El Niño and April SATs in MSA

Climate in the MSA region exhibits pronounced year-to-year variability that is strongly linked to the ENSO phenomenon7,18. April SATs over MSA are highly correlated (r=0.73) with the Niño-3.4 sea-surface temperature (SST) index, a common metric used to monitor ENSO variability19 that peaks during the December–January–February (DJF) season (Fig. 1). Positive DJF Niño-3.4 SST anomalies are associated with El Niño events and correspond to positive April SAT anomalies in MSA (Fig. 1d; hereafter ‘post-Niño Aprils’; see also Supplementary Fig. 2). While studies have suggested that this link with ENSO can be exploited to anticipate SAT anomalies after peak SSTs during the DJF season14,18,20, its impact on extremes in MSA during April remains poorly understand, especially in light of ongoing long-term warming9.

We explored the MSA April-El-Niño link further through a composite analysis of observed SATs during post-Niño Aprils. SAT anomalies from the GISTEMP2 and CRU3 (land) and HadISST21 (oceans) data sets show that, on average, post-Niño Aprils are characterized by warming over MSA (Fig. 2a,c), with enhanced warming over land. The April temperature response in the region to El Niño is among the largest in the world. This pattern is robust across the observational data sets, which show that, on average, post-Niño Aprils are accompanied by 0.6–0.7 °C of warming in MSA (Fig. 2a,b). A similar pattern of land amplification of the post-Niño April heat is seen in an ensemble of simulations of twentieth and twenty-first century climate performed with the Community Earth System Model Version 1 Large Ensemble (CESM1-LE) (Fig. 2e) and the CMIP5 suite of model members (Fig. 2g), confirming the observed MSA April-El-Niño link22,23.

Figure 2: Post-Niño April composites. Composites of (a–d) observed and (e–h) simulated (left) SAT and (right) cloud cover anomalies during April after El Niño events. The SAT observations are from the (a) GISTEMP datas et (1940–2016) and (c) CRU and HadISST1.1 data sets (1900–2014) whereas the cloud cover observations are from the (b) ERA-interim reanalysis data set and (d) the CRU and ICOADS data sets. The simulated composites are from (c) the CESM1-LE model with data from 1920 to 2015 (ref. 22) and the CMIP5 Ensemble from 1850 to 2015 (ref. 23; stippling indicates agreement between majority of the ensemble members). Composites were produced by averaging April anomalies after the peak of El Niño events where the simulated composites are the average composite among the 39 CESM1-LE members. El Niño events were identified as those years with peaks of the Niño-3.4 SST index larger than 0.5 standard deviations in each (observational and simulated) data set. The CESM1 composite SAT anomalies are scaled down by a factor of 0.45 over both land and ocean so that the April composite SST anomalies averaged over the Niño-3.4 region agree in magnitude with the observed April HadISST1.1 composite. No scaling was performed for the cloud cover data simulated by CESM nor for the SAT and cloud cover simulations from the CMIP5 ensemble. Full size image

These composite analyses reveal that peak seasonal SATs in the MSA region are highly sensitive to El Niño. Previous studies proposed that changes in atmospheric circulation associated with El Niño reduce cloud cover over the land areas of the Maritime Continent and Indian Ocean rim causing the associated warming20,24,25,26. To test whether clouds play a role causing extremes SATs over MSA, we performed a composite analysis of cloud cover anomalies during post-Niño Aprils using land- and ship-based observations together with reanalysis data as well as CESM1-LE and CMIP5 output (Fig. 2). We find that cloud cover observations from the ERA-interim data set27 and CRU (land) and ICOADS (ocean) data sets3,28 show, on average, reduced cloud cover over MSA during post-Niño Aprils (Fig. 2b,d). These reductions in cloud cover appear to be linked with increased cloud cover over the central Pacific, suggesting that the SST anomalies associated with decaying El Niño events could be driving anomalous overturning circulation in the atmosphere and thereby creating drought conditions over southeast Asia (Supplementary Figs 2 and 3; ref. 20). Our analysis shows that during April, the area of reduced rainfall and cloud cover has moved from the Maritime Continent, where El Niño impacts are more pronounced at their winter peak, to over MSA and the Philippines, explaining the region's sensitivity to ENSO.

The CESM1-LE and CMIP5 ensemble means simulate remarkably realistic patterns of surface temperature (Fig. 2e,g) and cloud cover anomalies (Fig. 2f,h), compared with observations from different data sets. This lends further support for a robust relationship between El Niño and hot April extremes in MSA inferred from observations. The CESM1-LE also simulates ENSO realistically (DiNezio et al. in prep) including the tropical SST patterns as well as circulation anomalies and teleconnections (see Methods for details on these simulations). The realism of CESM1 allows us to use data from the Large Ensemble, consisting of output from 39 simulations of twentieth and twenty-first century climate22, to explore the effect of long-term anthropogenic warming as well as El Niño on extreme seasonal temperatures over MSA.

Histogram analysis of April SATs

Do all El Niño events lead to hotter Aprils over MSA? We address this question by analysing the effect of El Niño and La Niña (El Niño 's cooler counterpart) on the distribution of April SATs. In both observations and simulations, after linearly detrending the data sets over their entire length to remove the long-term warming trend, we divided all April monthly mean SATs into distributions of post-El Niño and post-La Niña Aprils. The resulting distributions show that indeed the tails of the April MSA SATs are linked to ENSO variability. Both observations and model data show that post-El Niño Aprils are on average hotter (Fig. 3a–c, red bars) than post-La Niña Aprils (Fig. 3a–c, blue bars). Moreover, virtually all hot Aprils occurs following El Niño events (80% for GISTEMP, ∼73% for CRU, and ∼88% for CESM1-LE post-Niño April extremes had >29 °C detrended SAT values). The fact that the simulations accurately capture this shift in the distribution is indicative that the effect of ENSO on extreme SATs in MSA is robust.

Figure 3: Histograms of April SATs. SATs for Mainland Southeast Asia (MSA) are taken from the GISTEMP (1940–2016; a–c) and CRU (1901–2014; d–f) observational data sets along with simulations from the CESM1-LE (1920–2015; g–i). Histograms in the top panel (a,d,g) delineate all the April data (grey) into those occurring after El Niño (red) and La Niña (blue) events over the entire time period. Histograms in the middle panel (b,e,h) separate the available April surface temperature data into two equal time periods (dark blue—earlier; orange—latter) over the length of the individual data sets. The bottom panel histograms isolate post-Niño-April events in the observations (c,f) and simulations (i) over the time period indicated in the middle panel, i.e., they separate the post-Niño events (red) in the topmost panel into the subperiods indicated by the middle panel for each of the data sets. All data sets were linearly detrended prior to analyses where the top panel histograms (a,d,g) were detrended over the entire time period for each data set, whereas linear detrending was performed for each subdivided time period in the subsequent analysis. We found that over their respective time periods for both GISTEMP (1940–1977; 1978–2016) and CRU (1901–1958; 1959–2014) data sets, the mean was significantly larger in the more recent subperiod (t-test: H=H a ; P<0.01) whereas the variance was not significantly different between the subperiods (f-test: H=H o ; P>0.05). Full size image

Next, we looked into the effect of long-term regional warming on the observed and simulated distributions of all April SATs in MSA. Here, we divided the data sets into two equal subperiods and detrended the data over each subperiod (GISTEMP: 1940–1977 and 1978–2016; CRU: 1901–1958 and 1959–2015; CESM1-LE: 1920–1968 and 1969–2015). This ensures that the mean warming between the individual time periods is retained for each data set despite differing time periods (see also Supplementary Figs 4 and 5). The long-term warming results in distributions of April SATs with significant positive shifts in the mean according to a two-sample t-test (P<0.01). The distribution of SATs shifts due to this change in the mean and results in an increase of extreme SATs in the latter subperiod.

Lastly, we performed a similar analysis of only post-Niño Aprils (Fig. 3c,f,i). We find that the latter subperiods show more extreme post-Niño April SATs because of a significant shift in the mean of the distribution (two-sample t-test; P<0.01) but statistically indistinguishable variance (f-test; P>0.05). Changes in the tails of the distribution caused by shifts in the mean, similar to the ones identified here, are the most straightforward way in which global warming can lead to increased frequency of extremes16,29. Although increased extremes can also arise from increased variability, we do not observe statistically distinguishable variance between the two subperiods across any of these distributions (f-test; P>0.05). Thus, the mean shift in post-Niño April SATs underpins long-term warming as the most likely cause of the increasing extremes in MSA.

Attribution of extreme April SATs

Based on this assessment of the roles of El Niño and long-term warming in modulating April SATs, we built a statistical model to attribute the 2016 event and other April extremes in MSA. The model is a simple multiple regression in the form SAT April =αt+βt2+γT+δ, where April SATs in MSA (SAT April ) are regressed upon linear and quadratic components of the regional long-term warming trend18,30 represented by time in years (t and t2, respectively), a term proportional to DJF Niño-3.4 SST Index (T) capturing variability correlated with ENSO and a residual term (δ) capturing the effect of other climate variability and weather. This analysis yielded a fit that has a high correlation coefficient (r=0.83) with April temperature observations from 1940 to 2016 (Supplementary Fig. 6), indicating that long-term warming and DJF ENSO amplitude explain much of the SAT variability during April in MSA. The unexplained residual between our regression fit and the observed anomaly (termed ‘other’) appears to be related to mid-latitude weather over Asia or local weather (see Supplementary Discussion). However, as the statistical model explains a large component of April SATs, we used the regression model to attribute the relative contributions of long-term warming and ENSO in producing observed extremes in MSA.

We chose the 15 hottest Aprils (greater than the 80th percentile of the warmest Aprils) from the entire GISTEMP data set (1940–2016) and based on our regression model, investigated the influence of long-term warming versus El Niño in each April extreme (Fig. 4). All 15 events occur after 1980, i.e., in the latter, warmer subperiod of our previous analysis. April 2016 is the warmest on record, surpassing the previous record holder, April 1998, by 0.9 °C (Fig. 1c). Both of these record-breaking Aprils occurred after the peak of extremely strong El Niño events (1997–1998 and 2015–2016). The ENSO contribution typically accounts for 30–75% (0.25–1.15 °C) of the hot April SAT anomaly in MSA during those hot April events coinciding with El Niño years. Indeed, the eight hottest Aprils (>90th percentile) all coincided with El Niño years (labelled in red in Fig. 4a), with the notable exception of April 2001 where the residual term dominates. On the other hand, the recent hot Aprils of 2001, 2013 and 2014 do not occur during El Niño years, and as such, their anomalies (albeit minor) are mainly explained by long-term warming (Fig. 4). However, even though the regression model cannot fully explain the observed anomalies for these outliers (see residual component labelled ‘other’ in Fig. 4a), it does indicate that long-term warming is increasingly playing a role (∼25–60%) in producing extreme April SATs in the absence of El Niño events. The regression model also indicates an upward trend in the relative contribution of long-term warming, which increases across these 15 events until 2016, and will foreseeably increase as regional warming continues to influence hot Aprils.

Figure 4: Attributing hot Aprils in Mainland Southeast Asia. (a) Observations: The relative contribution of El Niño (green bars) versus the long-term warming trend (red bars) towards the 15 hottest April SATs (>80th percentile) in the GISTEMP record of Mainland Southeast Asia (MSA; 1940–2016) using a regression model. The residual of the observed anomaly and the regression fit is termed as ‘other’ variability (yellow bars). The years in red on the x-axis indicate the eight hottest extreme April events (>90th percentile). (b) Simulations: Median lead time for the next record-breaking April (years) compared to the baseline period (colours in legend) versus the median DJF Niño-3.4 SST anomaly (°C) for that forthcoming record-breaking year across all CESM1-LE members (with ±1σ ranges across the 39 members at each baseline period). A trendline for these datapoints calculated with the uncertainty is shown (black dashed line). Also depicted are the estimated probability density functions from the histograms of lead times as well as the DJF Niño-3.4 SST anomalies during the next record-breaking April at each baseline period. Full size image

The regression model indicates that the long-term warming trend caused ∼29% of the extreme 2016 April SAT in MSA (Fig. 4a). Despite its unprecedented strength, El Niño accounted for ∼49% of the record-breaking hot April of 2016, leaving 22% unexplained. If some amount of the unexplained portion of the 2016 April SAT, apart from the contribution of weather-related noise, arose from a nonlinear change in the rate of global warming30, the imprint of regional warming on the 2016 extreme would be even larger than our estimate. Regardless of this unexplained portion, our regression model holds predictive value18 as the DJF Niño-3.4 SST anomaly can be observed months before the potential hot April.

Future temperature extremes in MSA

With continued warming projected for the rest of this century8, will MSA experience more frequent, record-breaking hot Aprils? We investigated the likelihood of MSA extremes becoming more common in the future by analysing SAT data from the CESM1-LE. Starting from 1940 and using a non-overlapping moving window of 30 years, we calculated the hottest April in each of the ensemble members for that baseline period. For each of these record hot Aprils, we also calculated the DJF Niño-3.4 SST anomaly of that year. We then determined the lead time for the next, new, record-breaking April across each member (i.e. difference in years between the baseline record and new record). For each lead time, we also computed the DJF Niño-3.4 SST anomaly for that year in order to explore the control of El Niño on April extremes. Finally, we repeated this procedure for windows from 1940–1970 to 2030–2060 to understand the effect of warming on the lead-time for the next record-breaking April (Fig. 4b).

The median lead times across all ensemble members indicate that indeed, with future warming, record-breaking April SATs occur more frequently in MSA (Fig. 4b), a result in agreement with the statistics of record-breaking events31. The simulations suggest that record-breaking April SATs relative to an early twentieth century baseline occurred during large El Niño events (DJF Niño-3.4 SST anomaly >2 °C) whereas record-breaking April events in the twenty-first century can occur during El Niño events of smaller magnitudes. We note that 88.5% (414 out of 468) of all record-breaking extremes occur during El Niño years (DJF Niño-3.4 SST anomaly >0.5 °C), where 63% (260 out of 414) occur during strong events (DJF Niño-3.4 SST anomaly >2 °C). Thus, even though global warming will induce more extreme SATs in the future for the MSA region, our findings are advantageous for anticipating hot Aprils by monitoring the peak of Niño-3.4 SSTs a few months in advance.