Issues in the theoretical foundations of climate science

Katzav, Joel; Parker, Wendy S.

doi:10.1016/j.shpsb.2018.02.001

Cited by 14 publications

(7 citation statements)

References 54 publications

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“…The climatological reference period of 30 years is an established convention aiming at standardization and intercomparability of scientific results. However, there are no strict scientific reasons not to take 35 or 40 years (Katzav & Parker, 2018; Lovejoy & Schertzer, 2013). The choice of a specific reference period in observations can affect inferences based on the dataset such as consistency with the ensemble spread of the CMIP5, as has been shown by Hawkins and Sutton (2016).…”

Section: Understanding Dataset Uncertaintymentioning

confidence: 99%

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Zumwald

Knüsel

Baumberger

et al. 2020

WIREs Climate Change

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In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets. This article is categorized under: Paleoclimates and Current Trends > Modern Climate Change

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Section: Understanding Dataset Uncertaintymentioning

confidence: 99%

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Zumwald

Knüsel

Baumberger

et al. 2020

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

“…In turn, climate scientists may be tempted to consider the search for a more rigorous definition of little value. However, as Katzav and Parker (2018) note the theories at the foundations of climate science are not as clearly stated as one would wish for-at least from but not confined to a philosophical perspective. Katzav and Parker (2018) Katzav and Parker (2018) distinguish between statistical and combined physical-statistical descriptions.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 98%

“…However, as Katzav and Parker (2018) note the theories at the foundations of climate science are not as clearly stated as one would wish for-at least from but not confined to a philosophical perspective. Katzav and Parker (2018) Katzav and Parker (2018) distinguish between statistical and combined physical-statistical descriptions. Additionally they highlight the partly contrasting temporal and dynamical systems perspectives on climate (compare also, e.g., Conradie, 2015).…”

Section: Discussion and Concluding Remarksmentioning

confidence: 98%

“…There are the diverse, but generally reductionist views by the diverse communities of climate science. These include purely statistical perspectives either in terms of time intervals or ensembles, the view of climate as an attractor, or combined statistical-physical definitions (compare, e.g., Schneider and Dickinson, 1974;Leith, 1978;Lawhead, 2015;Conradie, 2015;Katzav and Parker, 2018). Some of these take a regional perspective, some consider a global climate (Mahony, 2017;Heymann, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…The absence of a clear definition of climate is at the heart of the considerations of Katzav and Parker (2018), who point to open questions of interest for the philosophy of climate science.…”

Section: Introductionmentioning

confidence: 99%

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Although the concept of climate is easy to understand, there is not any uncontroversial definition of it. Most definitions fall back to the simple formulation that `climate is the statistics of weather'. Recent attempts at a definition called versions of this saying vague. Climate is policy-relevant, and discussions on climate and climate change benefit from clarity on the topic. Beyond the policy relevance a definition should also be valid for scientific purposes and for individual views. It has to account for a general concept and individual instances of climate. Here, I try to highlight why the flexibility and the immediacy of the colloquial definition fit the topic. This defence shifts the lack of a clear definition towards the term `weather' and the time-scales separating weather and climate.

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Issues in the theoretical foundations of climate science

Cited by 14 publications

References 54 publications

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles

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