Ocean temperature and salinity components of the Madden-Julian oscillation observed by Argo floats

New diagnostics of the Madden-Julian Oscillation (MJO) cycle in ocean temperature and, for the first time, salinity are presented. The MJO composites are based on 4 years of gridded Argo float data from 2003 to 2006, and extend from the surface to 1,400 m depth in the tropical Indian and Pacific Oceans. The MJO surface salinity anomalies are consistent with precipitation minus evaporation fluxes in the Indian Ocean, and with anomalous zonal advection in the Pacific. The Argo sea surface temperature and thermocline depth anomalies are consistent with previous studies using other data sets. The near-surface density changes due to salinity are comparable to, and partially offset, those due to temperature, emphasising the importance of including salinity as well as temperature changes in mixed-layer modelling of tropical intraseasonal processes. The MJO-forced equatorial Kelvin wave that propagates along the thermocline in the Pacific extends down into the deep ocean, to at least 1,400 m. Coherent, statistically significant, MJO temperature and salinity anomalies are also present in the deep Indian Ocean

Virtual reality, ultrasound-guided liver biopsy simulator: Development and performance discrimination

Objectives: The aim of this article was to identify and prospectively investigate simulated ultrasound-guided targeted liver biopsy performance metrics as differentiators between levels of expertise in interventional radiology.Methods: Task analysis produced detailed procedural step documentation allowing identification of critical procedure steps and performance metrics for use in a virtual reality ultrasound-guided targeted liver biopsy procedure. Consultant (n=14; male=11, female=3) and trainee (n=26; male=19, female=7) scores on the performance metricswere compared. Ethical approval was granted by the Liverpool Research Ethics Committee (UK). Independent t-tests and analysis of variance (ANOVA) investigated differences between groups.Results: Independent t-tests revealed significant differences between trainees and consultants on three performance metrics: targeting, p=0.018, t=22.487 (22.040 to20.207); probe usage time, p=0.040, t=2.132 (11.064 to 427.983); mean needle length in beam, p=0.029, t=22.272 (20.028 to 20.002). ANOVA reported significant differences across years of experience (0–1, 1–2, 3+ years) on seven performance metrics: no-go area touched, p=0.012; targeting, p=0.025; length of session, p=0.024; probe usage time, p=0.025; total needle distance moved, p=0.038; number of skin contacts, p<0.001; total time in no-go area, p=0.008. More experienced participants consistently received better performance scores on all 19 performance metrics.Conclusion: It is possible to measure and monitor performance using simulation, with performance metrics providing feedback on skill level and differentiating levels of expertise. However, a transfer of training study is required

GCRF African SWIFT White Paper Policy Brief: The future of African weather forecasting

There is a huge opportunity for the African continent to benefit from the ‘silent revolution’ in weather forecasting that has been realised in the mid-latitudes throughout the twentieth century. While there are tremendous societal and economic benefits from advancing the science behind weather forecasting in sub-Saharan Africa, there are also significant barriers to realising advances. This policy brief examines the value of investment in African weather forecasting science and the technical & communication challenges that this will bring with wider implementation

GCRF African SWIFT and ForPAc SHEAR White Paper on the Potential of Operational Weather Prediction to Save Lives and Improve Livelihoods and Economies in Sub-Saharan Africa

The ‘silent revolution’ of numerical weather prediction (NWP) has led to significant social benefits and billions of dollars in economic benefits to mid-latitude countries, however the level of benefit in sub-Saharan Africa has been very limited, despite the potential to save lives, improve livelihoods, protect property and infrastructure and boost economies. Ongoing climate change in Africa, and the associated projected intensification of weather impacts in coming decades, makes the realisation of effective and more reliable weather forecasts and climate services even more urgent. It is widely recognised that to achieve this potential, investment is required in strengthening decision makers’ understanding of weather predictions and confidence in interpreting and appropriately applying forecasts, alongside transparent communication of the levels of skill and probability or certainty in forecast products. However, on all time scales of prediction, it is generally unrecognised that many forecasts that produce user-relevant metrics have such low skill that they are only marginally valuable to stakeholders, creating significant practical and ethical barriers to increasing uptake and generating benefits. Here, we present substantial evidence that even a modest investment in science for weather information and forecast techniques, to provide new technology and tools for Africa, can significantly increase the skill of user-relevant forecast products on all time scales. This will be a necessary enabler for building trust in and uptake of decision-relevant forecasts with the potential to deliver significant social and economic benefits. We present here an argument that incremental improvements in the skill of weather forecasting across all timescales in the African tropics, alongside strengthening communication and understanding of these forecasts, is fundamental to saving lives and enhancing livelihoods. Investing in the capacity and capability of National Meteorological Services and research institutions is essential to ensure lifesaving and life-enhancing services continue to be developed with and designed to serve the populations of sub-Saharan countries

Propagation of the Madden–Julian Oscillation and scale interaction with the diurnal cycle in a high-resolution GCM

The Madden–Julian Oscillation (MJO) is the chief source of tropical intra-seasonal variability, but is simulated poorly by most state-of-the-art GCMs. Common errors include a lack of eastward propagation at the correct frequency and zonal extent, and too small a ratio of eastward- to westward-propagating variability. Here it is shown that HiGEM, a high-resolution GCM, simulates a very realistic MJO with approximately the correct spatial and temporal scale. Many MJO studies in GCMs are limited to diagnostics which average over a latitude band around the equator, allowing an analysis of the MJO’s structure in time and longitude only. In this study a wider range of diagnostics is applied. It is argued that such an approach is necessary for a comprehensive analysis of a model’s MJO. The standard analysis of Wheeler and Hendon (Mon Wea Rev 132(8):1917–1932, 2004; WH04) is applied to produce composites, which show a realistic spatial structure in the MJO envelopes but for the timing of the peak precipitation in the inter-tropical convergence zone, which bifurcates the MJO signal. Further diagnostics are developed to analyse the MJO’s episodic nature and the “MJO inertia” (the tendency to remain in the same WH04 phase from one day to the next). HiGEM favours phases 2, 3, 6 and 7; has too much MJO inertia; and dies out too frequently in phase 3. Recent research has shown that a key feature of the MJO is its interaction with the diurnal cycle over the Maritime Continent. This interaction is present in HiGEM but is unrealistically weak

Indian summer monsoon onset forecast skill in the UK Met Office initialized coupled seasonal forecasting system (GloSea5-GC2)

Accurate and precise forecasting of the Indian monsoon is important for the socio-economic security of India, with improvements in agriculture and associated sectors from prediction of the monsoon onset. In this study we establish the skill of the UK Met Office coupled initialized global seasonal forecasting system, GloSea5-GC2, in forecasting Indian monsoon onset. We build on previous work that has demonstrated the good skill of GloSea5 at forecasting interannual variations of the seasonal mean Indian monsoon using measures of large-scale circulation and local precipitation. We analyze the summer hindcasts from a set of three springtime start-dates in late April/early May for the 20-year hindcast period (1992-2011). The hindcast set features at least fifteen ensemble members for each year and is analyzed using five different objective monsoon indices. These indices are designed to examine large and local-scale measures of the monsoon circulation, hydrological changes, tropospheric temperature gradient, or rainfall for single value (area-averaged) or grid-point measures of the Indian monsoon onset. There is significant correlation between onset dates in the model and those found in reanalysis. Indices based on large-scale dynamic and thermodynamic indices are better at estimating monsoon onset in the model rather than local-scale dynamical and hydrological indices. This can be attributed to the model's better representation of large-scale dynamics compared to local-scale features. GloSea5 may not be able to predict the exact date of monsoon onset over India, but this study shows that the model has a good ability at predicting category-wise monsoon onset, using early, normal or late tercile categories. Using a grid-point local rainfall onset index, we note that the forecast skill is highest over parts of central India, the Gangetic plains, and parts of coastal India - all zones of extensive agriculture in India. El Niño Southern Oscillation (ENSO) forcing in the model improves the forecast skill of monsoon onset when using a large-scale circulation index, with late monsoon onset coinciding with El Niño conditions and early monsoon onset more common in La Niña years. The results of this study suggest that GloSea5's ensemble-mean forecast may be used for reliable Indian monsoon onset prediction a month in advance despite systematic model errors

Geographic variation in the aetiology, epidemiology and microbiology of bronchiectasis

Bronchiectasis is a disease associated with chronic progressive and irreversible dilatation of the bronchi and is characterised by chronic infection and associated inflammation. The prevalence of bronchiectasis is age-related and there is some geographical variation in incidence, prevalence and clinical features. Most bronchiectasis is reported to be idiopathic however post-infectious aetiologies dominate across Asia especially secondary to tuberculosis. Most focus to date has been on the study of airway bacteria, both as colonisers and causes of exacerbations. Modern molecular technologies including next generation sequencing (NGS) have become invaluable tools to identify microorganisms directly from sputum and which are difficult to culture using traditional agar based methods. These have provided important insight into our understanding of emerging pathogens in the airways of people with bronchiectasis and the geographical differences that occur. The contribution of the lung microbiome, its ethnic variation, and subsequent roles in disease progression and response to therapy across geographic regions warrant further investigation. This review summarises the known geographical differences in the aetiology, epidemiology and microbiology of bronchiectasis. Further, we highlight the opportunities offered by emerging molecular technologies such as -omics to further dissect out important ethnic differences in the prognosis and management of bronchiectasis.NMRC (Natl Medical Research Council, S’pore)MOH (Min. of Health, S’pore)Published versio

The northern hemisphere circumglobal teleconnection in a seasonal forecast model and its relationship to European summer forecast skill

Forecasting seasonal variations in European summer weather represents a considerable challenge. Here, we assess the performance of a seasonal forecasting model at representing a major mode of northern hemisphere summer climate variability, the circumglobal teleconnection (CGT), and the implications of errors in its representation on sea7 sonal forecasts for the European summer (June, July, August). Using seasonal hindcasts initialised at the start of May, we find that the model skill for forecasting the interannual variability of 500 hPa geopotential height is poor, particularly over Europe and several other “centres of action” of the CGT. The model also has a weaker CGT pattern than is observed, particularly in August, when the observed CGT wavetrain is strongest. We investigate several potential causes of this poor skill. First, model variance in geopotential height in west-central Asia (an important region for the maintenance of the CGT) is lower than observed in July and August, associated with a poor representation of the link between this region and Indian monsoon precipitation. Second, analysis of the Rossby wave source shows that the source associated with monsoon heating is both too strong and displaced to the northeast in the model. This is related to errors in monsoon precipitation over the Bay of Bengal and Arabian Sea, where the model has more precipitation than is observed. Third the model jet is systematically shifted northwards by several degrees latitude over large parts of the northern hemisphere, which may affect the propagation characteristics of Rossby waves in the model