Editorial: Ecological Applications of Earth System Models and Regional Climate Models

The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system's predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.

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Calibrating Simple Climate Models to Individual Earth System Models: Lessons Learned From Calibrating Hector

Earth and Space Science ◽

10.1029/2019ea000980 ◽

2020 ◽

Vol 7 (11) ◽

Cited By ~ 1

Author(s):

Kalyn Dorheim ◽

Robert Link ◽

Corinne Hartin ◽

Ben Kravitz ◽

Abigail Snyder

Keyword(s):

Climate Models ◽

Lessons Learned ◽

Earth System ◽

System Models ◽

Earth System Models

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CPMIP: measurements of real computational performance of Earth system models in CMIP6

Geoscientific Model Development ◽

10.5194/gmd-10-19-2017 ◽

2017 ◽

Vol 10 (1) ◽

pp. 19-34 ◽

Cited By ~ 14

Author(s):

Venkatramani Balaji ◽

Eric Maisonnave ◽

Niki Zadeh ◽

Bryan N. Lawrence ◽

Joachim Biercamp ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Initial Conditions ◽

Performance Model ◽

Earth System ◽

System Models ◽

Performance Engineering ◽

Computational Performance ◽

Sensitive Dependence ◽

Earth System Models

Abstract. A climate model represents a multitude of processes on a variety of timescales 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. codes present particular challenges to computational performance. 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. codes present particular challenges to computational performance. We present results for these measures for a diverse suite of models from several modeling centers, and propose to use these measures as a basis for a CPMIP, a computational performance model intercomparison project (MIP).

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CPMIP: Measurements of Real Computational Performance of Earth System Models

10.5194/gmd-2016-197 ◽

2016 ◽

Cited By ~ 1

Author(s):

V. Balaji ◽

E. Maisonnave ◽

N. Zadeh ◽

B. N. Lawrence ◽

J. Biercamp ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Initial Conditions ◽

Earth System ◽

System Models ◽

Performance Engineering ◽

Multi Scale ◽

Computational Performance ◽

Sensitive Dependence ◽

Earth System Models

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.

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Can we trust empirical marine DMS parameterisations within projections of future climate?

Biogeosciences ◽

10.5194/bg-7-1645-2010 ◽

2010 ◽

Vol 7 (5) ◽

pp. 1645-1656 ◽

Cited By ~ 45

Author(s):

P. R. Halloran ◽

T. G. Bell ◽

I. J. Totterdell

Keyword(s):

Climate Models ◽

Global Climate Models ◽

Future Climate ◽

Climate Feedback ◽

Climate Projections ◽

Earth System ◽

Aerosol Formation ◽

System Models ◽

Annual Variability ◽

Earth System Models

Abstract. Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System models. Using these models we can investigate the DMS-climate feedback and reduce uncertainty surrounding projections of future climate. Here we examine two empirical DMS parameterisations within the context of an Earth System model and find them to perform marginally better than the standard DMS climatology at predicting observations from an independent global dataset. We then question whether parameterisations based on our present understanding of DMS production by phytoplankton, and simple enough to incorporate into global climate models, can be shown to enhance the future predictive capacity of those models. This is an important question to ask now, as results from increasingly complex Earth System models lead us into the 5th assessment of climate science by the Intergovernmental Panel on Climate Change. Comparing observed and predicted inter-annual variability, we suggest that future climate projections may underestimate the magnitude of surface ocean DMS change. Unfortunately this conclusion relies on a relatively small dataset, in which observed inter-annual variability may be exaggerated by biases in sample collection. We therefore encourage the observational community to make repeat measurements of sea-surface DMS concentrations an important focus, and highlight areas of apparent high inter-annual variability where sampling might be carried out. Finally, we assess future projections from two similarly valid empirical DMS schemes, and demonstrate contrasting results. We therefore conclude that the use of empirical DMS parameterisations within simulations of future climate should be undertaken only with careful appreciation of the caveats discussed.

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Mid-Holocene Antarctic sea-ice increase driven by marine ice sheet retreat

Climate of the Past ◽

10.5194/cp-17-1-2021 ◽

2021 ◽

Vol 17 (1) ◽

pp. 1-19

Author(s):

Kate E. Ashley ◽

Robert McKay ◽

Johan Etourneau ◽

Francisco J. Jimenez-Espejo ◽

Alan Condron ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Global Climate Models ◽

Earth System ◽

Ice Shelf ◽

Sea Ice Extent ◽

Glacial Meltwater ◽

System Models ◽

Antarctic Sea Ice ◽

Earth System Models

Abstract. Over recent decades Antarctic sea-ice extent has increased, alongside widespread ice shelf thinning and freshening of waters along the Antarctic margin. In contrast, Earth system models generally simulate a decrease in sea ice. Circulation of water masses beneath large-cavity ice shelves is not included in current Earth System models and may be a driver of this phenomena. We examine a Holocene sediment core off East Antarctica that records the Neoglacial transition, the last major baseline shift of Antarctic sea ice, and part of a late-Holocene global cooling trend. We provide a multi-proxy record of Holocene glacial meltwater input, sediment transport, and sea-ice variability. Our record, supported by high-resolution ocean modelling, shows that a rapid Antarctic sea-ice increase during the mid-Holocene (∼ 4.5 ka) occurred against a backdrop of increasing glacial meltwater input and gradual climate warming. We suggest that mid-Holocene ice shelf cavity expansion led to cooling of surface waters and sea-ice growth that slowed basal ice shelf melting. Incorporating this feedback mechanism into global climate models will be important for future projections of Antarctic changes.

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Can we trust simple marine DMS parameterisations within complex climate models?

Biogeosciences Discussions ◽

10.5194/bgd-7-1295-2010 ◽

2010 ◽

Vol 7 (1) ◽

pp. 1295-1320 ◽

Cited By ~ 2

Author(s):

P. R. Halloran ◽

T. G. Bell ◽

I. J. Totterdell

Keyword(s):

Interannual Variability ◽

Climate Models ◽

Global Climate Models ◽

Future Climate ◽

Climate Feedback ◽

Climate Projections ◽

Earth System ◽

Aerosol Formation ◽

System Models ◽

Earth System Models

Abstract. Dimethylsulphide (DMS) is a globally important aerosol precurser. In 1987 Charlson and others proposed that an increase in DMS production by certain phytoplankton species in response to a warming climate could stimulate increased aerosol formation, increasing the lower-atmosphere's albedo, and promoting cooling. Despite two decades of research, the global significance of this negative climate feedback remains contentious. It is therefore imperative that schemes are developed and tested, which allow for the realistic incorporation of phytoplankton DMS production into Earth System models. Using these models we can investigate the DMS-climate feedback and reduce uncertainty surrounding projections of future climate. Here we examine two empirical DMS parameterisations within the context of an Earth System model and find them to perform marginally better than the standard DMS climatology at predicting observations from an independent global dataset. We then question whether parameterisations based on our present understanding of DMS production by phytoplankton, and simple enough to incorporate into global climate models, can be shown to enhance the future predictive capacity of those models. This is an important question to ask now, as results from increasingly complex Earth System models lead us into the 5th assessment of climate science by the Intergovernmental Panel on Climate Change. Comparing observed and predicted interannual variability, we suggest that future climate projections may underestimate the magnitude of surface ocean DMS change. Unfortunately this conclusion relies on a relatively small dataset, in which observed interannual variability may be exaggerated by biases in sample collection. We therefore encourage the observational community to make repeat measurements of sea-surface DMS concentrations an important focus, and highlight areas of apparent high interannual variability where sampling might be carried out. Finally, we assess future projections from two similarly valid empirical DMS schemes, and demonstrate contrasting results. We therefore conclude that the use of empirical DMS parameterisations within simulations of future climate should be undertaken only with careful appreciation of the caveats discussed.

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Metrics for the Evaluation of the Southern Ocean in Coupled Climate Models and Earth System Models

Journal of Geophysical Research Oceans ◽

10.1002/2017jc013461 ◽

2018 ◽

Vol 123 (5) ◽

pp. 3120-3143 ◽

Cited By ~ 13

Author(s):

Joellen L. Russell ◽

Igor Kamenkovich ◽

Cecilia Bitz ◽

Raffaele Ferrari ◽

Sarah T. Gille ◽

...

Keyword(s):

Southern Ocean ◽

Climate Models ◽

Earth System ◽

System Models ◽

Coupled Climate Models ◽

Earth System Models

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When Less Is More: Opening the Door to Simpler Climate Models

Eos ◽

10.1029/2017eo079417 ◽

2017 ◽

Cited By ~ 1

Author(s):

L. Polvani ◽

A. Clement ◽

B. Medeiros ◽

J. Benedict ◽

I. Simpson

Keyword(s):

Climate Models ◽

Earth System Model ◽

System Model ◽

Earth System ◽

Less Is More ◽

System Models ◽

Model Project ◽

Community Earth System Model ◽

Earth System Models

Earth system models are resource intensive and complex. To cut through this complexity, the Community Earth System Model project will now be embracing a hierarchy of simpler climate models.

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