1. Visualizing Intermodel Comparison
For Climate Simulations
Xiaoying Pu, Bucknell University

2. we build climate models to simulate the past
To predict the future,
we build climate models to simulate the past

3. CVDP (n=42) PSL annual mean
The status quo: arbitrary arrangements
Hmmm…
CVDP (n=42) PSL annual mean

4. The Goal is intuitive visualization
Q: What’s a good tool for climate science?

5. How to ~ 200 model runs ... compare Plus Time-series ... visualize
... interpret
~ 200 model runs
Plus Time-series

6. Climate model grids are like pixels
PSL = hPa
@(lat, long)
CVDP
CMIP5
20+ modeling groups
~200 model runs
~100 years
Pick a variable
Get 2D array
Aggregates over time dimension

7. SSIM is based on three metrics
from skimage.measure import compare_ssim as SSIM # python
SSIM( , )
Luminance
Contrast
Structure
Mathspeak:
Average
Variance
Covariance
If not ssim, then RMSE

8. Build a Distance Matrix
Symmetric!
for (i, j) in :
SSIM( , ) 2
Question to audience: But what’s the problem?
Mention observation

9. Metric Multidimensional Scaling
For non-Euclidean distance
High dimension data
Distance / similarity preserved!
from sklearn import manifold
import seaborn as sns

10. Interpreting the 2D projection
CESM
Observation
A model run
GISS-E2-*
CVDP (n = 42),
Pressure at sea level
1 degree rectilinear grid
SSIM, Metric MDS,
with observation
Euclidean distance
Assigning “meanings” to axis?
NASA Goddard Institute for Space Studies
Interpreting the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

11. Interpreting the 2D projection
HadGEM2-ES
HadGEM2-AO
“Changes in model configuration that do not influence the atmospheric diagnostics”
Assigning “meanings” to axis?
Interpreting the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

12. Interpreting the 2D projection
HadGEM2-ES
HadGEM2-AO
HadGEM2-CC
ACCESS1-3
ACCESS1-0
Two institutions,
Share a large fraction of code base
Assigning “meanings” to axis?
BNU-ESM
FIO-ESM
Interpreting the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

13. Interpreting the 2D projection
“Common atmosphere and land surface”
MPI-ESM-LR
NorESM1-ME
“Shared atmosphere”
CCSM4
CMCC-CM
NorESM1-M
Assigning “meanings” to axis?
Interpreting the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

14. Interpreting the 2D projection
Small
Euclidean distance
Climate model lineages
sometimes
Assigning “meanings” to axis?
Interpreting the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

15. Questions about the 2D projection
Assigning “meanings” to axis?
IPSL-CM5A-MR
IPSL-CM5A-LR
Questions about the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

16. Questions about the 2D projection
Observation
CESM
What do the axes mean?
Assigning “meanings” to axis?
Questions about the 2D projection
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

17. Our thoughts on SSIM + MDS
Intuitive and information-rich
Cannot answer all the questions
Assigning “meanings” to axis?
Our thoughts on SSIM + MDS
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "Addressing interdependency in a multimodel ensemble by interpolation of model properties." Journal of Climate (2015):
Sanderson, Benjamin M., Reto Knutti, and Peter Caldwell. "A representative democracy to reduce interdependency in a multimodel ensemble." Journal of Climate (2015):

18. Future Work: validation
User study
A new collage
Hierarchical clustering
Scree plot
Dasgupta, Aritra, et al. "Bridging theory with practice: An exploratory study of visualization use and design for climate model comparison." IEEE transactions on visualization and computer graphics 21.9 (2015):

19. Thank you! Acknowledgement Mentors: Rick Brownrigg Bill Ladwig
Scientists:
Adam Phillips
Dennis Shea
Ben Sanderson
Doug Nychka
Clara Deser
@NCL Workshop
Mary Haley
And all the admins!
you!

20. Figure 1. Behind the scene: a failed attempt
Just in case
Time curve
Questions?
Figure 1. Behind the scene: a failed attempt

21. CVDP members (n = 42), PSL, 1 degree rectilinear grid
SSIM, Metric MDS, without observation

22. CVDP members (n = 42), PSL, 1 degree rectilinear grid
SSIM, Metric MDS, with observation

23. CMIP5 members (n = 192), PSL, 1 degree rectilinear grid
SSIM, Metric MDS, with observation (20thC)

24. MDS that shows temporal change
“Time curves” for sea ice data
If we don’t average over time...
Bach, B., Shi, C., Heulot, N., Madhyastha, T., Grabowski, T., & Dragicevic, P. (2016). Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data. IEEE Transactions on Visualization and Computer Graphics, 22(1), 559–56

25. Time curve

26. Just in case
Time curve