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Validation of Aeolus winds using ground-based radars in Antarctica and in northern Sweden
No full textWinds measured by lidar from the Aeolus satellite are compared with winds measured by two ground-based radars - MARA in Antarctica (70.77 degrees S, 11.73 degrees E) and ES-RAD (67.88 degrees N, 21.10 degrees E) in Arctic Sweden - for the period 1 July-31 December 2019. Aeolus is a demonstrator mission to test whether winds measured by Doppler lidar from space can have sufficient accuracy to contribute to improved weather forecasting. A comprehensive programme of calibration and validation has been undertaken following the satellite launch in 2018, but, so far, direct comparison with independent measurements from the Arctic or Antarctic regions have not been made. The comparison covers heights from the low troposphere to just above the tropopause. Results for each radar site are presented separately for Rayleigh (clear) winds, Mie (cloudy) winds, sunlit ("summer") and non-sunlit ("winter") seasons, and ascending and descending satellite tracks. Horizontally projected line-of-sight (HLOS) winds from Aeolus, reprocessed using baseline 2B10, for passes within 100 km of the radar sites, are compared with HLOS winds calculated from 1 h averaged radar horizontal wind components. The agreement in most data subsets is very good, with no evidence of significant biases (<1ms(-1)). Possible biases are identified for two subsets (about -2ms(-1) for the Rayleigh winds for the descending passes at MARA and about 2ms(-1) for the Mie winds for the ascending passes at ESRAD, both in winter), but these are only marginally significant. A robust significant bias of about 7ms(-1) is found for the Mie winds for the ascending tracks at MARA in summer. There is also some evidence for increased random error (by about 1ms(-1) / for the Aeolus Mie winds at MARA in summer compared to winter. This might be related to the presence of sunlight scatter over the whole of Antarctica as Aeolus transits across it during summer
Towards advancing scientific knowledge of climate change impacts on short-duration rainfall extremes
No full textStudy of Urban Heat Islands Using Different Urban Canopy Models and Identification Methods
No full textBacterial and Archaeal Communities and Bottom Waters of the Abyssal Patterns and Future Monitoring Considerations
No full textBacteria and archaea are key contributors to deep-sea biogeochemical cycles and food webs. The disruptions these microbial communities may experience during and following polymetallic nodule mining in the Clarion-Clipperton Zone (CCZ) of the North Pacific Ocean could therefore have broad ecological effects. Our goals in this synthesis are to characterize the current understanding of biodiversity and biogeography of bacteria and archaea in the CCZ and to identify gaps in the baseline data and sampling approaches, prior to the onset of mining in the region. This is part of a large effort to compile biogeographic patterns in the CCZ, and to assess the representivity of no-mining Areas of Particular Environmental Interest, across a range of taxa. Here, we review published studies and an additional new dataset focused on 16S ribosomal RNA (rRNA) gene amplicon characterization of abyssal bacterial and archaeal communities, particularly focused on spatial patterns. Deep-sea habitats (nodules, sediments, and bottom seawater) each hosted significantly different microbial communities. An east-vs.-west CCZ regional distinction was present in nodule communities, although the magnitude was small and likely not detectable without a high-resolution analysis. Within habitats, spatial variability was driven by differences in relative abundances of taxa, rather than by abundant taxon turnover. Our results further support observations that nodules in the CCZ have distinct archaeal communities from those in more productive surrounding regions, with higher relative abundances of presumed chemolithoautotrophic Nitrosopumilaceae suggesting possible trophic effects of nodule removal. Collectively, these results indicate that bacteria and archaea in the CCZ display previously undetected, subtle, regional-scale biogeography. However, the currently available microbial community surveys are spatially limited and suffer from sampling and analytical differences that frequently confound inter-comparison; making definitive management decisions from such a limited dataset could be problematic. We suggest a number of future research priorities and sampling recommendations that may help to alleviate dataset incompatibilities and to address challenges posed by rapidly advancing DNA sequencing technology for monitoring bacterial and archaeal biodiversity in the CCZ. Most critically, we advocate for selection of a standardized 16S rRNA gene amplification approach for use in the anticipated large-scale, contractor driven biodiversity monitoring in the region
Business-as-usual will lead to super and ultra-extreme heatwaves in the Middle East and North Africa
No full textImpact of ocean heat transport on the Arctic sea-ice decline : a model study with EC-Earth3
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Determining maximal achievable effect sizes of antidepressant therapies in placebo-controlled trials
No full textObjective Antidepressants outperform placebo with an effect size of around 0.30. It has been suggested that effect sizes as high as 0.875 are necessary for a minimal clinically important difference. Whether such effect sizes are achievable in placebo-controlled trials is unknown. Therefore, we aimed to assess what effect sizes are theoretically achievable in placebo-controlled trials of antidepressants. Methods Patient-level analyses comparing Hamilton Depression Rating Scale (HDRS-17) outcomes for simulated antidepressant therapies to placebo-treated participants (n = 2201) from clinical trials of selective serotonin reuptake inhibitors. Results An optimally effective antidepressant, where all treated participants achieve HDRS-17 scores comparable to those displayed by healthy volunteers (remission-type model), had a maximum effect size of 1.75, with a mean difference of 11.6 points on the HDRS-17. In simulations where patients received an additional 50% symptom reduction over that obtained with placebo (improvement-type model), the maximum effect size was 1.08 with a mean HDRS-17 difference of 7.2. When adjusting for normal rates of treatment discontinuation, maximum effect sizes were 1.10 (remission-type model) and 0.76 (improvement-type model) with HDRS-17 mean differences of 8.8 and 5.6, respectively. Conclusions Three methodological issues (i) a large and variable placebo response, (ii) a high rate of dropout and (iii) HDRS-17-ratings significantly larger than zero in healthy volunteers, reduce the degree of treatment-placebo separation achievable in depression trials. Assuming that those who discontinue treatment have only partial response, even a highly effective antidepressant would have difficulties surpassing such effect size cut-offs as have been suggested to signify a minimal clinically important difference