Model drift analysis to understand the causes of ... · Adapted from Magnusson et al. Observed...
Transcript of Model drift analysis to understand the causes of ... · Adapted from Magnusson et al. Observed...
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5th Climate Europe Webminar
Model drift analysis to understand the causes of systematic errors in climate
prediction systems
EmiliaSanchez-Gomez
KaterinaGoubanovaandcollaborators
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Climate Prediction System
External forcings GHG, Volcanoes, aerosols, solar
Forecast
verifica-on
skill
Observations Ocean, atmosphere, land, sea-ice
Initial conditions
Climate Model
Boundary conditions
Perturbing Ensembles
simulations Drift correction
Forecast
Products
downscaling
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Climate Prediction System
External forcings GHG, Volcanoes, aerosols, solar
Forecast
verifica-on
skill
Observations Ocean, atmosphere, land, sea-ice
Initial conditions Boundary conditions
Perturbing Ensembles
simulations Drift correction
Uncertainties affecting forecast performance
Forecast
Products
downscaling
Climate Model
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Climate Prediction System
External forcings GHG, Volcanoes, aerosols, solar
Forecast
verifica-on
skill
Observations Ocean, atmosphere, land, sea-ice
Initial conditions Boundary conditions
Perturbing Ensembles
simulations Drift correction
Forecast
Products
downscaling
Climate Model
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Evaluation of climate models
ModelEvalua-on
Meanclimate
Variability
Predic-veskill Mechanisms
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Drift Analysis to understand model biases
AdaptedfromMagnussonetal.AdaptedfromMagnussonetal.
Observedclimate
Modelclimate
T=0
-me
Unini-alized
simula-on
ini-alized
simula-on
ModeldriAIni-alshock
ThemodeldriAisthesequenceofphysicalprocessesbywhichmodeladjusttoits
equilibriumstateora>ractor
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Drift Analysis to understand model biases
AdaptedfromMagnussonetal.AdaptedfromMagnussonetal.
ThedriA(orbiasadjustment)analysisdependsonthespa-al
and-mescalesconsideredinthephysicalproblem:
o Fast atmospheric processes (convective processes, clouds) hours to days o Ocean subsurface (mixing processes) days to months
o Deep ocean circulation and gyres years, decades
Transpose-AMIPprotocol
Seasonalforecast
Decadalforecast
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Understanding climate model biases
Coupled models continue to suffer from severe equatorial SST biases over the Tropical Atlantic
SSTbiasintheTropicalOceans(fromToniazzoandWoolnough,2013)
Data:CMIP5
WhatistheoriginofSSTbiasesinthe
SouthEasternTropicalAtlan-c(SETA)?
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Drift Analysis to understand model biases
2-monthsaveragedevolu-onoftheSSTbiaswithrespecttoGLORYS2v3
q Model:
CNRM-CM:ARPEGE(~50km)(T359L31)andNEMO~0.250(ORCA025L75)
q Seasonalforecast:
§ IniQalizaQonfromERAIandGLORYS2v3
§ Startdate:1February
§ 10years:2000-2009
§ Three-members,6monthsleadQme
Goubanovaetal.2017(inprep.)
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Drift of ocean subsurface temperature
FEB
MAR
APR
MAY
JUN
JUL
Isotherme20°Cinthemodel
Isotherme20°CinGLORYS2v3
⁰C
Goubanovaetal.2017(inprep.)
Talongtheequator(2°S-2°N) Talongthecoast(2°)
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Drift in wind stress
Goubanovaetal.2017(inprep.)
Shading:windstressamplitude
Vector:windstressvector
Contour:zonalwindstress
Pa
ERAI CNRM-CMModel
Meanevolu-onoftheequatorialwindstress(2°S-2°N)
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Drift in wind stress
Goubanovaetal.2017(inprep.)
Shading:windstressamplitude
Vector:windstressvector
Contour:zonalwindstress
Pa
ERAI CNRM-CMModel
Meanevolu-onoftheequatorialwindstress(2°S-2°N)
Westerlybias
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Sensitivity experiments
Goubanovaetal.2017(inprep.)
TAUEQ : Seasonal forecast experiment with wind stress replacement (ERAI) at the equator
TAUEQ
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Sensitivity experiments
Thetaalongtheequator(2°S-2°N) Thetaalongthecoast(2°) Thetaalongtheequator(2°S-2°N) Thetaalongthecoast(2°)
FEB
MAR
APR
MAY
JUN
JUL
FEB
MAR
APR
MAY
JUN
JUL
CONTROL TAUEQ
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Sensitivity experiments
SETA
SSTbiasdailyevoluQonovertheSETA(⁰C):
--- CTRL-HR
--- TAUEQ
--- CTRL-LR
u Remoteforcingfromtheequatorcontributesto~50%
ofwarmSETASSTbiasinCNRM-CMmodel
Error
reduc-on
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Physical analysis SST biases in SETA
CTRL: Spurious particular warm horizontal advection
TAUEQ: warm horizontal advection disappears
CTRL-HR
CTRL-LR
TAUEQ
SSTbiasevoluQon(⁰C)
°C/day
RATE
XY-Adv
Z-Adv
FORC
DIFF
ENTR
CTRL
TAUEQ
SETA
Mixed-layer temperature tendency terms
TemperRATE=XY_Adv+Z_Adv+Atm.FORC+Vert.DIFF+ENTR+Res
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Conclusions
q Usefulness of driA analysis in Climate Predic-on Systems tounderstandtheoriginofmodelsystema-cbiases
(Toniazzo and Woolnough 2013; Vanniere et al. 2013,2014;Sanchez-Gomezetal.2016;Goubanovaetal.(inprep))
q Casestudy:TropicalAtlan-cSSTbias
- SeasonalforecastsCNRM-CMmodel
- AnalysisofthedriAtounderstandSSTbiasintheSETAregion
ü 50%ofSSTbiascanbeexplainedbyspuriouswarmadvecQonfrom the equator, due to atmospheric biases (equatorialwesterlywindbias).
ü Transpose-AMIPanalysisshowsthatatmosphericwesterlybiasdevelopveryfast(withinthefirst5days)(Roehrigetal.)