Downscaling regional climate model outputs for the Caribbean using a weather generator

Locally relevant scenarios of daily weather variables that represent the best knowledge of the present climate and projections of future climate change are needed by planners and managers to inform management and adaptation to climate change decisions. Information of this kind for the future is only readily available for a few developed country regions of the world. For many less-developed regions, it is often difficult to find series of observed daily weather data to assist in planning decisions. This study applies a previously developed single-site weather generator (WG) to the Caribbean, using examples from Belize in the west to Barbados in the east. The purpose of this development is to provide users in the region with generated sequences of possible future daily weather that they can use in a number of impact sectors. The WG is first calibrated for a number of sites across the region and the goodness of fit of the WG against the daily station observations assessed. Particular attention is focussed on the ability of the precipitation component of the WG to generate realistic extreme values for the calibration or control period. The WG is then modified using change factors (CFs) derived from regional climate model projections (control and future) to simulate future 30-year scenarios centred on the 2020s, 2050s and 2080s. Changes between the control period and the three futures are illustrated not just by changes in average temperatures and precipitation amounts but also by a number of well-used measures of extremes (very warm days/nights, the heaviest 5-day precipitation total in a month, counts of the number of precipitation events above specific thresholds and the number of consecutive dry days)

An ECOOP web portal for visualising and comparing distributed coastal oceanography model and in situ data

As part of a large European coastal operational oceanography project (ECOOP), we have developed a web portal for the display and comparison of model and in situ marine data. The distributed model and in situ datasets are accessed via an Open Geospatial Consortium Web Map Service (WMS) and Web Feature Service (WFS) respectively. These services were developed independently and readily integrated for the purposes of the ECOOP project, illustrating the ease of interoperability resulting from adherence to international standards. The key feature of the portal is the ability to display co-plotted timeseries of the in situ and model data and the quantification of misfits between the two. By using standards-based web technology we allow the user to quickly and easily explore over twenty model data feeds and compare these with dozens of in situ data feeds without being concerned with the low level details of differing file formats or the physical location of the data. Scientific and operational benefits to this work include model validation, quality control of observations, data assimilation and decision support in near real time. In these areas it is essential to be able to bring different data streams together from often disparate locations

A multi-level approach to program objectives: definitions and managerial implications

Projects are recognized as the building blocks of strategy. Outputs, outcomes, benefits and related concepts have been put forward by the program management community to bridge the gap between strategy and projects. Yet, firstly there appears to be some discordance among authors on the exact nature of these concepts. Secondly, these frameworks may not yet fully reflect the specific nature of strategy implementation. Therefore it is hard to accept them as the basis for communication between the project/program organisation and the business management when managing strategy implementation through programs of projects. We will borrow three concepts (resources, competencies and capabilities) from the resource based view of the company (RBV). We shall use them to define three levels of program objectives. We will illustrate these levels through a case of a strategic program in a professional information services company. We conclude with implications on current program management practice and research. Keywords: program management, program objectives, strategy implementation, benefits managemen

Introductory overview: the OpenMI 2.0 standard for integrating numerical models

The purpose of this paper is to introduce, explain and promote the Open Modelling Interface (OpenMI) version 2.0 standard for coupling environmental numerical models (simulations of environmental processes). It is intended to be accessible to readers of all levels of experience. During recent decades it has been recognised that the environment is made up of a complex set of interconnected processes. Therefore, understanding the environment requires not only understanding of the processes in isolation, but also the interactions between these processes. Traditional methods of simulating such environmental interactions have included passing the outputs of one numerical model into another or creating a single ‘super-model’ covering a variety of processes. OpenMI provides a standard method which could be applied to independent numerical model components allowing them to exchange data and therefore influence one another. This is achieved without fundamental changes to the core of the components themselves

Improving the accessibility and re-use of environmental models through provision of model metadata : a scoping study

This poster presents the results of a scoping study funded under a recent Natural Environment Research Council (NERC) Environmental Data Call. The work was undertaken by the British Geological Survey (Nottinghamshire, UK) in collaboration with HR Wallingford (Oxfordshire, UK). This investigation was designed to better understand the problem that whilst the input data used for modelling frequently has metadata data available, and metadata is often routinely created for the datasets created by modelling, there was perceived to be a lack of schemes and systems to record metadata about the modelling process itself. From this analysis gaps in metadata provision were identified, and recommendations for further work to address these were identified

Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparinginitial and bias-adjusted ERA-Interim data against gridded observational fields

Open data from physical model tests: Lessons learned from related initiatives

The HYDRALAB network of European physical model laboratories (www.hydralab.eu) has a range of facilities that includes flumes, basins, ice facilities, rotating tanks and environmental facilities. Each institution had its own data collection system, there are many proprietorial data formats, a shortage of meta-data and no central effort to curate or preserve this data in a findable, accessible, interoperable and reusable (FAIR) way. HYDRALAB+ (2015-2019) is a European Commission Horizon 2020 project to support this network, which requires FAIR data management. HYDRALAB is reviewing the steps taken to make data openly accessible in related disciplines, so that lessons learned can be applied to HDRALAB+. The chosen communities were: (i) the University of Hull’s digital repository, (ii) EMODnet Baltic Checkpoint, (iii) OpenEarth and (iv) the FP7 projects PEGASO and MEDINA and the EU MED project COASTGAP. It is clear that no one solution can deal with all situations: different data types and requirements can best be dealt with by different approaches. Standards for meta-data should be applied, but no existing standard covers the range of situations faced by HYDRALAB. All can be extended in a bespoke manner (which can potentially be included in an update of the standard) but it is highly likely that more than one standard (and none) will be used in such a diverse community. This is perfectly acceptable, so long as the standard is published. There is also a clear need for guidance on the development of repositories where large volumes of data are collected and an understanding of how much needs to be made available on-line. Although there can be conflicts of interest between institutions that are developing policies for data management and projects that want a uniform approach to data management across all partners, systems today can generally accommodate this

Application of a stochastic weather generator to assess climate change impacts in a semi-arid climate: The Upper Indus Basin

Assessing local climate change impacts requires downscaling from Global Climate Model simulations. Here, a stochastic rainfall model (RainSim) combined with a rainfall conditioned weather generator (CRU WG) have been successfully applied in a semi-arid mountain climate, for part of the Upper Indus Basin (UIB), for point stations at a daily time-step to explore climate change impacts. Validation of the simulated time-series against observations (1961–1990) demonstrated the models’ skill in reproducing climatological means of core variables with monthly RMSE of <2.0 mm for precipitation and ⩽0.4 °C for mean temperature and daily temperature range. This level of performance is impressive given complexity of climate processes operating in this mountainous context at the boundary between monsoonal and mid-latitude (westerly) weather systems. Of equal importance the model captures well the observed interannual variability as quantified by the first and last decile of 30-year climatic periods. Differences between a control (1961–1990) and future (2071–2100) regional climate model (RCM) time-slice experiment were then used to provide change factors which could be applied within the rainfall and weather models to produce perturbed ‘future’ weather time-series. These project year-round increases in precipitation (maximum seasonal mean change:+27%, annual mean change: +18%) with increased intensity in the wettest months (February, March, April) and year-round increases in mean temperature (annual mean +4.8 °C). Climatic constraints on the productivity of natural resource-dependent systems were also assessed using relevant indices from the European Climate Assessment (ECA) and indicate potential future risk to water resources and local agriculture. However, the uniformity of projected temperature increases is in stark contrast to recent seasonally asymmetrical trends in observations, so an alternative scenario of extrapolated trends was also explored. We conclude that interannual variability in climate will continue to have the dominant impact on water resources management whichever trajectory is followed. This demonstrates the need for sophisticated downscaling methods which can evaluate changes in variability and sequencing of events to explore climate change impacts in this region