Measuring disruptions

We define direct measures of disruption to precipitation over the entire region of interest (that is, 140° E–240° E, 25°S–15° N). This contrasts with a previous study that used a proxy measure of disruption based on rainfall amount at a single location14. We will use two different measures of disruption: the spatial correlation coefficient (R); and the root-mean-squared difference (D), between seasonal (December–January–Februrary) and long-term (multi-decadal) average rainfall patterns. R in any given year is equal to the (spatial) correlation coefficient between average seasonal rainfall in that year with average seasonal rainfall over all years in the multi-decadal period in which that year falls. R is therefore a measure of the degree to which the spatial pattern of rainfall in individual years deviates from the climatological pattern. In a similar fashion, D is a measure of the magnitude of rainfall anomalies in individual years, relative to the climatological pattern over the same region. In the following we will refer to Ω R , Ω D and Ω, where Ω R is the frequency of major disruption defined in terms of R, Ω D is the frequency of major disruption defined in terms of D, and Ω is used to refer to Ω R and Ω D collectively.

Fourteen multi-decadal periods are considered: 10 during the pre-industrial period, and one each during the early 20th century (E20C), the late 20th century (L20C), the early 21st century (E21C) and the late 21st century (L21C). Furter details on why these periods are chosen is given in ‘Methods’ section.

Major disruptions

The relationship between R2 and NINO3.4 sea-surface temperature (SST) anomalies is presented in Fig. 1a for the observations21,22, coupled climate models23 under 20th century forcing, and an atmospheric general circulation model (AGCM)24,25,26 forced using SSTs observed from 1951 to 2013 (see ‘Methods’ section). The results indicate that the frequency and strength of disruptions tends to increase as the magnitude of NINO3.4 anomalies increase, and that major disruptions (defined here to occur when R2<0.5) can occur during both El Niño and La Niña years—the two extreme phases of ENSO. Under this definition the observations exhibited major disturbances during the El Niño years of 1991/92, 1982/1983 and 1997/98, and during the La Niña years of 1988/1989, 2000/2001 and 2010/2011. Similar results are obtained if we define major disruption in terms of D (Fig. 1b, D>3.1 mm day−1, which is chosen so that Ω D and Ω R have a similar value in the pre-industrial period (1.1 events per decade)). These thresholds are based on an analysis of the 24 CMIP5 models selected which had at least 500 years available under pre-industrial forcing (identified in Supplementary Table 1). The strong asymmetry evident in observational, coupled model and AGCM data indicates that much greater disruption can occur during El Niño years than during La Niña years.

Figure 1: Scatter plots showing measures of disruption versus the NINO3.4 SST anomaly. (a) R2 and (b) D. R is the correlation coefficient between the rainfall map in a given year with the map of rainfall averaged over a longer reference period in which the individual year falls. D is the root-mean-squared difference between seasonal and long-term (multi-decadal) average rainfall patterns. R and D are calculated over the domain 140° E–240° E, 25° S–15° N. Observations (unfilled red circles21,22), with reference period 1979–2013. The coupled climate models (small filled mustard-coloured dots), with reference period L20C (that is, 1957–1992). AGCM (unfilled blue circles) with reference period 1951–2013. AGCM values given are the averages of values in a 10-member ensemble. Quadratics-of-best-fit are also shown using the same colour scheme. Note that El Niño events have positive NINO3.4 SST anomalies, and so they appear to the right of the y axis in both panels, while La Niña events appear to the left. The greater the disruption the smaller R becomes, and the larger D becomes. All values are for December–February. Full size image

When a major disturbance occurs during El Niño years (Supplementary Fig. 1a), rainfall tends to extend further east along the equator, there tends to be a reduction in rainfall in the western Pacific, and both the Intertropical Convergence Zone and the South Pacific Convergence Zone tend to move equatorward6,7,24,26,27,28,29. While major disturbances during La Niña years tend to exhibit the opposite of these features6,7,25,27,28,29 asymmetries in rainfall anomalies are clearly evident23 (Supplementary Fig. 1b). The models (Supplementary Fig. 1c and d) appear to do a reasonable job in capturing the observed behaviour (Supplementary Fig. 1a and b).

Changes in the frequency of major disruptions

Relative to the pre-industrial period, there is a 10% increase in the multi-model mean (MMM) of Ω R, MMM(Ω R ), in E20C (Fig. 2a, Supplementary Table 2), that is, a 10% increase in the frequency of major disruption to the spatial rainfall pattern (when R2 falls below 0.5). In the E20C period, 14 of the 24 models show an increase in Ω R , 9 models show decreases, and one model shows no change (P value=0.21; Supplementary Table 2). There is also a 31% increase in MMM(Ω R ) in L20C (Fig. 2a), with 17 increases and 5 decreases (P value=0.01), a 54% increase in E21C with 19 increases and 4 decreases (P value<0.01), and a 31% increase in L21C with 14 increases and 8 decreases (P value=0.15). These 21st century increases occur under the 21st century scenario with the highest greenhouse gas increases (RCP8.5, Fig. 2a), with the clearest indication of change occuring for E21C. It is interesting that the change in E21C is larger than for L21C. We will return to this issue later.

Figure 2: Percentage change in the frequency of major disruptions in the twentieth and twenty-first centuries. E20C, L20C, E21C and L21C frequency changes relative to the pre-industrial period. (a) Twenty-first century values under RCP8.5 only. 24 models. (b) As in a but twenty-first century percentage changes are provided for three different scenarios: RCP2.6 (blue), RCP4.5 (orange) and RCP8.5 (red). The results in b are based on changes obtained from the 20 models that were forced with all three scenarios (see ‘Methods’). Dashed lines and circles indicate percentage changes in Ω D , solid lines and squares indicate percentage changes in Ω R , both relative to their pre-industrial values. Filled circles and squares indicates that P rank <0.1. Bars indicate the 90% confidence interval of the multi-model mean (MMM) change for L21C. Full size image

The corresponding changes in Ω D are 26, 28, 90 and 126%, for E20C, L20C, E21C and L21C respectively (Fig. 2a). The associated P values are 0.03, 0.03, <0.01, and 0.01 respectively (Supplementary Table 3). The 21st century figures for Ω D are larger than the corresponding figures for Ω R (that is, 54 and 31%). This indicates that 21st century global warming has a greater impact on disruptions to the magnitude of year-to-year variability than it does on the spatial structure of the variability.

Additional analysis indicates that the increases in Ω arise from increases in the frequency of major disruptions during both extreme phases of ENSO. For example, there is a 21% increase in Ω R during El Niño years (P value=0.03), and a 91% increase in La Niña years (P value=0.05), in E21C relative to the pre-industrial period.

Factors responsible for the increase in major disruptions

We will show below that the increase in MMM(Ω) has contributions from: (i) an increase in the frequency of El Niño and La Niña events; and (ii) an increase in precipitation anomalies arising from a nonlinear interaction between unchanged ENSO-driven SST anomalies and background (global) warming5,24,25,26,27,28. The positive contributions from nonlinearity can be reinforced or partially offset in models, depending on what happens to the magnitude of ENSO-driven SST variability in each model.

Changes in ENSO frequency

There is a tendency for the frequency of both El Niño and La Niña events—defined in terms of SST variability (see ‘Methods’ section)—to increase. For example, the MMM frequency of La Niña events during the pre-industrial period is 2.3 per decade, and this figure increases by 4% during E20C, 10% during L20C, and by 22% during E21C and 9% during L21C under the RCP8.5 scenario. The MMM frequency of El Niño events during the pre-industrial period is 2.2 per decade, and this figure increases by 2% in E20C, 12% in L20C, and by 22 and 7% in E21C and L21C, respectively (both under RCP8.5). Note that only the increases in L20C and E21C are statistically significant.

Precipitation anomaly increases

A useful and important indicator of rainfall variability and disruption in the Pacific is the amount of rainfall received in the central-eastern equatorial Pacific5,14. By the end of the 20th century, the magnitude of precipitation anomalies (relative to a new, background state) in the central-eastern Pacific during El Niño and La Niña events tends to increase with time, as the world warms up. This intensification of ENSO-driven rainfall variability will increase the likelihood that a given El Niño or La Niña event will cause major disruption. For example, during La Niña events, 33% of models show an increase in the magnitude of rainfall anomalies averaged over the NINO3.4 region during E20C, 62% during L20C, 71% during E21C and 83% during L21C. The corresponding increase during El Niño events are 54%, 67%, 88% and 71%, respectively.

Modelled changes in the magnitude of NINO3.4 SST anomalies during ENSO events are varied and do not exhibit the degree of consistency that the modelled precipitation changes exhibit. For example, during El Niño events, only 58% of models show an increase in the magnitude of the NINO3.4 SST anomaly in E20C under RCP8.5. Correspondingly, in L20C, E21C, and L21C, only 63, 50 and 38% of models show an increase. During La Niña events 71% of models actually show a decrease in the magnitude of the NINO3.4 SST anomalies in E20C, while increases of 50, 58, and 67% are evident for the later periods (with E21C and L21C values again obtained under the RCP8.5 scenario).

Multiple pieces of evidence support the importance of an increase in precipitation anomalies arising from a nonlinear interaction between unchanged ENSO-driven SST anomalies and background warming. Here, nonlinearity refers to the fact that exactly the same ENSO SST anomaly, when combined with background warming, can result in a different precipitation anomaly (measured relative to a new climatological precipitation value). The first piece of evidence is given in Fig. 3, which shows the change in both the SST anomaly (Fig. 3a) and precipitation anomaly (Fig. 3b) during El Niño events, between the pre-industrial period and L21C. There is little agreement among all models on the sign of change in SST anomalies (that is, lack of stippling in Fig. 3a). Despite this there is agreement on an enhancement of the rainfall signal.

Figure 3: Changes in El Niño-driven surface temperature and rainfall anomalies. Changes in (a) surface temperature anomalies, and in precipitation anomalies in both (b) the climate models and (c) the AGCM. There is no change at all in the ENSO-driven SST anomalies used to generate c. Climate models: L21C relative to pre-industrial. AGCM: L21C relative to L20C. Maps were generated using Interactive Data Language (The Interactive Data Language (Version 8.2.3) [Software]. (2013). IDL, Exelis Visual Information Solutions, Inc., is a subsidiary of the Harris Corporation. http://www.harrisgeospatial.com/). Full size image

Additional evidence for intensification of rainfall anomalies through nonlinearity is provided by the similarity between the patterns of change in precipitation anomalies obtained from the SST-forced AGCM experiments (Fig. 3c)—in which there is no change at all in ENSO-driven SST anomalies—and the MMM pattern of change in the coupled climate models (Fig. 3b).

It is reassuring to note the magnitude of the nonlinear reinforcement of El Niño-driven rainfall anomalies over the NINO3.4 region in the coupled models and AGCM have a similar scale in Fig. 3 (∼0.61 mm day−1 (models) and 0.7 mm day−1 (AGCM)). A breakdown of the moisture budget in the AGCM, described in ‘Methods’, shows that the changes in rainfall primarily arise from changes in the mean circulation dynamics (Supplementary Fig. 2). These changes are partially offset by contributions from the covariant component comprising transient eddy and surface terms, and are weakly enhanced by a contribution from a thermodynamic component which reflects an increase in the available moisture.

An enhancement of the La Niña-driven rainfall response in the climate models is also evident (Supplementary Fig. 3b). This occurs despite there being very little consistency in the change in La Niña-driven SST variability (Supplementary Fig. 3a). This is also consistent with the impact of global warming on La Niña-driven rainfall responses in the AGCM in which there are no changes in the La Niña-driven SST anomalies at all (Supplementary Fig. 3c).

Our results are consistent with earlier research5,24,25,26,30,31, which collectively indicates that changes in ENSO-driven precipitation variability can be explained in terms of changes in four factors: (i) mean-state moisture content, (ii) the amplitude of ENSO-driven SST variability, (iii) spatially dependent changes in mean-state SST and (iv) in the structure of ENSO-driven SST variability. One of these studies31 highlighted the importance of the contrast between mean-state SST changes in the tropical Pacific and changes in mean-state SST throughout the tropics.

Estimates of the contribution of nonlinearity to changes in ENSO rainfall anomalies are given in Fig. 4. Figure 4a (La Niña) and Fig. 4b (El Niño) give the modelled relative frequency distributions of changes in NINO3.4 rainfall anomalies, while Fig. 4c,d give the modelled, relative frequency distributions of the nonlinear contribution to the change in the corresponding rainfall anomalies above. The method used to estimate nonlinearity is described in ‘Methods’. Changes arising from internal variability alone are estimated by differences between each multi-decadal pre-industrial period with every other multi-decadal pre-industrial period (grey bars). For El Niño (Fig. 4d), nonlinearity makes a positive (enhancing) contribution in all of the 20th and 21st century periods (that is, E20C, L20C, E21C, L21C). This contribution is largest in the 21st century (that is, in E21C and L21C), and smallest in E20C. In fact the change in E20C is very small, and is typically within the range of internal variability. This is not the case for the other three periods. Nonlinearity in L20C, E21C and L21C is larger than E20C, and is beyond the internal variability range depicted. This indicates that the changes in El Niño rainfall anomalies are at least partially caused by external forcing during L20C, E21C and L21C.

Figure 4: Relative frequency distributions of changes in NINO3.4 region. (a,b) rainfall anomalies and (c,d) nonlinear contributions. All changes are relative to 10 different pre-industrial periods. The grey bars represent the distribution of changes between the 10 pre-industrial periods. The grey bars therefore represent changes that can arise from internal climate variability alone. The boxplots represent the changes for E20C (light blue), L21C (dark blue), E21C (orange), L21C (red), relative to the 10 pre-industrial values. The whiskers indicate the minimum and the maximum change, the boxplot the 25th and 75th percentiles, and the median is indicated by the vertical line in the boxes. The approach used to estimate the nonlinear contribution is described in ‘Methods’. Full size image

For La Niña, the nonlinear contribution tends to be negative for all 20th and 21st century periods. However, only the 21st century changes in nonlinearity (Fig. 4c) are very largely outside the internal variability range depicted. As rainfall tends to decline in the NINO3.4 box during pre-industrial La Niña events, the negative value of the 21st century nonlinear contributions again indicates that nonlinearity acts to enhance the ENSO-driven rainfall anomaly. This nonlinear enhancement is consistent with, but extends, earlier research on El Niño-driven rainfall changes in coupled models5 and ENSO-driven changes in an AGCM24,25,26.

One puzzle, which we have not yet addressed, is why there is a decrease in Ω R from E21C to L21C under RCP8.5 (Fig. 2a, for example MMM(ΔΩ R )=−0.3 events/decade). This is, at least in part, due to a decline in the magnitude of El Niño-driven SST anomalies in most models between these two periods (Supplementary Fig. 5), consistent with earlier research32. Such declines oppose, and evidently overcome, the nonlinear enhancement of precipitation anomalies in L21C, relative to E21C (Fig. 4).

Impact of reducing 21st century global emissions