Taming uncertainty in climate prediction

Taming uncertainty in climate prediction
The results of the UQ process show an improved predictive model making it more reliable in projecting future climate change.

(PhysOrg.com) -- Uncertainty just became more certain. Atmospheric and computational researchers at Pacific Northwest National Laboratory used a new scientific approach called "uncertainty quantification," or UQ, that allowed them to better simulate precipitation. Their study is the first to apply a stochastic sampling method to select model inputs for precipitation representations and improve atmospheric simulations within a regional weather research and forecasting model. Their approach marks a significant advancement in representing precipitation, one of the most difficult climate components to simulate.

The word "uncertain" always seems to appear when describing Earth and atmospheric systems in numerical models. Trying to represent complexity through has limitations, not the least of which is a lack of sufficient computing power. Consider trying to model human body systems with numbers. Humans come in all shapes, sizes, ages, locations, and temperaments. It's the same with atmospheric systems. Getting a handle on the systems' uncertainties, to effectively and efficiently represent current weather and climate systems in a computer model, paves the way for scientists to apply those same techniques to predict the future climate changes. Sound predictions will give planners the tools to forecast the probability of and .

A PNNL team of and computational modelers used the Forecasting (WRF) model to validate a new approach to improving parameters used to estimate precipitation. Using observational data from the Southern Great Plains (SGP), gathered by a U.S. Department of Energy (ARM) Facility, they reduced the uncertainty for several parameters in the convective cloud scheme in WRF to improve the precipitation calculations.

"We used an interdisciplinary team and the powerful computing resources at multiple locations to tackle this challenge," said Dr. Yun Qian, a climate scientist at PNNL. "Precipitation is much more challenging to represent in climate simulations than, for example, temperature. And it's harder to predict. The UQ methodology provides a way to assess key parameters that are critical for precipitation calculation in regional and global climate models."

Using the vast amount of data collected at SGP, the team used a numerical technique to identify and improve the precipitation calculations in WRF. The team was the first to use a stochastic algorithm, an important to study parameterizations in regional climate simulations. The method, called Multiple Very Fast Simulated Annealing (MVFSA), randomly chooses numbers within distributions to minimize model errors. MVFSA is computationally more efficient, requiring a lower number of simulations to better match the observational data.

MVFSA identified five optimal parameters to reduce the model precipitation bias at a 25 kilometer climate grid. The team then improved precipitation simulations on a 12 kilometer grid, as well as temperature and wind results. Testing the model on another climate region showed that the MVFSA process produces improved results across spatial scales, processes, and other climatic regions.

The results of the UQ process show an improved model with better predictability making it more reliable in projecting future .

Working within the Community Atmospheric Model (CAM5), a global climate model, the team will test the optimized representations in convective precipitation scenarios. Finding that some representations were more important than others, the UQ approach will focus on how improving representations of convection in climate model helps to improve simulations of the global circulation and climate.


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The proof is in the clouds

More information: Yang B, et al. 2012. "Some Issues in Uncertainty Quantification and Parameter Tuning: A Case Study of Convective Parameterization Scheme in the WRF Regional Climate Model," Atmospheric Chemistry and Physics, 12, 2409-2427, doi:10.5194/acp-12-2409-2012
Citation: Taming uncertainty in climate prediction (2012, March 23) retrieved 25 August 2019 from https://phys.org/news/2012-03-uncertainty-climate.html
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Mar 23, 2012
Have the Brits launched there radiometer, TRUTHS?
"TRUTHS provides 'benchmark' measurements of key 'radiative forcing feedbacks' such as clouds and albedo (in the solar reflective spectral domain) with uncertainties small enough that future change, from a background of natural variability can be detected."
http://www.npl.co.uk/TRUTHS

OR the US?

http://map.nasa.g...ite.html

Mar 23, 2012
"The results show the model
bias for precipitation can be significantly reduced by using
five optimal parameters identified by the MVFSA algorithm,
especially for heavy precipitation with rain rates
over 20mmday1."
http://www.atmos-...2012.pdf
What does 'significant' mean, percents, orders of magnitude, ...?

"All models are wrong but some are useful.

Box, G. E. P. (1979). Robustness in the strategy of scientific model building. In R. L. Launer, and G. N. Wilkinson, (eds.) Robustness in Statistics. New York: Academic Press "

Mar 24, 2012
Ven responding that way to those with ideological beliefs such as rygg or Richie is futile as they dont want to listen and it just de-rails the topic at hand.

Just downrate them and report any really offensive posts m8.

Mar 24, 2012
LOL, it's just Vendicar, who puts offensive posts here all the time and who calls the people, who are just linking publications here, a retards without any arguments. Your perception of reality is completely reversed.

Mar 24, 2012
LOL, it's just Vendicar, who puts offensive posts here all the time and who calls the people, who are just linking publications here, a retards without any arguments. Your perception of reality is completely reversed.

Anyone can post a link to crap, which is just what VD calls out.

Cherry picked garbage is still garbage, linked or not.


Mar 24, 2012
Someone at PNNL recently read Taleb's Fooled by Randomness.

Point being?

Mar 24, 2012
I challenge any AGWites to read the paper and quantify what was called 'significant'.

Mar 24, 2012
You are innately challenged, Marjon, to say anything of factual significance.

Mar 24, 2012
You are innately challenged, Marjon, to say anything of factual significance.

So you can't quantify what the paper defines as 'significant'.

Mar 24, 2012
You are innately challenged, Marjon, to say anything of factual significance.

So you can't quantify what the paper defines as 'significant'.

Since you are not qualified to judge, as evidenced by your history here, neither have you standing to demand that others so judge.


Mar 24, 2012
You are innately challenged, Marjon, to say anything of factual significance.

So you can't quantify what the paper defines as 'significant'.

Since you are not qualified to judge, as evidenced by your history here, neither have you standing to demand that others so judge.


Either no one can read or no one can find how 'significant' is quantified.

Mar 25, 2012
An example of significant error:
"Some scientists call the cosmological constant the "worst prediction of physics." And when todays theories give an estimated value that is about 120 orders of magnitude larger than the measured value, its hard to argue with that title."
http://www.physor...firstCmt
This is an example of quantification of the error.

Mar 25, 2012
We don't care to do your homework for you Tard Boy.

Do it yourself.
...says the moron who prefers outdated Wikipedia references to current climate science research center references.


Mar 25, 2012
So no one out there can read a science journal paper?

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