The Role of Air–Sea Interaction for Prediction of

The Role of Air–Sea Interaction for Prediction of Australian Summer Monsoon Rainfall

HARRY H. HENDON, EUN-PA LIM, AND GUO LIU

Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia

(Manuscript received 3 March 2011, in final form 4 August 2011)

ABSTRACT

Forecast skill for seasonal mean rainfall across northern Australia is lower during the summer monsoon

than in the premonsoon transition season based on 25 years of hindcasts using the Predictive Ocean Atmo-

sphere Model for Australia (POAMA) coupled model seasonal forecast system. The authors argue that this

partly reflects an intrinsic property of the monsoonal system, whereby seasonally varying air–sea interaction

in the seas around northern Australia promotes predictability in the premonsoon season and demotes pre-

dictability after monsoon onset. Trade easterlies during the premonsoon season support a positive feedback

between surface winds, SST, and rainfall, which results in stronger and more persistent SST anomalies to the

north of Australia that compliment the remote forcing of Australian rainfall from El Nino in the Pacific. After

onset of the Australian summer monsoon, this local feedback is not supported in the monsoonal westerly

regime, resulting in weaker SST anomalies to the north of Australia and with lower persistence than in the

premonsoon season. Importantly, the seasonality of this air–sea interaction is captured in the POAMA

forecast model. Furthermore, analysis of perfect model forecasts and forecasts generated by prescribing

observed SST results in largely the same conclusion (i.e., significantly lower actual and potential forecast skill

during the monsoon), thereby supporting the notion that air–sea interaction contributes to intrinsically lower

predictability of rainfall during the monsoon.

1. Introduction

Northern portions of Australia experience a monsoonal

climate, with the majority of the annual rainfall occurring

in the summer (wet) half of the year (November–April).

The seasonal reversal of the circulation, which typifies a

monsoonal climate, typically occurs abruptly across north-

ern Australia in late December, when the trade easterlies

diminish, the subtropical ridge retreats poleward, and a

monsoonal trough with concomitant lower-tropospheric

westerlies establishes just to the north of the continent

over the course of a few days (Troup 1961; Hendon and

Liebmann 1990). Although the bulk of the wet season

rainfall occurs after the reorganization of the circulation

at monsoon onset, upward of 30% of the wet season

rainfall occurs prior to onset during September–November

(e.g., Nicholls et al. 1982). This period of premonsoon

rainfall is also referred to as the transition season, and, as

pointed out by Troup (1961), is characterized by increased

frequency of squall lines and thunderstorms.

Long-range prediction of rainfall during both the mon-

soon and the premonsoon transition season has many

practical applications especially for agriculture and wa-

ter resource management across northern Australia (e.g.,

McCown 1981; Mollah and Cook 1996; Everingham et al.

2008). Hence, there has been widespread interest and

research in developing long-range prediction of mon-

soon season rainfall. The observed relationship between

the El Nino–Southern Oscillation (ENSO) and transi-

tion season rainfall, whereby dry (wet) conditions tend to

accompany El Nino (La Nina; McBride and Nicholls 1983),

together with the persistence of ENSO SST anomalies in

the Pacific from austral winter [June–August (JJA)] to

spring [September–November (SON)], has been exploited

to develop predictions of transition season rainfall (e.g.,

Nicholls et al. 1982) and wet season onset (Nicholls 1984a;

Lo et al. 2007). Onset of the wet season is typically de-

fined as the date by which some small fraction of the total

wet season rainfall is achieved (e.g., Nicholls et al. 1982)

and typically occurs earlier than when the circulation

abruptly reorganizes at monsoon onset. Predicting wet

season onset is of utility, for instance, for management of

grazing stock (McCown 1981) and sugar cane harvest-

ing (e.g., Everingham et al. 2008). Although statistical

Corresponding author address: Harry H. Hendon, CAWCR/BoM,

GPO Box 1289, Melbourne VIC 3001, Australia.

E-mail: [email protected]

1278 J O U R N A L O F C L I M A T E VOLUME 25

DOI: 10.1175/JCLI-D-11-00125.1

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algorithms have had success in the premonsoon, they have

had limited success for predicting postonset rainfall (e.g.,

Nicholls et al. 1982) even though ENSO SST anomalies

tends to persist and even peak in austral summer.

In an effort to improve seasonal prediction of climate

in Australia, the Australian Bureau of Meteorology (BoM)

has been developing a dynamical model forecast system

[i.e., the Predictive Ocean Atmosphere Model for Aus-

tralia (POAMA)] based on a coupled ocean–atmosphere

climate model (e.g., Alves et al. 2003). Forecasts from the

POAMA system show good skill to lead times of two–

three seasons for predicting the state of ENSO (e.g.,

Hendon et al. 2009; Zhao and Hendon 2009). Capital-

izing on this ability to predict ENSO, which is the most

important driver of Australian-wide climate variability,

POAMA is able to provide skillful predictions of regional

Australian climate (e.g., rainfall and temperature) at lead

times up to about one season, especially in the eastern

and southern parts of the country during the cool seasons

when ENSO has a pronounced impact (e.g., Lim et al.

2009). Seasonal forecasts from POAMA for transition

season rainfall across northern Australia also show some

skill at lead times up to a few months (e.g., Figs. 1a–c).

However, forecasts from POAMA for summer monsoon

(postonset) rainfall are no better than climatology, even

at the shortest lead time (Figs. 1d–f; more details of the

POAMA system, forecasts, and verification are supplied

in section 3). Skill is higher over the surrounding ocean

points than over land for both seasons; however, skill

also drops over the ocean points from spring to summer.

A similar drop in skill for northern Australian rainfall in

going from spring to summer is also demonstrated by

FIG. 1. Correlation of seasonal mean rainfall forecasts for (left) SON and (right) DJF for lead times of

(top) 0, (middle) 2, and (bottom) 4 months. Forecasts are from POAMA1.5b for the period of 1982–2006

and verified against CMAP (Xie and Arkin 1997). The contour interval is 0.2. A correlation of 0.4 is

estimated to be significantly different from zero at the 95% level assuming 25 independent samples.

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other dynamical forecast models such as those that con-

tributed to the ENSEMBLES project (Hewitt and Griggs

2004; selected rainfall skill maps are available online

at www.ecmwf.int/research/EU_projects/ENSEMBLES/

results/stream2_seasonal.html).

The purpose of the present study is to explore in more

detail the causes for success of the forecasts of transition

season rainfall and the failure of the forecasts for post-

onset rainfall. We will argue that local air–sea interaction

in the warm seas surrounding northern Australia tends to

promote predictability of rainfall in the transition sea-

son prior to monsoon onset and to demote predictability

postonset in a fashion similar to that proposed by Nicholls

(1979) to explain Indonesian SST and rainfall variability.

We will further show that this seasonally varying air–sea

interaction is faithfully captured in the POAMA dynami-

cal coupled model. While not downplaying other physical

processes (e.g., unpredictable variability associated with

the Madden–Julian oscillation and land-based convection)

or model error for limiting the ability to predict summer

monsoon rainfall, we will argue that the reduced skill for

predicting rainfall postmonsoon onset (e.g., Fig. 1) is partly

accounted for by lower intrinsic predictability than in the

premonsoon as a result of local air–sea interaction.

In section 2, we will investigate the observational basis

for the role of local air–sea interaction for promot-

ing predictability of northern Australian rainfall in the

premonsoon and for diminishing predictability post-

onset. The POAMA coupled model forecast system,

the reforecasts (hindcasts) for 1982–2006 that we use to

assess forecast skill, and a series of experimental forecasts

aimed at elucidating the role of air–sea interaction for

rainfall prediction are described in section 3. Analysis of

hindcast prediction skill and depiction of the relevant air–

sea interaction by the POAMA model is provided in sec-

tion 4. Conclusions are provided in section 5.

2. Observed seasonally varying air–sea interaction

Insight as to why postonset monsoon rainfall in north-

ern Australia is less predictable than preonset rainfall

is gained from examination of the seasonality of the

relationship between northern Australia rainfall and

SST. Figure 2 shows the correlation of gridded SST with

the time series of rainfall averaged across northern

Australia (land points north of 258S) for the period 1982–

2006. The gridded SST data are from the monthly anal-

yses of Reynolds et al. (2002) and the northern Australian

rainfall index is computed by averaging the gridded

monthly rainfall over northern Australia using the Cli-

mate Prediction Center (CPC) Merged Analysis of Pre-

cipitation (CMAP; Xie and Arkin 1997). A correlation of

0.4 is assumed to be significantly different from zero at the

95% level assuming 25 independent samples (i.e., there

FIG. 2. Regression (vectors) of 10-m winds (NCEP–DOE reanalyses; Kanamitsu et al. 2002)

onto the rainfall index based on Australian land points north of 258S from CMAP overlaid on

the correlation (color shading) between the rainfall index and observed SST (Reynolds et al.

2002) for the period 1982–2006 in (a) SON and (b) DJF. The vector magnitude (m s21) is shown

in the top right of (a) and (b). Vectors are shown where the regression coefficient is significant

at the 90% level. A correlation of 0.4 is estimated to be significantly different from zero at the

95% level assuming 25 independent samples.



is no serial correlation from year to year). During SON

(premonsoon; Fig. 2a) northern Australian rainfall is

strongly positively correlated with SST in the seas sur-

rounding northern Australia and strongly negatively cor-

related with remote SST in the central equatorial Pacific.

This pattern of SST correlation, both locally and remotely,

is reminiscent of La Nina conditions during SON. In con-

trast, during the December–February (DJF) season the

correlation between northern Australia rainfall and SST

weakens everywhere and is even weakly negative to north

of Australia (Fig. 2b). These seasonal varying relation-

ships of rainfall and north Australian SST with were first

documented by Nicholls (1984b).

This contrast in correlation between north Australian

rainfall and local SST in SON (strongly positive) and DJF

(near zero or weakly negative) is also evident in the point-

wise correlation between oceanic rainfall around north-

ern Australia and SST (Fig. 3; see also Wu and Kirtman

2007). Rainfall and SST are positively correlated in the

seas surrounding north Australia in the premonsoon SON

season (Fig. 3a), but the correlation is near zero or even

weakly negative during the DJF monsoon season (Fig.

3b). In contrast to this behavior to the north of Australia,

SST and rainfall are strongly positively correlated in the

central Pacific in both seasons. The strong positive cor-

relation of rainfall and SST to the north of Australia in

SON and in the central equatorial Pacific in both SON and

DJF is indicative of SST forcing of rainfall (e.g., Wu and

Kirtman 2007). The weak correlation around northern

Australia during the monsoon is indicative of weak SST

forcing of rainfall variability or even of atmospheric forc-

ing of SST variability (Wu and Kirtman 2007).

This seasonal variation in the forcing of the atmosphere

by the ocean is further highlighted by considering the lag

correlation between SST surrounding northern Australia

and northern Australia rainfall (Fig. 4). During wet periods

in SON, SST tends to be warm and in phase with rainfall,

indicative of SST forcing of the atmosphere because the

atmospheric response to SST is relatively fast (e.g., Wu

FIG. 3. Point-wise correlation between seasonal mean SST and rainfall for (a) SON and

(b) DJF for the period 1982–2006. The contour interval is 0.2. A correlation of 0.4 is estimated

to be significantly different from zero at the 95% level assuming 25 independent samples.

FIG. 4. Lag correlation of 3-month mean SST surrounding

northern Australia (58–158S, 1008–1608E) with northern Australian

rainfall (land points north of 258S, 1128–1568E) in SON (solid

curve) and DJF (dotted curve). Lags are in months (x axis). A

negative lag means that SST leads rainfall. A correlation of 0.4 is

estimated to be significantly different from zero at the 95% level

assuming 25 independent samples.



and Kirtman 2007). However, during DJF, SST tends to

be in quadrature with rainfall, with warm SST preceding

increased DJF rainfall and cooler SSTs following in-

creased DJF rainfall. Such a quadrature relationship is

indicative of atmospheric forcing of the SST, whereby in-

creased winds (causing increased latent heat flux and in-

creased ocean mixing) and decreased insolation (together

causing surface cooling) that accompany increased rainfall,

which is presumed to be generated through internal at-

mospheric dynamics or remote forcing, acting to cool the

SST (e.g., Wu and Kirtman 2007).

We postulate that this seasonal dependence of the

rainfall–SST correlation reflects the seasonal variation

of air–sea interaction to the north of Australia. In the

premonsoon season, the region experiences trade east-

erlies (Fig. 5a). Here we use the monthly-mean 10-m winds

from the National Centers for Environmental Prediction–

Department of Energy (NCEP–DOE) reanalyses II

(Kanamitsu et al. 2002). Enhanced rainfall in SON is

associated with anomalous westerly surface winds (Fig. 2a),

which act to reduce the total wind speed because they

act in an easterly basic state (Fig. 5a). The correlation

between zonal wind and total wind speed (shading in

Fig. 5a) confirms this negative relationship in SON. The

reduced wind speed associated with anomalous westerlies

then acts to warm the ocean surface via reducing latent

and sensible heat fluxes and ocean mixing (e.g., Nicholls

1979, 1981, 1984c; Hendon 2003). The warm SSTs then

act to further lower surface pressure and enhance surface

convergence, thereby increasing anomalous rainfall and

westerly surface winds in a fashion expected by the re-

sponse of the tropical atmosphere to a region of local-

ized heating (e.g., Gill 1980). We note that this positive

feedback in SON also works in response to remote forcing

from La Nina (or conversely El Nino), whereby cold SSTs

in the east Pacific remotely drive anomalously westerlies

and wet conditions to the north of Australia (e.g., Klein

et al. 1999; Shinoda et al. 2004). The remotely forced

westerlies then act to reduce the wind speed, resulting in

a local warm SST that feeds back onto the remotely forced

wet westerlies. We also note that this same sort of pos-

itive feedback in a trade-easterly regime has also been

postulated to explain the development of the anomalous

Philippine Seas anticyclone that typically matures in the

northwest Pacific during the boreal summer season fol-

lowing the peak of El Nino (Wang et al. 2000).

Once the Australian monsoon onsets, the mean winds

to the north of Australia become westerly (Fig. 5b) and

this positive feedback between anomalous SST and

winds collapses: anomalous wet conditions in northern

Australia during DJF are still associated with anomalous

westerly surface winds (Fig. 2b), but these anomalous

westerlies are now positively correlated with wind speed

anomalies (Fig. 5b). Thus, westerly anomalies in DJF will

act to cool the ocean surface via increased surface heat

fluxes and stronger ocean mixing, thereby leading to in-

creased surface pressure, sinking motion, and reduced

rainfall. We note that the region where the point-wise

FIG. 5. Climatological 10-m wind vectors overlaid on the point-wise correlation between

10-m zonal winds and wind speeds (color shading) for (a) SON and (b) DJF for the period 1982–

2006 from NCEP–DOE reanalyses (Kanamitsu et al. 2002). The vector magnitude (m s21) is

shown in top right of (a) and (b).



correlation between SST and rainfall weakens dramati-

cally from SON to DJF (Fig. 3) is roughly the same region

where trade easterlies in SON are replaced by monsoonal

westerlies in DJF (i.e., over the seas around northern

Australia and south of Indonesia; Fig. 5).

This same region where easterly trades are replaced

by monsoonal westerlies and the feedback between zonal

wind, SST, and rainfall collapses also experiences weak

persistence of SST anomalies going from SON to DJF

(Fig. 6; see also Nicholls 1981). Here we measure per-

sistence simply as the lag correlation between the SST

anomaly at each grid point in SON and that in DJF for the

period 1982–2006. The region of weak persistence of SST

anomalies from SON to DJF to the north of Australia

matches well with where the point-wise correlation be-

tween SST and rainfall weakens dramatically and where

the correlation of zonal wind with wind speed changes

from negative to positive going from SON to DJF (cf.

Figs. 3 and 5). The weak persistence of SST from SON to

DJF around northern Australia is also in sharp contrast

to the central and eastern equatorial Pacific (Fig. 6),

where slow ENSO variations dominate, persistence is

high, and the correlation of zonal wind with wind speed

remains negative in both seasons (Fig. 5).

The seasonal variation of persistence of SST anomalies

to the north of Australia is investigated further by com-

puting the 1-month lag correlation using the monthly SST

anomaly for the box 58–158S, 1008–1608E (Fig. 7a; see also

Nicholls 1981). Strong persistence of SST anomalies (lag-1

correlation .0.8) occurs from about April to October,

after which the persistence of the November anomalies

into December plunges to near 0.4. A slow recovery from

little persistence then occurs by April. This strong sea-

sonality of the persistence of SST anomalies is also re-

flected in the seasonality of the standard deviation of

monthly SST anomalies (Fig. 7b): the strongest SST

variability to the north of Australia occurs in the pre-

monsoon season at the end of the period of high per-

sistence, and the weakest SST variability occurs during

the monsoon after the rapid decline in persistence (see

also Nicholls 1981). Hence, premonsoon SST anomalies

to the north of Australia are characterized by relatively

large amplitude and strong temporal persistence and are

correlated positively with local rainfall over both ocean

and adjacent land. Such SST anomalies would be expected

to promote seasonal predictability of rainfall. During the

Australian summer monsoon, the local SST anomalies

exhibit weak month-to-month persistence, have relatively

FIG. 6. Lag correlation between observed SON and DJF SST for the period 1982–2006. Contour

interval is 0.2. Solid (dashed) contour line indicates positive (negative) correlation.

FIG. 7. (a) Annual variation of the 1-month lag correlation of observed monthly SST aver-

aged over 58–158S, 1008–1608E. The calendar month on the abscissa indicates the base month

(e.g., 2 means February correlated with the following March and 12 means December corre-

lated with the following January). (b) Seasonal variation of the monthly standard deviation of

SST in the same box (8C).



weak amplitude and spatial coherence, and tend not to be

correlated with local rainfall. Local SST anomalies during

the monsoon would not be expected to promote pre-

dictability of monsoon rainfall. In the following section

we explore how this seasonally varying air–sea inter-

action is simulated in the POAMA forecast model and

investigate the implications for the long-range prediction

of rainfall.

3. Dynamical coupled model forecasts

POAMA (Alves et al. 2003) is based on an atmospheric

GCM with modest resolution (T47L17) coupled to a ver-

sion of the Geophysical Fluid Dynamics Laboratory

(GFDL) Modular Ocean Model version 2 (MOM2) global

ocean model (28 longitude by 18 latitude telescoping to

0.58 latitude, 88S–88N; Pacanowski 1995). The models are

coupled every 3 h without flux correction. The version

used here is POAMA1.5b, as described and evaluated in

Hendon et al. (2009), Zhao and Hendon (2009), and

Hudson et al. (2010). Forecasts are initialized with ob-

served ocean (Smith et al. 1991) and atmosphere–land

initial conditions (Hudson et al. 2010). Here we evaluate

forecast skill and assess the simulation of air–sea in-

teraction based on a 10-member ensemble (atmosphere

initial conditions are lagged by 6 h) of hindcasts for the

period 1982–2006. Forecasts were initialized on the first

of each month and run for 9 months. We refer to the

hindcast set using the fully coupled model as P15b.

P15b provides skillful forecasts of El Nino two–three

seasons in advance (e.g., the correlation of Nino-3.4 SST

index remains above 0.6 to beyond the 9-month lead

time; Hendon et al. 2009; Zhao and Hendon 2009). Mean

state drift, especially related to the overdevelopment of

the equatorial Pacific cold tongue, limits the utility of these

El Nino forecasts for regional climate prediction at longer

lead times because the atmospheric teleconnection of

ENSO degrades with the increasing forecast lead time

(Lim et al. 2009). The forecast model does, however, ad-

equately represent the seasonal evolution of the Australian

monsoonal circulation, for instance as depicted by the sea-

sonal development of the monsoonal westerlies to the

north of Australia (Fig. 8a). This good simulation of the

monsoonal circulation in P15b reflects a good depiction

of the seasonal evolution of rainfall across the broader

Maritime Continent region (not shown), but the forecast

model does underestimate Australian land-based mon-

soonal rainfall (Fig. 8b). Further inspection of the sim-

ulated rainfall over land indicates realistic values near

the coast but a more rapid decline inward from the coast

than is observed. A possible problem with the treatment

of land surface interactions in the POAMA model is

indicated and is the focus of additional investigation.

Some of the effects of mean biases, such as the lower-

than-observed mean rainfall over land as shown in Fig. 8b,

are removed by computing forecast anomalies relative

to the forecast climatology. This forecast climatology is

a function of start month and lead time (e.g., Stockdale

1997). Verification anomalies based on observed rainfall

and SST are similarly computed by creating the climatol-

ogy over the same 1982–2006 period in which the hind-

casts are available. We use the 10-member ensemble mean

in order to verify forecasts against observations. For vali-

dation and diagnosis of the sensitivity of predicted rainfall

to SST variations, we compute the relevant diagnostics

(e.g., the correlation between north Australian rainfall

and local SST) using individual members (rather than the

ensemble mean) and then average the results over all en-

semble members. In this fashion, signal and noise in these

sensitivity calculations are treated as per the calculations

based on observed behavior, where only one ‘‘member’’

is available.

To complement the main set of hindcasts from

POAMA1.5b, two additional sets of forecasts are made

in order to explore the importance of local SST vari-

ability for promoting predictability in the premonsoon

FIG. 8. Seasonal cycle of the mean observed and P15b ensemble mean (a) zonal winds at the 850-hPa

level in the domain 58–158S, 1008–1608E (m s21) and (b) Australian rainfall for land points north of 258S

(mm day21). Predictions are at 0-, 2-, and 4-month lead times.



and diminishing it postonset. The first set is aimed at

investigating the impact of an imperfect forecast of SST

for limiting the prediction of rainfall during the summer

monsoon (e.g., Fig. 1). That is, we address the question

of whether the reduced skill seen in Fig. 1 for the DJF

season compared to SON season results from less skillful

SST predictions in DJF compared to SON. To answer

this question we create a new set of forecasts whereby

‘‘perfect’’ SSTs are prescribed during the forecast. We

do this by decoupling the atmosphere from the ocean

model and prescribing the lower boundary SST in the

atmospheric model to be the observed variations of SST

during the forecast period. Initial conditions for the at-

mosphere and land are identical to those used in the fully

coupled hindcast set. The observed SST is prescribed to

vary daily based on a linear interpolation from observed

monthly mean SST (Reynolds et al. 2002). Here we take

the monthly mean to be valid at the midpoint of the

month and the simple mean of the current and previous

month’s means is valid on the first of the month. This set

of forecasts is similar to an (Atmospheric Model Inter-

comparison Project) AMIP-style integration (e.g., Taylor

et al. 2000), and we refer to it as Forecast-AMIP (F-AMIP)

to indicate that we have run with prescribed SSTs, but have

initialized the atmosphere–land conditions with observed

states on the first day of the forecasts. In a true AMIP-style

integration, the atmosphere–land states would not be ini-

tialized based on observed states, rather they would be the

model’s response to the prescribed SST.

The second additional set of forecasts is aimed at as-

sessing the impact of uncoupling the atmosphere from

the ocean. That is, we aim to explore whether any sub-

stantial differences between the F-AMIP experiments

and the original fully coupled predictions stem from the

artificial decoupling of the atmosphere from the ocean.

To asses this, we create another set of forecasts similar

to F-AMIP but where we prescribe the SST variation

during the forecast to be that predicted from the original

POAMA1.5b hindcasts. Similar to F-AMIP, we prescribe

the SST to vary daily based on a linear interpolation of the

monthly mean output from the POAMA1.5b forecasts.

For the first 15 days of the first month (when we do not

have available predictions of the previous monthly mean),

we prescribe the SST to be constant and equal to the pre-

dicted monthly mean for the first month. After these first

15 days, the linear interpolation from monthly to daily is

identical to that used for F-AMIP. That is, we take the

monthly mean to be valid at the midpoint of the month,

and the value at the first of the month is the simple mean

of the current and previous month. We refer to this set of

forecasts as POAMA-AMIP (P-AMIP). We note that

this interpolation of monthly means to daily values for

both PAMIP and FAMIP does not preserve the original

monthly mean SST in the fashion of Taylor et al. (2000),

but acts as a weak low-pass filter (equivalent to a 1–4–1

running monthly mean). For SSTs that are varying slowly

(both seasonally and interannually) over the 3 months of

the integration, as is the case for the SST in these experi-

ments, the difference between the monthly means com-

puted from the interpolated daily data and the original

monthly mean are small (e.g., root-mean-square differ-

ences ,0.28C; not shown) and these small differences

are not considered to be a source of difference between

the experiments.

For both F-AMIP and P-AMIP, we generate a

10-member ensemble from the first of September and

December for the period 1982–2006. We focus on the

zero-month lead forecasts for SON and DJF with F-AMIP

and P-AMIP because the differences in skill between the

P15b forecasts for SON and DJF are already evident

at lead 0 (e.g., Fig. 1). Anomalies for the F-AMIP and

P-AMIP forecasts are created in a similar fashion as for

the P15b forecasts, but we use the forecast climatology

from F-AMIP and P-AMIP, respectively, based on the

September and December starts for 1982–2006.

4. Seasonal variation of forecast skill and depictionof air–sea interaction

Forecast skill is assessed using the correlation of the

ensemble mean with observed. We assume that a corre-

lation of 0.4 is significantly different than 0 at the 95%

level assuming 25 independent samples. We note that

a similar assessment of forecast skill is obtained if we

use root-mean-square error rather than correlation (not

shown). For instance, the areas where the correlation is

greater than about 0.4 in Fig. 1 coincide with the areas

where the root-mean-square error is less than the stan-

dard deviation of the verification (which is a common

measure of the limit of a skillful forecast).

Forecast skill for north Australian rainfall (average of

land points north of 258S) at zero lead time for SON and

DJF is summarized in Fig. 9a. The seasonality of forecast

skill depicted in Fig. 1 for P15b is reflected in the skill for

predicting area-averaged rainfall in Fig. 9a: forecast skill

for north Australian rainfall is significant in SON, but ab-

sent in DJF. Overall skill is higher if ocean points around

northern Australia are included as well (i.e., rainfall is

averaged over land and ocean points 108–258S, 1128–

1568E; Fig. 9b), but skill still drops markedly in going

from SON to DJF.

Importantly, this result of lower skill in DJF compared

to SON holds for the F-AMIP predictions in which ob-

served SST is prescribed: lower skill in DJF occurs even

if perfect SST is prescribed. And, the reduced skill in

DJF compared to SON for the F-AMIP run appears not to



be a spurious result from decoupling the atmosphere from

the ocean because the same reduction in skill is displayed

by P-AMIP, in which POAMA’s predicted SST are pre-

scribed. There is some indication that the model produces

the ‘‘wrong answer’’ during DJF when SST is prescribed

(i.e., the correlation of predicted and observed rainfall is

now weakly negative), but these negative correlations are

weak and not significant. Hence, observed and forecast

SST, coupled or uncoupled, result in the same lack of

predictability of rainfall during the DJF monsoon season.

One interesting aspect of these forecast experiments is

that prescribing perfect SST (F-AMIP) does slightly

improve the prediction of rainfall in SON over that from

P15b, but not by much. This lack of greater improvement

for predicting SON rainfall by prescribing observed SST

is addressed further below, but can be assumed to stem

from the good prediction of SST in SON due to high

persistence of SST in the premonsoon season.

We next investigate whether the higher skill for rainfall

prediction in SON and the lack of skill in DJF is accom-

panied by a proper depiction of the seasonality of air–sea

interaction in the monsoon, which we diagnose here using

the correlation of rainfall with SST. Figure 10a shows the

observed and simulated correlations between rainfall av-

eraged over and around northern Australia and the aver-

aged SST surrounding northern Australia. Note that we

compute the correlations using individual ensemble mem-

bers and display the average correlation across all 10

members. We note that we obtain a range of correlations

from the individual ensemble members and that that the

spread of the correlations is consistent with our assessment

of significant correlation based on the sampling theory.

As previously discussed in section 2, observed north Aus-

tralian rainfall is strongly positively correlated with local

SST in SON, but uncorrelated with local SST in DJF (Fig.

10a). This seasonality in correlation is faithfully simulated

in the forecasts, whether or not SST is predicted (P15b

and P-AMIP) or prescribed as observed (F-AMIP). How-

ever, the positive relationship between rainfall and SST

in SON is simulated to be weaker in the forecasts than

observed, possibly indicative of problems of simulating

land-based rainfall with the POAMA model or, as dis-

cussed below, due to a bias in the El Nino teleconnection.

We also consider the simulation of the teleconnection

of El Nino to the monsoon by computing the correlation

of rainfall with the Nino-3.4 SST index (Fig. 10b). The

observed relationship is strongly negative in SON (r 5

20.75), and this relationship weakens in DJF (r 5 20.6).

Although the negative correlation between north Aus-

tralian rainfall and Nino-3.4 is faithfully simulated for SON

in all of the forecast experiments, the simulated correlation

is less negative than observed. For the experiments with

predicted SST (P15b and P-AMIP), the correlation in DJF

is more negative than in SON, contrary to the observed.

The more negative correlation in DJF compared to SON

when a forecast SST is used suggests a model bias in the

prediction of the El Nino–related SST anomalies and their

teleconnection to the monsoon. The more negative than

observed correlation in DJF probably stems from the

westward bias of the SST anomalies predicted by P15b

during El Nino that is evident even at a short lead time

(Zhao and Hendon 2009). This westward shift, which is

more pronounced at the mature phase of El Nino during

DJF, may result in a greater than observed impact of

El Nino because westward-shifted El Nino events pro-

duce a stronger impact in northern Australian rainfall

(e.g., Murphy and Ribbe 2004; Wang and Hendon 2007).

When observed SSTs are prescribed (F-AMIP), the less

negative correlation in DJF is faithfully depicted, but is

now weaker than observed (20.3 vs 20.6). This weaker-

than-observed correlation is probably not accounted for

by sampling uncertainty (e.g., the interquartile range

of correlations from the ensemble members is only 20.4

to 20.25). However, we note that a weaker-than-observed

FIG. 9. Correlation of predicted and observed rainfall anomalies averaged for (a) Australian land

points north of 258S and for (b) all ocean and land points over 108–258S, 1128–1568E. Forecasts are en-

semble means from P15b (POAMA), P-AMIP, and F-AMIP at zero lead time for SON (dark bars) and

DJF (light bars).



negative correlation between rainfall and the Nino-3.4

SST index also occurs in SON when observed SSTs are

prescribed, again suggesting a systematic atmospheric

model bias in the response to El Nino–related SST.

Finally, we consider the potential predictability of

Australian monsoon rainfall in order to assess whether

our conclusion of lower predictability in DJF than in

SON is independent of errors in the initial conditions or

in the model. Potential predictability is an assessment of

how reproducible each year’s forecast is relative to the

spread about the ensemble mean assuming no errors in

the model or initial conditions. It is an estimate of the

upper limit of predictability that is relevant to the as-

sessment of actual predictive skill if key physical pro-

cesses are captured by the model (e.g., the seasonality of

air–sea interaction), but that other model (and initial

condition) errors are acting to limit actual predictive skill.

To assess potential predictability, one member of the

ensemble is assumed to be reality and the ensemble mean

formed from the remaining members is then scored as the

forecast. In practice, we compute the potential predict-

ability by the equivalent method of analysis of variance

(e.g., Zhao and Hendon 2009), whereby the potential

predictability is expressed as a ratio of the predictable

variance (the variance of the ensemble mean) to the total

variance of the ensemble (ensemble mean plus spread;

using an unbiased estimate following Rowell et al. 1995).

Potential predictability of north Australian rainfall in all

three experiments is expressed as the percentage of ex-

plained variance by the ensemble mean [which is equiv-

alent to a squared correlation coefficient (R2) with R2 .

0.16 assumed significantly different than zero at the 95%

level based on 25 independent samples]. It is clear from

Fig. 11 that the potential predictability is systematically

higher than the actual predictive skill (cf. to the square

correlation values in Fig. 9a) and that the potential pre-

dictability is systematically higher in SON than in DJF.

This is most evident for the F-AMIP forecasts, whereby

observed SST is prescribed. Hence, observed or predicted

SST variations during DJF do not provide the same level

of reproducibility of predicted rainfall as they do in SON.

The greater difference in potential predictability be-

tween SON and DJF when observed SSTs are prescribed

as compared to when predicted SSTs are used suggests

that the predicted SST may be exerting unrealistic control

over rainfall in DJF and not enough influence in SON.

We have already seen this expressed in the stronger

relationship of north Australia rainfall with El Nino in DJF

when SSTs are predicted compared to when observed

FIG. 10. (a) Correlations between rainfall (land and ocean points

over 108–258S, 1128–1568E) and SST north of Australia (ocean

points 58–158S, 1008–1608E) from observations and zero-month

lead predictions from P15b, P-AMIP, and F-AMIP for SON (dark

bars) and DJF (light bars). Correlations are computed using

individual ensemble members and then averaged over all 10 mem-

bers. (b) As in (a), but for the same rainfall correlated with Nino-3.4

SST index.

FIG. 11. Potential predictability (ratio of ensemble mean vari-

ance to an unbiased estimate of total ensemble variance) for zero

lead-time predictions of north Australian rainfall (land points

north of 258S, 1128–1568E) from P15b, P-AMIP, and F-AMIP.

Dark bars are for SON and light bars are for DJF.



SSTs are prescribed (Fig. 10b). This overly strong de-

pendence of monsoon rainfall on El Nino is also evident

in other coupled models (Wang et al. 2008), suggesting

some systematic bias associated with the forecast SST

anomalies during El Nino (e.g., the westward bias of the

SST anomalies in the Pacific) or that some key processes

(including internal variability) are missing in current

forecast model. It may also, however, reflect less skillful

SST forecasts in DJF than in SON. We explore this by

scoring the forecasts of two relevant SST indices: the

Nino-3.4 SST index and the area mean SST to the north of

Australia (Fig. 12). As expected from our previous studies

of forecast skill for P15b (e.g., Hendon et al. 2009),

forecast skill at short lead times for Nino-3.4 is similarly

very high for both SON and DJF. However, forecast skill

for SST to the north of Australia is lower in DJF than in

SON. The lower skill for the DJF forecasts of SST to the

north of Australia is not unexpected given that we have

argued for a lack of positive air–sea feedbacks to the

north of Australia during DJF and that the model sim-

ulates an unrealistically strong dependence on ENSO.

We thus conclude that a reduced distinction between

monsoon rainfall predictability in SON and DJF when

predicted versus observed SSTs are used stems both from

lower forecast skill of SST to the north of Australia in

DJF and model error of the El Nino teleconnection to the

monsoon, which is more pronounced in DJF.

5. Conclusions

Based on hindcasts with the POAMA forecast model,

seasonal mean rainfall across northern Australia is less

predictable during the summer monsoon than in the pre-

monsoon transition season. We have argued that this lower

prediction skill during the monsoon reflects an intrinsic

property of the Australian monsoonal system whereby

seasonally varying air–sea interaction in the seas around

northern Australia promotes predictability in the pre-

monsoon season and demotes predictability after mon-

soon onset. Trade easterlies during the premonsoon

season support a positive feedback between surface wind,

SST, and rainfall, which results in stronger and more

persistent local SST anomalies that compliment the re-

mote forcing of rainfall from El Nino in the Pacific. After

the onset of the Australian summer monsoon, this feed-

back is not supported in the monsoonal westerly regime,

resulting in weaker SST anomalies to the north of

Australia and with lower persistence. Also, these weaker

SST anomalies do not cooperatively act with remote

forcing by El Nino. Importantly, the seasonality of this air–

sea interaction is captured in the POAMA forecast model.

Additional forecast experiments were conducted us-

ing observed rather than predicted SSTs and this was

shown to result in only a modest improvement forecast

skill. Importantly, prescribing ‘‘perfect’’ SST in the mon-

soon season still resulted in significantly lower forecast

skill than what can be achieved in the premonsoon sea-

son. Prescribing SST has previously been shown to lead to

spurious behavior in regions where the atmosphere is

strongly forcing the ocean (e.g., Wu and Kirtman 2007),

thus suggesting that these prescribed SST experiments

may be fatally flawed. However, an additional experi-

ment whereby the predicted SST from the POAMA model

was prescribed resulted in nearly identical behavior of

the forecast skill as in the original fully coupled version

of the POAMA model (i.e., high forecast skill in SON and

low forecast skill in DJF). Furthermore, the nature of the

forcing of the rainfall anomalies by SST, as diagnosed by

the point-wise correlation of SST anomalies with rainfall,

was similar in both the fully coupled and prescribed SST

runs and is very similar to the observed behavior. That is,

SST and rainfall are strongly positively correlated around

northern Australian during the premonsoon and this cor-

relation goes to zero after monsoon onset whether we use

predicted or observed SST. Hence, we conclude that it is

the nature of the SST anomalies themselves in DJF (low

amplitude and low persistence) that prevents a strong

contribution to seasonal rainfall predictability. Interest-

ingly, the weaker and less persistent SST anomalies dur-

ing the monsoon also stem from air–sea interaction (in

this case, strong atmospheric forcing of the ocean), so

our results do not underplay the primary role that the

interaction of the atmosphere and the ocean play for

seasonal rainfall variability and predictability in both

seasons.

Air–sea feedbacks are probably not the only cause of

reduced predictability during the monsoon season when

FIG. 12. Correlation of zero lead-time predictions from P15b for

Nino-3.4 SST index and SST north of Australia (local SST; 58–

158S, 1008–1608E). Dark bars are for SON and light bars are for

DJF.



rainfall over land is at its maximum: land-based convec-

tion inherently varies at shorter time and space scales than

oceanic convection (e.g., Ricciardulli and Sardeshmukh

2002). Furthermore, the MJO contributes more strongly

to the variability of the Australian summer monsoon in

DJF than in SON (e.g., Wheeler et al. 2009), and the sea-

sonal behavior of MJO is not predictable. Systematic

model biases (i.e., westward-shifted El Nino, too strong

ENSO teleconnection, too little rainfall over land, and

a poor representation of land surface feedbacks) are also

acting to limit prediction of summer monsoon rainfall.

However, the nature of air–sea feedbacks around north-

ern Australia during the monsoon appears to contribute

to an upper limit of predictability that is much reduced

compared to that during the premonsoon.

Acknowledgments. Support for this work was pro-

vided in part by the Managing Climate Variability R&D

Program (see online at http://www.managingclimate.gov.

au). Critical and constructive comments by the reviewers

are gratefully acknowledged.

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