boundary and initial flow induced variability in ccc-gcm

29th Climate Diagnostic and Prediction Workshop

1

Boundary and Initial Flow Induced Variability

in CCC-GCM

Amir Shabbar and Kaz HiguchiClimate Research Branch

Environment Canada


2

Introduction

• ENSO-forced SST boundary conditions and extreme initial flow atmospheric configurations are analyzed for variability over the PNA sector in a series of Canadian atmospheric GCM experiments

• Two-way ANOVA and EOF techniques are used to assess and quantify the effects of prescribed boundary conditions and initial (zonal and meridional) flow regimes on the modes of mid-latitude variability


3

Questions

• Does the extreme phase of flow configuration excite some known mode of variability?

• Are the effects of ENSO SSTs and internal atmospheric dynamics on the total variability independent?


4

Experimental Design• Partition the total variation of 500 hPa heights into two statistically

independent components that isolate the variability forced by

(a) the prescribed ENSO SSTs and sea-ice extents, and (b) the variability associated with the changes in the initial flow configuration

(i.e., zonal or meridional over the Pacific-North American sector).

• ANOVA partitions the total sum of squares SSTOT into four components

• SSTOT = SSSST + SSFLOW + SSSxF + SSINTER • SSSST is the between sum of squares that measures differences

between the warm and cold phases of ENSO

• SSFLOW is the between sum of squares that measures differences between the two initial flow configuration.

• SSSxF is the between sum of squares that measures the interaction between the boundary forcing and initial flow configuration effects

• SSINTER is the sum of squares from internal sources that remains after the effects of boundary forcing, initial conditions and interaction terms are taken into account.

The total number of degrees of freedom dfTOT is given by

• dfTOT = dfSST + dfFLOW+ dfSxW + dfINTER


5

Hypothesis Testing for Boundary and Initial Conditions

• The null hypotheses for the three effects are set as follows:

a) Hsst: μC1 = μC2 El Niño and La Niña has no effect on the 500 hPa response.

b) Hflow: μR1 = μR2 Zonal or meridional flow configuration has no effect on the 500 hPa response.

c) Hsxf: (μ11 - μ12) = (μ21 - μ22) ENSO response is not influenced by the initial flow configuration.


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El Niño- La Niña SST Composites

El Niño La Niña

1957-58 1955-56

1972-73 1970-71

1982-83 1973-74

1991-92 1975-76

1997-98 1988-89

Fig. 1. Composite o f sea surface temperature anom aly for Dec-Jan-Feb. Contour interval is 0.5 C. a) El N i o and b) La Ni a. Zero line is thickened. See text for years of composite.o ñ ñ

a)

b)


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Mean 500 hPa Flow for Initial Condition

Zonal Flow

Meridional Flow

1968 1962

1971 1966

1872 1970

1978 1976

1982 1986

Average of first 10 days of November are examined for zonaland meridional flow regimes

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d )c)

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Fig 2 (d) Am p_anom

F ig . 2 . C om p os ite o f th e firs t 1 0 d ay s o f N o v em b er o f 5 00 h P a g eo p o ten tial he igh ts in d ecam ete rs (d am ) fro mN C E P rean aly s is u sed a s in it ia l f lo w co n fig u ra tion . a) zo n a l f lo w m ean , b ) zo n a l f lo w ano m a ly, c ) m e rid ion a l f low m ean an d d ) m e rid io na l flow an o m a ly. C o n to u r in te rv al is 1 2 d am in a ) an d c ), and 2 d am in b ) an d d ) .


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Model and Observed Variability in 500 hPa Circulation for

ENSO Years

Overall, the amplitude of the wintertime variability in the ENSO simulations are attenuated compared to those found in the observed data

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F ig . 3 . S tan d a rd d e v ia tion o f (a ) m od e l sim u la ted a n d (b ) b a se d o n N C E P /N C A R re an a ly s is5 0 0 h P a g eo p o te n tia l he ig h ts in m ete rs fo r D ec -Ja n -F eb fo r 2 0 E N S O cases co rresp on d ing to 10 zo n a l an d 1 0 m e rid io n a l f lo w reg im e s . C o n to u r in te rva l is 5 m ete rs.


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Zonal and Meridional Initial Flow

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Fig 2(a) Zn l_m ean

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F ig . 2 . C o m p o s ite o f th e first 1 0 d ay s o f N o v em b er o f 5 0 0 h P a g eo p o ten tia l h e ig h ts in d ec am e te rs (d am ) fro mN C E P rean aly s is u sed a s in itia l flo w co n fig u ra tio n . a ) zo na l flo w m ean , b ) zo n al f lo w an o m aly, c ) m erid io n a l flow m ean an d d ) m erid io n al flo w an o m aly. C o n to u r in te rv al is 1 2 d am in a ) an d c), an d 2 d am in b ) a n d d) .


10

Ratio of Boundary-forced variance to total variance over PNA sector

CCC-GCM results show that specified SSTs and sea-ice boundary conditions induce significant amount of mid-latitude Variability in both seasonal and monthly means, the pattern of which resembles the PNA teleconnection

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F ig . 4 . R a tio o f b o u n d a ry -fo rced v a rian ce to th e to ta l v a rian ce fo r 5 0 0 h P a g eop o ten tia l h e ig h ts a s d er iv ed b y A N O VA . S ign ifica n t F -ra tio a tth e 5 % leve l o f s ig n ific an ce is ind ica te d b y shad in g . a ) D ec -Jan -F eb , o v er 54 % o f th e a rea the n u ll h y p o thesis o f no bo u n d a ry e ffec t c an b ere jec ted a t th e 5 % lev e l o f s ign if ican ce , b ) D ecem b er, 30 % o f th e a rea ex h ib it s ta tis tic al s ign ific an ce a t 5 % , c ) J an u a ry, 3 8% o f th e a rea ex h ib it s ta tis tic a l s ign ifica n ce a t 5 % an d d ) F eb ru ary, w h e re 4 7 % o f th e a re a sh o w sta tis tica l s ig n ific an ce a t 5% .


11

Ratio of Initial-forced variance to total variance over PNA sector

A PNA-like pattern can be identified in December (b), and thepattern takes a more classical PNA appearance in January (c)

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F ig . 5 . R a tio o f in it ia l flo w co n d ition v arian ce to th e to ta l v a riance fo r 5 00 h P a ge o p o ten tia l h e ig h ts as d e riv e d b y A N O VA . S ig n ifican t F -ra tioa t th e 1 0 % leve l fo r th e in itia l f lo w e ffec t is in d ic a ted b y sh ad in g . a ) D ec-Jan -F eb , o v er 3 4 % o f th e a rea the n u ll e ffec t o f no in itia l f lo w effec tcan b e re jec ted a t th e 1 0 % lev e l o f s ig n ifican ce , b ) D ecem b er, 1 9% o f th e a rea ex h ib it s ta tis tic a l s ig n ifican c e a t 1 0 % , c ) J an u ary, w h ere 2 3 % o f th e a rea ex h ib it s ta tis tica l s ig n ifican ce a t 10 % .


12

Difference between Zonal and Meridional Flow

The relationship between the zonal and meridional flow in the model is further analyzed by forming differences in the mean of 10-member zonal and 10-member meridional flow regimes. The January pattern shown is very similar to the PNA. The nature of air-sea interaction in the North Pacific may lead to this pattern.

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Fig. 12: Zonal - Am pl F low JAN

F ig. 6 . D iffe ren ce in 5 0 0 h P a g eo p o ten tia l h e ig h ts b e tw een th e m ean o f 1 0 -m em b er zo n a l an d th e m ean o f 1 0 -m e m b er am p lified flo w m o d e l s im u la tion o v e r th e P ac ific N o rth A m erica n sec to r fo r Ja nu a ry. C o n to u r in te rv a l is in m e te rs . T h e p a tte rn is s im ila r to th e P N A p a tte rn .


13

Pattern Correlation between five-sample and three-sample variance

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F ig .7 . R e la tiv e freq uency o f th e pa ttern co rre la tio n b e tw een th e o ne fiv e -m em b eran d 1 00 th ree -m em b er ensem ble s fo r th ein itia l flow e ffec t. a ) D ecem ber, b ) Jan ua ryan d c ) F eb ru a ry . O v er 8 0% o f th e co rre la -tio n s a re h ig h e r th an 0 .6 in Janu a ry . N o te d iffe rence in sca le in c ).

• The PNA pattern remains identifiable when the signal to noise ratio is reduced in the smaller ensemble of 3 sample.

• Shown are the pattern correlation between the original 5-member sample and 100 3-member sample. The results are strong for December (a) and January (b), but become weaker in February (c)


14

First Two EOFs of Dec-Feb 500 hPa Heights as derived from model

ensemble

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F ig . 8 . a ) E O F 1 fo r D ec -Jan -F eb o f 5 0 0 h P a g e o p o ten tia l h e ig h ts a s d e riv ed from m o d e l en s em b le . E O F 1 sh o w s th e P N A m o d e o f th e N o rth e rn H em is p h e re v a riab ili ty an d ex p la in so v e r 5 0 % o f th e sea so n a l v a ria ib il ity. b ) E O F 2 o f th e sam e f ie ld . E O F 2 sh o w s a m ix tu re o f th e N o rth A tlan tic O sc illa tio n (N A O ) an d th e P N A m o d es an d ex p la in s ab o u t 1 7 % o f th e sea so n a l v a riab il ity. T h e co n to u r in te rv a l is 0 .0 1 . T h e sig n s a re a rb itra ry an d u n its a red im en sio n le ss .


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Boundary and Initial Flow F- Ratio in EOF reconstructed

500 hPa Heights

Dec-Feb F-ratio in reconstructed data shows PNA-like pattern for both Boundary and Initial flow effects

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F ig . 9 . F -ra tio o f the D ec -Jan -F eb 5 00 h P a g eo p o te n tia l h e ig h ts a s re co n stru c ted fro m E O F 1 an d E O F 2 fo r a ) b o un d a ry -fo rced e ffec t a nd b ) fo r in itia l flo w c o n d itio n e ffec t. S h ad ed a rea sh o w s s ta tis tica l s ig n ifican c e a t 5 % fo r th e b o u n d ary -fo rc ed e ffec t, w h e re o ve r 8 3 % o f th e a re a th e n u ll hy p o th es is o f n o e ffec t can b e re je c ted . F o r th e in itia l f lo w c o nd itio n , s ig n if ica n ce is d e te rm ine d a t 1 0 % , a n d o v er 6 1% o f th e a re a , th e n u ll h y po th esis o f n o e ffe c t can be re jec ted .


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Summary

• Two way ANOVA on model ensemble shows that the specified time varying SSTs in the tropical Pacific contribute significantly to the North American variability as a PNA mode of circulation

• Moreover, initial flow configuration induces additional variability over the region through the PNA-like mode of variability

• Relative contribution from the tropical SSTs and the initial flow are linearly independent and additive

• ANOVA technique applied to 500 hPa anomaly filtered with the leading two EOFs shows considerable boundary-forced and to a lesser extent initial flow variability over the Pacific North American sector

boundary and initial flow induced variability in ccc-gcm

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