Met Office Decadal Activities€¦ · Met Office Decadal Activities . Skilful predictions of NAO!...
Transcript of Met Office Decadal Activities€¦ · Met Office Decadal Activities . Skilful predictions of NAO!...
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Met Office Decadal Activities
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Skilful predictions of NAO
• Skill extends over the whole satellite era since 1980 • Recent large signals are captured • Significant skill from more than a year ahead
Scaife et al 2014; Dunstone et al 2016
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Dunstone et al 2016
Multiple linear regression: Atlantic tripole, ENSO, polar vortex, Kara sea ice
Sources of NAO skill
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Sahel rainfall
• Significant skill for both multiyear (years 2 to 5, top row) … • … and inter-annual at 8 month lead (bottom row)
(Sheen et al. 2017)
Mul
tiyea
r In
ter-
annu
al
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(Sheen et al. 2017)
Sources of Sahel rainfall skill
• Multiyear driven by hemispheric temperature gradient which shifts the ITCZ Ø anomalous Hadley (meridional) circulation
• Interannual driven mainly by ENSO Ø anomalous Walker (zonal) circulation
Teleconnections Skill (detrended) M
ultiy
ear
Inte
r-an
nual
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European summer rainfall
• 80 ensemble members (40 each from May and Nov) • Every year from 1961 • r=0.47 • Captures some extreme years (e.g. 1976) and some low frequency variations (e.g. wet years 2007-2012) • Also some skill for southern Europe (r=0.39)
Dunstone et al, submitted
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European summer rainfall
Dunstone et al, submitted
Low frequency High frequency
Correlation between obs European rainfall and forecast T (colours) and moisture flux (arrows)
Correlation between obs European rainfall and forecast Q
Correlation between obs European rainfall and forecast U
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The signal to noise paradox
• Skill (anomaly correlation) of seasonal forecasts of the NAO (DJF from Nov)
• Model ensemble mean predicts the real world better than individual model members!
• High skill despite low signal to noise in model → “the signal to noise paradox” • N.B. You will not see this if you have a low skill score... Eade et al 2014, Scaife et al 2014, Dunstone et al 2016
Model predicting real world
Model predicting itself
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Will the mel)ng Arc)c sea ice promote cold European winters?
Atmosphere model
Coupled model
• Reduced ice → reduced Equator to pole temperature gradient → less wave activity • Response depends on wave propagation, and hence background refractive index • Observations (grey shading) suggest –ve NAO response • Need more models → coordinated multi-model experiments • New CMIP6 MIP, Polar Amplification MIP, to investigate the causes and consequences of polar amplification
Equatorward refraction
Neg
ativ
e N
AO
Obs
erva
tions
(Smith et al. 2017)
Obs
erva
tions
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Global warming slowdown: role of anthropogenic and volcanic aerosols
• Recent decrease in 15 year trends is simulated by CMIP5 models → externally forced • Partly recovery from Pinatubo
• But anthropogenic aerosols produce cooling trend over most recent 15 years • Pattern matches obs in many regions including the Pacific → negative PDO
• Slowdown was potentially externally forced by aerosols
(Smith et al. 2016)
15 year trends 15 year trend 1998-2012
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Slowdown in surface warming: recalibration of models
• Detection and attribution analysis to obtain scaling factors (beta) • Use data before 1995 • Scaling for Nat significantly less than one → models over-sensitive to volcanoes • Scaled projection (red dotted) in much better agreement with obs than unscaled (red dashed) • Need to understand response to external forcing better even for near term predictions
(Smith et al. 2016)
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UNSEEN: Unprecedented Simulated Extremes in ENsembles
Thompson et al, 2017
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Risk of exceeding 1.5oC
• Coming 2 years • 4% (initialised) vs 25% (uninitialised)
• Coming 5 years • 43% (initialised) vs 34% (uninitialised)