The links to the other sites that explained the terms being used were important and useful for me. I use Real Climate to stay informed, but sometimes I find some of the concepts hard to wrap my head around. Some background and definitions make it much easier for me to understand, like the Wikipedia page on ‘boundary conditions’ and the World Scientific page on empirical-statistical downscaling.
I thought the line “So what’s the fuzz all about?” in the first paragraph was a misinterpretation by a non-native english speaker of the expression “what’s the buzz all about” until I read the link to Kerr 2013!
Comment by Joseph O'Sullivan — 6 Apr 2013 @ 1:32 PM
Thank you for this article. Just a minor grammatical point: in the sentence that begins “ESD and RCMs have different strengths” you might want to replace “compliment” with “complement.”
Eli was talking with a friend, a regional modeler on Friday, about air pollution models. He was grousing how the dumbest models (basically persistence) do the best job if nothing changes. On the other hand they fail completely if something changes (for example the ozone model used in LA failed completely during the Olympics because there was much less traffic).
When something changes the complex chemical models do a much better, tho not perfect job, of capturing the change. There may be something similar going on here, where a combination of downscaling and persistence may be needed.
I’m curious as to Rasmus & Raypierre’s take on Lovejoy & Schertzer’s new book The Weather and Climate: Emergent Laws and Multifractal Cascades. which avers that many of the shortcomings of regional and global prediction can be overcome by multifractal cascade models.
The blurb claims that :
” By generalizing the classical turbulence laws, emergent higher-level laws of atmospheric dynamics are obtained and are empirically validated over time-scales of seconds to decades and length-scales of millimetres to the size of the planet. In generalizing the notion of scale, atmospheric complexity is reduced to a manageable scale-invariant hierarchy of processes, thus providing a new perspective for modelling and understanding the atmosphere.”
I guess my questions are: How are RCMs constructed? Are they the result of applying finer horizontal and vertical grids to GCMs? Are there differences in the underlying physics — GCM vs. RCM? What are the principal differences between projection from RCMs versus those using ESD? What is downscaled in ESD? Whence comes the data to initialize RCMs? ESD models? Are the data the same as those used in GCMs?
At first glance, it is no small wonder “that the problem is not so much the RCMs, but the global climate models’ (GCMs) ability to predict climate changes on a regional scale.”
The interesting post points towards an issue we spend a lot of time discussing during my climate change course at UBC: the gap between what science can deliver and what people expect science to deliver. Climate services are potentially very valuable, however, we need to understand and appreciate this gap, or scientists and decision-makers will continue to frustrate one another.
As a test of the regional models, it might be interesting to concentrate on outliers – areas where the models predict unusually large climate changes, and areas where in fact changes are large. To what extent do they match? Concentrating on outliers mitigates the problem that the models cannot cover all forces because large effects are likely to drown out the background noise. Also, of course, big changes have more practical importance.
Well, that is not that much connected to the topic of this post, but I’ll ask anyway:
Regarding the IPCC-models and positive feedbacks, such as the melting of permafrost, why are such feedbacks not included in the latest report? Seems to me that they are rather too important to neglect.
What if we take a break for let say 10 years and see what will happen. In the mean time put all our effort in what we know and do something about it with what we know.
If there is a hole in the dike the best thing to do is put your finger in it. Maybe over simplified but a lot easier than to figure out a way to lower the water level of the sea.
The world is getting sick of spending billions of dollars towards climate change research wile hardly anything is happening what is measurable. Yes we invented, solar panels, wind-turbines, electric cars, what did it bring except burning tons of energy to produce them.
I don’t say the work of scientist is not valuable, it is however you don’t save the world with data, we save the world with action.
10 years, 10 billion trees, 10 million sustainable jobs. You don’t need science to figure-out the value of that.
Well, thankfully, scientists know far, far more than you do. We know CO2 is a greenhouse gas–and have known this since the 1850s. We know adding CO2 to the atmosphere will warm the planet. And we know your silly, little mitigation suggestions won’t matter worth a fart in a hurricane.
Might I suggest that if you start to realize just how ignorant your opinion is, it might go a long way toward rectifying your ignorance.
People are very good at pushing systems’ levers, but generally push them in the wrong direction. You have an example there.
Problems with the loggers’ notion that rushing to plant more trees is useful: it’s mostly wrong because trees aren’t most of the answer. Forest holds carbon where it’s left undisturbed. Mature complicated forests are pretty tolerant of fire, left alone — the fire burns through under shaded canopy between big trees, clearing out brush. Forest is mostly soil, with a top layer of trees. The problem with the timber industry approach is, you can’t get that soil back quickly, nor preserve it, when you cut the trees. Nor is most of the actual timber preserved by logging; most goes to sawdust, or rots quickly.
Most of a standing tree is dead wood, surrounded by a thin layer of living tree. Once the tree dies the dead center is rapidly converted to living fungi — both above and below ground; the dead tree is rapidly converted to — mostly living soil.
“50 to 70% of stored carbon in a chronosequence of boreal forested islands derives from roots and root-associated microorganisms. Fungal biomarkers indicate impaired degradation and preservation of fungal residues in late successional forests.”