Spatiotemporal MVL Analysis of EPS: Application to DEMETER ...
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Spatiotemporal MVL Analysis of EPS: Application to DEMETER multi-model
Seasonal Predictions
José M. Gutiérrez [email protected]
Miguel Angel Rodríguez Sixto Herrera
Jesús Fernández Antonio S. Cofiño
Cristina Primo
Meteorology Group Instituto de Física de Cantabria (IFCA)
ECODYC10. Max Plack Institute Physics Complex Systems. Dresden, 25-29/1/2010
Juan M. López, Diego Pazó, ... Statistical Physics Group
Instituto de Física de Cantabria (IFCA)
Spatiotemporal Chaos Research Line
Santander Meteorology Group A multidisciplinary approach for weather & climate
From theory to operations
TOY Models (Physics) Lorenz96
OPERATIONAL Models (Physics + Engineering)
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dvdt
= −α∇p −∇φ + F − 2Ω× v
∂ρ∂t
= −∇⋅ (ρv)
pα = RT
Q = CpdTdt
−αdpdt
∂ρq∂t
= −∇⋅ (ρvq) + ρ(E −C)
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What we learned from our “theoretical” colleagues – Spatiotemporal growth of errors
What we jointly developed for practical applications: – MVL diagram
Application to DEMETER Hindcast
Going back to theory: – Extension to two-scale systems (Lorenz96)
Table of contents
Santander Meteorology Group A multidisciplinary approach for weather & climate
Error Growth (Primo et al. 2005, 2007)
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δxi(t) = xip (t) − xi(t)
Control
Temporal growth
Spatial growth
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MVL Diagram
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The MVL diagram provides a “fingerprint” of the system dynamics
Climatological fluctuations
A: Random and spatially uncorrelated C: Assimilated perturbations (e.g. bred vec.) B: Spatially correlated and non-assimilated (e.g. lagged)
Moreover, the initial transient is related to the initial perturbations.
Santander Meteorology Group A multidisciplinary approach for weather & climate
DEMETER Seasonal Hindcast
Ocean-atmosphere global circulation models running ensemble initial condition perturbations (9 members) for seasonal forecasts.
Feb 87 May 87 Aug 87 Nov 87 Feb 88 ...
Initial conditions
9 members for 6 months = 1 forecast Production for 1980-2001 starting 4 times per year:
1st February 1st May 1st August 1st November
x 7 models
Multi-model ensemble addresses the problem of model error.
thanks to Francisco Doblas-Reyes
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DEMETER GCMs
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GCMs building blocks
Different models share common building blocks (atmospheric and/or oceanic components), so they cannot be considered as equiprobable representations of the model error. Thus, we need some diagnostic tool to find similarities among models dynamically.
Santander Meteorology Group A multidisciplinary approach for weather & climate
MVL for T2m
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Robust to interannual variability
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MVL for z500
Robust to interannual variability
Santander Meteorology Group A multidisciplinary approach for weather & climate
Model weighting
All these methods weight models according to performance.
Thus, if a “good” model is included several times in the ensemble, then the results will be biased towards this particular model.
Santander Meteorology Group A multidisciplinary approach for weather & climate
Model Comparison & weighting
Santander Meteorology Group A multidisciplinary approach for weather & climate
MVL control vs MVL reanalysis
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δxi(t) = xip (t) − xi(t)
Control
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δxi(t) = xip (t) − xi(t)
Reanalysis
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Role of slow-fast dynamics
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Santander Meteorology Group A multidisciplinary approach for weather & climate
Two-Scale Lorenz96
h=0.3
h=1
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h=0.3 17
h=0.6
h=1
Santander Meteorology Group A multidisciplinary approach for weather & climate
Sensitivity of the coupling param.
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Fast variables vs Noise
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The fast variables play a role in the dynamics of slow variables even after saturation. They cannot be substituted by an “effective” noise.
Santander Meteorology Group A multidisciplinary approach for weather & climate Conclusions
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The MVL diagram is a powerfull diagnosis and characterization tool.
We (operational meteorology) can benefit from the advances in spatiotemporal nonlinear physics.
ALSO THE OTHER WAY AROUND.
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http://www.meteo.unican.es
S. Herrera, J. Fernández, M.A. Rodríguez and J.M. Gutiérrez (2010) Spatio-temporal Error Growth in the Multi-Scale Lorenz96 Model Nonlinear Processes in Geophysics, submitted.
J. Fernández, C. Primo, A. S. Cofiño, J.M. Gutiérrez, M.A. Rodríguez (2009) MVL Spatiotemporal analysis for model comparison. Application to the DEMETER Multi-model Ensemble Climate Dynamics, 33, 233-243. DOI: 10.1007/s00382-008-0456-9
J.M. Gutiérrez, C. Primo, M.A. Rodríguez and J. Fernández (2008) Spatiotemporal Characterization of Ensemble Prediction Systems. The Mean-Variance of Logarithms (MVL) Diagram Nonlinear Processes in Geophysics, 15, 109-114.