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.

The joint influence of personal achievement goals and classroom goal structures on achievement-relevant outcomes

The authors provide an analytic framework for studying the joint influence of personal achievement goals and classroom goal structures on achievement-relevant outcomes. This framework encompasses 3 models (the direct effect model, indirect effect model, and interaction effect model), each of which addresses a different aspect of the joint influence of the 2 goal levels. These 3 models were examined together with a sample of 1,578 Japanese junior high and high school students from 47 classrooms. Results provided support for each of the 3 models: Classroom goal structures were not only direct, but also indirect predictors of intrinsic motivation and academic self-concept, and some cross-level interactions between personal achievement goals and classroom goal structures were observed (indicating both goal match and goal mismatch effects). A call is made for more research that takes into consideration achievement goals at both personal and structural levels of representation. (PsycINFO Database Record (c) 2012 APA, all rights reserved)(journal abstract)

RuII(α-diimine) or RuIII(α-diimine·–)? Structural, spectroscopic, and theoretical evidence for the stabilization of a prominent metal-to-Ligand charge- transfer excited-state configuration in the ground state

The new compounds [Ru(R-DAB)(acac)2] (R-DAB = 1,4-diorganyl- 1,4-diazabuta-1,3-diene; R = tert-butyl, 4-methoxyphenyl, 2,6-dimethylphenyl; acac– = 2,4-pentanedionate) exhibit intrachelate ring bond lengths 1.297

An integrated multi-stage model of knowledge management in international joint ventures: identifying a trigger for knowledge exploration and knowledge harvest

This paper examines the evolution of knowledge management from the initial knowledge migration stage, through adaptation and creation, to the reverse knowledge migration stage in international joint ventures (IJVs). While many studies have analyzed these stages (mostly focusing on knowledge transfer), we investigated the path-dependent nature of knowledge flow in IJVs. The results from the empirical analysis based on a survey of 136 Korean parent companies of IJVs reveal that knowledge management in IJVs follows a sequential, multi-stage process, and that the knowledge transferred from parents to IJVs must first be adapted within its new environment before it reaches the creation stage. We also found that only created knowledge is transferred back to parents.

Importance of resolution and model configuration when downscaling extreme precipitation

Dynamical downscaling is frequently used to investigate the dynamical variables of extra-tropical cyclones, for example, precipitation, using very high-resolution models nested within coarser resolution models to understand the processes that lead to intense precipitation. It is also used in climate change studies, using long timeseries to investigate trends in precipitation, or to look at the small-scale dynamical processes for specific case studies. This study investigates some of the problems associated with dynamical downscaling and looks at the optimum configuration to obtain the distribution and intensity of a precipitation field to match observations. This study uses the Met Office Unified Model run in limited area mode with grid spacings of 12, 4 and 1.5 km, driven by boundary conditions provided by the ECMWF Operational Analysis to produce high-resolution simulations for the Summer of 2007 UK flooding events. The numerical weather prediction model is initiated at varying times before the peak precipitation is observed to test the importance of the initialisation and boundary conditions, and how long the simulation can be run for. The results are compared to raingauge data as verification and show that the model intensities are most similar to observations when the model is initialised 12 hours before the peak precipitation is observed. It was also shown that using non-gridded datasets makes verification more difficult, with the density of observations also affecting the intensities observed. It is concluded that the simulations are able to produce realistic precipitation intensities when driven by the coarser resolution data.

Gaussian anamorphosis in the analysis step of the EnKF: a joint state-variable/observation approach

The analysis step of the (ensemble) Kalman filter is optimal when (1) the distribution of the background is Gaussian, (2) state variables and observations are related via a linear operator, and (3) the observational error is of additive nature and has Gaussian distribution. When these conditions are largely violated, a pre-processing step known as Gaussian anamorphosis (GA) can be applied. The objective of this procedure is to obtain state variables and observations that better fulfil the Gaussianity conditions in some sense. In this work we analyse GA from a joint perspective, paying attention to the effects of transformations in the joint state variable/observation space. First, we study transformations for state variables and observations that are independent from each other. Then, we introduce a targeted joint transformation with the objective to obtain joint Gaussianity in the transformed space. We focus primarily in the univariate case, and briefly comment on the multivariate one. A key point of this paper is that, when (1)-(3) are violated, using the analysis step of the EnKF will not recover the exact posterior density in spite of any transformations one may perform. These transformations, however, provide approximations of different quality to the Bayesian solution of the problem. Using an example in which the Bayesian posterior can be analytically computed, we assess the quality of the analysis distributions generated after applying the EnKF analysis step in conjunction with different GA options. The value of the targeted joint transformation is particularly clear for the case when the prior is Gaussian, the marginal density for the observations is close to Gaussian, and the likelihood is a Gaussian mixture.

The effect of model configuration on modelled hillslope–riparian interactions

The transfer of hillslope water to and through the riparian zone forms a research area of importance in hydrological investigations. Numerical modelling schemes offer a way to visualise and quantify first-order controls on catchment runoff response and mixing. We use a two-dimensional Finite Element model to assess the link between model setup decisions (e.g. zero-flux boundary definitions, soil algorithm choice) and the consequential hydrological process behaviour. A detailed understanding of the consequences of model configuration is required in order to produce reliable estimates of state variables. We demonstrate that model configuration decisions can determine effectively the presence or absence of particular hillslope flow processes and, the magnitude and direction of flux at the hillslope–riparian interface. If these consequences are not fully explored for any given scheme and application, the resulting process inference may well be misleading.

Managing change in complex projects: configuration management, asset information and big data

As we enter an era of ‘big data’, asset information is becoming a deliverable of complex projects. Prior research suggests digital technologies enable rapid, flexible forms of project organizing. This research analyses practices of managing change in Airbus, CERN and Crossrail, through desk-based review, interviews, visits and a cross-case workshop. These organizations deliver complex projects, rely on digital technologies to manage large data-sets; and use configuration management, a systems engineering approach with mid-20th century origins, to establish and maintain integrity. In them, configuration management has become more, rather than less, important. Asset information is structured, with change managed through digital systems, using relatively hierarchical, asynchronous and sequential processes. The paper contributes by uncovering limits to flexibility in complex projects where integrity is important. Challenges of managing change are discussed, considering the evolving nature of configuration management; potential use of analytics on complex projects; and implications for research and practice.

Joint multiple dictionary learning for tensor sparse coding

Traditional dictionary learning algorithms are used for finding a sparse representation on high dimensional data by transforming samples into a one-dimensional (1D) vector. This 1D model loses the inherent spatial structure property of data. An alternative solution is to employ Tensor Decomposition for dictionary learning on their original structural form —a tensor— by learning multiple dictionaries along each mode and the corresponding sparse representation in respect to the Kronecker product of these dictionaries. To learn tensor dictionaries along each mode, all the existing methods update each dictionary iteratively in an alternating manner. Because atoms from each mode dictionary jointly make contributions to the sparsity of tensor, existing works ignore atoms correlations between different mode dictionaries by treating each mode dictionary independently. In this paper, we propose a joint multiple dictionary learning method for tensor sparse coding, which explores atom correlations for sparse representation and updates multiple atoms from each mode dictionary simultaneously. In this algorithm, the Frequent-Pattern Tree (FP-tree) mining algorithm is employed to exploit frequent atom patterns in the sparse representation. Inspired by the idea of K-SVD, we develop a new dictionary update method that jointly updates elements in each pattern. Experimental results demonstrate our method outperforms other tensor based dictionary learning algorithms.

A coupled cloud physics–radiation parameterization of the bulk optical properties of cirrus and its impact on the Met Office unified model global atmosphere 5.0 configuration

A new coupled cloud physics–radiation parameterization of the bulk optical properties of ice clouds is presented. The parameterization is consistent with assumptions in the cloud physics scheme regarding particle size distributions (PSDs) and mass–dimensional relationships. The parameterization is based on a weighted ice crystal habit mixture model, and its bulk optical properties are parameterized as simple functions of wavelength and ice water content (IWC). This approach directly couples IWC to the bulk optical properties, negating the need for diagnosed variables, such as the ice crystal effective dimension. The parameterization is implemented into the Met Office Unified Model Global Atmosphere 5.0 (GA5) configuration. The GA5 configuration is used to simulate the annual 20-yr shortwave (SW) and longwave (LW) fluxes at the top of the atmosphere (TOA), as well as the temperature structure of the atmosphere, under various microphysical assumptions. The coupled parameterization is directly compared against the current operational radiation parameterization, while maintaining the same cloud physics assumptions. In this experiment, the impacts of the two parameterizations on the SW and LW radiative effects at TOA are also investigated and compared against observations. The 20-yr simulations are compared against the latest observations of the atmospheric temperature and radiative fluxes at TOA. The comparisons demonstrate that the choice of PSD and the assumed ice crystal shape distribution are as important as each other. Moreover, the consistent radiation parameterization removes a long-standing tropical troposphere cold temperature bias but slightly warms the southern midlatitudes by about 0.5 K.

Exploration of relational factors: Sino-foreign joint-venture partnering

Purpose – Despite recent threats of economic contraction, China still offers attractive opportunities for foreign companies seeking to expand their business activities through joint venturing (JV) partnering entry strategies. Recent research has indicated a growing recognition of the importance of relational factors in JV partnering. The purpose of this paper is to build on recent research findings that identify critical relation success factors in JVs and explores these in the context of a Hong Kong-based civil aviation services company seeking to expand business activities in Greater China. Design/methodology/approach – While the extant management literature focuses primarily on factors relevant to the inter-partner relationship between partners in the formation stage of a joint venture, this research takes a dynamic stakeholder perspective in respect of the relevant relational factors over the evolution of a partnership. The research described in this paper is based on a case-based study that identifies and examines the relevance and importance of uniquely Chinese factors such as guanxi, renqing and mianzi in the specific context of a strategic partnering relationship. Findings – This phenomenological study provides empirical evidence of critical linkages of these to intrinsically Chinese notions of guanxi, mianzi and renqing – it links these to key strategic partnering success factors identified to be trust, conflict resolution, commitment and cooperation. This study thereby reinforces the importance of the uniquely Chinese relational context in cross-border JVs. Moreover, the research findings suggest that these factors underpin the dynamic bi-directional stakeholder relationship in a Sino-foreign strategic partnership. Originality/value – This study conceptually links the uniquely Chinese relational factors (guanxi, mianzi and renqing) to key success factors supporting the establishment of a strategic partnership in a Sino-foreign context; moreover, it contributes empirical evidence substantiating the proposed conceptual linkage.

Joint retrievals of cloud and drizzle in marine boundary layer clouds using ground-based radar, lidar and zenith radiances

Active remote sensing of marine boundary-layer clouds is challenging as drizzle drops often dominate the observed radar reflectivity. We present a new method to simultaneously retrieve cloud and drizzle vertical profiles in drizzling boundary-layer clouds using surface-based observations of radar reflectivity, lidar attenuated backscatter, and zenith radiances under conditions when precipitation does not reach the surface. Specifically, the vertical structure of droplet size and water content of both cloud and drizzle is characterised throughout the cloud. An ensemble optimal estimation approach provides full error statistics given the uncertainty in the observations. To evaluate the new method, we first perform retrievals using synthetic measurements from large-eddy simulation snapshots of cumulus under stratocumulus, where cloud water path is retrieved with an error of 31 g m−2 . The method also performs well in non-drizzling clouds where no assumption of the cloud profile is required. We then apply the method to observations of marine stratocumulus obtained during the Atmospheric Radiation Measurement MAGIC deployment in the Northeast Pacific. Here, retrieved cloud water path agrees well with independent three-channel microwave radiometer retrievals, with a root mean square difference of 10–20 g m−2.

Joint data assimilation of satellite reflectance and net ecosystem exchange data constrains ecosystem carbon fluxes at a high-elevation subalpine forest

We utilized an ecosystem process model (SIPNET, simplified photosynthesis and evapotranspiration model) to estimate carbon fluxes of gross primary productivity and total ecosystem respiration of a high-elevation coniferous forest. The data assimilation routine incorporated aggregated twice-daily measurements of the net ecosystem exchange of CO2 (NEE) and satellite-based reflectance measurements of the fraction of absorbed photosynthetically active radiation (fAPAR) on an eight-day timescale. From these data we conducted a data assimilation experiment with fifteen different combinations of available data using twice-daily NEE, aggregated annual NEE, eight-day f AP AR, and average annual fAPAR. Model parameters were conditioned on three years of NEE and fAPAR data and results were evaluated to determine the information content from the different combinations of data streams. Across the data assimilation experiments conducted, model selection metrics such as the Bayesian Information Criterion and Deviance Information Criterion obtained minimum values when assimilating average annual fAPAR and twice-daily NEE data. Application of wavelet coherence analyses showed higher correlations between measured and modeled fAPAR on longer timescales ranging from 9 to 12 months. There were strong correlations between measured and modeled NEE (R2, coefficient of determination, 0.86), but correlations between measured and modeled eight-day fAPAR were quite poor (R2 = −0.94). We conclude that this inability to determine fAPAR on eight-day timescale would improve with the considerations of the radiative transfer through the plant canopy. Modeled fluxes when assimilating average annual fAPAR and annual NEE were comparable to corresponding results when assimilating twice-daily NEE, albeit at a greater uncertainty. Our results support the conclusion that for this coniferous forest twice-daily NEE data are a critical measurement stream for the data assimilation. The results from this modeling exercise indicate that for this coniferous forest, average annuals for satellite-based fAPAR measurements paired with annual NEE estimates may provide spatial detail to components of ecosystem carbon fluxes in proximity of eddy covariance towers. Inclusion of other independent data streams in the assimilation will also reduce uncertainty on modeled values.

Mens rea in joint enterprise: a role for endorsement?

This paper argues that the problems commonly associated with the joint enterprise doctrine might be alleviated by supplementing the cognitive mens rea standard of foresight with a volitional element that looks to how the defendant related to the foreseen risk. A re-examination of the case law suggests that a mens rea conception of foresight plus endorsement might be within interpretative reach. The paper considers possible objections to such a development but ultimately rejects them. It concludes that it is not necessary to wait for Parliament to put in place reforms: joint enterprise is a creature of the common law, and the common law is able to tame it unaided.