Incorporation of a physically based melt pond scheme into the sea ice component of a climate model

The extent and thickness of the Arctic sea ice cover has decreased dramatically in the past few decades with minima in sea ice extent in September 2005 and 2007. These minima have not been predicted in the IPCC AR4 report, suggesting that the sea ice component of climate models should more realistically represent the processes controlling the sea ice mass balance. One of the processes poorly represented in sea ice models is the formation and evolution of melt ponds. Melt ponds accumulate on the surface of sea ice from snow and sea ice melt and their presence reduces the albedo of the ice cover, leading to further melt. Toward the end of the melt season, melt ponds cover up to 50% of the sea ice surface. We have developed a melt pond evolution theory. Here, we have incorporated this melt pond theory into the Los Alamos CICE sea ice model, which has required us to include the refreezing of melt ponds. We present results showing that the presence, or otherwise, of a representation of melt ponds has a significant effect on the predicted sea ice thickness and extent. We also present a sensitivity study to uncertainty in the sea ice permeability, number of thickness categories in the model representation, meltwater redistribution scheme, and pond albedo. We conclude with a recommendation that our melt pond scheme is included in sea ice models, and the number of thickness categories should be increased and concentrated at lower thicknesses.

Selected wheat seed defense proteins exhibit competitive binding to model microbial lipid interfaces

Puroindolines (Pins) and purothionins (Pths) are basic, amphiphilic, cysteine-rich wheat proteins that play a role in plant defense against microbial pathogens. We have examined the co-adsorption and sequential addition of Pins (Pin-a, Pin-b and a mutant form of Pin-b with Trp-44 to Arg-44 substitution) and β-purothionin (β-Pth) model anionic lipid layers, using a combination of surface pressure measurements, external reflection FTIR spectroscopy and neutron reflectometry. Results highlighted differences in the protein binding mechanisms, and in the competitive binding and penetration of lipid layers between respective Pins and β-Pth. Pin-a formed a blanket-like layer of protein below the lipid surface that resulted in the reduction or inhibition of β-Pth penetration of the lipid layer. Wild-type Pin-b participated in co-operative binding with β-Pth, whereas the mutant Pin-b did not bind to the lipid layer in the presence of β-Pth. The results provide further insight into the role of hydrophobic and cationic amino acid residues in antimicrobial activity.

The ocean's gravitational potential energy budget in a coupled climate model

This study examines, in a unified fashion, the budgets of ocean gravitational potential energy (GPE) and available gravitational potential energy (AGPE) in the control simulation of the coupled atmosphere–ocean general circulation model HadCM3. Only AGPE can be converted into kinetic energy by adiabatic processes. Diapycnal mixing supplies GPE, but not AGPE, whereas the reverse is true of the combined effect of surface buoyancy forcing and convection. Mixing and buoyancy forcing, thus, play complementary roles in sustaining the large scale circulation. However, the largest globally integrated source of GPE is resolved advection (+0.57 TW) and the largest sink is through parameterized eddy transports (-0.82 TW). The effect of these adiabatic processes on AGPE is identical to their effect on GPE, except for perturbations to both budgets due to numerical leakage exacerbated by non-linearities in the equation of state.

Aircraft observations and model simulations of concentration and particle size distribution in the Eyjafjallajökull volcanic ash cloud

The Eyjafjallajökull volcano in Iceland emitted a cloud of ash into the atmosphere during April and May 2010. Over the UK the ash cloud was observed by the FAAM BAe-146 Atmospheric Research Aircraft which was equipped with in-situ probes measuring the concentration of volcanic ash carried by particles of varying sizes. The UK Met Office Numerical Atmospheric-dispersion Modelling Environment (NAME) has been used to simulate the evolution of the ash cloud emitted by the Eyjafjallajökull volcano during the period 4–18 May 2010. In the NAME simulations the processes controlling the evolution of the concentration and particle size distribution include sedimentation and deposition of particles, horizontal dispersion and vertical wind shear. For travel times between 24 and 72 h, a 1/t relationship describes the evolution of the concentration at the centre of the ash cloud and the particle size distribution remains fairly constant. Although NAME does not represent the effects of microphysical processes, it can capture the observed decrease in concentration with travel time in this period. This suggests that, for this eruption, microphysical processes play a small role in determining the evolution of the distal ash cloud. Quantitative comparison with observations shows that NAME can simulate the observed column-integrated mass if around 4% of the total emitted mass is assumed to be transported as far as the UK by small particles (< 30 μm diameter). NAME can also simulate the observed particle size distribution if a distal particle size distribution that contains a large fraction of < 10 μm diameter particles is used, consistent with the idea that phraetomagmatic volcanoes, such as Eyjafjallajökull, emit very fine particles.

Incorporating toxicokinetics into an individual-based model for more realistic pesticide exposure estimates: A case study of the wood mouse

The potential risk of agricultural pesticides to mammals typically depends on internal concentrations within individuals, and these are determined by the amount ingested and by absorption, distribution, metabolism, and excretion (ADME). Pesticide residues ingested depend, amongst other things, on individual spatial choices which determine how much and when feeding sites and areas of pesticide application overlap, and can be calculated using individual-based models (IBMs). Internal concentrations can be calculated using toxicokinetic (TK) models, which are quantitative representations of ADME processes. Here we provide a population model for the wood mouse (Apodemus sylvaticus) in which TK submodels were incorporated into an IBM representation of individuals making choices about where to feed. This allows us to estimate the contribution of individual spatial choice and TK processes to risk. We compared the risk predicted by four IBMs: (i) “AllExposed-NonTK”: assuming no spatial choice so all mice have 100% exposure, no TK, (ii) “AllExposed-TK”: identical to (i) except that the TK processes are included where individuals vary because they have different temporal patterns of ingestion in the IBM, (iii) “Spatial-NonTK”: individual spatial choice, no TK, and (iv) “Spatial-TK”: individual spatial choice and with TK. The TK parameters for hypothetical pesticides used in this study were selected such that a conventional risk assessment would fail. Exposures were standardised using risk quotients (RQ; exposure divided by LD50 or LC50). We found that for the exposed sub-population including either spatial choice or TK reduced the RQ by 37–85%, and for the total population the reduction was 37–94%. However spatial choice and TK together had little further effect in reducing RQ. The reasons for this are that when the proportion of time spent in treated crop (PT) approaches 1, TK processes dominate and spatial choice has very little effect, and conversely if PT is small spatial choice dominates and TK makes little contribution to exposure reduction. The latter situation means that a short time spent in the pesticide-treated field mimics exposure from a small gavage dose, but TK only makes a substantial difference when the dose was consumed over a longer period. We concluded that a combined TK-IBM is most likely to bring added value to the risk assessment process when the temporal pattern of feeding, time spent in exposed area and TK parameters are at an intermediate level; for instance wood mice in foliar spray scenarios spending more time in crop fields because of better plant cover.

Bringing export performance metrics into Annual Reports: the APEV scale and the PERFEX scorecard

Annual company reports rarely distinguish between domestic and export market performance and even more rarely provide information about annual indicators of a specific export venture's performance. In this study, the authors develop and test a new measure for assessing the annual performance of an export venture (the APEV scale). The new measure comprises five dimensions: (1) annual export venture financial performance, (2) annual export venture strategic performance, (3) annual export venture achievement, (4) contribution of the export venture to annual exporting operations, and (5) satisfaction with annual export venture overall performance. The authors use the APEV scale to generate a scorecard of performance in exporting (the PERFEX scorecard) to assess export performance at the corporate level while comparatively evaluating all export ventures of the firm. Both the scale and the scorecard could help disclose export venture performance and could be useful instruments for annual planning, management, monitoring, and improvement of exporting programs.

A simple method for integrating a complex model into an ensemble data assimilation system using MPI

This paper details a strategy for modifying the source code of a complex model so that the model may be used in a data assimilation context, {and gives the standards for implementing a data assimilation code to use such a model}. The strategy relies on keeping the model separate from any data assimilation code, and coupling the two through the use of Message Passing Interface (MPI) {functionality}. This strategy limits the changes necessary to the model and as such is rapid to program, at the expense of ultimate performance. The implementation technique is applied in different models with state dimension up to $2.7 \times 10^8$. The overheads added by using this implementation strategy in a coupled ocean-atmosphere climate model are shown to be an order of magnitude smaller than the addition of correlated stochastic random errors necessary for some nonlinear data assimilation techniques.

Including an ocean carbon cycle model into iLOVECLIM (v1.0)

The atmospheric carbon dioxide concentration plays a crucial role in the radiative balance and as such has a strong influence on the evolution of climate. Because of the numerous interactions between climate and the carbon cycle, it is necessary to include a model of the carbon cycle within a climate model to understand and simulate past and future changes of the carbon cycle. In particular, natural variations of atmospheric CO2 have happened in the past, while anthropogenic carbon emissions are likely to continue in the future. To study changes of the carbon cycle and climate on timescales of a few hundred to a few thousand years, we have included a simple carbon cycle model into the iLOVECLIM Earth System Model. In this study, we describe the ocean and terrestrial biosphere carbon cycle models and their performance relative to observational data. We focus on the main carbon cycle variables including the carbon isotope ratios δ13C and the Δ14C. We show that the model results are in good agreement with modern observations both at the surface and in the deep ocean for the main variables, in particular phosphates, dissolved inorganic carbon and the carbon isotopes.