The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations

Alexander, K.; Easterbrook, Steve

doi:10.5194/gmd-8-1221-2015

Cited by 39 publications

(41 citation statements)

References 34 publications

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“…There are diverse component architectures across Earth system models (Alexander and Easterbrook, 2015), but they nonetheless share common features for the purposes of discussion of the coupling algorithms. Consider the simplest case, that of two components, called A and O (symbolizing atmosphere and ocean).…”

Section: Coupling Algorithms In Earth System Modelsmentioning

confidence: 99%

“…ESMs around the world embody these differently in code; this figure is not intended to define the software structure of all ESMs, which tend to be quite diverse (Alexander and Easterbrook, 2015). How the notional architecture of Fig.…”

mentioning

confidence: 99%

“…3. (Alexander and Easterbrook, 2015 For the purposes of this study, we describe the actual code architecture of the GFDL Flexible Modeling System (FMS). The atmosphere and ocean components are set up to run in parallel in distributed memory, communicating on the slow coupling timestep ∆t cpld , on the order of ∆t cpld = 3600 sec for its flagship application, decadal-centennial climate change.…”

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confidence: 99%

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Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

et al. 2016

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Abstract. Climate models represent a large variety of processes on a variety of timescales and space scales, a canonical example of multi-physics multi-scale modeling. Current hardware trends, such as Graphical Processing Units (GPUs) and Many Integrated Core (MIC) chips, are based on, at best, marginal increases in clock speed, coupled with vast increases in concurrency, particularly at the fine grain. Multiphysics codes face particular challenges in achieving finegrained concurrency, as different physics and dynamics components have different computational profiles, and universal solutions are hard to come by.We propose here one approach for multi-physics codes. These codes are typically structured as components interacting via software frameworks. The component structure of a typical Earth system model consists of a hierarchical and recursive tree of components, each representing a different climate process or dynamical system. This recursive structure generally encompasses a modest level of concurrency at the highest level (e.g., atmosphere and ocean on different processor sets) with serial organization underneath.We propose to extend concurrency much further by running more and more lower-and higher-level components in parallel with each other. Each component can further be parallelized on the fine grain, potentially offering a major increase in the scalability of Earth system models.We present here first results from this approach, called coarse-grained component concurrency, or CCC. Within the Geophysical Fluid Dynamics Laboratory (GFDL) Flexible Modeling System (FMS), the atmospheric radiative transfer component has been configured to run in parallel with a composite component consisting of every other atmospheric component, including the atmospheric dynamics and all other atmospheric physics components. We will explore the algorithmic challenges involved in such an approach, and present results from such simulations. Plans to achieve even greater levels of coarse-grained concurrency by extending this approach within other components, such as the ocean, will be discussed.

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Section: Coupling Algorithms In Earth System Modelsmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

et al. 2016

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“…This similarity likely indicates that the structure of the models that describe SOC dynamics in the ESMs is similar. One reason for the similarity may be that certain ESMs share a common code (Alexander and Easterbrook, 2015). Another reason may be rooted in the basic structure of the soil carbon model, wherein SOC is calculated as the balance between dead organic matter input to soil and carbon emissions from the decomposition of organic matter.…”

Section: Similarity In Influential Factors For Esm Outputsmentioning

confidence: 99%

Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models

et al. 2017

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Abstract. Future climate change will dramatically change the carbon balance in the soil, and this change will affect the terrestrial carbon stock and the climate itself. Earth system models (ESMs) are used to understand the current climate and to project future climate conditions, but the soil organic carbon (SOC) stock simulated by ESMs and those of observational databases are not well correlated when the two are compared at fine grid scales. However, the specific key processes and factors, as well as the relationships among these factors that govern the SOC stock, remain unclear; the inclusion of such missing information would improve the agreement between modeled and observational data. In this study, we sought to identify the influential factors that govern global SOC distribution in observational databases, as well as those simulated by ESMs. We used a data-mining (machine-learning) (boosted regression trees -BRT) scheme to identify the factors affecting the SOC stock. We applied BRT scheme to three observational databases and 15 ESM outputs from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) and examined the effects of 13 variables/factors categorized into five groups (climate, soil property, topography, vegetation, and land-use history). Globally, the contributions of mean annual temperature, clay content, carbon-to-nitrogen (CN) ratio, wetland ratio, and land cover were high in observational databases, whereas the contributions of the mean annual temperature, land cover, and net primary productivity (NPP) were predominant in the SOC distribution in ESMs. A comparison of the influential factors at a global scale revealed that the most distinct differences between the SOCs from the observational databases and ESMs were the low clay content and CN ratio contributions, and the high NPP contribution in the ESMs. The results of this study will aid in identifying the causes of the current mismatches between observational SOC databases and ESM outputs and improve the modeling of terrestrial carbon dynamics in ESMs. This study also reveals how a data-mining algorithm can be used to assess model outputs.

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“…At the high level , Alexander and Easterbrook (2015) showed some permutations of how large components can be coupled together in eight earth system models, and at the same time indicated the relative complexity of the key components. In general, the most complexity was found in the atmospheres of these models, although some earth system models of intermediate complexity (EMICs) were dominated by oceans.…”

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confidence: 99%

Crossing the Chasm: How to develop weather and climate models for next generation computers?

Budich¹,

Lawrence²,

Rezny³

2017

Preprint

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Abstract.Weather and climate models are complex pieces of software which include many individual components, each of which is evolving under the pressure to exploit advances in computing to enhance some combination of a range of possible improvements (higher spatio/temporal resolution, increased fidelity in terms of resolved processes, more quantification of uncertainty etc). However, after many years of a relatively stable computing environment with little choice in processing architecture or 5 programming paradigm (basically X86 processors using MPI for parallelism), the existing menu of processor choices includes significant diversity, and more is on the horizon. This computational diversity, coupled with ever increasing software complexity, leads to the very real possibility that weather and climate modelling will arrive at a chasm which will separate scientific aspiration from our ability to develop and/or rapidly adapt codes to the available hardware.In this paper we review the hardware and software trends which are leading us towards this chasm, before describing current 10 progress in addressing some of the tools which we may be able to use to bridge the chasm. This brief introduction to current tools and plans is followed by a discussion outlining the scientific requirements for quality model codes which have satisfactory performance and portability, while simultaneously supporting productive scientific evolution. We assert that the existing method of incremental model improvements employing small steps which adjust to the changing hardware environment is likely to be inadequate for crossing the chasm between aspiration and hardware at a satisfactory pace, in part because insti-15 tutions cannot have all the relevant expertise in house. Instead, we outline a methodology based on large community efforts in engineering and standardisation, one which will depend on identifying a taxonomy of key activities -perhaps based on 1 Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-186 Manuscript under review for journal Geosci. Model Dev. Discussion started: 5 September 2017 c Author(s) 2017. CC BY 4.0 License. existing efforts to develop domain specific languages, identify common patterns in weather and climate codes, and develop community approaches to commonly needed tools, libraries etc -and then collaboratively building up those key components. Such a collaborative approach will depend on institutions, projects and individuals adopting new interdependencies and ways of working.

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The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations

Cited by 39 publications

References 34 publications

Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

Coarse-grained component concurrency in Earth system modeling: parallelizing atmospheric radiative transfer in the GFDL AM3 model using the Flexible Modeling System coupling framework

Data-mining analysis of the global distribution of soil carbon in observational databases and Earth system models

Crossing the Chasm: How to develop weather and climate models for next generation computers?

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