Utilisation of pain counselling in osteopathic practice: Secondary analysis of a nationally representative sample of australian osteopaths

Objectives Advice, reassurance and education are recommended as first line treatments for musculoskeletal pain conditions such as low back pain. Osteopaths are registered primary contact allied health professionals in the Australian healthcare system who primarily manage acute and chronic musculoskeletal pain conditions. This study aimed to investigate the proportion of Australian osteopaths who do and do not utilise advice, reassurance and education (pain counselling) in their clinical practice, and determine the characteristics associated with the frequency of using pain counselling in clinical practice. Methods A secondary analysis of practice characteristics from a nationally representative sample of Australian osteopaths was undertaken. Participants completed a 27-item practice characteristics questionnaire between July-December 2016. Bivariate analyses were used to identify significant variables for inclusion in a backward multiple logistic regression model. Adjusted odds ratios (OR) were calculated for significant variables. Results Responses were received from 991 Australian osteopaths, representing 49% of the profession. Of these 264 (26.64%) indicated often utilising pain counselling, and 727 (73.36%) reported not often utilising pain counselling. Those who utilised pain counselling were more than twice as likely to report research evidence had a high impact on their clinical practice (OR 2.11), and nearly twice as likely to discuss physical activity with their patients (OR 1.84). Conclusions Pain counselling is under-utilised by nearly three quarters of the Australian osteopathic profession as a management strategy. Future studies are required to explore the reasons why most in the profession comprised in this sample are infrequently utilising this guideline recommendation. Given the frequency of chronic musculoskeletal pain conditions presenting to Australian osteopaths, strategies appear to be needed to advance the profession via professional development in accessing and using evidence-based care for pain conditions

Global analysis of the controls on seawater dimethylsulfide spatial variability

Dimethylsulfide (DMS) emitted from the ocean makes a significant global contribution to natural marine aerosol and cloud condensation nuclei, and therefore our planet&rsquo;s climate. Oceanic DMS concentrations show large spatiotemporal variability, but observations are sparse, so products describing global DMS distribution rely on interpolation or modelling. Understanding the mechanisms driving DMS variability, especially at local scales, is required to reduce uncertainty in large scale DMS estimates. We present a study of mesoscale and sub-mesoscale (&lt;100 km) seawater DMS variability that takes advantage of the recent expansion in high frequency seawater DMS observations and uses all available data to investigate the typical distances over which DMS varies in all major ocean basins. These DMS spatial variability lengthscales (VLS) are uncorrelated with DMS concentrations. DMS concentrations and VLS can therefore be used separately to help identify mechanisms underpinning DMS variability. When data are grouped by sampling campaigns, almost 80 % of the DMS VLS can be explained using the VLS of sea surface height anomalies, density, and chlorophyll-a. Our global analysis suggests that both physical and biogeochemical processes play an equally important role in controlling DMS variability, in contrast with previous results based on data from the low&ndash;mid latitudes. The explanatory power of sea surface height anomalies indicates the importance of mesoscale eddies in driving DMS variability, previously unrecognised at a global scale and in agreement with recent regional studies. DMS VLS differs regionally, including surprisingly high frequency variability in low latitude waters. Our results independently confirm that relationships used in the literature to parameterise DMS at large scales appear to be considering the right variables. However, contrasts in regional DMS VLS highlight that important driving mechanisms remain elusive. The role of sub-mesoscale features should be resolved or accounted for in DMS process models and parameterisations. Future attempts to map DMS distributions should consider the length scale of variability.</p

Evaluation of SO2, SO42− and an updated SO2 dry deposition parameterization in UKESM1

In this study we evaluate simulated surface SO2 and sulfate (SO42-) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground-based measurement networks in the USA and Europe for the period 1987–2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO42- in both Europe and the USA over the period 1987–2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3 while under-predicting surface SO42- concentrations by 25 %–35 %. In the cleaner western USA, the model over-predicts both surface SO2 and SO42- concentrations by factors of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO42- concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model's global performance, finding that UKESM1 over-predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA, and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model's SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO42-. In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the model's over-prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations, the simulated surface SO42- concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase. </jats:p

The role of anthropogenic aerosols in the anomalous cooling from 1960 to 1990 in the CMIP6 Earth System Models

The Earth system models (ESMs) that participated in the sixth Coupled Model Intercomparison Project (CMIP6) tend to simulate excessive cooling in surface air temperature (TAS) between 1960 and 1990. The anomalous cooling is pronounced over the Northern Hemisphere (NH) midlatitudes, coinciding with the rapid growth of anthropogenic sulfur dioxide (SO2) emissions, the primary precursor of atmospheric sulfate aerosols. Structural uncertainties between ESMs have a larger impact on the anomalous cooling than internal variability. Historical simulations with and without anthropogenic aerosol emissions indicate that the anomalous cooling in the ESMs is attributed to the higher aerosol burden in these models. The aerosol forcing sensitivity, estimated as the outgoing shortwave radiation (OSR) response to aerosol concentration changes, cannot well explain the diversity of pothole cooling (PHC) biases in the ESMs. The relative contributions to aerosol forcing sensitivity from aerosol–radiation interactions (ARIs) and aerosol–cloud interactions (ACIs) can be estimated from CMIP6 simulations. We show that even when the aerosol forcing sensitivity is similar between ESMs, the relative contributions of ARI and ACI may be substantially different. The ACI accounts for between 64 % and 87 % of the aerosol forcing sensitivity in the models and is the main source of the aerosol forcing sensitivity differences between the ESMs. The ACI can be further decomposed into a cloud-amount term (which depends linearly on cloud fraction) and a cloud-albedo term (which is independent of cloud fraction, to the first order), with the cloud-amount term accounting for most of the inter-model differences

UK community Earth system modelling for CMIP6

We describe the approach taken to develop the UK’s first community Earth System model, UKESM1. This is a joint effort involving the Met Office and the Natural Environment Research Council (NERC), representing the UK academic community. We document our model development procedure and the subsequent UK submission to CMIP6, based on a traceable hierarchy of coupled physical and Earth system models. UKESM1 builds on the well‐established, world‐leading HadGEM models of the physical climate system and incorporates cutting‐edge new representations of aerosols, atmospheric chemistry, terrestrial carbon and nitrogen cycles, and an advanced model of ocean biogeochemistry. A high‐level metric of overall performance shows that both the physical model, HadGEM3‐GC3.1 and UKESM1 perform better than most other CMIP6 models so far submitted for a broad range of variables. We point to much more extensive evaluation performed in other papers in this special issue. The merits of not using any forced climate change simulations within our model development process are discussed. First results from HadGEM3‐GC3.1 and UKESM1 include the emergent climate sensitivity (5.5K and 5.4K respectively) which is high relative to the current range of CMIP5 models. The role of cloud microphysics and cloud‐aerosol interactions in driving the climate sensitivity, and the systematic approach taken to understand this role is highlighted in other papers in this special issue. We place our findings within the broader modelling landscape indicating how our understanding of key processes driving higher sensitivity in the two UK models seems to align with results from a number of other CMIP6 models

Evaluation of ocean dimethylsulfide concentration and emission in CMIP6 models

Characteristics and trends of surface ocean dimethylsulfide (DMS) concentrations and fluxes into the atmosphere of four Earth system models (ESMs: CNRM-ESM2-1, MIROC-ES2L, NorESM2-LM, and UKESM1-0-LL) are analysed over the recent past (1980–2009) and into the future, using Coupled Model Intercomparison Project 6 (CMIP6) simulations. The DMS concentrations in historical simulations systematically underestimate the most widely used observed climatology but compare more favourably against two recent observation-based datasets. The models better reproduce observations in mid to high latitudes, as well as in polar and westerlies marine biomes. The resulting multi-model estimate of contemporary global ocean DMS emissions is 16–24 Tg S yr−1, which is narrower than the observational-derived range of 16 to 28 Tg S yr−1. The four models disagree on the sign of the trend of the global DMS flux from 1980 onwards, with two models showing an increase and two models a decrease. At the global scale, these trends are dominated by changes in surface DMS concentrations in all models, irrespective of the air–sea flux parameterisation used. In turn, three models consistently show that changes in DMS concentrations are correlated with changes in marine productivity; however, marine productivity is poorly constrained in the current generation of ESMs, thus limiting the predictive ability of this relationship. In contrast, a consensus is found among all models over polar latitudes where an increasing trend is predominantly driven by the retreating sea-ice extent. However, the magnitude of this trend between models differs by a factor of 3, from 2.9 to 9.2 Gg S decade−1 over the period 1980–2014, which is at the low end of a recent satellite-derived analysis. Similar increasing trends are found in climate projections over the 21st century

Operational Dust Prediction

Over the last few years, numerical prediction of dust aerosol concentration has become prominent at several research and operational weather centres due to growing interest from diverse stakeholders, such as solar energy plant managers, health professionals, aviation and military authorities and policymakers. Dust prediction in numerical weather prediction-type models faces a number of challenges owing to the complexity of the system. At the centre of the problem is the vast range of scales required to fully account for all of the physical processes related to dust. Another limiting factor is the paucity of suitable dust observations available for model, evaluation and assimilation. This chapter discusses in detail numerical prediction of dust with examples from systems that are currently providing dust forecasts in near real-time or are part of international efforts to establish daily provision of dust forecasts based on multi-model ensembles. The various models are introduced and described along with an overview on the importance of dust prediction activities and a historical perspective. Assimilation and evaluation aspects in dust prediction are also discussed

Comparison of particle number size distribution trends in ground measurements and climate models

Despite a large number of studies, out of all drivers of radiative forcing, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in nucleation, Aitken, and accumulation modes at 21 measurement sites in Europe and the Arctic. For 13 of those sites, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five earth system models (ESMs). We found that the trends of particle number concentrations were mostly consistent and decreasing in both measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, was typically stronger at northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories: for EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, and for other models the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showed clear differences in concentrations between remote and urban sites. To conclude, although all of the model simulations had identical input data to describe anthropogenic mass emissions, trends in differently sized particles vary among the models due to assumptions in emission sizes and differences in how models treat size-dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol-cloud interactions. Our analysis also indicates that between models there is a large variation in efficiency of long-range transportation of aerosols to remote locations. The differences in model results are most likely due to the more complex effect of different processes instead of one specific feature (e.g. the representation of aerosol or emission size distributions). Hence, a more detailed characterization of microphysical processes and deposition processes affecting the long-range transport is needed to understand the model variability.Peer reviewe

Comparison of particle number size distribution trends in ground measurements and climate models

Despite a large number of studies, out of all drivers of radiative forcing, the effect of aerosols has the largest uncertainty in global climate model radiative forcing estimates. There have been studies of aerosol optical properties in climate models, but the effects of particle number size distribution need a more thorough inspection. We investigated the trends and seasonality of particle number concentrations in nucleation, Aitken, and accumulation modes at 21 measurement sites in Europe and the Arctic. For 13 of those sites, with longer measurement time series, we compared the field observations with the results from five climate models, namely EC-Earth3, ECHAM-M7, ECHAM-SALSA, NorESM1.2, and UKESM1. This is the first extensive comparison of detailed aerosol size distribution trends between in situ observations from Europe and five earth system models (ESMs). We found that the trends of particle number concentrations were mostly consistent and decreasing in both measurements and models. However, for many sites, climate models showed weaker decreasing trends than the measurements. Seasonal variability in measured number concentrations, quantified by the ratio between maximum and minimum monthly number concentration, was typically stronger at northern measurement sites compared to other locations. Models had large differences in their seasonal representation, and they can be roughly divided into two categories: for EC-Earth and NorESM, the seasonal cycle was relatively similar for all sites, and for other models the pattern of seasonality varied between northern and southern sites. In addition, the variability in concentrations across sites varied between models, some having relatively similar concentrations for all sites, whereas others showed clear differences in concentrations between remote and urban sites. To conclude, although all of the model simulations had identical input data to describe anthropogenic mass emissions, trends in differently sized particles vary among the models due to assumptions in emission sizes and differences in how models treat size-dependent aerosol processes. The inter-model variability was largest in the accumulation mode, i.e. sizes which have implications for aerosol-cloud interactions. Our analysis also indicates that between models there is a large variation in efficiency of long-range transportation of aerosols to remote locations. The differences in model results are most likely due to the more complex effect of different processes instead of one specific feature (e.g. the representation of aerosol or emission size distributions). Hence, a more detailed characterization of microphysical processes and deposition processes affecting the long-range transport is needed to understand the model variability