Nonlinear El Niño impacts on the global economy under climate change

Domain: earth_science
Published: 2023-09-21
Editorial outcome: accept

Abstract
The El Niño-Southern Oscillation (ENSO) is a consequential climate phenomenon affecting global extreme weather events often with large scale socio-economic impacts. To what extent the impact affects the macroeconomy, how long the impact lasts, and how the impact may change in a warming climate are important questions for the field. Using a smooth nonlinear climate-economy model fitted with historical data, here we find a damaging impact from an El Niño which increases for a further three years after initial shock, amounting to multi-trillion US dollars in economic loss; we attribute a loss of US$2.1 T and US$3.9 T globally to the 1997-98 and 2015-16 extreme El Niño events, far greater than that based on tangible losses. We find impacts from La Niña are asymmetric and weaker, and estimate a gain of only US$0.06 T from the 1998-99 extreme La Niña event. Under climate change, economic loss grows exponentially with increased ENSO variability. Under a high-emissions scenario, increased ENSO variability causes an additional median loss of US$33 T to the global economy at a 3% discount rate aggregated over the remainder of the 21st century. Thus, exacerbated economic damage from changing ENSO in a warming climate should be considered in assessments of mitigation strategies.

Introduction
The El Niño-Southern Oscillation (ENSO) is a consequential climate phenomenon on Earth, alternating between the warm phase El Niño and cool phase La Niña1,2. During El Niño, anomalous sea surface temperature (SST) warming in the central and eastern equatorial Pacific weakens the easterly trade winds and shifts the atmospheric deep convection eastward, which in turn alters global atmospheric circulation through its teleconnections3–6. During La Niña, the reverse generally occurs but not symmetrically. The impact has a global reach, affecting a myriad of fields transcending extreme weathers, hydrological cycle, ecosystems and agriculture to human community7–10. In contrast to substantial advances in our understanding of ENSO’s physical dynamics and ENSO global climate teleconnection, how ENSO affects human society and how the impact might change under climate change remain important areas to explore.

Major El Niño and La Niña events are known to cause substantial economy reductions in affected countries and regions, directly through local weather extremes11–13: for example, the 1982-83 El Niño caused severe floods in southeast Brazil, with economic losses exceeding US$1.1 billion (ref. 14); the 1997-98 El Niño led to extreme weathers around the United States and direct economic losses of ~US$4 billion (ref. 15); the 1998-99 La Niña contributed to one of the most devastating Yangtze floods in China, with direct economic losses amounted to US$20 billion (ref. 16); the 2015-16 El Niño resulted in multi-year extreme drought and wildfire in Amazonia that cost about US$26 billion in total17. There are well-established connections between ENSO and subcomponents of economic production, such as crop yields and fisheries18,19, and losses at micro levels are reflected on macro levels20,21. However, the effect from El Niño and La Niña do not compensate22,23.

Climate change affects many aspects of modern human society including economy, public health and human conflict24–27. The mean temperature and precipitation have been common metrics for assessing the climate change impact. Recent advances suggest that ENSO variability is likely to increase under greenhouse warming28,29. Whether changes in ENSO cycles intensify the economic risks of greenhouse warming is an important issue. Here, by assessing ENSO’s impact on global economic production using historical climate and economic production data, we find that, in contrast to a non definitive effect from La Niña, there is a substantial negative impact from El Niño on global economic production that lasts for multi-years, leading to a larger growth effect than previously thought; further, the impact from future ENSO cycles increases in a warming climate, with an exacerbated loss of global economic production from increased ENSO amplitude under greenhouse warming.

Results
Nonlinear effect of ENSO on economic production
Most empirical econometric models treat ENSO as a simple linear predictor and focus on the negative impact of El Niño20,30,31. This approach assumes that La Niña has a positive impact symmetric to negative impact of El Niño. However, a limited nonlinear model incorporating a Heaviside step function to depict impact of La Niña and El Niño deferentially22,23 finds that La Niña may not always benefits; for example, the 1998-99 strong La Niña causes extreme rainfall and floods that reduce economic growth and damage livelihoods in many affected countries16,32, which substantially reduce the beneficial effect of a La Niña. This leads to heterogeneous economic effects across countries (Supplementary Fig. S1), and any benefit may be offset or overwhelmed by the losses. Further, ENSO is a nonlinear-dynamical system with asymmetric amplitude of SST anomalies and teleconnections between El Niño and La Niña33,34; for example, El Niño amplitude tends to be greater than that of La Niña, exerting a greater impact on global climate and human community than La Niña, and such impact can increase or change nonlinearly with amplitude of El Niño/La Niña. In addition, impact of an ENSO event could affect economic production in the ensuing years after global economic connectivity is disrupted by the ENSO event22. Therefore, an econometric model must reflect these features to realistically estimate ENSO’s effect on global economy.

As such, we establish a fixed-effect panel regression model35 using an ENSO index as a nonlinear predictor and incorporating lagged effects (see ‘Empirical econometric model’ in Methods). The ENSO index is Niño 3.4 SST anomaly (averaged over 5°S–5°N, 120°−170°W) in boreal winter (December, January and February, D(0)JF(1)), when ENSO typically peaks. The model accounts for: (1) country-fixed effect, such as different history and culture backgrounds of individual countries; (2) country-specific long-term linear and quadratic time trends in growth rates, derived from changing political institutions and economic policies of individual countries; and (3) nonlinear effect of annual mean country-specific temperature and precipitation, as in previous studies25,36, except that ENSO signal is removed through linear regression to obtain ENSO-independent annual temperature and precipitation. Importantly, we incorporate the nonlinear effect of ENSO in a quadratic function in which both the linear and the nonlinear components include lagged effects. These lag terms account for growth effects after the contemporaneous climate shock of an ENSO event.

We train our model using climate and economic data over 1960–2019 period. Time-invariant and time-trending covariates are allowed to interact with observed economic and climate variables. We include the mean temperature and precipitation terms to parallelly estimate the effect from mean state change and from ENSO. Consistent with previous findings25,36, the nonlinear effect of mean temperature operates in addition to the ENSO impact. In contrast to the mean temperature which has a significant contemporaneous effect25,36, we find that ENSO impact persists for 3 years (Supplementary Fig. S2), after which time little further impact is seen and uncertainty increases. We therefore focus on its effect with lags of 3 years after an event. A set of Bootstrap methods is applied to quantify uncertainty of point estimates in the model (Supplementary Fig. S3; see ‘Statistical significance test’ in Methods).

Our model reveals a nonlinear relationship between the Niño 3.4 index and its economic impact on country-level economic production (Fig. 1a). Both extreme El Niño and La Niña cause damage on economic growth, but the damage is far greater during El Niño than during La Niña; weak and moderate La Niña events produce a smaller benefit, which in amplitude is far smaller than the damage of weak El Niño events. Overall, there is a negative and statistically significant impact on economic growth during El Niño, but the impact is by and large insignificant for La Niña. Our model also finds that the contemporaneous effect is dominated by the linear component and its growth effect is dominated by the nonlinear component (Supplementary Table S1); the difference between the impact in the occurrence year and in the lagged years reflects a subsequent acceleration in the impact on economic activity that is fed by the initial shock.

We test the heterogeneity of such nonlinear effect by different groups of countries. Firstly, we measure monthly ENSO teleconnection to each country at different lags and accumulate teleconnections that are statistically significant to separate teleconnected and weakly-teleconnected countries (Supplementary Fig. S5–6; see ‘ENSO index and country-specific teleconnection’). ENSO-teleconnected countries exhibit a greater response to El Niño than weakly-teleconnected countries (Fig. 1b), because the economic impacts from ENSO are underpinned by the direct response of local weather and climate conditions23. We choose to involve all countries in our main analysis to account for indirect influences like trade. Secondly, agriculture-dependent countries, of which the GDP share of agriculture is >20%, show a greater response to El Niño than agriculture-independent countries because agriculture production is more likely to be affected by ENSO-induced local weather anomalies (Fig. 1c). Similarly, lower-income countries, that is, with a purchasing-power-parity-adjusted Gross Domestic Product (GDP) per capita in 1980 below the median25, exhibit a greater response to El Niño than high-income countries because their level of preparedness and the capacity to mitigate are relatively low, they are mostly located in the tropics where the ENSO teleconnections are strong, and their economies tend to have a high proportion of agriculture (Fig. 1d). The common nonlinear nature reflects the feature that the global spillovers and cascading effects dominate the globaleconomic impact of ENSO (see ‘Heterogeneity of response function’ in Methods).

Economic loss an order of magnitude greater than previously thought
In contrast to previous estimates37,38 that the extreme El Niño events of 1982–83, 1997–98 and 2015–16 each costed global economy tens of billions in US dollars, our result suggests an impact from each event is trillions of US dollars. These additional losses mainly come from unobservable and delayed response to the initial climate shock. Initially, El Niño drives direct losses in the severely-affected regions through extreme weathers as reflected in temperature and precipitation anomalies. Subsequently, cross-sector and cross-border spillovers occur, affecting global macroeconomy. Previous studies suggested the cascading effects may commence via several transmission channels. For example, extreme weathers lower crop yields and agricultural productivity, leading to food shortages, trade contractions and commodity price increases39; abnormal sea surface temperature and ocean current cause decreased fishery stocks and other marine resources40; damages on infrastructure increase reconstruction and maintenance costs and disrupt transportation networks41; fluctuation in rainfall and surface run-off cause hydroelectric power shortages and reduce water-dependent industrial outputs42; changes in disease vector dynamics caused by extreme weathers increase healthcare cost and reduce manual productivity43; and poor weather conditions reduce tourist arrivals and consumptions for tourism-dependent regions44.

Observed extreme El Niño events have considerable growth effect on global economy. The 1982–83, 1997–98 and 2015–16 events decreased growth by 1.0% in the occurrence year, but >5.0% cumulated over the subsequent 3 years (Supplementary Fig. S7). We estimate the loss value by multiplying the reduction in global GDP growth rate at each lag with the GDP value of the preceding year. The contemporaneous loss amounts to US$246, US$401 and US$739 billion for the extreme El Niño events of 1982–83, 1997–98 and 2015–16, respectively, (about 0.9–1.0% of global GDP at the time) in the El Niño occurrence year, and the cumulative loss over the occurrence year and the subsequent 3 years reaches US$1.3, US$2.1 and US$3.9 trillion (about 4–5% of global GDP at the time), respectively (Fig. 2a). In the subsequent 3 years, statistical uncertainty increases somewhat as the year advances, but the cumulative loss increases. The greater loss from the 2015–16 and the 1997–98 event than from the 1982–83 El Niño is due to a larger global economy. The contemporaneous loss is dominated by the linear effect and is only a small part of the total loss; the loss from growth effect is dominated by the nonlinear impact and is larger. By contrast, extreme La Niña events such as in 1988-89, 1998-99 and 2010-11 contribute to a fluctuating impact around zero, with a contemporaneous benefit being offset by a negative growth effect. For example, the cumulative gain of 1998-99 La Niña only amounts to US$0.06 trillion (Fig. 2b).

For a given year t, the effect of an ENSO event includes lagged impacts from ENSO events of the prior 3 years plus a contemporaneous impact of the ENSO at year t. For example, loss of global economic production in 2000 (t=year 2000) includes the contemporaneous and growth effect from 1997-98 strong El Niño and the ensuing 1998-1999 strong La Niña at different lags, as well as ENSO’s own contemporaneous effect in year 2000. The impact on growth rate is computed for all years over the 1960–2019 period (Fig. 2c). Owning to the asymmetric impact between El Niño and La Niña, there is a net reduction in global GDP growth rate in most years. Over the period of 1960–2019, there is an average reduction of 0.6% per annum in global GDP growth rate from ENSO cycles; the cumulative loss in economic production is US$13.5 trillion in total. Extreme El Niños in 1982–83, 1997–98 and 2015–16 account for 54% of the total loss over the 60 years.

We examine likely impacts of country-level heterogeneity by incorporating potential interaction of the common ENSO shock (as in Niño 3.4) with country-specific teleconnection (see ‘Heterogeneity of response function’ in Methods). Although the teleconnection strength provides some heterogeneity between individual countries (Supplementary Fig. S8a), a lack of statistical significance of interaction terms indicates that most of economic impact is reflected in a common shock after the spillovers and cascading effects are transmitted to the global macroeconomy (Supplementary Table S3). As such, including the country-level heterogeneity leads to a similar estimate of global economic loss (Supplementary Fig. S8b).

Loss in economic growth increases with ENSO amplitude
We use our econometric model to examine impact of change in future ENSO on global economy under emission scenarios of the Intergovernmental Panel on Climate Change (IPCC). We analyze outputs of available climate models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) (ref. 45). These models are forced with historical anthropogenic and natural forcing until 2014, and four Shared Socioeconomic Pathways (SSP) (ref. 46) of future greenhouse gas concentration trajectories from 2015 onwards (see ‘Climate and economic data’ in Methods).

To depict ENSO evolution in climate models, monthly SST anomalies referenced to the 1900–1999 climatology are constructed and then averaged over the Niño 3.4 region. The Niño 3.4 ENSO index is then quadratically-detrended over the entire period of 1900–2099. We compare standard deviation of the detrended Niño 3.4 index over the 100-year period of 2000–2099 (the 21st century) with that over the 100-years of the 1900–1999 period (the 20th century) to assess ENSO change under greenhouse warming. All four emission scenarios simulate an increase in future ENSO variability with a strong inter-model consensus as reported by previous studies28,29 (Fig. 3a; Supplementary Fig. S9). For example, in the high-emission scenario of SSP5-8.5, the multi-model ensemble median (mean) increase in future Niño 3.4 variability is 14.3% (15.5%). A total of 42 out of 48 available models (87.5%) generate an increase in ENSO variability (Supplementary Fig. S9a). The increased ENSO amplitude is also seen in the SSP3-7.0, SSP2-4.5 and SSP1-2.6 scenarios, with multi-model ensemble median (mean) increase in ENSO amplitude is 13.1%, 9.5%, and 7.5% (15.3%, 11.8%, and 10.6%), respectively (Supplementary Fig. S9b–d).

Conceivably, sequence of ENSO events might affect the ENSO’s impact on economy averaged over a period. For example, if an extreme El Niño occurs in the last year of the period, therefore its growth effect is felt beyond the period, it might affect the period-averaged impact. To avoid any potential dependence on ENSO event sequence, we develop a counterfactual 21st century Niño 3.4 timeseries such that it follows the same projected evolution (therefore the same sequence) but its standard deviation is scaled to have the same amplitude as that in the 20th century (Supplementary Fig. S10a; see ‘Counterfactual ENSO and scenario’ in Methods). The counterfactual future ENSO timeseries is taken as the ENSO evolution if future ENSO variability does not change under greenhouse warming. We then compare ENSO impact on economy using our econometric model with the projected and the counterfactual future ENSO timeseries as inputs. We begin our assessment of the economic impact by the difference in annual global GDP growth rate averaged over the 21st century. This is carried out for each model.

All climate models simulate a net economic growth reduction, and the net reduction becomes greater as the ENSO amplitude increases under all four IPCC emission scenarios, supported by a strong inter-model consensus (Fig. 3b). The multi-model ensemble median (mean) increase in the century-average reduction of global GDP growth is estimated to be 0.19%, 0.18%, 0.12%, and 0.10% (0.25%, 0.23%, 0.19%, and 0.17%) per annum under the SSP5-8.5, SSP3-7.0, SSP2-4.5, and SSP1-2.6 scenario, respectively (Fig. 3b). Further, there is a strong inter-model relationship between changes in ENSO SST variability and changes in century-averaged global GDP growth rate reduction, using all CMIP6 models under all four IPCC emission scenarios (Fig. 3c). Models that generate a greater increase in ENSO amplitude systematically generate a greater global GDP growth rate reduction.

Substantial additional loss from future increase in ENSO amplitude
We estimate the ENSO-induced economic production loss from the future growth rate reduction, using projections from the SSPs (ref. 46) that define the secular evolution of country-level population and economic development. The SSP projections cover the period from 2010 to the end of 21st century, forced under different emission levels (see ‘Climate and economic data’ in Methods). Assuming that loss from ENSO permanently imprints on the long-term economic development, we develop a counterfactual ENSO for future assuming no change (see ‘Counterfactual ENSO and scenario’ in Methods), and two “no-ENSO” future economic growth projections from 2020 onward, in which global economic production loss from the projected and the counterfactual ENSO in each year is taken out from the projected GDP timeseries (Supplementary Fig. S10b). The difference is taken as the additional economic loss from changing ENSO. Several fixed-rate discounting schemes, ranging from 1% to 5% per annum, where, for example, a 1% discount rate means that society values a given amount of consumption in 1 year roughly 1% less than its values today, are employed.

To illustrate, we use distributions of the additional economic loss to quantify the probability under different emission scenarios at a 3% discount rate (Fig. 4a–d). There is >80% chance of an additional economic loss from ENSO changes in all four IPCC emission scenarios, with a median increase ranging from US$14 trillion for the SSP2-4.5 scenario (Fig. 4c) to US$33 trillion for the SSP5-8.5 scenario (Fig. 4a), aggregated over the last 80 years of the 21st century. Reduction in emissions effectively cut the additional loss; under the SSP1-2.6 scenario, a strong mitigation pathway for achieving the Paris Agreement target of limiting warming to 1.5–2.0 °C relative to the pre-industrial level, the additional loss reduces by ~50% from that in the high-emission scenario of SSP5-8.5.

Importantly, there is a nonlinear relationship between changes in ENSO SST variability and additional economic losses, using all CMIP6 models under four IPCC emission scenarios. Models that generate a greater increase in ENSO amplitude tend to generate a greater additional loss in global economic production, and the tendency is systematic and statistically significant (Fig. 4e). This nonlinearity means a heightened risk of increased ENSO amplitude to global economy, with many models generating an additional loss in the order of hundreds of trillion US dollars and possibly as high as US$374 trillion, at a 3% discount rate in the high-emission scenario.

There are multiple sources of uncertainty, including model differences, emission scenarios, discount rates used, our econometric model, and ENSO event sequences. We assess the uncertainty by firstly quantifying the relative contribution from these individual factors, one by one by keeping other factors fixed at a chosen level, and subsequently the combined uncertainty (Fig. 4f) (see ‘Assessment of uncertainty’ in Methods). Uncertainty in climate models, that is, the inter-model differences in ENSO amplitude change, is the largest source of all other factors. Econometric model regression and ENSO sequences contribute to a relatively small uncertainty range, suggesting that our econometric model is reasonably stable and insensitive to economic parameters or random elements such as chaotic processes in the occurrence sequences of ENSO events. That there is a weak sensitivity to ENSO sequences is in contrast to finding of a simultaneous and independent study, which uses a linear econometric model and suggests a large uncertainty source from ENSO event sequences by assuming that El Niño and La Niña exert a perfectly symmetric but opposite impact47.

Our result of an increased loss in global economy from future ENSO cycles is underpinned by a greater impact of El Niño than that of La Niña and by an increased ENSO variability in the future climate. The greater El Niño impact than that of La Niña is in turn a consequence of the 3-years-long effect on economy that accelerates after the initial shock in the occurrence year; both the initial shock and the lagged effects are far smaller during La Niña. Therefore, a period of ENSO cycles sees a net reduction in global economic production dominated by the loss from extreme El Niño events. Under greenhouse warming, ENSO amplitude increases substantially in all likely emission scenarios, translating to an additional median loss in global economic production in the range of US$14-33 trillion over the last 80 years of the 21st century, that is generally larger in higher emission scenarios; the possibility of an additional loss in hundreds of trillion US dollars cannot be excluded. Achieving the Paris Agreement reduces about half of the increased economic loss. There are transmission pathways of impact from increased future ENSO variability that are not incorporated here, for example, through affecting ocean warming, ice shelf and ice sheet melt, which contribute to sea level rise48–50 leading to additional economic loss. The additional economic damage from changes in ENSO amplitude in the 21st century highlight the urgency of mitigating emissions of greenhouse gases.

Methods
Climate and economic data
To construct ENSO timeseries and its global teleconnection, we use multiple observational and reanalysis datasets: Monthly sea surface temperature data are from Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) (ref. 51), with a 1° grid resolution covering the period of 1950–2021. Monthly land-based surface air temperature and precipitation data are from ECMWF Reanalysis version 5 (ERA5) (ref. 52), with a 0.1° grid resolution for all land areas covering the period of 1950-2021. These variables are bilinearly interpolated to a horizontal grid of 1°×1°. Monthly anomalies referenced to the period of 1950-2021 are constructed by removing the monthly climatology, and then quadratically detrended at each grid to remove the long-term greenhouse warming and any low-frequency climate variability. We also use other products such as NOAA Extended Reconstructed SST version 5 (ERSSTv5) (ref. 53) and University of Delaware54 to confirm robustness of our results.

We use country-specific annual economic data from re-estimating the World Bank Development Indicators55, which contains Gross Domestic Product (GDP) per capita (in constant 2015 US$, inflation-adjusted) for all countries in the world covering the period of 1960–2019, although data for only a subset of years are available for some countries. We also use the data from Penn World Tables version 10.0 (ref. 56) to test robustness of our results. The population density data is obtained from the Gridded Population of the World (GPW) version 4 (ref. 57).

To project the future change of ENSO under greenhouse warming, we use climate outputs from the state-of-the-art climate models in the Coupled Model Intercomparison Project phase 6 (CMIP6) (ref. 45). The CMIP6 models are forced with historical anthropogenic and natural forcing from 1850 to 2014, and different emission scenarios thereafter from 2015 to 2100. There are four IPCC emission scenarios used, which are SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, from the scenario for achieving target of Paris Agreement to high-emission scenario. For a given model, outputs might not be available for all emission scenarios, and only the first available experiment from each model for each scenario is used (Supplementary Table S4). All the outputs are bilinearly interpolated to a horizontal grid of 1°×1°. Monthly anomalies referenced to the period of 1900–1999 are constructed by removing the monthly climatology, and then quadratically detrended at each grid over the 1900–2099 period to remove the long-term trend.

To estimate the economic production loss from future ENSO changes, we use projections from Shared Socioeconomic Pathways database version 2.0 (ref. 46), which consist of several scenarios of projected socio-economic development associated with various degrees of climate forcing over the 21st century. We use the timeseries of country-level economic production (in constant 2015 US$, inflation-adjusted) and population over the period of 2020–2099 under four SSP scenarios (SSP1, SSP2, SSP3, and SSP5) as the baseline to construct counterfactual global GDP growth and then calculated economic loss from ENSO in the future.

ENSO index and country-specific teleconnection
We use Niño 3.4 index to define the ENSO timeseries in the observation and CMIP6 models, which is the monthly SST anomaly averaged in the region of 5°S-5°N, 120°-170°W. Because ENSO generally peaks in boreal winter (December-February, DJF), we use DJF-averaged Niño 3.4 index to measure interannual variability of ENSO.

As the economic impact of ENSO is underpinned by the direct climate response to ENSO for individual countries, we construct country-specific ENSO teleconnection to evaluate the extent to which climate of individual countries is affected by ENSO as follows:
(1) We regress normalized monthly grid-point surface air temperature and precipitation anomalies onto normalized D(0)JF(1) Niño 3.4 index from May(0) to April(1) (noting the spring barrier58), yielding two fields of monthly regression coefficients \tau_{x,y,m} and \rho_{x,y,m} for surface air temperature T and precipitation P at each longitude-latitude grid point (x,y) and calendar month m, respectively. We use partial regression for temperature with precipitation’s impact removal to control the correlation between temperature and precipitation, and vice versa.
(2) We accumulate these monthly coefficients from May(0) to April(1) that are statistically significant above the 95% confidence level, and then take the absolute value of the sum to obtain the cumulative teleconnection strength \tau_{x,y} and \rho_{x,y} (Supplementary Fig. S5).
(3) We average the grid-point teleconnection strength to country-specific \tau_i and \rho_i, weighted by population density in 2020. We use population density weighting rather than other approaches (for example, areal weighting) for its better representation of reaction of human-based economic activity to climate anomaly within a given country. Economic impact transmitted from climate shock is generally driven by human interactions and transactions. Population density weighting put more emphasis on areas with higher concentration of people, which are typically the centers of economic activity. However, as we have shown, a common shock to most countries dominates, rendering our results insensitive to approaches adopted.
(4) We accumulate the country-specific temperature and precipitation teleconnection strengths as the total cumulative teleconnection strength \psi_i (Supplementary Fig. S6). We assign those countries with \psi_i > 0.5 to the “teleconnected” group, and 0 < \psi_i \le 0.5 to the “weakly-teleconnected” group (Fig. 1a; Supplementary Fig. S6b).

Empirical econometric model
A distributed-lag timeseries model estimated by the Ordinary Least Squares (OLS) is applied to assess the nonlinear effect of ENSO on global economic production. Economic growth for each country is represented by the first difference of natural logarithm of annual GDP per capita. Combining the factors including observed climate variables and unobserved time-invariant and time-trending covariates, we then build the model as:
\Delta log(y_{it}) = \sum_{l=0}^{n} [\alpha_{1,l} E_{t-l} + \alpha_{2,l} E_{t-l}^2 + \beta_{1,l} T_{it-l} + \beta_{2,l} T_{it-l}^2 + \lambda_{1,l} P_{it-l} + \lambda_{2,l} P_{it-l}^2] + \mu_i + \theta_{1i} t + \theta_{2i} t^2 + \epsilon_{it} (1)
where y_{it} is GDP per capita in country i and year t, l is the lag year to year t, E is DJF-averaged Niño 3.4 index in year t-l, T and P are annual mean surface air temperature and precipitation in year t-l, after removing the ENSO’s signal through linear regression.

Consistent with previous literatures with respect to ENSO’s socio-economic impact20,28, we include the country-fixed effect (\mu_i) as a “control” variable that represents time-invariant factors such as history, culture backgrounds and geographic location, and the country-specific linear and quadratic time trend (\theta_{1i} t + \theta_{2i} t^2) as “control” factors that change over time within a country, such as development, trade liberalization and technological progress. We also incorporate specific control variables such as trade openness, share of agricultural GDP and financial depth as sensitivity tests, and our estimation is insensitive to these factors. We drop the year-fixed effect that represents time-varying factors common across countries such as global recession, since the year-fixed effect introduces high risk of collinearity as ENSO timeseries could be correlated with time-specific factors, making it harder to disentangle the separate impacts from ENSO and time-fixed effects. ENSO can be treated as a time-specific global phenomenon that commonly affects countries. Including year-fixed effects weakens the statistical influence of ENSO, leading to an underestimation of the real impact of ENSO on economic growth59. In contrast to previous studies which depict different impact of El Niño and La Niña through a Heaviside step function22,23, we incorporate a continuous nonlinear effect of ENSO in a quadratic function in which both the linear and the nonlinear components include lagged effects. These lag terms account for growth effects after the contemporaneous climate shock of an ENSO event, allowing subsequent acceleration in the impact on economic activity after the initial shock.

Equation (1) is simultaneously fitted by all the country-year samples from 181 countries over the period of 1961–2019 (N=7404). Inclusions of annual mean temperature and precipitation are aimed to parallelly estimate the impact from mean state change, with assumption that ENSO-induced temperature and precipitation anomalies are independent to the mean change. We test this assumption by removing ENSO signal from annual mean temperature and precipitation by linear regression, which goes
T^*_{it} = T_{it} - E_t \cdot r(T_i, E_i) (2)
where T^*_{it} is the annual temperature after ENSO signal removal in country i and year t. r(T_i, E_i) is the linear regression coefficient of annual temperature T_i onto the DJF Niño 3.4 index E_i in country i during the period of 1950-2021. Same process is applied for annual mean precipitation P_{it}. There is little difference in the coefficients of ENSO impact after removing ENSO signal from annual mean temperature and precipitation.

We test different lag years to obtain the optimum one for ENSO’s growth effect. ENSO continue to have negative effect till year 3, after which time little further impact is seen and uncertainty increases (Supplementary Fig. S2; Supplementary Table S1). As such, a 3-year lag is applied (n=3) to estimate the growth effect of ENSO. We estimate loss from ENSO in each year as:
\Delta gE_t = exp(\sum_{l=0}^{n} [\alpha_{1,l} E_{t-l} + \alpha_{2,l} E_{t-l}^2]) - 1 (3)
where \Delta gE_t is the ENSO-induced change in growth rate of annual GDP per capita in year t. Hence, the in-dollar-value impact of ENSO in year t is obtained by
\Delta YE_t = \Delta gE_t \cdot y_{it} (4)
where \Delta YE_t is the ENSO-induced global per capita GDP change in year t.

Counterfactual ENSO and scenario
To avoid any influence from sequence of ENSO events when assessing change of ENSO-induced economic growth reduction under greenhouse warming, we develop counterfactual scenarios with hypothetical future ENSO timeseries. The counterfactual future ENSO is rescaled from original timeseries in the 21st century to be of the same amplitude as in the 20th century, that is:
E^C = E^{SSP} \cdot \frac{\sigma(E^{HIST})}{\sigma(E^{SSP})} (5)
where E^C is the counterfactual Niño 3.4 timeseries, E^{HIST} and E^{SSP} are Niño 3.4 timeseries under the 20th and 21st century, respectively. This approach retains the temporal evolution of ENSO in a SSP scenario to remove any uncertainty that might arise from the sequence of individual El Niño and La Niña events (Supplementary Fig. S10a). The growth rate difference between the original and counterfactual scenarios in each year \Delta gE_t is calculated as:
\Delta gE_t = exp(\sum_{l=0}^{n} [\alpha_{1,l} E^{SSP}_{t-l} + \alpha_{2,l} (E^{SSP}_{t-l})^2]) - exp(\sum_{l=0}^{n} [\alpha_{1,l} E^C_{t-l} + \alpha_{2,l} (E^C_{t-l})^2]) (6)
To project the economic development in global GDP absent ENSO in the future, we build a counterfactual global GDP growth based on SSP projections. We assume that ENSO permanently reshapes the global economy as its impact in each year aggregated over a long-term period. The counterfactual GDP growth starts at year 2020, in which the GDP is the same as particular SSP pathways. Then the cumulative growth rate reduction from ENSO in each year (both contemporaneous effect at that year and growth effect from previous years) is restored to the original GDP growth rate. As such, the counterfactual GDP is generated and aggregated to the end of 21st century, which is calculated as:
y^C_t = y^C_{t-1} \cdot (1 + g_t + \Delta gE_t) (7)
where y^C_t is the counterfactual global GDP per capita at year t, g_t is the original GDP growth rate in year t.

Statistical significance test
We implement several sets of Bootstrap methods60 to assess statistical significance of the historical response function and multi-model projections. To quantify uncertainty in estimates of \alpha_1 and \alpha_2, we apply different bootstrap strategies (1) Sampling by country in which the 181 countries are randomly resampled to construct another 10,000 realizations of 181-element lists. In the resampling process, any country is allowed to be selected again. These resampled lists of countries are used to re-estimate the Eq. (1) and obtain the estimates of \alpha_1 and \alpha_2. (2) Sampling by year, in which the 59 years from 1961–2019 period are randomly resampled to construct another 10,000 realizations of 59-element lists. In the resampling process, any year is allowed to be selected again. These resampled lists of years are used to re-estimate the Eq. (1) and obtain the estimates of \alpha_1 and \alpha_2. (3) Sampling by 5-year block, in which we divide the country-year data into 5-year blocks (like 1961–1965, 1966–1970 and so on), and resample these blocks as (2). The 1.96 s.d. of the 10,000 estimates of \alpha_1 and \alpha_2 represent the 95% confidence interval. We use strategy (1) in our presentation of results, but we also show that the robustness of our historical response function is insensitive to alternative bootstrap strategies (Supplementary Fig. S3).

We also apply several sensitivity tests by omitting some of data and re-estimating Eq. (1). In one such test, we re-estimate the econometric model under 50-year running window periods of 1960–2009, 1961–2010, …, and 1970–2019. In another, we randomly drop 3 individual years of data from the period of 1960–2019 to re-estimate the econometric model for 1000 times by the Bootstrap method. We find that our historical response function is insensitive to these tests (Supplementary Fig. S4).

A similar Bootstrap method allowing repeat is used to test statistical significance of a difference between the 20th and 21st century, or between a counterfactual and its original SSP scenario in the 21st century, in multi-model ensemble mean Niño 3.4 variability and economic growth reduction. Specifically, their individual values (corresponding to models) in each century are resampled randomly to construct 10,000 realizations of multi-model ensemble mean values, and standard deviation of the 10,000 realizations for each century is calculated. When the multi-model ensemble mean difference is greater than the sum of s.d. values of the 10,000 realizations of the two centuries, the multi-model ensemble mean difference is statistically significant above the 95% confidence level (Supplementary Fig. S9).

Heterogeneity of response function
To test the heterogeneity of ENSO effect across different groups of countries (Fig. 1), we allow interaction between both the linear and quadratic terms of Niño 3.4 and an indicator D_i for whether a country is, for example, a teleconnected country. We then incorporate it into the econometric model by
\Delta log(y_{it}) = \sum_{l=0}^{n} [\alpha_{1,l} E_{t-l} + \alpha_{2,l} E_{t-l}^2 + D_i (\alpha_{3,l} E_{t-l} + \alpha_{4,l} E_{t-l}^2)] + \beta_{1,l} T_{it-l} + \beta_{2,l} T_{it-l}^2 + \lambda_{1,l} P_{it-l} + \lambda_{2,l} P_{it-l}^2 + \mu_i + \theta_{1i} t + \theta_{1i} t^2 + \epsilon_{it} (8)
where
D_i = \begin{cases} 1 & \text{if country i is a teleconnected country} \\ 0 & \text{if country i is a weakly-teleconnected country} \end{cases}
where \alpha_{1,l} and \alpha_{2,l} represent the linear and nonlinear coefficients of the response function for weakly-teleconnected countries, and \alpha_{3,l} and \alpha_{4,l} represent the adjustments to \alpha_{1,l} and \alpha_{2,l} that are only applicable to teleconnected countries. Thus, the response functions of teleconnected and weakly-teleconnected countries are statistically different when \alpha_{3,l} and \alpha_{4,l} are significant. We apply the same approach to agriculture-dependent/independent and high-income/lower-income countries. We find that while some coefficients of linear terms \alpha_{3,l} are significant, coefficients of nonlinear terms \alpha_{4,l} are indistinguishable from zero for these groups of countries (Supplementary Table S2). This means that the common nonlinear nature reflects the feature that the global spillovers and cascading effects dominate global economic impact of ENSO. On the other hand, the heterogeneous magnitude of response is due to several different factors. One is linked to geographical locations in that strongly-teleconnected countries tend to be in the tropics and lower-income countries; another is economic structure in that there tends to be a high proportion of agriculture in GDP in lower-income countries; further, in lower-income countries, the level of preparedness and the capacity to mitigate are relatively low.

To test any country-level heterogeneity of ENSO effect across individual countries, we include the interaction of the common ENSO shock with country-specific teleconnection in the econometric model by
\Delta log(y_{it}) = \sum_{l=0}^{n} [\alpha_{1,l} E_{t-l} + \alpha_{2,l} E_{t-l}^2 + \gamma_{1,l} \psi_i E_{t-l} + \gamma_{2,l} (\psi_i E_{t-l})^2] + \beta_{1,l} T_{it-l} + \beta_{2,l} T_{it-l}^2 + \lambda_{1,l} P_{it-l} + \lambda_{2,l} P_{it-l}^2 + \mu_i + \theta_{1i} t + \theta_{2i} t^2 + \epsilon_{it} (9)
where \psi_i E_{t-l} is the interaction term between the common ENSO shock and country-specific teleconnection. We find that coefficients of the interaction terms only show a weak statistical significance (Supplementary Table S3), suggesting a major contribution of common ENSO shock to the global impact.

Assessment of uncertainty
Our projection of additional loss in global economic production contains several sources of uncertainty, including various future socio-economic baseline and climate forcing from different SSP scenarios, varying changes in ENSO amplitude across climate models, choices of discount rate, and historical function regression. Given the counterfactual GDP timeseries is associated with ENSO timeseries, the sequence of ENSO is also a potential factor of uncertainty. We therefore re-arrange the ENSO timeseries in each CMIP6 model to construct 10,000 timeseries, which have the same amplitude but different sequences of ENSO events. To quantify the relative contribution from the above factors to uncertainty, we hold four of five factors fixed and allow the fifth to change. The factors are fixed as follows: SSP scenario is fixed to SSP5-8.5, climate model projection of ENSO changes is fixed to the multi-model ensemble median, discount rates are fixed at 3%, historical function regression is fixed to the point estimate, and ENSO sequence is fixed to the original sequence of timeseries as simulated in each climate model. For the total uncertainty, each factor is allowed to vary.

The total uncertainty leads to a 95% confidence interval of changes in global economic loss from -US$247 trillion to US$20 trillion, while SSP scenario uncertainty alone leads to a 95% confidence interval of -US$36 trillion to -US$14 trillion, climate model uncertainty to a 95% confidence interval of -US$290 trillion to US$25 trillion, discount rate uncertainty to a 95% confidence interval of -US$90 trillion to -US$15 trillion, historical regression uncertainty to a 95% confidence interval of -US$55 trillion to -US$22 trillion, and ENSO sequence uncertainty to a 95% confidence interval of -US$47 trillion to -US$38 trillion (Fig. 4f). Climate models with various ENSO amplitude changes contribute most to the total uncertainty, suggesting the projected additional economic loss is highly dependent on ENSO amplitude change in the future.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an interesting paper. The role of the ENSO cycles on world economies has been investigated\nbefore, but this study presents additional details to the discussion. Specifically, it allows for nonlinear\neffects of ENSO--basically allowing for the possibility that both El Nino and La Nina events can be\ndamaging to economies.\nOne of the key messages of the study, which is presented as its main \"selling point\" is that the\nestimated effect in this study is the order of magnitude larger than that presented in previous studies.\nThe paper doesn't indicate why that may be the case. Presumably, the authors allude that their\nestimates are correct and therefore estimates of the previous studies are wrong, and that may very\nwell be the case, but it should be noted, rather explicitly, what are the key points of departure. Is it\ndata-related (because the present study uses more recent data) or the econometric technique? In any\ncase, it also would be interesting to see if the present study replicates the results of the previous\nstudies.\nThis study seems to be using nominal GDP per capita. If so, then the effects of different historical\nevents are not directly comparable. 100 million dollars in 1998 were worth more than 100 million\ndollars in 2015, for example.\nThe dependent variable is the natural log of the GDP per capita. Meaning that the estimated\nparameters are, approximately, semi-elasticities. But that also puts the high-income countries and\nlow-income countries in the same basket. It seems like the authors subsequently relax this\nhomogeneity assumption, but it is not immediately clear how the estimated impacts of ENSO events,\nin dollar value, have been obtained.\nThe effect on teleconnected and not teleconnected (or weakly teleconnected) countries seem to be\nvery similar (e.g., Fig1), which raises the concern of whether this estimated effect is spurious. I must\napologize for this comment because I have no reason to make such a claim, other than a hunch. But I\ndecided to voice this concern, just to be honest with myself, at the very least.\nIt would have been nice to have, at least, the main regression results presented in a table, just to get\na better idea of which parameters are driving the results.", "reviewer_2": "The paper is engaging and well-written. It tackles a relevant issue, which could spur interest in a large\ncommunity of readers. In particular, the authors study the effects of El Nino and la Nina events on\neconomic growth, using well-established empirical methods from climate econometrics and a global\npanel of countries over the period 1961-2019. My feeling is that the paper has potential, but the\nauthors should convincingly address some key issues, which I think currently undermine the validity\nand innovativeness of the study.\nFirst, a suggested interpretation of the estimated effects is grossly missing. The authors estimate\neconomic losses from ENSO by means of an econometric model relating country-specific economic\ngrowth rates to an ENSO index, country-specific temperatures, and country-specific precipitation (with\nmultiple powers and lags). They then use the marginal estimated effects to compute losses stemming\nfrom ENSO oscillations (please, pay attention to my third point below). However, they are silent on\nwhat mechanisms could drive economic losses from El Nino/La Nina. I feel this is relevant, especially\nwhen the effect of local weather change is controlled for. Intuitively, one would expect that El Nino\nevents cause changes in the experienced weather conditions in different countries, which - in turn -\naffect local economic activity and production. However, given the model estimated by the authors,\nsuch effects should be largely captured by the precipitation and temperature variables. Hence, the\n“additional effect” of ENSO is very interesting and surprisingly large, but – for this to be credible - I\nfeel the authors should make an interpretative effort, suggesting what mechanisms may lie behind the\nstatistical relationship the paper seems to spot robustly. This is even more relevant as the present\nstudy reports much larger losses than in previous literature.\nSecond, the innovativeness of the paper should be streamlined much more effectively. The authors\nsometimes seem to motivate their study on the basis of inconclusive and scant evidence about the\neffects of El Nino/La Nina on the macroeconomy (see e.g. the abstract at lines 18-19). While I agree\nthat further studies are definitely necessary, I have some issues grasping the novelty of the present\npaper with respect to the literature. For example, Smith and Ubilava (2017, GEC) - which the authors\ncite - offer a quite similar approach, finding non-linear effects, spanning across different years and\nvarying across countries and climatic zones. Cashin et al. 2017 (2017, JIntEco) - which the authors\ncite - offer a much more comprehensive assessment of the macroeconomic effects of El Nino and La\nNina events, though with a different methodology. I feel the authors should make crystal clear the\npiece of evidence they add to the literature. My reading is that the originality lies in i. the role of\nteleconnections (which would deserve more space in the paper), ii. the additional effect of ENSO\nbeyond that of local temperature and precipitations (see my point above), iii. the projected effects on\ngrowth, which are very large and need to be robust (see my third and fourth points immediately\nbelow).\nThird, the authors estimate a growth effect while they mostly report results in terms of a level effect.\nWhile the paper finds a robust non-linear effect of ENSO on growth, economic losses are mostly\nreported in terms of trillions of dollars of economic damage, which is suggestive of a level effect. I feel\nthis approach is problematic for two reasons: first, it does not account for the debate on the\npersistence of climate impacts on the macroeconomy (see e.g. Diaz and Moore 2017, NCC; Bastien-\nOlvera et al. 2021, ERL); second, it makes results more difficult to interpret from a quantitative\nstandpoint. Indeed, 1 trillion USD is relatively low for the US economy, while extremely large for a\nsmall African country; similarly, it is difficult to say whether a loss of 2.1 trillion (a figure found in the\nabstract) is large or small for the global economy without a meaningful term of comparison (e.g. the\nvalue of the global GDP in the year of the shock or the loss generated by another event, e.g. the\nglobal financial crisis). Further, while the authors account for inflation, it is not always clear in dollars\nof which year results are computed. More relevantly, I failed to get the exact meaning of equations 2\nand 4. To my reading, they do not reflect the impact on growth the authors would like to measure. I\nwould kindly encourage the authors to provide more details, possibly together with examples for some\ncountries. Indeed, it is not evident what losses in which countries are driving the global loss estimates\nthe authors come up with. More clarity along these lines would make the results much more trustable.\nUnpacking the results across countries would offer an intriguing perspective, but this is just a\nsuggestion.\nFourth. In their projection exercises, it is unclear what drivers of growth the authors consider and\nwhat they do not. For example, in the seminal paper by Burke et al. (2015, Nature) projections\ninclude socio-economic trends (proxied by historical growth rates of GDP). I have the impression the\nauthors exclude such trends when projecting the GDP of various countries. For example, this seems to\nbe the case in the section “Substantial additional loss in economic production from increased ENSO\namplitude” (from line 197). Accounting for such trends may change the projection results sensibly.\nSimilarly, accounting for future temperature will affect growth rates; however, the counterfactual\nscenario seems to assume that ENSO will change alongside future climatic and SSP scenarios while\ntemperatures are not. I think the authors should either change their projections or offer a crystal-clear\nand convincing explanation of their counterfactual and projection exercises.\nFifth, aggregation with discount rates may be problematic. Impacts on economic production are\naggregated with discount rates chosen by the authors. Figure 4 is the most communicative example. I\nfind this choice debatable. First, the last two decades have witnessed a fierce debate about the\nselection of discount rates, and results from impact studies typically showed large sensitivity to such a\nparameter. This paper is not an exception. Second, it is not clear to me why the authors need to\ndiscount back in time a projected impact. For example, they could just compute the loss as the ratio\nbetween the projected global GDP accounting for ENSO effects and the projected GDP without the\nENSO effect. This would be more similar, for example, to the approach by Burke et al. 2015 and to the\nmajority of damage functions.\nFinally, let me provide few other yet less substantive comments.\n• at lines 95-98 the authors state they include mean temperature and precipitation in their model to\nestimate the effects of mean state change and introduce the assumption that ENSO-induced\ntemperature and precipitation changes are independent of mean state changes. First, I am not\nconvinced that equation 1 reflects this assumption (rather, it reflects yearly deviation from country-\nspecific trends that may be caused by any events, including ENSO); second, I am not convinced the\nassumption of independence is realistic and/or innocent.\n• the effects of temperature and precipitations are not discussed and it is also not clear whether these\nvariables are included in the estimation in their population-weighted version or not. This may have\nimplications for how the authors capture exposure of economic production to the weather and for the\ninterpretation of results.\n• The links with the blossoming climate econometrics literature could be better surveyed and the\npaper better positioned in such a stream of studies. For example, El Nino and La Nina are known to\nexert impacts through changes in precipitation patterns (among other channels). Palagi et al. (2022,\nPNAS) and Kotz et al. (2022, Nature) are two extremely recent contributions emphasizing the impact\nof precipitation anomalies on growth and the macroeconomy. The authors could leverage these results\nto provide a more informative contextualization of their findings. This links back to my first point.\nReferences\nBastien-Olvera, B., Granella, F. & Moore, F. (2021). Persistent effect of temperature on GDP identified\nfrom lower frequency temperature variability, Environmental Research Letters, 17 (8).\nBurke, M., Hsiang, S. M., & Miguel, E. (2015). Global non-linear effect of temperature on economic\nproduction. Nature, 527(7577), 235-239.\nCashin, P., Mohaddes, K., & Raissi, M. (2017). Fair weather or foul? The macroeconomic effects of El\nNiño. Journal of International Economics, 106, 37-54.\nDiaz, D., & Moore, F. (2017). Quantifying the economic risks of climate change. Nature Climate\nChange, 7(11), 774-782.\nKotz, M., Levermann, A., & Wenz, L. (2022). The effect of rainfall changes on economic production.\nNature, 601(7892), 223-227.\nPalagi, E., Coronese, M., Lamperti, F., & Roventini, A. (2022). Climate change and the nonlinear\nimpact of precipitation anomalies on income inequality. Proceedings of the National Academy of\nSciences, 119(43), e2203595119.\nSmith, S. C., & Ubilava, D. (2017). The El Niño Southern Oscillation and economic growth in the\ndeveloping world. Global Environmental Change, 45, 151-164.", "reviewer_3": "This article, which aims to assess the overall economic cost of ENSO in a context of climate change,\ncovers an important subject area. My comments focus on how the ENSO impact is considered and the\nrelevance of an average assessment of its cost in terms of overall GDP (knowing that I am not a\nclimatologist). I find many issues that must be addressed for the assessment to be credible.\nMy comments relate to the estimation of the country-specific teleconnection strengths as well as the\nestimation of the economic cost of ENSO-related events. It seems fundamental to me to solidify these\nresults before any climate projection.\n1. ENSO index and country-specific teleconnection\nThe authors propose a measure of the degree of teleconnection specific to each country. More detail\nshould be given regarding the construction of the indicator reflecting the magnitude of teleconnection\npatterns.\nThe estimation of the regression coefficients ρ and τ raises questions in the absence of a clearly\nspecified model. Are the temperature and precipitation anomalies, used as explained variables,\nstandardized? Are precipitation included as a covariate in the regression when analyzing temperature\nand vice versa to control for the correlation between temperature and precipitation.\nMoreover, several factors can lead to a waxing and waning in the strength of the ENSO\nteleconnections: changes in the evolution of the SST pattern during ENSO, changes in the ENSO's\ntemporal behavior, changes in the mean base climatology, and its modulation of the teleconnection\nprocess, and simple sampling variations. It could then be helpful to review the robustness of the\ncountry-specific teleconnection metric assessed by the authors by analyzing its temporal stationarity\nthroughout the entire reference period.\nIn line 403, the authors state that the estimated coefficients are weighted by population density in\n2020. First, what is the source of the population density data? Second, the authors should give better\njustification with regard to this choice concerning the spatial aggregation procedure. Number of\neconomic studies aggregate ENSO teleconnections within countries borders using a fixed set of\npopulation weights, so that the relevant concept is the average weather experienced by a person in\nthe administrative area, not the average weather experienced by a place. However, this procedure\nimplies that the aggregation gives a higher weight to highly populated (urban) areas while areas\ncharacterized by low population densities may play a major economic role and while being impacted\nby ENSO shocks. This is particularly the case for agricultural regions, a sector through which the\nmajority of ENSO-related economic shocks spread.\nIn line 405, the authors divide the countries into two categories based on the accumulation of the\ncountry-specific temperature and precipitation teleconnection strengths (ψ_i) so that countries where\nψ_i<0.5 are weakly connected and countries where ψ_i>0.5 are connected. This categorization is of\nparticular concern to me. On what basis is this classification made? It seems from Figure S4 that the\nchoice of such a low threshold value completely crushes the country heterogeneity of ENSO shocks\nand that almost all countries belong to the category of teleconnected countries. In other words, what\nis the point of calculating a specific indicator for each country to ultimately arrive at such a broad\ncategorization? It seems to me, moreover, that this methodological choice is decisive for the rest of\nthe article and therefore the determination of the overall cost of ENSO.\nIt would be necessary to consider alternative global measurements of the level of teleconnection\nbased on more precise measurements (spatial variability, spatial extent, etc.) and to accompany the\nresults with a map showing the level of teleconnection by grid point (before the aggregation at the\ncountry level).\n2. Empirical econometric model\nThe authors test a distributed-lag timeseries model to estimate the economic cost of ENSO events. Is\nthis model estimated by the GLS or by the OLS? I think it is essential to have more information on the\npossible presence of multicollinearity (using the VIF for example). Indeed, the measurement of ENSO\nshocks being indexed only by time (shock common to all countries), the risk of quasi collinearity with\nthe country fixed effects (μ_i) is high and likely to bias the estimates.\nUsing a common ENSO shock to estimate its overall cost does not seem to constitute a significant\ncontribution compared to the previous literature on the subject which explicitly introduces a measure\nof the strength of the teleconnection specific to each country in the econometric specification or via\nthe interaction between the common shock and country specific weather anomalies. Why not integrate\nthe results from the calculation of country-specific teleconnections within the econometric\nspecification? Indeed, the distinction made in Figure 1 does not seem to show any difference between\nthe categories of countries and seems insufficient.\nThe distinction between rich and poor countries should also be clarified. On what basis is this\ndistinction made? This distinction may seem tautological from an economic point of view, as poor\ncountries are more vulnerable to climatic hazards... A more relevant distinction should be made on the\nbasis of the countries structural characteristics by distinguishing, for example, countries dependent on\nagriculture, countries whose economic fabric is more diversified and thus evaluating the heterogeneity\nestimated cost of ENSO events.\nThe annual measurement of the ENSO indicator (Nino 3.4) should be subject to robustness. The\nauthors should consider comparing their non-linear (quadratic) measurement to a measurement based\non event occurrence (based on threshold values) thus justifying the methodological contribution of the\npaper with respect to the prior literature relying on a Heaviside function in order to dissociate El Niño\nevents from La Nina events. It is important to note that the references mentionned by the authors use\nboth a Heaviside function and a quadratic specification in order to characterize ENSO events within the\nframework of their econometric specifications.\nAlthough the measure of the climate can reasonably be considered exogenous (not invalidating the\nhypotheses of the model), it would seem relevant to include control variables reflecting the\ncharacteristics of the countries such as the share of GDP in the Value added Agricultural, the degree of\ntrade openness."}, "author_response": "Response to Reviewer #1\nThis is an interesting paper. The role of the ENSO cycles on world economies has been\ninvestigated before, but this study presents additional details to the discussion. Specifically, it\nallows for nonlinear effects of ENSO--basically allowing for the possibility that both El Nino\nand La Nina events can be damaging to economies.\nWe thank the reviewer for the positive comments.\nOne of the key messages of the study, which is presented as its main \"selling point\" is that the\nestimated effect in this study is the order of magnitude larger than that presented in previous\nstudies. The paper doesn't indicate why that may be the case. Presumably, the authors allude\nthat their estimates are correct and therefore estimates of the previous studies are wrong, and\nthat may very well be the case, but it should be noted, rather explicitly, what are the key points\nof departure. Is it data-related (because the present study uses more recent data) or the\neconometric technique? In any case, it also would be interesting to see if the present study\nreplicates the results of the previous studies.\nPrevious estimates included direct and contemporaneous losses from El Niño/La Niña events,\nand produced results that are consistent with ours. For example, Smith andUbilava (2017) and\nGeneroso et al. (2020) used a Heaviside step function and allowed limited lags to describe the\ndifferent impact between El Niño and La Niña. Their approach therefore assumed the same\nimpact sensitivity for weak and strong events. Although the resultant loss is smaller, it is of the\nsame order of magnitude.\nHere we take into account, in our econometric model, the likelihood of an impact increasing or\nchanging nonlinearly with amplitude of El Niño/La Niña, and include all possible lags. Because\nof these two improvements, our continuously nonlinear econometric model (Fig. R1A), while\ncapturing main features of previous models, generates a greater loss than that in previous\nstudies. The continuously nonlinear model is important for assessing future ENSO impact as\nENSO amplitude is projected to increase under greenhouse warming.\nWe have discussed this point in Line 82-86.\nFig. R1A. Comparison of (a) results from the fully nonlinear model in our paper (same as Fig.\n1a) and (b) the Heaviside model, both with contemporaneous effect and 3-year lagged effect\nincluded; also the comparison of cumulative losses from observed extreme El Niño events from\n(a) the fully nonlinear model in our paper (same as Fig. 2a) and (b) the Heaviside model.\nComparison of (c) cumulative losses from observed extreme El Niño events under the fully\nnonlinear model in our paper (same as Fig. 2a) and (d) the Heaviside model.\nThis study seems to be using nominal GDP per capita. If so, then the effects of different\nhistorical events are not directly comparable. 100 million dollars in 1998 were worth more than\n100 million dollars in 2015, for example.\nWe use constant 2015 US$ GDP (Line 408 and 429). We have made this clearer.\nThe dependent variable is the natural log of the GDP per capita. Meaning that the estimated\nparameters are, approximately, semi-elasticities. But that also puts the high-income countries and\nlow-income countries in the same basket. It seems like the authors subsequently relax this\nhomogeneity assumption, but it is not immediately clear how the estimated impacts of ENSO\nevents, in dollar value, have been obtained.\nWe have improved this in the revised version. We have now added more detailed steps to Eq.\n2 to explain how the ENSO-induced change in GDP growth rate is calculated. Given that\ny\nlog(y )log(y )log(y )log( it )log(1g )\nit it it1 y it\nit1\nThen\n n \ngE exp   E  E2  1\nt 1,l tl 2,l tl\n \nl0\nwhere gE is the ENSO-induced change in growth rate of annual GDP per capita in year t.\nt\nHence the in-dollar-value impact of ENSO in year t is obtained by\nYE gEy\nt t it\ni\nwhere YE is the ENSO-induced global per capita GDP change in year t.\nt\nThe effect on teleconnected and not teleconnected (or weakly teleconnected) countries seem to\nbe very similar (e.g., Fig1), which raises the concern of whether this estimated effect is spurious.\nI must apologize for this comment because I have no reason to make such a claim, other than\na hunch. But I decided to voice this concern, just to be honest with myself, at the very least.\nWe thank the reviewer for raising this point, which lets us revisit the heterogeneity and\nhomogeneity of ENSO impact.\nFirst, we have revised Fig. 1 by changing the threshold of teleconnected countries to ψ>1, and\nthen depicting the comparison between teleconnected and weakly-teleconnected countries (Fig.\n1b), agriculture-dependent and -independent countries (Fig. 1c), rich and poor countries (Fig.\n1d), to show more relevant distinctions.\nNext, we have examined whether the response functions across different groups of countries\nare statistically distinguishable. We allow interaction between both the linear and quadratic\nterms of Niño3.4 and an indicator D for whether a country is, for example, a teleconnected\ni\ncountry. We then incorporate it into the econometric model (Eq. 1):\nn\nlog(y )  E  E2 D   E  E2  T  T2 P  P2  tt2\nit 1,l tl 2,l tl i 3,l tl 4,l tl 1,l itl 2,l itl 1,l itl 2,l itl i 1i 1i it\nl0\n1 if country i is a teleconnected country\nD \ni 0 if country i is a weakly-teleconnected country\nwhere  and  represent the linear and nonlinear coefficients of the response function for\n1,l 2,l\nweakly-teleconnected countries, and  and  represent the adjustments to  and \n3,l 4,l 1,l 2,l\nthat are only applicable to teleconnected countries. Thus, the response functions of\nteleconnected and weakly-teleconnected countries are statistically different in structure if both\n and  are significantly distinguishable from zero (p<0.05). We apply the same approach\n3,l 4,l\nto agriculture-dependent/-independent and rich/poor countries. We find that while some\ncoefficients of linear terms  are significant, coefficients of nonlinear terms  are\n3,l 4,l\nindistinguishable from zero (Table R1A).\nTherefore, we conclude that although the heterogeneity of ENSO impact does exist, for\nexample, teleconnected countries exhibit different asymmetry of the ENSO impact, with\nsomewhat greater negative effect from El Niño than weakly-teleconnected countries, the\noverall response functions are statistically indistinguishable between each other, indicating a\ndominant homogeneity of the nonlinear effect for all groups of countries.\nTable R1A. Same as Table S1, but with regression coefficients of interaction terms (,  )\n3 4\nadded.\nTeleconnected/Weakly- Agriculture-dependent/-\nRich/Poor countries\nteleconnected countries independent countries\n\n-0.0055*** -0.0053*** -0.0053***\n1,0\n\n-1.53×10-4 -0.0023** -0.0029***\n1,1\n\n0.0011 1.64×10-4 -0.0017*\n1,2\n\n5.80×10-4 4.74×10-4 -0.0012\n1,3\n\n7.67×10-4 5.88×10-4 -1.31×10-5\n2,0\n\n-0.0029*** -0.0025*** -0.0024***\n2,1\n\n-0.0033*** -0.0029*** -0.0030***\n2,2\n\n-4.41×10-4 -8.07×10-4 -0.0017**\n2,3\n\n5.00×10-4 5.31×10-4 4.37×10-4\n3,0\n\n-0.0046*** -9.81×10-4 6.87×10-4\n3,1\n\n-0.0033** -0.0022 0.0026*\n3,2\n\n-0.0029** -0.0040*** 4.86×10-4\n3,3\n\n-8.78×10-4 -8.01×10-4 7.26×10-4\n4,0\n\n0.0013 0.0011 7.98×10-4\n4,1\n\n0.0011 7.80×10-4 8.24×10-4\n4,2\n\n-0.0013 -9.35×10-4 0.0013\n4,3\nR2 0.2064 0.2056 0.2047\nIt would have been nice to have, at least, the main regression results presented in a table, just\nto get a better idea of which parameters are driving the results.\nWe included all the regression coefficients with their statistical significance in Table S1 of\nSupplementary Information.\nThank you again for your helpful and encouraging comments.\nResponse to Reviewer #2\nThe paper is engaging and well-written. It tackles a relevant issue, which could spur interest in\na large community of readers. In particular, the authors study the effects of El Nino and la Nina\nevents on economic growth, using well-established empirical methods from climate\neconometrics and a global panel of countries over the period 1961-2019. My feeling is that the\npaper has potential, but the authors should convincingly address some key issues, which I think\ncurrently undermine the validity and innovativeness of the study.\nWe thank the reviewer for the positive and encouraging comments.\nFirst, a suggested interpretation of the estimated effects is grossly missing. The authors\nestimate economic losses from ENSO by means of an econometric model relating country-\nspecific economic growth rates to an ENSO index, country-specific temperatures, and country-\nspecific precipitation (with multiple powers and lags). They then use the marginal estimated\neffects to compute losses stemming from ENSO oscillations (please, pay attention to my third\npoint below). However, they are silent on what mechanisms could drive economic losses from\nEl Nino/La Nina. I feel this is relevant, especially when the effect of local weather change is\ncontrolled for. Intuitively, one would expect that El Nino events cause changes in the\nexperienced weather conditions in different countries, which - in turn - affect local economic\nactivity and production. However, given the model estimated by the authors, such effects\nshould be largely captured by the precipitation and temperature variables. Hence, the\n“additional effect” of ENSO is very interesting and surprisingly large, but – for this to be\ncredible - I feel the authors should make an interpretative effort, suggesting what mechanisms\nmay lie behind the statistical relationship the paper seems to spot robustly. This is even more\nrelevant as the present study reports much larger losses than in previous literature.\nWe thank the reviewer for the suggestion. Some previous studies reported immediate\nobservable losses from ENSO events (for example, Changnon 1999; Zong et al. 2000), but\nthere are losses not directly observable or as a delayed response to the initial climate shock.\nFor example, in El Niño-affected regions (such as Australia), extended drought and forest fires\ncause not only infrastructure damage, loss of cattle stocks, and reduction in farming outputs,\nbut also unemployment of people working in the sector (Vos 1999). These impacts lead to high\ninflation and reduced purchasing power with an economic effect transcending the affected\nworkers and communities (Brunner 2002; Cashin et al. 2017). Through the high inflation, low\npurchasing power, and the associated low consumer confidence, the impact spills over to other\nsectors such as service and tourism of the broader economy and delays major investment and\nrecruitment decisions. In addition, through global trades, the reduced agriculture and cattle\nproduction leads to higher commodity prices, affecting global economy. Thus the impact is not\nlimited to the immediate tangible losses, but far larger, intangible and long-lasting.\nWe have now included more discussion in Line 144-150.\nReferences:\nBrunner, A. D. (2002). El Nino and world primary commodity prices: warm water or hot air?.\nReview of Economics and statistics, 84(1), 176-183.\nCashin, P., Mohaddes, K., & Raissi, M. (2017). Fair weather or foul? The macroeconomic\neffects of El Niño. Journal of International Economics, 106, 37-54.\nChangnon, S. A. (1999). Impacts of 1997–98 EI Niño–generated weather in the United\nStates. Bulletin of the American Meteorological Society, 80(9), 1819-1828.\nVos, R., Velasco, M., & Edgar de Labastida, R. (1999). Economic and social effects of El\nNiño in Ecuador, 1997-1998. Washington, DC: Inter-American Development Bank.\nZong, Y. & Chen, X. The 1998 flood on the Yangtze, China. Natural Hazards 22, 165-184\n(2000).\nSecond, the innovativeness of the paper should be streamlined much more effectively. The\nauthors sometimes seem to motivate their study on the basis of inconclusive and scant evidence\nabout the effects of El Nino/La Nina on the macroeconomy (see e.g. the abstract at lines 18-\n19). While I agree that further studies are definitely necessary, I have some issues grasping the\nnovelty of the present paper with respect to the literature. For example, Smith and Ubilava\n(2017, GEC) - which the authors cite - offer a quite similar approach, finding non-linear effects,\nspanning across different years and varying across countries and climatic zones. Cashin et al.\n2017 (2017, JIntEco) - which the authors cite - offer a much more comprehensive assessment\nof the macroeconomic effects of El Nino and La Nina events, though with a different\nmethodology. I feel the authors should make crystal clear the piece of evidence they add to the\nliterature.\nOur nonlinear model is different in some key characteristics from previous approaches such as\nusing a Heaviside model to depict the nonlinear impact. Our fully nonlinear model, which\nshares the feature of an asymmetric impact between El Niño and La Niña, allows the impact to\nincrease exponentially with El Niño amplitude, which is assumed to be linearly proportional to\nEl Niño amplitude in a Heaviside model. Additionally, our model allows the La Niña impact\nto change sign from a benefit during moderate La Niña to a damage during extreme La Niña,\ninstead of assuming one sign of impact for all La Niña events in a Heaviside model. Our fully\nnonlinear model is more realistic. Allowing the impact to change nonlinearly with El Niño or\nLa Niña amplitude is important for assessing ENSO impact on macroeconomy in a warming\nclimate, given that ENSO amplitude is projected to increase.\nMy reading is that the originality lies in i. the role of teleconnections (which would deserve\nmore space in the paper), ii. the additional effect of ENSO beyond that of local temperature\nand precipitations (see my point above), iii. the projected effects on growth, which are very\nlarge and need to be robust (see my third and fourth points immediately below).\nWe thank the reviewer for recognizing our points and for the suggestions to improve. We have\nadded the following discussion.\ni. In terms of the role of country-specific teleconnection, we stress that our inclusion of\ninteraction between common ENSO shock with country-specific teleconnection in the\neconometric model is to test the heterogeneity of ENSO impact across individual countries.\nWe find that the inclusion only makes a minor and insignificant change to the common\nENSO impact; see our detailed response to your third comment below.\nii. In terms of the effect of ENSO beyond that of local weather anomalies, please see our\ndetailed response to your first comment, we have stressed this point.\niii. In terms of the large effect on growth, we thank you for recognizing this aspect. Please see\nour response to your third and fourth comments regarding to the robustness of our\nprojection.\nThird, the authors estimate a growth effect while they mostly report results in terms of a level\neffect. While the paper finds a robust non-linear effect of ENSO on growth, economic losses\nare mostly reported in terms of trillions of dollars of economic damage, which is suggestive of\na level effect. I feel this approach is problematic for two reasons: first, it does not account for\nthe debate on the persistence of climate impacts on the macroeconomy (see e.g. Diaz and\nMoore 2017, NCC; Bastien-Olvera et al. 2021, ERL);\nWe should have made it clear that this is not the case. We introduced multiple lags in our\neconometric model to test whether and how ENSO affects the level or/and the growth effect.\nIf ENSO only affects the level of GDP, the contemporaneous and lagged effects of ENSO\nwould be opposite in sign. That is, a negative impact in the occurrence year of ENSO would\nbe followed by a positive “rebound” in the following year, leading to a sum of\ncontemporaneous and lagged effects (i.e., cumulative effect) close to zero.\nInstead, we find substantial and significant lagged effects of ENSO, which have the same sign\nas the contemporaneous effect, and the magnitude is greater than the level effect (Table S1).\nThis suggests an accelerated growth effect of ENSO after the initial shock. In our estimates,\nsuch as that presented in Fig. 2a, the losses mostly come from growth effect. Our result also\nsupports the persistence of ENSO impact on the macroeconomy.\nsecond, it makes results more difficult to interpret from a quantitative standpoint. Indeed, 1\ntrillion USD is relatively low for the US economy, while extremely large for a small African\ncountry; similarly, it is difficult to say whether a loss of 2.1 trillion (a figure found in the\nabstract) is large or small for the global economy without a meaningful term of comparison\n(e.g. the value of the global GDP in the year of the shock or the loss generated by another event,\ne.g. the global financial crisis).\nWe have also estimated economic losses separately in different groups of countries, to show\nthe heterogeneous response of different countries to ENSO (Fig. 1). For example, given the\nmagnitude of 1997-98 El Niño (+2.27 s.d.), poor countries (such as small African countries)\nexhibit a loss of 6.08% while rich countries (including US) experience a loss of 3.28% (Fig.\n1d). As our goal is to estimate ENSO’s global impact and its future change, we use all-country\nsamples to train the econometric model for our main results in this paper.\nWe estimate that the cumulative loss of US$1.3, US$2.1 and US$4.0 trillion from the 1982-83,\n1997-98 and 2015-16 extreme El Niño events accounted for about 4% of global GDP in total\nover the four years (Line 156). Data from World Bank shows that the 2008 global financial\ncrisis, for example, caused a cumulative loss in global GDP growth by ~5.7% (-2.3% from\n2007 to 2008 and -3.4% from 2008 to 2009).\nFurther, while the authors account for inflation, it is not always clear in dollars of which year\nresults are computed.\nWe have included that all the GDP data (including SSP projection) and estimated costs in this\npaper are in constant 2015 US$.\nMore relevantly, I failed to get the exact meaning of equations 2 and 4. To my reading, they do\nnot reflect the impact on growth the authors would like to measure. I would kindly encourage\nthe authors to provide more details, possibly together with examples for some countries. Indeed,\nit is not evident what losses in which countries are driving the global loss estimates the authors\ncome up with. More clarity along these lines would make the results much more trustable.\nUnpacking the results across countries would offer an intriguing perspective, but this is just a\nsuggestion.\nWe thank the reviewer for this suggestion. We have now provided more detailed steps of how\nthe observed ENSO-induced economic loss, in-dollar value, is calculated (Line 486-490).\nWe have attempted to assess the country-specific estimate of economic loss by including an\ninteraction of the common ENSO shock (Niño3.4) with country-specific teleconnection in the\neconometric model, similar to a previous study linking the common ENSO shock with local\nweather conditions (Generoso et al. 2020), as below:\nn\nlog(y )  E  E2 E  (E )2T  T2 P  P2  tt2\nit 1,l tl 2,l tl 1,l i tl 2,l i tl 1,l itl 2,l itl 1,l itl 2,l itl i 1i 2i it\nl0\nwhere E is the interaction term between the common ENSO shock and country-specific\ni tl\nteleconnection. The estimated regression coefficients are listed below:\nTable R2A. Same as Table S1, but with regression coefficients of interaction term ( )\nadded.\nLag 0 Lag 1 Lag 2 Lag3 Lag4 Lag5\n\n-0.0032*** -0.0042*** -0.0052*** -0.0055*** -0.0049*** -0.0051***\n1,0\n\n6.19×10-5 -0.0011 -0.0015 -0.0016 -0.0020*\n1,1\n\n9.26×10-4 2.39×10-4 1.06×10-4 3.79×10-4\n1,2\n\n4.83×10-4 -1.09×10-4 3.63×10-4\n1,3\n\n0.0021** 0.0026***\n1,4\n\n-0.0011\n1,5\n\n3.93×10-4 2.76×10-4 5.03×10-4 4.69×10-4 7.77×10-5 3.53×10-4\n2,0\n\n-0.0019*** -0.0023*** -0.0023*** -0.0022*** -0.0020***\n2,1\n\n-0.0024*** -0.0027*** -0.0024*** -0.0025***\n2,2\n\n-0.0012** -0.0014** -0.0015**\n2,3\n\n-0.0012* -8.26×10-4\n2,4\n\n9.36×10-4\n2,5\n\n2.05×10-4 3.33×10-4 2.36×10-4 2.02×10-4 8.63×10-5 1.37×10-4\n1,0\n\n-4.35×10-4 -4.76×10-4 -6.55×10-4* -6.70×10-4* -5.82×10-4\n1,1\n\n-3.77×10-4 -5.20×10-4 -6.42×10-4* -6.94×10-4*\n1,2\n\n-8.35×10-4** -8.44×10-4** -9.62×10-4**\n1,3\n\n-6.71×10-4** -7.95×10-4**\n1,4\n\n5.86×10-5\n1,5\n\n-2.80×10-5 -2.07×10-5 -2.18×10-5 -2.48×10-5 -2.12×10-5 -3.03×10-5\n2,0\n\n3.39×10-5 3.56×10-5 2.87×10-5 2.34×10-5 1.57×10-5\n2,1\n\n-3.67×10-7 2.91×10-6 -6.85×10-6 -3.83×10-6\n2,2\n\n5.04×10-6 6.74×10-6 1.17×10-5\n2,3\n\n1.45×10-5 6.85×10-6\n2,4\n\n-3.43×10-5\n2,5\nR2 0.1973 0.2006 0.2033 0.2055 0.2068 0.2075\nAs can be seen from Table R2A, coefficients of the interaction terms show little statistical\nsignificance. We compare the estimated global losses of observed extreme El Niño events. The\nresult from the model with interaction terms doesn’t show a significant difference from our\noriginal result (Fig. R2A). However, the model provides information on country-specific\nimpacts. We have calculated country-level in-dollar-value losses from 2015-16 extreme El\nNiño. As can be seen from Fig. R2B, countries with the greatest economy (like US and China)\ncontribute to much of loss, despite a smaller GDP growth loss, as expected.\nFig. R2A. Cumulative effect of three major extreme El Niño events in 1982/83 (yellow),\n1997/98 (red) and 2015/16 (purple) on the global total GDP under (a) the original econometric\nmodel (same as Fig. 2a), and (b) new econometric model with interaction terms added.\nShadings indicate the 95% confidence level for each event based on a Bootstrap method.\nFig. R2B. Country-level cumulative losses from 2015-16 extreme El Niño under the country-\nspecific econometric model. The map is created in the MATLAB computing environment usi\nng the M_Map mapping package (Pawlowicz, R., 2020. \"M_Map: A mapping package for M\nATLAB\", version 1.4m, [Computer software], available online at www.eoas.ubc.ca/~rich/ma\np.html) .\nFourth. In their projection exercises, it is unclear what drivers of growth the authors consider\nand what they do not. For example, in the seminal paper by Burke et al. (2015, Nature)\nprojections include socio-economic trends (proxied by historical growth rates of GDP). I have\nthe impression the authors exclude such trends when projecting the GDP of various countries.\nFor example, this seems to be the case in the section “Substantial additional loss in economic\nproduction from increased ENSO amplitude” (from line 197). Accounting for such trends may\nchange the projection results sensibly. Similarly, accounting for future temperature will affect\ngrowth rates; however, the counterfactual scenario seems to assume that ENSO will change\nalongside future climatic and SSP scenarios while temperatures are not. I think the authors\nshould either change their projections or offer a crystal-clear and convincing explanation of\ntheir counterfactual and projection exercises.\nWe followed Burke et al. (2015 & 2018) to train our econometric model by fitting with\nhistorical climate and economic data. The socio-economic linear and quadratic trends, were\nincluded as θ t and θ t2 in our model (Line 94-96 and 469-471).\n1i 2i\nFurther, we assume that the future increase of temperature doesn’t change the nonlinear ENSO\nimpact. Future loss from mean temperature warming could be estimated using the historical\nresponse function as Burke et al. (2018), but that is not the goal of our work.\nThe estimated loss using the econometric model is dependent on the coefficients ( ,  ),\n1,l 2,l\n(which are independent from rising temperature) and the projected future GDP. Two ENSO\ntime series are constructed, one based on the projected ENSO time series, and the other on\nscaled projected time series such that ENSO variability is the same as in the model historical\nperiod. Using each of these ENSO time series as input, we obtain two time series of future\nENSO impact on GDP growth rate, which are used to modify the projected growth rate. From\nthe two time series of modified growth rate and the projected, we calculate two new time series\nof GDP. The difference is the attributed as due to changes in ENSO.\nFifth, aggregation with discount rates may be problematic. Impacts on economic production\nare aggregated with discount rates chosen by the authors. Figure 4 is the most communicative\nexample. I find this choice debatable. First, the last two decades have witnessed a fierce debate\nabout the selection of discount rates, and results from impact studies typically showed large\nsensitivity to such a parameter. This paper is not an exception.\nWe agree. We used a mid-range discount rate as an example to gauge the economic damages\nfrom changing ENSO in dollar value that is comparable across years during the long period.\nChoice of discount rate is itself a source of uncertainty, and this is why we use a range of\ndiscount rate (1-5%) values.\nThe uncertainty from discount rates, while substantial, is relatively small compared with\nuncertainty from that projected ENSO change – which we show to be the dominant source (Fig.\n4f and Line 563-586). We also provide the result in terms of percentage GDP loss without\ninvolving a discount rate (Fig. 3), see our response below.\nSecond, it is not clear to me why the authors need to discount back in time a projected impact.\nFor example, they could just compute the loss as the ratio between the projected global GDP\naccounting for ENSO effects and the projected GDP without the ENSO effect. This would be\nmore similar, for example, to the approach by Burke et al. 2015 and to the majority of damage\nfunctions.\nYou are right. We have calculated the projected loss equivalent to a ratio of projected global\nGDP in 2099. The multi-model ensemble mean ratio between the projected GDP accounting\nfor ENSO effects and the projected GDP without the ENSO effect is 1.14, indicating a\ncumulative 14% reduction of global GDP by 2099. Moreover, we find that the ratio of projected\nGDP increases nonlinearly with the ratio of projected ENSO amplitude (Fig. R2C), similar to\nthe relationship in Fig. 4e. These conclusions are independent of an assumed discount rate or\na projected GDP value.\nFig. R2C. Nonlinear relationship between ratios of Niño3.4 variability (21st / 20th century) and\nratios of projected global GDP in 2099 (without projected ENSO’s effect / without\ncounterfactual ENSO’s effect) from CMIP6 models under four IPCC scenarios. Different\ncolors refer to four IPCC scenarios. The R square and P value of a nonlinear fit are given.\nFinally, let me provide few other yet less substantive comments.\n• at lines 95-98 the authors state they include mean temperature and precipitation in their model\nto estimate the effects of mean state change and introduce the assumption that ENSO-induced\ntemperature and precipitation changes are independent of mean state changes. First, I am not\nconvinced that equation 1 reflects this assumption (rather, it reflects yearly deviation from country-\nspecific trends that may be caused by any events, including ENSO); second, I am not\nconvinced the assumption of independence is realistic and/or innocent.\nWe have tested whether ENSO variation is independent of temperature and precipitation by\nremoving the ENSO signal from country-level mean temperature and precipitation, and then\nre-estimate the econometric model. As can be seen in Table R2B, there is little difference in\nthe coefficients of ENSO impact ( ,  ), mean temperature and precipitation effect ( ,\n1,l 2,l 1,l\n and  ,  ), suggesting our results remain virtually the same after excluding ENSO\n2,l 1,l 2,l\nsignal from the mean state.\nTable R2B. Comparison of regression coefficients of Niño3.4 index, mean temperature and\nprecipitation in the original econometric model (left) and the econometric model with ENSO\nremoval in the mean temperature and precipitation change.\nLag 3, ENSO removed\nLag 3, original\nin mean state change\n\n-0.0055*** -0.0051***\n1,0\n\n-0.0027*** -0.0029***\n1,1\n\n-6.20×10-4 -3.41×10-4\n1,2\n\n-9.73×10-4 -8.25×10-4\n1,3\n\n2.92×10-4 3.08×10-4\n2,0\n\n-0.0021*** -0.0021***\n2,1\n\n-0.0026*** -0.0026***\n2,2\n\n-0.0012** -0.0012**\n2,3\n\n0.0125*** 0.0123***\n1,0\n\n-0.0076** -0.0071**\n1,1\n\n-0.0013 -0.0015\n1,2\n\n-0.0023 -0.0016\n1,3\n\n-3.68×10-4*** -3.48×10-4***\n2,0\n\n2.31×10-4** 2.31×10-4**\n2,1\n\n7.33×10-5 1.16×10-4\n2,2\n\n-2.95×10-5 -7.23×10-5\n2,3\n\n-3.48×10-4 -4.84×10-4\n1,0\n\n-0.0010 -2.11×10-4\n1,1\n\n-3.51×10-4 1.95×10-5\n1,2\n\n-0.0042 -0.0049\n1,3\n\n-4.26×10-5 -4.48×10-5\n2,0\n\n3.79×10-5 3.84×10-6\n2,1\n\n-4.79×10-5 -6.42×10-5\n2,2\n\n2.50×10-4 2.88×10-4\n2,3\nR2 0.2041 0.2040\n• the effects of temperature and precipitations are not discussed and it is also not clear whether\nthese variables are included in the estimation in their population-weighted version or not. This\nmay have implications for how the authors capture exposure of economic production to the\nweather and for the interpretation of results.\nWhat we have shown is after mean temperature and precipitation are already included. Here\nthe mean temperature and precipitation are also population-weighted into country-average. We\nhave now added the discussion about effect of temperature and precipitation. Similar to\nfindings of Burke et al. (2015), the mean temperature affects economy nonlinearly, while mean\nprecipitation shows little and insignificant impact.\n• The links with the blossoming climate econometrics literature could be better surveyed and\nthe paper better positioned in such a stream of studies. For example, El Nino and La Nina are\nknown to exert impacts through changes in precipitation patterns (among other channels).\nPalagi et al. (2022, PNAS) and Kotz et al. (2022, Nature) are two extremely recent\ncontributions emphasizing the impact of precipitation anomalies on growth and the\nmacroeconomy. The authors could leverage these results to provide a more informative\ncontextualization of their findings. This links back to my first point.\nGreat point. ENSO affects macroeconomy directly through local weather conditions such as\ntemperatures and precipitation. We have provided the context that ENSO is a recognized major\ndriver of weather extremes in many affected regions, and cited the economic impacts revealed\nby previous investigation (for example, temperature extremes: Callahan and Mankin, 2022;\nprecipitation extremes: Palagi et al. 2022;Kotz et al. 2022). See Line 47.\nThank you again for your helpful and encouraging comments, which have guided us to\nimprove the paper a lot.\nResponse to Reviewer #3\nThis article, which aims to assess the overall economic cost of ENSO in a context of climate\nchange, covers an important subject area. My comments focus on how the ENSO impact is\nconsidered and the relevance of an average assessment of its cost in terms of overall GDP\n(knowing that I am not a climatologist). I find many issues that must be addressed for the\nassessment to be credible.\nMy comments relate to the estimation of the country-specific teleconnection strengths as well\nas the estimation of the economic cost of ENSO-related events. It seems fundamental to me to\nsolidify these results before any climate projection.\nWe thank the reviewer for the thorough and constructive comments, which improve our work.\nWe provide our response below.\n1. ENSO index and country-specific teleconnection\nThe authors propose a measure of the degree of teleconnection specific to each country. More\ndetail should be given regarding the construction of the indicator reflecting the magnitude of\nteleconnection patterns.\nWe have now provided more detailed steps for the construction of ENSO country-specific\nteleconnection.\nThe estimation of the regression coefficients ρ and τ raises questions in the absence of a\nclearly specified model. Are the temperature and precipitation anomalies, used as explained\nvariables, standardized?\nThe Niño3.4 index, temperature and precipitation anomalies are standardized in our study. The\nnormalization ensues that the regression coefficients τ and ρ are dimensionless, and comparable.\nWe have made clear in the revised manuscript (Line 442-443).\nAre precipitation included as a covariate in the regression when analyzing temperature and vice\nversa to control for the correlation between temperature and precipitation.\nWe have now used partial regression instead to calculate the regression coefficient of\ntemperature onto Niño3.4 index when removing the precipitation impact, and vice versa (Line\n446-448).\nMoreover, several factors can lead to a waxing and waning in the strength of the ENSO\nteleconnections: changes in the evolution of the SST pattern during ENSO, changes in the\nENSO's temporal behavior, changes in the mean base climatology, and its modulation of the\nteleconnection process, and simple sampling variations. It could then be helpful to review the\nrobustness of the country-specific teleconnection metric assessed by the authors by analyzing\nits temporal stationarity throughout the entire reference period.\nWe thank the reviewer for the suggestion. First, to test spatial variability of ENSO SST\nanomaly that may affect teleconnection, we apply a 36-year running window regression of DJF\nSST anomalies onto DJF Niño3.4 (1950-1985, 1951-1986, … 1986-2021, a total of 37\nwindows), and then compare the pattern correlation of the 37 samples with the overall regressed\nSST anomalies in the tropical Pacific (Fig. R3A-a). As can be seen in Fig. R3A-b, there is\nlittle significant change in ENSO SST pattern in different time periods, suggesting the ENSO\nSST pattern is rather stationary.\nSecond, to test temporal change of ENSO teleconnection, we apply a 36-year running window\nregression of May(0) to April(1) monthly surface air temperature and precipitation anomalies\nonto DJF Niño3.4 (1950-1985, 1951-1986, … 1986-2021), to construct 37 samples of\ncumulative temperature and precipitation teleconnection strength (τ and ρ) in the globe. We\ncompare the pattern correlation of these 37 samples with the overall cumulative teleconnection\n(Fig. R3B-a, b). As can be seen in Fig. R3B-c, d, the global teleconnection patterns of both\ntemperature and precipitation are stationary.\nFig. R3A. a, Regressed DJF SST anomalies onto Niño3.4 index (normalized) in the tropical\nPacific during 1950-2021. Only the regression coefficients above 95% significance level are\nshown as shading. The map is created in the MATLAB computing environment using the M\n_Map mapping package (Pawlowicz, R., 2020. \"M_Map: A mapping package for MATLAB\n\", version 1.4m, [Computer software], available online at www.eoas.ubc.ca/~rich/map.html)\n. b, Pattern correlation of regressed SST anomalies from 37 samples, with the overall regress\ned SST anomalies during 1951-2021.\nFig. R3B. a, Global cumulative ENSO teleconnection of temperature (τ), calculated as the sum\nof May(0) to April(1) monthly regression coefficients of surface air temperature anomalies onto\nDJF Niño3.4 (both normalized) during 1950-2021. b, Same as a, bur for precipitation (ρ). c,\nPattern correlation of cumulative ENSO teleconnection of temperature (τ) from 37 samples,\nwith the overall one during 1951-2021. d, Same as c, but for precipitation (ρ). The maps were\ncreated in the MATLAB computing environment using the M_Map mapping package\n(Pawlowicz, R., 2020. \"M_Map: A mapping package for MATLAB\", version 1.4m, [Computer\nsoftware], available online at www.eoas.ubc.ca/~rich/map.html) .\nIn line 403, the authors state that the estimated coefficients are weighted by population density\nin 2020. First, what is the source of the population density data?\nWe included this information. The population density data is from Gridded Population of the\nWorld (GPW) (Line 410-412).\nSecond, the authors should give better justification with regard to this choice concerning the\nspatial aggregation procedure. Number of economic studies aggregate ENSO teleconnections\nwithin countries borders using a fixed set of population weights, so that the relevant concept is\nthe average weather experienced by a person in the administrative area, not the average weather\nexperienced by a place. However, this procedure implies that the aggregation gives a higher\nweight to highly populated (urban) areas while areas characterized by low population densities\nmay play a major economic role and while being impacted by ENSO shocks. This is\nparticularly the case for agricultural regions, a sector through which the majority of ENSO-\nrelated economic shocks spread.\nWe thank the reviewer for raising this point. Because ENSO affects economy not only in the\nagriculture sector (though agriculture is one of the most affected components), but also many\nother economic activities such as tourism, trade and services, which are labor-related and a key\npart of the economy. The spill-over effect can be far greater than the direct and tangible loss.\nWe average the climate shock by population density only to gauge the impact sensitivity to\nvarying groups of economies. Ultimately when all countries are included, as the case in our\nstudy, the impact does not involve a country-specific depiction of ENSO teleconnection.\nIn line 405, the authors divide the countries into two categories based on the accumulation of the\ncountry-specific temperature and precipitation teleconnection strengths (ψ_i) so that\ncountries where ψ_i<0.5 are weakly connected and countries where ψ_i>0.5 are connected.\nThis categorization is of particular concern to me. On what basis is this classification made? It\nseems from Figure S4 that the choice of such a low threshold value completely crushes the\ncountry heterogeneity of ENSO shocks and that almost all countries belong to the category of\nteleconnected countries. In other words, what is the point of calculating a specific indicator for\neach country to ultimately arrive at such a broad categorization?\nA specific threshold for distinguishing “teleconnected” and “weakly-teleconnected” countries\nis to separately estimate the nonlinear economic impact from these two groups of countries.\nWe have applied a sensitivity test to gauge whether the choice of ψ threshold may affect our\nresult. For example, by using ψ>1 or ψ>1.5 instead, there is no significant difference in the\nresponse function (Fig. R3C).\nFig. R3C. Global nonlinear relationship between D(0)JF(1) Niño3.4 index (normalized) and 3-\nyear cumulative (from year 0 to year 3) change in log GDP per capita for teleconnected\ncountries with threshold of ψ>1.5 (brown curve), ψ>1 (red curve), and ψ>0.5 (yellow curve).\nShading indicating the 95% confidence level based on a Bootstrap method.\nWe agree with the reviewer that a low threshold (ψ>0.5) allows most of countries belong to the\ncategory of “teleconnected”. Hence, we choose ψ>1 as the new threshold to make a more\nbalanced distinction for the two groups (Fig. S5b).\nFig. S5. b, Classification of countries into groups of “teleconnected” defined as ψ>1 (red), and\n“weakly-teleconnected” defined as ψ≤1 (blue). The map is created in the MATLAB\ncomputing environment using the M_Map mapping package (Pawlowicz, R., 2020. \"M_Map:\nA mapping package for MATLAB\", version 1.4m, [Computer software], available online at\nwww.eoas.ubc.ca/~rich/map.html) .\nIt seems to me, moreover, that this methodological choice is decisive for the rest of the article\nand therefore the determination of the overall cost of ENSO.\nWe should have made it clear that this is not the case. We examined the heterogeneity effect\nonly in this section only as a gauge of the impact sensitivity to grouping of countries.\nSubsequently, we have included all countries, which does not involve a country-specific\ndepiction of ENSO teleconnection. As our ultimate goal is to estimate the global economic\nimpact of ENSO shocks and its future change, all of our main results hereafter are generated\nfrom the climate-economy model that is fitted by data from all countries, involving no country-\nspecific depiction of ENSO teleconnection.\nIt would be necessary to consider alternative global measurements of the level of\nteleconnection based on more precise measurements (spatial variability, spatial extent, etc.)\nand to accompany the results with a map showing the level of teleconnection by grid point\n(before the aggregation at the country level).\nIn the context of sensitivity tests (see above), we have now given a grid-point temperature\nand precipitation teleconnection map separately in Fig. S4.\nFig. S4. Global cumulative grid-point ENSO teleconnection of a temperature (τ) and b\nprecipitation (ρ), calculated as the sum of May(0) to April(1) monthly regression coefficients\nof normalized surface air temperature and precipitation anomalies onto normalized DJF\nNiño3.4. The maps were created in the MATLAB computing environment using the M_Map\nmapping package (Pawlowicz, R., 2020. \"M_Map: A mapping package for MATLAB\",\nversion 1.4m, [Computer software], available online at www.eoas.ubc.ca/~rich/map.html) .\n2. Empirical econometric model\nThe authors test a distributed-lag timeseries model to estimate the economic cost of ENSO events.\nIs this model estimated by the GLS or by the OLS?\nOur model is estimated by the Ordinary Least Squares (OLS). We have now included it (Line\n460).\nI think it is essential to have more information on the possible presence of multicollinearity\n(using the VIF for example). Indeed, the measurement of ENSO shocks being indexed only by\ntime (shock common to all countries), the risk of quasi collinearity with the country fixed\neffects (μ_i) is high and likely to bias the estimates.\nThere is little collinearity of ENSO shocks with a country-fixed effect represented by country-\nspecific ENSO teleconnection. Taking the teleconnection as a part of characteristics for\nindividual countries, we examine the correlation between country-specific teleconnection (ψ)\ni\nand country-fixed effect (μ) in our econometric model. As can be seen in Fig. R3D, there’s no\ni\nevidence of a significant collinearity between them.\nFig. R3D. Relationship between the country-specific ENSO teleconnection (ψ, x-axis) and\ni\ncountry-fixed effect (μ, y-axis) extracted from the econometric model. The very low\ni\ncorrelation (0.02) indicates little collinearity between them.\nUsing a common ENSO shock to estimate its overall cost does not seem to constitute a\nsignificant contribution compared to the previous literature on the subject which explicitly\nintroduces a measure of the strength of the teleconnection specific to each country in the\neconometric specification or via the interaction between the common shock and country\nspecific weather anomalies. Why not integrate the results from the calculation of country-\nspecific teleconnections within the econometric specification? Indeed, the distinction made in\nFigure 1 does not seem to show any difference between the categories of countries and seems\ninsufficient.\nWe thank the reviewer for the suggestion. To test this hypothesis, we have introduced the\ninteraction terms to assess how ENSO affects economy under different teleconnection strengths.\nThis approach is similar to a previous study that linking the common ENSO shock with local\nweather conditions (Generoso et al. 2020). The econometric model is therefore written by\nn\nlog(y )  E  E2 E  (E )2T  T2 P  P2  tt2\nit 1,l tl 2,l tl 1,l i tl 2,l i tl 1,l itl 2,l itl 1,l itl 2,l itl i 1i 2i it\nl0\nwhere E is the interaction term between the common ENSO shock and country-specific\ni tl\nteleconnection. The estimated regression coefficients are listed below:\nTable R3A. Same as Table S1, but with regression coefficients of interaction term ( )\nadded.\nLag 0 Lag 1 Lag 2 Lag3 Lag4 Lag5\n\n-0.0032*** -0.0042*** -0.0052*** -0.0055*** -0.0049*** -0.0051***\n1,0\n\n6.19×10-5 -0.0011 -0.0015 -0.0016 -0.0020*\n1,1\n\n9.26×10-4 2.39×10-4 1.06×10-4 3.79×10-4\n1,2\n\n4.83×10-4 -1.09×10-4 3.63×10-4\n1,3\n\n0.0021** 0.0026***\n1,4\n\n-0.0011\n1,5\n\n3.93×10-4 2.76×10-4 5.03×10-4 4.69×10-4 7.77×10-5 3.53×10-4\n2,0\n\n-0.0019*** -0.0023*** -0.0023*** -0.0022*** -0.0020***\n2,1\n\n-0.0024*** -0.0027*** -0.0024*** -0.0025***\n2,2\n\n-0.0012** -0.0014** -0.0015**\n2,3\n\n-0.0012* -8.26×10-4\n2,4\n\n9.36×10-4\n2,5\n\n2.05×10-4 3.33×10-4 2.36×10-4 2.02×10-4 8.63×10-5 1.37×10-4\n1,0\n\n-4.35×10-4 -4.76×10-4 -6.55×10-4* -6.70×10-4* -5.82×10-4\n1,1\n\n-3.77×10-4 -5.20×10-4 -6.42×10-4* -6.94×10-4*\n1,2\n\n-8.35×10-4** -8.44×10-4** -9.62×10-4**\n1,3\n\n-6.71×10-4** -7.95×10-4**\n1,4\n\n5.86×10-5\n1,5\n\n-2.80×10-5 -2.07×10-5 -2.18×10-5 -2.48×10-5 -2.12×10-5 -3.03×10-5\n2,0\n\n3.39×10-5 3.56×10-5 2.87×10-5 2.34×10-5 1.57×10-5\n2,1\n\n-3.67×10-7 2.91×10-6 -6.85×10-6 -3.83×10-6\n2,2\n\n5.04×10-6 6.74×10-6 1.17×10-5\n2,3\n\n1.45×10-5 6.85×10-6\n2,4\n\n-3.43×10-5\n2,5\nR2 0.1973 0.2006 0.2033 0.2055 0.2068 0.2075\nAs can be seen from Table R3A, coefficients of interaction term show little statistical\nsignificance. We further compare the estimated global losses of observed extreme El Niño\nevents. The result from the model with interaction terms doesn’t show significant difference\nwith original result (Fig. R3E), suggesting the interaction between common ENSO shock with\ncountry-specific teleconnection has a minor effect.\nFig. R3E. Cumulative effect of three major extreme El Niño events in 1982/83 (yellow),\n1997/98 (red) and 2015/16 (purple) on the global total GDP under a original econometric\nmodel (same as Fig. 2a), and b new econometric model with interaction term added. Shadings\nindicate the 95% confidence level for each event based on a Bootstrap method.\nThe distinction between rich and poor countries should also be clarified. On what basis is this\ndistinction made?\nHere we classify the rich and poor countries following the distinction of Burke et al. (2015),\nthat is, rich (poor) countries are those with purchasing-power-parity-adjusted (PPP) per capita\nincome in 1980 above (below) the median. We have provided the details in the revised version.\nThis distinction may seem tautological from an economic point of view, as poor countries are\nmore vulnerable to climatic hazards...\nGreat point. This is what we have stressed. Poor countries show larger nonlinear response to\nENSO mainly because they happen to be more strongly affected by ENSO (histograms of Fig.\n1d) (Line 135-136).\nA more relevant distinction should be made on the basis of the countries structural\ncharacteristics by distinguishing, for example, countries dependent on agriculture, countries\nwhose economic fabric is more diversified and thus evaluating the heterogeneity estimated cost\nof ENSO events.\nWe thank the reviewer for the suggestion. We have additionally estimated the response of\nagriculture-dependent countries (defined by GDP share of agriculture greater than 20%), whose\neconomy is more likely affected by weather extremes from ENSO teleconnection. As expected,\nagriculture-dependent countries exhibit larger response to El Niño than agriculture-\nindependent countries. We have included this result in the new Fig. 1c.\nFig. 1. c, Global nonlinear relationship between D(0)JF(1) Niño3.4 index (normalized) and 3-\nyear cumulative (from year 0 to year 3) change in log GDP per capita for agriculture-dependent\n(green curve) and agriculture-independent countries (blue curve) during 1960-2019, with\nshading indicating the 95% confidence level based on a Bootstrap method. Histograms below\nshow the distribution of country-specific ENSO teleconnection strength for agriculture-\ndependent (green bars) and agriculture-independent (blue bars) countries, respectively.\nThe annual measurement of the ENSO indicator (Nino 3.4) should be subject to robustness.\nThe authors should consider comparing their non-linear (quadratic) measurement to a\nmeasurement based on event occurrence (based on threshold values) thus justifying the\nmethodological contribution of the paper with respect to the prior literature relying on a\nHeaviside function in order to dissociate El Niño events from La Nina events. It is important\nto note that the references mentionned by the authors use both a Heaviside function and a\nquadratic specification in order to characterize ENSO events within the framework of their\neconometric specifications.\nTo compare our result with previous estimates, we have built a similar Heaviside model with\ndifferent lags according to prior studies (Smith andUbilava, 2017; Generoso et al. 2020):\nn\nlog(y ) I(.) E 1I(.) E  T  T2  P  P2  t t2\nit 1,l tl 2,l tl 1,l itl 2,l itl 1,l itl 2,l itl i 1i 2i it\nl0\nwhere I(.) is the Heaviside indicator to determine the ENSO regimes. We therefore train the\nHeaviside model and estimate the global losses from observed extreme El Niño events. Fig\nR3F shows consistent results between these two models, both of which indicate a negative\nimpact from El Niño but insignificant for La Niña, and estimate similar losses from extreme El\nNiño events in the historical record.\nFig. R3F. a, Same as Fig. 1a. c, Same as Fig. 2a. b and d, Same as a and c, but for the results\nfrom the Heaviside model.\nAlthough our estimated loss from ENSO during the historical period is, mostly, consistent with\nprior studies in the historical estimate, we use an index-based fully nonlinear model, instead of\na threshold-based Heaviside model, to assess future projection under climate change mainly\nfor two reasons. First, threshold-based model is sensitive to the threshold value, which\nintroduces potential uncertainty in the projected loss from ENSO events. Second, El Niño’s\nimpact increases nonlinearly with amplitude, which however is assumed to be linearly\nproportional to amplitude in a Heaviside approach; for La Niña, the impact changes sign from\na benefit for small-amplitude events to a damage for large-amplitude events, but is assumed to\nbe one sign for all events in a Heaviside approach. One additional advantage is that index-based\nmodel is able to construct a continuous counterfactual ENSO and GDP timeseries, which\nallows us estimate continuously the projected loss under climate change.\nAlthough the measure of the climate can reasonably be considered exogenous (not invalidating\nthe hypotheses of the model), it would seem relevant to include control variables reflecting the\ncharacteristics of the countries such as the share of GDP in the Value added Agricultural, the\ndegree of trade openness.\nEffects that controlling characteristics of individual countries, such as share of GDP in\nagriculture, trade openness, are involved in the country-fixed effect μ, and in projected GDP\ni\nfor each country.\nWe thank you again for your helpful comments, which have guided us to explore deeper, and\nmake our key points clearer. The paper is so much the better.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have responded to my original comments and suggestions. Those responses are\nsatisfactory. I still find it troubling that the effect of ENSO events is largely homogenous around the\nglobe. But I can live with that. My remaining comments are as follows:\n- are the results sensitive to omitting some years from the data? For instance, the authors could do a\nvariant of rolling- or expanding-window regressions. Say, run the regression using 1960-2000 data;\nthen 1961-2001 data, and so on. More broadly, the authors may consider some variant of cross-\nvalidation -- drop a bunch of random years, re-estimate the coefficients, repeat the process many\ntimes, and present the distribution of the coefficients.\n- are the results robust to falsification checks? For instance, use the future ENSO data. Say,\nregress growth in period t on ENSO in periods (t+5,t+4, and t+3). There should be no statistically significant\ncoefficients associated with ENSO variables.\nReviewer #2 (Remarks to the Author):\nI think the paper has improved and that some of my comments have been addressed rather\nconvincingly by the authors. However, in my reading, a few outstanding issues remain, perhaps\nregarding my most serious concerns.\nFirst. Despite some efforts at amending a few lines of the text, I feel the interpretation of results\nis still poor. Specifically, it is unclear what mechanism may drive the ENSO-related losses. My reading of\nthe main econometric model of this study (Equation 1) is that it assumes and tests how GDP growth is\naffected by (i) an ENSO index and (iii) local climate/weather, beyond a series of controlling factors.\nResults show that ENSO exerts a non-linear effect on growth conditional on local weather conditions. I\nwonder how. The effect cannot go through changes in yearly temperature and precipitations – as\nsometimes seems to emerge from the text (e.g. “As the economic impact of ENSO is underpinned by\nthe direct climate response to ENSO for individual countries”) since they are accounted for in the\neconometric model. This means that El Nino and La Nina induce impacts on the economy through\nother channels that are relevant to growth. For example, it might well be that one of these channels is\nan increase in wildfires (as in the example the authors use in their response to my comment) yet only\nconditional on the fact that yearly precipitation and temperatures do not already reflect such an\nincrease. However, fires are just one among many possible candidates.\nInterpreting the effect is even more relevant as the authors find a little role for teleconnections.\nFurther, in response to the authors’ reply to my comment, I feel the present analysis cannot say\nwhether the macroeconomic effects are due to sectoral spillovers/cascading effects or because the\ndirect effect of ENSO is sufficiently large in certain regions to reflect on the macroeconomic statistics.\nHowever, I appreciate such further reasoning, which I think should be further expanded to convince\nthe reader about the large magnitude of the effects.\nFinally, on this point, I feel the authors should justify why an inverted U shape for the ENSO effect is\nreasonable. Specifically, why should an increase in the DJF Nino3.4 index be beneficial to growth up to\na certain level? What is such a level? Is it reasonable? Is it robust across specifications? The figures\nprovide little information as the ENSO index is normalized.\nSecond, I still have issues with the persistence of the effect the authors find. I fully agree that the\nimpact they find in the analysis is persistent, adverse to growth in most cases, and going beyond a\ntemporary shift in the level of output. This was motivating me to ask why they report losses in levels,\nwhich I think is misleading. Indeed, the results seem to indicate that, on average, a change in the\nENSO index will lower the average growth rate of GDP, with the economy taking a few years to adjust\nto the new long-term growth pattern. I fear the authors tend to interpret their econometric model as if\nit would estimate an impulse-response effect, but this is not the case if my reading is correct. In short,\nI am sympathetic to the growth-effect view expressed by the authors, while I think there is an\nexcessive and incoherent effort at expressing “damages” in lost trillions in a certain number of years\n(which is reminiscent of a level effect).\nFinally, let me flag three minor points.\n- I feel a figure showing the geographical distribution and intensity of losses is worth space in the\nmain text. I suggest discussing more/including Fig. R2B or similar in the main text.\n- As far as the projection exercise is concerned, what growth rate of output (g_t) is used for future\ntime periods (eq. 6).? Further, I still feel the difference between the projected GDP in a certain SSP\nand the historical growth rate of a country is NOT accounted for in the projections.\n- Please clarify exactly how the ENSO signal has been removed from country-specific temperature and\nprecipitations for the robustness exercise. Table R2B is relevant; it is not clear how this information is\nused in the text.\nReviewer #3 (Remarks to the Author):\nI would like to extend my appreciation to the authors for the considerable enhancements made to the\npaper and for addressing earlier remarks. However, there remain a few aspects that necessitate\nfurther elucidation.\n1 - The initial clarification pertains to the insensitivity of the results with respect to the psi\nthreshold parameter. It would be valuable to understand the authors' interpretation of this\nseemingly paradoxical finding.\n2 - The explanation regarding the weighting applied to meteorological variables (based on population\ndensity) could be more explicit. The estimated results remain stable across various classifications;\nhowever, the only notable distinction is tied to the proportion of agriculture in GDP (low/high). The\nauthors should delve deeper into this outcome and provide a clearer interpretation. Identifying an\neconomic cost without pinpointing the transmission channel seems problematic, particularly in a\nprojection exercise context.\n3 - It is crucial to confirm that the obtained results do not rely on the population-based\nweighting method. For instance, I would be interested to see if the estimated costs associated with the\n2015/2016 El Niño event affect China (despite its weak teleconnection) and the United States similarly\nwhen using an alternative weighting approach.\n4 - The authors have satisfactorily addressed the matter of ENSO temporal stability. It is worth noting,\nhowever, that the literature indicates that the occurrence of ENSO CP, EP, and mixed phenomena can\nconsiderably influence global temperatures and precipitation. My primary concern was that literature\nprojections suggest a potentially higher frequency of CP and mixed-type events, which would have\nvarying impacts on temperatures and precipitation.\n5 - The provided response to the inquiry about introducing control variables appears inadequate.\nIndeed, individual fixed effects only account for the impact of time-invariant factors, and\nincorporating variables such as trade, the proportion of agriculture, development, and financial\ndepth is essential, as it could modify the estimated coefficients associated with ENSO effects.\nFailing to do so poses a significant risk of omitted variable bias.\nWe thank the reviewers for their positive and helpful comments.\nResponse to Reviewer #1\nThe authors have responded to my original comments and suggestions. Those responses are\nsatisfactory. I still find it troubling that the effect of ENSO events is largely homogenous\naround the globe. But I can live with that. The homogeneity we highlighted here (Line 137-139)\nis for the nonlinear nature of the ENSO impact on global economy, rather than the “magnitude”.\nThe magnitude of response to ENSO varies across countries (Fig. 1).\nMy remaining comments are as follows:\n- are the results sensitive to omitting some years from the data? For instance, the authors could\ndo a variant of rolling- or expanding-window regressions. Say, run the regression using 1960-\n2000 data; then 1961-2001 data, and so on. More broadly, the authors may consider some\nvariant of cross-validation -- drop a bunch of random years, re-estimate the coefficients, repeat\nthe process many times, and present the distribution of the coefficients.\nGreat point. We have tested the sensitivity of our regression model by re-estimating in running-\nwindow periods of 1960-2009, 1961-2010, …, and 1970-2019. A similar nonlinear relationship\nholds across windows (Fig. R1A-a). We also have applied a 1000-time bootstrap test by\nrandomly dropping 3 individual years from the 1960-2019 period, and then re-estimated our\nmodel; uncertainty exists as expected but the nature of the nonlinearity stands (Fig. R1A-b),\nand the uncertainty range is comparable to previous Bootstrap tests for re-sampling by\ncountries, years, or 5-year blocks (Supplementary Fig. S3).\nWe have now added these sensitivity test results to Supplementary Fig. S4.\nFig. R1A. Sensitivity test of nonlinear effect of ENSO on global economy for (a) re-estimating\nby the running-window periods of 1960-2009, 1961-2010, …, and 1970-2019 (colored curves);\n(b) re-estimating by randomly dropping 3 individual years from 1960-2019 based on the\nBootstrap method. Black curve indicates the original relationship. Shading indicates the 95%\nconfidence level from the Bootstrap test.\n- are the results robust to falsification checks? For instance, use the future ENSO data. Say,\nregress growth in period t on ENSO in periods (t+5,t+4, and t+3). There should be no\nstatistically significant coefficients associated with ENSO variables.\nGood point. We have applied a falsification check by regressing growth at year t onto future\nNiño3.4 index at year t+3, t+4, and t+5. As can be seen from Table R1A, there’s no\n \nstatistically significant 1 and 2 .\n \nTable R1A. Regression coefficients for and when regressing growth onto future\n1 2\nNiño3.4 index (t+3, t+4, and t+5). All coefficients have no statistically significant on 90%\nconfidence level.\nt+3 t+4 t+5\n\n-6.96×10-4 -0.0012 -2.50×10-4\n1\n\n6.30×10-4 6.71×10-4 -2.59×10-4\nI think the paper has improved and that some of my comments have been addressed rather\nconvincingly by the authors. However, in my reading, a few outstanding issues remain, perhaps\nregarding my most serious concerns.\nWe thank the reviewer for the positive feedback and further comments.\nFirst. Despite some efforts at amending a few lines of the text, I feel the interpretation of results\nis still poor. Specifically, it is unclear what mechanism may drive the ENSO-related losses.\nWe thank the reviewer for delving into a detailed mechanism. There are many previous studies\nwhich provided interpretation of how ENSO drives direct losses, whereby triggering cross-\nsector and cross-border impact on macroeconomy.\nWe have provided more information in the revised manuscript (see Line 144-157), which reads\nas:\n“… Initially, El Niño drives direct losses in the severely-affected regions through\nextreme weathers as reflected in temperature and precipitation anomalies. Subsequently, cross-\nsector and cross-border spillovers occur, affecting global macroeconomy. Previous studies\nsuggested the cascading effects may commence via several transmission channels. For\nexample, extreme weathers lower crop yields and agricultural productivity, leading to food\nshortages, trade contractions and commodity price increases (Schlenker & Roberts, 2009);\nabnormal sea surface temperature and ocean current cause decreased fishery stocks and other\nmarine resources (Lehodey et al. 2006); damages on infrastructure increase reconstruction and\nmaintenance costs and disrupt transportation networks (Kousky, 2014); fluctuation in rainfall\nand surface run-off cause hydroelectric power shortages and reduce water-dependent industrial\noutputs (Hamlet & Lettenmaier, 2007); changes in disease vector dynamics caused by extreme\nweathers increase healthcare cost and reduce manual productivity (Hales et al. 1999); and poor\nweather conditions reduce tourist arrivals and consumptions for tourism-dependent\nregions(Scott et al. 2012).”\n- Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe\ndamages to US crop yields under climate change. Proceedings of the National Academy of\nSciences, 106(37), 15594-15598.\n- Lehodey, P., Alheit, J., Barange, M., Baumgartner, T., Beaugrand, G., Drinkwater, K., ... &\nWerner, F. (2006). Climate variability, fish, and fisheries. Journal of Climate, 19(20), 5009-\n5030.\n- Kousky, C. (2014). Informing climate adaptation: A review of the economic costs of natural\ndisasters. Energy Economics, 46, 576-592.\n- Hamlet, A. F., & Lettenmaier, D. P. (2007). Effects of 20th century warming and climate\nvariability on flood risk in the western U.S. Water Resources Research, 43(6).\n- Hales, S., Weinstein, P., Souares, Y. & Woodward, A. El Niño and the dynamics of\nvectorborne disease transmission. Environmental Health Perspectives 107, 99-102 (1999).\n- Scott, D., Gössling, S., & Hall, C. M. (2012). Tourism and climate change: Impacts,\nadaptation and mitigation. Routledge.\nMy reading of the main econometric model of this study (Equation 1) is that it assumes and\ntests how GDP growth is affected by (i) an ENSO index and (iii) local climate/weather, beyond\na series of controlling factors. Results show that ENSO exerts a non-linear effect on growth\nconditional on local weather conditions. I wonder how. The effect cannot go through changes\nin yearly temperature and precipitations – as sometimes seems to emerge from the text (e.g.\n“As the economic impact of ENSO is underpinned by the direct climate response to ENSO for\nindividual countries”) since they are accounted for in the econometric model. This means that\nEl Nino and La Nina induce impacts on the economy through other channels that are relevant\nto growth. For example, it might well be that one of these channels is an increase in wildfires\n(as in the example the authors use in their response to my comment) yet only conditional on\nthe fact that yearly precipitation and temperatures do not already reflect such an increase.\nHowever, fires are just one among many possible candidates.\nInterpreting the effect is even more relevant as the authors find a little role for teleconnections.\nFurther, in response to the authors’ reply to my comment, I feel the present analysis cannot say\nwhether the macroeconomic effects are due to sectoral spillovers/cascading effects or because\nthe direct effect of ENSO is sufficiently large in certain regions to reflect on the\nmacroeconomic statistics. However, I appreciate such further reasoning, which I think should\nbe further expanded to convince the reader about the large magnitude of the effects.\nIt is not a trivial task to quantify the direct effect and cascading effect, but we can gauge by\napplying the same econometric approach (Eq. 1) to different sectors of the global economy,\nallowing various lagged effects, By doing so, allowing contemporaneous effect only, we find\nthat impact on agricultural production (principally reflect the direct growth reduction) is clear,\nbut there is a large uncertainty for non-agricultural production such as industry and service (Fig.\nR2A-a); however, in a model version allowing lagged effect (Fig. R2A-b), reduction of non-\nagricultural sectors increases in the subsequent years, with an overall nonlinear effect\nsomewhat greater than agricultural sector, indicating a lagged but cross-sector spillover effect\nthat is greater than the initial shock. Given that agriculture production is a small part of the\nglobal economy, it follows that the cascading effect is substantial.\nFig. R2A. Global nonlinear relationship between D(0)JF(1) Niño3.4 index (normalized) and\nchange in log GDP per capita in (a) ENSO occurrence year (year 0) and (b) 3-year cumulative\n(from year 0 to year 3) for different sectors of economy including agriculture (red), non-\nagriculture (black), industry (blue), and service (green). Shadings indicate the 95% confidence\nlevel based on a Bootstrap method.\nFinally, on this point, I feel the authors should justify why an inverted U shape for the ENSO\neffect is reasonable. Specifically, why should an increase in the DJF Nino3.4 index be\nbeneficial to growth up to a certain level? What is such a level? Is it reasonable? Is it robust\nacross specifications? The figures provide little information as the ENSO index is normalized.\nA positive increase in the Niño3.4 index is detrimental to growth (there seems to be a\nmisunderstanding).\nHowever, A moderate La Niña could benefit economic growth, as can be seen from Fig. 1a.\nLa Niña events are typically associated with increased precipitation in regions including\nWestern Pacific, part of South America and Southern Africa. Higher rainfall increases soil\nmoisture and improve growing conditions, which boost agricultural yields and benefit the\noverall economy. It also leads to a higher water level in surface runoffs which improves water\navailability for hydroelectric power generation and human consumption (e.g., Ropelewski &\nHalpert, 1987; Cane et al. 1994). But La Niña also causes droughts in many other regions\nincluding America, Chile, and Peru, which would offset the benefit.\nBut when a La Niña is extreme, intense precipitation causes huge floods that destroy crops\nreducing agricultural output, destroy infrastructures, and cause life and property losses (e.g.,\nthe record-breaking floods in China derived in the 1998-99 extreme La Niña). In addition,\nassociated droughts in other regions added to the losses. So, large La Niña slows economic\ngrowth, contributing to the U shape effect.\nWe have added another x-axis by converting to an unnormalized Niño3.4 index (in degree C,\nFig. R2B).\n- Ropelewski, C. F., & Halpert, M. S. (1987). Global and regional scale precipitation patterns\nassociated with the El Niño/Southern Oscillation. Monthly Weather Review, 115(8), 1606-1626.\n- Cane, M. A., Eshel, G., & Buckland, R. W. (1994). Forecasting Zimbabwean maize yield using\neastern equatorial Pacific sea surface temperature. Nature, 370(6486), 204-205.\nFig. R2B. Same as Fig. 1a, but with additional x-axis of unnormalized D(0)JF(1) Niño3.4 index\n(°C) in the top.\nSecond, I still have issues with the persistence of the effect the authors find. I fully agree that\nthe impact they find in the analysis is persistent, adverse to growth in most cases, and going\nbeyond a temporary shift in the level of output. This was motivating me to ask why they report\nlosses in levels, which I think is misleading. Indeed, the results seem to indicate that, on average,\na change in the ENSO index will lower the average growth rate of GDP, with the economy\ntaking a few years to adjust to the new long-term growth pattern. I fear the authors tend to\ninterpret their econometric model as if it would estimate an impulse-response effect, but this is\nnot the case if my reading is correct. In short, I am sympathetic to the growth-effect view\nexpressed by the authors, while I think there is an excessive and incoherent effort at expressing\n“damages” in lost trillions in a certain number of years (which is reminiscent of a level effect).\nThe reviewer is right. An estimated growth effect (reduction of GDP growth rate) was missing\nin terms of historical extreme El Niño events. In the revised version, we show that 1982-83,\n1997-98 and 2015-16 El Niño events decreased growth by 1.0% in the occurrent year, but more\nthan 5.0% cumulated over subsequent three years (Fig. R2C; Line 159-160). We presented\ncumulative in-dollar-value loss, which is translated from the cumulative in-percentage growth\neffect, as to efficiently compare with previous estimates.\nFig. R2C. Cumulative growth effect of ENSO on global economic production in terms of\nD(0)JF(1) Niño3.4 at each year. Red and black lines indicate the growth effect only in the\nENSO occurrence year and cumulated after subsequent three years, respectively. Shadings\nshow the 95% confidence interval based on a Bootstrap method. Extreme El Niño (Niño3.4 >\n1.5 s.d.) and La Niña (Niño3.4 < -1.25 s.d.) events are marked as red and blue bars, respectively.\nFinally, let me flag three minor points.\n- I feel a figure showing the geographical distribution and intensity of losses is worth space in\nthe main text. I suggest discussing more/including Fig. R2B or similar in the main text.\nWe agree with the reviewer regarding the potential interest. We have now discussed more about\nour country-specific approach in the main text and Methods (Line 186-193); we have also\nincluded Supplementary Fig. S8 for detailed information.\n- As far as the projection exercise is concerned, what growth rate of output (g_t) is used for\nfuture time periods (eq. 6).? Further, I still feel the difference between the projected GDP in a\ncertain SSP and the historical growth rate of a country is NOT accounted for in the projections.\ng refers to the GDP growth rate at year t based on the original output of SSP database.\nt\nWe did not compare with the historical growth rate; instead, we compared with two futures\nwith same ENSO evolution, one with increased ENSO amplitude but one with the same ENSO\namplitude as in the model historical period.\n- Please clarify exactly how the ENSO signal has been removed from country-specific\ntemperature and precipitations for the robustness exercise. Table R2B is relevant; it is not clear\nhow this information is used in the text.\nWe removed ENSO signal from country-level annual temperature and precipitation by linear\nregression, which goes\nT* T E r T,E\nit it t i i\nwhere T* is the annual temperature after ENSO signal removal in country i and year t. r T,E\nit i i\nis the linear regression coefficient of annual temperature T onto the DJF Niño3.4 index E in\ni\ncountry i during the period of 1950-2021. Same process was applied for annual precipitation.\nRelated discussions are now included in the Method (Line 520-527).\nResponse to Reviewer #3\nI would like to extend my appreciation to the authors for the considerable enhancements made\nto the paper and for addressing earlier remarks. However, there remain a few aspects that\nnecessitate further elucidation.\nThank you for your further comments and positive feedback.\n1 - The initial clarification pertains to the insensitivity of the results with respect to the psi\nthreshold parameter. It would be valuable to understand the authors' interpretation of this\nseemingly paradoxical finding.\nThe insensitivity of the response function can largely be attributed to the fact that the samples\nof countries do not differ substantially under different thresholds of ψ. There are 75 countries\nwith ψ>0.5, 63 countries with ψ>1, and 50 countries with ψ>1.5 (Fig. R3A). The majority of\nteleconnected countries fall into these three thresholds.\nFig. R3A. Distribution of country-specific ENSO teleconnection strength, with the shading\nshows the samples under different ψ thresholds of ψ>0.5, ψ>1, and ψ>1.5.\n2 - The explanation regarding the weighting applied to meteorological variables (based on\npopulation density) could be more explicit.\nWe thank the reviewer for the suggestion. There is advantage of population density weighting\ncompared to other approaches such as a simple areal weighting. One such advantage is a better\nrepresentation of the reaction of human-based economic activity to climate anomaly.\nEconomic impact transmitted from climate shock is generally driven by human interactions\nand transactions. However, as we have shown, a common shock across most countries\ndominates, rendering our results insensitive to approaches adopted.\nWe have now added the detailed discussion in Method (Line 478-484).\nThe estimated results remain stable across various classifications; however, the only notable\ndistinction is tied to the proportion of agriculture in GDP (low/high). The authors should delve\ndeeper into this outcome and provide a clearer interpretation.\nIt is the nature of “nonlinearity” that remains stable but the amplitude of the impact is rather\ndifferent. For example, the impact (in terms of growth rate) is approximately twice as large for\nstrongly teleconnected countries as for weakly-teleconnected countries, for a +2 SD ENSO,\nsee Fig. 1b.\nThe common nonlinear nature reflects the feature that the global spillovers and cascading\neffects dominate the ENSO global economic impact. On the other hand, the heterogeneous\nmagnitude of the response is due to several different factors. One is linked to geographical\nlocations in that strongly-teleconnected countries tend to be in the tropics and poor countries;\nanother is economic structure in that there tends to be a high proportion of agriculture in GDP\nin poor countries; further, in poor countries, the level of preparedness and the capacity to\nmitigate are low. We have added discussion in the revised manuscript. See Line 137-139 and\n610-617.\nIdentifying an economic cost without pinpointing the transmission channel seems problematic,\nparticularly in a projection exercise context.\nWe thank the reviewer for emphasizing transmission channels. Previous studies have identified\nthese channels. There is no suggestion that these will substantially change in the future. We\nhave provided more information in the revised manuscript (see Line 144-157), which reads as:\n“… Initially, El Niño drives direct losses in the severely-affected regions through\nextreme weathers as reflected in temperature and precipitation anomalies. Subsequently, cross-\nsector and cross-border spillovers occur, affecting global macroeconomy. Previous studies\nsuggested the cascading effects may commence via several transmission channels. For\nexample, extreme weathers lower crop yields and agricultural productivity, leading to food\nshortages, trade contractions and commodity price increases (Schlenker & Roberts, 2009);\nabnormal sea surface temperature and ocean current cause decreased fishery stocks and other\nmarine resources (Lehodey et al. 2006); damages on infrastructure increase reconstruction and\nmaintenance costs and disrupt transportation networks (Kousky, 2014); fluctuation in rainfall\nand surface run-off cause hydroelectric power shortages and reduce water-dependent industrial\noutputs (Hamlet & Lettenmaier, 2007); changes in disease vector dynamics caused by extreme\nweathers increase healthcare cost and reduce manual productivity (Hales et al. 1999); and poor\nweather conditions reduce tourist arrivals and consumptions for tourism-dependent\nregions(Scott et al. 2012).”\n- Schlenker, W., & Roberts, M. J. (2009). Nonlinear temperature effects indicate severe\ndamages to US crop yields under climate change. Proceedings of the National Academy of\nSciences, 106(37), 15594-15598.\n- Lehodey, P., Alheit, J., Barange, M., Baumgartner, T., Beaugrand, G., Drinkwater, K., ... &\nWerner, F. (2006). Climate variability, fish, and fisheries. Journal of Climate, 19(20), 5009-\n5030.\n- Kousky, C. (2014). Informing climate adaptation: A review of the economic costs of natural\ndisasters. Energy Economics, 46, 576-592.\n- Hamlet, A. F., & Lettenmaier, D. P. (2007). Effects of 20th century warming and climate\nvariability on flood risk in the western U.S. Water Resources Research, 43(6).\n- Hales, S., Weinstein, P., Souares, Y. & Woodward, A. El Niño and the dynamics of\nvectorborne disease transmission. Environmental Health Perspectives 107, 99-102 (1999).\n- Scott, D., Gössling, S., & Hall, C. M. (2012). Tourism and climate change: Impacts,\nadaptation and mitigation. Routledge.\n3 - It is crucial to confirm that the obtained results do not rely on the population-based\nweighting method. For instance, I would be interested to see if the estimated costs associated\nwith the 2015/2016 El Niño event affect China (despite its weak teleconnection) and the United\nStates similarly when using an alternative weighting approach.\nFollowing your suggestion, we re-estimated the country-level economic loss by using\nalternative weighting approaches, for example, area-weighted ENSO teleconnection, under a\ncountry-specific framework. The economic cost of 2015-16 El Niño event amounts to US$507\nand US$912 billion for China and US under the population-weighted approach (Fig. R3B-a),\nand US$520 and US$926 under the area-weighted approach (Fig. R3B-b). The slight\ndifference means that spillovers and cascading effects dominate.\nFig. R3B. Country-level cumulative losses from 2015-16 extreme El Niño by fitting the\ncountry-specific econometric model under (a) population-weighted teleconnection and (b)\narea-weighted teleconnection. The maps were created in the MATLAB computing\nenvironment using the M_Map mapping package (Pawlowicz, R., 2020. \"M_Map: A mapping\npackage for MATLAB\", version 1.4m, [Computer software], available online at www.eoas.ub\nc.ca/~rich/map.html) .\n4 - The authors have satisfactorily addressed the matter of ENSO temporal stability. It is worth\nnoting, however, that the literature indicates that the occurrence of ENSO CP, EP, and mixed\nphenomena can considerably influence global temperatures and precipitation. My primary\nconcern was that literature projections suggest a potentially higher frequency of CP and mixed-\ntype events, which would have varying impacts on temperatures and precipitation.\nThe SST anomaly center tends to locate in the equatorial eastern Pacific during strong El Niño\n(EP), mostly captured by Niño3, but in central Pacific during La Niña or moderate El Niño,\nmostly captured by Niño4. A combination index of Niño3.4 has been a common metrics to\ndescribe all ENSO events. Niño3.4 SST variability simulated by latest models (as used by our\nstudy) captures well the overall ENSO variability of all types (Cai et al. 2022), including the\nmixed type of events.\n- Cai, W., Ng, B., Wang, G., Santoso, A., Wu, L., & Yang, K. (2022). Increased ENSO sea\nsurface temperature variability under four IPCC emission scenarios. Nature Climate Change,\n12(3), 228-231.\n5 - The provided response to the inquiry about introducing control variables appears inadequate.\nIndeed, individual fixed effects only account for the impact of time-invariant factors, and\nincorporating variables such as trade, the proportion of agriculture, development, and financial\ndepth is essential, as it could modify the estimated coefficients associated with ENSO effects.\nFailing to do so poses a significant risk of omitted variable bias.\nWe thank the reviewer for emphasizing this point. There are three kinds of control variables:\ncountry-fixed factor, year-fixed factor, and country-specific time-varying factor. Consistent\nwith previous literatures with respect to ENSO’s socio-economic impact (e.g., Hsiang et al.\n2011; Smith & Ubilava, 2017), we included the country-fixed effect (μ) to represent time-\ni\ninvariant factors (e.g., geographic location, agricultural country or not), as well as the country-\nspecific linear and quadratic time trend (θ t+ θ t2) for factors that change over time within a\n1i 2i\ncountry (e.g., development, trade liberalization, technological progress).\nWe excluded year-fixed effect for the two reasons:\n- Year-fixed effect introduces risk of collinearity as ENSO time-fixed effects could be\ncorrelated with time-specific factors, making it harder to disentangle the separate impacts\nfrom ENSO and time-fixed effects on economic growth.\n- ENSO is a time-specific global phenomenon, like other major events (e.g., financial crisis)\nthat commonly affect countries. Including year-fixed effects weakens the statistical\ninfluence of ENSO, leading to an underestimation of the real impact of ENSO on economic\ngrowth (Dell et al. 2014).\nWe have added detailed interpretation in Methods (Line 499-510).\n- Hsiang, S. M., Meng, K. C., & Cane, M. A. (2011). Civil conflicts are associated with the\nglobal climate. Nature, 476(7361), 438-441.\n- Smith, S. C., & Ubilava, D. (2017). The El Niño Southern Oscillation and economic growth\nin the developing world. Global Environmental Change, 45, 151-164.\n- Dell, M., Jones, B. F., & Olken, B. A. (2014). What do we learn from the weather? The new\nclimate-economy literature. Journal of Economic literature, 52(3), 740-798.\nREVIEWERS' COMMENTS\nReviewer #2 (Remarks to the Author):\nI would like to thank the authors for having considered and addressed all my doubts.\nReviewer #3 (Remarks to the Author):\nI have no further remarks regarding this paper. I appreciate the revisions made by the authors,\nwhich I believe have improved an already high-quality initial submission. I would like to thank\nthem for considering and responding to the various comments.\nResponse to Reviewer #2\nReviewer #2 (Remarks to the Author):\nI would like to thank the authors for having considered and addressed all my doubts.\nThank you for accepting the paper!\nResponse to Reviewer #3\nReviewer #3 (Remarks to the Author):\nI have no further remarks regarding this paper. I appreciate the revisions made by the authors,\nwhich I believe have improved an already high-quality initial submission. I would like to thank\nthem for considering and responding to the various comments.\nThank you for accepting the paper!"}]

Decision letter