Probing Earth’s core with geo- and
paleomagnetism
Monika Korte
Deutsches GeoForschungsZentrum Potsdam
Outline
1. Global magnetic field models
– numerical simulations and inverse models
2. Magnetic field characteristics at the core-mantle
boundary and inferences for core dynamics and
couplings
3. Recent new results on geomagnetic field excursions
Numerical dynamo simulations
•
Numerical solutions of a system of coupled partial differential equations
as pioneered by Glatzmaier and Roberts (1995).
•
Solutions are controlled by non-dimensional parameters of force
balances:
• Ekman number – viscous/Coriolis
• Prandtl number – viscous/thermal diffusivity
• mag. Prandtl number – magnetic/thermal diffusivity
• Rayleigh number – bouyancy/Coriolis force
•
These parameters have to be inferred from other
geophysical observations or experiments
•
Several parameters in the models are far from
the estimated Earth values, in particular the low
viscosity of Earth’s core cannot be reached yet
=> application of scaling laws
[Glatzmaier and Roberts, 1995]
Numerical dynamo simulations – how realistic are they?
Criteria for “Earthlike” dynamo simulations, based on observed characteristics of
Earth's magnetic field like, e.g., dipolarity, symmetries, reversal frequency, temporal
variability … [Christensen et al., 2010; Davies & Constable, 2014]
Magn. Reynolds number
•
Anticipated general flow
structure;
Convection columns likely more
numerous and thinner;
[Christensen, 2011]
Earth
Ekman number
Geomagnetic inverse models as observational
constraints on geodynamo processes
Spherical harmonic representation
B = -grad F
R 
F (r , ,  )  RE    E 
l 1 m  0  r 
l max
Potential
l
Earth‘s radius
l 1
g
m
l

cos( m  )  hlm sin( m  ) Pl m (cos  )
Gauss-coefficients
Spherical harmonics,
degree l, order m
•
Coefficients determined from observations by inverse methods
•
Simple downward-continuation to CMB assuming insulating mantle
=> maps of radial field (Br) at CMB
•
Convenient representation of spectral power, e.g. (axial) dipole to
non-dipole
•
Investigation of symmetries (hemispheric, zonal)
•
Further inversion for fluid flow at the top of the core possible
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
Ørstedt
Swarm
CHAMP
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
< 200 yrs
Observatory data
Good spatial and temporal resolution;
Core flow inversions;
e.g. CM4 [Sabaka et al.,
2002], COV-OBS [Gillet et
al., 2015], C3FM [Wardinski &
Lesur, 2012]
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
< 200 yrs
Observatory data
Good spatial and temporal resolution;
Core flow inversions;
e.g. CM4 [Sabaka et al.,
2002], COV-OBS [Gillet et
al., 2015], C3FM [Wardinski &
Lesur, 2012]
~ 400 yrs
Historical
directional
observations
Variable resolution;
Dipole moment extrapolated prior to 1840;
gufm1 [Jackson et al., 2000]
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
< 200 yrs
Observatory data
Good spatial and temporal resolution;
Core flow inversions;
e.g. CM4 [Sabaka et al.,
2002], COV-OBS [Gillet et
al., 2015], C3FM [Wardinski &
Lesur, 2012]
~ 400 yrs
Historical
directional
observations
Variable resolution;
Dipole moment extrapolated prior to 1840;
gufm1 [Jackson et al., 2000]
~ 3 kyrs
Archaeomagnetic
& volcanic data
Strongly heterogeneous data distribution;
Rather unreliable for southern hemisphere;
Not recommended for core field studies;
e.g. ARCH3k.1 [Korte et al.,
2009], AFM [Licht et al.,
2013], SHA.DIF.14k [PavonCarrasco et al., 2014]
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
< 200 yrs
Observatory data
Good spatial and temporal resolution;
Core flow inversions;
e.g. CM4 [Sabaka et al.,
2002], COV-OBS [Gillet et
al., 2015], C3FM [Wardinski &
Lesur, 2012]
~ 400 yrs
Historical
directional
observations
Variable resolution;
Dipole moment extrapolated prior to 1840;
gufm1 [Jackson et al., 2000]
~ 3 kyrs
Archaeomagnetic
& volcanic data
Strongly heterogeneous data distribution;
Rather unreliable for southern hemisphere;
Not recommended for core field studies;
e.g. ARCH3k.1 [Korte et al.,
2009], AFM [Licht et al.,
2013], SHA.DIF.14k [PavonCarrasco et al., 2014]
~ 12 kyrs
Paleomagnetic
sediment
and
volcanic
data
Limited temporal and spatial resolution with
reasonable global data coverage;
Large-scale & time averaged characteristics;
e.g. CALS10k.1b [Korte &
Constable, 2011], pfm9k.1
[Nilsson et al., 2014];
HFM.OL1.A1, CALS10k.2
[Constable et al., submitted]
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
< 200 yrs
Observatory data
Good spatial and temporal resolution;
Core flow inversions;
e.g. CM4 [Sabaka et al.,
2002], COV-OBS [Gillet et
al., 2015], C3FM [Wardinski &
Lesur, 2012]
~ 400 yrs
Historical
directional
observations
Variable resolution;
Dipole moment extrapolated prior to 1840;
gufm1 [Jackson et al., 2000]
~ 3 kyrs
Archaeomagnetic
& volcanic data
Strongly heterogeneous data distribution;
Rather unreliable for southern hemisphere;
Not recommended for core field studies;
e.g. ARCH3k.1 [Korte et al.,
2009], AFM [Licht et al.,
2013], SHA.DIF.14k [PavonCarrasco et al., 2014]
Limited temporal and spatial resolution with
reasonable global data coverage;
Large-scale & time averaged characteristics;
e.g. CALS10k.1b [Korte &
Constable, 2011], pfm9k.1
[Nilsson et al., 2014];
HFM.OL1.A1, CALS10k.2
[Constable et al., submitted]
Complex global surface field characterization;
Geodynamo behaviour during extreme events;
IMOLE [Leonhard et al.,
2009]; IMMAB [Leonhard and
Fabian, 2007]; IMIBE [Lanci
et al., 2008]; LSMOD [Brown
et al., work in progress]
~ 12 kyrs
Individual
excursions,
reversals
Paleomagnetic
sediment
and
volcanic
data
Inverse global SH models on different time-scales
Time
interval
Data sources
Characteristics
Models &
References
< 20 yrs
Satellite vector
data
Highest spatial & temporal resolution;
Core flow inversions;
e.g. CHAOS [Olsen et al.,
2014], GRIMM [Lesur et al.,
2010], POMME [Maus et al.,
2010]
< 200 yrs
Observatory data
Good spatial and temporal resolution;
Core flow inversions;
e.g. CM4 [Sabaka et al.,
2002], COV-OBS [Gillet et
al., 2015], C3FM [Wardinski &
Lesur, 2012]
~ 400 yrs
Historical
directional
observations
Variable resolution;
Dipole moment extrapolated prior to 1840;
gufm1 [Jackson et al., 2000]
~ 3 kyrs
Archaeomagnetic
& volcanic data
Strongly heterogeneous data distribution;
Rather unreliable for southern hemisphere;
Not recommended for core field studies;
e.g. ARCH3k.1 [Korte et al.,
2009], AFM [Licht et al.,
2013], SHA.DIF.14k [PavonCarrasco et al., 2014]
Limited temporal and spatial resolution with
reasonable global data coverage;
Large-scale & time averaged characteristics;
e.g. CALS10k.1b [Korte &
Constable, 2011], pfm9k.1
[Nilsson et al., 2014];
HFM.OL1.A1, CALS10k.2
[Constable et al., submitted]
Complex global surface field characterization;
Geodynamo behaviour during extreme events;
IMOLE [Leonhard et al.,
2009]; IMMAB [Leonhard and
Fabian, 2007]; IMIBE [Lanci
et al., 2008]; LSMOD [Brown
et al., work in progress]
100 kyrs
Including two excursions;
[Panovska & Constable, work
in progress]
5 Myr
Time-averaged models for normal & reversed field;
Long term persistent structure & symmetries;
LSN1, LSR1 [Johnson &
Constable, 1997], [Kelly &
Gubbins, 1997], [McFadden
& Johnson, 2015]
~ 12 kyrs
Individual
excursions,
reversals
Paleomagnetic
sediment
and
volcanic
data
CMB magnetic field characteristics
linked to core dynamics
Main field
Vertical component
at CMB
2005
Secular variation
[Lesur et al., 2010]
•
•
•
•
•
Paired intense high latitude flux patches in present field and many time averages
Westward drifting equatorial flux patches
Reverse flux patches
Stronger secular variation in Atlantic than Pacific hemisphere on many time scales
Weaker intensity with stronger secular variation in southern hemisphere
Pi
σPi
[Constable, Korte & Panovska, submitted]
Fluid flow at the top of the outer core
Main field
Z
Secular variation
Axial dipole weakened by:
- Normal flux transported equatorward
- Reverse flux transported poleward
Growth and decay
mostly balanced,
except for small
remainder caused
by SAA reverse flux
patch
(growth)
(decay)
[Finlay et al., 2016]
•
Planetary-scale gyre advectively drives present dipole decay.
•
Long-standing question about stratification at the top of the core
[Finlay et al., 2016]
Dynamics & stratification of the core
•
MAC waves (magnetic, Archimedes, Coriolis force) describe zonal flow and can
account for half of the observed decadal length-of-day variations. They
require a ~ 130 km stratified layer at the top of the core. [Buffet et al., 2016]
•
Consequences of revised thermal and electrical diffusivity of core material for
geodynamo: [Gubbins et al., 2015]
- Core convection vigorous in lower part and weak in upper part
- Weakly convecting rather than stably stratified layer at top of the core as
most likely scenario
•
Consistency between geomagnetic satellite data and a stably stratified layer at
the top of the core: [Lesur et al., 2015]
- SV generated by purely toroidal flow does not agree with the magnetic field
as observed by the CHAMP satellite (2000-2010)
- However, weak large scale poloidal flow in an upper layer might explain the
observations.
Misfit to CHAMP data
[Lesur et al., 2015]
Purely toroidal flow
General flow
Mantle influence on the geodynamo
Radial magnetic field at CMB,
time averages
T
400 yr
[Mosca et al., 2012]
3 ky
10 ky
[Masters et al., 2000]
•
[Amit et al., 2015]
Heterogeneous CMB heat flux causes long-term
deviations from GAD and axial symmetry
[e.g. Olson, 2016]
GK93
5Myr
•
Simulations using probabilistic tomography results
show low-latitude magnetic flux patches
[Amit et al., 2015]
JC95
5Myr
Inner core influence on the geodynamo
(Agreement in terms of flux concentration with gufm1)
weak ICB heterogeneity
strong ICB heterogeneity
[Aubert et al., 2013]
[from Mound et al., 2015]
•
IC translation generates heat flow anomaly at ICB
•
Gravitational coupling of IC and mantle  giant, westward drifting flow gyre
[Davies et al., 2013]
[Aubert, 2013]
•
Differential IC growth distorts the gyre  localization of strong SV in Atlantic
longitudinal sector [Aubert et al., 2013]
•
IC control can explain high latitude flux patches
•
ICB control might exceed CMB control
[Mound et al., 2015]
New global views on geomagnetic field excursions:
Laschamp (~41 ka)
(M. Brown, M. Korte, I. Wardinski, work in progress)
Data processing and modelling
method
1. Sediments kept on their
independent chronology. Lows in
intensity not forced to align.
2. RPI scaling by global dipole
model PADM2M [Ziegler et al.,
Sediment records:
• 30 directional
• 42 relative paleointensity
(RPI)
Volcanic data
• 73 Inclinations
• 72 Declinations
• 41 absolute paleointensities
2011]
3. Spherical harmonic inverse
models for interval 50 – 30 ka
4. Regularization parameters
chosen based on power
spectrum (space) and visual fit
to time series data (time)
Sorry, unpublished results deleted here in this version …
Conclusions from work on global characteristics of
excursions
•
A wide range of excursion behaviour found in our numerical simulation
•
Inverse and numerical models suggest that field behaviour at Earth’s
surface is complex during excursions
•
Axial dipole strength seems to be the main driver for excursions
•
Globally reversed directions are only observed if axial dipole reverses to a
certain degree – this implies an increase in intensity around the excursion
midpoint
Open questions & required studies
•
How strong is the influence of mantle and/or inner core on geodynamo
processes?
 persistence/recurrence of geomagnetic field morphology at CMB, e.g.
intense/reverse flux patches …
 symmetries in the geomagnetic field …
 boundary conditions in dynamo simulations …
 heat flow heterogeneity through CMB, linked to LLSVPs …
 inner core anisotropy …
•
Does a stably stratified layer exist at the top of the outer core?
 core flow constraints …
 core dynamics …
 core properties: composition, conductivity …
 seismology ?
•
How diverse are geomagnetic excursions and reversals and what is
their main driver?
 global inverse reconstructions …
 reversals and excursions in “Earth-like” dynamo simulations …
•
Important ingredient: further observational constraints (data) on all
time-scales