The Indo-Pacific warm pool (IPWP) has warmed and grown substantially during the past century. The IPWP is Earth’s largest region of warm sea surface temperatures (SSTs), has the highest rainfall, and is fundamental to global atmospheric circulation and hydrological cycle. The region has also experienced the world’s highest rates of sea-level rise in recent decades, indicating large increases in ocean heat content and leading to substantial impacts on small island states in the region. Previous studies have considered mechanisms for the basin-scale ocean warming, but not the causes of the observed IPWP expansion, where expansion in the Indian Ocean has far exceeded that in the Pacific Ocean. We identify human and natural contributions to the observed IPWP changes since the 1950s by comparing observations with climate model simulations using an optimal fingerprinting technique. Greenhouse gas forcing is found to be the dominant cause of the observed increases in IPWP intensity and size, whereas natural fluctuations associated with the Pacific Decadal Oscillation have played a smaller yet significant role. Further, we show that the shape and impact of human-induced IPWP growth could be asymmetric between the Indian and Pacific basins, the causes of which remain uncertain. Human-induced changes in the IPWP have important implications for understanding and projecting related changes in monsoonal rainfall, and frequency or intensity of tropical storms, which have profound socioeconomic consequences.

Keywords

Recent studies suggest greenhouse gas–induced warming to be the major cause for global ocean temperature ( 14 ) and tropical Indian Ocean SST changes ( 12 , 15 – 17 ), but its role in the observed IPWP region changes ( 14 , 16 , 17 ) is not clear. We provide the first quantitative attribution of the observed IPWP warming and expansion changes during the past 60 years, examining anthropogenic and natural contributions to the IPWP warming and expansion. We address this by comparing observed 1953–2012 changes with climate model–simulated changes using CMIP5 (Coupled Model Intercomparison Project Phase 5) ( 18 ) historical climate change simulations that account for anthropogenic forcing (greenhouse gases, aerosols, and other anthropogenic forcing agents) combined with natural (solar and volcanic activities) forcings (ALL), greenhouse gas forcing only (GHG), or natural forcings only (NAT).

( A to D ) HadISST ( 39 ) observations (A) and CMIP5 ( 18 ) multimodel means for each type of forcing: (B) anthropogenic plus natural external (29 models), (C) greenhouse gas only (6 models), and (D) natural external only (6 models). In each panel, trends are °C per 60 years, and the mean IPWP area is shown for two periods: 1953–1959 (dashed line) and 2000–2012 (solid lines). Gray boxes in (A) depict the area over which warm pool regions are calculated for the IPWP, divided into Indian and Pacific sectors by the 120°E meridian.

The Indo-Pacific warm pool (IPWP), where sea surface temperatures (SSTs) exceed 28°C (which is an estimated threshold for atmospheric deep convection), supports the Walker circulation’s rising branch and largely determines rainfall distribution throughout the tropics to extratropics ( 1 , 2 ). It plays a key role in climate and monsoon variability for many developing countries throughout Asia and Africa ( 3 – 7 ), but also influences remote regions and large-scale climate modes of variability ( 8 – 10 ). From year to year, IPWP intensity and size fluctuate with the El Niño–Southern Oscillation (ENSO) ( 5 , 11 – 13 ). The ongoing IPWP warming and expansion in recent decades ( 5 , 7 , 11 , 12 ) ( Fig. 1A ) are, by one estimate, responsible for more tropical Indo-Pacific SST variance than anomalies associated with ENSO ( 5 ).

RESULTS

Observed and modeled changes Models simulate the observed Indo-Pacific warming and IPWP expansion (Fig. 1B) reasonably well, albeit with greater warming and expansion in the central to eastern Pacific (fig. S1), a region affected by persistent biases (for example, excessively strong equatorial Pacific cold tongue) (19). We focus our analysis on 29 of 42 models (tables S1 and S2; see Materials and Methods) that simulate a realistic IPWP (that is, comparable size to observations; fig. S2) to reduce the impact of biases because there is a close relationship between IPWP mean size with changes in intensity and area (fig. S3). Specific forcing experiments show that realistic changes occur only when greenhouse gases are included (Fig. 1, B to D) but that the response is stronger than observed in GHG-only experiments, which exclude negative contributions from other anthropogenic forcings, such as aerosols (16, 17). To examine long-term IPWP intensity and area changes, we considered nonoverlapping 5-year annual means over the 60-year period. Mean IPWP SST and area are calculated over the Indo-Pacific region enclosed by the 28°C isotherm between 25°S to 25°N and 40°E to 130°W. We also independently analyze the Indian and Pacific Ocean warm pools (Fig. 1A). The IPWP warmed and expanded steadily until the late 1990s, followed by weaker trends, as observed in global mean temperature (Fig. 2) (20). The ALL and anthropogenic forcing (ANT; estimated as ALL minus NAT) simulations show realistic increasing trends, whereas GHG-only trends are significantly larger than observed. In contrast, NAT-only simulations have varying decadal trends, resulting in no significant long-term trend. The signal induced by ANT-only is therefore close to that from ALL forcing (Table 1). Preindustrial control simulations from the models are used to provide a measure of the range of trends arising from unforced internal climate variability, which the observed trends exceed (Fig. 2). Fig. 2 Time series of 5-year mean anomalies of warm pool intensity and area. (A) Observed (39) (black) SST anomalies (°C) with (solid) and without (dashed) PDO influence are compared with multimodel mean–simulated responses to anthropogenic plus natural external forcings (ALL; green), anthropogenic forcing (ANT; calculated as ALL minus NAT; orange), greenhouse gas only forcing (GHG; red), and natural external only forcings (NAT; blue) for the Indo-Pacific (top), Indian Ocean (center), and Pacific Ocean (bottom) warm pools. Gray dashed lines represent the 5 to 95% range of internal variability taken from control (CTL) simulations. Linear trends [error bars representing 5 to 95% confidence intervals (CIs)] for observations with (black) and without (white) PDO and multimodel mean over the period 1953–2012 are displayed to the right. (B) The same as (A) but for warm pool area anomalies (as a percentage of each 1971–2000 mean) in the three warm pool regions. Table 1 Comparison of trends in warm pool intensity and area between observations and climate model simulations. Multimodel means of linear trend slopes are defined as the signal (S ALL , S ANT , S GHG , or S NAT ), and the SD of trends across nonoverlapping CTL chunks is defined as the noise (N). The 5 to 95% CIs are shown in Fig. 2. Signal-to-noise ratios (SNRs) are then calculated from slopes of SST and area series averaged over the three warm pool regions during 1953–2012 for ALL and ANT simulations. Observational (39) trend slopes with (S OBS ) and without the influence of the PDO (S OBS* ) are given for comparison. Units for S and N are °C and % per 60 years for warm pool intensity and area, respectively. View this table: Despite studies reporting tropical Indian Ocean warming at a rate of up to three times faster than the tropical Pacific (fig. S4A) (5, 12, 17, 21), trends are comparable if only area-mean SSTs averaged in the expanding warm pool of both oceans are compared, due to the larger increase in warm pool size in the Indian Ocean (Fig. 2 and Table 1). Therefore, the zonal intensity gradient (Indian minus Pacific) between the two warm pool sectors has experienced little change (fig. S4B). Year-to-year variations of approximately 10 to 15% (relative to the climatological mean) in IPWP size occur with ENSO (12, 13, 22), far less than the observed expansion of over 30% since the 1950s (Fig. 2B and Table 1). Warm pool expansion in the Indian Ocean (51%) has also far exceeded that in the Pacific Ocean (22%) (Fig. 2B and Table 1). These results are consistent among observational data sets despite some regional differences, and are different to similar expansion rates in both basins associated with a uniform warming (fig. S5).

Detection of human influence To detect and quantify contributions from ALL, GHG, ANT, and NAT forcings to long-term variations in IPWP intensity and area, we use an optimal fingerprinting technique (23). In this method, observations are regressed via generalized linear regression onto one or two multimodel-simulated signals (see Materials and Methods for details). We conduct single-signal analyses by regressing observations onto model-simulated responses to ALL, ANT, GHG, and NAT forcings estimated from the average of the selected model ensemble. We conduct a two-signal analysis in which observations are simultaneously regressed onto ANT and NAT response to estimate the contribution of both anthropogenic and natural forcings to changes in warm pool properties. Unforced control (CTL) simulations are used to obtain an estimate of the internal climate variability, in addition to conducting a residual consistency test (23) to compare model-simulated internal variability with observations. Resulting best estimates and uncertainty ranges of scaling factors, which scale estimates of the responses to individual combinations of forcings to best reproduce observed changes, are used to determine whether external forcings are present in observations. Intensity and area changes are not perfectly correlated (r = 0.87, P < 0.01), and thus, we combine normalized intensity and area anomalies to capture additional information on changes that may improve detection and attribution (24). The influence of external forcing is detected when a scaling factor is significantly greater than zero, and considered consistent with observations when it is consistent with unity. Scaling factors based on single-signal optimal analyses are shown in the left panels of Fig. 3. Except for the Pacific warm pool area, scaling factors for ALL, GHG, and ANT are significantly greater than zero for long-term warm pool intensity and area changes, including combined changes. This indicates that the overall effect of external anthropogenic and natural forcing, or the effect of greenhouse gas forcing or anthropogenic forcing alone, can be detected. In most cases, uncertainty ranges for the scaling factors on the ALL and ANT responses include unity, indicating consistency with observations. Best estimates for ALL and ANT scaling factors are slightly above one for warm pool intensities and Indian Ocean warm pool area (Fig. 3, A and B), highlighting some underestimation of the response in the multimodel mean. In contrast, best estimates for GHG are below one, meaning that GHGs acting alone would have produced larger changes than the observed. Strong agreement of best estimates with observed trends is found in all three warm pool regions when combining intensity and area changes (Fig. 3C). The influence of NAT is not robustly detected in any case considered. The residual consistency test is passed in most of the single-signal cases, indicating that the residual variability that remains in the observations after removing the scaled response is consistent with model internal variability. Fig. 3 Results from optimal detection analysis of warm pool intensity and area with corresponding attributable trends. (A to C) Scaling factors of warm pool SST (A), area (B), and combined SST and area anomalies (C) for single-signals of anthropogenic plus natural external (ALL; green), greenhouse gas only (GHG; red), anthropogenic (ANT; orange), and natural external only (NAT; blue) forcings (left), and for two signals of ANT (x axis) and NAT (y axis) (center). In two-signal panels, dark, medium, and light colors indicate the Indo-Pacific, Indian, and Pacific warm pools, respectively. Best estimates (data points) and 5 to 95% Cls (error bars) of scaling factors are displayed, and the 5 to 95% joint confidence for two signals are represented by ellipses. Detectable response to an individual forcing occurs when scaling factors are significantly greater than zero. Consistency between observed and simulated responses is determined when scaling factors are not significantly different from unity. Also shown in corresponding bar graphs (right) of (A) and (B) are ANT- and NAT-attributable intensity and area trends (bar and 5 to 95% CI) from two-signal analysis (center) compared to observed trends (horizontal solid lines). The right panel of (C) compares the detection strength (mean and 5 to 95% CI) of the multimodel fingerprint of ANT in model runs (orange) and observations (black). Results from two-signal (ANT and NAT) analyses of warm pool intensity and area changes are shown in the center panels of Fig. 3. The ANT influence is detected in all cases with clear separation from the NAT influence except for the Pacific warm pool area. The ANT scaling factors for IPWP changes are closest to unity with more confidence compared to the Indian and Pacific Oceans separately. Overall, ANT signals for warm pool intensity must be scaled up, and ANT signals for warm pool area need to be scaled up for the Indian Ocean, but CIs encompass unity. NAT is not robustly detected because the joint confidence ellipses include zero on the NAT axis in all cases. ANT-attributable trends (calculated by multiplying two-signal scaling factors with multimodel mean trends) are very close to observed intensity (Fig. 3A) and area (Fig. 3B) trends. Considering combined changes increases the “detection strength,” which is a representation of the projection of any model run or observations onto the single variable or combined fingerprint (24), and further increases confidence that intensity and area changes are not due to internal variability alone (Fig. 3C). Our detection results are robust to the use of different SST data sets and different model sampling (see Materials and Methods).

Internal variability influence To understand the models’ underestimation of the warm pool warming, we assess the contribution of internal climate variability evident in observations (Fig. 2). The dominant mode of multidecadal variability in the Indo-Pacific is the Pacific Decadal Oscillation (PDO) (25). Changes in IPWP intensity and area associated with the observed PDO variability during the last 60 years have augmented that due to anthropogenic forcing. The contribution of the PDO to the observed IPWP warming and expansion is approximately 12 to 18% (Table 1). Removing the PDO influence from observations (based on linear regression) results in better agreement with multimodel anthropogenic responses in intensity trends and Indian Ocean warm pool expansion (Fig. 2) (26, 27).