Analysis of the seasonal cycle within the first international urban land-surface model comparison

A number of urban land-surface models have been developed in recent years to satisfy the growing requirements for urban weather and climate interactions and prediction. These models vary considerably in their complexity and the processes that they represent. Although the models have been evaluated, the observational datasets have typically been of short duration and so are not suitable to assess the performance over the seasonal cycle. The First International Urban Land-Surface Model comparison used an observational dataset that spanned a period greater than a year, which enables an analysis over the seasonal cycle, whilst the variety of models that took part in the comparison allows the analysis to include a full range of model complexity. The results show that, in general, urban models do capture the seasonal cycle for each of the surface fluxes, but have larger errors in the summer months than in the winter. The net all-wave radiation has the smallest errors at all times of the year but with a negative bias. The latent heat flux and the net storage heat flux are also underestimated, whereas the sensible heat flux generally has a positive bias throughout the seasonal cycle. A representation of vegetation is a necessary, but not sufficient, condition for modelling the latent heat flux and associated sensible heat flux at all times of the year. Models that include a temporal variation in anthropogenic heat flux show some increased skill in the sensible heat flux at night during the winter, although their daytime values are consistently overestimated at all times of the year. Models that use the net all-wave radiation to determine the net storage heat flux have the best agreement with observed values of this flux during the daytime in summer, but perform worse during the winter months. The latter could result from a bias of summer periods in the observational datasets used to derive the relations with net all-wave radiation. Apart from these models, all of the other model categories considered in the analysis result in a mean net storage heat flux that is close to zero throughout the seasonal cycle, which is not seen in the observations. Models with a simple treatment of the physical processes generally perform at least as well as models with greater complexity.

The Joint UK Land Environment Simulator (JULES), model description – part 1: energy and water fluxes

This manuscript describes the energy and water components of a new community land surface model called the Joint UK Land Environment Simulator (JULES). This is developed from the Met Office Surface Exchange Scheme (MOSES). It can be used as a stand alone land surface model driven by observed forcing data, or coupled to an atmospheric global circulation model. The JULES model has been coupled to the Met Office Unified Model (UM) and as such provides a unique opportunity for the research community to contribute their research to improve both world-leading operational weather forecasting and climate change prediction systems. In addition JULES, and its forerunner MOSES, have been the basis for a number of very high-profile papers concerning the land-surface and climate over the last decade. JULES has a modular structure aligned to physical processes, providing the basis for a flexible modelling platform.

Host model uncertainties in aerosol radiative forcing estimates: results from the AeroCom Prescribed intercomparison study

Simulated multi-model “diversity” in aerosol direct radiative forcing estimates is often perceived as a measure of aerosol uncertainty. However, current models used for aerosol radiative forcing calculations vary considerably in model components relevant for forcing calculations and the associated “host-model uncertainties” are generally convoluted with the actual aerosol uncertainty. In this AeroCom Prescribed intercomparison study we systematically isolate and quantify host model uncertainties on aerosol forcing experiments through prescription of identical aerosol radiative properties in twelve participating models. Even with prescribed aerosol radiative properties, simulated clear-sky and all-sky aerosol radiative forcings show significant diversity. For a purely scattering case with globally constant optical depth of 0.2, the global-mean all-sky top-of-atmosphere radiative forcing is −4.47Wm−2 and the inter-model standard deviation is 0.55Wm−2, corresponding to a relative standard deviation of 12 %. For a case with partially absorbing aerosol with an aerosol optical depth of 0.2 and single scattering albedo of 0.8, the forcing changes to 1.04Wm−2, and the standard deviation increases to 1.01W−2, corresponding to a significant relative standard deviation of 97 %. However, the top-of-atmosphere forcing variability owing to absorption (subtracting the scattering case from the case with scattering and absorption) is low, with absolute (relative) standard deviations of 0.45Wm−2 (8 %) clear-sky and 0.62Wm−2 (11 %) all-sky. Scaling the forcing standard deviation for a purely scattering case to match the sulfate radiative forcing in the Aero- Com Direct Effect experiment demonstrates that host model uncertainties could explain about 36% of the overall sulfate forcing diversity of 0.11Wm−2 in the AeroCom Direct Radiative Effect experiment.

A model for the consolidation of rafted sea ice

Rafting is one of the important deformation mechanisms of sea ice. This process is widespread in the north Caspian Sea, where multiple rafting produces thick sea ice features, which are a hazard to offshore operations. Here we present a one-dimensional, thermal consolidation model for rafted sea ice. We consider the consolidation between the layers of both a two-layer and a three-layer section of rafted sea ice. The rafted ice is assumed to be composed of layers of sea ice of equal thickness, separated by thin layers of ocean water. Results show that the thickness of the liquid layer reduced asymptotically with time, such that there always remained a thin saline liquid layer. We propose that when the liquid layer is equal to the surface roughness the adjacent layers can be considered consolidated. Using parameters representative of the north Caspian, the Arctic, and the Antarctic, our results show that for a choice of standard parameters it took under 15 h for two layers of rafted sea ice to consolidate. Sensitivity studies showed that the consolidation model is highly sensitive to the initial thickness of the liquid layer, the fraction of salt release during freezing, and the height of the surface asperities. We believe that further investigation of these parameters is needed before any concrete conclusions can be drawn about rate of consolidation of rafted sea ice features.

Controls on inorganic nitrogen leaching from Finnish catchments assessed using a sensitivity and uncertainty analysis of the INCA-N model

The semi-distributed, dynamic INCA-N model was used to simulate the behaviour of dissolved inorganic nitrogen (DIN) in two Finnish research catchments. Parameter sensitivity and model structural uncertainty were analysed using generalized sensitivity analysis. The Mustajoki catchment is a forested upstream catchment, while the Savijoki catchment represents intensively cultivated lowlands. In general, there were more influential parameters in Savijoki than Mustajoki. Model results were sensitive to N-transformation rates, vegetation dynamics, and soil and river hydrology. Values of the sensitive parameters were based on long-term measurements covering both warm and cold years. The highest measured DIN concentrations fell between minimum and maximum values estimated during the uncertainty analysis. The lowest measured concentrations fell outside these bounds, suggesting that some retention processes may be missing from the current model structure. The lowest concentrations occurred mainly during low flow periods; so effects on total loads were small.

A mathematical model of the sterol regulatory element binding protein 2 cholesterol biosynthesis pathway

Cholesterol is one of the key constituents for maintaining the cellular membrane and thus the integrity of the cell itself. In contrast high levels of cholesterol in the blood are known to be a major risk factor in the development of cardiovascular disease. We formulate a deterministic nonlinear ordinary differential equation model of the sterol regulatory element binding protein 2 (SREBP-2) cholesterol genetic regulatory pathway in an hepatocyte. The mathematical model includes a description of genetic transcription by SREBP-2 which is subsequently translated to mRNA leading to the formation of 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR), a main precursor of cholesterol synthesis. Cholesterol synthesis subsequently leads to the regulation of SREBP-2 via a negative feedback formulation. Parameterised with data from the literature, the model is used to understand how SREBP-2 transcription and regulation affects cellular cholesterol concentration. Model stability analysis shows that the only positive steady-state of the system exhibits purely oscillatory, damped oscillatory or monotic behaviour under certain parameter conditions. In light of our findings we postulate how cholesterol homestasis is maintained within the cell and the advantages of our model formulation are discussed with respect to other models of genetic regulation within the literature.

An aggregated wind power generation model based on MERRA reanalysis data: MATLAB model and example data for the April 2014 wind farm distribution of Great Britain.

The MATLAB model is contained within the compressed folders (versions are available as .zip and .tgz). This model uses MERRA reanalysis data (>34 years available) to estimate the hourly aggregated wind power generation for a predefined (fixed) distribution of wind farms. A ready made example is included for the wind farm distribution of Great Britain, April 2014 ("CF.dat"). This consists of an hourly time series of GB-total capacity factor spanning the period 1980-2013 inclusive. Given the global nature of reanalysis data, the model can be applied to any specified distribution of wind farms in any region of the world. Users are, however, strongly advised to bear in mind the limitations of reanalysis data when using this model/data. This is discussed in our paper: Cannon, Brayshaw, Methven, Coker, Lenaghan. "Using reanalysis data to quantify extreme wind power generation statistics: a 33 year case study in Great Britain". Submitted to Renewable Energy in March, 2014. Additional information about the model is contained in the model code itself, in the accompanying ReadMe file, and on our website: http://www.met.reading.ac.uk/~energymet/data/Cannon2014/

Key conclusions of the first international urban land surface model comparison project

he first international urban land surface model comparison was designed to identify three aspects of the urban surface-atmosphere interactions: (1) the dominant physical processes, (2) the level of complexity required to model these, and 3) the parameter requirements for such a model. Offline simulations from 32 land surface schemes, with varying complexity, contributed to the comparison. Model results were analysed within a framework of physical classifications and over four stages. The results show that the following are important urban processes; (i) multiple reflections of shortwave radiation within street canyons, (ii) reduction in the amount of visible sky from within the canyon, which impacts on the net long-wave radiation, iii) the contrast in surface temperatures between building roofs and street canyons, and (iv) evaporation from vegetation. Models that use an appropriate bulk albedo based on multiple solar reflections, represent building roof surfaces separately from street canyons and include a representation of vegetation demonstrate more skill, but require parameter information on the albedo, height of the buildings relative to the width of the streets (height to width ratio), the fraction of building roofs compared to street canyons from a plan view (plan area fraction) and the fraction of the surface that is vegetated. These results, whilst based on a single site and less than 18 months of data, have implications for the future design of urban land surface models, the data that need to be measured in urban observational campaigns, and what needs to be included in initiatives for regional and global parameter databases.

An energy budget agent-based model of earthworm populations and its application to study the effects of pesticides

Earthworms are important organisms in soil communities and so are used as model organisms in environmental risk assessments of chemicals. However current risk assessments of soil invertebrates are based on short-term laboratory studies, of limited ecological relevance, supplemented if necessary by site-specific field trials, which sometimes are challenging to apply across the whole agricultural landscape. Here, we investigate whether population responses to environmental stressors and pesticide exposure can be accurately predicted by combining energy budget and agent-based models (ABMs), based on knowledge of how individuals respond to their local circumstances. A simple energy budget model was implemented within each earthworm Eisenia fetida in the ABM, based on a priori parameter estimates. From broadly accepted physiological principles, simple algorithms specify how energy acquisition and expenditure drive life cycle processes. Each individual allocates energy between maintenance, growth and/or reproduction under varying conditions of food density, soil temperature and soil moisture. When simulating published experiments, good model fits were obtained to experimental data on individual growth, reproduction and starvation. Using the energy budget model as a platform we developed methods to identify which of the physiological parameters in the energy budget model (rates of ingestion, maintenance, growth or reproduction) are primarily affected by pesticide applications, producing four hypotheses about how toxicity acts. We tested these hypotheses by comparing model outputs with published toxicity data on the effects of copper oxychloride and chlorpyrifos on E. fetida. Both growth and reproduction were directly affected in experiments in which sufficient food was provided, whilst maintenance was targeted under food limitation. Although we only incorporate toxic effects at the individual level we show how ABMs can readily extrapolate to larger scales by providing good model fits to field population data. The ability of the presented model to fit the available field and laboratory data for E. fetida demonstrates the promise of the agent-based approach in ecology, by showing how biological knowledge can be used to make ecological inferences. Further work is required to extend the approach to populations of more ecologically relevant species studied at the field scale. Such a model could help extrapolate from laboratory to field conditions and from one set of field conditions to another or from species to species.

Sensitivity of simulated regional Arctic climate to the choice of coupled model domain

The climate over the Arctic has undergone changes in recent decades. In order to evaluate the coupled response of the Arctic system to external and internal forcing, our study focuses on the estimation of regional climate variability and its dependence on large-scale atmospheric and regional ocean circulations. A global ocean–sea ice model with regionally high horizontal resolution is coupled to an atmospheric regional model and global terrestrial hydrology model. This way of coupling divides the global ocean model setup into two different domains: one coupled, where the ocean and the atmosphere are interacting, and one uncoupled, where the ocean model is driven by prescribed atmospheric forcing and runs in a so-called stand-alone mode. Therefore, selecting a specific area for the regional atmosphere implies that the ocean–atmosphere system can develop ‘freely’ in that area, whereas for the rest of the global ocean, the circulation is driven by prescribed atmospheric forcing without any feedbacks. Five different coupled setups are chosen for ensemble simulations. The choice of the coupled domains was done to estimate the influences of the Subtropical Atlantic, Eurasian and North Pacific regions on northern North Atlantic and Arctic climate. Our simulations show that the regional coupled ocean–atmosphere model is sensitive to the choice of the modelled area. The different model configurations reproduce differently both the mean climate and its variability. Only two out of five model setups were able to reproduce the Arctic climate as observed under recent climate conditions (ERA-40 Reanalysis). Evidence is found that the main source of uncertainty for Arctic climate variability and its predictability is the North Pacific. The prescription of North Pacific conditions in the regional model leads to significant correlation with observations, even if the whole North Atlantic is within the coupled model domain. However, the inclusion of the North Pacific area into the coupled system drastically changes the Arctic climate variability to a point where the Arctic Oscillation becomes an ‘internal mode’ of variability and correlations of year-to-year variability with observational data vanish. In line with previous studies, our simulations provide evidence that Arctic sea ice export is mainly due to ‘internal variability’ within the Arctic region. We conclude that the choice of model domains should be based on physical knowledge of the atmospheric and oceanic processes and not on ‘geographic’ reasons. This is particularly the case for areas like the Arctic, which has very complex feedbacks between components of the regional climate system.

Applying the chain formation model to magnetic properties of aggregated ferrofluids

The magnetization properties of aggregated ferrofluids are calculated by combining the chain formation model developed by Zubarev with the modified mean-field theory. Using moderate assumptions for the inter- and intrachain interactions we obtain expressions for the magnetization and initial susceptibility. When comparing the results of our theory to molecular dynamics simulations of the same model we find that at large dipolar couplings (lambda>3) the chain formation model appears to give better predictions than other analytical approaches. This supports the idea that chain formation is an important structural ingredient of strongly interacting dipolar particles.

Considering the Feasibility of Semantic Model Design in the Built-Environment

Building Information Modeling (BIM) is the process of structuring, capturing, creating, and managing a digital representation of physical and/or functional characteristics of a built space [1]. Current BIM has limited ability to represent dynamic semantics, social information, often failing to consider building activity, behavior and context; thus limiting integration with intelligent, built-environment management systems. Research, such as the development of Semantic Exchange Modules, and/or the linking of IFC with semantic web structures, demonstrates the need for building models to better support complex semantic functionality. To implement model semantics effectively, however, it is critical that model designers consider semantic information constructs. This paper discusses semantic models with relation to determining the most suitable information structure. We demonstrate how semantic rigidity can lead to significant long-term problems that can contribute to model failure. A sufficiently detailed feasibility study is advised to maximize the value from the semantic model. In addition we propose a set of questions, to be used during a model’s feasibility study, and guidelines to help assess the most suitable method for managing semantics in a built environment.

Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models

We utilize energy budget diagnostics from the Coupled Model Intercomparison Project phase 5 (CMIP5) to evaluate the models' climate forcing since preindustrial times employing an established regression technique. The climate forcing evaluated this way, termed the adjusted forcing (AF), includes a rapid adjustment term associated with cloud changes and other tropospheric and land-surface changes. We estimate a 2010 total anthropogenic and natural AF from CMIP5 models of 1.9 ± 0.9 W m−2 (5–95% range). The projected AF of the Representative Concentration Pathway simulations are lower than their expected radiative forcing (RF) in 2095 but agree well with efficacy weighted forcings from integrated assessment models. The smaller AF, compared to RF, is likely due to cloud adjustment. Multimodel time series of temperature change and AF from 1850 to 2100 have large intermodel spreads throughout the period. The intermodel spread of temperature change is principally driven by forcing differences in the present day and climate feedback differences in 2095, although forcing differences are still important for model spread at 2095. We find no significant relationship between the equilibrium climate sensitivity (ECS) of a model and its 2003 AF, in contrast to that found in older models where higher ECS models generally had less forcing. Given the large present-day model spread, there is no indication of any tendency by modelling groups to adjust their aerosol forcing in order to produce observed trends. Instead, some CMIP5 models have a relatively large positive forcing and overestimate the observed temperature change.

Impacts of increasing the aerosol complexity in the Met Office global numerical weather prediction model.

The inclusion of the direct and indirect radiative effects of aerosols in high-resolution global numerical weather prediction (NWP) models is being increasingly recognised as important for the improved accuracy of short-range weather forecasts. In this study the impacts of increasing the aerosol complexity in the global NWP configuration of the Met Office Unified Model (MetUM) are investigated. A hierarchy of aerosol representations are evaluated including three-dimensional monthly mean speciated aerosol climatologies, fully prognostic aerosols modelled using the CLASSIC aerosol scheme and finally, initialised aerosols using assimilated aerosol fields from the GEMS project. The prognostic aerosol schemes are better able to predict the temporal and spatial variation of atmospheric aerosol optical depth, which is particularly important in cases of large sporadic aerosol events such as large dust storms or forest fires. Including the direct effect of aerosols improves model biases in outgoing long-wave radiation over West Africa due to a better representation of dust. However, uncertainties in dust optical properties propagate to its direct effect and the subsequent model response. Inclusion of the indirect aerosol effects improves surface radiation biases at the North Slope of Alaska ARM site due to lower cloud amounts in high-latitude clean-air regions. This leads to improved temperature and height forecasts in this region. Impacts on the global mean model precipitation and large-scale circulation fields were found to be generally small in the short-range forecasts. However, the indirect aerosol effect leads to a strengthening of the low-level monsoon flow over the Arabian Sea and Bay of Bengal and an increase in precipitation over Southeast Asia. Regional impacts on the African Easterly Jet (AEJ) are also presented with the large dust loading in the aerosol climatology enhancing of the heat low over West Africa and weakening the AEJ. This study highlights the importance of including a more realistic treatment of aerosol–cloud interactions in global NWP models and the potential for improved global environmental prediction systems through the incorporation of more complex aerosol schemes.

The statistical cusp: a flux transfer event model

Traditionally, the cusp has been described in terms of a time-stationary feature of the magnetosphere which allows access of magnetosheath-like plasma to low altitudes. Statistical surveys of data from low-altitude spacecraft have shown the average characteristics and position of the cusp. Recently, however, it has been suggested that the ionospheric footprint of flux transfer events (FTEs) may be identified as variations of the “cusp” on timescales of a few minutes. In this model, the cusp can vary in form between a steady-state feature in one limit and a series of discrete ionospheric FTE signatures in the other limit. If this time-dependent cusp scenario is correct, then the signatures of the transient reconnection events must be able, on average, to reproduce the statistical cusp occurrence previously determined from the satellite observations. In this paper, we predict the precipitation signatures which are associated with transient magnetopause reconnection, following recent observations of the dependence of dayside ionospheric convection on the orientation of the IMF. We then employ a simple model of the longitudinal motion of FTE signatures to show how such events can easily reproduce the local time distribution of cusp occurrence probabilities, as observed by low-altitude satellites. This is true even in the limit where the cusp is a series of discrete events. Furthermore, we investigate the existence of double cusp patches predicted by the simple model and show how these events may be identified in the data.