Simulation, prediction and analysis of Earth rotation parameters

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1 Simulation, prediction and analysis of Earth rotation parameters with a dynamic Earth system model Florian Seitz Earth Oriented Space Science and Technology (ESPACE)

2 Earth rotation parameters and their physical interpretation Besides their necessity for various applications (e.g., the realisation of time and reference systems, navigation) Earth rotation parameters (ERP) are interesting for various disciplines of geosciences, since dynamic processes in the Earth system are reflected in their temporal variations. Analysis of ERP time series allows for conclusions with respect to processes and changes in the Earth system on various temporal scales However: Since ERP are integral quantities their physical interpretation is very difficult: Specific features in the time series cannot be related to contributions of individual system components particular causative processes without further information Independent information from physical modelling is required 2

3 Physical model of Earth rotation Development of the physically consistent and comprehensive Dynamic Earth System Model DyMEG: Composed of a discrete number of interacting system components Consistent modelling of -rotational variations -gravity field variations -geometrical surface variations that are caused by various dynamic processes in the Earth system Focus of this presentation: Model results for inter-annual variations of polar motion caused by processes in the coupled atmosphere-hydrosphere h h system. 3

4 Polar motion: Signal characteristics Journées 2011 "Systèmes de référence spatio temporels" ", Vienna x component 4

5 Balance of angular momentum in the Earth system The model approach for Earth rotation is based on the balance of angular momentum in an Earth-fixed coordinate system (Euler-Liouville Equation): d dt with angular momentum L(t): I(t): h(t): ω(t): H( t) + ω(t) H( t) = L( t) external gravitational torques (Sun, Moon, planets) Earth s tensor of inertia relative angular momenta Earth rotation vector H( t) = I( t)ω( t) + h() t Numerical solution of the Euler-Liouville Equation for ω(t) in DyMEG: Simulation of polar motion and the Earth s angular velocity (ΔLOD). 5

6 Experiment 1: Realistic atmospheric and hydrospheric forcing Numerical values for ΔI(t) and h(t) from atmospheric reanalyses of NCEP - assimilates meteorological observation data ocean circulation model ECCO - unconstrained version (c ) - forced by NCEP fields of wind stress, heat and freshwater fluxes consistent representation of dynamics and mass transports in the subsystems atmosphere and ocean water, groundwater and snow fields from the global hydrological model LaD neglected: earthquakes, volcanoes, postglacial uplift, core/mantle, 6

7 Model results for polar motion x-component: corr.: 0,98; RMS-diff.: 29,5 mas y-component: corr.: 0,99; RMS-diff.: 23,3 mas 7

8 Experiment 2: ERP predictions - scenario runs over 200 years ΔI(t) and h(t) from ensemble runs of the fully coupled atmosphere-hydrosphere model ECOCTH of the MPI for meteorology (Hamburg, Germany) Simulation of the atmospheric-hydrospheric angular momentum variability over a time frame of 200 years ( ) ECOCTH has also been used for simulations in the frame of the 4th IPCC AR full consistency: conservation of mass, energy and momentum five equiprobable model runs (different initial conditions for the state of 1860) Absolutely free model: atmosphere oceans hydrology force each other mutually there is no information about real time only statistical conclusions can be drawn! 8

9 Model results for polar motion ( ) x-components of polar motion: All runs feature a clear beat between annual and Chandler oscillation IERS C01/C04 9

10 Model results for polar motion ( ) Chandler signal annual signal Runs show similar annual signals but twice as strong as observed Chandler components are very different 10

11 Model results for polar motion ( ) Chandler signal +26 a +54 a Background noise of ECOCTH is capable of exciting realistic Chandler amplitude variations Korr.: 0.74 Korr.:

12 Strongest contributor to Chandler excitation? (Atmosphere: Mass + Motion) (Atmosphere + Ocean: Motion) (Atmosphere: Motion) (Ocean: Mass + Motion) (Atmosphere + Ocean: Mass) (Ocean: Motion) (Atmo. + Ocean: Mass + Motion) atmosphere a bit stronger than the ocean wind dominates motion significantly stronger the mass 12

13 Final remarks and conclusions Simulations with DyMEG allow for a meaningful geophysical interpretation of ERP Naturally, the interpretability of model results depends on the applied forcing ECOCTH allows for statistical interpretations of long-term variations of ERP. ECOCTH is capable of producing realistic variations of the Chandler oscillation Experiments reveal the dominance of wind excitation (=random white noise) for the continuous forcing of the Chandler oscillation. 13

14 END Journées 2011 "Systèmes de référence spatio temporels" ", Vienna 14

15 Dynamic model for Earth rotation (DyMEG) Journées 2011 " "Systèmes de référence spatio temporels" ", Vienna 15

16 Influence of the initial values Model results for polar motion over 200 yrs ECOCTH ( ) x 0 = 0 x 0 = 0.3 x 0 =

17 White noise Chandler wobble excitation (1) ", Vienna tio temporels" référence spat Jo ournées 2011 "Systèmes de r Experiment: Substitution tion of atmospheric and oceanic forcing by uniformly distributed random numbers (white noise) Result: Resonant excitation of the Chandler oscillation over 1000 years Formation of maxima and nodes according to energy level and phase of random excitations cf. Seitz et al.,

18 White noise Chandler wobble excitation (2) ", Vienna tio temporels" référence spat "Systèmes de r ournées 2011 " Jo Observed CW Similar characteristics of simulated and observed free polar motion Atmospheric background noise due to random variability (weather) is the most likely excitation mechanism of the Chandler oscillation 18

19 Rotational deformation Modeled as temporal variation of the Earth s centrifugal potential: 2 3 Ω a ΔC 21 () t = ( R ( k 2)m 1 () t +I ( k 2)m 2 ()) t 3GM 2 3 Ω a ΔS 21( t ) = ( R( 2)m 2( ) ( 2)m 1( )) k t I 3GM k t k = k + Δk + Δk * O A with the pole tide Love number. IERS-Conv. 2010: k 2 = i 19

20 Quality factors in the literature CW period Q [range] Source ± [50, 400] Wilson & Haubrich (1976) Lenhardt & Groten (1985) ± [47, >1000] Wilson & Vicente (1990) ± [30, 500] Kuehne et al. (1996) ± [35, 100] Furuya & Chao (2001) Schuh et al. (2001) 82 IERS-Conv. (2010) 20

21 Results for k 2 ", Vienna tio temporels" référence spat "Systèmes de r ournées 2011 " Jo k2 = i Model Chandler period = d Q-Factor = ± ± IERS-Conv. 2010: k 2 = i 21

22 Forward modeling (DyMEG, ) k2 = i Model forcing: NCEP + ECCO Model Chandler period = d Q-Factor = 82 Full PM C01/C04 Chandler C01/C04 Corr-Coef.: 0.82 RMS-Diff.: 82.2 mas Corr-Coef Coef.: 0.89 RMS-Diff.: 55.7 mas 22

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