The critical role of volcano monitoring in risk reduction

International audienceData from volcano-monitoring studies constitute the only scientifically valid basis for short-term forecasts of a future eruption, or of possible changes during an ongoing eruption. Thus, in any effective hazards-mitigation program, a basic strategy in reducing volcano risk is the initiation or augmentation of volcano monitoring at historically active volcanoes and also at geologically young, but presently dormant, volcanoes with potential for reactivation. Beginning with the 1980s, substantial progress in volcano-monitoring techniques and networks ? ground-based as well space-based ? has been achieved. Although some geochemical monitoring techniques (e.g., remote measurement of volcanic gas emissions) are being increasingly applied and show considerable promise, seismic and geodetic methods to date remain the techniques of choice and are the most widely used. Availability of comprehensive volcano-monitoring data was a decisive factor in the successful scientific and governmental responses to the reawakening of Mount St. elens (Washington, USA) in 1980 and, more recently, to the powerful explosive eruptions at Mount Pinatubo (Luzon, Philippines) in 1991. However, even with the ever-improving state-of-the-art in volcano monitoring and predictive capability, the Mount St. Helens and Pinatubo case histories unfortunately still represent the exceptions, rather than the rule, in successfully forecasting the most likely outcome of volcano unrest

Area deprivation across the life course and physical capability in mid-life: findings from the 1946 British Birth Cohort

Physical capability in later life is influenced by factors occurring across the life course, yet exposures to area conditions have only been examined cross-sectionally. Data from the National Survey of Health and Development, a longitudinal study of a 1946 British birth cohort, were used to estimate associations of area deprivation (defined as percentage of employed people working in partly skilled or unskilled occupations) at ages 4, 26, and 53 years (residential addresses linked to census data in 1950, 1972, and 1999) with 3 measures of physical capability at age 53 years: grip strength, standing balance, and chair-rise time. Cross-classified multilevel models with individuals nested within areas at the 3 ages showed that models assessing a single time point underestimate total area contributions to physical capability. For balance and chair-rise performance, associations with area deprivation in midlife were robust to adjustment for individual socioeconomic position and prior area deprivation (mean change for a 1-standard-deviation increase: balance, −7.4% (95% confidence interval (CI): −12.8, −2.8); chair rise, 2.1% (95% CI: −0.1, 4.3)). In addition, area deprivation in childhood was related to balance after adjustment for childhood socioeconomic position (−5.1%, 95% CI: −8.7, −1.6). Interventions aimed at reducing midlife disparities in physical capability should target the socioeconomic environment of individuals—for standing balance, as early as childhood

Assessing the Impact of Lead and Floe Sampling on Arctic Sea Ice Thickness Estimates from Envisat and CryoSat‐2

Multidecadal observations of sea ice thickness, in addition to those available for extent, are key to understanding long‐term variations and trends in the amount of Arctic sea ice. The European Space Agency's Envisat (2002–2010) and CryoSat‐2 (2010–present) satellite radar altimeter missions provide a continuous 17‐year dataset with the potential to estimate sea ice thickness. However, the satellite footprints are not equal in area and so different distributions of floes and leads are sampled by each mission. Here, we compare lead and floe sampling from Envisat and CryoSat‐2 to investigate the impact of geometric sampling differences on Arctic sea ice thickness estimates. We find that Envisat preferentially samples wider, thicker sea ice floes, and that floes in less consolidated ice regions are effectively thickened by off‐nadir ranging to leads. Consequently, Envisat sea ice thicknesses that are an average of 80 cm higher than CryoSat‐2 over first‐year ice and 23 cm higher over multiyear ice. By considering the along‐track distances between lead and floe measurements, we are able to develop a sea ice thickness correction that is based on Envisat's inability to resolve discrete surfaces relative to CryoSat‐2. This is a novel, physically based approach to addressing the bias between the satellites and reduces the average thickness difference to negligible values over first‐year and multiyear ice. Finally, we evaluate our new bias‐corrected Envisat sea ice thickness product using independent airborne, moored‐buoy and submarine data. The European Space Agency's Envisat and CryoSat‐2 satellites have the potential to produce a continuous record of Arctic sea ice thickness since 2002, but this is complicated by the fact that the satellites do not sample the sea ice surface in the same way. We find that Envisat is only able to sample larger, thicker sea ice relative to CryoSat‐2, because of its poorer resolution. In this paper we account for these differences in sampling to combine Arctic sea ice thickness estimates from two the satellite missions. Applying a sea ice thickness bias correction to Envisat data reduces the ice thickness difference between Envisat and CryoSat‐2 from an average of 53.0 to 0.5 c

Improving upon the efficiency of complete case analysis when covariates are MNAR.

Missing values in covariates of regression models are a pervasive problem in empirical research. Popular approaches for analyzing partially observed datasets include complete case analysis (CCA), multiple imputation (MI), and inverse probability weighting (IPW). In the case of missing covariate values, these methods (as typically implemented) are valid under different missingness assumptions. In particular, CCA is valid under missing not at random (MNAR) mechanisms in which missingness in a covariate depends on the value of that covariate, but is conditionally independent of outcome. In this paper, we argue that in some settings such an assumption is more plausible than the missing at random assumption underpinning most implementations of MI and IPW. When the former assumption holds, although CCA gives consistent estimates, it does not make use of all observed information. We therefore propose an augmented CCA approach which makes the same conditional independence assumption for missingness as CCA, but which improves efficiency through specification of an additional model for the probability of missingness, given the fully observed variables. The new method is evaluated using simulations and illustrated through application to data on reported alcohol consumption and blood pressure from the US National Health and Nutrition Examination Survey, in which data are likely MNAR independent of outcome

A Simulation of Snow on Antarctic Sea Ice Based on Satellite Data and Climate Reanalyses

Although snow plays an important role in the energy and mass balance of sea ice, it is little studied in the Southern Ocean. We present a Lagrangian model of snow on sea ice, CASSIS, that simulates the daily creation and drift of floes. Drifting floes accumulate snow from the atmosphere and the Antarctic ice sheet, and lose snow to the ocean and snow-ice formation. The depth of snow on Southern Ocean sea ice increases in all sectors between autumn and spring 1981–2021, reaching 40 cm in much of the Weddell Sea, coastal Amundsen Sea and south east Indian Ocean. The root mean square difference between seasonally-averaged model and ship-based snow depths is 13.1 cm, and between modeled and airborne snow depths from Operation IceBridge is 13.5 cm. Our model offers an alternative long-term snow depth record to that from passive microwave (PM) radiometry, which does not capture the seasonal growth of the snow cover. We find that although the average circumpolar snow layer thickness has increased by 16 mm between 1981 and 2021 (P = 0.004), there has been a decrease of 13 mm in the Southern Pacific Ocean (P = 0.133, but significant in spring and autumn), driven by a reduction of summer sea ice extent in this region. Our model paves the way for improved satellite-based estimates of Antarctic sea ice thickness

Appropriate inclusion of interactions was needed to avoid bias in multiple imputation

OBJECTIVE: Missing data are a pervasive problem, often leading to bias in complete records analysis (CRA). Multiple imputation (MI) via chained equations is one solution, but its use in the presence of interactions is not straightforward. STUDY DESIGN AND SETTING: We simulated data with outcome Y dependent on binary explanatory variables X and Z and their interaction XZ. Six scenarios were simulated (Y continuous and binary, each with no interaction, a weak and a strong interaction), under five missing data mechanisms. We use directed acyclic graphs to identify when CRA and MI would each be unbiased. We evaluate the performance of CRA, MI without interactions, MI including all interactions, and stratified imputation. We also illustrated these methods using a simple example from the National Child Development Study (NCDS). RESULTS: MI excluding interactions is invalid and resulted in biased estimates and low coverage. When XZ was zero, MI excluding interactions gave unbiased estimates but overcoverage. MI including interactions and stratified MI gave equivalent, valid inference in all cases. In the NCDS example, MI excluding interactions incorrectly concluded there was no evidence for an important interaction. CONCLUSIONS: Epidemiologists carrying out MI should ensure that their imputation model(s) are compatible with their analysis model