Statistical inference of OH concentrations and air mass dilution rates from successive observations of non-methane hydrocarbons in single air masses

Bayesian inference has been used to determine rigorous estimates of hydroxyl radical concentrations () and air mass dilution rates (K) averaged following air masses between linked observations of nonmethane hydrocarbons (NMHCs) spanning the North Atlantic during the Intercontinental Transport and Chemical Transformation (ITCT)-Lagrangian-2K4 experiment. The Bayesian technique obtains a refined (posterior) distribution of a parameter given data related to the parameter through a model and prior beliefs about the parameter distribution. Here, the model describes hydrocarbon loss through OH reaction and mixing with a background concentration at rate K. The Lagrangian experiment provides direct observations of hydrocarbons at two time points, removing assumptions regarding composition or sources upstream of a single observation. The estimates are sharpened by using many hydrocarbons with different reactivities and accounting for their variability and measurement uncertainty. A novel technique is used to construct prior background distributions of many species, described by variation of a single parameter . This exploits the high correlation of species, related by the first principal component of many NMHC samples. The Bayesian method obtains posterior estimates of , K and following each air mass. Median values are typically between 0.5 and 2.0 × 106 molecules cm−3, but are elevated to between 2.5 and 3.5 × 106 molecules cm−3, in low-level pollution. A comparison of estimates from absolute NMHC concentrations and NMHC ratios assuming zero background (the “photochemical clock” method) shows similar distributions but reveals systematic high bias in the estimates from ratios. Estimates of K are ∼0.1 day−1 but show more sensitivity to the prior distribution assumed.

Effect of uncertainty in surface mass balance--elevation feedback on projections of the future sea level contribution of the Greenland ice sheet

We apply a new parameterisation of the Greenland ice sheet (GrIS) feedback between surface mass balance (SMB: the sum of surface accumulation and surface ablation) and surface elevation in the MAR regional climate model (Edwards et al., 2014) to projections of future climate change using five ice sheet models (ISMs). The MAR (Modèle Atmosphérique Régional: Fettweis, 2007) climate projections are for 2000–2199, forced by the ECHAM5 and HadCM3 global climate models (GCMs) under the SRES A1B emissions scenario. The additional sea level contribution due to the SMB– elevation feedback averaged over five ISM projections for ECHAM5 and three for HadCM3 is 4.3% (best estimate; 95% credibility interval 1.8–6.9 %) at 2100, and 9.6% (best estimate; 95% credibility interval 3.6–16.0 %) at 2200. In all results the elevation feedback is significantly positive, amplifying the GrIS sea level contribution relative to the MAR projections in which the ice sheet topography is fixed: the lower bounds of our 95% credibility intervals (CIs) for sea level contributions are larger than the “no feedback” case for all ISMs and GCMs. Our method is novel in sea level projections because we propagate three types of modelling uncertainty – GCM and ISM structural uncertainties, and elevation feedback parameterisation uncertainty – along the causal chain, from SRES scenario to sea level, within a coherent experimental design and statistical framework. The relative contributions to uncertainty depend on the timescale of interest. At 2100, the GCM uncertainty is largest, but by 2200 both the ISM and parameterisation uncertainties are larger. We also perform a perturbed parameter ensemble with one ISM to estimate the shape of the projected sea level probability distribution; our results indicate that the probability density is slightly skewed towards higher sea level contributions.

Moisture can be the dominant environmental parameter governing cadaver decomposition in soil

Forensic taphonomy involves the use of decomposition to estimate postmortem interval (PMI) or locate clandestine graves. Yet, cadaver decomposition remains poorly understood, particularly following burial in soil. Presently, we do not know how most edaphic and environmental parameters, including soil moisture, influence the breakdown of cadavers following burial and alter the processes that are used to estimate PMI and locate clandestine graves. To address this, we buried juvenile rat (Rattus rattus) cadavers (∼18 g wet weight) in three contrasting soils from tropical savanna ecosystems located in Pallarenda (sand), Wambiana (medium clay), or Yabulu (loamy sand), Queensland, Australia. These soils were sieved (2 mm), weighed (500 g dry weight), calibrated to a matric potential of -0.01 megapascals (MPa), -0.05 MPa, or -0.3 MPa (wettest to driest) and incubated at 22 °C. Measurements of cadaver decomposition included cadaver mass loss, carbon dioxide-carbon (CO2-C) evolution, microbial biomass carbon (MBC), protease activity, phosphodiesterase activity, ninhydrin-reactive nitrogen (NRN) and soil pH. Cadaver burial resulted in a significant increase in CO2-C evolution, MBC, enzyme activities, NRN and soil pH. Cadaver decomposition in loamy sand and sandy soil was greater at lower matric potentials (wetter soil). However, optimal matric potential for cadaver decomposition in medium clay was exceeded, which resulted in a slower rate of cadaver decomposition in the wettest soil. Slower cadaver decomposition was also observed at high matric potential (-0.3 MPa). Furthermore, wet sandy soil was associated with greater cadaver decomposition than wet fine-textured soil. We conclude that gravesoil moisture content can modify the relationship between temperature and cadaver decomposition and that soil microorganisms can play a significant role in cadaver breakdown. We also conclude that soil NRN is a more reliable indicator of gravesoil than soil pH.