Download presentation

Presentation is loading. Please wait.

Published byDeja Fayne Modified over 2 years ago

1
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N You need how many runs?! Michael F. Wehner Lawrence Berkeley National Laboratory mfwehner@lbl.gov

2
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N How many runs should we make? The answer to this question has always been: As many as you can afford. A more quantitative reply is possible if the question is more specific. How many realizations are necessary to know the mean value of a field to within a specified tolerance and statistical certainty? Or How many realizations are necessary to know that differences between models are statistically significant?

3
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N How many runs? Q. What is the minimum number of realizations (n) required to estimate model mean output within a specified tolerance (E) and statistical confidence ( )? A. For a Gaussian distributed random variable: s 2 =sample variance, =population variance N=Number of available realizations Z and are properties of the Gaussian function and

4
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N How many runs? The number of runs required depends on: Which fields are deemed important. How well defined they need to be. Statistical certainty Tolerance What scale is needed. Temporal Spatial The internal variability of the model.

5
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Chickens and Eggs But how can we use this formula to estimate ensemble size before we perform the integrations? How to estimate ensemble variance? If N=20, =95% then 0.58s 2 < 2 <2.1s 2 The answer lies in postulating ergodicity of the climate system. For example, the modeled system is considered ergodic if the inter-realization variance of the mean of each decade from an ensemble of transient runs is statistically identically to the variance of the decadal mean from a long stationary control run. If the model is ergodic, we can use the control run sample variance estimate in the equation for n.

6
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Is the modeled climate ergodic on decadal time scales? Nine transient runs (20c3m) N=9; 0.45s 2 < 2 <3.7s 2 500 years of control run 2 (picntrl) N=50; 0.69s 2 < 2 <1.5s 2 F-test at 90% confidence No significant difference

7
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Is the modeled climate ergodic? Decadal mean annual surface air temperature E=0.5K, =95% Centered pattern correlation = 0.95 Control Run

8
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Is the modeled climate ergodic? Decadal mean annual precipitation E = 10% of the mean value, =95% Centered pattern correlation = 0.96 Control Run

9
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Strong seasonal dependence Decadal mean seasonal surface air temperature E=0.5K, =95% DJFJJA

10
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Strong seasonal dependence Decadal mean seasonal precipitation E = 10% of the mean value, =95% DJFJJA

11
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N What about interannual time scales? Pretty hopeless to determine an annual or seasonal mean at these accuracies for single gridpoints. Either relax the accuracy or spatially average.

12
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N What about interannual time scales?

13
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N

16
Other considerations. Double these estimates to perform differences between scenarios to the same accuracies. Extreme events ~10 to estimate 20 year return value of annual daily extrema Pair control runs with transient runs A clever way to account for drift and/or initialize. Doubles the number of runs. The variability of the new model may be different than the current model. PCM variability is considerably larger than CCSM3.0

17
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N How many runs? In the absence of a clearly defined set of specifications: The final answer …

18
C O M P U T A T I O N A L R E S E A R C H D I V I S I O N Remains As many as you can!

Similar presentations

OK

Error Propagation. Uncertainty Uncertainty reflects the knowledge that a measured value is related to the mean. Probable error is the range from the mean.

Error Propagation. Uncertainty Uncertainty reflects the knowledge that a measured value is related to the mean. Probable error is the range from the mean.

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on object oriented programming with c++ pdf Ppt on astronomy and astrophysics courses Ppt on brain drain Powerpoint ppt on spinal injuries Ppt on soil pollution Presentation ppt on motivation Ppt on standing order definition Ppt on places in our neighbourhood Ppt on bond length periodic table Ppt on 7 wonders of the modern world