by Nic Lewis
These comments constitute a response to erroneous statements and misrepresentations made in a report published by the Met Office in July 2013: “The recent pause in global warming (3): What are the implications for projections of future warming?” (the Report).
Posted in climate models, Sensitivity & feedbacks
by Judith Curry
Update: New comment from Xie
My mind has been blown by a new paper just published in Nature.
Posted in Attribution, climate models
by Judith Curry
Recent observed global warming is significantly less than that simulated by climate models. This difference might be explained by some combination of errors in external forcing, model response and internal climate variability.
Posted in Attribution, climate models
by Judith Curry
What are the implications of climate model deficiencies in simulating multi-decadal natural internal variability for IPCC’s climate change detection and attribution arguments?
Posted in Attribution, climate models
by Judith Curry
The Weather Forecasting Improvement Act of 2013 introduced by Environment Subcommittee Vice Chairman Jim Bridenstine will prioritize the mission of NOAA to include the protection of lives and property, and make funds available to improve weather-related research, operations and computing resources.
Posted in climate models, Policy
by Judith Curry
Arguably the most poorly documented aspect of climate models is how they are calibrated, or ‘tuned’
Posted in climate models
by Judith Curry
To solve these pressing problems, there needs to be much better recognition of the importance of probability models in climate science and a more integrated view of climate modelling whereby climate prediction involves the fusion of numerical climate models and statistical models. – Stephenson et al.
Posted in climate models, Uncertainty
by Judith Curry
Over the last two weeks, there have been some interesting exchanges in the blogosphere on the topic of interpreting an ensemble of models.
Posted in climate models
by Judith Curry
Rather than reducing biases stemming from an inadequate representation of basic processes, additional complexity has multiplied the ways in which these biases introduce uncertainties in climate simulations. – Bjorn Stevens and Sandrine Bony
Posted in climate models
by Judith Curry
If climate scientists were credit-rating agencies, climate sensitivity would be on negative watch. But it would not yet be downgraded. – The Economist
by Judith Curry
[A] technique called direct statistical simulation dramatically reduces the time and brute-force computing that current simulation techniques require. The process does a good job of modeling fluid jets, fast-moving flows that form naturally in oceans and in the atmosphere. The findings are a key step toward bringing powerful statistical models rooted in basic physics to bear on climate science.
Posted in climate models
by Judith Curry
In the past 6 months or so, we have seen numerous different plots of the CMIP5 climate model simulations versus observations.
Posted in climate models, Communication, Uncertainty
by Anastassia Makarieva, Victor Gorshkov, Douglas Sheil, Antonio Nobre, Larry Li
It’s official: our controversial paper has been published. After a burst of intense attention (some of you may remember discussions at Climate Etc., the Air Vent and the Blackboard), followed by nearly two years of waiting, our paper describing a new mechanism driving atmospheric motion has been published in Atmospheric Chemistry and Physics.
Posted in climate models
by Judith Curry
by Judith Curry
My perspective on climate models (uncertainty monster, DOE presentation, RS presentation) have been regarded as outside the ‘mainstream’. Here are some new papers by leading climate modelers that provide new evidence and arguments on the concerns that I have been raising.
Posted in climate models
by Judith Curry
Overall, climate modeling has made enormous progress in the past several decades, but meeting the information needs of users will require further advances in the coming decades. – NRC
Posted in climate models
by Judith Curry
Should probabilistic qualities be assigned to climate model projections?
Are the approaches used by the IPCC for assessing climate model projection quality – confidence building, subjective Bayesian, and likelihood – appropriate for climate models?
What are some other approaches that could be used?
Posted in climate models, Scientific method
by Judith Curry
Estimates of climate-change impacts will get less, rather than more, certain. But this should not excuse inaction, say Mark Maslin and Patrick Austin.
Posted in climate models
by Judith Curry
The CMIP5 decadal simulations are now available for seven climate models. The first intercomparison results have just been published.
Posted in climate models
by Judith Curry
The new International Journal of Uncertainty Quantification has some very interesting papers. Lets take a look at a paper entitled ‘Error and Uncertainty Quantification and Sensitivity Analysis in Mechanics Computational Models.’
Posted in climate models, Uncertainty
by Anonymous
[O]ur notion of software quality with respect to climate models is theoretically and conceptually vague. It is not clear to us what differentiates high from low quality software; nor is it clear which aspects of the models or modelling processes we might reliably look to make to that assessment.
Posted in climate models
by Judith Curry
“It’s a national embarrassment. It has resulted in large unnecessary costs for the U.S. economy and needless endangerment of our citizens. And it shouldn’t be occurring.
What am I talking about? The third rate status of numerical weather prediction in the U.S. It is a huge story, an important story, but one the media has not touched, probably from lack of familiarity with a highly technical subject. And the truth has been buried or unavailable to those not intimately involved in the U.S. weather prediction enterprise.” — Cliff Mass, UW
Posted in climate models
by Judith Curry
In my original essay on this topic October 2010, my short answer to this question was “I’m not sure.” My current thinking on this topic reframes the question in the context of fitness for purpose of climate models across a range of uses and applications.
Posted in climate models
by Frank Lemke
A recent post in our Global Warming Prediction Project discusses the question “What Drives Global Warming?” based on a self-organized interdependent nonlinear dynamic system of equations of 6 variables (ozone, aerosols, clouds, sun activity, CO2, global temperature). It also predicts using this system global warming 6 years ahead (monthly resolved) and it compares the known IPCC AR4 projections with this system prediction and the observed anomalies of the past 23 years.
Posted in Attribution, climate models
