Nonlinear country-heterogenous impact of the Indian Ocean Dipole on global economies

Domain: earth_science
Published: 2024-06-12
Editorial outcome: minor_revision

Abstract
A positive Indian Ocean Dipole features an anomalously high west-minus-east sea surface temperature gradient along the equatorial Indian Ocean, affecting global extreme weathers. Whether the associated impact spills over to global economies is unknown. Here, we develop a nonlinear and country-heterogenous econometric model, and find that a typical positive event causes a global economic loss that increases for further two years after an initial shock, inducing a global loss of hundreds of billion US dollars, disproportionally greater to the developing and emerging economies. The loss from the 2019 positive event amounted to US$558 B, or 0.67% in global economic growth. Benefit from a negative dipole event is far smaller. Under a high-emissions scenario, a projected intensification in Dipole amplitude causes a median additional loss of US$5.6 T at 3% discount rate, but likely as large as US$24.5 T. The additional loss decreases by 64% under the target of the Paris Agreement.

Introduction
A positive IOD (pIOD) develops in boreal summer (June-August) and matures in September, October and November (SON), exerting a substantial impact. During the 1997 strong pIOD, floods and malaria outbreaks occurred in East Africa, leading to fatalities in the thousands and displacement in hundreds of thousands. On the other side of the Indian Ocean, severe droughts and wildfires plagued Indonesia and Australia and the associated smoke and haze affected the health and economic activities of tens of millions of people. The impact extended to remote regions such as central and South America through atmosphere teleconnection. The 2019 strong pIOD event saw similar effects. Sri Lanka, India and East Africa experienced floods causing many deaths and displacing millions of people, whereas Indonesia and Australia saw a devastating bushfire season. In eastern and southeastern Australia, the fires burned million hectares, destroyed 2400 buildings and killed 34 human lives and over one billion animals. The reduced output of Australia farming sector alone, known to be severely affected by the IOD, amounted to about AU$5.0 B.

Studies of impacts from extreme weathers and climate have found ways for transmission to the broader economy. For example, droughts and fires decreased employment in the farming sector and reduced demand; reduced farming production puts upward pressure on consumer prices; reduced rainfall and surface run-off decrease economic activity of water-dependent industries; poor weather conditions reduce tourist arrivals and consumptions for tourism-dependent regions; smoke haze-induced air pollution from fires reduces workers productivity and increases road closures and uncertainty about safety; and low crop yields and agricultural outputs lead to trade contractions and commodity price increases.

The potential spillovers have motivated studies of the impact on global economy, for example, from El Niño-Southern Oscillation (ENSO), the most consequential mode of climate variability originated in the tropical Pacific Ocean. One type of econometric model assumes an impact symmetric about El Niño and La Niña but country-heterogenous governed by ENSO teleconnections. Other models emphasize a nonlinear impact asymmetric about El Niño and La Niña, because El Niño, stronger in amplitude than La Niña, typically results in 1–2% growth reduction to global economy, but the effect is virtually absent during La Niña. These models converge on a persistent impact lasting for several years after the initial shock, causing a loss in trillions of US dollars during an extreme El Niño mostly through the spill-over and cascading effects. Similar to ENSO asymmetry, a pIOD grows to a greater amplitude than that of an nIOD; unlike ENSO, to date, the impact of the IOD on global economies is unknown.

Climate change affects global economy, and the associated impact is usually assessed using mean temperature and rainfall changes. The impact from changing climate variability under greenhouse warming is less known, in part because how climate variability itself might change is uncertain. Recent advances suggest that variability of the IOD is likely to increase over the 21st century under greenhouse warming, with moderate pIOD events being converted more frequently to strong pIOD events. The extent to which the projected IOD change exacerbates the economic risks of greenhouse warming, particularly to the already vulnerable economies, is an important issue.

Here we establish a nonlinear econometric model that incorporates country-level impacts from the IOD on global economic growth. We find that a typical pIOD event leads to an economic loss of hundreds of billion US dollars, disproportionately large in developing economies, and that there is an exponential increase in the loss over the remaining 21st century from a projected intensification of future IOD.

Results
Unlike some econometric models that focus on different climate zones, here we incorporate a country-heterogenous assessment. For ENSO, because the impact on the global economy is strong and by and large worldwide, a country-heterogenous approach may not be essential. By contrast, for the IOD, the impact is weaker and the affected regions are mostly developing economies. As such, the cascading effect and spillovers could be more regionally distinct, therefore a country-heterogenous approach is necessary.

Our country-heterogenous assessment is based on climate teleconnection of the IOD and population density, similar to previous approaches (see “Climate teleconnections” in “Methods”). We include the IOD climate teleconnections in fields of surface temperature and rainfall anomalies because both can simultaneously exert an impact. To combine their influence but avoid dominance by either anomaly field due to their amplitude contrast, the monthly anomaly field is normalized by its respective monthly standard deviation at individual grid-points. Influences from the IOD could commence and peak at different months in different regions, therefore we regress the normalized anomalies, from May to December covering the entire IOD life cycle of eight months, one month at a time, onto the IOD index in SON, when an IOD peaks. Although most of strong pIODs such as the 1961, 1994, and 2019 events occurred independently of ENSO and many pIODs events occurred with a La Niña condition as in 2007 and 2008, some IOD events are accompanied by ENSO events. We therefore remove ENSO teleconnections through a partial regression, which appears to be effective.

For each field, we obtain eight monthly patterns of statistically significant regression coefficients; the regression coefficient values are then cumulated over the eight months. The amplitudes of cumulated surface temperature and rainfall teleconnection are combined to form climate teleconnections and averaged over individual countries weighted by grid-point population density to yield a country-heterogenous IOD teleconnection pattern. Eastern African countries and Indonesia, both developing and emerging economies, are among countries with the strongest teleconnections.

Because pIOD amplitude is greater than nIOD amplitude such that the impact may increase nonlinearly with the amplitude, and given that economic impact from climate variability can affect economic production in the ensuing years, our econometric model incorporates these features. As such, we build into a fixed-effect panel regression model by adding the IOD teleconnections as a nonlinear predictor and incorporating lagged effects (see “Empirical econometric model” in “Methods”). The amplitude of the predictor is parameterized as the product of “IOD teleconnection and an IOD index”. Our model is therefore continuously nonlinear and country-heterogenous, combining the characteristics of nonlinearity and country-heterogeneity.

We train our model using economic data and the IOD index over the 1960–2020 period. Like in the previous models for ENSO, time-invariant and time-trending covariates are allowed to interact with observed economic and climate variables, and the nonlinear effect of mean temperature operates in addition to the IOD impact. We allow delayed impacts for a different number of years. A version with three years of delayed impacts (last column of Supplementary Table S1) shows that the IOD impact persists for another two years, after which time further impact is mild, as seen in the associated coefficients in year 3, when neither the linear nor the nonlinear is statistically significant. The impact is strongest in the year after the occurrence (year 1; year 0 is occurrence year) when both the linear and nonlinear terms are statistically significant. In the occurrence year (year 0) or year 2 after the occurrence, only the linear term is statistically significant. Thus, the contemporaneous impact on economy is linear and mild but accelerates the year after, before tapering in year 2. This version is adopted. We focus on the effect for the three years (year 0 to year 2). A set of bootstrap methods are applied to quantify the uncertainty of point estimates in the model (see “Bootstrap tests” in Methods), and the tests show that the model is stable.

Our IOD impact model reveals a strong effect increasing exponentially with the IOD. For each IOD index value, we calculate the impact on Gross Domestic Production (GDP) growth cumulated over years 0, 1, and 2 for all countries, to establish the relationship between accumulated anomalous growth rate and the IOD index value. Firstly, there is an asymmetric impact between pIOD and nIOD, with the impact far greater than that for pIOD. For example, a 1.0 s.d. pIOD causes 1.89% (±0.64%) loss in Kenya GDP growth that is statistically significant, while a 1.0 s.d. nIOD causes 1.09% (±1.25%) benefit in Kenya GDP growth that is not statistically significant. Secondly, the nonlinear impact is greater for countries with a greater teleconnection. For example, the impact is greater in Kenya than in Australia; the difference is because the teleconnection is weaker in Australia than in Kenya, where weather-sensitive sectors are a substantial portion of a smaller economy.

For each IOD index value, the associated impact in terms of GDP values cumulated over years 0, 1, and 2 (in 2015 US dollars) is calculated by multiplying the IOD-induced change in GDP growth rate with the respective GDP value of the previous year, for all countries. During the 2019 pIOD, the total loss is approximately US$37.8 B in Australia and US$4.7 B in Kenya, larger in Australia because the Australia economy is larger. In some other developed countries, though with a weak teleconnection, the impact is similarly substantial because of a large economy, for example, the loss to US economy by the 2019 pIOD is US$62.3 B.

The global impact is substantial. Aggregated over all countries, the 2019 pIOD event resulted in a loss of US$558 B, amounting to a reduction in global GDP growth of 0.67%. More generally, for a one-standard deviation of a pIOD index value, there is an average loss in the global GDP growth rate of ~0.24%. By contrast, the benefit from an nIOD event is hardly statistically significant, not only because of the smaller nIOD amplitude but also because of the nonlinear economic response. By comparison, there is a ~1.5% cumulative loss in global economy for one standard deviation of ENSO. The 2015/16 strong El Niño led to a loss of US$3.9 T, amounting to a ~5% cumulative loss in global economy.

We calculate the effect of the IOD on economic production for a given year. Due to the lagged impact, for a given year t, the impact from the IOD on a country includes the contemporaneous and lagged effects from the previous two years. For example, the impact on GDP growth in 2019 includes the effect of year 2 of the 2017, year 1 of the 2018, and year 0 of the 2019 IOD index values. The evolution of the IOD impact on Kenya economic growth rate shows that pIOD events have largest negative impact in year 1 after occurrence and the associated economic loss is far greater than the economic benefit of an nIOD event. The time series of effect on GDP growth is negatively skewed, with a skewness of −1.74. The skewness of economic impact is additional to the IOD skewness, as reflected in a quadratic relationship between the two time series after the IOD time series is shifted forward by one year to maximize their coherence due to the one year lag. The value of nonlinear coefficient, referred to as Ω, is −0.54.

As a result of the nonlinear economic impact, over time, the loss from pIOD is not offset by the benefit from nIOD. For Kenya, the net effect results in an averaged loss of 0.37% per annum averaged over the period, dominated by strong pIOD events. The amplitude of the nonlinear coefficient (Ω) in the relationship between temporal evolution of the loss and the IOD index measures the extent to which economic loss from pIOD outsizes the benefit of nIOD.

The value of nonlinear coefficient, computed for every country shows that across countries, the nonlinearity increases exponentially with the strength of the IOD teleconnection. In countries with a strong teleconnection, such as Kenya and Indonesia, the nonlinearity and hence the loss in growth rate is greater than in countries with a moderate teleconnection, such as Australia or Sri Lanka. Thus, the asymmetric impact between pIOD and nIOD renders an economic loss after a period of IOD cycles, and the loss increases exponentially with teleconnection strength.

We conduct a sensitivity test in which we include the ENSO’s common global shock component to our IOD economic impact model. The result from this combined model shows that the economic effect of the IOD is largely independent of that of ENSO. Although some of the effect is shared, the effect of the IOD in this combined model is only marginally smaller (19%) than that in our IOD-only economic impact model.

We apply our econometric model to examine the impact of changing IOD on global economy in a warming climate. We examine IOD changes under several emission scenarios of the Intergovernmental Panel on Climate Change (IPCC), using outputs of available climate models that participated in the Coupled Model Intercomparison Project phase 6 (CMIP6). These models are forced with historical anthropogenic and natural forcing until 2014, and four Shared Socioeconomic Pathways (SSP) of future greenhouse gas concentration trajectories from 2015 onwards, that is, SSP5-8.5, SSP3-7.0, SSP2-4.5, and SSP1-2.6. To describe model IOD evolution, an Empirical Orthogonal Function (EOF) analysis is applied to the detrended SST anomalies in the domain of 5°S-5°N, 40°E-100°E. The first EOF pattern, which resembles the observed IOD pattern, and the associated principal component (PC) scaled to unity over the 200 years, are taken as our model IOD pattern and index. We use the ability of a model to simulate the IOD amplitude asymmetry between pIOD and nIOD to select models, as it is an indication of the realism of the model in simulating the IOD processes. A total of 16 models are selected for comparison of the economic impact from changing IOD between different scenarios of greenhouse warming. We compare the standard deviation of the IOD index over the 2000-2099 (21st century) with that over the 1900–1999 period (20th century) to assess IOD changes.

There is a strong inter-model consensus on an increase in future IOD variability under all scenarios. In the SSP5-8.5, 87.5% of the models generate an increase in variability, with a multi-model ensemble median (mean) increase of 29.5% (36.7%) translating to a higher frequency of strong pIOD events. For each model, IOD climate teleconnection is constructed in the same way as the observed with ENSO impacts removed. In terms of the teleconnection strengths, unlike the amplitude of IOD variability, multi-model ensemble mean shows no inter-model consensus nor a systematic pattern of a statistically significant change even under the high emissions scenario. As such, we assess the economic impact of changes in IOD amplitude.

We develop a counterfactual 21st century IOD time series such that it follows the same projected 21st century evolution and IOD event sequences but its standard deviation is scaled to the same amplitude as that in the 20th century. Sequences of IOD events can potentially affect the economic impact; because of the lagged effects, an IOD event, occurring early as opposed to at the end of the period, makes a difference. The counterfactual future IOD time series is taken as the IOD evolution if future IOD variability does not change. For each model and each country, using the projected and the counterfactual future IOD time series as input to our econometric model, we obtain a projected and a counterfactual time series of GDP growth change, where each value represents the combined effect of the contemporaneous and growth effects. The difference between them in GDP growth averaged over the 21st century is taken as a measure of the impact from the projected IOD change.

The reduction in economic growth increases with the projected IOD amplitude increase in all emission scenarios. Take Kenya as an example, an additional reduction is seen under all four IPCC emission scenarios, each supported by a strong inter-model consensus. The multi-model ensemble median (mean) increase in the century-average reduction of GDP growth is 0.27%, 0.24%, 0.21%, and 0.15% (0.32%, 0.31%, 0.25%, and 0.15%) per annum for the SSP5-8.5, SSP3-7.0, SSP2-4.5, and SSP1-2.6 scenario, respectively. Overall, there is a statistically significant sensitivity of an additional loss of 1.0% per annum per 1.0 s.d. increase in future IOD amplitude based on all CMIP6 models under the four emission scenarios. The inter-model consensus is seen in all affected countries, and the sensitivity increases with IOD teleconnection, stronger in Kenya and Indonesia than in Australia.

We estimate the IOD-induced economic production loss from the projected IOD based on the Shared Socioeconomic Pathways (SSP) that provide a 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 the different emission scenarios. Assuming that losses from an IOD event permanently alter the long-term economic development, we construct two “no-IOD” future economic growth projections from 2020 to 2099, using the time series of growth rate from the projected and the counterfactual IOD time series, each including the contemporaneous and lagged effects, to construct the SSP economic growth rate time series. Two new GDP time series are obtained by compounding growth using the modified growth rate time series. Additional GDP loss from changing IOD variability, discounted by different schemes ranging from 1 to 5% per annum, is calculated as the difference between the two time series of GDP. The change is then cumulated over the last 80 years (2020-2099). Such calculations are carried out for all countries and aggregated globally.

The projected increase in IOD variability likely causes substantial additional global economic losses. Using a 3% discount rate and bootstrapping the econometric regression model in 1000 different combinations of 181 countries allowing repeats, and 1000 realizations of IOD sequences from a given IOD time series, the high emissions scenario (SSP5-8.5) leads to a median additional global loss cumulative over the period of 2020-2099 is US$5.6 T. There is a large range in the projected loss with the 90% confidence interval spanning from a loss of US$24.5 T to a small benefit of US$0.5 T.

The same analysis is carried out for other emission scenarios. There is a more than 85% chance of an additional loss in any of the four emission scenarios. Overall, the additional losses decrease with lowering emissions, due to commensurately smaller increases in the IOD amplitude. Under the SSP1-2.6 scenarios, a strong mitigation pathway to achieve the warming target of 1.5–2.0 °C warming relative to the pre-industrial levels, the smaller increase in IOD amplitude reduces the additional loss to a median US$2.0 T; though the loss is still substantial, it represents a reduction of approximately 64% from the SSP5-8.5 scenarios, highlighting the benefit of mitigation.

The losses increase with a lower discount rate, or vice versa, which is one of the multiple sources of uncertainty, including model differences, emission scenarios, regression of the econometric model, and IOD event sequences. Assessment of their relative contribution to uncertainty, one by one by keeping other factors fixed at a chosen level, finds that the inter-model difference in IOD amplitude changes is the largest source of all factors. Discount rates are the second largest source whereas econometric model regression and sequences of IOD events contribute to only a small uncertainty range.

Importantly, in all scenarios, the additional loss in the 21st century increases exponentially with the projected increase in IOD amplitude. The exponential increase is in turn underpinned by the nonlinear economic impact that is larger during pIOD than during nIOD, in addition to the asymmetric IOD amplitude itself. The result reinforces the additional risk of increased IOD amplitude to global economy, with many models projecting an additional loss in the order of tens of trillion US dollars. On the flip side, the nonlinearity underpins the effectiveness of mitigation, as seen in the decreased loss in SSP1-2.6, in which curtailing the increase in IOD amplitude exponentially reduces the additional loss.

Alternatively, we calculate the global GDP growth rate with and without IOD changes for the four emission scenarios as in other studies, and then average the growth rate across all positive IOD events, defined as when an SON IOD index exceeds 1.0 s.d., over the 80-year period, for all emission scenarios. For example, the increased intensity of pIOD under SSP5-8.5 leads to an increase in loss from 0.20 to 0.29%, i.e., 45% greater because of the increased pIOD intensity alone, not counting the increased loss due to the increased frequency.

Methods
We depict the observed IOD using the Ocean Reanalysis System 5 (ORAS5) over the period of 1958-2020, a reanalysis that assimilates observed upper ocean temperatures and resolves key oceanic feedback responsible for the positive skewness of the IOD. Our IOD index is obtained from an empirical orthogonal function analysis on quadratically detrended SON SST anomalies in the equatorial Indian Ocean (5°S-5°N, 40°E-100°E), yielding a dipole pattern, and a principal component (PC1) that is scaled to unity; PC1 is highly correlated with the DMI with a coefficient r=0.92. We use land-based surface air temperature and rainfall data from ECMWF Reanalysis version 5 (ERA5) to construct the IOD climate teleconnection over the same period of 1958-2020.

The climate teleconnections In both surface temperature and rainfall anomalies are included, because both can simultaneously exert an impact; for example, during a pIOD event, lower rainfall and higher temperatures in Indonesia and Australia both contribute to drought and bushfires. To combine their influence but avoid dominance by either anomaly field due to their amplitude contrast, monthly anomaly field is normalized by its respective monthly standard deviation at individual grid-point. Influences from the IOD could commence and peak at different months in different regions, we therefore examine climate teleconnection from May to December. We remove ENSO’s influence on grid-point anomalies through a partial regression onto the IOD index (PC1) and an ENSO index (Niño 3.4 SST) month by month.

Specifically, to remove impact from ENSO and to obtain IOD teleconnection for monthly surface air temperature T at each longitude-latitude grid point (x,y) and calendar month m from May to December, partial regression is calculated through

τ x,y,m = R TI - R TE R EI / (1 - R EI^2) * θ T / θ I

where τ is the partial regression coefficient of T. R TI and R TE refer to correlation coefficients of T with the IOD and with the ENSO index, respectively. R EI is the correlation coefficient between the IOD and the ENSO index. θ T is the standard deviation of T and θ I is the standard deviation of the IOD index after linearly removing the ENSO influence. The same calculation is conducted for P to obtain its partial regression coefficient ρ. The effectiveness of this approach is tested by applying to grid-point SST; a partial regression coefficient between grid-point SST anomalies after removing ENSO and the IOD index shows no ENSO pattern in the equatorial eastern Pacific.

We add τ from May to December that are statistically significant above the 95% confidence level, to yield amplitude of the cumulative temperature teleconnection τ. The same is carried out to ρ to obtain amplitude of rainfall teleconnection ρ. The total “climate” teleconnection is the sum of temperature and rainfall teleconnection ψ = τ + ρ. The grid-point “climate” teleconnection is then converted to the country-heterogenous teleconnection ψ by weighting the grid-point population density as of 2020 obtained from the Gridded Population of the World (GPW) version 4.

We obtain country-level inflation-adjusted annual Gross Domestic Product (GDP) per capita from the World Bank Development Indicators, for all countries in the world over the period of 1960–2020, although data for only a subset of years are available for some countries. Data from Penn World Tables version 10.0 is also used to test robustness of our results. We model the economic impact of the IOD on country-level GDP using a general distributed-lag regression model, in which country-heterogenous impacts of the IOD are incorporated through:

log(y i,t) - log(y i,t-1) = Σ l=0 to n α 1,l ψ i I t-l + α 2,l (ψ i I t-l)^2 + β 1,l T i,t-l + β 2,l T^2 i,t-l + λ 1,l P i,t-l + λ 2,l P^2 i,t-l + μ i + θ 1,i t + θ 2,i t^2 + ε i,t

where y i,t is GDP per capita in country i and year t, l is the lag year to year t, I is the IOD index in year t-l, ψ i is the country-heterogenous teleconnection strength, T and P are annual mean surface air temperature and rainfall in year t-l, after removing the IOD’s signal through linear regression.

The model includes a term μ i, which accounts for country-fixed, time-invariant factors, such as different history and culture backgrounds of individual countries. The model also includes country-level long-term linear and quadratic time trends in growth rates θ 1,i t + θ 2,i t^2 from changing political institutions and economic policies of individual countries, and nonlinear effects of annual mean country-level temperature and precipitation. The country-heterogenous impact of the IOD is a quadratic function of (ψ i I t-l) and allows lagged impacts accounting for growth effects after the initial shock contemporaneous with an IOD event. We have not included year-fixed effects, as they might weaken the statistical influence of the IOD, leading to an underestimation of the real impact. Also, year-fixed effect could introduce risk of collinearity as IOD time-fixed effects could be correlated with a time-specific factor, making it hard to separate the impacts from the IOD.

We obtain the optimum duration of the IOD’s growth effects by testing the impact of the IOD on global economy for a different number of years. The impact persists for another two years after the occurrence year, after which time a further increase is not statistically significant. During the occurrence year, the contemporary effect is mostly through a direct loss, which is mild in terms of a global total loss, likely because the climatic impact is largest in developing countries, in which the economies are relatively small. In the subsequent years in terms of climatic effect, drought and its effect such as drought-induced crop failures may continue but far weaker than in the occurrence year; however, the subsequent cascading effect becomes substantial due to impact on global trades, commodity prices, and low inventories, and due to a low capacity to deal with to the adverse effect, contributing to the nonlinear effect. As such, we estimate the economic impact aggregated over the contemporaneous year and the two years after. The model generates a time series of the IOD-induced change in GDP growth.

Reducing the model to a nonlinear version without the country-heterogenous feature by setting the teleconnection to be a value of 1.0 in every country, we find a non meaningful result, in which both pIOD and nIOD could lead to a benefit in the occurrence year. The failure of such a model with a common global shock only is in contrast to the model of ENSO impact, in which a common global effect captures much of the impact. Including similar terms for ENSO, that is, nonlinear and country-heterogenous terms, generates coefficients that are not statistically significant, consistent with a previous finding that for ENSO a common global shock dominates. Comparing to the impact of ENSO, impact from the IOD on global economy is about 16% of that from ENSO.

We construct an alternative model in which we add the ENSO’s common global shock component to our IOD impact model. The result from this combined model shows that the economic effect of the IOD is largely independent of that of ENSO. For example, the effect of the IOD from this combined model is only marginally smaller than that in the IOD-only economic impact model.

To quantify the uncertainty of estimated coefficients (α 1 and α 2) of econometric model, we use several of bootstrap strategies. One is to sample by country, in which the 181 countries are randomly resampled to construct 1000 realizations of 181-element lists. A country is allowed to be selected again during the resampling. Then we use these resampled lists of countries to re-estimate Eq. (2), extracting the re-estimated coefficients α 1 and α 2. Another approach samples by year. The 60 years (1961-2020) are randomly resampled to construct 1000 realizations of 60-element lists, again allowing re-selection. The resampled lists of years are used to re-estimate Eq. (2) to obtained coefficients α 1 and α 2. In the third approach, we sample by five-year block. We group the data into five-year blocks (that is, 1961–1965, 1966–1970, …, 2016–2020), and resample these blocks as in other approach. The estimated coefficients are stable in all three strategies. We use strategy one in presenting our results.

We also use the Bootstrap method to test statistical significance of multi-model ensemble mean difference in IOD amplitude between the 20th and 21st century. The IOD amplitude in each century from each model are resampled randomly allowing repeat, to construct 1000 realizations of multi-model ensemble mean values. A multi-model ensemble mean difference greater than the sum of standard deviations of the 1000 realizations of the two centuries is statistically significant above the 95% confidence level.

To project future IOD change and the associated impact, we use climate outputs from climate models in the Coupled Model Intercomparison Project phase 6 (CMIP6), and socio-economic outputs from Shared Socioeconomic Pathways (SSP) database version 2.0. The climate models are forced with historical anthropogenic and natural forcing from 1850 to 2014, and different emission scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5, from low to high emissions) thereafter from 2015 to 2100. The first available experiment from each model is used. Monthly anomalies referenced to the climatology over the 1900–1999 period are reconstructed, and then quadratically detrended at each grid over the period of 1900-2099. The socio-economic projections contain different scenarios of socio-economic development, including country-level GDP and population, under various degrees of climate forcing from 2010 to 2100. The world GDP is projected to reach US$1200 T, US$330 T, US$635 T, and US$672 T by 2100 under SSP5, SSP3, SSP2, and SSP1.

During strong pIOD, anomalous cooling in the east results as a consequence of nonlinear oceanic positive feedbacks along the equator, referred to as nonlinear oceanic zonal and vertical advection. Anomalous cooling along the eastern equatorial Indian Ocean pushes atmosphere convection and the convergence zone to the west, generating strong equatorial easterlies, which in turn intensify the equatorial nonlinear oceanic zonal and vertical advection, conducive to the cooling. The ability of a model to simulate the skewness is an indication of the realism of the model in simulating the IOD nonlinear oceanic positive feedbacks.

On this basis, SON grid-point SST anomalies referenced to the 1900–1999 climatology are constructed, an EOF analysis is applied to quadratically detrended SON SST anomalies in the domain of 5°S-5°N, 40°E-100°E. The first EOF pattern resembles the observed IOD pattern, and the associated principal component (PC1), scaled to unity over the 200 years, is taken as our model IOD index. The skewness of PC1 is used to select models. We use a threshold of 33% of the observed skewness, which is 0.7. Based on the high emissions scenario of SSP5-8.5, we initially select 24 out of available 48 models using this threshold. However, outputs under other emission scenarios are available only in 16 common models. These 16 models are selected for assessment and comparison of economic impact from changing IOD under greenhouse warming.

We compare standard deviation of the IOD index over the 2000-2099 (21st century) with that over the 1900–1999 period (20th century) to assess changes in IOD variability and strong pIOD frequency under greenhouse warming in the selected models. Under greenhouse warming, the eastern equatorial Indian Ocean warms slower than the west, facilitating a westward moment of atmospheric convection, and increased occurrences of strong pIOD events. As such, majority of models that simulate the positive skewness, that is, the positive feedbacks, produce an increase in IOD variability.

There is a strong inter-model consensus on an increase in future IOD variability. In the high-emissions scenario of SSP5-8.5, a total of 14 out of 16 selected models (87.5%), but models under all scenarios generate a faster warming in the west than in the east, and a strong inter-model consensus on an increase in IOD variability, with a multi-model ensemble median (mean) increase of 29.5%, 29.2%, 26.2% and 19.1% (36.7%, 37.6%, 33.7%, 19.1%) for SSP5-8.5, SSP3-7.0, SSP2-4.5, and SSP1-2.6, respectively. The increase in IOD variability translates to a higher frequency of strong pIOD events, defined as when the IOD index is greater than a 1.5 standard deviation value.

Combined climate teleconnection from surface temperature and rainfall is constructed in the same way as for the observed and shows no systematic change, in contrast to the IOD amplitude. We therefore examine the impact of the changing IOD amplitude, assuming that the econometric model based of historical climate and economic data operates.

Our projection of additional GDP loss is affected by uncertainty in several factors including projected socio-economic baseline, emission level, discount schemes, econometric model regression, IOD amplitude changes, and sequence of IOD events (five factors). We test their relative influence one by one. For example, to quantify uncertainty associated with IOD event sequences, we re-order an IOD time series to generate 1000 lists; for each list we re-build the counterfactual IOD time series and GDP growth to estimate the future additional loss. To do so, we hold four out of five factors fixed: SSP scenario is fixed to SSP5-8.5, climate model projection of IOD amplitude changes is fixed to the multi-model ensemble median value, discount rate is fixed at 3%, econometric model regression is fixed to the original point estimate, and IOD sequence is fixed to the original sequence from each climate model. The total uncertainty is a combination of the above sources of uncertainty, in which each factor is allowed to change.

The total uncertainty encompasses a 95% confidence interval of changes in global economic loss from -US$22.5 trillion to US$0.7 trillion, while SSP scenario uncertainty alone leads to a 95% confidence interval of -US$5.0 trillion to -US$1.9 trillion, climate model uncertainty to a 95% confidence interval of -US$33.6 trillion to US$1.0 trillion, discount rate uncertainty to a 95% confidence interval of -US$13.9 trillion to -US$2.2 trillion, econometric model regression uncertainty to a 95% confidence interval of -US$9.4 trillion to US$0.6 trillion, and IOD sequence uncertainty to a 95% confidence interval of -US$7.1 trillion to -US$5.1 trillion. We highlight that climate models with varying IOD amplitude changes contribute most to the total uncertainty, reinforcing that the additional GDP loss is underpinned by IOD amplitude change.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Report for NCOMMS-23-47036\nNonlinear country-specific impact of the Indian Ocean Dipole on global economies\nThe article aims to measure the economic effects generated by the IOD phenomenon on 180 countries\nusing an ARDL econometric model and a country-specific method based on a teleconnection strength\nparameter. This article is closely aligned in philosophy and methodology with a study published in\n2023 in Nature Communications on the individualized economic effects of ENSO. It is well-structured,\nthe empirical work appears well-executed, and it holds interest for publication in a journal like Nature\nCommunications.\nHowever, I would like to modestly address some points through questions and requests for clarification\nthat could further enhance the article in the context of a revision.\n- A significant contribution of the paper lies in highlighting non-linear effects of IOD on production.\nHowever, could this result stem from the chosen modeling, which might overestimate the presence of\nnon-linear effects by using the quadratic form (alpha1 + alpha2)? Have you tested a linear model and\ncompared its predictive capabilities to your non-linear model? If the non-linear model exhibits superior\npredictive abilities, then it is legitimate to use and estimate it; otherwise, doubts might arise about the\nfactors driving the results.\n- Have you tested a common IOD shock in addition to interaction terms, similar to your previous\narticle on ENSO, which is methodologically very close?\n- Do T and P capture the residual effects of temperatures and precipitation after controlling for IOD\n(and also ENSO) effects, is that correct? Could there also be an indirect effect captured by an\ninteraction term like T*IOD, for example?\n- You mention using a 2-lag model, deemed optimal, but based on what criteria? The S1 table shows\nthat lag3 has a higher R-squared. Of course, I am not sure if it is the raw or adjusted R-squared;\nI presume it is the adjusted R-squared.\n- Only the alpha2_1 coefficients are significant at 1%, but the rest are significant at 10% only.\nIsn't this problematic given the large number of observations you have?\n- The effects of the squared term are significant, but not for lag0 (contemporary effect), and similarly,\nlinear effects are non-significant for lag0 and lag1. How do you explain this from both a climatic and\neconomic perspective? What happens in terms of channels/mechanisms to explain this?\n- The article controls for several sources of uncertainty, which is very interesting. However, a potential\nsource of bias and uncertainty is not mentioned, in my opinion: the quality and relevance of GDP data\nin certain countries, especially in developing and African countries, where data quality and availability\nmay be low. What about that? (the authors used both World Bank and Penn WT but what about\nMaddison or other data). This could potentially affect the dynamics in ARDL models in time series and\nthe significance of lags. Would it not be interesting to test the model's robustness since 1980 or 1990\nas a robustness exercise?\n- We observe, especially with Figure 1a, that the vast majority of countries have a teleconnection\nstrength coefficient below 1.5, and unlike ENSO, as the authors state, the distribution is more\nasymmetric. Perhaps this justifies the non-linear model. However, it would be interesting to know the\neconomic damages recorded by moderately teleconnected or even weakly teleconnected countries and\nto add them to Figure 1.b, which only concerns three of the most teleconnected countries. In terms of\neconomic policy impact and for the general public, knowing that IOD could have significant effects of a\ncertain magnitude in the United States or European countries could have a significant impact on the\nfight against climate change, as one might expect less damage from this type of teleconnections.\n- In Figure 4b, is it logical for SSP2-4.5 to be associated with a negative coefficient of such a small\nmagnitude?\n- It would be interesting in the discussion to provide a comparison with ENSO results to put in\nperspectives the magnitude of losses caused by IOD at a global and regional levels.\n- Regarding climate variability and climate change effects on the global economy, it might be good to\ncite, in addition to references 31 and 32, the two articles by Kotz et al. (2021, 2022) in Nature Climate\nChange and Nature because they are recent significant contributions on this topic and address climate\nvariability for temperatures and precipitation.\n- The article's form could be improved. There are a few typos, such as ‘Rsquare’ instead of ‘RSquared’\nas an example.", "reviewer_2": "The authors provide a timely and interesting analysis of the impacts of the Indian Ocean Dipole on the\neconomic growth of countries across the world. The paper builds on standard climate-econometric\ntechniques but applies a nuanced understanding of climate science including EOF analysis and\nassessments of teleconnection strength to construct a powerful indicator of national-level exposure to\nIOD. The empirical results are interesting and the combination with future projections of IOD make for\nhighly relevant findings.\nHowever, I have a number of questions regarding the methodology and framing which, for me, would\nbe essential to address before publication. I list these in order of importance:\nMajor:\n1. The independence of impacts from ENSO\nRecent literature, including by the authors, has shown strong impacts of El Nino on economic growth.\nWhile the present analysis takes care to define the IOD teleconnections as independent of El Nino,\nthey do not assess whether the economic impacts from IOD which they identify are independent from\nthose caused by El Nino. Given that we know that positive IOD are often associated with El Nino, it is\ncrucial for our understanding and the implications of the results to assess an empirical model which\nincludes both an El Nino and an IOD index as independent variables to test how much they\nindependently impact global economic output.\n2. Projections of future impacts\nThe calculation of future economic impacts due to the IOD change should be made clearer, currently\nthere are no equations for this in the methods section. In particular, the description on L241-246 was\nunclear to me in how the discount rate was applied. The discount rate should be used to weight the\ncumulative sum of future GDP losses, but in these lines it is described as being applied to the\ncompounding growth rates. This method needs to be clarified before I can assess its validity.\nMoreover, presenting economic damages as cumulative GDP losses over the whole of the century likely\ninflates the numbers compared to other presentations. Most assessments of future economic impacts\nof climate change typically report impacts as an annual % reduction of future GDP. Obviously, the\nauthors are at liberty to choose how they present their results, but I think it would be valuable and\nmake for a more honest comparison to at least also present the impacts of IOD that they calculate in\nthis way which is consistent with other literature (e.g. Burke 2015, Kalkuhl & Wenz 2020).\n3. Bootstrapping and significance of empirical regressions\nThe authors should include additional tables which show the results of the assessment of statistical\nsignificance when using the other two bootstrap resampling approaches. Moreover, the authors should\ndiscuss what assumptions and types of uncertainty these different bootstrapping approaches are likely\nto assess and justify their main choice. I think this is particularly important to consider in light of the\nfact that the main independent variable (IOD) is common, and therefore perfectly correlated, across\nall countries. This means there are much fewer independent observations than normally assumed in a\npanel regression where independent variables are not so correlated across groups. This issue\ncompared to normal panel models should be explicitly dealt with when presenting the significance of\nthe econometric results.\n4. Climate teleconnection\nThis is an interesting and subtle way to construct a countries potential exposure to IOD. However, a\nfew methodological choices seem subjective and alternative approaches are not explored. E.g.:\nCan the authors demonstrate the effectiveness of the partial regression by also showing the direct\ncorrelation of sea surface temperatures with IOD without removing the ENSO effect?\nWhy are climate teleconnections only from May to December included?\nMinor:\n5. Fixed-effects choices\nPanel fixed-effects models typically include two way (country and year) fixed effects for causal\nidentification. The year fixed effects capture unobserved confounders which are common across\ncountries in given years. Here, these are not included. This is likely due to the fact that IOD variation\nis common across countries and so including year-fixed effects would remove the variance in which\nthe model is interested in identifying. So this is a justifiable choice, but the authors should make that\njustification explicit.\n6. Description as “country-specific” results\nIn my opinion, the description of the empirical model as “country-specific” is mis-leading. A global\npanel model estimates an aggregated response across countries. Even when including interaction\nterms (here with the teleconnection strength) to describe cross-country heterogeneity, the model still\nestimates a global heterogeneity based on this strength rather than a specific country-level response.\nL144 – do the authors distinguish between countries with a larger/smaller agricultural share in their\neconometric specification? It appears not based on their methods, and that the only way different\nimpacts across countries are described in the model are through the teleconnection strength.\nTherefore it seems like this interpretation of their results that Australia suffers a larger impact due to\nits larger agricultural sector is not justified by their methods or results.\nL133 – can the authors show a table indicating this insignificant result?\n140 - the impact is of an opposite sign for negative/positive IOD and this should be stated, not just\nthat the impact is greater for positive than negative.\nL207-210 – should be made clearer that these sentences refer to the teleconnection strengths/\npatterns.\nThere are a number of minor wording issues which should be corrected, in particular missing “the”,\ne.g. L87 “the impact on the global economy”.", "reviewer_3": "Please see attachment.\nReferee report for “Nonlinear country-specific impact of the Indian Ocean Dipole on global\neconomies”\nThis paper estimates the effect of the Indian Ocean Dipole (IOD) on economic growth, and projects the\neconomic impacts of an intensified IOD amplitude under future climate change. The IOD is a mode of\ninterannual global climate variability originating in sea surface temperature anomalies in the equatorial\nIndian Ocean. These anomalies are correlated with strong variation in surface air temperature and rainfall\nin some parts of the world (i.e., highly “teleconnected”) but much less so in other parts of the world (i.e.,\nnot highly “teleconnected”). The paper exploits these differences in teleconnection to estimate the effect\nof the IOD on economic growth by country. An important finding is that the effects are highly\nnonlinear/assymetric- while a large positive IOD has a large negative effect on economic growth, a\nnegative IOD has virtually no effect on growth. The harmful effects of positive IODs are stronger for\nmore teleconnected countries. Based on these findings, the paper projects that intensified IOD amplitude\nunder climate change will have large economic costs compared to a no-climate-change counterfactual.\nThese costs will be disproportionately borne by today’s developing and emerging economies, as these\nhappen to be the ones with greatest teleconnection. (Climate change is not projected to alter the patterns\nof teleconnection.)\nThe basic research design of this paper combines previous work on the effects of another mode of\ninterannual global climate variability- ENSO (e.g., Hsiang et al., 2011) with previous work on the effects of\nclimate on economic growth (e.g. Burke et al., 2015). ENSO originates in sea surface temperature\nanomalies in the eastern equatorial Pacific and its effects have been studied considerably more than those\nof the IOD. As such, this paper fills in important gap by studying the economic effects of the IOD.\nWhile the paper takes on an important topic, more work needs to be done on the econometric\nestimation and climate change impact projections to ensure the robustness of the results. The main estimating\nequation (Equation 2) includes a multiplicative interaction of an annual IOD index value with a country-\nspecific measure of teleconnection. This interaction term is modeled as a quadratic function to account for\nnonlinearity in the country-specific effects of IOD. Additional regressors include country-level annual\nmean surface air temperature and rainfall.\n1. Importantly, the IOD index values used in the estimating equation are constructed via a partial\nregression that removes the impact of ENSO (Equation 1). This approach seems opaque. The\nauthors should consider also running an alternative version of the estimating equation that includes\nas regressors both the IOD index x IOD teleconnection interaction as well as an ENSO index x\nENSO teleconnection interaction. Such a specification will directly control for the effect of ENSO\nand will allow for a transparent comparison between the effects of IOD and ENSO. Both effects\ncould then also be carried through to the climate change impact projections, allowing for a\ncomparison of the respective impacts.\n2. The estimating equation includes country-level annual mean surface air temperature and rainfall,\nboth modeled as quadratic. However, the estimated effects of these variables on economic growth\nare never displayed, nor do they seem to be accounted for in the climate change impact\nprojections. Considering that prior work has shown temperature (Burke et al., 2015) and rainfall\n(Kotz et al., 2022) to affect economic growth, the authors should present the estimated effects of\nthese variables and carry these forward when projecting the impacts of climate change. This will\nallow for comparison between the impacts of different climatic variables on future economic costs.\n3. The estimating equation includes contemporaneous and lagged effects of all variables. However,\nonly the cumulated effect is ever plotted. It would be useful to also see the separated effects\npresented in a supplementary figure.\nOther Points\n1. Figure 2c plots the nonlinearity of economic impact (referred to as alpha) against economic\nteleconnection strength. It is unclear to me what exactly is alpha as I could not find it defined\nanywhere. The estimating equation (Equation 2) refers to alpha_1 and alpha_2, but not alpha\nwithout a subscript.\n2. There are multiple references to an acronym “SON”, but I could not find it defined anywhere.\nReferences\nBurke, M., Hsiang, S.M. and Miguel, E., 2015. Global non-linear effect of temperature on economic\nproduction. Nature, 527(7577), pp.235-239.\nHsiang, S.M., Meng, K.C. and Cane, M.A., 2011. Civil conflicts are associated with the global climate.\nNature, 476(7361), pp.438-441.\nKotz, M., Levermann, A. and Wenz, L., 2022. The effect of rainfall changes on economic production.\nNature, 601(7892), pp.223-227."}, "author_response": "We thank three reviewers for the helpful and positive comments. Please find our response\nbelow (in blue).\nResponse to Reviewer #1\nThe article aims to measure the economic effects generated by the IOD phenomenon on 180\ncountries using an ARDL econometric model and a country-specific method based on a\nteleconnection strength parameter. This article is closely aligned in philosophy and\nmethodology with a study published in 2023 in Nature Communications on the individualized\neconomic effects of ENSO. It is well-structured, the empirical work appears well-executed, and\nit holds interest for publication in a journal like Nature Communications.\nHowever, I would like to modestly address some points through questions and requests for\nclarification that could further enhance the article in the context of a revision.\nThank you very much for your detailed, thoughtful, and helpful comments.\n- A significant contribution of the paper lies in highlighting non-linear effects of IOD on\nproduction. However, could this result stem from the chosen modelling, which might\noverestimate the presence of non-linear effects by using the quadratic form (alpha1 + alpha2)?\nHave you tested a linear model and compared its predictive capabilities to your non-linear\nmodel? If the non-linear model exhibits superior predictive abilities, then it is legitimate to use\nand estimate it; otherwise, doubts might arise about the factors driving the results.\nThank you for the helpful comments. We did test using a linear model. The resultant Alpha1\ncoefficients are similarly statistically significant in the two years after the occurrence (see Table\n1-R1).\nNonlinear model Linear Model\n-0.0015* 3.43×10-4 -9.39×10-4\n𝛼 𝛼 𝛼\n1,0 (8.51×10-4) 2,0 (2.60×10-4) 1,0 (7.66×10-4)\n-7.52×10-\n-0.0018* -0.0029***\n𝛼 𝛼 4*** 𝛼\n1,1 (9.01×10-4) 2,1 1,1 (8.09×10-4)\n(2.62×10-4)\n-0.0017* -4.99×10-5 -0.0014*\n𝛼 𝛼 𝛼\n1,2 (9.00×10-4) 2,2 (2.82×10-4) 1,2 (8.47×10-4)\n-7.15×10-4 -2.51×10-4 -0.0011\n𝛼 𝛼 𝛼\n1,3 (9.03×10-4) 2,3 (2.79×10-4) 1,3 (8.26×10-4)\nTable 1-R1 | Comparison of linear and nonlinear models.\nWe use a nonlinear model because either an extreme positive or an extreme negative IOD\ncauses a damage, which would not be captured by a linear model. The superior performance\nof the nonlinear model is supported by the fact that it explains ~50% more variability than that\nexplained by the linear model; for example, the nonlinear model shows a standard deviation of\n1.29% of variability in Kenya GDP growth rate over the 60-year period, but the value for the\nlinear model is only 0.84%.\n- Have you tested a common IOD shock in addition to interaction terms, similar to your previous\narticle on ENSO, which is methodologically very close?\nWe did. If we use the same common shock model as in our ENSO model, we obtain a\nnonmeaningful result. For example, in the occurrence year the impact is a benefit for both\npositive IOD and negative IOD events. For the IOD, a country-specific resolution is essential.\nWe have included this information in the revised version. See Lines 384-387.\n- Do T and P capture the residual effects of temperatures and precipitation after controlling for\nIOD (and also ENSO) effects, is that correct? Could there also be an indirect effect captured by\nan interaction term like T*IOD, for example?\nOur model adds the IOD impact to the original model – and this approach captures the residual\neffect after controlling for T and P that are independent from the IOD, that is, after the influence\nby the IOD on T and P is removed. Your idea of an interaction term like “T*IOD” is a\nworthwhile one. It could be a way to study how warming might exacerbate the impact of the\nIOD. We have tried to introduce such a term (see the (𝜸 𝑻 𝑰 ) term below) but have not\n𝒍 𝒊,𝒕−𝒍 𝒕−𝒍\nbeen able to find coefficients that are statistically significant (Table 2-R1). Whether this is\nanything to do with our short data length is unclear, and is definitely a future direction to pursue.\n𝑙𝑜𝑔(𝑦 )−𝑙𝑜𝑔(𝑦 )\n𝑖,𝑡 𝑖,𝑡−1\n𝑛\n= ∑{𝛼 𝜓 𝐼 +𝛼 (𝜓 𝐼 )2 +𝛽 𝑇 +𝛽 𝑇2 +𝜸 𝑻 𝑰\n1,𝑙 𝑖 𝑡−𝑙 2,𝑙 𝑖 𝑡−𝑙 1,𝑙 𝑖,𝑡−𝑙 2,𝑙 𝑖,𝑡−𝑙 𝒍 𝒊,𝒕−𝒍 𝒕−𝒍\n𝑙=0\n+𝜆 𝑃 +𝜆 𝑃2 }+𝜇 +𝜃 𝑡+𝜃 𝑡2 +𝜀\n1,𝑙 𝑖,𝑡−𝑙 2,𝑙 𝑖,𝑡−𝑙 𝑖 1,𝑖 2,𝑖 𝑖,𝑡\n-1.21×10-4\n𝛾\n0 (6.17×10-4)\n-2.29×10-4\n𝛾\n1 (6.45×10-4)\n-3.88×10-5\n𝛾\n2 (6.71×10-5)\n-8.56×10-5\n𝛾\n3 (6.59×10-5)\nTable 2-R1 | Coefficients for interaction term, not statistically significant at any lags.\n- You mention using a 2-lag model, deemed optimal, but based on what criteria? The S1 table\nshows that lag3 has a higher R-squared. Of course, I am not sure if it is the raw or adjusted R-\nsquared; I presume it is the adjusted R-squared.\nIt is adjusted R-squared.\n- Only the alpha2_1 coefficients are significant at 1%, but the rest are significant at 10% only.\nIsn't this problematic given the large number of observations you have?\nThe 10% significance is definitely not high but is unusual, and certainly what we can get. The\nlag 2 Alpha2 at the 1% significance suggests that the spillover or cascading effect is largest the\nyear after the occurrence. This appears reasonable as the cascading effect takes time to be\nreflected in the economic data the year after. It is not clear how the result might be different if\nwe would have more data than 60 years.\n- The effects of the squared term are significant, but not for lag0 (contemporary effect), and\nsimilarly, linear effects are non-significant for lag0 and lag1. How do you explain this from\nboth a climatic and economic perspective? What happens in terms of channels/mechanisms to\nexplain this?\nGreat question. During the occurrence year, the contemporary effect is mostly through a direct\nloss, which is mild, in part likely because the climatic impact is largest in developing countries,\nin which the economies are relatively small. In the subsequent years drought and its effect such\nas drought-induced crop failures may continue but weaker than the occurrence year; however,\nthe cascading effect becomes substantial due to impact on global trades, commodity prices, and\nlow inventories, and from a low capacity to deal with the adverse effect, contributing to the\nnonlinear effect. We have added this information in the revised version. See Lines 375-381.\n- The article controls for several sources of uncertainty, which is very interesting. However, a\npotential source of bias and uncertainty is not mentioned, in my opinion: the quality and\nrelevance of GDP data in certain countries, especially in developing and African countries,\nwhere data quality and availability may be low. What about that? (the authors used both World\nBank and Penn WT but what about Maddison or other data). This could potentially affect the\ndynamics in ARDL models in time series and the significance of lags. Would it not be\ninteresting to test the model's robustness since 1980 or 1990 as a robustness exercise?\nGreat suggestions. Our robustness test is mainly through the Bootstrap method. We have added\na Supplementary Table (S2) regarding robustness with different years used.\nWe use data that are commonly used, for example, by Burk et al. 2015, Kotz et al. (2021, 2022),\nand Callahan et al. (2023). When we fit the model with the data since 1990 or 1980, some of\nthe originally significant coefficients become not significant due to a shorter length of data\n(Table 3-R1). Length of data seems rather important.\n1960-2020 1990-2020\n-0.0015* 3.43×10-4 -8.96×10-4 6.38×10-4\n𝛼 𝛼 𝛼 𝛼\n1,0 (8.51×10-4) 2,0 (2.60×10-4) 1,0 (0.0011) 2,0 (3.04×10-4)\n-0.0018* -7.52×10-4*** -0.0015 -6.94×10-4**\n𝛼 𝛼 𝛼 𝛼\n1,1 (9.01×10-4) 2,1 (2.62×10-4) 1,1 (0.0012) 2,1 (3.10×10-4)\n-0.0017* -4.99×10-5 -0.0014 -1.16×10-4\n𝛼 𝛼 𝛼 𝛼\n1,2 (9.00×10-4) 2,2 (2.82×10-4) 1,2 (0.0012) 2,2 (3.46×10-4)\n-7.15×10-4 -2.51×10-4 -3.29×10-4 -1.61×10-4\n𝛼 𝛼 𝛼 𝛼\n1,3 (9.03×10-4) 2,3 (2.79×10-4) 1,3 (0.0012) 2,3 (3.41×10-4)\nTable 3-R1 | Coefficients for using data since 1990.\nThe Maddison data appear to be of lower quality and not used by research on a similar topic.\n- We observe, especially with Figure 1a, that the vast majority of countries have a\nteleconnection strength coefficient below 1.5, and unlike ENSO, as the authors state, the\ndistribution is more asymmetric. Perhaps this justifies the non-linear model. However, it would\nbe interesting to know the economic damages recorded by moderately teleconnected or even\nweakly teleconnected countries and to add them to Figure 1.b, which only concerns three of the\nmost teleconnected countries. In terms of economic policy impact and for the general public,\nknowing that IOD could have significant effects of a certain magnitude in the United States or\nEuropean countries could have a significant impact on the fight against climate change, as one\nmight expect less damage from this type of teleconnections.\nThanks for the suggestion; see the result below for USA, for example, in terms of percentage\nof economic growth, it is small, but the total loss is ~60 billion of US dollars for the 2019 pIOD\nevent (Fig. 1-R1). We have added this information in the paper, see Lines 153-155.\nFig. 1-R1 | Same as Fig. 1b, but adding the nonlinear relationship of United States (green)\nand United Kingdom (black).\n- In Figure 4b, is it logical for SSP2-4.5 to be associated with a negative coefficient of such a\nsmall magnitude?\nThe range captures all possibilities, assuming each model is equally likely. This is determined\nby the change in IOD amplitude, which is somewhat similar to that of SSP1-2.6.\n- It would be interesting in the discussion to provide a comparison with ENSO results to put in\nperspectives the magnitude of losses caused by IOD at a global and regional levels.\nGood suggestion. We commented on global level as we don’t have country-specific model for\nENSO. For one standard deviation of the IOD time series, there is a loss of 0.24% cumulative\nloss in global economy. The 2019 strong pIOD event resulted in a loss of US$558B, amounting\nto a reduction in the cumulative global GDP growth of 0.67%. By comparison, there is a ~1.5%\ncumulative loss in global economy per one standard deviation of the ENSO time series. The\n2015/16 strong El Niño led to a loss of US$3.9T, amounting to a ~5% cumulative loss in global\neconomy. We have added this information. See Lines 161-163.\n- Regarding climate variability and climate change effects on the global economy, it might be\ngood to cite, in addition to references 31 and 32, the two articles by Kotz et al. (2021, 2022) in\nNature Climate Change and Nature because they are recent significant contributions on this\ntopic and address climate variability for temperatures and precipitation.\nDone, thank you for the suggestion.\n- The article's form could be improved. There are a few typos, such as ‘Rsquare’ instead of\n‘RSquared’ as an example.\nDone! Thank you.\nResponse to Reviewer #2\nThe authors provide a timely and interesting analysis of the impacts of the Indian Ocean Dipole\non the economic growth of countries across the world. The paper builds on standard climate-\neconometric techniques but applies a nuanced understanding of climate science including EOF\nanalysis and assessments of teleconnection strength to construct a powerful indicator of\nnational-level exposure to IOD. The empirical results are interesting and the combination with\nfuture projections of IOD make for highly relevant findings.\nHowever, I have a number of questions regarding the methodology and framing which, for me,\nwould be essential to address before publication. I list these in order of importance:\nThank you very much for your detailed, thoughtful, and helpful comments.\n1.The independence of impacts from ENSO\nRecent literature, including by the authors, has shown strong impacts of El Niño on economic\ngrowth. While the present analysis takes care to define the IOD teleconnections as independent\nof El Niño, they do not assess whether the economic impacts from IOD which they identify are\nindependent from those caused by El Niño. Given that we know that positive IOD are often\nassociated with El Niño, it is crucial for our understanding and the implications of the results\nto assess an empirical model which includes both an El Niño and an IOD index as independent\nvariables to test how much they independently impact global economic output.\nAn IOD can occur independently of ENSO; in fact, most of the strongest positive IOD events\n(2019, 1961, and 1994) occurred in a non-El Niño year, and 2007 and 2008 positive IOD events\noccurred when the Pacific saw a La Niña event (Cai et al. 2013; Wang and Cai et al. 2024). It\nis true a positive IOD could be forced by an El Niño but an El Niño can also be forced by a\npositive IOD too. There is no obvious way to completely separate them, or their impact, because\ntheir mutual forcing/interaction is always present.\nOur current work finds that for the IOD impact country-heterogeneity is essential; our Liu and\nCai et al. paper finds that for ENSO a common shock dominates and that a nonlinear country-\nheterogeneous approach does not yield a statistically significant impact. We therefore test a new\nmodel that combines the two approaches with both an El Niño and an IOD index as independent\nvariables, i.e., with IOD information removed from the ENSO index.\n𝑙𝑜𝑔(𝑦 )−𝑙𝑜𝑔(𝑦 )\n𝑖,𝑡 𝑖,𝑡−1\n𝑛\n= ∑{𝜶 𝝍 𝑰 +𝜶 (𝝍 𝑰 )𝟐 +𝜸 𝑬 +𝜸 𝑬𝟐 +𝛽 𝑇\n𝟏,𝒍 𝒊 𝒕−𝒍 𝟐,𝒍 𝒊 𝒕−𝒍 𝟏,𝒍 𝒕−𝒍 𝟐,𝒍 𝒕−𝒍 1,𝑙 𝑖,𝑡−𝑙\n𝑙=0\n+𝛽 𝑇2 +𝜆 𝑃 +𝜆 𝑃2 }+𝜇 +𝜃 𝑡+𝜃 𝑡2 +𝜀\n2,𝑙 𝑖,𝑡−𝑙 1,𝑙 𝑖,𝑡−𝑙 2,𝑙 𝑖,𝑡−𝑙 𝑖 1,𝑖 2,𝑖 𝑖,𝑡\nWe find that the properties for each component, such as lag years and nonlinearity, are similar\nto their respective original models.\nCompared with the respective original models, an in-dollar loss of individual events is reduced\n(Fig. 1-R2) because original ENSO and IOD “share a common loss” that is not reflected in\ntheir respective impacts.\nThe standard deviation of ENSO- and IOD-induced impact during 1960-2020 is 0.53% and\n0.10%, respectively, indicating the ratio between IOD’s and ENSO’s impact is ~19%. This is\nsimilar to the estimate from the original respective models of ~16%.\nFig. 1-R2. | Similar to Fig. 1c of this paper and Fig. 2a-b of Liu et al. (2023). Shown are\ncumulative effects of a) two extreme pIOD and nIOD events and b) three extreme El Niño and\nLa Niña events, but from the new model discussed above. The properties are similar.\nReferences\nCai, W. et al. Projected response of the Indian Ocean Dipole to greenhouse warming. Nature\ngeoscience 6, 999-1007 (2013).\nWang, G. and Cai, W. et al. Change Indian Ocean Dipole in a warming climate. Nature Review\nEarth and Environment, in press (2014).\n2. Projections of future impacts\nThe calculation of future economic impacts due to the IOD change should be made clearer,\ncurrently there are no equations for this in the methods section. In particular, the description on\nL241-246 was unclear to me in how the discount rate was applied. The discount rate should be\nused to weight the cumulative sum of future GDP losses, but in these lines it is described as\nbeing applied to the compounding growth rates. This method needs to be clarified before I can\nassess its validity.\nThank you for picking this up. Our description is incorrect. The discount rate was applied to\nweight the cumulative sum of future GDP losses. We have changed. See Lines 248-250.\nMoreover, presenting economic damages as cumulative GDP losses over the whole of the\ncentury likely inflates the numbers compared to other presentations. Most assessments of future\neconomic impacts of climate change typically report impacts as an annual % reduction of future\nGDP. Obviously, the authors are at liberty to choose how they present their results, but I think\nit would be valuable and make for a more honest comparison to at least also present the impacts\nof IOD that they calculate in this way which is consistent with other literature (e.g. Burke 2015,\nKalkuhl & Wenz 2020).\nThank you for the suggestion. We have calculated the global GDP growth rate with and without\nIOD changes for the four emission scenarios. We average the growth rate across positive IOD\nevents defined as when an SON IOD index exceeds a 1.0 s.d value (Fig. 2-R2). The increased\nintensity of pIOD in the future leads to an additional increase of 35%-47% per event in these\nscenarios (45% for SSP5-8.5).\nAccumulating all events would lead to an even greater additional loss because the frequency\nalso increases (Fig. 3-R2).\nFig. 2-R2 | Comparison of IOD-induced global GDP loss with and without IOD changes.\na-d, pIOD-induced loss of global GDP growth with projected changing IOD (y-axis) and\ncounterfactual IOD (x-axis), averaged in the period of 2020-2099 under the a SSP5-8.5, b\nSSP3-7.0, c SSP2-4.5, and d SSP1-2.6 scenario. A pIOD is defined as SON IOD index exceeds\n1 s.d. Coloured dots indicate different climate models. Red pentagrams indicate the multi-model\nensemble mean. The multimodel average for a-d (with, without) IOD changes are (-0.29%, -\n0.20%), (-0.28%, -0.19%), (-0.28%, -0.20%), and (-0.27%, -0.22%), for the a SSP5-8.5, b\nSSP3-7.0, c SSP2-4.5, and d SSP1-2.6 scenario, with a 45%, 47%, 40%, and 35% increase,\nrespectively.\nFig. 3-R2 | Comparison of numbers of pIOD events with and without IOD changes. a-d,\nSame as Fig. 1R2, but for numbers of pIOD events with projected changing IOD (y-axis) and\ncounterfactual IOD (x-axis), averaged in the period of 2020-2099 under the a SSP5-8.5, b\nSSP3-7.0, c SSP2-4.5, and d SSP1-2.6 scenario. The multimodel average for a-d (with, without)\nIOD changes are (23.4, 16.2), (22.4, 15.3), (20.4, 14.7), and (18.5, 16.3), for the a SSP5-8.5, b\nSSP3-7.0, c SSP2-4.5, and d SSP1-2.6 scenario, with a 44%, 46%, 39%, and 14% increase,\nrespectively.\nWe have added the information in the revised version. See Lines 283-289. We have also\nincluded the two additional figures in the Supplementary Information (S11, S12).\n3. Bootstrapping and significance of empirical regressions\nThe authors should include additional tables which show the results of the assessment of\nstatistical significance when using the other two bootstrap resampling approaches. Moreover,\nthe authors should discuss what assumptions and types of uncertainty these different\nbootstrapping approaches are likely to assess and justify their main choice. I think this is\nparticularly important to consider in light of the fact that the main independent variable (IOD) is\ncommon, and therefore perfectly correlated, across all countries. This means that there are\nmuch fewer independent observations than normally assumed in a panel regression where\nindependent variables are not so correlated across groups. This issue compared to normal panel\nmodels should be explicitly dealt with when presenting the significance of the econometric\nresults.\nIt is true that the IOD is common to all countries; it is because the countries are commonly\naffected by the IOD that allows us to examine the impact. The interannual IOD index has little\nautocorrelation beyond lag 1 on both sides (0.15).\nThank you for the suggestion, we have now added additional Tables testing the statistical\nsignificance of different lists of years and 5-year blocks of countries (Table 1-R2, below). The\nTable is now included in the revised paper (Supplementary Table S2), with a comment in the\nmain text, see Lines 134-137.\nSampling by Sampling by Sampling by 5-\ncountry year year block\n-0.0016* -0.0015* -0.0016*\n\n1,0 (5.93×10-4) (7.24×10-4) (6.35×10-4)\n-0.0018* -0.0017* -0.0018*\n\n1,1 (6.69×10-4) (8.17×10-4) (8.78×10-4)\n-0.0017* -0.0017* -0.0017*\n\n1,2 (6.82×10-4) (7.74×10-4) (7.65×10-4)\n-7.24×10-4 -6.94×10-4 -7.86×10-4\n\n1,3 (7.59×10-4) (7.90×10-4) (5.90×10-4)\n4.05×10-4 3.51×10-4 3.45×10-4\n\n2,0 (2.57×10-4) (1.64×10-4) (1.76×10-4)\n-7.61×10-4*** -7.49×10-4*** -7.37×10-4***\n\n2,1 (1.74×10-4) (2.61×10-4) (3.09×10-4)\n-2.41×10-5 -2.42×10-5 -3.21×10-5\n\n2,2 (2.25×10-4) (2.12×10-4) (1.93×10-4)\n-2.50×10-4 -2.56×10-4 -2.56×10-4\n\n2,3 (2.28×10-4) (1.43×10-4) (1.15×10-4)\nTable 1-R2 | Regression coefficients 𝜶 from three bootstrap resampling approaches in\nvarious lag years. Values in the brackets denote s.d. of 1,000 bootstrapped coefficients.\nSuperscript *, ** and *** indicate the estimate of coefficient is statistically significant above\nthe 90%, 95% and 99% confidence level, respectively.\n4. Climate teleconnection\nThis is an interesting and subtle way to construct a countries potential exposure to IOD.\nHowever, a few methodological choices seem subjective and alternative approaches are not\nexplored. E.g.: Can the authors demonstrate the effectiveness of the partial regression by also\nshowing the direct correlation of sea surface temperatures with IOD without removing the\nENSO effect?\nWe have added. Without removal of ENSO, one sees strong ENSO anomalies (see Fig. 4-R2\nbelow).\nFig. 4-R2 | Illustration of effectiveness of a partial regression. a-h, Monthly regression\ncoefficients of observed sea surface temperature onto the observed SON IOD index, from May\nto December month by month. Only the regression coefficients above the 95% confidence level\nare shown. Without removing ENSO, ENSO signal is clearly seen in the Pacific.\nWhy are climate teleconnections only from May to December included?\nThis is because an IOD develops in June, but ends by November, and no IOD survives beyond\nDecember. This is determined by strong seasonal cycle in the Indian Ocean. We have added this\ninformation to the paper. See Lines 102-103.\nMinor:\nFixed-effects choices.\nPanel fixed-effects models typically include two way (country and year) fixed effects for causal\nidentification. The year fixed effects capture unobserved confounders which are common\nacross countries in given years. Here, these are not included. This is likely due to the fact that\nIOD variation is common across countries and so including year-fixed effects would remove\nthe variance in which the model is interested in identifying. So this is a justifiable choice, but\nthe authors should make that justification explicit.\nWe thank the reviewer for raising this point. As you pointed out, the IOD is a time-specific\nglobal phenomenon, like one of other major events (e.g., financial crisis), that commonly affects\ncountries. Including year-fixed effects weakens the statistical influence of the IOD, leading to\nan underestimation of the real impact of the IOD on economic growth, as pointed out previously\n(Dell et al. 2015). Also, year-fixed effect could introduce risk of collinearity as IOD time-fixed\neffects could be correlated with time-specific factors, making it harder to separate impacts from\nthe IOD. We have made these clear, see Lines 366-369.\nDescription as “country-specific” results\nIn my opinion, the description of the empirical model as “country-specific” is mis-leading. A\nglobal panel model estimates an aggregated response across countries. Even when including\ninteraction terms (here with the teleconnection strength) to describe cross-country heterogeneity,\nthe model still estimates a global heterogeneity based on this strength rather than a specific\ncountry-level response.\nWe have changed to “country-heterogenous” throughout the paper.\nL144 – do the authors distinguish between countries with a larger/smaller agricultural share in\ntheir econometric specification? It appears not based on their methods, and that the only way\ndifferent impacts across countries are described in the model are through the teleconnection\nstrength. Therefore, it seems like this interpretation of their results that Australia suffers a larger\nimpact due to its larger agricultural sector is not justified by their methods or results.\nWe have removed the inference, see Lines 146-148.\nL133 – can the authors show a table indicating this insignificant result?\nWe are sorry, our statement there was incorrect; we meant a nonmeaningful result. Some\ncoefficients are significant but for year 0 (occurrence year) both a positive and a negative IOD\nproduce a benefit due to a positive Alpha2,0. We have added this information to the paper, see\nLines 385-388.\n140 - the impact is of an opposite sign for negative/positive IOD and this should be stated, not\njust that the impact is greater for positive than negative.\nDone. Thank you for the suggestion. We have added a comparison. For example, a +1.0 s.d.\npIOD causes -1.89% (±0.64%) loss in Kenya GDP growth that is statistically significant, while\na -1.0 s.d. nIOD causes +1.09% (±1.25%) benefit in Kenya GDP growth that is not statistically\nsignificant. See Lines 142-145.\nL207-210 – should be made clearer that these sentences refer to the teleconnection strengths/\npatterns.\nGreat point. Made clear now. See Lines 214-215.\nThere are a number of minor wording issues which should be corrected, in particular missing\n“the”, e.g. L87 “the impact on the global economy”.\nThanks, changed in appropriate places.\nResponse to Reviewer #3\nThis paper estimates the effect of the Indian Ocean Dipole (IOD) on economic growth, and\nprojects the economic impacts of an intensified IOD amplitude under future climate change.\nThe IOD is a mode of interannual global climate variability originating in sea surface\ntemperature anomalies in the equatorial Indian Ocean. These anomalies are correlated with\nstrong variation in surface air temperature and rainfall in some parts of the world (i.e., highly\n“teleconnected”) but much less so in other parts of the world (i.e., not highly “teleconnected”).\nThe paper exploits these differences in teleconnection to estimate the effect of the IOD on\neconomic growth by country. An important finding is that the effects are highly\nnonlinear/assymetric- while a large positive IOD has a large negative effect on economic\ngrowth, a negative IOD has virtually no effect on growth. The harmful effects of positive IODs\nare stronger for more teleconnected countries. Based on these findings, the paper projects that\nintensified IOD amplitude under climate change will have large economic costs compared to a\nno-climate-change counterfactual. These costs will be disproportionately borne by today’s\ndeveloping and emerging economies, as these happen to be the ones with greatest\nteleconnection. (Climate change is not projected to alter the patterns of teleconnection.)\nThe basic research design of this paper combines previous work on the effects of another mode\nof interannual global climate variability- ENSO (e.g., Hsiang et al., 2011) with previous work\non the effects of climate on economic growth (e.g. Burke et al., 2015). ENSO originates in sea\nsurface temperature anomalies in the eastern equatorial Pacific and its effects have been studied\nconsiderably more than those of the IOD. As such, this paper fills in important gap by studying\nthe economic effects of the IOD.\nWhile the paper takes on an important topic, more work needs to be done on the econometric\nestimation and climate change impact projections to ensure the robustness of the results. The\nmain estimating equation (Equation 2) includes a multiplicative interaction of an annual IOD\nindex value with a countryspecific measure of teleconnection. This interaction term is modeled\nas a quadratic function to account for nonlinearity in the country-specific effects of IOD.\nAdditional regressors include country-level annual mean surface air temperature and rainfall.\nThank you for the interest and positive comments on our attempt.\n1. Importantly, the IOD index values used in the estimating equation are constructed via a partial\nregression that removes the impact of ENSO (Equation 1). This approach seems opaque. The\nauthors should consider also running an alternative version of the estimating equation that\nincludes as regressors both the “IOD index x IOD teleconnection” interaction as well as an\nENSO index x ENSO teleconnection interaction. Such a specification will directly control for\nthe effect of ENSO and will allow for a transparent comparison between the effects of IOD and\nENSO. Both effects could then also be carried through to the climate change impact projections,\nallowing for a comparison of the respective impacts.\nWe thank the reviewer for the vision of such a unified model assessment. We build a model of\ncountry-specific IOD impact and country-specific ENSO impact as below:\n𝑙𝑜𝑔(𝑦 )−𝑙𝑜𝑔(𝑦 ) = ∑𝑛 {𝛼 𝜓 𝐼 +𝛼 (𝜓 𝐼 )2 +𝜸 𝝋 𝑬 +\n𝑖,𝑡 𝑖,𝑡−1 𝑙=0 1,𝑙 𝑖 𝑡−𝑙 2,𝑙 𝑖 𝑡−𝑙 𝟏,𝒍 𝒊 𝒕−𝒍\n𝜸 (𝝋 𝑬 )𝟐 +𝛽 𝑇 +𝛽 𝑇2 +𝜆 𝑃 +𝜆 𝑃2 }+𝜇 +𝜃 𝑡+𝜃 𝑡2 +\n𝟐,𝒍 𝒊 𝒕−𝒍 1,𝑙 𝑖,𝑡−𝑙 2,𝑙 𝑖,𝑡−𝑙 1,𝑙 𝑖,𝑡−𝑙 2,𝑙 𝑖,𝑡−𝑙 𝑖 1,𝑖 2,𝑖\n𝜀 ,\n𝑖,𝑡\nwhere ENSO teleconnection is obtained as in Liu et al. (2023). As can be seen from the\nregression coefficients shown in Table 1-R3, there is little significance of a country-specific\nENSO impact. Our work on ENSO impact (Liu et al. 2023) found that a teleconnection-\nweighted approach produces nonlinear (Alpha1) and nonlinear (Alpha2) coefficients that are\nmostly NOT statistically significant, suggesting that ENSO is mostly consequential in terms of\nits cascading effect and its common spill-over to the global economy. We have included this\ninformation, see Lines 388-390.\nHowever, the “mother” board of our current model and that in Liu et al 2023 is the same (based\non Burke et al. 2015) and in this sense because we have made the IOD independent from ENSO,\nour results here and in Liu et al. 2023 are by and large addable. Globally, impact from the IOD\nis about ~16.0% of that from ENSO. We have added discussion to this effect; see Lines 391-\n392.\nIOD x IOD teleconnection with ENSO x ENSO teleconnection\n-0.0016** -1.37×10-4\n \n1,0 (6.35×10-4) 2,0 (1.08×10-4)\n-5.19×10-4 2.02×10-4*\n \n1,1 (6.50×10-4) 2,1 (1.10×10-4)\n-9.43×10-4 -6.54×10-5\n \n1,2 (6.51×10-4) 2,2 (1.18×10-4)\n-5.60×10-4 -4.69×10-5\n \n1,3 (6.02×10-4) 2,3 (1.19×10-4)\nTable 1-R3 | Coefficients of ENSO x ENSO teleconnection interaction.\n2. The estimating equation includes country-level annual mean surface air temperature and\nrainfall, both modeled as quadratic. However, the estimated effects of these variables on\neconomic growth are never displayed, nor do they seem to be accounted for in the climate\nchange impact projections. Considering that prior work has shown temperature (Burke et al.,\n2015) and rainfall (Kotz et al., 2022) to affect economic growth, the authors should present the\nestimated effects of these variables and carry these forward when projecting the impacts of\nclimate change. This will allow for comparison between the impacts of different climatic\nvariables on future economic costs.\nAgain, this is a grand vision which we would aspire to eventually have. The impact on global\neconomy by changing temperature and rainfall has been studied substantially; that is why we\nhave not focused. Our model reveals a similar nonlinear simultaneous effect of temperature\nwith Burke et al. (2015 & 2018), of which both linear and quadratic coefficients are statistically\nsignificant 𝛽 =0.0107, standard error 0.0031, p<0.001; 𝛽 =-3.36×10-4, standard error 9.66×10-\n1 2\n5, p<0.001). The slight difference between our estimation and Burke et al (2015) is probably\ndue to: (1) we remove IOD’s signal in the annual temperature; (2) we exclude the year-fixed\neffect to avoid collinearity as IOD time-fixed effects could be correlated with time-specific\nfactors. Projecting the economic impact from changes in temperature and rainfall is not what\nwe are set out to obtain. We are focusing on economic impact of the IOD, and its future changes,\nassuming that the functional link in terms of Alpha1 and Alpha2 remains the same in the future.\nWe have added discussion on this; see Lines 367-371.\n3. The estimating equation includes contemporaneous and lagged effects of all variables.\nHowever, only the cumulated effect is ever plotted. It would be useful to also see the separated\neffects presented in a supplementary figure.\nThank you for the suggestion. We have now included this information (see Fig. 1-R3 below)\nas Supplementary Fig. 4. The call out is at Line 132.\nFig. 1-R3 | Same as Fig.1c. but for IOD-induced global GDP change at each lag year.\nOther Points\nFigure 2c plots the nonlinearity of economic impact (referred to as alpha) against economic\nteleconnection strength. It is unclear to me what exactly is alpha as I could not find it defined\nanywhere. The estimating equation (Equation 2) refers to alpha_1 and alpha_2, but not alpha\nwithout a subscript.\nSorry for the confusion. Alpha is completely different from Alpha1 and Alpha2. We have\nchange Alpha to Omega.\n2. There are multiple references to an acronym “SON”, but I could not find it defined anywhere.\nDefined. It is September, October, and November. See Line 42. Thanks.\nReferences\nThe three papers are cited.\nBurke, M., Hsiang, S.M. and Miguel, E., 2015. Global non-linear effect of temperature on\neconomic production. Nature, 527(7577), pp.235-239.\nHsiang, S.M., Meng, K.C. and Cane, M.A., 2011. Civil conflicts are associated with the global\nclimate. Nature, 476(7361), pp.438-441.\nKotz, M., Levermann, A. and Wenz, L., 2022. The effect of rainfall changes on economic\nproduction. Nature, 601(7892), pp.223-227."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I thank the authors of the article for their responses and the quality of the discussions. I am pleased\nto see that several of my suggestions have further improved the article. This article now should be\npublished in Nature Comm'.\nHere are my feedback points that may not necessarily require a response:\nYou mentioned that the NL model explains approximately 50% more variability than that explained by\nthe linear model; it would have been interesting to provide the adjusted R-squared and the sum of\nsquares explained elsewhere. Please correct in the text that this refers to the adjusted R-squared,\nwhich is still not the case, and that the choice of lags in the ARDL model was made using this\nindicator.\nIt is interesting to learn that the common shock of IOD is not meaningful and represents only 16% of\nthat of ENSO. Similarly, the indirect effect captured by an interaction term like T*IOD is not\nsignificant. That being said, perhaps the effect of climate change would be more visible by considering\ndeltaT...\nThank you for the new Figure 1, which now incorporates the USA and the UK; I believe this is\ninformative.\nLastly, thank you for conducting robustness tests, particularly by considering sub-periods. The fact\nthat the linear coefficient no longer emerges significantly (although it remains stable in magnitude)\nover the most recent period can indeed be explained by the reduction in sample size, and we may be\ncapturing less of the long-term average effect. However, wouldn't bootstrapping help compensate for\nthis? This aligns with the a priori absence of the effect of the interaction term T*IOD: the acceleration\nof global warming would not therefore modify the effect of IOD on economic growth.", "reviewer_2": "I thank the authors for their detailed responses to my comments and suggestions which have satisfied\na number of my concerns.\nOne thing still remain which I believe the authors could present better to communicate the robustness\nof their results.\n1. Independence of ENSO and IOD\nIn their response to my first point in the previous round of reviews, the authors conducted an\nadditional analysis which showed that while the effect of IOD on the economy is largely independent of\nthat of ENSO, some of the effect is shared and hence the independent effect of IOD is marginally\nsmaller (19%). I think it is in the interest of scientific integrity (and would as such actually support the\nmain manuscript) for the authors to report the additional figure they shared in their response letter in\nthe supplementary materials and to comment on this inter-dependence between ENSO and IOD\nimpacts in the main manuscript. The authors should not worry, as this will not undermine their results\nbut strengthen the reader's confidence in the manuscript and ability to infer the broader picture from\nthe results.\nI thank the authors for addressing all my other points in a very good manner! Best wishes to them for\ntheir future research. Max Kotz.", "reviewer_3": "Thank you for addressing my comments from the first review. I find the paper is considerably stronger\nnow.\nHowever, I continue to be concerned about the partial, incomplete nature of the projection exercise,\nwhich only projects the impacts of future IOD changes but not temperature and rainfall changes,\neven though these are estimated in the model (Equation 2).\nAs I understand, it is feasible to project the full impact of changes in climatic variables implied by the\nempirical model, though please correct me if I am wrong. The projected impacts of IOD changes will\nnot be altered when doing this; they will simply be put into larger perspective, and the projection\nexercise will be more transparent."}, "author_response": "Response to Reviewer #1\nI thank the authors of the article for their responses and the quality of the discussions. I am\npleased to see that several of my suggestions have further improved the article. This article\nnow should be published in Nature Comm'.\nThank you!\nHere are my feedback points that may not necessarily require a response:\nYou mentioned that the NL model explains approximately 50% more variability than that\nexplained by the linear model; it would have been interesting to provide the adjusted R-\nsquared and the sum of squares explained elsewhere. Please correct in the text that this\nrefers to the adjusted R-squared, which is still not the case, and that the choice of lags in\nthe ARDL model was made using this indicator. It is interesting to learn that the common\nshock of IOD is not meaningful and represents only 16% of that of ENSO. Similarly, the\nindirect effect captured by an interaction term like T*IOD is not significant. That being said,\nperhaps the effect of climate change would be more visible by considering delta T...\nAdjusted R-squared is mentioned. The “T” here means Delta T.\nThank you for the new Figure 1, which now incorporates the USA and the UK; I believe this\nis informative. Lastly, thank you for conducting robustness tests, particularly by considering\nsub-periods. The fact that the linear coefficient no longer emerges significantly (although it\nremains stable in magnitude) over the most recent period can indeed be explained by the\nreduction in sample size, and we may be capturing less of the long-term average effect.\nHowever, wouldn't bootstrapping help compensate for this? This aligns with the a priori\nabsence of the effect of the interaction term T*IOD: the acceleration of global warming\nwould not therefore modify the effect of IOD on economic growth.\nBootstrapping only helps compensate for this mildly.\nReviewer #1 (Remarks on code availability):\nVery small code written for Matlab. Not easy if you have not access to Matlab.\nRight, that is the code we developed.\nResponse to Reviewer #2\nI thank the authors for their detailed responses to my comments and suggestions which\nhave satisfied a number of my concerns.\nOne thing still remain which I believe the authors could present better to communicate the\nrobustness of their results.\n1. Independence of ENSO and IOD\nIn their response to my first point in the previous round of reviews, the authors conducted\nan additional analysis which showed that while the effect of IOD on the economy is largely\nindependent of that of ENSO, some of the effect is shared and hence the independent\neffect of IOD is marginally smaller (19%). I think it is in the interest of scientific integrity\n(and would as such actually support the main manuscript) for the authors to report the\nadditional figure they shared in their response letter in the supplementary materials and to\ncomment on this inter-dependence between ENSO and IOD impacts in the main\nmanuscript. The authors should not worry, as this will not undermine their results but\nstrengthen the reader's confidence in the manuscript and ability to infer the broader picture\nfrom the results. I thank the authors for addressing all my other points in a very good\nmanner! Best wishes to them for their future research. Max Kotz.\nThank you Dr Kotz, we have added an additional figure as you suggested.\nResponse to Reviewer #3\nThank you for addressing my comments from the first review. I find the paper is\nconsiderably stronger now. However, I continue to be concerned about the partial,\nincomplete nature of the projection exercise, which only projects the impacts of future IOD\nchanges but not temperature and rainfall changes, even though these are estimated in the\nmodel (Equation 2).\nAs I understand, it is feasible to project the full impact of changes in climatic variables\nimplied by the empirical model, though please correct me if I am wrong. The projected\nimpacts of IOD changes will not be altered when doing this; they will simply be put into\nlarger perspective, and the projection exercise will be more transparent.\nOur study aims to investigate the impact from changing IOD. We avoid repeating work of\nprevious studies."}]

Decision letter