1. Climate Networks & Extreme Events
Jürgen Kurths
Potsdam Institute for Climate Impact Research
&
Institut of Physics, Humboldt-Universität zu Berlin
King‘s College, University of Aberdeen

2. Main Collaborators: - PIK Potsdam N. Boers, J. Donges, N. Marwan, N
Main Collaborators: - PIK Potsdam N. Boers, J. Donges, N. Marwan, N. Molkenthin, J. Runge,
V. Petoukhov, V. Stolbova
- UC Sta Barbara B. Bookhagen
- Uni North Carol. N. Malik, P. Mucha
- INPE (Brazil) J. Marengo
UWA (Austral.) M. Small
Uni Utrecht H. Dijkstra
- Acad Sc (Czech) M. Palus, J. Hlinka

3. Contents Introduction Climate networks Event synchronization
Extreme floods in Central Andes
Monsoon dynamics in India
Conclusions

4. Working in operational prediction of extreme events - dangerous for (y)our life these days?

5. Headline News (June 12, 2014) Strong drought that spring in North Korea Kim Jong Un: responsible are the meteorologists due to their bad forecasts

7. Main building of PIK: Michelson House
Albert Abraham Michelson made experiments here in 1881 when he worked in Berlin&Potsdam (Germany)

8. Telegraph Hill: Scientific Breakthroughs
Secular Station Potsdam
1832/33 Opto-Mechanical Telegraph Line Station No. 4 Potsdam
Ernst von Rebeur-Paschwitz
First Solution of Einstein‘s Equations
Reinhard Süring
1889 First Record of Teleseismic Earthquake
Albert Einstein
Karl Schwarzschild
Friedrich Robert Helmert
1904 Interstellar Matter Large Refractor
1881 Michelson Experiment
Johannes Hartmann
Potsdam Datum Point Helmert Tower
Albert Abraham Michelson,

9. PIK: Mission
PIK addresses crucial scientific questions in the fields of global change, climate impact and sustainable development.
Researchers from the natural and social sciences work together to generate interdisciplinary insights and to provide society with sound information for decision making.
The main methodologies are systems and scenarios analysis, modelling, computer simulation, and data integration

10. Transdisciplinary Concepts and Methods
Research Domain IV
Transdisciplinary Concepts and Methods

11. Research Domain 4: Transdisciplinary Concepts and Methods

12. Humboldt Universität zu Berlin
Founded in 1809 teaching & research
30 Nobel laureats (Planck, Einstein, van ´t Hoff, Nernst, Hahn, Koch…)
University of Excellence
Wilhelm von Humboldt

13. Complex Networks Origin in Social Networks

14. Social Networks

16. Complex Network Approach to Climate

17. System Earth

19. Network Reconstruction from a continuous dynamic system
(structure vs. functionality) New (inverse) problems arise! Is there a backbone underlying the climate system?

20. Basic Idea: Use of rich instrumentarium of complex network (graph) theory for system Earth and sustainability Hope: Deepened understanding of system Earth (with other techniques NOT possible)

21. Climate Networks Earth system Observation sites Climate network
Network analysis
Time series

22. Infer long-range connections – Teleconnections

23. Complex network approach to climate system

25. Visual Analytics tools
temperature climate networks
scalable for > edges
graphics card implementation
2D node layout (360 degree circular projection) avoiding edge clutter at the equator
Thomas Nocke, PIK

27. Artifacts and Interpretation of (Climate) Network Approach

28. Reconstructing causality from data
- Common drivers
- Indirect links ?
Achievements
Causal algorithm to efficiently detect linear and nonlinear links (Phys. Rev. Lett. 2012)
Quantifying causal strength with Momentary Information Transfer (Phys. Rev. E 2012)
Reconstructing Walker Circulation from data (J. Climate 2014, )

29. Reconstructing causality from data
Classic techniques Advanced method
Correlation/regression conditional independencies

30. Identifying causal gateways and mediators in complex spatio-temporal systems
Step 1: Dimension reduction via VARIMAX (principal components, rotation, significance)
Step 2: Causal reconstruction: identify causalities based on conditional dependencies (different time lags)
Step 3: Causal interaction quantification: identify strongest paths
Step 4: Hypothesis testing of causal mechanisms
Nature Commun, 6, 8502 (2015)

32. Atmospheric data Reanalysis data – NCEP/NCAR (Boulder)
surface pressure
1948 – 2012
Spatial resolution: 2.5º → 10,512 grid points
Weekly data: each node time series of 3, points

33. 60 strongest VARIMAX components refer to main climatic patterns
ENSO: “0” – western uplift, “1” – eastern downdraft limbs
Monsoon: “33” Arabian Sea high-surface-pressure sector, “26” tropical Atlantic West African Monsoon system

34. Identification of causal pathways
Effects of sea level pressure anomalies in ENSO region to pressure variability in the Arabic Sea via the Indonesian Archipelago

35. Extreme Events Strong Rainfall during Monsoon Challenge: Predictability

36. Motivation: Objectives:
the predictability of the Indian monsoon remains a problem of vital importance
Objectives:
to reveal spatial structures in network of extreme events over the Indian subcontinent and their seasonal evolution during the year.

37. New Technique: Event Synchronization

42. Rainfall amount (mm/day)
METHOD
Step 2. Event synchronization – use time lags to compare individual events between two grid points
Step 1. Apply a threashold to time series of each grid point to obtain event series
Step 3. Construct the network by creating links between points with the highest synchronization values
2. Event synchronization
Rainfall amount (mm/day)
Time (days)
Nodes: geographical locations
Links: synchronization of extreme rainfall events between nodes
1. Network approach
Network measures
degree
betweenness
Average link lengths
Network
Quiroga et.al. 2002
Malik et.al. 2011
Boers et.al. 2013

43. Extreme Rainfall Events of the South American Monsoon System
TRMM 3B42 V7 daily satellite data
Measured: Jan 1, 1998 – March 31, 2012
Spatial resolution: 0.25 x 0.25
Spatial coverage:
Method: event synchronization
Extreme event: > 99 % percentile
Dec-Feb (DJF) – summer monsoon months

45. (a) Topography and simplied SAMS mechanisms. (b) 99th percentile
of hourly rainfall during DJF derived from TRMM 3B42V7. (c) Fraction of total
DJF rainfall accounted for by events above the 99th percentile. (d) Rainfall time
series of concatenated DJF seasons and the corresponding 99th percentile for a
grid cell located at the ECA at 17S and 66W.

46. Non-symmetric Adjacency Matrix (in – out)
> 0 – sink: extreme events here preceded by those at another location
< 0 – source: extreme events follow at another location

47. SESA – Southeast South America
ECA – Eastern Central Andes

52. > 60 % (90 % during El Nino conditions) of extreme rainfall events in Eastern Central Andes (ECA) are preceded by those in Southeastern South America (SESA)
Low pressure anomaly from Rossby-wave activity propagates northwards (cold front) and low-level wind channel from Amazon
Nature Commun. (2014), GRL (2014), J. Clim. (2014), Clim. Dyn. (2015)

53. Model comparison via networks
TRMM data, ECHAM6 (Global circulation model), ECMWF (Re-Analysis), ETA (regional climate model)
→ Strong differences found
ECHAM6 closest to data
Clim. Dyn. 2015

54. Indian Monsoon

55. Data:
APHRODITE: daily rainfall, rain-gauge interpolated, 0.5 °/0.25° resolution ( )
TRMM: daily rainfall, satellite-derived, 0.25° ( )
NCEP/NCAR: reanalysis, 2.5 °, T, P, winds, vorticity, divergency

56. Spatial patterns of extreme rainfall: TRMM
Common network measures for three time periods: pre-monsoon (MAM), Summer (ISM) and Winter monsoon (WM).
Links between a set of 153 reference grid points to other grid points and Surface Vector Winds mean From top to bottom: North Pakistan (NP), Tibetan Plateau (TP), Eastern Ghats (EG) .
Stolbova V., Martin P., Bookhagen B., Kurths J., Nonlin. Proc. in Geophysics, 2014

57. Spatial patterns of extreme rainfall: APHRODITE
Common network measures for three time periods: pre-monsoon (MAM), Summer (ISM) and Winter monsoon (WM).
Links between a set of 45 reference grid points to other grid points and Surface Vector Winds mean From top to bottom: North Pakistan (NP), Tibetan Plateau (TP), Eastern Ghats (EG) .
Nonlin. Proc. in Geophysics, 2014

58. Spatial patterns of extreme rainfall
Network approach allows to reveal spatial structures of extreme rainfall synchronization.
Identified essential spatial domains (North Pakistan, Eastern Ghats and Tibetan Plateau) for the synchronization of extreme rainfall during the Indian Summer Monsoon which appear during the pre-monsoon season, evolve during ISM and disappear during the post-monsoon season.
Findings open possibility to account spatial distribution of essential patterns in determining the ISM timing and strength by observation of rainfall variability within dominant patterns.

59. Summary Complex climate networks promising approach
Network divergence: a general tool to analyze extreme event propagation in complex systems
Explains intraseasonal variability of moisture flux from the Amazon to the subtropics: Rossby Waves
Prediction of floods in the Central Andes
Approach in its infancy – many open problems

60. Our papers on climate networks
Europhys. Lett. 87, (2009)
Phys. Rev. E 81, R (2010)
Climate Dynamics 39, 971 (2012)
PNAS 108, (2011)
Phys. Rev. Lett. 106, (2012)
Europhys. Lett. 97, (2012)
Climate Past 8, 1765 (2012)
Geophys. Res. Lett. 40, 2714 (2013)
Climate Dynamics 41, 3 (2013)
J. Climate 27, 720 (2014)
Nature Scientific Reports 4, 4119 (2014)
Climate Dynamics (2014)
Geophys. Res. Lett. 41, 7397 (2014)
Nature Commun. 5, 5199 (2014)
Climate Dynamics 44,1567 (2015)
J. Climate 28, 1031 (2015)
Climate Past 11, 709 (2015)
Climate Dynamics (online 2015)
Nature Commun. 6, 8502 (2015)

61. Codes available
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn software package, CHAOS 25, (2015)
Causal network identification: Python software script by J. Runge

63. Test of the climate network reconstruction method: Networks from special flows
Advection-diffusion dynamics on a background flow
Analytic and numerical treatment compared with correlation-based reconstruction of simulated data
Nature Scientific Rep. 4, 4119 (2014)
Nonlin. Proc. Geophys. 21, 651 (2014)

66. Algorithmic parameters causal
Ƭ (max) 4 weeks
Significance (student´s test)
Tigramite approach (time series graph-based measure of information transfer)