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Modeling the Climate System:
Is model-based science like model-based engineering?
Steve Easterbrook
Email: sme@cs.toronto....
2
Climate Modeling vs S/W Engineering
Continuous math
E.g. partial differential
equations
Models of
“How things are”
Discr...
3
Outline
1. What are climate models?
In which we step back in time and meet a 19th Century Swedish chemist
and a famous c...
4
5
Complex software systems…
Easterbrook, S. M., & Johns, T. C. (2009). Engineering the Software for Understanding
Climate ...
6
Alexander, K., Easterbrook, S. (2015). The software architecture of climate models: a graphical
comparison of CMIP5 and ...
7
The First Computational Climate Model
1895: Svante Arrhenius constructs an energy balance model to test his
hypothesis t...
8
Pittsburgh
Stockholm
Paris
London
Milestonesof19th
CenturyClimateScienc
Vienna
Image: Watercolour Waterson projection by...
9Image Source: https://chriscolose.wordpress.com/2010/02/18/greenhouse-effect-revisited/
Warm objects radiate infra-red
10Image Source: http://rabett.blogspot.ca/2010/03/simplest-explanation.html
Absorption fingerprint of greenhouse gases
In ...
11
Schematic of the model equations
12
Arrhenius’s Model Outputs
Image Source: Arrhenius, S. (1896). On the Influence of Carbonic Acid in the Air upon the Tem...
13
First Computer Model of Weather
1950s: John Von Neumann develops a killer app for the first
programmable electronic com...
14Image Source: Lynch, P. (2008). The ENIAC Forecasts: A Recreation. Bulletin of the American Meteorological Society
15
Basic physical equations
Zonal (East-West) Wind:
Meridional (North-South) Wind:
Temperature:
Precipitable Water:
Air pr...
16
Towards Numerical Forecasts
1910s: Lewis Fry Richardson performs the first numerical weather
forecast, imagines a giant...
17
(What a forecast factory actually looks like)
The Yellowstone supercomputer at the NCAR Wyoming Supercomputing Center, ...
18
Towards Earth System Models
Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere
Land surface
+ land ice?L...
19Image Source: IPCC Fifth Assessment Report, Jan 2014. Working Group 1, Fig 1.14(b)
Grid scale in a high resolution model
20
From: Knutti, R., & Sedláček, J. (2012). Robustness and uncertainties in the new CMIP5 climate model projections. Natur...
21
Can we limit warming to >+2ºC?
From: MR Allen et al. Nature 458, 1163-1166 (2009)
22
Can we artificially cool the planet?
From: Berdahl, M., et al. (2014). Arctic cryosphere response in the Geoengineering...
23
Some Observations
1) A model is never complete…
…but is sometimes good enough
24
?Model
Weakness
Develop
Hypothesis
Run
Experime
nt
Interpret
Results
Peer
Review
Try another hypothesis
OK
?
New Model
...
25
Some Observations
2) Models are for testing and improving our
understanding of the world
(e.g. for “what-if” experiment...
26
Understanding What-if Experiments
E.g. How do volcanoes
affect climate?
Sources: (a) http://www.imk-ifu.kit.edu/829.php...
27
Some Observations
3) Models enable communication and collaboration
28
Inter-disciplinary work is hard!
29
Coupled model
Atmospheric Dynamics
and Physics
Ocean Dynamics
Sea Ice
Land Surface
Processes
Atmospheric
Chemistry
Ocea...
30
Some Observations
(A corollary)
4) Model Integration is inevitable
…unfortunately, it’s never easy
31
Example: NCAR, Boulder
Alexander, K., Easterbrook, S. (2015). The software architecture of climate models: a graphical
...
32
Some Observations
5) A solitary model has very little value
33
Comparing multiple models
Model
Hierarchies
Multi-Model
Ensembles
Multiple
Versions
Sources: (a) Knutti, R. et al. (201...
34
Some Observations
6) When the model and the world disagree, the
model is often right…
35
Observational data is often wrong
Thompson, D. W. J., Kennedy, J. J., Wallace, J. M., & Jones, P. D. (2008). A large di...
36
Some Observations
7 Complex models have emergent phenomena
…and when the model surprises you,
learning happens
37Source: http://www.vets.ucar.edu/vg/T341/index.shtml
Global Precipitation in CCSM CAM3
38
Some Observations
8) Most of what we need to know to interpret a
model’s output isn’t in the model itself…
39
A Climate
Model
Configuration
?
Scientific
Question
Model
Development,
Selection &
Configuration
Running
Model
Interpre...
40
Summary
1. A model is never complete, but is sometimes good enough
2. Models are for improving our understanding and as...
41Image: https://www.flickr.com/photos/good_day/211972522/
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Modeling the Climate System: Is model-based science like model-based engineering?

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Keynote Talk given at the ACM/IEEE 18th International Conference on Model Driven Engineering Languages and Systems (Models 2015), Ottawa, September 2015.

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Modeling the Climate System: Is model-based science like model-based engineering?

  1. 1. Modeling the Climate System: Is model-based science like model-based engineering? Steve Easterbrook Email: sme@cs.toronto.edu Blog: www.easterbrook.ca/steve Twitter: @SMEasterbrook
  2. 2. 2 Climate Modeling vs S/W Engineering Continuous math E.g. partial differential equations Models of “How things are” Discrete math E.g. state machines, graphs, FOL Models of “How things should be” Modeling as a strategy for testing our ideas about the world when direct experimentation isn’t possible. Modeling to support collaboration and communication in a diverse community of practice. Modeling to support wise decision-making about our future course of action. Modeling as a strategy for testing our ideas about the world when direct experimentation isn’t possible. Modeling to support collaboration and communication in a diverse community of practice. Modeling to support wise decision-making about our future course of action.
  3. 3. 3 Outline 1. What are climate models? In which we step back in time and meet a 19th Century Swedish chemist and a famous computer scientist. 1. How are they used? In which we perform two dangerous experiments on the life support systems of planet earth but live to tell the tale. 1. What are the engineering challenges? In which we share war stories about the difficulties of model management in the real world. 1. Conclusion: So how is climate modeling like software modeling? In which I wave my arms a lot.
  4. 4. 4
  5. 5. 5 Complex software systems… Easterbrook, S. M., & Johns, T. C. (2009). Engineering the Software for Understanding Climate Change. Computing in Science and Engineering, 11(6), 65–74. Easterbrook, S. M., & Johns, T. C. (2009). Engineering the Software for Understanding Climate Change. Computing in Science and Engineering, 11(6), 65–74.
  6. 6. 6 Alexander, K., Easterbrook, S. (2015). The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations. Geoscientific Model Development 8, 1221-1232. Alexander, K., Easterbrook, S. (2015). The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations. Geoscientific Model Development 8, 1221-1232. Complex software eco-systems…
  7. 7. 7 The First Computational Climate Model 1895: Svante Arrhenius constructs an energy balance model to test his hypothesis that the ice ages were caused by a drop in CO2; (Predicts global temperature rise of 5.7°C if we double CO2) Stockholm
  8. 8. 8 Pittsburgh Stockholm Paris London Milestonesof19th CenturyClimateScienc Vienna Image: Watercolour Waterson projection by Stamen Design
  9. 9. 9Image Source: https://chriscolose.wordpress.com/2010/02/18/greenhouse-effect-revisited/ Warm objects radiate infra-red
  10. 10. 10Image Source: http://rabett.blogspot.ca/2010/03/simplest-explanation.html Absorption fingerprint of greenhouse gases In some wavelength bands, the atmosphere is transparent to infra-red. Emissions to space are from the (warmer) ground In bands where greenhouse gases block infra-red from the ground, emissions to space come from the (cooler) upper atmosphere
  11. 11. 11 Schematic of the model equations
  12. 12. 12 Arrhenius’s Model Outputs Image Source: Arrhenius, S. (1896). On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground.
  13. 13. 13 First Computer Model of Weather 1950s: John Von Neumann develops a killer app for the first programmable electronic computer ENIAC: weather forecasting Imagines uses in weather control, geo-engineering, etc.
  14. 14. 14Image Source: Lynch, P. (2008). The ENIAC Forecasts: A Recreation. Bulletin of the American Meteorological Society
  15. 15. 15 Basic physical equations Zonal (East-West) Wind: Meridional (North-South) Wind: Temperature: Precipitable Water: Air pressure: 1904: Vilhelm Bjerknes identified the “primitive equations” These capture the flow of mass and energy in the atmosphere; Sets out a manifesto for practical forecasting
  16. 16. 16 Towards Numerical Forecasts 1910s: Lewis Fry Richardson performs the first numerical weather forecast, imagines a giant computer to do this regularly; First plan for massively parallel computation Image Source: Lynch, P. (2008). The origins of computer weather prediction and climate modeling.
  17. 17. 17 (What a forecast factory actually looks like) The Yellowstone supercomputer at the NCAR Wyoming Supercomputing Center, Cheyenne
  18. 18. 18 Towards Earth System Models Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Land surface + land ice?Land surfaceLand surfaceLand surfaceLand surface Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice New Ocean & sea-ice Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol Carbon (+ N?) cycle Atmospheric chemistry Ocean & sea-ice model Sulphur cycle model Non-sulphate aerosols Carbon cycle model Land carbon cycle model Ocean carbon cycle model Atmospheric chemistry Atmospheric chemistry Off-line model development Strengthening colours denote improvements in models 1975 1985 1992 1997 2004/05 2009/11 HadCM3 HadGEM1 HadGEM2 Image:UKMetOffice©CrownCopyright
  19. 19. 19Image Source: IPCC Fifth Assessment Report, Jan 2014. Working Group 1, Fig 1.14(b) Grid scale in a high resolution model
  20. 20. 20 From: Knutti, R., & Sedláček, J. (2012). Robustness and uncertainties in the new CMIP5 climate model projections. Nature Climate Change, (October), 1–5.
  21. 21. 21 Can we limit warming to >+2ºC? From: MR Allen et al. Nature 458, 1163-1166 (2009)
  22. 22. 22 Can we artificially cool the planet? From: Berdahl, M., et al. (2014). Arctic cryosphere response in the Geoengineering Model Intercomparison Project G3 and G4 scenarios. Journal of Geophysical Research: Atmospheres, 119(3), 1308–1321. Globalaveragenear- surfacetemperature(°C) ArticSeaIceExtent (millionsofkm2 )
  23. 23. 23 Some Observations 1) A model is never complete… …but is sometimes good enough
  24. 24. 24 ?Model Weakness Develop Hypothesis Run Experime nt Interpret Results Peer Review Try another hypothesis OK ? New Model Version Model building is “doing science”
  25. 25. 25 Some Observations 2) Models are for testing and improving our understanding of the world (e.g. for “what-if” experiments)
  26. 26. 26 Understanding What-if Experiments E.g. How do volcanoes affect climate? Sources: (a) http://www.imk-ifu.kit.edu/829.php (b) IPCC Fourth Assessment Report, 2007. Working Group 1, Fig 9.5.
  27. 27. 27 Some Observations 3) Models enable communication and collaboration
  28. 28. 28 Inter-disciplinary work is hard!
  29. 29. 29 Coupled model Atmospheric Dynamics and Physics Ocean Dynamics Sea Ice Land Surface Processes Atmospheric Chemistry Ocean Biogeochemistry Overlapping Communities
  30. 30. 30 Some Observations (A corollary) 4) Model Integration is inevitable …unfortunately, it’s never easy
  31. 31. 31 Example: NCAR, Boulder Alexander, K., Easterbrook, S. (2015). The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations. Geoscientific Model Development 8, 1221-1232. Alexander, K., Easterbrook, S. (2015). The software architecture of climate models: a graphical comparison of CMIP5 and EMICAR5 configurations. Geoscientific Model Development 8, 1221-1232.
  32. 32. 32 Some Observations 5) A solitary model has very little value
  33. 33. 33 Comparing multiple models Model Hierarchies Multi-Model Ensembles Multiple Versions Sources: (a) Knutti, R. et al. (2013). Climate model genealogy: Generation CMIP5 and how we got there. (b) http://efdl.as.ntu.edu.tw/research/timcom/
  34. 34. 34 Some Observations 6) When the model and the world disagree, the model is often right…
  35. 35. 35 Observational data is often wrong Thompson, D. W. J., Kennedy, J. J., Wallace, J. M., & Jones, P. D. (2008). A large discontinuity in the mid-twentieth century in observed global-mean surface temperature. Nature, 453(7195), 646–649.
  36. 36. 36 Some Observations 7 Complex models have emergent phenomena …and when the model surprises you, learning happens
  37. 37. 37Source: http://www.vets.ucar.edu/vg/T341/index.shtml Global Precipitation in CCSM CAM3
  38. 38. 38 Some Observations 8) Most of what we need to know to interpret a model’s output isn’t in the model itself…
  39. 39. 39 A Climate Model Configuration ? Scientific Question Model Development, Selection & Configuration Running Model Interpretation of results Papers & Reports Scope of typical model evaluations Scope of fitness-for-purpose validation of a modeling system Is this model configuration appropriate to the question? Are the model outputs used appropriately? From models to modeling systems
  40. 40. 40 Summary 1. A model is never complete, but is sometimes good enough 2. Models are for improving our understanding and asking “what-if” questions. 3. Models enable close cross-disciplinary collaboration. 4. Model integration is difficult and inevitable. 5. A solitary model has very little value. 6. When the model and the data disagree, it’s often the data that are wrong. 7. Complex models have emergent phenomena… …and a model is most valuable when it surprises you 8. A model won’t make sense out of context
  41. 41. 41Image: https://www.flickr.com/photos/good_day/211972522/

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