Climate Networks & Extreme Events Potsdam Institute for Climate Impact Research & Institut...

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

juergen.kurths@pik-potsdam.dejuergen.kurths@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

METHODMETHOD

.,0

,1

else

QifA

Qijij

ij

Network

thresholdais

matrixncorrelatioaisQQij

ij

Nodes: geographical locations

Links: synchronization of extreme rainfall events between nodes

1. Network approach

i

j

ijQ?

Network measures

,1

1

N

AD

N

nij

j

,,

,

N

Vvji

vv ji

jiB

,RALL ijijiijj

degree

betweenness

Average link lengths

,

)2()2(

ji

ijss

ijcjicQ

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/,,,min

1111 ttttttttj

m

j

m

j

m

j

m

i

l

i

l

i

l

i

l

ij

lm

.,0

,,2/1

,0,1

else

ttif

ttif

J jm

il

ijlm

jm

il

ij

,

1 1

i js

l

s

mijJjic

2. Event synchronization

Rain

fall

amou

nt

(mm

/day

)

Time (days)

1l

1m

1l

1m

i

j

l

m

1l

1m

1l

1m

i

j

l

m

t

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)