Sentences with phrase «zhai emergent constraints»

Borodina, A., Fischer, E. M. & Knutti, R. Emergent constraints in climate projections: a case study of changes in high - latitude temperature variability.

Bracegirdle, T. J. & Stephenson, D. B. On the robustness of emergent constraints used in multimodel climate change projections of Arctic warming.

Personally, I'm doubtful that emergent constraint approaches generally tell one much about the relationship to the real world of aspects of model behaviour other than those which are closely related to the comparison with observations.

There have been quite a number of papers published in recent years concerning «emergent constraints» on equilibrium climate sensitivity (ECS) in comprehensive global climate models (GCMs), of both the current (CMIP5) and previous (CMIP3) generations.

In Part 1 of this article the nature and validity of emergent constraints [1] on equilibrium climate sensitivity (ECS) in GCMs were discussed, drawing mainly on the analysis and assessment of 19 such constraints in Caldwell et al. (2018), [2] who concluded that only four of them were credible.

We shall see in this 3 - part article that emergent constraint approaches have the potential to offer useful insights into cloud behaviour, however the main focus will be on to what extent they narrow the uncertainty range of ECS in GCMs.

Caldwell regarded a proposed emergent constraint as not credible if it lacks an identifiable physical mechanism; is not robust to change of model ensemble; or if its correlation with ECS is not due to its proposed physical mechanism.

In Part 1 of this article the nature and validity of emergent constraints [i] on equilibrium climate sensitivity (ECS) in GCMs were discussed, drawing mainly on the analysis and assessment of 19 such constraints in Caldwell et al (2018; henceforth Caldwell), [ii] who concluded that only four of them were credible.

It thus falls within the «emergent constraint» paradigm.

An extract of the rows of Table 1 of Part 1 detailing those four emergent constraints is given below.

1) We use an Emergent Constraint (EC) approach, which means that we use GCMs to determine the relationship between variability and ECS.

These transient constraints have tended to come in lower than the other estimates based on paleo - climate or emergent constraints, and thus have been embraced by (let's say) more «optimistic» commentators (though until the mismatches are resolved it would be premature to only favor only one class of results).

5) As Kevin Cowtan rightly says, we did check that the coverage of the observational data in HadCRUT4 did not affect our emergent constraint.

Keeping only models with enough structural differences often reduces the reliability of identified emergent constraints.

An emergent constraint can be trusted if it meets certain criteria.

Emergent constraints attempt to use

If the models» behavior after the modification deviates from that expected from the emergent constraint (red arrows in Figure 1), the relationship may have been found by chance.

Finally, the relevance of constrained climate - change projections depends on statistical conditions that characterize emergent constraints.

In a previous post, I described the concept of emergent constraints, which allow us to narrow uncertainties in climate change projections through empirical relationships that relate a model's climate response to observable metrics.

The credibility of an emergent constraint relies upon the strength of the statistical relationship, a clear understanding of the mechanisms underlying the relationship, and the accuracy of observations.

In either case, assigning probabilities to ECS based on emergent constraints makes me uncomfortable.

It appears that you corrected your example Emergent Constraint on 25th Jan..

But I do want to advocate for caution, as you nicely did in your 2014 paper on data mining for emergent constraints.

Since the AR4, there are a few examples of emergent constraints where observations are used to constrain multi-model ensemble projections.

Regarding the emergent constraint used in Brient & Schneider (2016), it is noteworthy that if the models are weighted by reference to their consistency with the data, regression of ECS on TLC reflection variability explains almost none of the intermodel ECS variation — the R - squared is negligible.

Would you agree, or is there reason to suspect that emergent constraints are less well suited to hydrological fields and / or regional scales?

The plain - language explanations of the various limitations of the linear regression approach to emergent constraints were especially useful.

These points all support the need for caution regarding emergent constraints that you advocate.

Various attempts have been made to narrow the likely range of the equilibrium climate sensitivity (ECS) through exploitation of «emergent constraints.»

It thus falls within the «emergent constraint» paradigm.

Is there anything to object to in this work, leaving aside issues with the whole emergent constraint approach?

You could go further and talk about tuning to «emergent constraints» for climate sensitivity, observational metrics that are correlated with climate sensitivity when looking across model ensembles.

Your answer might depend on whether you find this literature on emergent constraints convincing or not.

Personally, I'm doubtful that emergent constraint approaches generally tell one much about the relationship to the real world of aspects of model behaviour other than those which are closely related to the comparison with observations.

As with all emergent constraints, this means it can be difficult to use natural variations to constrain the climate response of clouds.

It is also the first paper introducing the concept of an «emergent constraint» in the climate sciences.

Other branches of climate science use the concept of emergent constraints.

An early application of emergent constraints concerned the snow - albedo feedback.

To my knowledge, the first attempt at establishing an emergent constraint was made by Allen and Ingram in 2002.

The success of the Hall and Qu study paved the way for a number of studies seeking emergent constraints for equilibrium climate sensitivity (ECS).

Despite this successful application of an emergent constraint, the generation of models that followed (CMIP5) continued to exhibit a large spread in seasonal variability of snow - albedo changes (Qu and Hall 2014).

This diversity of emergent constraints highlights the commitment of the climate community to narrowing uncertainties in climate projections.

And beyond the post-facto model evaluation, it will be interesting to see whether new climate models will take advantage of emergent constraints to improve their simulation of present - day climate and to reduce uncertainties in future projections.

Conversely, many emergent constraints on ECS can be understood as encoding properties of shortwave low - cloud feedbacks (Qu et.

al 2016), results that may point the way toward mechanistic emergent constraints on high - cloud feedback.

The upcoming CMIP6 project will likely boost the enthusiasm for emergent constraints.

The three main criteria for a robust emergent constraint are satisfied: the physical mechanisms are well understood, the statistical relationship between the quantities of interest is strong, and uncertainties in the observed variations are weak, allowing Hall and Qu to constrain the snow - albedo feedback under global warming.

These uncertainties may partly explain the typically weak correlations found between paleoclimate indices and climate projections, and the difficulty in narrowing the spread in models» climate sensitivity estimates from paleoclimate - based emergent constraints (Schmidt et.

Correcting for precipitation biases in the tropical western Pacific using an emergent constraint methodology, however, reduces the magnitude of these increases by ∼ 50 %.

Evaluating Emergent Constraints on Equilibrium Climate Sensitivity.