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  • View in gallery

    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.

  • View in gallery

    Regression (vectors) of 10-m winds (NCEP–DOE reanalyses; Kanamitsu et al. 2002) onto the rainfall index based on Australian land points north of 25°S 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 s−1) 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.

  • View in gallery

    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.

  • View in gallery

    Lag correlation of 3-month mean SST surrounding northern Australia (5°–15°S, 100°–160°E) with northern Australian rainfall (land points north of 25°S, 112°–156°E) 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.

  • View in gallery

    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 s−1) is shown in top right of (a) and (b).

  • View in gallery

    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.

  • View in gallery

    (a) Annual variation of the 1-month lag correlation of observed monthly SST averaged over 5°–15°S, 100°–160°E. The calendar month on the abscissa indicates the base month (e.g., 2 means February correlated with the following March and 12 means December correlated with the following January). (b) Seasonal variation of the monthly standard deviation of SST in the same box (°C).

  • View in gallery

    Seasonal cycle of the mean observed and P15b ensemble mean (a) zonal winds at the 850-hPa level in the domain 5°–15°S, 100°–160°E (m s−1) and (b) Australian rainfall for land points north of 25°S (mm day−1). Predictions are at 0-, 2-, and 4-month lead times.

  • View in gallery

    Correlation of predicted and observed rainfall anomalies averaged for (a) Australian land points north of 25°S and for (b) all ocean and land points over 10°–25°S, 112°–156°E. Forecasts are ensemble means from P15b (POAMA), P-AMIP, and F-AMIP at zero lead time for SON (dark bars) and DJF (light bars).

  • View in gallery

    (a) Correlations between rainfall (land and ocean points over 10°–25°S, 112°–156°E) and SST north of Australia (ocean points 5°–15°S, 100°–160°E) 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 members. (b) As in (a), but for the same rainfall correlated with Niño-3.4 SST index.

  • View in gallery

    Potential predictability (ratio of ensemble mean variance to an unbiased estimate of total ensemble variance) for zero lead-time predictions of north Australian rainfall (land points north of 25°S, 112°–156°E) from P15b, P-AMIP, and F-AMIP. Dark bars are for SON and light bars are for DJF.

  • View in gallery

    Correlation of zero lead-time predictions from P15b for Niño-3.4 SST index and SST north of Australia (local SST; 5°–15°S, 100°–160°E). Dark bars are for SON and light bars are for DJF.

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

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  • 1 Centre for Australian Weather and Climate Research, Bureau of Meteorology, Melbourne, Victoria, Australia
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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 Atmosphere 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 predictability 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 Niño 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.

Corresponding author address: Harry H. Hendon, CAWCR/BoM, GPO Box 1289, Melbourne VIC 3001, Australia. E-mail: hhh@bom.gov.au

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 Atmosphere 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 predictability 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 Niño 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.

Corresponding author address: Harry H. Hendon, CAWCR/BoM, GPO Box 1289, Melbourne VIC 3001, Australia. E-mail: hhh@bom.gov.au

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 northern 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 monsoon and the premonsoon transition season has many practical applications especially for agriculture and water 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 monsoon season rainfall. The observed relationship between the El Niño–Southern Oscillation (ENSO) and transition season rainfall, whereby dry (wet) conditions tend to accompany El Niño (La Niña; 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 defined 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 harvesting (e.g., Everingham et al. 2008). Although statistical 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 Australia (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). Capitalizing 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 other dynamical forecast models such as those that contributed 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).

Fig. 1.
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.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 postonset 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 season 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 dynamical 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 promoting predictability of northern Australian rainfall in the premonsoon and for diminishing predictability postonset. 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 section 4. Conclusions are provided in section 5.

2. Observed seasonally varying air–sea interaction

Insight as to why postonset monsoon rainfall in northern 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 25°S) for the period 1982–2006. The gridded SST data are from the monthly analyses of Reynolds et al. (2002) and the northern Australian rainfall index is computed by averaging the gridded monthly rainfall over northern Australia using the Climate Prediction Center (CPC) Merged Analysis of Precipitation (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 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 surrounding northern Australia and strongly negatively correlated with remote SST in the central equatorial Pacific. This pattern of SST correlation, both locally and remotely, is reminiscent of La Niña conditions during SON. In contrast, 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 relationships of rainfall and north Australian SST with were first documented by Nicholls (1984b).

Fig. 2.
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 25°S 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 s−1) 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.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 northern 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 correlation 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 forcing of SST variability (Wu and Kirtman 2007).

Fig. 3.
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.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 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 increased DJF rainfall. Such a quadrature relationship is indicative of atmospheric forcing of the SST, whereby increased winds (causing increased latent heat flux and increased ocean mixing) and decreased insolation (together causing surface cooling) that accompany increased rainfall, which is presumed to be generated through internal atmospheric dynamics or remote forcing, acting to cool the SST (e.g., Wu and Kirtman 2007).

Fig. 4.
Fig. 4.

Lag correlation of 3-month mean SST surrounding northern Australia (5°–15°S, 100°–160°E) with northern Australian rainfall (land points north of 25°S, 112°–156°E) 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.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 easterlies (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 response of the tropical atmosphere to a region of localized heating (e.g., Gill 1980). We note that this positive feedback in SON also works in response to remote forcing from La Niña (or conversely El Niño), 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 positive 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 following the peak of El Niño (Wang et al. 2000).

Fig. 5.
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 s−1) is shown in top right of (a) and (b).

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 increased surface pressure, sinking motion, and reduced rainfall. We note that the region where the point-wise correlation between SST and rainfall weakens dramatically 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 persistence 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 between 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).

Fig. 6.
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.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

The seasonal variation of persistence of SST anomalies to the north of Australia is investigated further by computing the 1-month lag correlation using the monthly SST anomaly for the box 5°–15°S, 100°–160°E (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 seasonality of the persistence of SST anomalies is also reflected 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 premonsoon season at the end of the period of high persistence, 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 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 predictability of monsoon rainfall. In the following section we explore how this seasonally varying air–sea interaction is simulated in the POAMA forecast model and investigate the implications for the long-range prediction of rainfall.

Fig. 7.
Fig. 7.

(a) Annual variation of the 1-month lag correlation of observed monthly SST averaged over 5°–15°S, 100°–160°E. The calendar month on the abscissa indicates the base month (e.g., 2 means February correlated with the following March and 12 means December correlated with the following January). (b) Seasonal variation of the monthly standard deviation of SST in the same box (°C).

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

3. Dynamical coupled model forecasts

POAMA (Alves et al. 2003) is based on an atmospheric GCM with modest resolution (T47L17) coupled to a version of the Geophysical Fluid Dynamics Laboratory (GFDL) Modular Ocean Model version 2 (MOM2) global ocean model (2° longitude by 1° latitude telescoping to 0.5° latitude, 8°S–8°N; 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 observed 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 interaction 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 Niño two–three seasons in advance (e.g., the correlation of Niño-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 Niño 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, adequately represent the seasonal evolution of the Australian monsoonal circulation, for instance as depicted by the seasonal 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 monsoonal rainfall (Fig. 8b). Further inspection of the simulated 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.

Fig. 8.
Fig. 8.

Seasonal cycle of the mean observed and P15b ensemble mean (a) zonal winds at the 850-hPa level in the domain 5°–15°S, 100°–160°E (m s−1) and (b) Australian rainfall for land points north of 25°S (mm day−1). Predictions are at 0-, 2-, and 4-month lead times.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 climatology over the same 1982–2006 period in which the hindcasts are available. We use the 10-member ensemble mean in order to verify forecasts against observations. For validation 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 ensemble 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 variability for promoting predictability in the premonsoon 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 atmosphere 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 Intercomparison 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 initialized 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 assessing the impact of uncoupling the atmosphere from the ocean. That is, we aim to explore whether any substantial 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 predicted 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 experiments, the difference between the monthly means computed from the interpolated daily data and the original monthly mean are small (e.g., root-mean-square differences <0.2°C; 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 depiction of air–sea interaction

Forecast skill is assessed using the correlation of the ensemble mean with observed. We assume that a correlation 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 standard 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 25°S) 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 absent 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 10°–25°S, 112°–156°E; Fig. 9b), but skill still drops markedly in going from SON to DJF.

Fig. 9.
Fig. 9.

Correlation of predicted and observed rainfall anomalies averaged for (a) Australian land points north of 25°S and for (b) all ocean and land points over 10°–25°S, 112°–156°E. Forecasts are ensemble means from P15b (POAMA), P-AMIP, and F-AMIP at zero lead time for SON (dark bars) and DJF (light bars).

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

Importantly, this result of lower skill in DJF compared to SON holds for the F-AMIP predictions in which observed 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 prescribed. 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 accompanied 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 averaged over and around northern Australia and the averaged SST surrounding northern Australia. Note that we compute the correlations using individual ensemble members 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 Australian 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). However, 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 discussed below, due to a bias in the El Niño teleconnection.

Fig. 10.
Fig. 10.

(a) Correlations between rainfall (land and ocean points over 10°–25°S, 112°–156°E) and SST north of Australia (ocean points 5°–15°S, 100°–160°E) 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 members. (b) As in (a), but for the same rainfall correlated with Niño-3.4 SST index.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

We also consider the simulation of the teleconnection of El Niño to the monsoon by computing the correlation of rainfall with the Niño-3.4 SST index (Fig. 10b). The observed relationship is strongly negative in SON (r = −0.75), and this relationship weakens in DJF (r = −0.6). Although the negative correlation between north Australian rainfall and Niño-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 Niño–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 Niño 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 Niño during DJF, may result in a greater than observed impact of El Niño because westward-shifted El Niño events produce 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 (−0.3 vs −0.6). 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 −0.4 to −0.25). However, we note that a weaker-than-observed negative correlation between rainfall and the Niño-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 Niño–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 assessment of actual predictive skill if key physical processes 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 predictability 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 explained variance by the ensemble mean [which is equivalent 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 predictability 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.

Fig. 11.
Fig. 11.

Potential predictability (ratio of ensemble mean variance to an unbiased estimate of total ensemble variance) for zero lead-time predictions of north Australian rainfall (land points north of 25°S, 112°–156°E) from P15b, P-AMIP, and F-AMIP. Dark bars are for SON and light bars are for DJF.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

The greater difference in potential predictability between 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 Niño in DJF when SSTs are predicted compared to when observed SSTs are prescribed (Fig. 10b). This overly strong dependence of monsoon rainfall on El Niño is also evident in other coupled models (Wang et al. 2008), suggesting some systematic bias associated with the forecast SST anomalies during El Niño (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 Niño-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 Niño-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 simulates 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 Niño teleconnection to the monsoon, which is more pronounced in DJF.

Fig. 12.
Fig. 12.

Correlation of zero lead-time predictions from P15b for Niño-3.4 SST index and SST north of Australia (local SST; 5°–15°S, 100°–160°E). Dark bars are for SON and light bars are for DJF.

Citation: Journal of Climate 25, 4; 10.1175/JCLI-D-11-00125.1

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 premonsoon 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 premonsoon season and demotes predictability after monsoon 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 remote forcing of rainfall from El Niño in the Pacific. After the onset of the Australian summer monsoon, this feedback 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 Niño. Importantly, the seasonality of this air–sea interaction is captured in the POAMA forecast model.

Additional forecast experiments were conducted using observed rather than predicted SSTs and this was shown to result in only a modest improvement forecast skill. Importantly, prescribing “perfect” SST in the monsoon season still resulted in significantly lower forecast skill than what can be achieved in the premonsoon season. 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 experiment 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 correlation 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. Interestingly, the weaker and less persistent SST anomalies during 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 rainfall over land is at its maximum: land-based convection 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 seasonal behavior of MJO is not predictable. Systematic model biases (i.e., westward-shifted El Niño, 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 northern 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 provided 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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