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 juergen.kurths@pik-potsdam.de http://www.pik-potsdam.de/members/kurths/

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

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 Secular Station Potsdam 1832/33 Opto-Mechanical Telegraph Line Station No. 4 Potsdam Ernst von Rebeur-Paschwitz 1861-1895 First Solution of Einstein‘s Equations Reinhard Süring 1866-1950 1889 First Record of Teleseismic Earthquake Albert Einstein 1879-1955 Karl Schwarzschild 1873-1916 Friedrich Robert Helmert 1843-1917 1904 Interstellar Matter Large Refractor 1881 Michelson Experiment Johannes Hartmann 1865-1936 1870-1950 Potsdam Datum Point Helmert Tower Albert Abraham Michelson, 1852-1931

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

Transdisciplinary Concepts and Methods Research Domain IV Transdisciplinary 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 Earth system Observation sites Climate network Network analysis Time series

Infer long-range connections – Teleconnections

Complex network approach to climate system

Visual Analytics tools temperature climate networks scalable for > 100.000 edges graphics card implementation 2D node layout (360 degree circular projection) avoiding edge clutter at the equator Thomas Nocke, PIK

Artifacts and Interpretation of (Climate) Network Approach

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, )

Reconstructing causality from data Classic techniques Advanced method Correlation/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: 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.

New Technique: Event Synchronization

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

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

(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.

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 America ECA – 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: APHRODITE: 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 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 1998-2012. 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

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 1998-2012. From top to bottom: North Pakistan (NP), Tibetan Plateau (TP), Eastern Ghats (EG) . Nonlin. Proc. in Geophysics, 2014

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.

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

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)