climate networks & extreme events potsdam institute for climate impact research & institut...
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Climate Networks & Extreme Events
Potsdam Institute for Climate Impact ResearchPotsdam Institute for Climate Impact Research&&
Institut of Physics, Humboldt-Universität zu Berlin Institut of Physics, Humboldt-Universität zu Berlin &&
King‘s College, University of AberdeenKing‘s College, University of Aberdeen
[email protected]@pik-potsdam.de
Jürgen KurthsJürgen Kurths
http://www.pik-potsdam.de/members/kurths/
Main Collaborators:Main Collaborators:- - PIKPIK PotsdamPotsdam N. Boers, J. Donges, N. N. Boers, J. Donges, N. Marwan, N. Molkenthin, J. Runge, Marwan, N. Molkenthin, J. Runge, V. Petoukhov, V. Stolbova V. Petoukhov, V. Stolbova -- UC Sta Barbara UC Sta Barbara B. Bookhagen B. Bookhagen - Uni North Carol.- Uni North Carol. N. Malik, P. MuchaN. Malik, P. Mucha- INPE (Brazil)- INPE (Brazil) J. MarengoJ. Marengo-UWA (Austral.)UWA (Austral.) M. SmallM. Small-Uni Utrecht Uni Utrecht H. DijkstraH. Dijkstra- - Acad Sc (Czech) Acad Sc (Czech) M. Palus, J. HlinkaM. Palus, J. Hlinka
Contents
• Introduction
• Climate networks
• Event synchronization
• Extreme floods in Central Andes
• Monsoon dynamics in India
• Conclusions
Working in operational prediction of extreme events - dangerous for (y)our life these
days?
Headline News (June 12, 2014)
Strong drought that spring in North Korea
Kim Jong Un: responsible are the meteorologists due to their bad
forecasts
Main building of PIK: Michelson House
Albert Abraham Michelson made experiments here in 1881 when he worked in Berlin&Potsdam (Germany)
Telegraph Hill: Scientific Breakthroughs
8
1889 First Record of Teleseismic Earthquake
Ernst von Rebeur-Paschwitz1861-1895
1904 Interstellar Matter Large Refractor
Johannes Hartmann1865-1936
1832/33 Opto-Mechanical Telegraph Line Station No. 4 Potsdam
1870-1950 Potsdam Datum Point Helmert Tower
Friedrich Robert Helmert1843-1917
Secular Station Potsdam
Reinhard Süring1866-1950
1881 Michelson Experiment
Albert Abraham Michelson, 1852-1931
First Solution of Einstein‘s Equations
Karl Schwarzschild1873-1916
Albert Einstein1879-1955
• 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
PIK: Mission
Research Domain IVTransdisciplinary Concepts and Methods
Research Domain 4:Transdisciplinary Concepts and Methods
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
Complex Networks
Origin in Social Networks
Social Networks
Complex Network Approach to
Climate
System Earth
Network Reconstruction from a continuous dynamic system (structure vs. functionality)
New (inverse) problems arise!Is there a backbone underlying the
climate system?
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)
Climate Networks
Observation sitesEarth system
Time series
Climate network
Network analysis
Infer long-range connections –
Teleconnections
Complex network approach to climate system
2D node layout (360 degree circular projection) avoiding edge clutter at the equator
Thomas Nocke, PIK
Visual Analytics toolstemperature climate networks scalable for > 100.000 edgesgraphics card implementation
Artifacts and Interpretation of
(Climate) Network Approach
Reconstructing causality from data
28
? Y
X
Z
W
Artefacts due to - Indirect links- Common drivers
Achievements1.Causal algorithm to efficiently detect linear and nonlinear links (Phys. Rev. Lett. 2012) 2.Quantifying causal strength with Momentary Information Transfer (Phys. Rev. E 2012)3.Reconstructing Walker Circulation from data (J. Climate 2014, )
Reconstructing causality from data
Classic techniques Advanced methodCorrelation/regression conditional independencies
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)
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 3,339 points
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
Identification of causal pathways
Effects of sea level pressure anomalies in ENSO region to pressure variability in the Arabic Sea via the Indonesian Archipelago
Extreme Events
Strong Rainfall during Monsoon
Challenge: Predictability
Motivation:Motivation:the predictability of the Indian
monsoon remains a problem of vital importance
ObjectivesObjectives::to reveal spatial structures in network of extreme
events over the Indian subcontinent and their seasonal evolution during the year.
New Technique:
Event Synchronization
42
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1. Network approach
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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/,,,min
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Quiroga et.al. 2002Malik et.al. 2011Boers et.al. 2013
Extreme Rainfall Events of theSouth 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
(a) Topography and simplied SAMS mechanisms. (b) 99th percentileof hourly rainfall during DJF derived from TRMM 3B42V7. (c) Fraction of totalDJF rainfall accounted for by events above the 99th percentile. (d) Rainfall timeseries of concatenated DJF seasons and the corresponding 99th percentile for agrid cell located at the ECA at 17S and 66W.
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
SESA – Southeast South AmericaECA – Eastern Central Andes
• > 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)
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
Indian Monsoon
Data:Data:• APHRODITEAPHRODITE:: daily rainfall, rain-
gauge interpolated, 0.5 °/0.25° resolution (1951-2007)
• TRMM: daily rainfall, satellite-derived, 0.25° (1998-2013)
• NCEP/NCAR: reanalysis, 2.5 °, T, P, winds, vorticity, divergency
Spatial patterns of extreme rainfall: TRMM
Links between a set of 153 reference grid points to other grid points and Surface Vector Winds mean 1998-2012. From top to bottom: North Pakistan (NP), Tibetan Plateau (TP), Eastern Ghats (EG) .
Common network measures for three time periods: pre-monsoon (MAM), Summer (ISM) and Winter monsoon (WM).
Stolbova V., Martin P., Bookhagen B., Kurths J., Nonlin. Proc. in Geophysics, 2014
Spatial patterns of extreme rainfall: APHRODITE
Links between a set of 45 reference grid points to other grid points and Surface Vector Winds mean 1998-2012. From top to bottom: North Pakistan (NP), Tibetan Plateau (TP), Eastern Ghats (EG) .
Common network measures for three time periods: pre-monsoon (MAM), Summer (ISM) and Winter monsoon (WM).
Nonlin. Proc. in Geophysics, 2014
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.
Spatial patterns of extreme rainfall
• 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
Summary
Our papers on climate networks
• Europhys. Lett. 87, 48007 (2009)• Phys. Rev. E 81, 015101R (2010)• Climate Dynamics 39, 971 (2012)• PNAS 108, 20422 (2011)• Phys. Rev. Lett. 106, 258701 (2012)• Europhys. Lett. 97, 40009 (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)
Codes available
• Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn software package, CHAOS 25, 113101 (2015)
https://github.com/pik-copan/pyunicorn
• Causal network identification: Python software script by J. Runge http://tocsy.pik-potsdam.de/tigramite.php
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)
Algorithmic parameters causal
• Ƭ (max) 4 weeks
• Significance 0.001 (student´s test)
• Tigramite approach (time series graph-based measure of information transfer)