# Spatial Coherence and Seasonal Predictability of Monsoon Onset over Indonesia

Vincent Moron, Andrew W Robertson, Rizaldi Boer

► To cite this version:

Vincent Moron, Andrew W Robertson, Rizaldi Boer. Spatial Coherence and Seasonal Predictability of Monsoon Onset over Indonesia. Journal of Climate, 2009, 22 (3), pp.840-850. 10.1175/2008JCLI2435.1. hal-02894957

HAL Id: hal-02894957

https://hal.science/hal-02894957

Submitted on 30 Sep 2021

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# NOTES AND CORRESPONDENCE

## Spatial Coherence and Seasonal Predictability of Monsoon Onset over Indonesia

VINCENT MORON

CEREGE, UMR 6635 CNRS, Université d'Aix-Marseille, Aix en Provence, and Institut Universitaire de France, Paris, France, and International Research Institute for Climate and Society, Columbia University, Palisades, New York

ANDREW W. ROBERTSON

International Research Institute for Climate and Society, Columbia University, Palisades, New York

RIZALDI BOER

Laboratory of Climatology, Bogor Agricultural University, Bogor, Indonesia

(Manuscript received 18 January 2008, in final form 30 May 2008)

### ABSTRACT

The seasonal potential predictability of monsoon onset during the August–December season over Indonesia is studied through analysis of the spatial coherence of daily station rainfall and gridded pentad precipitation data from 1979 to 2005. The onset date, defined using a local agronomic definition, exhibits a seasonal northwest-to-southeast progression from northern and central Sumatra (late August) to Timor (mid-December). South of the equator, interannual variability of the onset date is shown to consist of a spatially coherent large-scale component, together with local-scale noise. The high spatial coherence of onset is similar to that of the September–December seasonal total, while postonset amounts averaged over 15–90 days and September–December amount residuals from large-scale onset show much less spatial coherence, especially across the main islands of monsoonal Indonesia. The cumulative rainfall anomalies exhibit also their largest amplitudes before or near the onset date. This implies that seasonal potential predictability over monsoonal Indonesia during the first part of the austral summer monsoon season is largely associated with monsoon onset, and that there is much less predictability within the rainy season itself. A cross-validated canonical correlation analysis using July sea surface temperatures over the tropical Pacific and Indian Oceans (20°S–20°N, 80°–280°E) as predictors of local-scale onset dates exhibits promising hindcast skill (anomaly correlation of ~0.80 for the spatial average of standardized rain gauges and ~0.70 for standardized gridded pentad precipitation data).

### 1. Introduction

Rainfall over Indonesia is governed by the austral-Asian monsoon, whose onset progresses from northwest to southeast during the austral spring (Aldrian and Susanto 2003; Naylor et al. 2007). This is also the season when the El Niño–Southern Oscillation (ENSO) exerts its strongest influence on Indonesian rainfall, particularly during the September–December monsoon onset season (Hamada et al. 2002). The impact of ENSO

then diminishes during the core of the rainy season in December–February (Haylock and McBride 2001; Hendon 2003; Aldrian et al. 2005, 2007; Giannini et al. 2007), suggesting that the timing of monsoon onset may be potentially predictable.

The date of onset of the rainy season is of particular importance for the agriculture sector over Indonesia (Naylor et al. 2002, 2007). It determines the suitable time for planting crops, while delayed onset during El Niño years (Hamada et al. 2002; Boer and Wahab 2007) can lead to crop failure. For irrigated rice farmers in Java, information on onset timing is also important for developing strategies (Boer and Subbiah 2005; Naylor et al. 2007) to avoid exposure of the second rice crop to higher drought risk at dry season planting (April–July),

<footnote>*Corresponding author address:* Vincent Moron, CEREGE, UMR 6635 CNRS, and Université d'Aix-Marseille, Europôle de l'Arbois, BP 80, 13545 Aix en Provence, France.
E-mail: moron@cerege.fr</footnote>
particularly for farmers located at the tail end of the irrigation system. Farmers in Indonesia often suffer from “false rains” in which isolated rainfall events around the expected onset date do not signal the sustained onset of the monsoon. Such false starts occurring in September prompt potato farmers in Pengalengan in West Java to start planting. In the eastern part of Indonesia, such as East Nuna Tenggara, multiple false starts can cause multiple failures, with farmers sometimes planting up to four times in a season.

This paper discusses the seasonal potential predictability of monsoon onset during the August–December season over Indonesia. The approach taken is based on quantifying the spatial coherence of specific rainfall properties: the September–December (SOND hereafter) rainfall total, rainfall onset date, and postonset rainfall totals following Haylock and McBride (2001) and Moron et al. (2006, 2007). The seasonal predictability of large-scale monsoon onset is then estimated based on sea surface temperatures (SST) in July using a cross-validated canonical correlation analysis (CCA). The two precipitation datasets [rain gauge and Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP)] are described in section 2, together with the definition of onset. Results are presented in section 3, with conclusions drawn in section 4.

## 2. Data and method

### a. Global Summary of the Day (GSOD) station data

Daily rainfall at rain gauges for the period 1979–2004 was extracted from National Oceanic and Atmospheric Administration (NOAA) CPC Global Summary of the Day (GSOD) dataset, archived at the National Center for Atmospheric Research (NCAR), and originating through the World Meteorological Organization (WMO) Global Telecommunication System (GTS). There are 91 available stations for Indonesia. The station years having at least 50% of daily data are extracted and the 57 stations having at least 10 available years are selected. Missing entries (< 13%) were filled using a simple stochastic weather generator (Wilks 1999), considering the wet-to-wet and dry-to-wet persistence and a gamma distribution for wet days, computed on a monthly basis at each station. If a month is completely missing (< 3% of station months for SOND), this method simulates a climatological daily sequence for that month.

### b. CMAP

Gridded pentad CMAP on a 2.5° latitude–longitude grid was selected within a window (12°S–6°N, 90–130°E) over the 1979–2005 period, based only on rain gauges and satellite estimates (Xie and Arkin 1996). Over this window,

there are typically 1–2 rain gauges per grid box including land (P. Xie 2007, personal communication).

### c. Definition of onset

Monsoon onset date can be defined in various ways. We used an agronomical definition (e.g., Sivakumar 1988) based on local rainfall amounts using thresholds to define the onset, requiring a certain amount of rainfall within a specified period of time, with no extended dry spell occurring afterward. This local definition is sensitive to small-scale processes but is used here in order to be relevant to agricultural management, and to prevent any a priori inflation of spatial coherence.

Onset date is defined to be the first wet day of the first 5-day sequence receiving at least 40 mm that is not followed by a dry 10-day sequence receiving less than 5 mm within the following 30 days from the onset date. Onset is computed from 1 August because August–September are the driest months over Indonesia (Aldrian and Susanto 2003; Aldrian et al. 2007). The latter criterion helps to avoid “false starts,” which could be defined, for example, as the difference between the first 5-day wet sequence receiving at least 40 mm and the onset as defined above. The identification of false starts is sensitive to the choice of the postonset dry spell length. In fact, the sensitivity of crops to postonset dry spells varies. In tropical countries, dry spells with a length of more than 7 days would have serious impact on crop yields (Niewolt 1989). Other studies found that 21 rice varieties being exposed to dry spells with a length of 16 days during the vegetative stage will have a delayed harvesting time between 2 and 27 days and reduced yield between 10% and 91% (Dikshit et al. 1987). Indeed, false starts defined with a 10-day dry spell in the following 30 days occur in 46% of station years, ranging from less than 40% in northern and central Sumatra and Kalimantan to a maximum > 50% in western and central Java. These percentages decrease by a factor of 2–3 when the length of the postonset dry spell is chosen to be 15 days. The mean onset date is also earlier (by one or two weeks in mean) with a postonset dry spell lasting 15 days rather than 10 days. Nevertheless, this parameter (and the others entering the onset definition) has only a very weak impact on the large-scale and regional-scale interannual variability of onset dates (e.g., the spatial averages of CMAP and GSOD onset-date anomalies computed with both parameters are correlated at 0.99 and 0.97, respectively). Increasing the length of the initial wet spell reduces the noise introduced by weather variability, but the threshold of 5 days is used to facilitate comparison between CMAP and GSOD datasets. The National Agency for Meteorology and Geophysics of Indonesia (BMG) defines the monsoon to start when, after 1 September,
two consecutive 10-day sequences each receive at least 50 mm of rain. While changing the length and/or the amount of rainfall of the initial wet spell modifies the climatological mean onset date, its impact on interannual variability is again found to be much smaller. The onset date is undefined for two cases in CMAP and the missing entries are filled with the latest available onset dates for the corresponding grid points.

### d. Spatial coherence estimates

The spatial coherence of interannual precipitation anomalies is estimated empirically in terms of the interannual variance of spatially averaged standardized anomalies given by the Standardized Anomaly index (SAI; Katz and Glantz 1986). Use of the SAI in the context of tropical rainfall is discussed extensively in Moron et al. (2006, 2007). The interannual variance of the SAI (var[SAI]) measures the spatial coherence between M stations (or grid points) because it depends on the interstation correlations; it ranges from var[SAI] = 0 when two samples of equal size, perfectly covariant, are perfectly out of phase; var[SAI] = 1/M when all the correlations are zero; and var[SAI] = 1 when all stations are perfectly correlated.

The SAI is an empirical estimate of the shared in-phase “signal” across the network. The “noise” component can be defined in terms of the (square rooted spatial average) squared deviations relative to the SAI. This definition of signal and noise is analogous to the distinction between externally forced and internally generated variance in ensembles of general circulation model (GCM) simulations (i.e., Rowell 1998), with stations or grid-points playing the role of GCM ensemble members. A signal-to-noise ratio (SNR) can be formed by dividing the SAI by the noise, but this second-order statistic is more sensitive to sampling issues than the SAI used here.

Statistical significance of interannual correlations is assessed against 1000 synthetic time series of the same length and spectral density as the observed pair, but random phase (Janicot et al. 1996), with the two-sided 90%, 95%, and 99% significance levels indicated in the following by one (*), two (**), and three (***), asterisks, respectively.

## 3. Results

### a. Onset date

The mean onset dates determined from CMAP and GSOD, plotted in Fig. 1a, exhibit a northwest-southeast progression from late August in northern-central Sumatra and Kalimantan to mid-December in Timor. The dates agree well between the two datasets, while there is

a large interstation variability over Java (Fig. 1a) that could be related to small-scale topographic features. Onset occurred before 1 November, 1 December, and 1 January in 67% (65%), 79% (86%), and 94% (95%) of cases, respectively, in CMAP (GSOD). Mean onset dates computed for subsets of GSOD stations averaged by subregion (Table 1) are in good agreement with Naylor et al. (2007; their Fig. 1). Using their definition (i.e., the first day when accumulated rainfall from 1 August reaches 200 mm) leads to similar median dates to those shown in Table 1, except in northern areas (not shown). Moreover, the interannual variability is highly consistent between both definition with cross correlations > 0.85*** for all regions displayed in Table 1 except for northern Sumatra (r = 0.52**). Onset date is less relevant in the northern regions because of the differing seasonality of rainfall north of the equator (Aldrian and Susanto 2003).

The interannual variability of onset date for the 14 stations over western and central Java is shown in Fig. 1b in terms of the individual standardized anomaly time series (dotted). The signal that is common to the 14 stations, defined by the SAI (heavy solid), accounts for a moderate fraction (var[SAI]=0.41) of the total variance at the individual stations, indicating substantial interstation noise. However, the SAI is correlated at 0.80*** (0.83***) with the large-scale SAI [leading principal component (PC) time series] computed from CMAP onset dates over all 128 grid points (heavy dashed blue and red curves, respectively), suggesting that the signal in onset over western and central Java is related to the large scale despite considerable small-scale noise. This is also seen in the other subregions (Table 1). The influence of ENSO is clearly visible in Fig. 1b, with big delays in large-scale onset during the 1982 and 1997 El Niño events. In fact, the correlation between large-scale SAI (leading PC time series) of CMAP is correlated at 0.84*** (0.84***) with the Niño 3.4 SST anomalies in October, corresponding to the mean onset date across the domain. Some skewness is also visible with delayed onsets exhibiting larger amplitudes than early onsets.

The leading EOFs of CMAP and GSOD onset dates are plotted in Fig. 2. The leading CMAP EOF accounts for 36% of total variance (EOF#2 accounts for 9% of total variance), and consists of a large-scale monopolar pattern with highest loadings over “monsoonal” Indonesia, (i.e., from southern Sumatra to the Timor Sea; Aldrian and Susanto 2003, their Fig. 2). Loadings remain substantial toward the southeast but fall off rapidly over northern Sumatra, the Malay Peninsula, and northern Kalimantan where they are generally close to zero. The loadings of the leading EOF of GSOD onset dates (31% of the variance) are generally similar to those of CMAP,
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>FIG. 1. (a) Mean onset date computed in CMAP (shading) and GSOD (dot) as the first wet day of a 5-day sequence receiving > 40 mm from 1 Aug without a dry 10-day sequence receiving < 5 mm in the following 30 days from onset. (b) Standardized onset date for western and central Java GSOD stations (dotted lines) with the average, i.e., SAI (solid black line), together with the CMAP SAI (blue dashed line) and standardized leading PC time series (red dashed line) computed from all 128 CMAP grid points. The dashed horizontal lines delineate the 95% confidence interval of a set of 14 white noise time series. Note that one std dev corresponds to an averaged deviation of ~20 days for western and central Java.</figcaption></figure>

while their PC time series are correlated > 0.90***; there is thus a high level of consistency at large scale between these two contrasting datasets. Similarly, the cross correlations between the SAIs of each region defined in Table 1 are always positive and significant at the one-sided 95% level or greater.

As discussed in section 2d, the station-scale noise can be defined in terms of the (square rooted spatial average) squared deviations of the stations' rainfall relative to the SAI. The noise variance computed in this fashion

for each of the subregions (not shown) is fairly uniform in space, though somewhat smaller in southern Kalimantan, southern Sumatra and western and central Java. However, differences in the spatial sampling between subregions do not allow for confidence in this second-order statistic.

### b. *Seasonal rainfall total and postonset amounts*

The temporal correlations between the leading PC of onset (Fig. 1b) and the leading PC of SOND seasonal
<table><caption>TABLE 1. Statistics of GSOD station onset date by subregion (N is number of stations), computed from the SAI of each region. The hindcast skill refers to the correlation between the observed and hindcast SAI with a cross-validated CCA using July SSTs as predictors. One, two, and three asterisks indicate correlation significant at the two-sided 90%, 95%, and 99% level according to a random-phase test (Janicot et al. 1996).</caption><thead><tr><th></th><th>N</th><th>25%, 50%, and 75% percentiles of the spatial average</th><th>Var [SAI]</th><th>Correlation with large-scale SAI of CMAP</th><th>Correlation with PC#1 of CMAP</th><th>Hindcast skill</th></tr></thead><tbody><tr><td>Western and central Java (west of 112°E)</td><td>14</td><td>16 Oct, 28 Oct, 11 Nov</td><td>0.41</td><td>0.80***</td><td>0.83***</td><td>0.59***</td></tr><tr><td>Eastern Java (east of 112°E)</td><td>7</td><td>13 Nov, 21 Nov, 2 Dec</td><td>0.44</td><td>0.74***</td><td>0.72***</td><td>0.61***</td></tr><tr><td>Southern Sumatra (south of 1°S)</td><td>6</td><td>5 Sep, 19 Sep, 17 Oct</td><td>0.60</td><td>0.86***</td><td>0.88***</td><td>0.74***</td></tr><tr><td>Central Sumatra (between 1°S and 2°N)</td><td>7</td><td>15 Aug, 24 Aug, 31 Aug</td><td>0.43</td><td>0.76***</td><td>0.74***</td><td>0.51**</td></tr><tr><td>Northern Sumatra (north of 2°N)</td><td>6</td><td>1 Sep, 11 Sep, 15 Sep</td><td>0.23</td><td>0.46**</td><td>0.41**</td><td>0.22</td></tr><tr><td>Southern Kalimantan (south of 1°S)</td><td>6</td><td>17 Sep, 22 Sep, 25 Oct</td><td>0.72</td><td>0.80***</td><td>0.79***</td><td>0.84***</td></tr><tr><td>Central Kalimantan (north of 1°S)</td><td>5</td><td>11 Aug, 29 Aug, 12 Sep</td><td>0.48</td><td>0.70***</td><td>0.69***</td><td>0.46**</td></tr><tr><td>Eastern Indonesia (east of 120°E and south of 8°S)</td><td>5</td><td>30 Nov, 13 Dec, 25 Dec</td><td>0.57</td><td>0.63**</td><td>0.60**</td><td>0.49**</td></tr></tbody></table>

total exceed −0.90*** for both datasets. The variance explained by the leading EOF of SOND total (51% in CMAP and 32% in GSOD) is even larger than that of onset, presumably because of the seasonal integration of rainfall that filters out some of the local-scale noise inherent in the definition of onset date. In fact, 46% (CMAP) and 67% (GSOD) of onsets occurred between 1 September and 31 December. This suggests that at least some of the spatially consistent interannual variability of SOND amount is actually conveyed by the anomalous timing of the monsoon onset.

Three approaches are used to test this hypothesis, by estimating the spatial coherence of rainfall amount beyond the onset date. (i) First, the spatial coherence of the rainfall summed over the 15, 30, 60, and 90 days

following the local onset date is computed. Postonset rainfall is a priori independent of the timing of the onset of the monsoon, although both may be influenced by ENSO and local-scale SST. The disadvantage of this approach is that postonset amounts refer to different temporal windows depending on the particular year and station location. Nonetheless, 30-day amounts, for example, refer to periods before 1 January in 69% (CMAP) and 88% (GSOD) of cases. (ii) In the second approach, the component of the SOND total accounted for by the large-scale onset, defined as the leading PC of each dataset (Fig. 2), is removed using a least squares linear regression. The remaining residual is thus associated only with postonset amounts, and all information linearly related to large-scale onset is removed a priori. (iii) The last method is to compare cumulative spatial-

<figure><img src="image_4.png" /><figcaption>FIG. 2. Leading EOF of CMAP (shading) and GSOD (dot) onset dates, plotted as correlations with the principal component time series. The time series of onset date at each grid point were standardized prior to EOF analysis.</figcaption></figure>
<table><caption>TABLE 2. Interannual variance of the SAI (Var[SAI]) of the 57 GSOD stations, and 128 grid points of CMAP for local onset date, and postonset 15-, 30-, 60-, and 90-day rainfall totals. Var[SAI] ranges between 0 (correlation of −1 between two equal-sized and perfectly covarying samples), $1/m$ (= 0.02 for $m$ = 57 and 0.008 for $m$ = 128) where $m$ is the number of locations for spatially independent variations, and 1 (perfect correlation between stations) (Moron et al. 2007).</caption><thead><tr><th></th><th>Var(SAI) GSOD</th><th>Var(SAI) CMAP</th></tr></thead><tbody><tr><td>Onset</td><td>0.30</td><td>0.31</td></tr><tr><td>15 day</td><td>0.03</td><td>0.05</td></tr><tr><td>30 day</td><td>0.03</td><td>0.08</td></tr><tr><td>60 day</td><td>0.05</td><td>0.11</td></tr><tr><td>90 day</td><td>0.06</td><td>0.14</td></tr><tr><td>SOND</td><td>0.26</td><td>0.46</td></tr><tr><td>SOND residuals</td><td>0.10</td><td>0.16</td></tr></tbody></table>

average rainfall anomalies computed from 1 August as expressed as percentage of the long-term mean for early and late onset years.

Estimates of var[SAI] for each quantity are given in Table 2. The spatial coherence is high (i.e., large var [SAI]) for both onset date and seasonal total but falls to near zero for postonset rainfall and SOND residuals. There is, nonetheless, a weak increase of spatial coherence as the length of the postonset averaging period increases from 15 to 90 days, expected because of the progressive cancellation of meteorological events as the length of considered period grows. The difference between CMAP and GSOD results could come from the area, mainly oceanic, that is not sampled in GSOD and/or smoothing provided by gridbox-pentad averages in CMAP.

Standardized anomaly time series of postonset 90-day amounts for each CMAP grid box are shown in Fig. 3a, together with the SAI. Spatial coherence is generally low in most years, with the exceptions of the 1982 and 1997 large El Niño events. The loadings of the leading EOF of the postonset 90-day amount are displayed in Fig. 3b. These are weak over monsoonal Indonesia, especially between southern Sumatra to Sulawesi, where those of the leading EOF of onset peak (Fig. 2), and this mode explains less variance (22% in CMAP and 11% in GSOD) than does the leading EOF of CMAP onset date (36% in CMAP and 32% in GSOD). The temporal behavior of the leading PC is nevertheless consistent with that of onset date; that is, the postonset season tends to be anomalously dry when onset is anomalously late, and vice versa, at least for CMAP ($r$ between leading PC of onset and of postonset 90-day amount is −0.87*** in CMAP and −0.19 in GSOD; the postonset PCs being correlated at 0.37* between the two datasets). Note that the second EOF of postonset 90-day amount in GSOD (not shown) explains 10% of

total variance and is correlated at −0.60*** (respectively 0.51**) with the leading PC of onset date in GSOD (respectively the leading PC of postonset 90-day in CMAP). The fact that the loadings are rather large over the eastern Indian Ocean and scattered patches of the northern and eastern oceanic margins of the domain (Fig. 3b) could be evidence of a deterministic signal and warrants further study.

The leading EOF of SOND residuals (Fig. 3c) shares some similarities with that of postonset 90-day rainfall amounts (Fig. 3b), at least for CMAP (25% explained variance); both have relatively high homogeneous loadings over eastern Indonesia, and weak loadings across monsoonal Indonesia. The leading EOF of GSOD (16% explained variance) lacks similarity with its CMAP counterpart, and their PCs are not significantly correlated ($r$ = 0.23). Nearby stations often have quite different loadings, such as over Java (Fig. 3c). By construction, the leading PC of SOND residuals is orthogonal to the leading PC of onset date.

Figure 4 shows the spatial average of the cumulative rainfall anomalies (averaged over the 57 stations across Indonesia in the upper panel and the 14 stations of western and central Java in the lower panel) computed from 1 August and expressed as percentage relative to long-term mean for the 6 latest and earliest mean onset dates. A constant modulation of rainfall anomalies would lead to a straight horizontal line at the mean rainfall anomaly. The largest positive (negative) cumulative anomalies occurred in both cases before or around the early (late) onset dates while the curves usually tend to zero thereafter (Fig. 4). The spatially averaged rainfall anomalies at the end of the rainy season, somewhere in March–April, are still consistent with the phase of the onset date but the amplitude of these anomalies is weak (Fig. 4). It suggests that the strongest spatially coherent signal at large scale (Fig. 4a) and for a particular subset of stations (Fig. 4b) is before or near the onset date while it tends to cancel thereafter.

### c. *Seasonal predictability of onset*

The substantial spatial coherence of onset date suggests seasonal predictability. To provide a measure of the latter, regression models are built using cross-validated CCA between July SST over the tropical Pacific and Indian Oceans (20°N–20°S, 80°–240°E) as predictors and GSOD or CMAP onset dates as predictands. Note that the 14% of missing entries in GSOD were first filled with a simple linear regression using the closest CMAP grid point as predictor. The models were built using the Climate Predictability Tool (CPT) software developed at the International Research Institute for Climate Prediction (IRI; http://iri.columbia.edu/outreach/software/);
<figure><img src="image_5.png" /><figcaption>FIG. 3. (a) Individual standardized anomalies of rainfall total for the 90-day period after the local onset date at the 128 CMAP grid points (dots) with the SAI (solid). The dashed horizontal lines delineate the 95% confidence interval of a set of 128 white noise time series. (b) Leading EOF of postonset 90-day amounts in CMAP (shading) and GSOD (dot). (c) Leading EOF of SOND residuals in CMAP (shading) and GSOD (dot). Units in (b) and (c) are correlations with the respective principal component time series.</figcaption></figure>

the predictor and predictand fields were prefiltered using EOFs, with the number of modes retained determined by maximizing the model's goodness of fit under cross validation, with 5 yr withheld at a time. The leading 5 and 2 (1) EOF modes are retained in SST and CMAP (GSOD) and most of the cross-validated skill is associated with the

leading CCA mode whose predictand pattern (i.e., SST pattern) is almost identical for CMAP and GSOD.

Homogeneous correlation maps of the leading CCA mode are shown in Figs. 5a and 5b for SST and onset date, respectively. The SST anomaly structure (Fig. 5a) exhibits a classical ENSO pattern, together with high
<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

FIG. 4. Spatial average of cumulative rainfall anomalies (a) for all 57 stations and (b) 14 stations from western and central Java (Table 1) computed from 1 Aug and expressed as percentage from the long-term mean for the six latest (in red) and earliest (in blue) onsets (computed from the spatial average of onset dates). The dashed line indicates each year and the full bold line indicates the mean of the 6 yr. The time series are low-pass filtered with a Butterworth filter (cutoff frequency = 1/30 cpd). The asterisks indicate the station-average onset date.

correlations around Indonesia, such that warm ENSO events are associated with delayed onset (Hamada et al. 2002; Hendon 2003). The corresponding structure in onset dates (Fig. 5b) indicates that the delayed onsets

extend right across Indonesia, with high loadings over monsoonal Indonesia, decreasing weakly (strongly) toward eastern (northwestern) Indonesia. The regression model hindcast skill is plotted in Fig. 5c in terms of
<figure><img src="image_8.png" /><figcaption>FIG. 5. Homogeneous correlation maps of (a) SST, and (b) onset date from CMAP (shading) and GSOD (circles), of the leading canonical correlation analysis (CCA) mode (c) MOS skill (i.e., correlation between observed and hindcast onset date) associated with the leading CCA mode between July SST and onset dates.</figcaption></figure>

anomaly correlation, with regional averages given in Table 1 (last column). Skill values are highest over monsoonal Indonesia, exceeding 0.5** from southern Sumatra to southern Kalimantan and Timor, reaching 0.80*** for the SAI computed over all stations (0.70*** for CMAP). The subisland subsets of stations in Table 1 achieve station-averaged skills ranging from 0.22 (northern Sumatra) to 0.84*** (southern Kalimantan). The spatial variability of skill over Java could be due to random sampling but also to deterministic signals as-

sociated with small-scale orographic features and/or orientation relative to low-level winds.

4. Conclusions

The spatial coherence of onset date and postonset rainfall is analyzed from GSOD rain gauges and the CMAP dataset. The onset date is defined using an ag-
ronomic approach, that is, the first significant wet spell
(here 40 mm in 5 days) without any potentially damaging
dry spell (here 10 days receiving less than 5 mm) there-
after (here in the 30 postonset days). This definition is
best suited for the end user's purpose but suffers from the
subjective choice of the parameters. Nevertheless, these
parameters broadly reflect the needs and risks associated
with major crops of Indonesia, such as lowland rice. The
long-term mean onset dates, as well as the frequency of
false starts are sensitive to these subjective parameters
and future applications should carefully consider the
impact of these choices on specific crops. However, for
our main purpose of analyzing the spatial coherence of
anomalous onset dates, the sensitivity to these param-
eters largely vanishes.

The interannual variability of rainy season onset over
monsoonal Indonesia is shown from both gridded
pentad CMAP and daily station GSOD rainfall datasets
to be characterized by a large-scale coherent signal,
together with a moderate amount of local-scale noise
(Figs. 1b and 2). Considering small subsets of GSOD
stations recovers this signal, despite the complexity of
the island topography (Table 1). The interannual anom-
alies are dominated by delayed onsets (Fig. 1b). Con-
versely, the spatial coherence of interannual rainfall
anomalies beyond the onset date is weak, as revealed by
the amount of rainfall in the 15 to 90 days after the onset
and the SOND residuals from large-scale onset (Table 2
and Fig. 3a). The leading EOF of postonset 90-day
CMAP amounts exhibits weak and rather inconsistent
loadings over the main islands with high loadings re-
stricted to eastern Indian Ocean and scattered patches
of the northern and eastern margins (Fig. 3b). However,
this signal is strongly consistent in sign with onset date in
CMAP (i.e., late onset associated with smaller postonset
amount and vice versa). The leading EOF of SOND
residuals from large-scale onset lacks consistency be-
tween the GSOD and CMAP datasets but both none-
theless exhibit large spatially coherent loadings over
eastern Indonesia, but not over the eastern Indian
Ocean (Fig. 3c). The spatial average of cumulative
rainfall anomalies also exhibit their largest amplitudes
before and near the onset date (Fig. 4), while the post-
onset cumulative rainfall anomalies tend almost
monotonically toward zero. There may thus be some
predictability in postonset seasonal amounts, but most
of the spatially coherent signal in SOND seasonal total,
especially across islands, is merely related to the onset.

Our main finding is that most of the large-scale in-
terannual signal of SOND seasonal rainfall total is
conveyed by variations in the onset date of the rainy
season. This implies that (i) rainfall monitoring at a
small set of stations spread across Indonesia should be
sufficient to establish interannual anomalies of onset
date, and (ii) the scale of the interannual variability of

the onset suggests a large-scale forcing and potential
seasonal predictability. Indeed large-scale onset is found
to be highly correlated with an ENSO SST pattern
during July (Fig. 5a), that is, at least one month and half
before the mean local-scale onset date. A cross-
validated CCA using July SST in tropical Indian and
Pacific Oceans (20°N–20°S, 80°–240°E) as a predictor
leads to promising skill values for the large-scale onset
date (r = 0.80*** for GSOD; Fig. 5b,c). Further work is
needed to examine the associated circulation changes
and to investigate the roles of ENSO and Indian Ocean
climate variability (Hendon 2003).

The spatial variation of hindcast skill (Fig. 5c) and
onset EOF loadings (Fig. 2) warrants further study.
Both exhibit maxima from southern Sumatra to south-
ern Kalimantan—quite close to the equator—and de-
creases gradually southward across Java and Sonde
Islands and more rapidly northward (Figs. 2 and 5c,
Table 1). The latter decrease could be related to the
year-round rainfall there (Aldrian and Susanto 2003),
and the onset date should be viewed merely as an in-
crease of the rainfall rather than the transition between
a real dry and wet season. In that case, the onset date is
sensitive to the subjective choices used to define it and is
clearly less robust. This does not apply to monsoonal
Indonesia south of 5°S. The highest EOF loadings and
SST-related skill over southern Sumatra to southern
Kalimantan coincide with the largest interquartile range
of interannual variability (Table 1). This subequatorial
band is perhaps the most sensitive to the spatial shift of
the ITCZ that probably triggers the onset of the rainy
season. The complex orography across Java could also
enhance the intraregional noise even between close
stations but we must also keep in mind that the spatial
sampling is highest over Java (Fig. 1a). Similarly, the
nature of spatial coherence for postonset rainfall and
SOND residuals over eastern Indonesia and the eastern
Indian Ocean (postonset rainfall only), as well as sea-
land contrast needs further investigation using better
sampled datasets and/or regional model simulations.

The large-scale signal in onset is still strongly present
in multistation small subisland regions (Table 1), indi-
cating the potential to downscale the large-scale onset
signal to the near-local scale. However, it is clear that
individual stations exhibit considerable noise (Table 1).
Thus, careful consideration needs to be given to the
trade-off between potentially more-accurate forecasts
at the aggregated scale versus local specificity for use in
climate risk management. The large-scale nature of
seasonal predictability of onset should enable improved
agricultural planning in the future, together with better
identification of false starts to the rainy season via real-
time monitoring and short-term forecasts of the large-
scale evolving monsoon circulation. Forecasts of the Madden-Julian oscillation may lend an additional source of predictability at intraseasonal lead times (Wheeler and McBride 2005).

*Acknowledgments.* We are grateful to P. Xie for information on the rain gauge measurements used in the CMAP, and to two anonymous reviewers whose comments helped us to clarify the paper. This research was supported by grants from NOAA (NA050AR4311004), the U.S. Agency for International Development's Office of Foreign Disaster Assistance (DFD-A-00-03-00005-00), and the U.S. Department of Energy's Climate Change Prediction Program (DE-FG02-02ER63413).

## REFERENCES

Aldrian, E., and R. D. Susanto, 2003: Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. *Int. J. Climatol.*, **23**, 1435–1452.

—, D. Sein, D. Jacob, L. Dümenil Gates, and R. Podzun, 2005: Modeling Indonesian rainfall with a coupled regional model. *Climate Dyn.*, **25**, 1–17.

—, L. Dümenil Gates, and F. H. Widodo, 2007: Seasonal variability of Indonesian rainfall in ECHAM4 simulations and in the reanalyses: The role of ENSO. *Theor. Appl. Climatol.*, **87**, 41–59.

Boer, R., and A. R. Subbiah, 2005: Agriculture drought in Indonesia. *Monitoring and Predicting Agricultural Drought: A Global Study*, V. S. Boken, A. P. Cracknell, and R. L. Heathcote, Eds., Oxford University Press, 330–344.

—, and I. Wahab, 2007: Use of seasonal surface temperature for predicting optimum planting window for potato at Pengalengan, West Java, Indonesia. *Climate Prediction and Agriculture: Advance and Challenge*, M. V. K. Sivakumar and J. Hansen, Eds., Springer, 135–141.

Dikshit, U. N., D. Parida, and D. Satpathy, 1987: Genetic evaluation and utilization: Drought tolerance. *Int. Rice Res. News.*, **12**, 6–7.

Giannini, A., A. W. Robertson, and J. H. Qian, 2007: A role for tropical tropospheric temperature adjustment to ENSO in the seasonality of monsoonal Indonesia precipitation predictability. *J. Geophys. Res.*, **112**, D16110, doi:10.1029/2007JD008519.

Hamada, J. I., M. D. Yamanaka, J. Matsumoto, S. Fukao, P. A. Winarso, and T. Sribimawati, 2002: Spatial and temporal variations of the rainy season over Indonesia and their link to ENSO. *J. Meteor. Soc. Japan*, **80**, 285–310.

Haylock, M., and J. L. McBride, 2001: Spatial coherence and predictability of Indonesian wet season rainfall. *J. Climate*, **14**, 3882–3887.

Hendon, H. H., 2003: Indonesian rainfall variability: Impacts of ENSO and local air-sea interaction. *J. Climate*, **16**, 1776–1790.

Janicot, S., B. Fontaine, and V. Moron, 1996: Sahel drought and ENSO. *Geophys. Res. Lett.*, **23**, 515–518.

Katz, R. W., and M. H. Glantz, 1986: Anatomy of a rainfall index. *Mon. Wea. Rev.*, **114**, 764–771.

Moron, V., A. W. Robertson, and M. N. Ward, 2006: Seasonal predictability and spatial coherence of rainfall characteristics in the tropical setting of Senegal. *Mon. Wea. Rev.*, **134**, 3468–3482.

—, —, —, and P. Camberlin, 2007: Spatial coherence of tropical rainfall at regional scale. *J. Climate*, **20**, 5244–5263.

Naylor, R. L., W. Falcon, N. Wada, and D. Rochberg, 2002: Using El Niño Southern Oscillation climate data to improve food policy planning in Indonesia. *Bull. Indones. Econ. Stud.*, **38**, 75–91.

—, D. S. Battisti, D. J. Vimont, W. P. Falcon, and M. B. Burke, 2007: Assessing the risks of climate variability and climate change for Indonesian rice agriculture. *Proc. Natl. Acad. Sci. USA*, **104**, 7752–7757.

Niewolt, S., 1989: Estimating agricultural risks of tropical rainfall. *Agric. For. Meteor.*, **45**, 251–263.

Rowell, D. P., 1998: Assessing potential seasonal predictability with an ensemble of multi-decadal GCM simulations. *J. Climate*, **11**, 1073–1093.

Sivakumar, M. V. K., 1988: Predicting rainy season potential from the onset of rains in southern Sahelian and Sudanian climatic zones of West Africa. *Agric. For. Meteor.*, **42**, 295–305.

Wheeler, M. C., and J. L. McBride, 2005: Intraseasonal variability of the Australian-Indonesian monsoon region. *Intraseasonal Variability of the Atmosphere-Ocean System*, W. K. M. Lau and D. E. Walliser, Eds., Praxis Publishing, 125–173.

Wilks, D. S., 1999: Interannual variability and extreme-value characteristics of several stochastic daily precipitation models. *Agric. For. Meteor.*, **93**, 153–169.

Xie, P., and P. A. Arkin, 1996: Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions. *J. Climate*, **9**, 840–858.