Evaluation of Wetland CH₄ in the JULES Land Surface Model Using Satellite Observations

Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model and is capable of generating estimates of wetland methane emissions. In this study we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 to 0.88 for most regions, however the seasonal cycle amplitude is typically underestimated (by between 1.8 ppb and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data is found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration out-performs the other but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1 ppb, 19.5 ppb and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the CaMa-Flood model to explicitly represent river and floodplain water dynamics and find these JULES-CaMa-Flood simulations are capable of providing wetland extent more consistent with observations in this regions, highlighting this as an important area for future model development.

Temperature-dependent dynamic property of magnetorheological gel composite: experiment and modelling

Of all the smart materials that could vary with the change of external excitations, magnetorheological gel (MRG) is one of the most preeminent composites which appear controllable and reversible responses according to the magnitude of external magnetic field. Temperature is identified as another important driver of the alteration of dynamic property of MRG, which so far has not been studied systematically. The temperature-dependent dynamic property of MRG under different magnetic field strengths are investigated by three kinds of experiments –– strain amplitude, frequency and magnetic field sweep test. The experimental results demonstrate that the storage and loss moduli of MRG display a temperature-induced stiffening effect with a magnetic field, while a temperature-induced softening effect without a magnetic field. Besides, storage modulus improves with magnetic field strength, whereas loss modulus firstly appears a rapid growth and then a gradual reduction with the increment of magnetic field strength. This temperature-dependency of dynamic property is also interpreted through different mechanisms related to the transformation of microstructures of MRG. Furthermore, a modified magnetic dipole model which could predict the relationship between storage modulus and magnetic field strength, combines with the classical Arrhenius equation expressing the effect of temperature on viscosity, to describe the temperature-dependency of storage modulus of MRG under different magnetic field strengths. This paper may provide some useful guidance for designing an MR device.

Temporal downscaling of precipitation time-series projections to forecast green roofs future detention performance

A strategy to simulate rainfall by the means of different Multiplicative random Cascades (MRC) was developed to evaluate their applicability to produce inputs for green roof infrastructures models taking into account climate change. The MRC reproduce a (multi)fractal distribution of precipitation through an iterative and multiplicative random process. The initial model was improved with a temperature dependency and an additional function to improve its capability to reproduce the temporal structure of rainfall. The structure of the models with depth and temperature dependency was found to be applicable in eight locations studied across Norway (N) and France (F). The resulting time-series from both reference period and projection based on RCP 8.5 were applied to two green roofs (GR) with different properties. The different models lead to a slight change in the performance of GR, but this was not significant compared to the range of outcomes due to ensemble uncertainty in climate modelling and the stochastic uncertainty due to nature of the process. The moderating effect of the green infrastructure was found to decrease in most of the Norwegian cities, especially Bergen (N), while increasing in Lyon (F).