Predictability of Persistent Thailand Rainfall during the Mature Monsoon Season in 2011 Using Statistical Downscaling of CGCM Seasonal Prediction
ABSTRACT
Predictability of above-normal rainfall over
(ProQuest: ... denotes formulae omitted.)
1. Introduction
The
Seasonal-scale predictions conducted by atmosphere- ocean coupled general circulation models (CGCMs) are expected to be useful in minimizing damage induced by such seasonal-scale climate events such as La Niña. Current CGCMs have the ability to predict large-scale tropical variables for up to six months or longer (Jin et al. 2008; Wang et al. 2009). El Niño-Southern Oscillation (ENSO) is one of the major interannual climate phenomena controlled by air-sea interactions and can be a useful predictand to evaluate a seasonal prediction system. On the other hand, most CGCMs lack the resolution to reproduce regional rainfall, which is affected by small-scale atmospheric eddies and topography. Furthermore, many CGCMs even have difficulty in reproducing large-scale phenomena such as the monsoon rainfall extending over the Indochina region.
Statistical downscaling is one approach that may help overcome the drawbacks inherent in predictions made with CGCMs. Its common strategy is to establish an empirical statistical relationship between regional-scale climate and large-scale variables. The CGCM output is used as a predictor of a large-scale field and is converted to a regional-scale predictand by a function describing a statistical relationship. Several statistical downscaling schemes are based on regression analysis or similar linear methods (Wetterhall et al. 2005; Feddersen and Andersen 2005; Kang et al. 2007). However, a single statistical function may be not sufficient, because it is likely to be built upon a mixture of several phenomena and may not necessarily be based on the physical mechanism of the individual phenomenon. From this perspective, Chu et al. (2008) derived an empirical statistical relationship between local precipitation over northern
In terms of
Many hydrologists, meteorologists, social scientists, and policymakers around the world are focusing on the
In the following section, we describe the observational data used in this paper, our seasonal prediction system, how the predictor was chosen, and the downscaling method. Statistical relationships isolated by SVDA are analyzed in section 3a and the prediction results of
2. Data and methodology
The entire statistical downscaling process in this paper is summarized in Fig. 1. The details are as described in sections 2a-2c.
a. Observational data and CGCM hindcast products
The observed regional precipitation dataset used in this study is the Asian Precipitation-Highly Resolved Observational Data Integration Toward Evaluation (APHRODITE) version APHRO-V1003R1 (Yatagai et al. 2009), which has 0.258 horizontal resolution and covers the period from 1964 to 2007. APHRODITE is used both for identifying the statistical relationship and for validation. For validation after 2008, we adapted the
The CGCM hindcast products were taken from outputs of the seasonal prediction system built on the Model for
b. Choice of predictor
To perform the best downscaled prediction, an optimal predictor should be chosen from outputs of the CGCM hindcast simulations. There are two requirements in choosing a predictor. First, it has to be well simulated by the GCM. Second, there should be a stable relationship between the predictor and the predictand (Wilby et al. 1999; Kang et al. 2007; Chu et al. 2008).
To meet the first requirement, we compared the raw hindcast outputs of precipitation, sea level pressure (SLP), geopotential height at 500 hPa (Z500), and SST with observations. Figure 2 shows the horizontal distributions of ASO time correlation coefficients (TCC) between the raw hindcast outputs and the corresponding observations on each grid. In the TCC for precipitation, significant predictability exists only in the central tropical
It is well known that the weather systems over Indochina (
Figures 3a, 3c, and 3e show the observed regressions of ASO SSTA with unnormalized ASO Niño-3, EMI, and DMI, respectively. Here, as EMI and DMI are influenced in part by the Niño-3 index, the ENSO effect is first removed using the linear regression with respect to the Niño-3 index and then the regression is obtained. The canonical El Niño features a warming in the eastern equatorial Pacific(Fig. 3a), whereas the CP El Niño is characterized by a warming over the central
On the other hand, the CP El Niño is not associated with the anticyclonic response in WNP (Fig. 3d). Instead, an anomalous cyclone exists around
To evaluate the three major phenomena simulated by our CGCM, we show in Fig. 4 the results from the regression analysis of the ASO hindcasts. The SST anomalies of the canonical ENSO and CP ENSO projected by the CGCM (Figs. 4a,c) extend too far west compared to the patterns extracted from the observation (Figs. 3a,c). This is a systematic bias of MIROC5 originated with too strong SST-wind coupling. Despite the existence of such a systematic bias, the simulated phase variations of the Niño-3 index and EMI are in good agreement with the observed variations. A temporal correlation coefficient is 0.83 for the Niño-3 index and 0.70 for EMI. On the other hand, the simulated IOD is not in agreement with the observation. Although the simulated pattern is similar to the observed pattern (Fig. 4e), our model underestimates the amplitude of IOD and the variation is not in good agreement with the observation (Fig. 4f). Generally, IOD prediction is more difficult than ENSO prediction. Differences of predictability among the phenomena potentially influence results of this study.
The concept of predictor choice mentioned above has also been emphasized in previous studies (e.g., Kang et al. 2007; Chu et al. 2008). However, the prediction skill of the selected variables in GCMs for each year and month has never been fully discussed. Figure 2 clearly shows that atmospheric variables, which were used as predictors in previous studies, do not necessarily have sufficient predictability over the western North Pacific, and that oceanic variables are sometimes more appropriate as a predictor.
c. Statistical downscaling method
We expanded the method of Chu et al. (2008) to downscale the regional rainfall in Indochina. We used SVDA to obtain statistical relationships between large-scale tropical SST and local rainfall in Indochina. It gives the coupled patterns and time series as follows:
..., (1)
..., (2)
where Pi (x) and Qi(y) indicate the ith mode of the singular vector of the large-scale SST from an CGCM hindcast (predictor) and of regional rainfall (predictand), respectively. The terms Ai (t) and Bi(t) denote time expansion coefficients of the ith mode for the predictor and the predictand, respectively, and m is the total number of SVD modes. Given ttarget as the target prediction month, SST(ttarget, x) can be obtained from outputs of a CGCM seasonal prediction. The future time expansion coefficient for SST is calculated as
... (3)
Because the time coefficient for target precipitation, PRCP(ttarget, y), is unknown, Ai (ttarget) is used as a substitute. Then the downscaling transform function can be given as
..., (4)
where N is the total number of SVD modes.
Here, we carried out cross validation to identify time stable modes. In cross validation, the target month for the prediction is excluded from the training period of the statistical analysis of SVDA to prevent the signals of forecast months from being included in the statistical function. This procedure is repeated 33 times, and yields rainfall predictions for 33 years. During the cross validation, each relationship between the rainfall and SST in the transfer functions should be maintained. On the basis of this criterion, the leading six modes are identified as the time stable modes (not shown) and retained in our downscaling scheme.
3. Results
a. Statistical relationship from SVDA
Here we analyze the statistical relationship derived by SVDA [Eqs. (1) and (2)]. We examine spatial patterns of SST and regional precipitation through values regressed onto expansion coefficients Ai (t) and Bi (t), instead of singular vectors Pi (x) and Qi (y), because a singular vector is normalized and thus unitless.
The leading statistical relationships extracted by the SVDA between predicted SST and observed Indochina rainfall are shown in Figs. 5a-c. Although an ENSO-like SST anomaly pattern is visible in the regressed SST field (Fig. 5b), the time coefficients of the leading mode indicate decadal-scale variations rather than interannual variability, and show a continuous La Niña-like phase over the past two decades. In addition, SST anomalies over the entire Indian Ocean show an opposite sign to the ENSO-like anomalies in the eastern tropical Pacific. These notable signals include a La Niña-like tendency and warmer conditions in the Indian Ocean over the past few decades, which is consistent with the hiatus hypothesis discussed in Luo et al. (2012). Therefore, the first SVD mode seems to mainly reflect the recent decadal tendency. In the same manner, Figs. 5d-f show the results of the second mode. This mode shows a typical ENSO-like pattern in the tropical Pacific with dominant interannual variability. The difference from the first mode is found in the Indian Ocean; the IOD pattern is visible. It is also observed that some IOD events have occurred with ENSO events. Impacts of both phenomena are visible in the Indochina regional rainfall. The results of the third mode shown in Figs. 5g-i reflect the features of the CP El Niño as shown in Figs. 3c and 3d.
As seen above, the statistical relationship between the Indochina regional rainfall and the remote oceans depends on which phenomenon develops, and the SVD analysis can cover each relationship according to each phenomenon. Here, the contribution ratio of the leading mode of SVDA is small at less than 15% in both the observed and the simulated fields; nevertheless, typical linear analyses (e.g., an empirical orthogonal function) show that ENSO is a distinctly dominant phenomenon among the tropical upper oceans. This discrepancy arises because the local rainfall in Indochina is influenced by not only ENSO but also by other modes of climate variability, such as IOD and CP El Niño. It means that different phenomena have different physical relationships. That is why we adopt not only the leading mode but also all significant modes.
Note that the correlation coefficient between the time expansion coefficients of SST and local precipitation is 0.80 in Fig. 5a, 0.75 in Fig. 5d, and 0.72 in Fig. 5g. This is an unavoidable bottleneck for the approach of this study, because we assume that these two time series are identical in the process represented in Eq. (4). With this in mind, the skill of our downscaling approach based on SVDA is examined in the next section.
b. Prediction of
The results of downscaling obtained from Eqs. (3) and (4) are compared with observations over the latter half of the 2011 monsoon season. Figure 6c shows the precipitation anomaly obtained from the TRMM/3B43 data. More-than-normal rainfall is found not only around
In the background mean state (Fig. 6a), the decaying La Niña signal dominates in the PacificOceanwith peak anomalies in the vicinity of the equator and in the eastern subtropics. As a measure of the atmospheric anomalies, contours of OLR anomalies are also shown in Fig. 6a. Convection was active and brings much rainfall over Indochina. In the Indian Ocean, the IOD structure is observed with warmer SST to the west and colder SST to the east. The eastern cooling around
So far, the results shown in Fig. 6 suggest that our downscaling approach with the CGCM prediction has the ability to predict
4. Discussion
Figure 7b exposes that this downscaling prediction is not always possible anytime and anywhere. It is natural to think that there are both years when prediction is easy and years when prediction is difficult. As seen in Fig. 4, the skill of seasonal prediction differs depending on the dominating phenomenon such as ENSO, IOD, the new type of El Niño, and so forth (Luo et al. 2005; Kim et al. 2009; Hendon et al. 2009). Furthermore, even for the same ENSO, a developing phase shows higher predictability than a decaying phase (Jin et al. 2008). All of these factors are combined in the resulting skill shown in Fig. 7. Therefore, we checked another particular rainfall event in recent years. Figure 8 shows anomalies in ASO 2002 and ASO 2008. During the monsoon season of 2002, a CP El Niño was developing in the
The background state of 2008 was quite similar to the condition of 2011: the La Niña tendency in the
There are two potential causes for skill loss: one is prediction errors in CGCM hindcast and the other is the imperfect statistical relationship extracted by the SVD analysis. Although the time-mean skill has a critical upper limit due to the unavoidable imperfect statistical relationship, reliability of the relationship depends on SVD modes. The significance of the statistical relationship in each mode can be known in advance by analyzing the degree of coincidence between two expansion coefficients derived from SVDA (e.g., black and red lines in Figs. 5a,d,g). Meanwhile, we can predict in advance which mode is likely to develop in a coming season by referring to Ai (ttarget) in Eq. (4), which gives a future coefficient of a target predictand. Combining these two estimations, the reliability of each forecast can also be predicted. With the addition of such reliability information, forecasted products would surely become useful.
5. Summary and conclusions
This paper describes a methodology to forecast the prolonged and anomalously heavy
These results tell us that regional predictability fluctuates depending on the dominating phenomenon in the background large-scale field, which might be easy or difficult to predict. Therefore, if we have advance knowledge of the tendency of the large-scale circulation, we can estimate whether the following forecast would be reliable. In this regard, the SVDA approach is a powerful tool. Each mode revealed by SVDA has the potential to identify each physical mechanism controlling the relationship between a large-scale and regional-scale phenomenon, and thus helps us to understand the factor that controls the predictability. The modes derived from our SVDA analysis captured each growing or decaying phase of ENSO, IOD, and the new type of CP El Niño. Mode-by-mode analysis will tell us not only the physical processes behind but also the strengths and weaknesses of a CGCM simulation, and how to reduce biases in each mode.
This paper highlights the potential of the one-tier seasonal forecasting system to predict cases like the
Moreover, monsoon rainfall in the late rainy season of
Acknowledgments. The authors are grateful to
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MASAHIDE KIMOTO AND
(Manuscript received
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