First Steps Towards Modeling a Multi-Scale Earth System

Author(s):  
Klaus Regenauer-Lieb ◽  
Thomas Poulet ◽  
Delphine Siret ◽  
Florian Fusseis ◽  
Jie Liu ◽  
...  
Keyword(s):  
2020 ◽  
Author(s):  
Kim M. Cobb

<p>The study of past climate trends, variability, and extremes has yielded unique insights into Earth’s changing climate, yet paleoclimate science must overcome a number of key challenges to maximize its utility in a century defined by accelerating climate change. First, the paleoclimate archive itself is at grave risk, given that i) many records end in the late 20<sup>th</sup> century, and no concerted efforts exist to extend them to the present-day, and ii) many paleoclimate archives are disappearing under continued climate change and other forms of human disturbance. Second, many paleoclimate records are comprised of oxygen isotopes, yet the coordinated, multi-scale observational and modeling infrastructures required to unravel the mechanisms governing water isotope variability are as yet underdeveloped. Lastly, in part owing to the aforementioned deficiencies, paleoclimate data assimilation efforts remain fraught with large uncertainties, despite their promise in constraining many aspects of future climate impacts, including extreme events and hydrological trends and variability. Paleoclimate science for the 21<sup>st</sup> century requires deep investments in the full integration of paleoclimate data and approaches into frameworks for climate risk and hazard assessments. In that sense, paleoclimate scientists will continue to play a key role in the communication of climate change science to key stakeholders, including the general public. Their understanding of the Earth system also equips them to contribute valuable insights to teams comprised of researchers, practitioners, and  decision-makers charged with leveraging science to inform solutions, in service to society.</p>


2020 ◽  
Author(s):  
Shiming Xu ◽  
Jialiang Ma ◽  
Lu Zhou ◽  
Yan Zhang ◽  
Jiping Liu ◽  
...  

Abstract. High-resolution sea ice modeling is becoming widely available for both operational forecasts and climate studies. Sea ice kinematics is the most prominent feature of high-resolution simulations, and with rheology models such as Viscous-Plastic, current models are able to reproduce multi-fractality and linear kinematic features in satellite observations. In this study, we carry out multi-scale sea ice modeling with Community Earth System Model (CESM) by using a grid hierarchy (22 km, 7.3 km, and 2.5 km grid stepping in the Arctic). By using atmospherically forced experiments, we simulate consistent sea ice climatology across the 3 resolutions. Furthermore, the model reproduces reasonable sea ice kinematics, including multi-fractal deformation and scaling properties that are temporally changing and dependent on circulation patterns and forcings (e.g., Arctic Oscillation). With the grid hierarchy, we are able to evaluate the model's effective spatial resolution regarding the statistics of kinematics, which is estimated to be about 6 to 7 times that of the grid's native resolution. Besides, we show that in our model, the convergence of the Elastic-Viscous-Plastic (EVP) rheology scheme plays an important role in reproducing reasonable kinematics statistics, and more strikingly, simulates systematically thinner sea ice than the standard, non-convergent experiments in landfast ice regions of Canadian Arctic Archipelago. Given the wide adoption of EVP and subcycling settings in current models, it highlights the importance of EVP convergence especially for climate studies and projections. The new grids and the model integration in CESM are openly provided for public use.


Author(s):  
Isaac Lyngaas ◽  
Matt Norman ◽  
Youngsung Kim

In this work, we demonstrate the process for porting the cloud resolving model (CRM) used in the Energy Exascale Earth System Model Multi-Scale Modeling Framework (E3SM-MMF) from its original Fortran code base to C++ code using a portability library. This porting process is performed using the Yet Another Kernel Library (YAKL), a simplified C++ portability library that specializes in Fortran porting. In particular, we detail our step-by-step approach for porting the System for Atmospheric Modeling (SAM), the CRM used in E3SM-MMF, using a hybrid Fortran/C++ framework that allows for systematic reproduction and correctness testing of gradually ported YAKL C++ code. Additionally, analysis is done on the performance of the ported code using OLCF’s Summit supercomputer.


2016 ◽  
Author(s):  
V. Balaji ◽  
E. Maisonnave ◽  
N. Zadeh ◽  
B. N. Lawrence ◽  
J. Biercamp ◽  
...  

Abstract. A climate model represents a multitude of processes on a variety of time and space scales; a canonical example of multi-physics multi-scale modeling. The underlying climate system is physically characterized by sensitive dependence on initial conditions, and natural stochastic variability, so very long integrations are needed to extract signals of climate change. Algorithms generally possess weak scaling and can be I/O and/or memory bound. Such weak-scaling, I/O and memory-bound multi-physics codes present particular challenges to computational performance. Traditional metrics of computational efficiency such as performance counters and scaling curves do not tell us enough about real sustained performance from climate models on different machines. They also do not provide a satisfactory basis for comparative information across models. We introduce a set of metrics that can be used for the study of computational performance of climate (and Earth System) models. These measures do not require specialized software or specific hardware counters, and should be accessible to anyone. They are independent of platform, and underlying parallel programming models. We show how these metrics can be used to measure actually attained performance of Earth system models on different machines, and identify the most fruitful areas of research and development for performance engineering. We present results for these measures for a diverse suite of models from several modeling centres, and propose to use these measures as a basis for a CPMIP, a computational performance MIP.


2020 ◽  
Vol 101 (10) ◽  
pp. E1743-E1760 ◽  
Author(s):  
Gabriele G. Pfister ◽  
Sebastian D. Eastham ◽  
Avelino F. Arellano ◽  
Bernard Aumont ◽  
Kelley C. Barsanti ◽  
...  

ABSTRACTTo explore the various couplings across space and time and between ecosystems in a consistent manner, atmospheric modeling is moving away from the fractured limited-scale modeling strategy of the past toward a unification of the range of scales inherent in the Earth system. This paper describes the forward-looking Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA), which is intended to become the next-generation community infrastructure for research involving atmospheric chemistry and aerosols. MUSICA will be developed collaboratively by the National Center for Atmospheric Research (NCAR) and university and government researchers, with the goal of serving the international research and applications communities. The capability of unifying various spatiotemporal scales, coupling to other Earth system components, and process-level modularization will allow advances in both fundamental and applied research in atmospheric composition, air quality, and climate and is also envisioned to become a platform that addresses the needs of policy makers and stakeholders.


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