On the coupling between the stratosphere and the troposphere

vii+111hlm.;24c

The stratosphere as a puppeteer of European winter climate

No abstract

Modeling Evidence that Ozone Depletion Has Impacted Extreme Precipitation in the Austral Summer

The impacts of stratospheric ozone depletion on the extremes of daily precipitation in the austral summer are explored using two global climate models. Both models indicate that stratospheric ozone losses since the late 1970s may have increased the frequency and intensity of very heavy precipitation in austral summer over southern high and subtropical latitudes, and may have decreased the frequency and intensity over southern midlatitudes. This hemispheric wide pattern of extreme precipitation response projects strongly onto a previously identified pattern of seasonal mean precipitation response, both of which are shown to be likely of dynamic rather than thermodynamic origin

Pharmacokinetic parameter sets of alfentanil revisited: optimal parameters for use in target controlled infusion and anaesthesia display systems

Background In open TCI and anaesthesia display systems, the choice of pharmacokinetic (PK) parameter sets of opioids is clinically relevant. Accuracy and bias of the PK models may be affected by administration mode and the co-administered hypnotic drug. We retrospectively evaluated the performance of eight PK parameter sets for alfentanil in two data sets (infusion and bolus application). Methods With the dosing history from two studies in orthopaedic patients anaesthetized with propofol or inhalation anaesthetics the alfentanil plasma concentration over time was calculated with eight PK parameter sets. Median absolute performance error (MDAPE), log accuracy, median performance error (MDPE), log bias, Wobble, and Divergence were computed. Mann-Whitney rank test with Bonferroni correction was used for comparison between bolus and infusion data, repeated measures analysis of variance on ranks was used for comparison among parameter sets. Results The parameters by Scott (original and weight adjusted) and Fragen had a MDAPE ≤30% and a median log accuracy <0.15 independent of the administration mode, while MDPE was within ±20% and log bias nearly within ±0.1, respectively. The sets by Maitre and Lemmens were within these limits only in the bolus data. All other parameter sets were outside these limits. Conclusions In healthy orthopaedic patients, the PK parameters by Scott and by Maitre were equally valid when alfentanil was given as repeated boluses. When given as infusion, the Maitre parameters were less accurate and subject to a significant bias. We cannot exclude that the difference between bolus and infusion is partially because of the different hypnotics use

The predictability of the extratropical stratosphere on monthly time-scales and its impact on the skill of tropospheric forecasts

This is the final version of the article. Available from Wiley via the DOI in this record.Extreme variability of the winter- and spring-time stratospheric polar vortex has been shown to affect extratropical tropospheric weather. Therefore, reducing stratospheric forecast error may be one way to improve the skill of tropospheric weather forecasts. In this review, the basis for this idea is examined. A range of studies of different stratospheric extreme vortex events shows that they can be skilfully forecasted beyond 5 days and into the sub-seasonal range (0–30 days) in some cases. Separate studies show that typical errors in forecasting a stratospheric extreme vortex event can alter tropospheric forecast skill by 5–7% in the extratropics on sub-seasonal time-scales. Thus understanding what limits stratospheric predictability is of significant interest to operational forecasting centres. Both limitations in forecasting tropospheric planetary waves and stratospheric model biases have been shown to be important in this context.This work is supported by the Natural Environmental Research Council (NERC) funded project Stratospheric Network for the Assessment of Predictability (SNAP) (Grant H5147600) and partially supported by the SPARC. ACP and RGH acknowledge funding through the EU ARISE project (Grant 284387) (EU-FP7). We also acknowledge Steven Pawson and Lawrence Coy from NASA for providing Figure 1. We wish to thank Lorenzo Polvani from Columbia University for providing Figure 4 and Amy Butler from NOAA for her contribution to Figure 5. We thank Adrian Simmons of ECMWF for his insightful review and two anonymous reviewers for their comments and suggestions that improved the quality of the manuscript

The Arctic predictability and prediction on seasonal-to-interannual timescales (APPOSITE) data set version 1

This is the final version of the article. Available from the publisher via the DOI in this record. Discussion paper (published on 15 Oct 2015)Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi- 5 model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model 10 intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate vari15 ability on these timescales, such as the El Niño Southern Oscillation.This work was supported by the Natural Environment Research Council (grant NE/I029447/1). Helge Goessling was supported by a fellowship of the German Research Foundation (DFG grant GO 2464/1-1). Data storage and processing capacity was kindly provided by the British Atmospheric Data Centre (BADC). Thanks to Yanjun Jiao (CCCma) for his assistance with the CanCM4 simulations and to Bill Merryfield for his comments on a draft of the pape