Coordinating an operational data distribution network  for CMIP6 data

Petrie, Ruth; Denvil, Sébastien; Ames, Sasha; Levavasseur, Guillaume; Fiore, Sandro; Allen, Christopher; Antonio, Fabrizio; Berger, KatharinaPier; Bretonnière, Pièrre-Antoine; Cinquini, L.; Dart, Eli; Dwarakanath, Prashanth; Druken, K. A.; Evans, Ben; Franchistéguy, Laurent; Gardoll, Sébastien; Gerbier, Eric; Greenslade, Mark; Hassell, David; Iwi, Alan; Juckes, Martin; Kindermann, Stephan; Łaciński, Łukasz; Mirto, Maria; Nasser, Atef Ben; Nassisi, Paola; Nienhouse, Eric; Nikonov, S.; Nuzzo, Alessandra; Richards, Clare; Ridzwan, Syazwan; Rixen, M.; Serradell, Kim; Snow, Kate; Stephens, Ag; Stockhause, Martina; Vahlenkamp, Hans; Wagner, Rick

doi:10.5194/gmd-14-629-2021

Cited by 48 publications

(26 citation statements)

References 10 publications

(24 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Every new iteration of the Coupled Model Intercomparison Project (CMIP) is based on the premise that newer generations of GCMs will demonstrate improvements over the previous ones, as models progressively improve in terms of their computational efficiency, resolution and representation of physical processes. In the latest 6th phase of CMIP (CMIP6, O'Neill, 2016;Petrie et al 2021), GCMs are forced with a new set of scenarios, which are a combination of Shared Socioeconomic Pathways (SSPs; Riahi et al 2017) and Representative Concentration Pathways (RCPs; van Vuuren et al 2011), to understand the Earth system response to increased anthropogenic forcing. The pathways addressed in this paper range from SSP1-2.6 representing one of the weaker forcing scenarios, to SSP5-8.5 representing the strongest forcing trajectory (O'Neill et al 2016;Riahi et al 2017;Zelinka et al 2020).…”

mentioning

confidence: 99%

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

et al. 2021

View full text Add to dashboard Cite

We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (2040–2059 and 2080–2099) relative to the reference period (1995–2014) under four Shared Socioeconomic Pathways (SSPs, SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andes—a result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP1–2.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the reference period and the magnitude of simulated sub-regional climate change in the future period. Overall, these results could be useful for regional climate change impact assessments across South America.

show abstract

mentioning

confidence: 99%

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

et al. 2021

View full text Add to dashboard Cite

show abstract

“…To evaluate the performance of the proposed framework with real climate data, a dataset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) [98], [99] in NetCDF-CF format [60], [61] has been used as input for the different operators; in particular, the following historical 12 See the Ophidia documentation for the full list of statistics computed: http://ophidia.cmcc.it/documentation/users/primitives/OPH_GSL_STATS. html dataset produced at CMCC with the CMCC-CM2-VHR4 model named [100]: CMIP6.HighResMIP.CMCC.CMCC-CM2-VHR4.hist-1950.…”

Section: Input Datamentioning

confidence: 99%

Towards HPC and Big Data Analytics Convergence: Design and Experimental Evaluation of a HPDA Framework for eScience at Scale

2021

Self Cite

View full text Add to dashboard Cite

Over the last two decades, scientific discovery has increasingly been driven by the large availability of data from a multitude of sources, including high-resolution simulations, observations and instruments, as well as an enormous network of sensors and edge components. In such a dynamic and growing landscape where data continue to expand, advances in Science have become intertwined with the capacity of analysis tools to effectively handle and extract valuable information from this ocean of data. In view of the exascale era of supercomputers that is rapidly approaching, it is of the utmost importance to design novel solutions that can take full advantage of the upcoming computing infrastructures. The convergence of High Performance Computing (HPC) and data-intensive analytics is key to delivering scalable High Performance Data Analytics (HPDA) solutions for scientific and engineering applications. The aim of this paper is threefold: reviewing some of the most relevant challenges towards HPDA at scale, presenting a HPDAenabled version of the Ophidia framework and validating the scalability of the proposed framework through an experimental performance evaluation carried out in the context of the Centre of Excellence in Simulation of Weather and Climate in Europe (ESiWACE). The experimental results show that the proposed solution is capable of scaling over several thousand cores and hundreds of cluster nodes. The proposed work is a contribution in support of scientific large-scale applications along the wider convergence path of HPC and Big Data followed by the scientific research community.

show abstract

“…If preservation and broad sharing of most projectgenerated data is intended to take place, a data productionorientation is necessary. This must encompass data preparation and curation tasks, such as ensuring that data and metadata conform to standards, that files are structured in consistent formats, that data access and preservation are possible, that data biases and errors are documented, and that data can be accessed and cited via persistent identifiers (McGinnis and Mearns, 2021;Petrie et al, 2021).…”

Section: Initial Rcn Project Findings Knowledge Production Vs Data Productionmentioning

confidence: 99%

Open Science Expectations for Simulation-Based Research

2021

View full text Add to dashboard Cite

There is strong agreement across the sciences that replicable workflows are needed for computational modeling. Open and replicable workflows not only strengthen public confidence in the sciences, but also result in more efficient community science. However, the massive size and complexity of geoscience simulation outputs, as well as the large cost to produce and preserve these outputs, present problems related to data storage, preservation, duplication, and replication. The simulation workflows themselves present additional challenges related to usability, understandability, documentation, and citation. These challenges make it difficult for researchers to meet the bewildering variety of data management requirements and recommendations across research funders and scientific journals. This paper introduces initial outcomes and emerging themes from the EarthCube Research Coordination Network project titled “What About Model Data? - Best Practices for Preservation and Replicability,” which is working to develop tools to assist researchers in determining what elements of geoscience modeling research should be preserved and shared to meet evolving community open science expectations.Specifically, the paper offers approaches to address the following key questions:• How should preservation of model software and outputs differ for projects that are oriented toward knowledge production vs. projects oriented toward data production?• What components of dynamical geoscience modeling research should be preserved and shared?• What curation support is needed to enable sharing and preservation for geoscience simulation models and their output?• What cultural barriers impede geoscience modelers from making progress on these topics?

show abstract

Coordinating an operational data distribution network for CMIP6 data

Cited by 48 publications

References 10 publications

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

Towards HPC and Big Data Analytics Convergence: Design and Experimental Evaluation of a HPDA Framework for eScience at Scale

Open Science Expectations for Simulation-Based Research

Contact Info

Product

Resources

About