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NASA scientists have developed a new model that is among the first to simulate the strength of updrafts in storms. This model was applied to a global warming scenario to give a possible peek at what future weather might look like

NASA's Goddard Institute for Space Studies is a leading research center, located in New York, which studies Earth's past, present and future climates.

The Institute has recently announced a new study which discusses what future storms might look like in a global warming scenario.

NASA's scientists at the Institute developed a new climate model for the study.  The model is among the first to successfully simulate the strength of updrafts in storms.  This allows the model to give a more complete picture of the strength of storms that are occurring around the world, and those that may someday occur.

The model is the first to successfully simulate the observed difference between land and sea storms.  It also is the first model to simulate how the strength of storms may change in a warming environment.

The model is run over regions several hundred miles wide.  It does not directly simulate thunderstorms and lightning, but instead identifies conditions conducive to producing storms of varying strengths.

The model was applied to a future scenario in which the temperature had risen 5 degrees and CO2 levels in the air had doubled.  This simulation found that the land would be warmed more than the sea, and that thunderstorms on land would be produced at higher altitudes than they are today, leading to higher intensity.

The model predicts that some regions will have less humid climates, which would indicate fewer thunderstorms.  However, Anthony Del Genio, Ph.D., lead author illustrates why this scenario may be more dangerous, particularly to western wildfire-prone states:
"These findings may seem to imply that fewer storms in the future will be good news for disastrous western U.S. wildfires, but drier conditions near the ground combined with higher lightning flash rates per storm may end up intensifying wildfire damage instead"
Central and Eastern U.S. are particularly prone to severe thunderstorms.  These storms arise when strong updrafts combine with horizontal winds to produce thunderstorms and deadly tornados.  The study indicates that this most extreme class of storms will become increasingly common, in these areas, with warming.

These increases in storm severity are due to two factors.  First, the land warms more than the sea, respectively.  Second the freezing level, will raise to a higher altitude, where stronger updrafts are present.  These factors are both common to all climate change simulations, but this is the first simulation to explore their effects on storm intensity.

A movie of cloud cover in 2000 generated from data from the GOES-11 satellite, which was used to verify the model, is posted on NASA's website.

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So What Else Are the Simulations Missing?
By jskirwin on 9/2/2007 11:50:24 AM , Rating: 3
The model is among the first to successfully simulate the strength of updrafts in storms.

Updrafts haven't been simulated until now? What else is missing?

Isn't this like using SimCity 1.0 to predict crime in New York City?

By JasonMick (blog) on 9/2/2007 12:05:52 PM , Rating: 2
Ha good quote!

I agree that modeling is still somewhat crude, even with today's incredible multi-core, multi-rack supercomputers. I was just speaking with a professor on Friday about some of his research on a type of biological reactions and he was discussing how, past models with a few hundred atoms, it would take to much power to precisely calculate the atomic interactions. Instead the reactions are modeled with a coarser grain model that may miss some of these effects and be slightly less accurate.

It is sorta scary that modeling, which everything from airplanes to weather reports rely upon for information is similarly coarse grained. Still, generally, well built coarse-grain models can be relatively effective in making general predictions as long as you are aware of their limitations.

Hopefully someday in the future, weather can be modeled on a cubic foot of air/square foot of land-sea basis with full physics modeling--an unthinkable level of detail today. Imagine the weather report always being right! It would really change human behavior. But that is still very far away in terms of computing power. Until then climatologists are left to use their more coarse-grain models to make more general predictions, as is true with many other branches of scientific modeling today.

RE: So What Else Are the Simulations Missing?
By TomZ on 9/2/2007 12:31:53 PM , Rating: 2
I don't understand your implication that models have to go down to the molecular level in order to be accurate. The point of a model is to abstract out details that are not of interest. This is the only way that modeling is practical.

For example, we design and simulate electronic circuits all day long, using models that abstract away details of quantum mechanics, electron flow, etc. These models provide a close enough approximation to reality in order to meet the requirements. So what would be the point of modeling down the the most tiny inteactions?

By JasonMick (blog) on 9/2/2007 12:46:17 PM , Rating: 2
These models provide a close enough approximation to reality in order to meet the requirements

That was my point, precisely. I also was acknowledging that these models cannot predict some behaviors.

For example solid state electronics concepts such as tunneling and channel creation are only well modelled on a atomic basis. Large scale models would fail to anticipate these quantum effects.

My point is that it would give you more data if you could do a fine grain model on everything, such as your electrical example, but it would be too computationally intensive for today's computers.

Fortunately coarse-grain models such as electrical circuits modeling provide a good enough system to provide general necessary information about behavior, as you and I both stated.

We are both in agreement on this point, I was simply providing additional information on the short-comings of coarse grain models, which I believe the poster was referring to.

By TomZ on 9/2/2007 12:52:52 PM , Rating: 2
It is sorta scary that modeling, which everything from airplanes to weather reports rely upon for information is similarly coarse grained.

OK, then we both agree that these types of models are not really "scary." I realize you already backpedaled from that statement in the sentence that followed, but it still kind of sounded like FUD to me, especially when you brought up safety-critical systems like aircraft design.

RE: So What Else Are the Simulations Missing?
By Kuroyama on 9/2/2007 12:15:20 PM , Rating: 3
ALL models work by oversimplifying the problem. You then do simulations to see whether the model fits the data. Open any textbook (say economics for instance) and you should have little trouble discovering vast simplifications in the models. Even "common sense" is often wrong, for instance sometimes higher prices of necessities actually lead to higher demand!

This is true even in hard core sciences. I worked in a quantum physics theory group one summer as an undergrad and basically the way it worked was the professor would propose a simplification of the QM to make things computable, then we would write up software and simulate, then the experimental group would do experiments and see if the simulations fit the experimental results.

So, no, this is not Simcity 1.0.

By masher2 (blog) on 9/2/2007 1:46:25 PM , Rating: 2
> > "ALL models work by oversimplifying the problem. You then do simulations to see whether the model fits the data"

Exactly so. It's a valid-- indeed a critical methodology. The problem is that GCM models have yet to "fit the data". Their abilities to predict future behavior have, so far, been nonexistent. The models are continually revised to bring them into alignment with current history....and when the next batch of data comes out, the models are again off base.

"We don't know how to make a $500 computer that's not a piece of junk." -- Apple CEO Steve Jobs

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