Abstract We describe the scientific and technical implementation of two models for a core set of experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The models used are the physical atmosphere‐land‐ocean‐sea ice model HadGEM3‐GC3.1 and the Earth system model UKESM1 which adds a carbon‐nitrogen cycle and atmospheric chemistry to HadGEM3‐GC3.1. The model results are constrained by the external boundary conditions (forcing data) and initial conditions. We outline the scientific rationale and assumptions made in specifying these. Notable details of the implementation include an ozone redistribution scheme for prescribed ozone simulations (HadGEM3‐GC3.1) to avoid inconsistencies with the model's thermal tropopause, and land use change in dynamic vegetation simulations (UKESM1) whose influence will be subject to potential biases in the simulation of background natural vegetation. We discuss the implications of these decisions for interpretation of the simulation results. These simulations are expensive in terms of human and CPU resources and will underpin many further experiments; we describe some of the technical steps taken to ensure their scientific robustness and reproducibility.
A new climate model, HadGEM3 N96ORCA1, is presented that is part of the GC3.1 configuration of HadGEM3. N96ORCA1 has a horizontal resolution of ~135 km in the atmosphere and 1° in the ocean and requires an order of magnitude less computing power than its medium-resolution counterpart, N216ORCA025, while retaining a high degree of performance traceability. Scientific performance is compared to both observations and the N216ORCA025 model. N96ORCA1 reproduces observed climate mean and variability almost as well as N216ORCA025. Patterns of biases are similar across the two models. In the northwest Atlantic, N96ORCA1 shows a cold surface bias of up to 6 K, typical of ocean models of this resolution. The strength of the Atlantic meridional overturning circulation (16 to 17 Sv) matches observations. In the Southern Ocean, a warm surface bias (up to 2 K) is smaller than in N216ORCA025 and linked to improved ocean circulation. Model El Niño/Southern Oscillation and Atlantic Multidecadal Variability are close to observations. Both the cold bias in the Northern Hemisphere (N96ORCA1) and the warm bias in the Southern Hemisphere (N216ORCA025) develop in the first few decades of the simulations. As in many comparable climate models, simulated interhemispheric gradients of top-of-atmosphere radiation are larger than observations suggest, with contributions from both hemispheres. HadGEM3 GC3.1 N96ORCA1 constitutes the physical core of the UK Earth System Model (UKESM1) and will be used extensively in the Coupled Model Intercomparison Project 6 (CMIP6), both as part of the UK Earth System Model and as a stand-alone coupled climate model.
Abstract For simulations intended to study the influence of anthropogenic forcing on climate, temporal stability of the Earth's natural heat, freshwater, and biogeochemical budgets is critical. Achieving such coupled model equilibration is scientifically and computationally challenging. We describe the protocol used to spin‐up the UK Earth system model (UKESM1) with respect to preindustrial forcing for use in the sixth Coupled Model Intercomparison Project (CMIP6). Due to the high computational cost of UKESM1's atmospheric model, especially when running with interactive full chemistry and aerosols, spin‐up primarily used parallel configurations using only ocean/land components. For the ocean, the resulting spin‐up permitted the carbon and heat contents of the ocean's full volume to approach equilibrium over 5,000 years. On land, a spin‐up of 1,000 years brought UKESM1's dynamic vegetation and soil carbon reservoirs toward near‐equilibrium. The end‐states of these parallel ocean‐ and land‐only phases then initialized a multicentennial period of spin‐up with the full Earth system model, prior to this simulation continuing as the UKESM1 CMIP6 preindustrial control (piControl). The realism of the fully coupled spin‐up was assessed for a range of ocean and land properties, as was the degree of equilibration for key variables. Lessons drawn include the importance of consistent interface physics across ocean‐ and land‐only models and the coupled (parent) model, the extreme simulation duration required to approach equilibration targets, and the occurrence of significant regional land carbon drifts despite global‐scale equilibration. Overall, the UKESM1 spin‐up underscores the expense involved and argues in favor of future development of more efficient spin‐up techniques.
We describe a toolkit for the design and visualization of flexible artificial heart valves. The toolkit consists of interlinked modules with a visual programming interface. The user of the toolkit can set the initial geometry and material properties of the valve leaflet, solve for the flexing of the leaflet and the flow of blood around it, and display the results using the visualization capabilities of the toolkit. The interactive nature of our environment is highlighted by the fact that changes in leaflet properties are immediately reflected in the flow field and response of the leaflet. Hence the user may, in a single session, investigate a broad range of designs, each one of which provides important information about the blood flow and motion of the valve during the cardiac cycle.
Data used for analysis in "Explainable Clustering Applied to the Definition of Terrestrial Biome" - using Decision Tree and Clustering techniques to identify biomes. Land surface properties: TreeCover - Vegetation Continuous Fields (VCF) collection 6 fractional tree cover from DiMiceli et al. 2015, regridded as per Kelley et al. 2019 NonTreeCover - VCF fractional herb cover Urban cover from the History Database of the Global Environment, Version 3.1 (HYDE) Klein Goldewijk et al. 2011 Crop cover (from HYDE) Pasture Cover (from HYDE) PopDen (population density from HYDE) Climate: MAP_CRU - Mean annual precipitation from version 4.01 of the Climatic Research Unit Time Series high resolution gridded dataset (CRU TS v4.01) (Harris & Jones 2017) MAT - Mean annual temperature from CRU) MADD_CRU - Mean annual dry days from CRU - i.e seasonality of rainfall MTWM - Mean Maximum Temperature of the warmest month from CRU MTCM - Mean Minumum Temperature of the coldest month from CRU SW1 - direct downwards SW simulated using the SLASH model using CRU cload cover SW2 - diffuse downwards SW simulated using the SLASH model using CRU cload cover BurntArea_GFED_four_s - Burnt area from Global Fire Emissions Database, Version 4.1 (GFEDv4.1) (Van Der Werf et al. 2017) MaxWind(Mean Max Windspeed from CRU-(National Centers for Environmental Prediction (Harris 2019) Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D. H.,Kelly, M., and Townshend, J. R. G. (2015). Mod44bmodis/terra vegetation continuous fields yearly l3global 250m sin grid v006 (v006). Harris, I. (2019). CRU JRA v1. 1: A forcings dataset ofgridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data, January1901–December 2017, University of East Anglia Climatic Research Unit, Centre for Environmental DataAnalysis. Harris, I. and Jones, P. (2017). CRU TS4. 01: Climatic Re-search Unit (CRU) Time-Series (TS) version 4.01 ofhigh-resolution gridded data of month-by-month vari-ation in climate (Jan. 1901–Dec. 2016).Centre forEnvironmental Data Analysis, 25. Kelley, D. I., Bistinas, I., Whitley, R., Burton, C., Marthews,T. R., and Dong, N. (2019). How contemporary biocli-matic and human controls change global fire regimes Klein Goldewijk, K., Beusen, A., Van Drecht, G., and DeVos, M. (2011). The HYDE 3.1 spatially explicitdatabase of human-induced global land-use changeover the past 12,000 years.Global Ecology and Bio-geography, 20(1):73–86. Van Der Werf, G. R., Randerson, J. T., Giglio, L.,Van Leeuwen, T. T., Chen, Y., Rogers, B. M., Mu, M.,Van Marle, M. J., Morton, D. C., Collatz, G. J., et al.(2017). Global fire emissions estimates during 1997–2016.Earth System Science Data, 9(2):697–720.
R. Evans, M. E. Fisher, B. Widom, R. M. Lynden-Bell, P. Phillips, S. F. O'Shea, R. K. Thomas, J. A. Morrison, D. Nicholson, J. Suzanne, M. H. W. Chan, A. Inaba, J. S. Rowlinson, M. L. Klein, F. van Swol, J. R. Henderson, J. P. R. B. Walton, M. P. Allen, M. R. Moldover, J. O. Indekeu, A. J. Willatt, A. Robledo, M. M. T. da Gama, J. H. Thurtell, S. Nordholm, B. A. Pethica, C. M. Knobler, J. C. Earnshaw, U. M. B. Marconi, N. Quirke, K. E. Gubbins, U. Heinbuch, J. Fischer, N. Clift and R. M. White, J. Chem. Soc., Faraday Trans. 2, 1986, 82, 1817 DOI: 10.1039/F29868201817
Climateprediction.net aims to harness the spare CPU cycles of a million individual users' PCs to run a massive ensemble of climate simulations using an up-to-date, full-scale 3D atmosphere-ocean climate model. Although it has many similarities with other public-resource computing projects, it is distinguished by the complexity of its computational task, its system demands and the level of participant interaction, data volume and analysis procedures. For simulations running on individual PCs, there is a requirement for visualizations that are compelling and readily grasped, since most users will be interested in the output from the model, but will have a limited level of scientific experience. This paper describes the design and implementation of these visualizations.
Abstract. The need for open science has been recognized by the communities of meteorology and climate science. However, while these domains are mature in terms of applying digital technologies, these are lagging behind where the implementation of open science methodologies is concerned. In a session on Weather and Climate Science in the Digital Era at the 14th IEEE International eScience conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. The studies presented in the conference session showed the added value of shared data, software and platforms through, for instance, combining data sets from disparate sources, increased accuracy and skill of simulations and forecasts at local scales, and improved consistency of data products. We observed that sharing data and code is important, but not sufficient to achieve open weather and climate science and that here are important issues to address. At the level of technology, the implementation of the FAIR principles to many datasets used in weather and climate science remains a challenge due to their origin, scalability, or legal barriers. Furthermore, the complexity of current software platforms limits collaboration between researchers and optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the system of science as a whole. There is a need for new roles and responsibilities at the interface of science and digital technology, e.g., data stewards and research software engineers. This requires the personnel portfolio of academic institutions to be more diverse, and in addition, a broader consideration of the impact of academic work, beyond publishing and teaching. Besides, new policies regarding open weather and climate science should be developed in an inclusive way to engage all stakeholders, including non-academic parties such as meteorological institutions. We acknowledge that open weather and climate science requires effort to change, but the benefits are large. As can already be observed from the studies presented in the conference it leads to much faster progress in understanding the world.
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