We present a survey of computational and applied mathematical techniques that have the potential to contribute to the next generation of high-fidelity, multi-scale climate simulations. Examples of the climate science problems that can be investigated with more depth with these computational improvements include the capture of remote forcings of localized hydrological extreme events, an accurate representation of cloud features over a range of spatial and temporal scales, and parallel, large ensembles of simulations to more effectively explore model sensitivities and uncertainties. Numerical techniques, such as adaptive mesh refinement, implicit time integration, and separate treatment of fast physical time scales are enabling improved accuracy and fidelity in simulation of dynamics and allowing more complete representations of climate features at the global scale. At the same time, partnerships with computer science teams have focused on taking advantage of evolving computer architectures such as many-core processors and GPUs. As a result, approaches which were previously considered prohibitively costly have become both more efficient and scalable. In combination, progress in these three critical areas is poised to transform climate modeling in the coming decades.

1. Opportunities with very large high resolution
climate model datasets
Extreme event attribution
Projections
Machine learning
Michael F. Wehner
Lawrence Berkeley National Laboratory
mfwehner@lbl.gov

2. US DOE Policy 411.2A
SUBJECT: SCIENTIFIC INTEGRITY
When expressing opinions on policy matters to the public and media,
research personnel must make it clear when they are expressing their
personal views, rather than those of the Department, the U.S.
Government, or their respective institutions. Public representation of
Government or DOE positions or policies must be cleared through
their program management to include DOE headquarters.

6. Icosahedral High Resolution
More modern discretizations

7. Resolution
1km
Cloud system resolving models
are a transformational change
25km
Upper limit of climate models
with cloud parameterizations
200km
Typical resolution of
IPCC AR4 models
Surface Altitude (feet)

8. Technology
Moore’s law is alive and well.
The largest computers continually get faster. And so do models
1990 AMIP1: Many modeling groups required a calendar year to complete a 10 year
integration of a stand alone atmospheric general circulation model. Typical grid
resolution was T21 or about 600km (64X32x10)
2017: I get ~1 simulated year/ wall clock day for the same calculation except at 25km
(1152x768x30)
This calculation used only 7680 processors on a 120,000 processor machine
• 5 million processor hours.
• 25 km grid cell
• Took about 3 months to complete in 2012. Typically, I get better throughput now.

10. Tropical
Storm
Cat1
Cat2
Cat3
Cat4
Cat5
Figures by Prabhat

11. Cat1
Cat2
Cat3
Cat4
Cat5
Figures by Prabhat

12. Cat4
Cat5
Figures by Prabhat

13. Tropical Cyclone min pressure vs max wind speed
Total # TC / year
observations 87±8
cam5.1 84±9
Total # hurricanes / year
observations 49±7
cam5.1 52
Figures by Cheng-Ta Chen

14. The strongest hurricanes get more intense.
+0.85oC +1.5oC +2oC +4.0oC
m/s
Average annual most intense tropical cyclone wind speed (m/s)

17. Real storms can be tracked by hand. They happen in real time!
Tracking of simulated storms must be automated. There are too many to count.
Two approaches.
Traditional, “parametric” feature tracking based on conditions.
• Hurricanes: co-located vorticity maxima, pressure minima, warm cores.
• Extratropical cyclones: co-located vorticity maxima, pressure minima.
• Atmospheric rivers: precipitable water, integrated water transport, etc. (ARTMIP)
• Blocking, fronts, meso-scale convective systems.
Supervised Machine-learning
• Convolutional neural networks.
• Need to have a training data set.
Tracking storms

18. Two steps:
1. Candidate detection
2. Continuity in time & space. (Stitching/tracking)
Toolkit for Extreme Climate Analysis
https://github.com/LBL-EESA/TECA
Highly parallel
(I routinely use 29200
processors for TC tracking)
TECA2: parallel parametric feature tracking

19. Tropical cyclone detection.
Movie courtesy of Burlen Loring

20. Can Deep Learning Work for Climate Science?
- 20 -
Similarities to Computer Vision
• Tasks:
– Pattern Classification
– Clustering
– Feature Learning
– Anomaly Detection
Differences
• Unique attributes of Climate Data
– Multi-channel / Multi-variate
– Spatio-temporal
– Statistics are likely different

21. Challenge: Multi-Variate Data
- 21 -

22. - 22 -
Task: Find Extreme Weather Patterns in a box

23. Supervised Learning
Training Input: Cropped, Centered, Multi-variate patches with Labels*
• Tropical Cyclone (TC)
• Atmospheric River (AR)
• Weather Front (WF)
• TC & AR labels are provided by TECA, which
implemented human-specified criteria
• WF is a hand crafted data set (5 FTE-years)
Output: Binary (Yes/No) on Test patches
• Is there a TC in the patch?
• Is there an AR in the patch?
• Is there a WF in the patch?
Currently, we have separate convolutional neural nets for these 3 storm types.
– Our goal is to have just one machine learning algorithm for all storms.
- 23 -

24. CLASSIFICATIO
N
Image
Dimensi
on
Variables Total Examples
(+ve) (-ve)
Tropical
Cyclone
32x32 PSL,UBOT,VBOT,TMQ,
U850,V850,T200,T500
10000 10000
Atmospheric
Rivers
148x224 TMQ, Land Sea mask 6500 6800
Weather
Fronts
27x60 T2m, Precip, PSL 5600 6500
Machine learning Training Data

25. Logistic
Regression
K-Nearest
Neighbor
Support
Vector
Machine
Random
Forest
ConvNet
Train Test Train Test Train Test Train Test Trai
n
Test
Tropical
Cyclone
96.8 95.85 98.1 97.85 97.0 95.85 99.2 99.4 99.3 99.1
Atmosphe
ric Rivers
81.97 82.65 79.7 81.7 81.6 83.0 87.9 88.4 90.5 90.0
Weather
Fronts
84.9 89.8 72.46 76.45 84.35 90.2 80.97 87.5 88.7 89.4
Hyper-parameter optimization applied with Spearmint for all methods
Supervised Classification Accuracy

26. Weather Front Detection
- 26 -
Contributors: Jim Biard, Ken Kunkel, Evan Racah

27. - 27 -

28. - 28 -

29. - 29 -

30. Current status
Contact Prabhat about Machine Learning details
prabhat@lbl.gov
Hyper-Parameter Optimization
• Tuning #layers, #filters, learning rates, schedule is a black art
Performance and Scaling
• Current networks take days to train on O(10) GB datasets, we have O(10TB)
datasets on hand
Scarcity of Labeled Data
• Community needs to self-organize and run labeling campaigns
Interpretability and Visualization
• ‘Black Box’ classifier
Deep Learning is viable for Pattern Detection in Climate Data
• Supervised architectures can match TECA performance
• Open challenges in semi-supervised, unsupervised learning and
interpretability
• Need more ground truth catalogs and training data!
- 30 -

31. • When extreme weather
happens, the public wants to
know
– “Is this climate change?”
Extreme Event Attribution

32. • Not quite the correct question, better to ask:
– “How has the risk of this event changed because of climate change?”
Or
– “How did climate change affect the magnitude of this event?”
Extreme Event Attribution

33. Severe floods occurred along
the Colorado Front Range
during the second week of
September 2013, impacting
several thousands of people
and many homes, roads, and
businesses.
Lyons, CO
usatoday.com
• At least 10 deaths; 11,000 evacuated
• Nearly 19,000 homes damaged, and
over 1,500 destroyed, costing $2 bn
• Several highway bridges
damaged/destroyed, and rail lines
affected
South Platte River, CO
nytimes.com
The 2013 Colorado Floods
P Pall, C Patricola, M Wehner, D Stone, C Paciorek, W Collins. In press.

34. Colorado Floods September 2013
A more constrained numerical experiment
Step 1 Step 2 Step 3 Step 4 Step 5
… with a best estimate of a
about a doubling in odds of
heavy rainfall occurrence.
Simulations suggest a
substantial human-
induced influence on
South Platte rainfall…
NCEP RE-
ANALYSIS
WRF MODEL SOUTH PLATTE
BASIN (CO)
INCREASE IN
ODDS OF HEAVY
RAINFALL
DISTRIBUTIONS OF
ENSEMBLE
RAINFALL
Use Sep 2013
weather from
NCEP re-
analysis, both
under human and
adjusted natural
conditions
…to drive an
ensemble of
100 regional
model
simulations
(WRF 12km)
… then
extract rain
over South
Platte
basin.
Human
Natural
(adjusted T, u,v, RH, etc.)

35. Colorado Sep 2013 floods: Mechanistic approach
• We find a substantial shift in our rainfall distributions over the South Platte basin
(increase in mean of ~30%)
-> beyond a thermodynamic (~7-14%/K) induced increase, given ΔT = ~1.5-2K
• But increase in precipitable water (~15%) appears broadly consistent with C-C
• The 30% increase is a result of increased cumulus convective energy
• Not a result of changes in larger scale dynamics or uplifting.
• The “storm that was” was more violent than the “storm that might have been”
.
7-DAY RAINFALL
P. Pall, et al. (2016) Diagnosing Anthropogenic Contributions to Heavy Colorado Rainfall in
September 2013. to appear in Weather and Climate Extremes

36. zarzycki@ucar.edu - University of Colorado, Boulder, CO, April 2016
Typhoon Haiyan
• Use VR-CESM in “forecast mode”
• ATM: GFS analysis
• OCN: NOAA OI
• Ensembles of 120 hr forecasts
Init: 12Z 11-04-2013
NOAA IR Obs: 11-07 21Z
111km: 11-07 21Z
8km: 11-07 21Z

37. zarzycki@ucar.edu - University of Colorado, Boulder, CO, April 2016
Typhoon Haiyan
• Forecast pretty good!
• Little overall change in forecast track
Obs.
All-Hist
Nat-Hist

38. Present day storm (red) was slightly weaker than the counterfactual storm (blue)
Colder counterfactual SST alone (green) weakened the storm.
Counterfactual initial conditions alone (yellow)intensified the storm.
Changes in winds and shear had little effect.
Colder upper air temperature changes alone intensified the storm.
Lots of unanswered questions. CAM5 vs MIROC5?
Typhoon Haiyan

40. Video courtesy of Andreas Prein NCAR
Convective outbreak in May 2010
• Objective based analysis allows to evaluate model on
the storm scale
Observation WRF 4 km

41. Hurricane Katrina
• Hindcast that was (red)
• Hindcast that might have been (blue)
• 3km WRF

42. • No detectible anthropogenic effect
on cyclone intensity in 2005
• Accumulated precipitation increases
at Clausius-Clapeyron rates.
• 3km WRF
Max wind speed

43. • End of 21st century (RCP8.5)
• But intensity increases in a much
warmer world
• 9 & 27km WRF
Max wind speed

44. • Not an ideal candidate
• Track is not as stable
to perturbations and
simulation start date
00UCT 25 Oct 2005
18UCT 24 Oct 2005
Superstorm
Sandy
Factual Counterfactual

45. Super storm Sandy
No discernible change in intensity
But storm surge was worse because of sea level rise
(GFDL ran detailed storm surge calculations)

46. • Christina finds little anthropogenic effect on Hurricane Katrina in 2005
but an intensification if a similar storm occurs in 2100.
• Andreas finds more MCS events and that they move slower in a
warmer world. Maximum rain rates up to 40% more in 2100.
• Our project at LBNL estimates that 28 sustained petaflops is required
for a global 2km climate model.
• We provided a technology path forward based on consumer
electronics design practices (Eliminate waste with a reduced
instruction set.)
• Each hourly 2D variable would require 6TB/year and would need to
be written at 200MB/sec.
– But many variables are of interest so the total is a lot more than
this.
– Some but not all tasks would better suited for in-line calculations.
Cloud system resolving models

47. • Over 4PB of a single hi-resolution global model is available now.
• Community Atmospheric Model (CAM5.1)
• 25km
Done now
• 5 realizations of a world that was(1996-2015)
• 5 realizations of a world that ParisCOP21 wanted (2105-2115) 1.5K over
preindustrial
• 5 realizations of a world that is also not very likely (2105-2115) 2.0K over
preindustrial
• Done soon
• 5 realizations of a world that might have been (1996-2005)
• 5 realizations of a world that we currently are headed towards (2080-2100)
– RCP8.5 (3.5K over preindustrial)
Available data.

48. http://portal.nersc.gov/c20c/
C20C+ detection and attribution subproject

49. • As climate models get to finer resolution, higher frequency
data becomes more interesting, causing dataset sizes to
increase yet more.
• Better simulated storms.
• More realistic extreme weather.
• New questions can be asked. And answered!
• Supervised machine learning works great for finding things
we already know something about (i.e.storms).
• Can unsupervised machine learning reveal other climate
features?
• New classes of storms?
• New modes of variability?
Conclusions

50. New Journal!
Intended as a bridge between the Statistics and climate/weather/ocean communities
http://advances-statistical-climatology-meteorology-oceanography.net/index.html

51. Contact me if you want some data!
Thank you!
mfwehner@lbl.gov