Category Archives: climate models

Verification, validation, and uncertainty quantification in scientific computing

by Judith Curry

I think I am gaining some insight into the debate between scientists versus engineers regarding climate model verification and validation.

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Climate models as ink blots

by Judith Curry

From Roger Pielke Jr.:  A fundamental problem with climate science in the public realm, as conventionally practiced by the IPCC, is the essential ink blot nature of its presentation. By “ink blot” I mean that there is literally nothing that could occur in the real world that would allow those who are skeptical of scientific claims to revise their views due to unfolding experience. That is to say, anything that occurs with respect to the climate on planet earth is “consistent with” projections made by the climate science community. 

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Santer on timescales of temperature trends

by Judith Curry

Santer et al. have a new paper in press entitled “Separating Signal and Noise in Atmospheric Temperature Changes: The Importance of Time Scale.”

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Water: too little, too much

by Judith Curry

Next week, I will be in Boulder attending a workshop on the topic of understanding and predicting conditions associated with either too much or too little water.

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Should we assess climate model predictions in light of severe tests?

by Judith Curry

This question is posed and addressed in a recent article by Joel Katzav in EOS.

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reanalyses.org

by Judith Curry

How do you estimate the state of the global atmosphere and ocean when observational data sets are incomplete, imperfect and noisy?

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NASA Earth Science Advisory Subcommittee

by Judith Curry

This week I am attending a meeting of the Earth Science Subcommittee of the NASA Advisory Council.  As described in the Public Notice for the meeting, the topic of this meeting is evaluation of NASA’s  Earth Science Modeling and Activities.

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NCAR Community Climate System Model Version 4

by Judith Curry

A new paper describing the latest version of the NCAR climate model has just been published at the Journal of Climate.  This is the version of the model that is being used for the IPCC AR5.

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Separating natural and anthropogenically-forced decadal climate variability

by Judith Curry

The issue of separating natural from anthropogenically forced variability, particularly in context of the attribution of 20th century climate change, has been a topic of several previous threads at Climate Etc.  The issue of natural vs anthropogenically forced climate variability/change has been a key issue of contention between the climate establishment and skeptics.  There are some encouraging signs that the climate establishment is maturing in their consideration of this issue.

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Nonlinearities, feedbacks and critical thresholds

by Judith Curry

Juoakola spotted an interesting paper, that I missed when it was originally published:

NONLINEARITIES, FEEDBACKS AND CRITICAL THRESHOLDS WITHIN THE EARTH’S CLIMATE SYSTEM

JOSÉ A. RIAL , ROGER A. PIELKE SR., MARTIN BENISTON , MARTIN CLAUSSEN, JOSEP CANADELL , PETER COX, HERMANN HELD , NATHALIE DE NOBLET-DUCOUDRÉ , RONALD PRINN, JAMES F. REYNOLDS and JOSÉ D. SALAS

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Scenarios: 2010-2030. Part I

by Judith Curry

On the time scale of a few decades ahead, regional variations in weather patterns and climate will be strongly influenced by natural internal variability. The potential applications of high resolution decadal climate change predictions are described in this CLIVAR doc.  Based upon my own interaction with decision makers, I see a need on these time scales that is primarily associated with infrastructure decisions.  Sectors that seem particularly interested in predictions on this time timescale are city and regional planners, the military, and the financial sector.

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Climate model verification and validation: Part II

I’m starting a new thread for this topic, since interest in the previous thread has re-generated owing to this AGU abstract by Steve Easterbrook, entitled Do Over or Make Do?  Climate Models as a Software Development Challenge.

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Confidence in radiative transfer models

by Judith Curry

The calculation of atmospheric radiative fluxes is central to any argument related to the atmospheric greenhouse/Tyndall gas effect.  Atmospheric radiative transfer models rank among the most robust components of climate model, in terms of having a rigorous theoretical foundation and extensive experimental validation both in the laboratory and from field measurements.   However, I have not found much in the way of actually explaining how atmospheric radiative transfer models work and why we should have confidence in them (at the level of technical blogospheric discourse).  In this post, I lay out some of the topics that I think need to be addressed  in such an explanation regarding infrared radiative transfer.  Given my limited time this week, I mainly frame the problem here and provide some information to start a dialogue on this topic, I hope that other experts participating can fill in (and I will update the main post).

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Climate model verification and validation

by Judith Curry

On the thread building confidence in climate models , a small amount of text was devoted to verification and validation (V&V).  In raising the level of the game, I included the following bullet:

• Fully documented verification and validation of climate models

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Water vapor mischief

There is a provocative new paper available at an online discussion journal:

Makarieva, Gorshkov, Sheil, Nobre, Li:  Where do winds come from?  A new theory on how water vapor condensation influences atmospheric pressure and dynamics. link

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The culture of building confidence in climate models

by Judith Curry

As climate models become increasingly relevant to policy makers,  they are being criticized  for not undergoing a formal verification and validation (V&V) process analogous to that used in engineering and regulatory applications. Further, claims are being made that climate models have been falsified by failing to predict specific future events.

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What can we learn from climate models?

by Judith Curry

Short answer:  I’m not sure.

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