Sentences with phrase «modeling the climate just»

Modeling the climate just got a little more complex.

Not exact matches

To figure out the economic cost of a decade of extreme methane release — say from 2015 to 2025 — the researchers added the extra methane and temperature increases to the climate models through to 2200 — that's how they got the $ 60 trillion cost globally from just the East Siberian Arctic Shelf.

Darin Kingston of d.light, whose profitable solar - powered LED lanterns simultaneously address poverty, education, air pollution / toxic fumes / health risks, energy savings, carbon footprint, and more Janine Benyus, biomimicry pioneer who finds models in the natural world for everything from extracting water from fog (as a desert beetle does) to construction materials (spider silk) to designing flood - resistant buildings by studying anthills in India's monsoon climate, and shows what's possible when you invite the planet to join your design thinking team Dean Cycon, whose coffee company has not only exclusively sold organic fairly traded gourmet coffee and cocoa beans since its founding in 1993, but has funded dozens of village - led community development projects in the lands where he sources his beans John Kremer, whose concept of exponential growth through «biological marketing,» just as a single kernel of corn grows into a plant bearing thousands of new kernels, could completely change your business strategy Amory Lovins of the Rocky Mountain Institute, who built a near - net - zero - energy luxury home back in 1983, and has developed a scientific, economically viable plan to get the entire economy off oil, coal, and nuclear and onto renewables — while keeping and even improving our high standard of living

And now Variety comes out of nowhere with this report where the numbers just don't seem to make sense in the current climate, especially for ESPN for reasons already mentioned including the existing Fight Pass business model.

And climate models predict wet regions will become wetter and dry regions drier, which means more rain for all of the UK, not just the coasts.

Co-author Prof Derk - Jan Dijk said: «Just as mathematical models are used to predict climate change, they can now be used to predict how changing our light environment will influence our biological rhythms.

Their computer models included not just energy use and production, but also the broader economy and the climate system.

«Climate models have improved greatly in the last 10 years, which allows us to look in detail at the simulation of daily weather rather than just monthly averages,» said Pierce.

Among the most uncertain elements in climate models are the effects of aerosols and their interactions with clouds — just the things involved in albedo modification — she says.

Titanic international projects that are just kicking off, including the National Science Foundation - funded Ocean Observatories Initiative and Southern Ocean Carbon and Climate Observations and Modeling project, promise to pile on reams of new data and knowledge in the coming years — not all of it expected to be postcard pretty.

Mills stresses that there is a lot more going into premium increases than just new models that factor in climate change, including population increases along the coast, deterioration of infrastructure such as levies, and destruction of naturally protective wetlands.

Dennis Avery of the Hudson Institute cited Doran's study, and claimed that «The American people are being hoodwinked not just by the green activists, but by the scientists who get billions of dollars for creating global climate models that can't even forecast backward, let alone forward.»

The following is just an example, and my original question was that I wonder how climate models adjust to non-linearities in the atmosphere.

Just for the sake of illustration, though, here's one scenario where higher Holocene variability could go along with lower climate sensitivity: Suppose that some unknown stabilizing mechanism makes the real world less sensitive to radiative forcing than our current models.

Often it is also used to attribute the most extreme example of disbelief in climate change to a people that probably are just a little more critical of, for example, computer modelling results, as those crafting the models.

Professor England's study has been tested against climate models, and indeed just the trade winds could account for the missing heat.

This point might become clearer once it's realised that climate models are not developed just to the climate change problem, but as much more general tools to quantify the net effects of all the different processes we know about.

Uncertainty exists for crop models, just as for climate models, and this must be acknowledged.

Also just a few months ago Millar et al. 2017 had to admit that the climate models indeed had been running much too hot and that the 1.5 °C target can be reached as well with a tripling of CO2 emissions.

Previous proofs have relied on complex climate models, but this proof doesn't need such models — just careful observations of the land, ocean and atmospheric gases.»

It was originally just a climate model benchmark, wasn't it?

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I would like to see this evidence that models just based on known physics «model climate and paleoclimate rather well».

There is an implicit assumption that the models are very close to resembling Earth's climate and just need tweaking, but no acknowledgement that the system is probably much more complex than we know or can model.

The «significant gap in GCMs» is not because «clearly the science isn't yet well understood»; it just corresponds to the fact that Global Climate Models are not Regional Climate Models.

There is nothing wrong per se in splicing records together to get a continuous series — for instance I have just done the exact same thing in creating a series of solar forcing functions for climate model runs — but these things should be clearly explained.

Using models to distinguish between the forcing histories is thus likely to require a tighter focus on regional changes, or in climate patterns, more than the just the mean temperature.

I suppose in the abstract this would be dull as doornails if not unhelpful, and so probably it's best to explain it with examples and in the context of climate modeling, but I wanted to describe it in the abstract, just because I think what keeps a lot of people from appreciating climate science (or even why it's hard to appreciate) has to do with very basic ideas about not just «the scientific process» but with the narrower or perhaps more easily describable process of modeling.

I keep re-reading Von Storch's articles on what climate models are, and so on, but also keep on coming out with the idea that Mann did just what Von Storch says shold be done.

(For instance, Gavin just gave 14 + as a common number of «knobs» used in modeling 20th century climate.)

But it's still the case that you just don't need to look at a climate model to evaluate the correctness of the experiment.

This point might become clearer once it's realised that climate models are not developed just to the climate change problem, but as much more general tools to quantify the net effects of all the different processes we know about.

Because the physics controlling δ18O is well understood, and we are able to implement δ18O in climate models, we can actually just use δ18O as a proxy for, well, δ18O.

(I am used to just corresponding with by climate modeling colleagues.)

It is quite strange that this paper seems to review future of tropical rainforest in the face of rising CO2 and rising temperature — unfortunately, it completely lacks to mention change in precipitation, which is just - another - very - important (climate change) metric — and it completely fails to mention modelling work of Peter Cox group — that predicts decline in rain forest productivity and growth due to decline in precipitation..

Although the Met Office Hadley Center model projects extreme drying and warming in the Amazon due to ongoing climate change, and there may even be a commitment to long - term decline of part of the Amazon forest even at just 2 degrees global warming above pre-industrial, other climate models show less of a drying or even none at all.

Thus, the noise in the climate models is not reproducing just inter-annual fluctuations, but instrumental noise.

Even if the study were right... (which it is not) mainstream scientists use * three * methods to predict a global warming trend... not just climate computer models (which stand up extremely well for general projections by the way) under world - wide scrutiny... and have for all intents and purposes already correctly predicted the future -(Hansen 1988 in front of Congress and Pinatubo).

I don't wish to imply that climate scientists have not adequately considered these issues; just that clearer explanations of these points would help those of us outside the community understand the accuracy and limitations of the models better.

I have said that if a model is set up to match a certain signal (not just climate) yet matches a signal that contains an additional cyclical factor which can change that signal significantly over the short and medium term then you would not expect it to show great accuracy over the short to medium term.

I'm not claiming this is or isn't a realistic numerical model of climate response, I'm just trying to accurately describe how the model works.

[Response: That «modeller» is me (I don't like that label, as I've done sea - going measurements and published papers on data analysis and theory — my topic is climate, and models are just one tool for its investigation).

(1) In this case even if they were correct and the models failed to predict or match reality (which, acc to this post has not been adequately established, bec we're still in overlapping data and model confidence intervals), it could just as well mean that AGW stands and the modelers have failed to include some less well understood or unquantifiable earth system variable into the models, or there are other unknowns within our weather / climate / earth systems, or some noise or choas or catastrophe (whose equation has not been found yet) thing.

I am just asking what people think are the most likely scenarios, i.e. which climate model forecasts you think «ahead of time» will prove to be most accurate?

My main argument that speaks for an anthropogenic influence is the long - term downward trend since 1930 inferred from the SST data in the subpolar Atlantic, and the fact that climate models driven by anthropogenic forcing predict just such a relatively cold patch in this same area.

In terms of the gold that a climate science denier might find in the paper, at the very least, they could argue that the fact that the troposphere isn't warming more quickly than the surface shows that the climate models are unreliable — even though the models predict just the pattern of warming that we see — with the troposphere warming more quickly than the surface over the ocean but less quickly than the surface over land.

Dennis Avery of the Hudson Institute cited Doran's study, and claimed that «The American people are being hoodwinked not just by the green activists, but by the scientists who get billions of dollars for creating global climate models that can't even forecast backward, let alone forward.»

The approximately 20 - year lag (between atmospheric CO2 concentration change and reaching equilibrium temperature) is an emerging property (just like sensitivity) of the global climate system in the GCM models used in the paper I linked to above, if I understood it correctly.