002. Estimating the social cost of carbon emissions. Government Economic Service Working Paper. HM Treasury, London]. Indeed, only at the higher end of the values of social cost of carbon found here would many climate change related policies - such as the Kyoto Protocol - pass a cost-benefit analysis. This conclusion, however, does not necessarily undermine the ethical and political economic reasons for supporting international collective action on climate change. © 2006 Elsevier Ltd. All rights reserved.

South Africa is likely to experience higher temperatures and less rainfall as a result of climate change. Resulting changes in regional water endowments and soil moisture will affect the productivity of cropland, leading to changes in food production and international trade patterns. High population growth elsewhere in Africa and Asia will put further pressure on natural resources and food security in South Africa. Based on four climate change scenarios from two general circulation models (CSIRO and MIROC) and two IPCC SRES emission scenarios (A1B, B1), this study assesses the potential impacts of climate change on global agriculture and explores two alternative adaptation scenarios for South Africa. The analysis uses an updated GTAP-W model, which distinguishes between rainfed and irrigated agriculture and implements water as an explicit factor of production for irrigated agriculture. For South Africa to adapt to the adverse consequences of global climate change, it would require yield improvements of more than 20 percent over baseline investments in agricultural research and development. A doubling of irrigation development, on the other hand, will not be sufficient to reverse adverse impacts from climate change in the country.

A claim has been that 97% of the scientific literature endorses anthropogenic climate change (Cook et al., 2013. Environ. Res. Lett. 8, 024024). This claim, frequently repeated in debates about climate policy, does not stand. A trend in composition is mistaken for a trend in endorsement. Reported results are inconsistent and biased. The sample is not representative and contains many irrelevant papers. Overall, data quality is low. Cook's validation test shows that the data are invalid. Data disclosure is incomplete so that key results cannot be reproduced or tested. © 2014 Elsevier Ltd.

Climate Data Records of soil moisture are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. To respond to this need, in 2012 the European Space Agency (ESA) released the first multi-decadal, global satellite-observed soil moisture (SM) dataset as part of its Climate Change Initiative (CCI) program. This product, named ESA CCI SM, combines various single-sensor active and passive microwave soil moisture products into three harmonised products: a merged ACTIVE, a merged PASSIVE, and a COMBINED active + passive microwave product. Compared to the first product release, the latest version of ESA CCI SM includes a large number of enhancements, incorporates various new satellite sensors, and extends its temporal coverage to the period 1978–2015. In this study, we first provide a comprehensive overview of the characteristics, evolution, and performance of the ESA CCI SM products. Based on original research and a review of existing literature we show that the product quality has steadily increased with each successive release and that the merged products generally outperform the single-sensor input products. Although ESA CCI SM generally agrees well with the spatial and temporal patterns estimated by land surface models and observed in-situ, we identify surface conditions (e.g., dense vegetation, organic soils) for which it still has large uncertainties. Second, capitalising on the results of > 100 research studies that made use of the ESA CCI SM data we provide a synopsis of how it has contributed to improved process understanding in the following Earth system domains: climate variability and change, land-atmosphere interactions, global biogeochemical cycles and ecology, hydrological and land surface modelling, drought applications, and meteorology. While in some disciplines the use of ESA CCI SM is already widespread (e.g. in the evaluation of model soil moisture states) in others (e.g. in numerical weather prediction or flood forecasting) it is still in its infancy. The latter is partly related to current shortcomings of the product, e.g., the lack of near-real-time availability and data gaps in time and space. This study discloses the discrepancies between current ESA CCI SM product characteristics and the preferred characteristics of long-term satellite soil moisture products as outlined by the Global Climate Observing System (GCOS), and provides important directions for future ESA CCI SM product improvements to bridge these gaps.

Dispersed transient absorption spectra collected at variable excitation intensities in combination with time-resolved signals were used to explore the underlying connectivity of the electronic excited-state manifold of the carotenoid rhodopin glucoside in the light-harvesting 2 complex isolated from Rhodopseudomonas acidophila. We find that the S state, which was recently identified as an excited state in carotenoids bound in bacterial light-harvesting complexes, exhibits a different response to the increase of excitation intensity than the S(1) state, which suggests that the models used so far to describe the excited states of carotenoids are incomplete. We propose two new models that can describe both the time-resolved and the intensity-dependent data; the first postulates that S(1) and S* are not populated in parallel after the decay of the initially excited S(2) state but instead result from the excitation of distinct ground-state subpopulations. The second model introduces a resonantly enhanced light-induced transition during excitation, which promotes population to higher-lying excited states that favors the formation of S* over S(1). Multiwavelength target analysis of the time-resolved and excitation-intensity dependence measurements were used to characterize the involved states and their responses. We show that both proposed models adequately fit the measured data, although it is not possible to determine which model is most apt. The physical origins and implications of both models are explored.

In this study we evaluate the skill of a new, merged soil moisture product (ECV_SM) that has been developed in the framework of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative projects. The product combines in a synergistic way the soil moisture retrievals from four passive (SMMR, SSM/I, TMI, and AMSR-E) and two active (ERS AMI and ASCAT) coarse resolution microwave sensors into a global data set spanning the period 1979-2010. The evaluation uses ground-based soil moisture observations of 596 sites from 28 historical and active monitoring networks worldwide. Besides providing conventional measures of agreement, we use the triple collocation technique to assess random errors in the data set. The average Spearman correlation coefficient between ECV_SM and all in-situ observations is 0.46 for the absolute values and 0.36 for the soil moisture anomalies, but differences between networks and time periods are very large. Unbiased root-mean-square differences and triple collocation errors show less variation between networks, with average values around 0.05 and 0.04m3m-3, respectively. The ECV_SM quality shows an upward trend over time, but a consistent decrease of all performance metrics is observed for the period 2007-2010. Comparing the skill of the merged product with the skill of the individual input products shows that the merged product has a similar or better performance than the individual input products, except with regard to the ASCAT product, compared to which the performance of ECV_SM is inferior. The cause of the latter is most likely a combination of the mismatch in sampling time between the satellite observations and in-situ measurements, and the resampling and scaling strategy used to integrate the ASCAT product into ECV_SM on the other. The results of this study will be used to further improve the scaling and merging algorithms for future product updates.

In acknowledgement of the complexity of environmental challenges, research on learning in environmental policy has grown substantially over the past two decades across a range of disciplines. Despite this growth, there are few comprehensive assessments of the literature on learning in environmental policy. This article fills this gap by providing insights on the overall coherence and impact of this body of scholarship. To do so, we analyze a sample of 163 articles from 2004 to 2014 using a standardized coding framework. The results provide an in-depth assessment of the status of the literature on learning in the context of environmental policy, as well as the quality of the literature. We demonstrate that despite the diversity in research questions and goals, the literature is lacking with respect to diversity in cases and context, theoretical development, clear conceptualization and operationalization of learning, and advancements in empirical approaches to study learning. From these insights, we discuss the challenges and opportunities for scholars in studying learning and provide recommendations for building the theoretical and methodological rigor of the field.