Hurricane Ida’s blackout-heatwave compound risk in a changing climate

Domain: earth_science
Published: 2025-05-15
Editorial outcome: minor_revision

Abstract
The emerging tropical cyclone (TC)-blackout-heatwave compound risk under climate change is not well understood. In this study, we employ projections of TCs, sea level rise, and heatwaves, in conjunction with power system resilience modeling, to evaluate historical and future TC-blackout-heatwave compound risk in Louisiana, US. We find that the return period for a compound event comparable to Hurricane Ida (2021), with approximately 35 million customer hours of simultaneous power outage and heatwave exposure in Louisiana, is around 278 years in the historical climate of 1980–2005. Under the SSP5-8.5 emissions scenario, this return period is projected to decrease to 16.2 years by 2070–2100, a ~17 times reduction. Under the SSP2-4.5 scenario, it decreases to 23.1 years, representing a ~12 times reduction. Heatwave intensification is the primary driver of this increased risk, reducing the return period by approximately 5 times under SSP5-8.5 and 3 times under SSP2-4.5. Increased TC activity is the second driver, reducing the return period by 40% and 34% under the respective scenarios. These findings enhance our understanding of compound climate hazards and inform climate adaptation strategies.

Introduction
In August 2021, Hurricane Ida, a Category 4 storm, struck Louisiana with intense winds, heavy rainfall, and storm surges, resulting in widespread flooding and damage to the state’s infrastructure systems. Subsequent to the hurricane’s landfall, the state experienced a loss of ~200 million customer hours of electricity, affecting roughly 2.15 million customers for an average power outage duration of 96 h. Data from the United States (U.S.) Department of Energy reveals that Hurricane Ida caused the most extensive power outage in Louisiana’s history, largely surpassing Hurricane Katrina (Category 5; 2005) and Hurricane Laura (Category 4; 2020), which led to losses of approximately 140 million and 100 million customer hours of electricity, respectively1–3. Furthermore, a prolonged heatwave occurred in the aftermath of Hurricane Ida, particularly affecting households that lost power and thus had no air conditioning (3; 93% of Louisiana households used air conditioning in 2020 corresponding to ref. 4). Consequently, Louisiana residents experienced a total of 35 million customer hours of compound blackout-heatwave risk (with a heat index surpassing 37.8°C/100°F; 5). Customers exposed to the compound hazard endured an average of approximately 98 h of heatwave conditions5. Prolonged heat exposure can cause hospitalization and mortality risks6, especially among vulnerable populations7. Understanding how often Ida-like compound blackout-heatwave events may occur is critical for the development of risk mitigation strategies for populations vulnerable to hurricanes.

A “compound climate event” can result in large impacts due to the combination of climate drivers and hazards such as floods, wildfires, heatwaves, and droughts8. Traditional risk assessment methods typically consider one hazard at a time, potentially leading to an underestimation of risk, since the physical drivers causing extreme events may exhibit spatial and/or temporal dependencies and interact to exacerbate the overall impact. Hurricanes or more generally tropical cyclones (TCs), as drivers of extreme wind, rainfall, and storm surge, inherently lead to compound impacts on coastal regions9 and are responsible for nine of the ten largest power outages in the United States over a recent two-decade period10. While extreme winds are the primary source of damage to power systems, the presence of storm surges and heavy rainfall resulted in extensive flood inundation during Hurricane Ida, which caused additional physical damage and hindered power system resilience as repair crews were unable to access affected areas2. In addition, sea-level rise (SLR) may intensify coastal flood inundation by extending and prolonging the flood coverage, further exacerbating power system damage and delaying recovery operations.

Due to the seasonal peak of intense heat being ahead of that of major TCs, TC-heatwave compound events have so far been rare worldwide11. However, a previous study12 found that for Harris County (a portion of Houston situated on higher ground and affected by hurricane winds), Texas (TX), the percentage of residents experiencing 5-day TC-blackout-heatwave compound hazard conditions could increase by a factor as large as 23 over the course of the 21st century under the high emissions scenario RCP8.5. Also, in recent years, TC-heatwave compound events have happened in the Gulf Coast region. Hurricane Ida may represent the first hurricane landfall on the mainland United States associated with a long-lasting (i.e., multi-day), large-scale blackout-heatwave compound hazard (during Hurricane Laura the state-average heat index was also high but did not reach the threshold of 37.8°C/100°F). During Hurricane Ida and Laura, at least eleven and eight Louisianans, respectively, died of heat-related illnesses1,13. Investigating this emerging compound threat, enhanced by climate change, will contribute to our knowledge of and adaptation to compound climate hazards.

In this study, we integrate hazard projection and power system analysis to examine TC-blackout-heatwave compound risk for Louisiana over the 21st century under the combined influence of SLR and changes in heatwave and storm climatology. We highlight the change in the return period/recurrence interval of Ida-like compound events from the historical to future climates. We further quantify the relative importance of the change in various climatological variables (i.e., heat stress, sea level, storm frequency, storm intensity) in driving the changes in the compound risk.

Our framework is an extension of the previous study in ref. 12 to incorporate multiple hazards, including storm surge, rainfall, and SLR, in addition to wind and heatwave, to more comprehensively model TC-blackout-heatwave compound risk, for the entire State of Louisiana under various climate conditions. Specifically, we combine projections of heatwaves14, TC hazards (including wind, storm surge, and rainfall15), and SLR16–18, driven by CMIP6 GCMs14 under a moderate (Shared Socioeconomic Pathway 2–4.5) and a high (SSP5-8.5) emissions scenarios, to assess both a likely scenario and an upper bound of the risk. We generate a large number of compound hazard events (~30,000 stochastic sequences) based on the projections for historical (1980–2005) and future (2081–2100) climates to estimate how hazard probabilities may change over the 21st century (Methods). Then we utilize a physics-based power outage and restoration model for Louisiana to simulate wind/surge/rainfall-induced power system failure and recovery for each hazard event, to estimate the TC-blackout-heatwave compound risk. In doing so, we extend the existing wind-impact-only simulation method (county level12,19) to a wind-rainfall-surge coupled framework for power damage and recovery process modeling to consider a larger study area including coastal regions (state level; Methods). Considering the uncertainty surrounding the impact of climate change on the frequency of TCs making landfall along the Gulf Coast, we assume a constant TC frequency but also assess the sensitivity of the compound risk to TC frequency projection.

To focus on the impact of climate change, we assume that the power system, population distribution, and recovery plans in the study region will remain unchanged. However, we assume that the coastal levees will be elevated following design guidance, as this enhancement may be considered necessary to prevent the region from frequent inundation due to SLR (“Methods”).

Results
Historical cases
We first examine power outage simulations of the historical cases of Hurricanes Ida and Laura, which are the two major events over the last decade that caused widespread power disruptions in Louisiana1. Ida devastated the eastern half of Louisiana, which is more densely populated (including the city of New Orleans), whereas Laura grazed the western side. Ida destroyed 31,000 poles (reported by local utility company Entergy20) that carry lower-voltage distribution lines in the neighborhoods, twice as many as those in Hurricane Laura (14,000 poles) and Katrina (2005; 17,000 poles).

As shown in Fig. 1a and b, the model’s estimates for the overall impact of Hurricanes Ida and Laura on Louisiana compare relatively well with the observation. Hurricane Ida led to 47% (48% in simulation) of customers being out of power within the first 24 h, and it took ~10 days (11 days in simulation) for 90% of customers to restore power. Meanwhile, up to 60% of Louisiana customers were under heatwave conditions within 6 days after Hurricane Ida’s landfall. About 42% independent customers experienced compound power outage and heatwave hazards for at least one day after the hurricane’s landfall, and the percentage of customers experiencing the compound hazard surpassed 15% during a period of time (see Supplementary Fig. 1). Hurricane Laura led to 27% (29% in simulation) of customers being out of electricity initially and it took ~6 days (8 days in simulation) for 90% of customers to restore power (see Supplementary Fig. 2 for spatial distribution).

To measure the overall severity of the blackout associated with each TC, we compute the cumulative interruption hours throughout Louisiana (the total of all customers’ power outage duration), a commonly used metric in evaluating power system reliability21. The model simulation estimates in total 189 (156–242; ±3 standard deviations) million power interruption hours for Ida, which is consistent with the observed 206 million hours, and 110 (77–153) million power interruption hours for Laura, which compares relatively well with the observed 99 million interruption hours. As a comparison, Hurricane Katrina led to ~140 million power interruption hours. The model estimation for the spatial and temporal distribution of power outage also correlates well with observations (the average relative error is <10% between the modeled and observed county-level power outage), as illustrated in Fig. 1c for the power outages at 24 h, 5 days, and 8 days after landfall at the county level for Hurricane Ida.

Blackout and compound hazards
Integrating power outage and recovery modeling with projections of future TC, SLR, and heatwaves, we examine the TC-induced blackout-heatwave compound risk in Louisiana. We generate 10,000 simulations of synthetic hazard events for each of the historical (1980–2005) and future (2081–2100) SSP5-8.5 and SSP2-4.5 scenarios. Each stochastic simulation includes a continuous 20-year sequence of TC occurrences, along with the physical simulation of TC tracks, wind speeds, rainfall amounts, storm surge levels, and heatwaves. We track each customer’s exposure (i.e., duration) to blackout or compound blackout-heatwave hazard in the power outage and recovery modeling process for each synthetic hazard event. Then, we integrate the customer-level results to obtain state-level statistics and estimate the return periods (i.e., reciprocal of annual exceedance probability) of event total interruption hours for the historical and future climates. As demonstrated by the substantial shift of the return period curves (Fig. 2), the power outage risk will increase dramatically from the historical to the future climate. Specifically, the historical return period of a power outage of 206 million customer hours, as in Hurricane Ida, is 64 years (Table 1). The return period of a power outage like Hurricane Ida’s total outage is projected to be 35.8 years in the future under the SSP5-8.5 emissions scenario, compared to 38.2 years under SSP2-4.5. The total power outage for an event with Ida’s historical return period of 64 years is approximately 413 million customer hours under SSP5-8.5 and 265 million customer hours under SSP2-4.5. The return period of a TC-blackout-heatwave compound hazard of 35 million customer hours, as in Hurricane Ida, is 278 years in the historical climate. This return period is expected to decrease in the future climate to 16.2 years under SSP5-8.5 (~17× reduction) and 23.1 years under SSP2-4.5 (~12× reduction). A compound hazard event with Ida’s historical return period of 278 years is projected to cause about 435 million customer hours of impact under SSP5-8.5 and 138 million under SSP2-4.5. Such an event would induce an average blackout-heatwave compound hazard duration of approximately 8.8 days (SSP5-8.5) or 2.8 days (SSP2-4.5) for each of Louisiana’s 2.13 million customers.

The power outage level under SSP2-4.5 is similar to that under SSP5-8.5 at Ida’s return period or shorter, although the power outage level becomes higher in SSP5-8.5 at longer return periods (Fig. 2a), due to higher extreme TC hazards and SLR in SSP5-8.5. The difference between the two emissions scenarios is larger for the compound risk. Specifically, the return period of the compound hazard impact at Ida’s level under SSP5-8.5 is slightly shorter than that under SSP2-4.5, and the return period of larger compound impacts becomes dramatically shorter under SSP5 8.5 (Fig. 2b), due to combined effects of larger increases in extreme heatwaves, TC hazards, and the sea level under SSP5-8.5. For example, the return period of extremely severe compound events, such as those with triple the impact of Hurricane Ida (i.e., 100 million customer-hours of compound hazards), is expected to be 2.5 times longer under SSP2-4.5 compared to SSP5-8.5. However, for less severe events, such as those with a third of Ida’s impact (i.e., 10 million customer-hours of compound hazards), we do not find a statistically significant difference in the event return period/frequency between the two emissions scenarios. These findings suggest that the combined effects of global warming and increasing hurricane intensity amplify the risk of the most extreme compound events. Nonetheless, the moderate emissions scenario may still lead to a similar increase of frequency for events of Ida’s impact magnitude, compared to the high emissions scenario.

Spatial pattern of compound risk
To investigate the spatial distribution of the compound risk, we estimate the county-average compound hazard interruption days for each synthetic hazard event. Figure 3 shows the compound hazard interruption days with Ida’s historical return period of 278 years for each county in Louisiana in the historical and future climates. The coastal counties face a greater compound risk than inland counties for both historical and future climates (Fig. 3a–c). For example, the counties with an average compound hazard impact larger than 20 days in the future climates are mostly coastal counties. Coastal counties often face a greater compound risk since hurricane winds reach peak strength before the storm makes landfall, and storms weaken as they move inland, causing less damage to the inland power infrastructure. Moreover, the floods induced by storm surge and heavy rainfall can severely damage coastal power sectors. The flooding also hampers the recovery efforts of local contractors by submerging electrical components in water and obstructing local traffic and logistics with debris.

The general spatial disparities in compound risks are also substantial and will increase with climate change. For example, in the historical climate, the most impacted county may face on average a 1.8-day compound hazard with the return period of 278 years, and the least impacted county does not face any compound risk (Fig. 3a). In the future, under the SSP5-8.5 scenario, the county with the greatest impact may face an average of 12.7 days of compound hazard exposure for an event with the return period of 278 years. Under the SSP2-4.5 scenario, this average is 3.1 days. Conversely, the least impacted area is projected to experience only 1.1 days under SSP5-8.5 and 0.1 days under SSP2-4.5 (Fig. 3b–c). To assess the spatial disparity in the compound risk, we use the Gini coefficient22, a statistical measure of inequality commonly applied to income, wealth, or consumption distribution. The coefficient ranges from 0 (perfect equality, where all counties face the same average compound hazard duration) to 1 (full inequality, where only one county faces the hazard). In the historical climate, the Gini coefficient is approximately 0.312. However, under future scenarios, it increases to 0.632 for SSP5-8.5 and 0.411 for SSP2-4.5. These results suggest that intensifying hurricane hazards disproportionally impact coastal regions and vulnerable communities distant from substations, and thus, climate change is likely to exacerbate existing disparities and inequalities in TC-blackout-heatwave compound risks across Louisiana.

We also examine the distribution of compound hazard durations for residents in densely populated counties. Figure 3d illustrates the distribution of compound hazard interruption days for affected customers in Orleans Parish, for a 278-year compound event. In the historical climate, only 3% of affected customers may experience a compound hazard lasting more than 120 h (5 days). In the future, under the SSP5-8.5 scenario, nearly 60% of affected customers are projected to face a compound hazard exceeding 120 h, with nearly 30% experiencing durations longer than 240 h (10 days) and 10% encountering durations exceeding 360 h (15 days). Under SSP2-4.5, 20% of affected customers may face compound hazards exceeding 120 h, 3% may experience durations longer than 240 h, and no customers are expected to encounter durations beyond 360 hours. Hence, climate change not only increases the average compound hazard impact but also intensifies the tail risk that vulnerable residents may encounter, especially under the high emissions scenario.

Drivers of changes in compound risk
The change in the compound risk is driven by the change in three climate factors: 1) heatwaves (heat index) 2) TC climatology, and 3) the sea level. As we assume that TC frequency remains unchanged in the future, the changes in TC climatology include changes in TC characteristics, particularly intensity (which drives changes in wind, storm tide, and rainfall). To determine the relative effect of the changes in these factors, we estimate the changes in the compound risk due to changes in temperature, SLR, and TC climatology, respectively, by adjusting each variable to its future value or distribution and calculating the resulting return period of Ida’s compound hazard (i.e., 35 million customer hours of simultaneous power outage and heatwave impact), as shown in Fig. 4a.

As discussed earlier, when all climate change factors are considered, Ida’s return period is projected to decrease from 278 years in the historical climate to 16.2 years under SSP5-8.5 and 23.1 years under SSP2-4.5 in the future climate. Among these factors, changes in heatwaves are the largest contributors to this reduction. Due to heatwave changes alone, Ida’s return period would drop to 47.6 years under SSP5-8.5 and 84.3 years under SSP2-4.5, representing approximately a five-fold and three-fold decrease, respectively. This substantial impact is driven by dramatic increases in temperature and humidity: the annual number of heatwave days (above 37.8°C) is expected to rise eightfold from the historical climate to the future climate, based on the ensemble average of the six GCMs for the study region.

Changes in TC characteristics, especially intensity also contribute to the reduction, decreasing Ida’s return period to 167.8 years under SSP5-8.5 and 185.2 years under SSP2-4.5. By comparison, the impact of SLR is relatively small, reducing Ida’s return period to 251.2 years under SSP5-8.5 and 263.3 years under SSP2-4.5. SLR appears to have a relatively low impact because we assume the levees along the coast will be elevated. Also, the impact of SLR is limited to coastal regions, and it is averaged out when the compound hazard impact is calculated for the entire state. The contribution of the various climatological drivers to future compound risk is consistent across the two different emission scenarios.

Considering that the projection of TC frequency is subject to considerable uncertainty23,24, we assumed a constant TC frequency in above analyses. Here, we investigate the sensitivity of the estimated compound risk to the projection of TC frequency change. In one scenario, we apply the TC frequency in the applied TC model15, which projects relatively high increases in TC frequency in the future climates. Under SSP5-8.5, Ida’s return period decreases to 7.9 years, and under SSP2-4.5, it decreases to 15.2 years, compared to 16.2 years (SSP5-8.5) and 23.1 years (SSP2-4.5) when accounting for all climate change factors except TC frequency changes. In another scenario, we consider a 30% decrease in TC frequency, the lower bound of TC frequency projections ensembled in ref. 24. In this case, Ida’s return period becomes 28.4 years under SSP5-8.5 and 40.6 years under SSP2-4.5. While longer than the projections with increased or constant TC frequency, these return periods represent a similarly dramatic decrease from 278 years in the historical climate. This sensitivity test indicates the relatively small impact of TC frequency change compared to the combined effects of other climate change factors.

Methods
Synthetic TC, storm surge, tide, and rainfall modeling
We use the synthetic TC hazard dataset generated in ref. 15 for the North Atlantic basin and select the TC tracks passing within 300 km of Louisiana, with a maximum wind speed of at least 22 m/s. The dataset contains synthetic TC tracks generated with the statistical-deterministic TC model34, which has been applied to TC hazard assessment9,35,36. The synthetic TC tracks for the historical period (between 1980 and 2005) were generated based on the National Centers for Environmental Prediction (NCEP) reanalysis. The dataset also contains bias-corrected and weighted-average climate projections of TCs for the future period (2070 to 2100) under Shared Socioeconomic Pathway (SSP) emissions scenarios, SSP5 8.5 and SSP2 4.5. The dataset was generated based on projections from six CIMP6 models (selected given data availability and following previous studies): Canadian Earth System Model (CanESM5), Centre National de Recherches Météorologiques Climate Model (CNRM-CM6-1), UK Earth System Model (UKESM1-0-LL), EC-Earth3, IPSL-CM6A-LR (Institut Pierre-Simon Laplace Climate Model), and Model for Interdisciplinary Research on Climate (MIROC6). The TC storm tides were modeled in ref. 15 using the Advanced Circulation (ADCIRC) hydrodynamic model37,38. We extract peak storm tides at nodes (~1 km resolution) along the coastline of Louisiana for each TC and match these to the county level. The rain fields were simulated in ref. 15 for each synthetic TC using the physics-based Tropical Cyclone Rainfall (TCR) model39. We apply area-averaged TCR estimates at the county level, and we employ the maximum 24 h rainfall accumulation from each storm event, since the 24-hour storm duration is often utilized for rainfall risk assessment39,40.

To perform sequential risk analysis, we generate 10,000 stochastic samples of storm sequences for each of the historical and future climate conditions. These samples were derived by sampling storms (according to a Poisson process with a rate as the TC annual frequency) and their associated hazards from the TC hazard datasets described above. Each stochastic sequence consists of 20 consecutive years of TC activity. For the primary analysis in this study, we maintain a constant TC frequency in the future climate.

For the sensitivity analysis, we consider the increased TC frequency projected by the statistical-deterministic TC model in ref. 15 and the decreased TC frequency by up to 30% projected by a range of climate models ensembled in ref. 24.

For each sampled storm, we generate the spatial-temporal wind field, employing the classical Holland wind profile41 and accounting for the effects of surface friction and large-scale background wind following ref. 42, and converting one-minute mean winds to 3 s wind gusts using gust factors43. We estimate the coastal flood area by comparing the height of peak storm tide (if over levee height) to the ground surface elevations specified by the USGS 30 m DEM44, assuming that areas would be inundated when the storm tide exceeds the ground elevation.

In Louisiana, the actual seawall heights vary largely along the coastline, ranging from 2 to 5 meters and often changing over short distances. Due to the difficulty in acquiring the precise data, our simulations do not incorporate partially available measurements45–47. We assigned the current seawall level based on estimated 100-year flood level (estimated in ref. 15) for each coastal county in Louisiana, according to the typical design guidance. For example, the 100-year flood level for New Orleans is approximately 3.4 meters above the North American Vertical Datum of 1988 (NAVD88)9. This approximation introduces a degree of inaccuracy into our flood modeling. Acknowledging this limitation, we subsequently focused on binary flood data—whether a flood occurs or not—when developing our power system damage and recovery models. We observe that the occurrence of flooding is a critical factor that largely hinders the restoration efforts of the power system in coastal counties. However, the inundation depth of the flooding appear to have a less substantial impact. When compared to the areas affected by the TC’s wind and rainfall, the storm surge flooded regions are generally smaller. Therefore, the majority of the structural damage to the power system may not be caused by coastal flooding.

The future coastal levee plan is uncertain. In the future climate simulations, we assume the coastal levee will be elevated by ~ 2 m, based on the historical 100-year return level plus one percentile SLR. This design strategy is commonly used by governmental agencies to plan the seawall height, and it is within the framework proposed by the U.S. Army Corps of Engineers for the New Orleans Region, Lafayette, and Lake Charles47. A sensitivity test was performed on future compound risks given different elevations of the coastal levee from 0–3 meters above the current level. If the levee were not elevated, the surge impact on the compound risk would be substantially higher than estimated in this study. On the other hand, when the levee is elevated by higher than 2 m, the estimated compound risk is not sensitive to the variation of the assumed levee increment up to the test case of 3 meters (see Supplementary Fig. 3). The generated wind, rainfall, and coastal flood conditions from each sampled storm drive the power grid outage and recovery analysis.

Heatwave projections
Following ref. 11, the daily HI is determined as a function of daily maximum near-surface (2 m) air temperature, daily mean specific humidity, and daily mean surface pressure. To maintain consistency with the TC simulation, we obtain these data for Louisiana from the NCEP reanalysis and the six GCMs stated above during and after landfall for each sampled synthetic storm (each synthetic storm is associated with a climatological time of occurrence and development). The future HI projected by the GCM is bias-corrected12 by adding the difference between the NCEP reanalysis and the GCM-estimated historical HI. According to the historical analysis in ref. 11, the HI will drop upon TC landfall and will recover to the ambient average within around ten days. To account for this dependence between TCs and heatwaves, we add the composite of the impact of TC passage to the meteorological variables used to calculate the HI, where the composite impact is estimated based on historical data (Fig. 3a in ref. 11).

Sea level rise projections
We employ sea-level projections produced by the Intergovernmental Panel on Climate Change Sixth Assessment Report16,18 (AR6) using the Framework for Assessing Changes To Sea-level17 (FACTS). Localized probabilistic SLR projections under the SSP5-8.5 and SSP2-4.5 emission scenarios with “medium confidence” are incorporated in this analysis (there are two confidence levels in the datasets, which are low and medium levels). The local sea level projection takes into account ground uplift or subsidence, oceanographic effects, and spatially variable responses of the geoid and the lithosphere to shrinking land ice. The projection of SLR was developed for tide-gauge stations. For each TC sequence realization, we first sample a near-by SLR time series (a realization) from the projection for each county. Then, we add the SLR to the storm tide level at each time point for each county.

Power outage and recovery model
We apply a physics-based power system model, which explicitly simulates component-level damage to predict the total power outage, accounting for the effects of future evolving factors, e.g., climate change, infrastructure upgrade, and utility maintenance. The physics-driven modeling of the power system allows us to better understand the impact of climate change and effectiveness of risk mitigation measures compared to if we used purely data-driven models48,49. Specifically, we extend the power grid outage and recovery model developed by refs. 12,19 to simulate TC impact on the electric power system in Louisiana (see the methodology diagram in Supplementary Fig. 4, power topology in Louisiana in Supplementary Figs. 5, 6, and electric utility service areas in Louisiana in Louisiana Public Service Commission50). The power grid failure model first applies probabilistic fragility functions to estimate the damage states of five main vulnerable component types of the power network: transmission substations, transmission lines, distribution nodes, distribution lines, and local distribution circuits. Component failures alter the power grid topology and may separate the power grid into disconnected sub-grids. A direct-current (DC) flow simulation is then performed to capture the power availability in each sub-grid (similar to approaches in refs. 51–54). The power system is open and connects with systems outside the study area via transmission lines; the performance of the power grid outside the study area is assumed to be under normal operation.

The fragility curves in refs. 12,19 only considered the wind damage. Here we extend the fragility functions to consider the effects of coastal floods and rainfall. For example, the probability of failure of a substation given specific wind, rainfall, and coastal flood levels is estimated based on a log-normal fragility function as Eq. (1):

P(D≥d_i | H=h=w+α*r+β*f) = 1/(h*σ_i*sqrt(2*π)) * integral_0^h exp(-(ln(x)-μ_i)^2 / (2*σ_i^2)) dx

where hazard (H/h) is considered as a linear combination of wind speed (w), rainfall amount (r), and flood condition (f, flooded or not; Boolean variable) with two parameters α and β. With the shape (σ_i) and location (μ_i) parameters, the log-normal distribution describes the probability of potential damage (D) in each of four states (d_i), i.e., i={low, moderate, severe, complete} damage. Fragility function refers to the latent distribution of a component’s ability to withstand outer forces (hazard). Some components may not withstand any force at all, while others can withstand very large outer force. Given a certain outer force, the probability of damage to the component is equal to the integral of fragility from 0 to that force level, i.e., the probability that the strength of the component is lower than outer force. The fragility functions for other components (support structures, distribution nodes, poles, conductors, and circuits) are similarly modeled with exponential, logistic, or uniform distributions. These fragility functions are similar to those in refs. 12,19 except that the effects of rainfall and flood are incorporated. The parameters are estimated by the Markov chain Monte Carlo (MCMC) method to minimize the mean squared error between simulated and observed county-level power outages1 under Hurricanes Laura and Ida with equal weight.

The recovery model, developed based on emergency response plans and operational data, applies estimated recovery resources based on a priority-oriented strategy to repair damaged transmission substations, transmission lines, and critical facilities vital to public safety, health, and welfare before local distribution networks12,19. Debris should be removed before utilities become able to reinstate the power system. This debris-cleaning time is sampled from a uniform distribution between 48 and 72 h (estimated from utility reports)2. We also account for that, without structural failure of the distribution system, residents may turn on the main power switch themselves 24–48 h after being flooded55.

There is a chance that TCs will make landfall in sequence, and the second TC comes before the damage caused by the first TC is fully recovered56. We account for this temporal compounding effect in our power system outage and recovery analysis. For each sampled sequential hazard time series, the initial state of the power system when a TC arrives is set based on the condition of the restoration state from the previous TC. If the power system is indeed not fully recovered from the previous TC impact, the emergency response plans following the second TC are also adjusted considering the recovery process for the first TC. Specifically, the response plans will re-evaluate and prioritize the restoration tasks and redirect the repair efforts based on this updated priority list.

The power grid outage and recovery models were calibrated (to determine the model parameters) for the study area using observed power outage data for Hurricane Ida and Laura using simulated wind and observed rainfall57 and flood58. The same wind field modeling method applied to the synthetic storms is used for these two historical storms with storm characteristics (i.e., track, intensity, and size) taken from the extended best track data59.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In their manuscript “Hurricane Ida’s blackout-heatwave compound hazard risk in a changing climate”, the authors present a\nrisk study on the compound hazard of hurricanes and heatwaves in Louisiana. They particularly focus on failure of the power\ngrid occurring simultaneously with a post-hurricane heatwave. I find the study very interesting and inspirational.\nMethodologically speaking the study is well executed, with particularly noting the inclusion of all three hazards in their\nhazard modeling. I therefore recommend minor revisions; please find my comments below.\n1. Reading a sentence can be hard at times due to the use of brackets, as they are both used in the referencing style, to\ndifferentiate between SSP245 and SSP585 and to add extra information. I particularly struggled getting through certain\nsections of the Results section. I encourage the authors to critically revise the use of brackets and re-write or exclude them\nwherever possible.\n2. Following my previous point, there are also a few runoff sentences that were hard for me to follow, and for which I would\nrecommend to split them up. I noted down the following cases: lines 167 – 169, lines 250 – 254, and lines 308- 311.\n3. The manuscript contains several mentions of the word “significant” which, to me, would imply that a significance testing\nwas done. If this is the case, could you please also include the p-value; if this is not the case, I recommend to use different\nwording.\n4. Please add a legend to Figures 1 and 2, similarly to how this was done in Figure 3d.\n5. I don’t fully understand how the stochastic simulations were done. The manuscript states that “each stochastic simulation\nincludes a continuous 20-year sequence of TC occurrences”. This 20-year period does line up with the future time period\n(which is also 20 years), so I guess you start the simulation in 2081 and then let it run for 20 years. But the historical period\ncovers 26 years, so what 20-year period did you use in this case?\n6. Are you using an intensity (wind/rain/surge) threshold for the synthetic tracks? I could not find any notion of this in the\nMethods-section but I might have overlooked it.\n7. Line 171 – 173: Could you please quantify this statement?\n8. Line 216: this sentence only mentions the percentage of customers facing a >120-hr compound hazard, but the following\nsentences also mention other time periods with associated percentages for the future climate scenarios. Could you please\nalso state the associated percentages for the other time periods for the historical period?\n9. Line 80: could you please (also) give the temperature in Celsius for those not familiar with Fahrenheit?\n10. Lines 376, 424 and 440 contain a reference error.", "reviewer_2": "Lin and co-authors present analysis of the return period of an event that combines a damaging hurricane passage, resulting\nin electrical outages, with a heatwave, as a function of climate state, in southern Louisiana. They compare an historical\nperiod (1980-2005) and a future period (2081-2100) under two climate forcing scenarios, and show that in either scenario,\nthere is a sharp reduction in the return period of given level of person-hours of exposure to heatwave under power outage.\nThe dominant cause of this reduced return period is increased frequency of heatwaves, but sea level rise and hurricane\nintensity increases also contribute.\nThe manuscript is clearly written, and the results are interesting, but I believe it could be improved in number of areas. There\nare a couple of areas where the clarity could be better, and there are some questions that are raised in the reader's mind, but\nnot answered.\n1) In lines 167 and following, the \"higher\" and \"lower\" return periods are discussed. I think this would be clearer if the\nlanguage were changed to \"longer\" and \"shorter\" return periods.\n2) In lines 198 and following, there is an interesting discussion of an increase in the spatial disparity of impacts, however the\ncause of this change is not identified. Can the authors clarify if this is this due to a change in the spatial structure of hurricane\nimpacts? Of heat? Storm surge?\n3) Only six models from the CMIP6 ensemble are used, but no explanation is given for the selection criteria. Were they\nmodels that had the best representations of hurricane storm tracks? A representative range of climate sensitivity? Some\nother good characteristic?\n4) The historical period chosen is quite short compared to the period for which reasonably good data exists. It would be\nhelpful to present results for two historical periods, say 1961-1990 and 1991-2020, and compare observed changes to\nchanges in the CMIP6 models' historical periods. This would help the reader assess the reliability of the models for this\npurpose in this study area.\n(Remarks on code availability)\nThe code repository appears to include only code to generate the figures from the pre-prepared .mat files. It would be helpful\nto have the code to generate the analyses from the GCM data as well, but perhaps this was already provided in the\nreferences cited? I don't know what Nature's standards are for this.", "reviewer_3": "Recommendation: Accept with minor revisions\nThe work is novel and useful. The scientific method is sound, although it does rely heavily on previous work (especially ref.\n14, Xi, Lin, and Gori (2023) and also Ouyang and Dueñas-Osorio (2014)) which I was unable to review in significant detail\ngiven my time constraints. All previous work cited was peer reviewed in well regarded journals however so I assume the\nmethods were sound. Indeed, my cursory readings of the supporting work indicates this.\nThere are a handful of bad references in the manuscript ((Error! Reference source not found.)) These should have been\ncorrected before submission. I think I was able to puzzle out which references were missing.\nMajor Comments\nOverall Evaluation:\nScientific merit: good\nClarity: good\nImpact of work: good\nBig Picture Questions and Issues\nHow often do heatwaves follow tropical storms, generally? My limited understanding is that temperatures after hurricanes\nand tropical storms are typically cooler than normal given the cloud cover, rainfall, and moisture retention. So, some\nexplanation of the synoptic conditions which could give rise to a heatwave following a hurricane or tropical storm would be\nhelpful for setting the scene. I am not a meteorologist. Can a high pressure system build after the storm leading to clear skies\nand warming? Do conditions depend on the subtropical ridge in late summer? Can the storm move out of the region quickly\nleading to a spike in temperatures? I'd like to see a meteorological/climatological explanation of how common the\nphenomenon is.\nUse of SSP5-8.5 over SSP3-7.0 as the high emissions scenario needs some justification, given the trend in the literature\naway from SSP5-8.5 towards SSP3-7.0. I don't have a problem with using SSP5-8.5, but there should be a sentence or two\nin the manuscript explaining and defending this choice. I presume the authors used SSP5-8.5 because previous work used\n4.5 and 8.5 as intermediate and high emissions scenarios?\nA discussion of the energy landscape in Louisiana would also be helpful, e.g., a discussion of the roles of investor-owned\nutilities Entergy and Cleco, the Midcontinent Independent System Operator (MISO), the SERC Regional Corporation;\nLouisiana Public Service Commission (LPSC). This could potentially be put in a supplementary section if current word limit\nconstraints are binding, although I think a paragraph within the main body of the text would be useful for readers who have\nsome familiarity with electricity systems, but not with the Louisiana grid. On a similar note, I don't understand why only the\nsouthern part of the state was impacted by power outages during tropical storms Ida and Laura given the connectivity of the\nnetwork. Some explanation would be helpful.\nThe authors should be clearer upfront about their modeling flow. A figure could be helpful showing the data sources and\nmodels used at each step in the chain of analyses. For example, I asked in my comments below whether the synthetic\nstorms tracks are from an academic request to WindRiskTech, for example. Such information, and information on the heat\nwave projections, the grid modeling, would all be useful for a reader to have in mind early in reading the paper.\nI have a large number of specific comments. They are relatively minor comments but I think addressing them would\nstrengthen the work.\nSpecific Comments:\nABSTRACT\nLine 27\n\"by a factor of ~17×(10x) to 16.2 (28.4) years\" <- one multiplication sign is the times symbol ×, the other is the letter x. I\nsuggest choosing one or the other for both instances.\nLines 28-31\n\"The significant increase in risk can be primarily attributed to 29 projected escalations in heatwaves, which result in an\napproximate 5(2)-fold decrease in 30 compound hazard return period, and in TC activity, which cause an estimated 2(1)-fold decrease\n31 in the return period.\"\n<- The numbers don't match the statement for the SSP2 4.5 scenario. You say the decrease in return period (10-fold\ndecrease) can ~primarily~ be attributed to heatwaves (2-fold decrease) and TC activity (1-fold decrease).\nFor the SSP5 8.5 scenario the statement makes more sense: the decrease in return period (17-fold) can primarily be\nattributed to heatwaves (5-fold decrease) and TC activity (2-fold decrease).\nMaybe it would be worth noting what other factors account for the remainder of the changes in return period. E.g., after the\nsentence on line 31 ending in \"in the return period.\" you could add a line: \"Other factors include ...\".\n(Coming back to this comment having read more of the paper, SLR appears to be the other factor considered.)\nINTRODUCTION\nLine 50\n\"Louisiana residents experienced a total of 35 million hours of compound blackout-heatwave hazard risk\" <- person hours?\nLine 51\n\"with a heat index surpassing 37.8°C/100°F, in accordance with Louisiana's heat advisory criteria\" <- I believe this heat\nindex condition is a national condition, not unique to Louisiana, though I may be mistaken.\nLines 55-56\n\"Understanding how often Ida-like compound blackout-heatwave events may occur is critical for the development of coastal\nrisk mitigation strategies.\"\n<- this is only relevant for coastal communities facing TCs, not for coastal communities in general. Possible edit:\n\"Understanding how often Ida-like compound blackout-heatwave events may occur is critical for the development of risk\nmitigation strategies for populations vulnerable to tropical cyclones.\"\nLines 64-65\n\"and are responsible for nine of the ten largest power 65 outages in the United States over the past two decades\" <- the\nreference is from 2018. There have been at least two major non-TC power outage events since 2018: 2019 California\nwildfires 2019 and 2021 Texas winter storm. Could soften the claim: \"and are responsible for nine of the ten largest power\n65 outages in the United States over a recent two-decade period\". That's the best I can think of.\nLines 75-76\n\"the TC-blackout-heatwave compound hazard risk would increase by a factor as large as 23 over the course of 21st century\nunder the high emissions scenario RCP8.5.\" <- what does this mean? the \"risk will increase by a factor of 23?\"\nIn the paper they say: \"The expected percentage of Harris residents experiencing at least one longer-than-5-day TC-\nblackout-heatwave compound hazard in a 20-year period could increase dramatically by a factor of 23 (from 0.8% to 18.2%)\nover the 21st century.\"\nCould rephrase your sentence:\n\"However, a previous study (11) found that for Harris County, TX (a portion of Houston situated on higher ground and\naffected by hurricane winds), the percentage of residents experiencing specified 5-day TC-blackout-heatwave compound\nhazard conditions could increase by a factor as large as 23 over the course of 21st century under the high emissions\nscenario RCP8.5.\"\nThere may be a better way of wording this.\nLines 76-77\n\"In recent years, TC-heatwave compound hazards have happened in the Gulf Coast region.\" <- \"In recent years, TC-\nheatwave compound events have happened in the Gulf Coast region.\"\nLines 82-83\n\"Investigating this emerging compound threat, possibly enhanced by climate change, will contribute to our knowledge of and\nadaptation to compound climate hazards.\" <- can drop the word \"possibly\". You've just referenced studies to show the effect;\nyour own study shows the effect.\nLine 99\n\"We generate a large number of compound hazard events\" <- how many?\nLine 104\n\"We extends the existing wind-impact-only simulation method\" <- \"We extend the existing wind-impact-only simulation\nmethod\"\n[Jumping to the Materials and Methods before the Results] [2.5/10.5 pages complete]\nRESULTS\nHISTORICAL CASES\nLines 119-120\n\"Ida devastated the eastern half of Louisiana, which is more densely populated (including the city of New Orleans), whereas\nLaura grazed the western side\"\n<-\n\"Ida devastated the eastern half of Louisiana, which is more densely populated (including the city of New Orleans), whereas\nLaura grazed the western side. The eastern half of Louisiana is largely served by the investor owned utility (IOU) Entergy\nwhile some of the western half is served by the IOU Cleco who control generation, transmission and distribution in the\nregion.\"\nAnd I'd add a supplementary half page or page going over the broad outline of the network. Discuss the market shares of\nEntergy and Cleco, their generation fleets, transmission networks, distribution systems; MISO; SERC Regional Corporation;\nLouisiana Public Service Commission (LPSC)\nLine 124\n\"compare relatively well with the observation\" <- should this be \"compare relatively well with the simulation\" ?\nLines 123-132\n\"As shown in Fig. 1a and b, the model’s estimates for the overall impact of Hurricanes Ida and Laura on Louisiana compare\nrelatively well with the observation. Hurricane Ida led to 47% (48% in simulation) of customers being out of power within the\nfirst 24 hours and it took ~10 days (11 days in simulation) for 90% of customers to restore power. Meanwhile, up to 60% of\nLouisiana residents were under heatwave conditions within 6 days after Hurricane Ida’s landfall. On average, 42% of\ncustomers experienced compound power outage-heatwave hazards for at least a day after the hurricane's landfall, based on\nthe overlap of county-level power outage and heat index data. Hurricane Laura led to 27% (29% in simulation) of customers\nbeing out of electricity initially and it took ~6 days (8 days in simulation) for 90% of customers to restore power. To measure\nthe overall severity of the blackout associated with each TC, we compute the cumulative interruption hours of customers\nthroughout Louisiana (the total of all customers’ power outage duration), a commonly used metric in evaluating power\nsystem reliability (20). The model estimates in total 189 (156-242; ±3(cid:1)) million power interruption hours for Ida, which is\nconsistent with the observed 206 million hours, and 110 (77-153) million power interruption hours for Laura, which compares\nrelatively well with the observed 99 million interruption hours. As a comparison, Hurricane Katrina led to ~140 million power\ninterruption hours. The model estimation for the spatial and temporal distribution of power outage also correlates well with\nobservations (the average relative error is <10% between the modeled and observed county-level power outage), as\nillustrated in Fig. 1c for the peak power outages within 24 hours and 5 days after landfall power outages at the county level\nfor Hurricane Ida.\"\n<- Paragraph could be broken in two. There is a lot of information here. I realize Fig 1a and 1b are meant to convey this\ninformation, but I feel like it could be done better.\n\"Meanwhile, up to 60% of Louisiana residents were under heatwave conditions within 6 days after Hurricane Ida’s landfall.\"\n<- This isn't really the relevant metric. What percent of Louisiana residents within the affected power outage region were\nunder heatwave conditions?\n\"On average, 42% of customers experienced compound power outage-heatwave hazards for at least a day after the\nhurricane's landfall, based on the overlap of county-level power outage and heat index data. Hurricane Laura led to 27%\n(29% in simulation) of customers being out of electricity initially and it took ~6 days (8 days in simulation) for 90% of\ncustomers to restore power.\"\n<- *** Could you create a bar chart / histogram showing the number of people-days with outage + heatwave conditions?\n\"To measure the overall severity of the blackout associated with each TC, we compute the cumulative interruption hours of\ncustomers throughout Louisiana (the total of all customers’ power outage duration), a commonly used metric in evaluating\npower system reliability (20). The model estimates in total 189 (156-242; ±3(cid:1)) million power interruption hours for Ida, which\nis consistent with the observed 206 million hours, and 110 (77-153) million power interruption hours for Laura, which compares\nrelatively well with the observed 99 million interruption hours. As a comparison, Hurricane Katrina led to ~140 million power\ninterruption hours. The model estimation for the spatial and temporal distribution of power outage also correlates well with\nobservations (the average relative error is <10% between the modeled and observed county-level power outage), as\nillustrated in Fig. 1c for the peak power outages within 24 hours and 5 days after landfall power outages at the county level\nfor Hurricane Ida.\"\n<- A reference to the modeling framework would be useful here, although I suppose that belongs in Materials and Methods.\nStill, I think I'd include a reference to Refs 11 and/or 18, whichever is the more recent or canonical reference + a brief phrase\nor sentence indicating what kind of model it is. As the paragraph stands, as a reader, I am left curious about how exactly\nanyone could do what you've done here. Even if you just say that you used \"a transmission and distribution modeling\nframework (11, 18)\" that would help, I think.\nSomething like:\nLines 134-135\n\"The model estimates in total 189 (156-242; ±3(cid:1)) million power interruption hours...\" <- \"The transmission and distribution\nmodel (10, 18) estimates in total 189 (156-242; ±3(cid:1)) million power interruption hours...\" Or if it's a generation, transmission,\nand distribution model, you could say that. Just a little more detail for the reader to understand what's going on.\nBLACKOUT AND COMPOUND HAZARDS\nLine 162\n\"~17x (10x)\" <- do you want the letter x or the times symbol? You used both in your abstract. Either I think is fine, just be\nconsistent.\nSPATIAL PATTERN OF COMPOUND HAZARD RISK (185-222) [38]\nLines 213-222 <- how realistic do you think it is that 9% of customers will experience power outages > 15 days (under SSP5\n8.5)? Is there precedent for power outages of this duration after tropical storms? I'm wondering if there are different factors\nwhich come into play when power outages reach a certain length of time, maybe additional resources are mobilized, etc.\nDRIVERS OF CHANGES IN COMPOUND HAZARD RISK (224-259) [36]\nSeems fine, see comment on Fig 4b.\nDISCUSSION (262-321) [60]\nLines 270-272\n\"When examining the impact of various climate scenarios, such as the high emission scenario SSP5 8.5 and the moderate\nemission scenario SSP2 4.5, it appears that the risk associated with Ida- or smaller-scale compound hazard events may not\nexhibit substantial difference.\"\n<- There seems to be a significant difference between scenarios shown in Fig 2b. What am I missing here? Thinking this\nthrough. You estimate the current return period of an Ida-like power outage + compound heat event to be 278 years, with 35\nmillion customer hours of simultaneous power outage and heatwave exposure in Louisiana... So the horizontal dashed line\ncaptures the magnitude of the Ida-like compound event. The intersection with the historical conditions occurs at 278 years.\nThe intersections with SSP2 4.5 and SSP5 8.5 are at 28.4 and 16.2 years, respectively. I suppose that's not so great a\ndifference. If I'm reading this correctly, then I think you're fine. If I'm reading this incorrectly you may with to clarify your text.\nLines 272-274\n\"This result indicates that utility companies urgently need to prepare for the compound events to prevent major impacts.\"\n<- maybe this should be rephrased:\n\"This result indicates that utility companies urgently need to prepare for the compound events to prevent major impacts,\nregardless of whether a moderate or high emissions projection is considered.\"\nLines 279-288\nThis is important. Well written. Although I'd consider adding a policy prescription:\n\"However, backup generators and solar panels may be cost-prohibitive for many middle and low-income communities,\nlimiting their effectiveness in reducing heat stress.\"\n<- indicating the need for proactive policy measures such as subsidies or tax breaks for such interventions.\nLines 289-299\nAlso important and well written. You've outlined some policies that will reduce the potential health impacts of extreme heat\ngenerally speaking, but can you think of specific policies that could be implemented in the aftermath of tropical cyclone\nevents? I could imagine it being challenging to manage cooling centers following a hurricane. On the other hand, investing\nin cooling equipment and back up generators at relief centers and designated shelters could provide significant returns on\ninvestment.\nAlso, given that you have a sense of the relative importance of your three factors (lines 224-225), heat index, TC climatology,\nand SLR, could this feed into your discussion of prioritizing interventions to reduce the risk and impacts of this compound\nhazard? I suppose to do this we'd also need a sense of the relative costs of such interventions.\nLines 300-321\nI don't know enough about current emergency management efforts at the state and federal level, but it could be worth\nincluding at least a couple of sentences about how your research could feed into state offices of emergency services and\nFEMA, say. I don't know enough to suggest the sentences, but some note about how the work could be integrated into\nexisting planning frameworks might be appropriate.\nMATERIALS AND METHODS\nSYNTHETIC TCS, STORM SURGE, TIDE AND RAINFALL MODELING\nLine 325\n\"We use the synthetic TC hazard dataset generated in ref. 14 for the North Atlantic basin and select the TC tracks passing\nwithin 200 km of Louisiana.\"\n<- I'd prefer something like: \"We use the Xi et al. (2023) synthetic TC hazard dataset (14, henceforth referred to as the Xi\n2023 dataset) for the North Atlantic basin and select the TC tracks passing within 200 km of Louisiana.\"\nIt's easier for the reader if you state the name of the dataset. If it doesn't have a name, you might consider \"Xi 2023\". I think\nthat's informative and concise, but you could consider alternatives, e.g., XLG 2023, something easier for the reader than \"ref.\n14\". Just a suggestion; I find it frustrating to have to bounce back and forth to the references when reading journal articles.\n[[Having just reviewed the Nature guidelines, I see this is not the recommended practice. That's unfortunate.]]\nMore important comment. The question that came to my mind when reading this sentence -- actually, when reading the\nabstract -- was: what synthetic storm tracks are they using? Proprietary WindRiskTech tracks or open source STORM\ntracks? Looks like you're using the Emmanuel tracks? That may be worth stating explicitly. I can't remember how many\nmodels WindRiskTech allows for academic research. Are you using a free subset? If so I think this should be stated. It would\nbe useful for others trying to replicate the work. Also, I haven't read through your code and data, but are you publishing the\nstorm tracks? I'm assuming if they're WindRiskTech tracks you are not able to publish them which limits reproducibility,\nunless other researchers buy the same set.\nLines 332-333\n\"based on six CMIP6 climate models: CanESM5, CNRM-CM6-1, UKESM1- 0-LL, EC-Earth3, IP-SL-CM6A-LR and\nMIROC6.\" <- maybe here you could spell out for the reader where you obtained the data, if it cost money, etc. Again, this\nwould be useful for other researchers, like myself, thinking about how they could replicate the results.\nLines 371-373\n\"we assume the coastal levee will be elevated to the historical 100-year return level plus one percent quantile SLR (~2\nmeters averaging over SSP2 4.5 and SSP5 8.5 for New Orleans calculated from ref. 14)\" <- I don't understand what you\nmean by \"plus one percent quantile SLR\". Please clarify.\nLine 376\n\"(Error! Reference source not found.).\" <- needs to be corrected\nLines 376-378\n\"A sensitivity test was performed on future compound hazard risks given different elevations of the coastal levee from 0-3\nmeters above the current level. \"\n<- I see the supplementary figure S1, but do you report the change in Rita-like return intervals under all the sensitivity tests.\nI think that would be a useful table...\nHETWAVE PROJECTIONS\nLine 384\n\"Following ref. 5, the daily HI is determined\"\n<- I can't find HI defined in this manuscript. Nor could I find it described in ref. 5 (The Ebi et al. 2021 paper). This reference\nshould be fixed and HI should be defined clearly within this manuscript (or Supplement) so that readers don't have to\ndownload an external reference to understand the metric.\nShould this be ref. 4, the NWS definition of the HI? You could perhaps cite the website:\nhttps://www.wpc.ncep.noaa.gov/html/heatindex_equation.shtml, although the link may change over time. Better would be the\nsource publication information, e.g.,\nSteadman, R. G., 1979: The Assessment of Sultriness. Part I: A Temperature-Humidity Index Based on Human Physiology\nand Clothing Science. J. Appl. Meteor. Climatol., 18, 861–873, https://doi.org/10.1175/1520-\n0450(1979)018<0861:TAOSPI>2.0.CO;2.\nRothfusz, L. P. (1990). The Heat Index Equation (or, More Than You Ever Wanted to Know About Heat Index). Technical\nAttachment (SR 90-23). National Weather Service, Southern Region Headquarters, Fort Worth, Texas. Available at:\nhttps://www.weather.gov/media/ffc/ta_htindx.PDF\nand you could reproduce the calculation in the Supplementary information, again so the reader doesn't have to look up the\noriginal references but can see specifically what you used to calculate the HI.\n(I'm not an expert in heat indices; I found these references through a cursory search; you may know of better references.)\nLines 390-391\n\"According to the historical analysis in ref. 4\" <- should this be ref. 14?\nLine 391-392\n\"the HI will drop upon TC landfall and will recover to the ambient average within around ten days.\"\n<- I'm a little unclear on this. So, this means that temperatures drop at TC landfall then recover after the TC, within ten days.\nAverage power outages are 96 hours according to your statement in the introduction (line 42). That's four days. I don't think I\nunderstand how the heatwave is a compounding hazard if temperatures are lower during the power outage and only recover\nsix days after the power outage. I may be misreading this because HI is not defined so I may be misunderstanding what's\nhappening here. This requires significant clarification.\nDoes this mean that high HI is equivalent to low temperature? Is there a typo in the sentence I have highlighted?\nNote: if it is feasible, I would be interested to see a timeline plot with time on the x-axis, marking the time of landfall of\nHurricane Ida and/or an Ida-like synthetic storm or set of Ida-like synthetic storms with a vertical line, showing the\nprogression of the physical hazards (observed or synthetic or synthetic ensemble): e.g., a measure of the strength of the\nwind field, a measure of the storm surge and/or a measure of the sea level + a time series of the number of people or\nhouseholds without power (and/or some measure of damage to the grid), so that we can see the relative timing of the hazard\nand the exposure. Could add the impacts as well, measures of mortality and/or morbidity resulting from all these factors.\nSuch a time course figure could be highly informative and intuitive if it is feasible.\nYou have something similar in Figure 1a and 1b, but more could be done. Actually, as I look more closely at Fig 1a, this is\nperhaps sufficient. We are able to see clearly in the red and blue lines the percentage of customers without power and in\nyellow the percentage of customers under heat stress.\nI could imagine another plot showing the number of people for whom the yellow curve is above the blue curve from days 0 to\n6, but this may not be necessary. More comments below in a discussion of the figures.\nLine 394\n\"Fig. 3a in ref. 4\" <- Ref. 4 is \"National Weather Service, New Orleans, New Orleans/Baton Rouge (2021)\" <- I don't see a\nFig 3a in this reference. Perhaps you mean ref 14.? There is a Fig 3a in ref. 14, so I presume this is what you are referring to.\nSEA LEVEL RISE PROJECTIONS\nPOWER OUTAGE AND RECOVERY MODEL\nLines 408-412\n\"We apply a physics-based power system model, which explicitly simulates component level damage to predict the total\npower outage, accounting for the effects of future evolving factors, e.g., climate change, infrastructure upgrade, and utility\nmaintenance. The physics-driven modeling of the power system allows us to better understand the impact of climate change\nand effectiveness of risk mitigation measures compared to if we used purely data-driven models (47,48).\"\n<- Are the references 47 and 48 to data-driven models? If so, what are the key references to the physics based models you\nuse? Ah, I see you state this in the next paragraph (refs. 11 and 18.) You could note that in the paragraph above:\n\"We apply a physics-based power system model (11, 18), which explicitly...\"\nLine 420\n\"A DC flow simulation\" <- define DC flow simulation. As someone who is not an expert in energy modeling, I assume this\nmeans direct current flow simulation. Questions that come to my mind: How do DC flow simulations compare to AC flow\nsimulations? They're simpler, right, but still useful?\nLine 422-423\n\"the performance of the power grid 423 outside the study area is assumed to be under normal operation.\"\n<- is this a reasonable and weak assumption, or a bold simplifying a assumption?\nLines 424-443\nThe fragility curves in refs. Error! Reference source not found. ... Error! Reference source not found. and 18 except that the\neffects of rainfall and flood are incorporated. The parameters are estimated by the Markov chain Monte Carlo (MCMC)\nmethod to minimize the mean squared error between simulated and observed county-level power outages.\n<- I'm unable to assess the validity of these methods a references is missing. Following ref. 18 which is not missing takes me\nto the Ouyang and Dueñas-Osorio (2014) paper. What is described here is fairly short on details but seems reasonable,\ngiven the Ouyang and Dueñas-Osorio framework. Eq (1) seems sensible.\n*** Note that a figure in this present manuscript could potentially benefit from a figure analogous to Fig 2 in the Ouyang and\nDueñas-Osorio (2014) paper, but showing southern Louisiana rather than Harris County.\nLines 441-443\n\"The parameters are estimated by the Markov chain Monte Carlo (MCMC) method to minimize the mean squared error\nbetween simulated and observed county-level power outages.\"\n<- It's not clear to me from reading the manuscript what training data were used to perform this MCMC estimation. This\ninformation may be contained in the missing reference. Even if it is, it may be worth restating here: what data were used to\nestimate the parameters of the fragility functions?\n= = = = =\nREFERENCES\n<- Note: it appears that the references are not all in Nature format and should be harmonized before publication. Most of\nthem seem fine, but there are some problematic entries, some of which I've identified, some of which I may have missed.\n1. U.S. department of energy <- capitalization Department of Energy\n2. Hurricane Ida Power Restoration,Utility Report, Entergy (2021) <- missing period at end of line. Also I was not able to find\nthis reference looking online. More detail would be helpful on how to find the reference, which looks interesting.\n3. Nicholas Bogel-Burroughs and Katy Reckdahl. The Greatest Killer in New Orleans Wasn’t the Hurricane. It Was the Heat..\nNew York Times (2021). <- double period.\n4. National Weather Service, New Orleans, New Orleans/Baton Rouge (2021) <- not a complete reference; where can I find\nthe statement of the heat advisory criteria?\n15. \"Fox-Kemper, B., et al. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1211–\n1362, doi:10.1017/9781009157896.011.\"\n<- According to my copy of Zotero, the Nature-style citation should be:\n\"1. Ocean, Cryosphere and Sea Level Change. in Climate Change 2021 – The Physical Science Basis: Working Group I\nContribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (ed. Intergovernmental\nPanel on Climate Change (IPCC)) 1211–1362 (Cambridge University Press, Cambridge, 2023).\ndoi:10.1017/9781009157896.011.\"\nEtc.\n= = = = =\nFIGURES\nFigure 1.\nDo you not have the yellow heatwave curve for Hurricane Laura? If it could be added that would strengthen the paper.\nFig 1c. It's a little hard to see exactly what the differences are between the top panel (observations) and the lower panel\n(simulations) in this subfigure. I could imagine a third row showing differences (it looks like the differences would be minimal.\nI could also imagine a third column showing Sep 7 say as that's when the power comes back online. Alternatively I could\nimagine a table showing some kind of summary statistics indicating the skill of the simulations for this (these?) test case(s),\nbecause just looking at the figures all I can make out is that the simulations appear to match the observations well. Of all\nthese comments I think the Sep 7 addition would be highest on my list of potential additions, unless you've already done it\nand found it to be uninformative.\nFig 1c. It would be very useful to see the figures for Laura in addition to the figures for Ida, since Laura affected the west part\nof the state. I'd be interested to see how the map changes as presumably at least some of the transmissions lines to New\nOrleans were spared?\nFigure 2\nGreat figure. Needs color legend boxes on at least 2a, in the top left corner for example. red line SSP5 8.5, yellow SSP2 4.5,\nblue historical. The current color scheme appears to be ok for colorblind viewers.\nInterruption Hour By Power Outage (Million Hour) is good Interruption Hour By Compound Hazard (Million Hour) is also fine,\nbut maybe there's a better way to describe these:\nYou have space at the tops of the subfigures 2a and 2b. You could potentially provide titles: \"Return Periods of Power\nOutages\" and \"Return Periods of Power Outages and Extreme Heat\". That might catch the eye a little better, allow the reader\nto quickly grasp what's in the figures.\nx-axes need to be labeled in 2a and 2b or at least in 2b.\n2c: the numbers in parentheses in the main cells have not been defined. We can infer that they represent the 95%\nconfidence intervals, but this should be stated explicitly in the caption.\nFinally, in the table 2c, I'd consider putting SSP2 4.5 in the middle column and SSP5 8.5 in the right column so that the\nnumbers are monotonic reading from left to right.\nFigure 3\nIn Fig 3d, for the lines in the top left of the figure, the line types should differ, one could be solid, another short dash, another\nlong dash, in addition to the color differences. This would make the figure easier to read, though you'll have to free up some\nspace to show the dash type in the legend. I think it can be done. If it turns out to make the figure too busy, it's probably fine\nto leave as is.\nFigure 3 title is \"Estimated average interruption days of compound hazard\" <- maybe consider \"Estimated average power\ninterruption days with heat compound hazard\" to be more explicit (or something similar that uses the words power and heat)\nIn Fig 3d, The breaks for the interruption hours of compound hazard are 0-60 hours (0-2.5 days), 60-120, (2.5-5 days), 120-\n180 (5-7.5 days), 180-240 (7.5-10 days), 240-300, (10-12.5 days), 300-360 (12.5-15 days), and > 15 days. When I tried to do\nthe hours to days calculation in my head I was off by a factor of two, embarrassingly, so I was confused by the long duration\nin the tail of the distribution. Perhaps it would be better to label the durations by day rather than by hour. This would match\nFig 3a-c.\nFigure 4b: I'm not sure if you need that much of the x-axis for this figure since the range appears to be around 0-65, not 0 to\n278. It's probably fine as it is, but maybe it would be easier to see the distinctions between scenarios zoomed in on the x\ndimension.\nSUPPLEMENTARY MATERIALS\nSupplementary Fig S1\n\"Therefore, the potential inaccuracies ... do not detract from the paper's main conclusions.\" <- \"Therefore, the potential\ninaccuracies ... should not detract from the paper's main conclusions.\" (soften the claim)\nI don't understand why there is such a discrete edge or east-west cut off line at about 30 degrees north. Knowing very little\nabout the grid in Louisiana, but a little about the grid in California, I am curious how Entergy, Cleco, MISO, and the SERC\nReliability Corporation (and even LPSC) respond in these conditions and how they responded in response to Ida. If\ntransmission lines and substations are down, why aren't any regions outside of the counties shown affected? Why is it such\na clean break on the map?\nA little background about the system would be helpful.\nSupplementary Fig S3\nFigure needs lat lon on the axis and/or graticules. Figure also needs a scale.\nI feel like you could include more than just the topology of the system in a figure like this. I'm not that familiar with figures of\npower transmission networks, but this seems like a missed opportunity to bring in more interesting details of the system. To\nmy eye there are four clusters of network activity in the southern portion of the state (the one below the 30 degree north\ncutoff), presumably corresponding to Lake Charles, LaFayette, Baton Rouge, and New Orleans. There is another question\njust to the west at Beaumont Texas and to the east at Gulfport Florida.\nA question that comes to mind is how would this picture look if rendered in the style of Fig. S1, i.e., coloring the network by\nthe average annual interruption days under compound hazard.\nA related question: how do these events play out through Entergy, Cleco, MISO and the SERC Reliability Corporation? How\ndid Laura and Ida play out according to the operators?\nThere is an interesting EIA report written on September 14, 2021, with text including:\n\"Because of the electric load decrease caused by Hurricane Ida, electric generation in the southern MISO region was forced\nto drop by a corresponding amount to keep the grid in balance. Hourly nuclear generation decreased significantly, falling\nfrom 5.2 gigawatts (GW) on August 29 to 3.7 GW on August 30. The River Bend Station nuclear plant was cut back to 40%\nof its capacity. In addition, the Waterford 3 nuclear plant located 25 miles west of New Orleans was shut down as a safety\nprecaution because of high winds on the morning of August 29.\n\"Natural gas-fired power plants also reduced electric generation. Hourly natural gas-fired electric generation in the southern\nMISO region peaked at 20 gigawatts (GW) on August 26, but it only reached 12 GW on August 30.\"\n[Interesting!]\nIf you feel up to writing it, a couple of paragraphs about how the electrical system operates could be helpful for the reader\nwho may be unfamiliar with some of the elements of transmission system management during TCs.\nSupplementary Fig S4\nThis should be rendered in higher resolution, so that the text is legible in a PDF.\n= = = = =\nFurther miscellaneous comments:\nWhat does the National Association of Regulatory Utility Commissioners (NARUC) think about hurricanes and future\nhurricane heatwave risk?\nWhat's the air conditioning penetration rate in Louisiana? It may be worth mentioning somewhere that the air conditioning\npenetration rate is about 97% according to the US EIA.\nWhat are the risks to the energy grid for Louisiana during TCs? River Bend and Waterford 3 nuclear power plants. What\ndoes the thermal fleet look like (coal, oil, natural gas)? Renewables, including biomass? What about the battery fleet? Are\nthere any metrics of Louisiana's grid stability that we can compare to those of other states?\nEntergy and Cleco have data on how the number of outages, peak outages, time to restore, number of hours each customer\nor service point was out of commission, poles, spans of wire, transformers, substations, transmission lines. Can you calibrate\nyour fragility models against the aggregate Entergy and Cleco datasets? Or is this what you did? If this is not discussed\nwithin this manuscript I presume it was within the references you have cited and I just didn't see it. But as a modeler I'm\ninterested in knowing how you validated your fragility functions and your model outputs.\n(Remarks on code availability)\nThe code is very superficial, providing (very) simple scripts at the very last stage of the analysis, converting final data output\ninto figures for the manuscript. Much more useful would be the full code base taking the user from the input data (e.g.,\nsynthetic storm tracks, heat index projections, etc.) all the way through to the final data and figures. Unless I have some\nmade some error in interpretation, it would not be possible for me to reproduce the authors' results starting with the raw input\ndata.", "reviewer_1_attached": "Response for “Hurricane Ida’s blackout-heatwave compound risk in a changing climate”\nReviewer #1 (Remarks to the Author):\nIn their manuscript “Hurricane Ida’s blackout-heatwave compound hazard risk in a changing\nclimate”, the authors present a risk study on the compound hazard of hurricanes and heatwaves\nin Louisiana. They particularly focus on failure of the power grid occurring simultaneously with\na post-hurricane heatwave. I find the study very interesting and inspirational. Methodologically\nspeaking the study is well executed, with particularly noting the inclusion of all three hazards in\ntheir hazard modeling. I therefore recommend minor revisions; please find my comments below.\nWe thank the reviewer for the thorough review and great questions and suggestions.\n1. Reading a sentence can be hard at times due to the use of brackets, as they are both used in the\nreferencing style, to differentiate between SSP245 and SSP585 and to add extra information. I\nparticularly struggled getting through certain sections of the Results section. I encourage the\nauthors to critically revise the use of brackets and re-write or exclude them wherever possible.\nThank you, we’ve removed the brackets and stated the results for SSP245 and SSP585\nseparately.\n2. Following my previous point, there are also a few runoff sentences that were hard for me to\nfollow, and for which I would recommend to split them up. I noted down the following cases:\nlines 167 – 169, lines 250 – 254, and lines 308- 311.\nThank you. We’ve checked these and other long sentences in the manuscript and revised\nthem to improve clarity.\n3. The manuscript contains several mentions of the word “significant” which, to me, would\nimply that a significance testing was done. If this is the case, could you please also include the p-\nvalue; if this is not the case, I recommend to use different wording.\nThanks, we have replaced “significant” with “substantial” when it’s not related to\nstatistical testing.\n4. Please add a legend to Figures 1 and 2, similarly to how this was done in Figure 3d.\nGood suggestion. Added.\n5. I don’t fully understand how the stochastic simulations were done. The manuscript states that\n“each stochastic simulation includes a continuous 20-year sequence of TC occurrences”. This 20-\nyear period does line up with the future time period (which is also 20 years), so I guess you start\nthe simulation in 2081 and then let it run for 20 years. But the historical period covers 26 years,\nso what 20-year period did you use in this case?\nSorry for the confusion. We simulate hazards and risk under two climate conditions\n(1980-2005 and 2081-2100). Within each climate condition, we assume the hazards are\nstationary (we study how hazards and risk change from 1980-2005 climate state to 2081-2100\nclimate state; we don’t study how hazards and risk change, e.g., from 2081 to 2100). For each\nclimate condition, we apply stochastic sampling to generate (stationary) sequence of hazards\nover 20-year period, and we study the risk over the 20-year period timeframe (e.g., the\nprobability of experiencing 5-day compound hazard over 20 years under the given climate\ncondition). Thus, the 20-year stochastic sampling period is not chosen to align with the climate\nsimulation periods. In another words, we could use 10-year or 30-year period and ask what is the\nprobability of experiencing 5-day compound hazard over 10 pr 30 years under the given climate\ncondition. To make this clear we revised the description in the Introduction, Results, and\nMethods:\n“We generate a large number of compound hazard events (~300,000 stochastic\nsequences) based on the projections for historical (1980-2005) and future (2081-2100) climates\nto estimate how hazard probabilities may change over the 21st century (Methods).”\n“We generate 10,000 simulations of synthetic hazard events for each of the historical\n(1980-2005) and future (2081-2100) SSP5 8.5 and SSP2 4.5 scenarios. Each stochastic\nsimulation includes a continuous 20-year sequence of TC occurrences, along with the physical\nsimulation of TC tracks, wind speeds, rainfall amounts, storm surge levels, and heatwaves.”\n“To perform sequential risk analysis, we generate 10,000 stochastic samples of storm\nsequences for both historical and future climate periods. These samples were derived by\nsampling storms (according to a Poisson process with a rate as the TC annual frequency) and\ntheir associated hazards from the TC hazard datasets described above. Each stochastic sequence\nconsists of 20 consecutive years of TC activity.”\n6. Are you using an intensity (wind/rain/surge) threshold for the synthetic tracks? I could not find\nany notion of this in the Methods-section but I might have overlooked it.\nSorry we did not mention that. We follow our threshold in former paper and we’ve now\nadded to the beginning of Methods as “Each synthetic storm passes within 300 km of Louisiana,\nwith a maximum wind speed of at least 22 m/s. ”.\n7. Line 171 – 173: Could you please quantify this statement?\nThis statement is a discussion on the curves. The quantitative statement in the case of Ida\nwere included in the previous paragraph. The statement is also followed by two quantitative\ncases with a much larger and a much smaller events, compared to Ida. To avoid the confusion,\nwe have changed the “Specifically” proceeding these quantitative statements to “For example”.\n8. Line 216: this sentence only mentions the percentage of customers facing a >120-hr\ncompound hazard, but the following sentences also mention other time periods with associated\npercentages for the future climate scenarios. Could you please also state the associated\npercentages for the other time periods for the historical period?\nFor the future climate we stated the percentage for a >120-hr compound hazard\nand >240-hr, >360. In the historical climate, the percentage is zero for these longer duration (as\nshown on Fig. 3d, purple bars disappear for these longer durations), so we didn’t report these for\nthe historical climate.\n9. Line 80: could you please (also) give the temperature in Celsius for those not familiar with\nFahrenheit?\nWe’ve added as “during Hurricane Laura the state-average heat index was also high but\ndid not reach the threshold of 37.8°C/100°F”.\n10. Lines 376, 424 and 440 contain a reference error.\nThank you, fixed."}, "author_response": "Response to Reviewers\n\nWe thank the reviewer for the thorough review and great questions and suggestions.\n\n1. Reading a sentence can be hard at times due to the use of brackets, as they are both used in the\nreferencing style, to differentiate between SSP245 and SSP585 and to add extra information. I\nparticularly struggled getting through certain sections of the Results section. I encourage the\nauthors to critically revise the use of brackets and re-write or exclude them wherever possible.\nThank you, we’ve removed the brackets and stated the results for SSP245 and SSP585\nseparately.\n\n2. Following my previous point, there are also a few runoff sentences that were hard for me to\nfollow, and for which I would recommend to split them up. I noted down the following cases:\nlines 167 – 169, lines 250 – 254, and lines 308- 311.\nThank you. We’ve checked these and other long sentences in the manuscript and revised\nthem to improve clarity.\n\n3. The manuscript contains several mentions of the word “significant” which, to me, would\nimply that a significance testing was done. If this is the case, could you please also include the p-\nvalue; if this is not the case, I recommend to use different wording.\nThanks, we have replaced “significant” with “substantial” when it’s not related to\nstatistical testing.\n\n4. Please add a legend to Figures 1 and 2, similarly to how this was done in Figure 3d.\nGood suggestion. Added.\n\n5. I don’t fully understand how the stochastic simulations were done. The manuscript states that\n“each stochastic simulation includes a continuous 20-year sequence of TC occurrences”. This 20-\nyear period does line up with the future time period (which is also 20 years), so I guess you start\nthe simulation in 2081 and then let it run for 20 years. But the historical period covers 26 years,\nso what 20-year period did you use in this case?\nSorry for the confusion. We simulate hazards and risk under two climate conditions\n(1980-2005 and 2081-2100). Within each climate condition, we assume the hazards are\nstationary (we study how hazards and risk change from 1980-2005 climate state to 2081-2100\nclimate state; we don’t study how hazards and risk change, e.g., from 2081 to 2100). For each\nclimate condition, we apply stochastic sampling to generate (stationary) sequence of hazards\nover 20-year period, and we study the risk over the 20-year period timeframe (e.g., the\nprobability of experiencing 5-day compound hazard over 20 years under the given climate\ncondition). Thus, the 20-year stochastic sampling period is not chosen to align with the climate\nsimulation periods. In another words, we could use 10-year or 30-year period and ask what is the\nprobability of experiencing 5-day compound hazard over 10 pr 30 years under the given climate\ncondition. To make this clear we revised the description in the Introduction, Results, and\nMethods:\n“We generate a large number of compound hazard events (~300,000 stochastic\nsequences) based on the projections for historical (1980-2005) and future (2081-2100) climates\nto estimate how hazard probabilities may change over the 21st century (Methods).”\n“We generate 10,000 simulations of synthetic hazard events for each of the historical\n(1980-2005) and future (2081-2100) SSP5 8.5 and SSP2 4.5 scenarios. Each stochastic\nsimulation includes a continuous 20-year sequence of TC occurrences, along with the physical\nsimulation of TC tracks, wind speeds, rainfall amounts, storm surge levels, and heatwaves.”\n“To perform sequential risk analysis, we generate 10,000 stochastic samples of storm\nsequences for both historical and future climate periods. These samples were derived by\nsampling storms (according to a Poisson process with a rate as the TC annual frequency) and\ntheir associated hazards from the TC hazard datasets described above. Each stochastic sequence\nconsists of 20 consecutive years of TC activity.”\n\n6. Are you using an intensity (wind/rain/surge) threshold for the synthetic tracks? I could not find\nany notion of this in the Methods-section but I might have overlooked it.\nSorry we did not mention that. We follow our threshold in former paper and we’ve now\nadded to the beginning of Methods as “Each synthetic storm passes within 300 km of Louisiana,\nwith a maximum wind speed of at least 22 m/s. ”.\n\n7. Line 171 – 173: Could you please quantify this statement?\nThis statement is a discussion on the curves. The quantitative statement in the case of Ida\nwere included in the previous paragraph. The statement is also followed by two quantitative\ncases with a much larger and a much smaller events, compared to Ida. To avoid the confusion,\nwe have changed the “Specifically” proceeding these quantitative statements to “For example”.\n\n8. Line 216: this sentence only mentions the percentage of customers facing a >120-hr\ncompound hazard, but the following sentences also mention other time periods with associated\npercentages for the future climate scenarios. Could you please also state the associated\npercentages for the other time periods for the historical period?\nFor the future climate we stated the percentage for a >120-hr compound hazard\nand >240-hr, >360. In the historical climate, the percentage is zero for these longer duration (as\nshown on Fig. 3d, purple bars disappear for these longer durations), so we didn’t report these for\nthe historical climate.\n\n9. Line 80: could you please (also) give the temperature in Celsius for those not familiar with\nFahrenheit?\nWe’ve added as “during Hurricane Laura the state-average heat index was also high but\ndid not reach the threshold of 37.8°C/100°F”.\n\n10. Lines 376, 424 and 440 contain a reference error.\nThank you, fixed.\n\nReviewer #2 (Remarks to the Author):\nLin and co-authors present analysis of the return period of an event that combines a damaging\nhurricane passage, resulting in electrical outages, with a heatwave, as a function of climate state,\nin southern Louisiana. They compare an historical period (1980-2005) and a future period (2081-\n2100) under two climate forcing scenarios, and show that in either scenario, there is a sharp\nreduction in the return period of given level of person-hours of exposure to heatwave under\npower outage. The dominant cause of this reduced return period is increased frequency of\nheatwaves, but sea level rise and hurricane intensity increase also contribute.\nThe manuscript is clearly written, and the results are interesting, but I believe it could be\nimproved in number of areas. There are a couple of areas where the clarity could be better, and\nthere are some questions that are raised in the reader's mind, but not answered.\nWe thank the reviewer for the thorough review and great questions and suggestions.\n\n1) In lines 167 and following, the \"higher\" and \"lower\" return periods are discussed. I think this\nwould be clearer if the language were changed to \"longer\" and \"shorter\" return periods.\nThanks for catching this detail. Accordingly, we have changed the words here and in\nanother two places.\n\n2) In lines 198 and following, there is an interesting discussion of an increase in the spatial\ndisparity of impacts, however the cause of this change is not identified. Can the authors clarify if\nthis is this due to a change in the spatial structure of hurricane impacts? Of heat? Storm surge?\nThank you for the great question. Several factors likely contribute to disparate impacts.\nFirst, local distribution network topology plays a significant role - communities more distant\nfrom urban centers and substations typically face higher risks due to their lengthier series-\nconnected distribution lines. Although network topology itself is static regardless of climate\nconditions, climate impacts manifest nonlinearly, potentially amplifying existing disparities\nbetween communities. Among climate factors, changes in hurricane hazards especially coastal\nflooding from storm surge and sea level rise appears particularly significant in creating\ndisparities, as their effects are geographically concentrated in coastal areas, leading to\nsystematically longer outage durations in these regions. Intensification of other hurricane hazards\nsuch as wind and rainfall tend to also have the largest impact in coastal regions. In contrast, heat\nwaves tend to vary more continuously across geographic areas, suggesting they may contribute\nless to systematic disparities between communities. We have added to the text “These results\nsuggest that, intensifying hurricane hazards that disproportionally impact coastal regions and\nvulnerable communities distant from substations, climate change is likely to exacerbate existing\ndisparities and inequalities in TC-blackout-heatwave compound risks across Louisiana.”\n\n3) Only six models from the CMIP6 ensemble are used, but no explanation is given for the\nselection criteria. Were they models that had the best representations of hurricane storm tracks? A\nrepresentative range of climate sensitivity? Some other good characteristic?\nThank you for the question. In this study, we applied existing TC hazard datasets to\nestimate the power outage risk (in Introduction “…we combine projections of heatwaves (14),\nTC hazards (including wind, storm surge, and rainfall, 15), and SLR (16, 17, 18),…”, In Method\n“We use the synthetic TC hazard dataset generated in ref. 15 for the North Atlantic basin…”).\nRef. 15 (and many other TC risk analysis using this prominent TC model) use about 6 selected\nGCMs. The 6 GCMs used by Ref. 15 are those that recorded climate variables required for\ngenerating the synthetic TCs by the hurricane model, and they were also used in previous studies.\nThese 6 GCMs provide a wide range of predictions, but they may not fully cover the uncertainty\nrange of the hurricane projections. Most other CMPI6 models do not provide sufficient input for\nthe TC simulation approach. It is also computationally difficult to test all CMIP6 models, given\nthat we need to generate at least thousands of storms and storm tide scenarios for each model.\nAlso, Ref. 15 and other studies have compared the historical simulations from selected GCMs\nwith historical observations, and they have bias corrected the projections based on the\ncomparison with observations (as detailed in Ref. 15 and earlier studies). No study exists yet that\ndefine the GCM set that guaranty the representation of the full hurricane track distributions, as\nthe latter is not fully understood yet for the future climate. Thus, the common practice is to select\nmore GCMs (not one or two as in many studies) that cover a large range of predictions and\nspecify the name of the GCMs (be clear the projections are based on these specific GCMs) for\npossible comparisons across studies. To clarify this, we have added to the revised manuscript\n“The dataset was generated based on projections from six CIMP6 models (selected given data\navailability and following previous studies): Canadian Earth System Model (CanESM5), Centre\nNational de Recherches Météorologiques Climate Model (CNRM-CM6-1), UK Earth System\nModel (UKESM1-0-LL), EC-Earth3, IPSL-CM6A-LR (Institut Pierre-Simon Laplace Climate\nModel), and Model for Interdisciplinary Research on Climate (MIROC6). ”\n\n4) The historical period chosen is quite short compared to the period for which reasonably good\ndata exists. It would be helpful to present results for two historical periods, say 1961-1990 and\n1991-2020, and compare observed changes to changes in the CMIP6 models' historical periods.\nThis would help the reader assess the reliability of the models for this purpose in this study area.\nThank you for this thoughtful suggestion regarding historical period comparisons. As\nmentioned above, existing hurricane hazard datasets were used in the study. While analyzing\nlonger historical periods would indeed provide additional context, our study specifically focuses\non the climate change impacts on power infrastructure under future scenarios rather than\nproviding a comprehensive model evaluation of CMIP6 performance. The historical period in\nour study serves primarily as a baseline for bias correction and future projections. Evaluations of\nCMIP6 model performance in simulating tropical cyclones in our study region have been\ndocumented in previous studies, as mentioned in Method “The dataset contains synthetic TC\ntracks generated with the statistical-deterministic TC model (34), which has been applied to TC\nhazard assessment (9, 35, 36).” These existing assessments provide confidence in the models'\ncapability to simulate the key atmospheric processes relevant to our analysis.\n\nReviewer #2 (Remarks on code availability):\nThe code repository appears to include only code to generate the figures from the pre-\nprepared .mat files. It would be helpful to have the code to generate the analyses from the GCM\ndata as well, but perhaps this was already provided in the references cited? I don't know what\nNature's standards are for this.\nThank you for these questions. Indeed, as mentioned above the we used TC datasets\ngenerated by Ref. 15, and thus the datasets and code to generate the TC hazards from GCMs\nwere provided by Ref. 15 (Nature Climate Change). We now have added this clarification in our\ndata and code statements.\nThis is also the first time we use the new code platform provided by Nature\nCommunications. The current code platform is unable to fully execute our model because it\nrelies on parallel computation settings that require both GPU and CPU interaction in MATLAB\nand some data processing relies on python. Additionally, the platform mandates that all results be\ndirectly compliable, while a complete run of our model would take several days to generate the\nfull risk profile. Thus, we only uploaded the results data and code to reproduce figures as what\nwe did for former papers.\nTo address these challenges and enhance the reproducibility of our results, we will\nupload the data and code and make a simplified calculation case available on GitHub upon\npublication. This case will represent the system, with geospatial information for distribution\nnodes blocked (as detailed distribution grid data cannot be published due to security and privacy\nrestrictions). It will also include a TC hazard input file that details the multi-hazard damage\nscenarios and recovery processes.\n\nReviewer #3 (Remarks to the Author):\nRecommendation: Accept with minor revisions\nThe work is novel and useful. The scientific method is sound, although it does rely heavily on\nprevious work (especially ref. 14, Xi, Lin, and Gori (2023) and also Ouyang and Dueñas-Osorio\n(2014)) which I was unable to review in significant detail given my time constraints. All previous\nwork cited was peer reviewed in well regarded journals however so I assume the methods were\nsound. Indeed, my cursory readings of the supporting work indicates this.\nThere are a handful of bad references in the manuscript ((Error! Reference source not found.))\nThese should have been corrected before submission. I think I was able to puzzle out which\nreferences were missing.\nMajor Comments\nOverall Evaluation:\nScientific merit: good\nClarity: good\nImpact of work: good\nWe thank reviewer for the thorough review and providing many constructive suggestions and\nquestions.\n\nBig Picture Questions and Issues\nHow often do heatwaves follow tropical storms, generally? My limited understanding is that\ntemperatures after hurricanes and tropical storms are typically cooler than normal given the\ncloud cover, rainfall, and moisture retention. So, some explanation of the synoptic conditions\nwhich could give rise to a heatwave following a hurricane or tropical storm would be helpful for\nsetting the scene. I am not a meteorologist. Can a high pressure system build after the storm\nleading to clear skies and warming? Do conditions depend on the subtropical ridge in late\nsummer? Can the storm move out of the region quickly leading to a spike in temperatures? I'd\nlike to see a meteorological/climatological explanation of how common the phenomenon is.\nThank you for the good question. Summer is naturally hot, and people usually rely on air\nconditioning to cope with heatwaves, which might cause them to underestimate the impact of\nsuch events. However, when tropical cyclones (TCs) lead to widespread power outages,\ndisabling air conditioning, the issue of heatwaves becomes more pronounced. Even if the\ntemperature slightly drops right after a TC (due to rainfall etc), the humid and hot summer\nenvironment can still exacerbate the effects of a heatwave.\nThe phenomenon of heatwaves following TCs is not yet fully understood in meteorology.\nTypically, TCs bring cloud cover and rainfall, resulting in cooler temperatures (but still hot).\nHowever, certain atmospheric conditions can lead to heatwaves in the aftermath of a TC (make it\neven worse).\nSynoptic Conditions Leading to Post-TC Heatwaves:\nInfluence of the Subtropical Ridge: The position and intensity of the subtropical ridge in\nlate summer can significantly affect weather patterns, increasing the likelihood of post-TC\nheatwaves.\nRapid TC Movement: If a TC moves out of the region quickly, the clearing skies and\ndescending air can cause rapid warming, potentially triggering a heatwave.\nResearch Insights:\nA study by Matthews et al. (2019) in Nature Climate Change highlights that TCs\ntypically occur during already hot conditions, meaning a heatwave may be present even before\nthe TC. However, post-TC air conditions can further worsen the situation, amplifying the impact\nof the heatwave.\nRecent research in China has also investigated the interactions between TCs and\nheatwaves. For example, marine heatwaves in the East China Sea have been found to enhance\nthe intensity of the strongest typhoons. However, the physical mechanisms behind these\ninteractions remain unclear and require further exploration.\nThe occurrence of heatwaves following TCs varies across regions, and the underlying\nmechanisms remain complex and poorly understood. Further research is necessary to uncover the\nphysical processes driving these interactions and their implications.\nOur study focuses on the impact of TC and heatwave compound hazards on the power\nsystem and people, with the frequency of TCs and heatwaves simulated from GCMs and\ndownscaling models. To understand this frequency and mechanisms is a boarder topic that is now\nunder increasing research. We believe our previous study on the impact of TC and heatwave\ncompound hazards have motivated some of these scientific investigations and our current study\nwill continue to do so. The motivation for this study is the Matthew paper (which first discussed\nthe indication of TC and heatwave compounding) and recent events in Louisiana (Ida and Laura).\nReferences:\nMatthews, T., Wilby, R. L., & Murphy, C. (2019). An emerging tropical cyclone–deadly\nheat compound hazard. Nature Climate Change, 9(8), 602–606. (cited in our paper)\nChoi, H. Y., Park, M. S., Kim, H. S., & Lee, S. (2024). Marine heatwave events\nstrengthen the intensity of tropical cyclones. Communications Earth & Environment, 5(1), 69.\n\nUse of SSP5-8.5 over SSP3-7.0 as the high emissions scenario needs some justification, given\nthe trend in the literature away from SSP5-8.5 towards SSP3-7.0. I don't have a problem with\nusing SSP5-8.5, but there should be a sentence or two in the manuscript explaining and\ndefending this choice. I presume the authors used SSP5-8.5 because previous work used\n4.5 and 8.5 as intermediate and high emissions scenarios?\nYes, we used the 4.5 and 8.5 scenarios in our previous research—thank you for your\ncareful reading. While we are building on previous work, in this case, we aim to assess both a\nroutine scenario and an upper bound of the risk. To clarify, we have added “driven by CMIP6\nGCMs (14) under both the high (Shared Socioeconomic Pathway 5 8.5; SSP5 8.5) and moderate\n(SSP2 4.5) emission scenarios, to assess both a likely scenario and an upper bound of the risk.”\n\nA discussion of the energy landscape in Louisiana would also be helpful, e.g., a discussion of the\nroles of investor-owned utilities Entergy and Cleco, the Midcontinent Independent System\nOperator (MISO), the SERC Regional Corporation; Louisiana Public Service Commission\n(LPSC). This could potentially be put in a supplementary section if current word limit constraints\nare binding, although I think a paragraph within the main body of the text would be useful for\nreaders who have some familiarity with electricity systems, but not with the Louisiana grid. On a\nsimilar note, I don't understand why only the southern part of the state was impacted by power\noutages during tropical storms Ida and Laura given the connectivity of the network. Some\nexplanation would be helpful.\nThank you for your constructive feedback. We now add discussion on the energy\nlandscape in the supplementary section (and referred it in the main text), as “As shown in Figure\nS8, Louisiana's energy infrastructure is characterized by a diverse composition. The eastern half\nof Louisiana is largely served by the investor owned utility (IOU) Entergy while some of the\nwestern half is served by the IOU Cleco, who control generation, transmission and distribution in\nthe region. Key entities in Louisiana include:\nl Investor-Owned Utilities: Entergy and Cleco are the state's principal electricity providers,\nsupplying power to the majority of residents and businesses.\nl Midcontinent Independent System Operator (MISO): MISO is a regional transmission\norganization that oversees the electrical grid across multiple states, including Louisiana,\nensuring reliable power transmission and market operations.\nl SERC Reliability Corporation: SERC is a regional entity responsible for monitoring and\nassessing the reliability of the bulk power system, contributing to the stability of electricity\nsupply.\nl Louisiana Public Service Commission (LPSC): LPSC is the state regulatory authority\noverseeing the operations of public utilities, including electricity and natural gas services, to\nensure fair and reasonable service provision.\nDuring tropical storms Ida and Laura, the southern part of Louisiana experienced more severe\npower outages despite the interconnected nature of the grid. This disparity is primarily due to the\nstorms' paths and intensities, which concentrated on the southern coastal regions, leading to\nsignificant damage to the local power infrastructure. For instance, Hurricane Ida caused the\ncollapse of transmission towers in the New Orleans area, resulting in widespread outages. In\ncontrast, northern regions, being farther from the storm centers, faced less direct impact and\nconsequently experienced fewer power disruptions.”.\n\nThe authors should be clearer upfront about their modeling flow. A figure could be helpful\nshowing the data sources and models used at each step in the chain of analyses. For example, I\nasked in my comments below whether the synthetic storms tracks are from an academic request\nto WindRiskTech, for example. Such information, and information on the heat wave projections,\nthe grid modeling, would all be useful for a reader to have in mind early in reading the paper.\nThank you for your comment. We have added a methodology diagram and referred it in\nthe main text as “see Fig. S5 on methodology diagram”.\nSupplementary Figure S5. Illustration of modeling procedure for the power system resilience and\npopulation affected during a TC event.\n\nI have a large number of specific comments. They are relatively minor comments but I think\naddressing them would strengthen the work.\nSpecific Comments:\nABSTRACT\nLine 27\n\"by a factor of ~17×(10x) to 16.2 (28.4) years\" <- one multiplication sign is the times symbol ×,\nthe other is the letter x. I suggest choosing one or the other for both instances.\nThank you for careful reading, we’ve changed all of them to be ×.\n\nLines 28-31\n\"The significant increase in risk can be primarily attributed to 29 projected escalations in\nheatwaves, which result in an approximate 5(2)-fold decrease in 30 compound hazard return\nperiod, and in TC activity, which cause an estimated 2(1)-fold decrease 31 in the return period.\"\n<- The numbers don't match the statement for the SSP2 4.5 scenario. You say the decrease in\nreturn period (10-fold decrease) can ~primarily~ be attributed to heatwaves (2-fold decrease) and\nTC activity (1-fold decrease).\nFor the SSP5 8.5 scenario the statement makes more sense: the decrease in return period (17-\nfold) can primarily be attributed to heatwaves (5-fold decrease) and TC activity (2-fold\ndecrease).\nMaybe it would be worth noting what other factors account for the remainder of the changes in\nreturn period. E.g., after the sentence on line 31 ending in \"in the return period.\" you could add a\nline: \"Other factors include ...\".\n(Coming back to this comment having read more of the paper, SLR appears to be the other factor\nconsidered.)\nThank you for the comments. The sentences were not very clear. We have revised them as\n“ Heatwave intensification is the primary driver of this increased risk, reducing the return period\nby approximately 5× under SSP5-8.5 and 3× under SSP2-4.5. Increased TC activity is the second\ndriver, reducing the return period by 40% and 34% under the respective scenarios.” SLR effects\nwere small, so we didn’t include it in the abstract (with word limits).\n\nINTRODUCTION\nLine 50\n\"Louisiana residents experienced a total of 35 million hours of compound blackout-heatwave\nhazard risk\" <- person hours?\nThank you. Updated with customer hours.\n\nLine 51\n\"with a heat index surpassing 37.8°C/100°F, in accordance with Louisiana's heat advisory\ncriteria\" <- I believe this heat index condition is a national condition, not unique to Louisiana,\nthough I may be mistaken.\nThank you for your observation. The National Weather Service (NWS) just sets general\nguidelines for heat advisories, typically issuing them when the heat index is expected to reach\n100°F (37.8°C) or higher for at least two days, with nighttime temperatures not dropping below\n75°F (23.9°C). When we wrote the paper, it is 105°F for most region and 100°F for Louisiana.\nWe’ve updated the reference. https://www.weather.gov/safety/heat-ww\n\nLines 55-56\n\"Understanding how often Ida-like compound blackout-heatwave events may occur is critical for\nthe development of coastal risk mitigation strategies.\"\n<- this is only relevant for coastal communities facing TCs, not for coastal communities in\ngeneral. Possible edit: \"Understanding how often Ida-like compound blackout-heatwave events\nmay occur is critical for the development of risk mitigation strategies for populations vulnerable\nto tropical cyclones.\"\nThank you for the clarification. We have made the corresponding updates.\n\nLines 64-65\n\"and are responsible for nine of the ten largest power 65 outages in the United States over the\npast two decades\" <- the reference is from 2018. There have been at least two major non-TC\npower outage events since 2018: 2019 California wildfires 2019 and 2021 Texas winter storm.\nCould soften the claim: \"and are responsible for nine of the ten largest power 65 outages in the\nUnited States over a recent two-decade period\". That's the best I can think of.\nThank you for pointing out. The metrics we use here is customer hour outage. The\nimpacted customer number are large under 2019 California wildfires and 2021 Texas winter\nstorm correspondingly from https://www.visualcapitalist.com/sp/ranked-the-largest-power-\noutages-in-the-us-2013-2023/\nThey calculated the affected population by the data from U.S. Department of Energy (DOE).\nHowever, it is hard to calculate the customer hour. The data we are using are in the following\nfigure.\nAccording to the poweroutage.us data, the 2019 California power outages likely involved less\nthan 5 million customer hours (1.1 million customers × 48 hours), which is comparable to the\nimpact of Hurricane Irene.\nThe 2021 Texas winter storm impacted approximately 13 million people, with an average\nrecovery time of less than 42 hours (source: Texas Tribune), resulting in around 500 million\ncustomer hours of impact.\nHowever, calculating the total customer hours for each event remains difficult due to the\ncomplexity of these events. We have adopted your suggested phrasing in the final version. Thank\nyou for your insightful comment and curiosity.\n\nLines 75-76\n\"the TC-blackout-heatwave compound hazard risk would increase by a factor as large as 23 over\nthe course of 21st century under the high emissions scenario RCP8.5.\" <- what does this mean?\nthe \"risk will increase by a factor of 23?\"\nIn the paper they say: \"The expected percentage of Harris residents experiencing at least one\nlonger-than-5-day TC-blackout-heatwave compound hazard in a 20-year period could increase\ndramatically by a factor of 23 (from 0.8% to 18.2%) over the 21st century.\"\nCould rephrase your sentence:\n\"However, a previous study (11) found that for Harris County, TX (a portion of Houston situated\non higher ground and affected by hurricane winds), the percentage of residents experiencing\nspecified 5-day TC-blackout-heatwave compound hazard conditions could increase by a factor as\nlarge as 23 over the course of 21st century under the high emissions scenario RCP8.5.\"\nThere may be a better way of wording this.\nThank you for the careful reading, we’ve rephrased based on your help.\n\nLines 76-77\n\"In recent years, TC-heatwave compound hazards have happened in the Gulf Coast region.\" <-\n\"In recent years, TC-heatwave compound events have happened in the Gulf Coast region.\"\nWe’ve updated.\n\nLines 82-83\n\"Investigating this emerging compound threat, possibly enhanced by climate change, will\ncontribute to our knowledge of and adaptation to compound climate hazards.\" <- can drop the\nword \"possibly\". You've just referenced studies to show the effect; your own study shows the\neffect.\nThank you. Revised.\n\nLine 99\n\"We generate a large number of compound hazard events\" <- how many?\nThank you. We’ve edited the main text, as “We generate a large number of compound\nhazard events (~300,000 stochastic sequences) based on…”\n\nLine 104\n\"We extends the existing wind-impact-only simulation method\" <- \"We extend the existing wind-\nimpact-only simulation method\"\nThank you. We’ve edited the main text.\n\n[Jumping to the Materials and Methods before the Results] [2.5/10.5 pages complete]\nRESULTS\nHISTORICAL CASES\nLines 119-120\n\"Ida devastated the eastern half of Louisiana, which is more densely populated (including the\ncity of New Orleans), whereas Laura grazed the western side\"\n<-\n\"Ida devastated the eastern half of Louisiana, which is more densely populated (including the\ncity of New Orleans), whereas Laura grazed the western side. The eastern half of Louisiana is\nlargely served by the investor owned utility (IOU) Entergy while some of the western half is\nserved by the IOU Cleco who control generation, transmission and distribution in the region.\"\nAnd I'd add a supplementary half page or page going over the broad outline of the network.\nDiscuss the market shares of Entergy and Cleco, their generation fleets, transmission networks,\ndistribution systems; MISO; SERC Regional Corporation; Louisiana Public Service Commission\n(LPSC)\nThank you, we’ve added a detail description in supplementary. We didn’t add the details\nof the market shares as they don’t affect our results and may confuse general readers with limited\nknowledge about the grid configuration.\n\nLine 124\n\"compare relatively well with the observation\" <- should this be \"compare relatively well with\nthe simulation\" ?\nThank you, we are saying the modeling results compare well with the observations.\nWe’ve checked the manuscript.\n\nLines 123-132\n\"As shown in Fig. 1a and b, the model’s estimates for the overall impact of Hurricanes Ida and\nLaura on Louisiana compare relatively well with the observation. Hurricane Ida led to 47% (48%\nin simulation) of customers being out of power within the first 24 hours and it took ~10 days (11\ndays in simulation) for 90% of customers to restore power. Meanwhile, up to 60% of Louisiana\nresidents were under heatwave conditions within 6 days after Hurricane Ida’s landfall. On\naverage, 42% of customers experienced compound power outage-heatwave hazards for at least\na day after the hurricane's landfall, based on the overlap of county-level power outage and heat\nindex data. Hurricane Laura led to 27% (29% in simulation) of customers being out of electricity\ninitially and it took ~6 days (8 days in simulation) for 90% of customers to restore power. To\nmeasure the overall severity of the blackout associated with each TC, we compute the cumulative\ninterruption hours of customers throughout Louisiana (the total of all customers’ power outage\nduration), a commonly used metric in evaluating power system reliability (20). The model\nestimates in total 189 (156-242; ±3𝜎) million power interruption hours for Ida, which is\nconsistent with the observed 206 million hours, and 110 (77-153) million power interruption\nhours for Laura, which compares relatively well with the observed 99 million interruption hours.\nAs a comparison, Hurricane Katrina led to ~140 million power interruption hours. The model\nestimation for the spatial and temporal distribution of power outage also correlates well with\nobservations (the average relative error is <10% between the modeled and observed county-level\npower outage), as illustrated in Fig. 1c for the peak power outages within 24 hours and 5 days\nafter landfall power outages at the county level for Hurricane Ida.\"\n<- Paragraph could be broken in two. There is a lot of information here. I realize Fig 1a and 1b\nare meant to convey this information, but I feel like it could be done better.\nThank you. We’ve broke the paragraph into three.\n\n\"Meanwhile, up to 60% of Louisiana residents were under heatwave conditions within 6 days\nafter Hurricane Ida’s landfall.\"\n<- This isn't really the relevant metric. What percent of Louisiana residents within the affected\npower outage region were under heatwave conditions?\n\"On average, 42% of customers experienced compound power outage-heatwave hazards for at\nleast a day after the hurricane's landfall, based on the overlap of county-level power outage and\nheat index data. Hurricane Laura led to 27% (29% in simulation) of customers being out of\nelectricity initially and it took ~6 days (8 days in simulation) for 90% of customers to restore\npower.\"\n<- *** Could you create a bar chart / histogram showing the number of people-days with outage\n+ heatwave conditions?\nYes, we’ve provided that in the supplementary. And added to the main text “About 42%\nindependent customers experienced compound power outage and heatwave hazards for at least\none day after the hurricane’s landfall, and the peak of simultaneous outages and heatwave effects\nsurpassed 15% at one point (see Fig. S1).”\n\n\"To measure the overall severity of the blackout associated with each TC, we compute the\ncumulative interruption hours of customers throughout Louisiana (the total of all customers’\npower outage duration), a commonly used metric in evaluating power system reliability (20). The\nmodel estimates in total 189 (156-242; ±3𝜎) million power interruption hours for Ida, which is\nconsistent with the observed 206 million hours, and 110 (77-153) million power interruption\nhours for Laura, which compares relatively well with the observed 99 million interruption hours.\nAs a comparison, Hurricane Katrina led to ~140 million power interruption hours. The model\nestimation for the spatial and temporal distribution of power outage also correlates well with\nobservations (the average relative error is <10% between the modeled and observed county-level\npower outage), as illustrated in Fig. 1c for the peak power outages within 24 hours and 5 days\nafter landfall power outages at the county level for Hurricane Ida.\"\n<- A reference to the modeling framework would be useful here, although I suppose that belongs\nin Materials and Methods. Still, I think I'd include a reference to Refs 11 and/or 18, whichever is\nthe more recent or canonical reference + a brief phrase or sentence indicating what kind of model\nit is. As the paragraph stands, as a reader, I am left curious about how exactly anyone could do\nwhat you've done here. Even if you just say that you used \"a transmission and distribution\nmodeling framework (11, 18)\" that would help, I think.\nThank you; we included discussion on the framework and analysis details in Methods.\nWe now added a method diagram as Supplementary Fig. S5. We’ve also added the suggested\ncitations. Additionally, we are extending the existing methods to a multi-hazard framework,\nrather than simply adopting them as is, as discussed in detail in Method.\n\nLines 134-135\n\"The model estimates in total 189 (156-242; ±3𝜎) million power interruption hours...\" <- \"The\ntransmission and distribution model (10, 18) estimates in total 189 (156-242; ±3𝜎) million power\ninterruption hours...\" Or if it's a generation, transmission, and distribution model, you could say\nthat. Just a little more detail for the reader to understand what's going on.\nThank you. We didn’t add the citations here as we have extended the model to a new\nmodel for this study, as discussed in Method. To clarify we add “The model simulation…”.\n\nBLACKOUT AND COMPOUND HAZARDS\nLine 162\n\"~17x (10x)\" <- do you want the letter x or the times symbol? You used both in your abstract.\nEither I think is fine, just be consistent.\nWe’ve updated to times symbol.\n\nSPATIAL PATTERN OF COMPOUND HAZARD RISK (185-222) [38]\nLines 213-222 <- how realistic do you think it is that 9% of customers will experience power\noutages > 15 days (under SSP5 8.5)? Is there precedent for power outages of this duration after\ntropical storms? I'm wondering if there are different factors which come into play when power\noutages reach a certain length of time, maybe additional resources are mobilized, etc.\nAdding more workers could indeed expedite recovery efforts. Currently, during a tropical\ncyclone, utility companies typically rely on mutual aid agreements to borrow workers from\nneighboring states, with the number of workers ranging from 4,000 to 6,000. We follow the\nutility’s strategies (cited as refs. 12 and 19 in Methods), that is the number of workers is\nestimated empirically based on the severity of the event. For instance, a Category II storm\nrequires approximately 800 workers, a Category III storm around 2,500, and Category IV-V\nstorms between 4,500 and 6,500 workers. Thus, in future climates when stronger storms are\nmore often, additional resources are indeed mobilized.\nNationwide, utility companies collectively have access to around 9,000 to 12,000\ncontractors. Mobilizing additional workers could significantly accelerate recovery. For\nhurricanes such as Laura and Ida, utility reports indicate that approximately 6,500 contractors\nwere deployed, yet around 4-6% of the affected population experienced power outages lasting\nover 15 days. If more contractors were proactively engaged, the system repairs could potentially\nbe completed more quickly. However, there is a limit, and we don’t know if this limit can be\nheightened in the future.\n\nDRIVERS OF CHANGES IN COMPOUND HAZARD RISK (224-259) [36]\nSeems fine, see comment on Fig 4b.\n\nDISCUSSION (262-321) [60]\nLines 270-272\n\"When examining the impact of various climate scenarios, such as the high emission scenario\nSSP5 8.5 and the moderate emission scenario SSP2 4.5, it appears that the risk associated with\nIda- or smaller-scale compound hazard events may not exhibit substantial difference.\"\n<- There seems to be a significant difference between scenarios shown in Fig 2b. What am I\nmissing here? Thinking this through. You estimate the current return period of an Ida-like power\noutage + compound heat event to be 278 years, with 35 million customer hours of simultaneous\npower outage and heatwave exposure in Louisiana... So the horizontal dashed line captures the\nmagnitude of the Ida-like compound event. The intersection with the historical conditions occurs\nat 278 years. The intersections with SSP2 4.5 and SSP5 8.5 are at 28.4 and 16.2 years,\nrespectively. I suppose that's not so great a difference. If I'm reading this correctly, then I think\nyou're fine. If I'm reading this incorrectly you may with to clarify your text.\nYou are reading correctly. We edited the text in the result a bit to clarify.\n\nLines 272-274\n\"This result indicates that utility companies urgently need to prepare for the compound events to\nprevent major impacts.\"\n<- maybe this should be rephrased:\n\"This result indicates that utility companies urgently need to prepare for the compound events to\nprevent major impacts, regardless of whether a moderate or high emissions projection is\nconsidered.\"\nThank you, we’ve rephrased.\n\nLines 279-288\nThis is important. Well written. Although I'd consider adding a policy prescription:\n\"However, backup generators and solar panels may be cost-prohibitive for many middle and low-\nincome communities, limiting their effectiveness in reducing heat stress.\"\n<- indicating the need for proactive policy measures such as subsidies or tax breaks for such\ninterventions.\nThank you. We extend as “However, backup generators and solar panels may be cost-\nprohibitive for many middle- and low-income communities, limiting their effectiveness in\nreducing heat stress. Proactive policy measures, such as subsidies or tax breaks, may be needed\nto make these interventions accessible and effective for vulnerable populations.”\n\nLines 289-299\nAlso important and well written. You've outlined some policies that will reduce the potential\nhealth impacts of extreme heat generally speaking, but can you think of specific policies that\ncould be implemented in the aftermath of tropical cyclone events? I could imagine it being\nchallenging to manage cooling centers following a hurricane. On the other hand, investing in\ncooling equipment and back up generators at relief centers and designated shelters could provide\nsignificant returns on investment.\nAlso, given that you have a sense of the relative importance of your three factors (lines 224-225),\nheat index, TC climatology, and SLR, could this feed into your discussion of prioritizing\ninterventions to reduce the risk and impacts of this compound hazard? I suppose to do this we'd\nalso need a sense of the relative costs of such interventions.\nWe edited the discussion to include more discussions about these. “Also, equipping relief\ncenters with reliable cooling and backup power systems can further enhance emergency response\ncapacity. Prioritizing these interventions based on estimated compound risk can guide effective\nresource allocations.”\n\nLines 300-321\nI don't know enough about current emergency management efforts at the state and federal level,\nbut it could be worth including at least a couple of sentences about how your research could feed\ninto state offices of emergency services and FEMA, say. I don't know enough to suggest the\nsentences, but some note about how the work could be integrated into existing planning\nframeworks might be appropriate.\nThat's a great question. Since relief centers are managed by FEMA, we believe the\nsolution proposed for the previous question could be effectively applied to this question as well.\n\nMATERIALS AND METHODS\nSYNTHETIC TCS, STORM SURGE, TIDE AND RAINFALL MODELING\nLine 325\n\"We use the synthetic TC hazard dataset generated in ref. 14 for the North Atlantic basin and\nselect the TC tracks passing within 200 km of Louisiana.\"\n<- I'd prefer something like: \"We use the Xi et al. (2023) synthetic TC hazard dataset (14,\nhenceforth referred to as the Xi 2023 dataset) for the North Atlantic basin and select the TC\ntracks passing within 200 km of Louisiana.\"\nIt's easier for the reader if you state the name of the dataset. If it doesn't have a name, you might\nconsider \"Xi 2023\". I think that's informative and concise, but you could consider alternatives,\ne.g., XLG 2023, something easier for the reader than \"ref. 14\". Just a suggestion; I find it\nfrustrating to have to bounce back and forth to the references when reading journal articles.\n[[Having just reviewed the Nature guidelines, I see this is not the recommended practice. That's\nunfortunate.]]\nThank you. It's a pity that only the format like 'Ref. 14' is allowed.\nMore important comment. The question that came to my mind when reading this sentence --\nactually, when reading the abstract -- was: what synthetic storm tracks are they using?\nProprietary WindRiskTech tracks or open source STORM tracks? Looks like you're using the\nEmmanuel tracks? That may be worth stating explicitly. I can't remember how many models\nWindRiskTech allows for academic research. Are you using a free subset? If so I think this\nshould be stated. It would be useful for others trying to replicate the work. Also, I haven't read\nthrough your code and data, but are you publishing the storm tracks? I'm assuming if they're\nWindRiskTech tracks you are not able to publish them which limits reproducibility, unless other\nresearchers buy the same set.\nWe used a TC track dataset generated in Ref. 15 using a TC model that is also used by\nWindRiskTech. In Method, we mentioned “We use the synthetic TC hazard dataset generated in\nref. 15 for the North Atlantic basin and select the TC tracks passing within 300 km of Louisiana,\nwith a maximum wind speed of at least 22 m/s. The dataset contains synthetic TC tracks\ngenerated with the statistical-deterministic TC model (34), which has been applied to TC hazard\nassessment (9, 35, 36).” Ref 34 is the model used in WindRiskTech for generating tracks.\nHowever, the model existed before WindRiskTech and Ref. 15 and a previous study (Ref. 8)\ngenerated the dataset using the model in collaboration with Kerry Emanuel, the owner of\nWindRiskTech, but also use the model for academic research. To not confuse reader, we don’t\nmention WindRiskTech but cite the model with the journal publication. The track datasets are\navailable upon request to Kerry Emanuel for academic use, as included in the data statement of\nRef. 15 and Ref. 8. The hazards (wind, surge, rainfall) of the TC tracks generated by Ref. 15\nwere posted by Ref. 15. This study used these datasets to generate the power system damage data\ndue to these TC tracks and their hazards, and the damage data will be posted, as included in the\ndata availability statement.\n\nLines 332-333\n\"based on six CMIP6 climate models: CanESM5, CNRM-CM6-1, UKESM1- 0-LL, EC-Earth3,\nIP-SL-CM6A-LR and MIROC6.\" <- maybe here you could spell out for the reader where you\nobtained the data, if it cost money, etc. Again, this would be useful for other researchers, like\nmyself, thinking about how they could replicate the results.\nThank you for that, we spell out the whole name in supplementary (“Canadian Earth\nSystem Model (CanESM5), Centre National de Recherches Météorologiques Climate Model\n(CNRM-CM6-1), UK Earth System Model (UKESM1-0-LL), EC-Earth3, IPSL-CM6A-LR\n(Institut Pierre-Simon Laplace Climate Model), and Model for Interdisciplinary Research on\nClimate (MIROC6).”). Again, Ref. 15 and Ref. 8 used these GCMs, we didn’t directly use these\nGCMs. Generally, it doesn’t cost money to use GCM outputs.\n\nLines 371-373\n\"we assume the coastal levee will be elevated to the historical 100-year return level plus one\npercent quantile SLR (~2 meters averaging over SSP2 4.5 and SSP5 8.5 for New Orleans\ncalculated from ref. 14)\" <- I don't understand what you mean by \"plus one percent quantile\nSLR\". Please clarify.\nSorry we made an error in wording. It is now corrected as “plus one percentile SLR”.\n\nLine 376\n\"(Error! Reference source not found.).\" <- needs to be corrected\nCorrected.\n\nLines 376-378\n\"A sensitivity test was performed on future compound hazard risks given different elevations of\nthe coastal levee from 0-3 meters above the current level. \"\n<- I see the supplementary figure S1, but do you report the change in Rita-like return intervals\nunder all the sensitivity tests.\nYes. Sorry for the confusion; the caption for the figure was not clear. We have changed it to changed it to\n“Supplementary Figure S4. Estimated average interruption days of compound hazard during\nevents with a return period of 278 years under SSP5 8.5 for coastal counties with different added\nlevee heights.” So the figure show results for different levee increments for a specific return\nperiod (like Ida), consistent with Fig. 3b (for the same scenario but only for 2m increment).\n\nHEATWAVE PROJECTIONS\nLine 384\n\"Following ref. 5, the daily HI is determined\"\n<- I can't find HI defined in this manuscript. Nor could I find it described in ref. 5 (The Ebi et al.\n2021 paper). This reference should be fixed and HI should be defined clearly within this\nmanuscript (or Supplement) so that readers don't have to download an external reference to\nunderstand the metric.\nThanks for checking even on the references. Sorry for the inconsistency. We’ve fixed the\nreference. It is ref.11: T. Matthews, R.L.Wilby and C. Murphy “An emerging tropical cyclone–\ndeadly heat compound hazard”. Nat. Clim. Chang., 9(8), 602-606(2019).\n\nLines 390-391\n\"According to the historical analysis in ref. 4\" <- should this be ref. 14?\nSorry. It is ref.11. As revised in last comment.\n\nLine 391-392\n\"the HI will drop upon TC landfall and will recover to the ambient average within around ten\ndays.\"\n<- I'm a little unclear on this. So, this means that temperatures drop at TC landfall then recover\nafter the TC, within ten days. Average power outages are 96 hours according to your statement in\nthe introduction (line 42). That's four days. I don't think I understand how the heatwave is a\ncompounding hazard if temperatures are lower during the power outage and only recover six\ndays after the power outage. I may be misreading this because HI is not defined so I may be\nmisunderstanding what's happening here. This requires significant clarification.\nDoes this mean that high HI is equivalent to low temperature? Is there a typo in the sentence I\nhave highlighted?\nThe heat Index will temporarily drop after the TC passes, but it will quickly recover to\nthe ambient HI. If the ambient HI is high (above the heatwave threshold), a heatwave will occur\neven with the drop. We considered this drop in our analysis as “To account for this dependence\nbetween TCs and heatwaves, we add the composite of the impact of TC passage to the\nmeteorological variables used to calculate the HI, where the composite impact is estimated based\non historical data (Fig. 3a in ref. 11).”\n\nLine 394\n\"Fig. 3a in ref. 4\" <- Ref. 4 is \"National Weather Service, New Orleans, New Orleans/Baton\nRouge (2021)\" <- I don't see a Fig 3a in this reference. Perhaps you mean ref 14.? There is a Fig\n3a in ref. 14, so I presume this is what you are referring to.\nSorry this is the same mistake as above, it is Fig. 3a in ref.11. Corrected.\n\nPOWER OUTAGE AND RECOVERY MODEL\nLines 408-412\n\"We apply a physics-based power system model, which explicitly simulates component level\ndamage to predict the total power outage, accounting for the effects of future evolving factors,\ne.g., climate change, infrastructure upgrade, and utility maintenance. The physics-driven\nmodeling of the power system allows us to better understand the impact of climate change and\neffectiveness of risk mitigation measures compared to if we used purely data-driven models\n(47,48).\"\n<- Are the references 47 and 48 to data-driven models? If so, what are the key references to the\nphysics based models you use? Ah, I see you state this in the next paragraph (refs. 11 and 18.)\nYou could note that in the paragraph above:\n\"We apply a physics-based power system model (11, 18), which explicitly...\"\nYes, your understanding is correct. As there are many parameters to describe for the\nphysics model, we put them on the next several paragraphs.\n\nLine 420\n\"A DC flow simulation\" <- define DC flow simulation. As someone who is not an expert in\nenergy modeling, I assume this means direct current flow simulation. Questions that come to my\nmind: How do DC flow simulations compare to AC flow simulations? They're simpler, right, but\nstill useful?\nYes, we’ve got the direct current (DC) full name in the main text. While DC power flow\nmodels simplify the analysis, they are widely adopted in power system risk assessment literature\ndue to their demonstrated accuracy in capturing historical cascading failures and topological\ndisruptions. As referenced in (ref. 50):\nY. Yang, T. Nishikawa, and A.E. Motter. \"Small Vulnerable Sets Determine Large\nNetwork Cascades in Power Grids.\" Science, 358(2017).\n\nLines 422-423\n\"the performance of the power grid 423 outside the study area is assumed to be under normal\noperation.\"\n<- is this a reasonable and weak assumption, or a bold simplifying a assumption?\nThe assumption of normal operation for the power grid outside the study area is well\njustified. This boundary condition is frequently adopted even in county-level analyses (e.g.,\nOuyang and Duenas-Osorio, 2014), and becomes more reasonable for state-level studies (e.g.,\nYang et al., 2017) where the relative impact of external networks is further diminished.\nY. Yang, T. Nishikawa, and A.E. Motter. \"Small Vulnerable Sets Determine Large\nNetwork Cascades in Power Grids.\" Science, 358(2017).\nM. Ouyang, L. Duenas-Osorio, Multi-dimensional hurricane resilience assessment of\nelectric power systems. Struct. Saf. 48, 15–24 (2014).\n\nLines 424-443\nThe fragility curves in refs. Error! Reference source not found. ... Error! Reference source not\nfound. and 18 except that the effects of rainfall and flood are incorporated. The parameters are\nestimated by the Markov chain Monte Carlo (MCMC) method to minimize the mean squared\nerror between simulated and observed county-level power outages.\n<- I'm unable to assess the validity of these methods a references is missing. Following ref. 18\nwhich is not missing takes me to the Ouyang and Dueñas-Osorio (2014) paper. What is described\nhere is fairly short on details but seems reasonable, given the Ouyang and Dueñas-Osorio\nframework. Eq (1) seems sensible.\n*** Note that a figure in this present manuscript could potentially benefit from a figure\nanalogous to Fig 2 in the Ouyang and Dueñas-Osorio (2014) paper, but showing southern\nLouisiana rather than Harris County.\nSorry about the error in the final compiling of the referencing. We’ve fixed the references\nand added a figure in the supplementary to show the procedure.\n\nLines 441-443\n\"The parameters are estimated by the Markov chain Monte Carlo (MCMC) method to minimize\nthe mean squared error between simulated and observed county-level power outages.\"\n<- It's not clear to me from reading the manuscript what training data were used to perform this\nMCMC estimation. This information may be contained in the missing reference. Even if it is, it\nmay be worth restating here: what data were used to estimate the parameters of the fragility\nfunctions?\nWe used MCMC method as a tool to fit model parameters using observations. That is we\nperform many simulations with varying parameters and find the parameters that generate results\nclosest to the observations. The observations are power outage data during Hurricanes Laura and\nIda and we gave them equal weight of importance. To clarify, we added to the main text “The\nparameters are estimated by the Markov chain Monte Carlo (MCMC) method to minimize the\nmean squared error between simulated and observed county-level power outages (1) under\nHurricanes Laura and Ida with equal weight.”\n\n= = = = =\nREFERENCES\n<- Note: it appears that the references are not all in Nature format and should be harmonized\nbefore publication. Most of them seem fine, but there are some problematic entries, some of\nwhich I've identified, some of which I may have missed.\n1. U.S. department of energy <- capitalization Department of Energy\nFixed.\n2. Hurricane Ida Power Restoration,Utility Report, Entergy (2021) <- missing period at end of\nline. Also I was not able to find this reference looking online. More detail would be helpful on\nhow to find the reference, which looks interesting.\nFixed. It is a series of news reports following\n“https://www.entergynewsroom.com/article/entergy-system-hurricane-ida-update-9-2-21-9-m/”.\nEvery day there is a report.\n3. Nicholas Bogel-Burroughs and Katy Reckdahl. The Greatest Killer in New Orleans Wasn’t the\nHurricane. It Was the Heat.. New York Times (2021). <- double period.\nFixed.\n4. National Weather Service, New Orleans, New Orleans/Baton Rouge (2021) <- not a complete\nreference; where can I find the statement of the heat advisory criteria?\nFixed. Changed to 4. National Weather Service, Heat Watch vs. Warning (2024)\nhttps://www.weather.gov/safety/heat-ww\n15. \"Fox-Kemper, B., et al. Cambridge University Press, Cambridge, United Kingdom and New\nYork, NY, USA, pp. 1211–1362, doi:10.1017/9781009157896.011.\"\n<- According to my copy of Zotero, the Nature-style citation should be:\n\"1. Ocean, Cryosphere and Sea Level Change. in Climate Change 2021 – The Physical Science\nBasis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental\nPanel on Climate Change (ed. Intergovernmental Panel on Climate Change (IPCC)) 1211–1362\n(Cambridge University Press, Cambridge, 2023). doi:10.1017/9781009157896.011.\"\nThank you for that comment. They do not require format formation at the first period. But\nwe will try to fix any references that could not be found.\n\n= = = = =\nFIGURES\nFigure 1.\nDo you not have the yellow heatwave curve for Hurricane Laura? If it could be added that would\nstrengthen the paper.\nLaura has some time point reaches 98 F but not break 100 F. It is not regarded as\nheatwave.\n\nFig 1c. It's a little hard to see exactly what the differences are between the top panel\n(observations) and the lower panel (simulations) in this subfigure. I could imagine a third row\nshowing differences (it looks like the differences would be minimal. I could also imagine a third\ncolumn showing Sep 7 say as that's when the power comes back online. Alternatively I could\nimagine a table showing some kind of summary statistics indicating the skill of the simulations\nfor this (these?) test case(s), because just looking at the figures all I can make out is that the\nsimulations appear to match the observations well. Of all these comments I think the Sep 7\naddition would be highest on my list of potential additions, unless you've already done it and\nfound it to be uninformative.\nThank you for your valuable feedback. We see your point about the difficulty in\ndistinguishing between the top (observations) and bottom (simulations) panels in the\nsubfigure. A third row showing the differences could indeed be a helpful addition,\nespecially since it appears that the differences are minimal. Alternatively, we added a\nthird column for September 7, which would highlight when power is restored. This could\nprovide a clearer visual distinction and help emphasize the recovery process.\nThe model estimation for the spatial and temporal distribution of power outage also\ncorrelates well with observations (the average relative error is <10% between the modeled and\nobserved county-level power outage), as illustrated in Fig. 1c for the peak power outages within\n24 hours, 5 days and 8 days after landfall power outages at the county level for Hurricane Ida.\n\nFig 1c. It would be very useful to see the figures for Laura in addition to the figures for Ida, since\nLaura affected the west part of the state. I'd be interested to see how the map changes as\npresumably at least some of the transmissions lines to New Orleans were spared?\nThank you for your suggestion. I agree that including figures for Laura alongside those\nfor Ida would provide valuable insight, especially since Laura affected the western part of the\nstate. It would indeed be interesting to compare how the map changes, particularly given that\nsome transmission lines to New Orleans may have been spared. We have added a figure in the\nsupplementary materials to reflect this. However, we only have simulated power line failure data,\nand while transmission lines may have been a major source of initial failure, they may not have\nbeen the dominant factor in the recovery process. As such, we did not add the figure to include\ntransmission line failure data.\n\nFigure 2\nGreat figure. Needs color legend boxes on at least 2a, in the top left corner for example. red line\nSSP5 8.5, yellow SSP2 4.5, blue historical.\nInterruption Hour By Power Outage (Million Hour) is good Interruption Hour By Compound\nHazard (Million Hour) is also fine, but maybe there's a better way to describe these:\nYou have space at the tops of the subfigures 2a and 2b. You could potentially provide titles:\n\"Return Periods of Power Outages\" and \"Return Periods of Power Outages and Extreme Heat\".\nThat might catch the eye a little better, allow the reader to quickly grasp what's in the figures.\nx-axes need to be labeled in 2a and 2b or at least in 2b.\nThank you for careful reading. We’ve fixed the details.\n\n2c: the numbers in parentheses in the main cells have not been defined. We can infer that they\nrepresent the 95% confidence intervals, but this should be stated explicitly in the caption.\nFinally, in the table 2c, I'd consider putting SSP2 4.5 in the middle column and SSP5 8.5 in the\nright column so that the numbers are monotonic reading from left to right.\nWe’ve fixed the details.\n\nFigure 3\nIn Fig 3d, for the lines in the top left of the figure, the line types should differ, one could be solid,\nanother short dash, another long dash, in addition to the color differences. This would make the\nfigure easier to read, though you'll have to free up some space to show the dash type in the\nlegend. I think\nit can be done. If it turns out to make the figure too busy, it's probably fine to leave as is.\nThank you for your comments. Yes, it would be too busy. This journal is available online\nonly, and for consistency, we typically use a single line style.\n\nFigure 3 title is \"Estimated average interruption days of compound hazard\" <- maybe consider\n\"Estimated average power interruption days with heat compound hazard\" to be more explicit (or\nsomething similar that uses the words power and heat)\nThanks. We changed to “blackout-heatwave compound hazard”.\n\nIn Fig 3d, The breaks for the interruption hours of compound hazard are 0-60 hours (0-2.5 days),\n60-120, (2.5-5 days), 120-180 (5-7.5 days), 180-240 (7.5-10 days), 240-300, (10-12.5 days), 300-\n360 (12.5-15 days), and > 15 days. When I tried to do the hours to days calculation in my head I\nwas off by a factor of two, embarrassingly, so I was confused by the long duration in the tail of\nthe distribution. Perhaps it would be better to label the durations by day rather than by hour. This\nwould match Fig 3a-c.\nWe changed to bi-labels, thank you for your comments.\n\nFigure 4b: I'm not sure if you need that much of the x-axis for this figure since the range appears\nto be around 0-65, not 0 to 278. It's probably fine as it is, but maybe it would be easier to see the\ndistinctions between scenarios zoomed in on the x dimension.\nThank you for the comments. We want to match with 4a for easier comparison.\n\nSUPPLEMENTARY MATERIALS\nSupplementary Fig S1\n\"Therefore, the potential inaccuracies ... do not detract from the paper's main conclusions.\" <-\n\"Therefore, the potential inaccuracies ... should not detract from the paper's main conclusions.\"\n(soften the claim)\nAdjusted.\n\nI don't understand why there is such a discrete edge or east-west cut off line at about 30 degrees\nnorth. Knowing very little about the grid in Louisiana, but a little about the grid in California, I\nam curious how Entergy, Cleco, MISO, and the SERC Reliability Corporation (and even LPSC)\nrespond in these conditions and how they responded in response to Ida. If transmission lines and\nsubstations are down, why aren't any regions outside of the counties shown affected? Why is it\nsuch a clean break on the map? A little background about the system would be helpful.\nThis is because the coastal region experiences significant flood inundation, requiring the\nremoval of obstacles (e.g., fallen trees) as a first step. Additionally, repairing local power poles to\nrestore the network can only proceed after clearing the roads, as road access is essential for\npower network repairs. Please see the figure.\nWe clarified in the main text that only coastal counties are considered as potentially\nimpacted by storm surge and flooding.\n\nSupplementary Fig S3\nI feel like you could include more than just the topology of the system in a figure like this. I'm\nnot that familiar with figures of power transmission networks, but this seems like a missed\nopportunity to bring in more interesting details of the system. To my eye there are four clusters\nof network activity in the southern portion of the state (the one below the 30 degree north\ncutoff), presumably corresponding to Lake Charles, LaFayette, Baton Rouge, and New Orleans. There is\nanother question just to the west at Beaumont Texas and to the east at Gulfport Florida.\nA question that comes to mind is how would this picture look if rendered in the style of Fig. S1,\ni.e., coloring the network by the average annual interruption days under compound hazard.\nA related question: how do these events play out through Entergy, Cleco, MISO and the SERC\nReliability Corporation? How did Laura and Ida play out according to the operators?\nThere is an interesting EIA report written on September 14, 2021, with text including:\n\"Because of the electric load decrease caused by Hurricane Ida, electric generation in the\nsouthern MISO region was forced to drop by a corresponding amount to keep the grid in balance.\nHourly nuclear generation decreased significantly, falling from 5.2 gigawatts (GW) on August 29\nto 3.7 GW on August 30. The River Bend Station nuclear plant was cut back to 40% of its\ncapacity. In addition, the Waterford 3 nuclear plant located 25 miles west of New Orleans was\nshut down as a safety precaution because of high winds on the morning of August 29.\n\"Natural gas-fired power plants also reduced electric generation. Hourly natural gas-fired electric\ngeneration in the southern MISO region peaked at 20 gigawatts (GW) on August 26, but it only\nreached 12 GW on August 30.\"\n[Interesting!]\nIf you feel up to writing it, a couple of paragraphs about how the electrical system operates could\nbe helpful for the reader who may be unfamiliar with some of the elements of transmission\nsystem management during TCs.\nThank you for the points. We’ve included the service area in Fig. S4 (original, now S8).\nSo we currently did not update this figure. Coloring network section is interesting. We did not do\nthat in the earlier version because transmission line usually are recovered immediately post TC\nand there are too many distribution lines, where we could not show them fully to readers. The\noperational aspects are highly complex; therefore, our study focuses on long-term conditions\nwhere recovery processes play a more significant role, enabling us to draw solid conclusions. But\nwe do think it is interesting to show the failure probability of each transmission sector by annual\nfailure probability. So we now have added a figure in supplementary to show that.\nSupplementary Figure S7. Annual failure probability of transmission lines under the historical\nclimate.\nSupplementary Discussion for Supplementary Figure S7: To illustrate transmission line features,\nFigure S7 presents the annual failure probability of transmission lines under historical climate\nconditions, derived from component-level failure simulations incorporating wind speed data. The\nanalysis mapped historical TCs to individual transmission poles and lines, using vulnerability\ncurves to calculate component-specific failure rates. These rates were then converted to annual\nfailure probabilities. The methodology considers a transmission line to have failed if any single\ncomponent along its length fails, resulting in higher failure probabilities for longer transmission\nlines due to their increased number of potential failure points.\n\nSupplementary Fig S4\nThis should be rendered in higher resolution, so that the text is legible in a PDF.\nWe are currently unable to directly upload the high-resolution version. The high-\nresolution map can be accessed here: https://www.lpsc.louisiana.gov/Utilities_Map. We will\ndiscuss the matter with the publisher. We requested the corresponding data from Louisiana\nPublic Service Commission (LPSC) and cross-checked it with Entergy’s data, which, while not\nfully covering Louisiana, allows for zooming in at various levels, to control the data quality.\nPlease note that we may not have the rights to publicly share the data at such a detailed level.\n\n= = = = =\nFurther miscellaneous comments:\nWhat does the National Association of Regulatory Utility Commissioners (NARUC) think about\nhurricanes and future hurricane heatwave risk?\nIt has many plan books to support energy system resilience against hurricanes,\nheatwaves, and other threats: Energy Resilience Reference Guide: A comprehensive resource for\nstate utility commissions, offering tools and metrics to guide resilience investment decisions\nacross sectors (https://pubs.naruc.org/pub/45930E31-AD27-1228-C5A0-3FFCFD9DAD95);\nIntegrated System Resilience: Supports frameworks for tackling extreme weather, cyber-attacks,\nand critical infrastructure risks, with a focus on aligning resources and evaluating\npolicies(https://www.naruc.org/core-sectors/critical-infrastructure-and-cybersecurity/integrated-\nsystem-resilience);Critical Infrastructure Policy: Enhances emergency preparedness and recovery\nefforts, ensuring reliable utility services during major events(https://www.naruc.org/events/all-\nevents/grid-resilience-cohorts-workshop/).\n\nWhat's the air conditioning penetration rate in Louisiana? It may be worth mentioning\nsomewhere that the air conditioning penetration rate is about 97% according to the US EIA.\nAccording to the U.S. Energy Information Administration (EIA), approximately 93% of\nLouisiana households used air conditioning in 2020\n(https://www.eia.gov/consumption/residential/data/2020/state/pdf/State%20Air%20Conditioning\n.pdf). We have added to the introduction “93% of Louisiana households used air conditioning in\n2020 corresponding to ref. 4”. Thank you for the thoughtful question.\n\nWhat are the risks to the energy grid for Louisiana during TCs? River Bend and Waterford 3\nnuclear power plants. What does the thermal fleet look like (coal, oil, natural gas)? Renewables,\nincluding biomass? What about the battery fleet? Are there any metrics of Louisiana's grid\nstability that we can compare to those of other states?\nLouisiana's energy grid faces significant risks during tropical cyclones, including\ninfrastructure damage, energy production disruptions, and vulnerabilities due to reliance on\nnatural gas.\nKey Risks During TCs:\nInfrastructure Damage: High winds and flooding can damage power lines, substations,\nand plants, causing widespread outages.\nProduction Disruptions: Offshore oil and gas production is heavily impacted; after\nFrancine, 30% of crude oil and 41% of natural gas production in the Gulf of Mexico remained\noffline.\nNuclear Power Plants:\nRiver Bend Station: 974 MW, ~8% of state demand.\nWaterford 3: 1,152 MW. Both facilities are built to withstand extreme weather.\nThermal Fleet:\nNatural Gas: Dominates the energy mix, supported by local supply.\nCoal and Oil: Minimal; primarily used for emergencies or peaking power.\nRenewable Energy:\nBiomass: Significant utilization from agriculture and forestry.\nSolar & Wind: Limited contribution compared to other states.\nBattery Storage:\nEarly development stage, with plans for 4.5 GW of solar and storage projects led by\nEntergy to improve reliability.\nGrid Stability Metrics:\nLouisiana's grid stability metrics aren't directly comparable to other states, but its reliance\non natural gas and vulnerability to TCs highlight the need for diversification and resilience\ninvestments.\nSince this paper is mainly discussing about recovery period, these may not be the most\nimportant impact as the power source usually recover quicker than transmission and distribution\nutilities. Thus, we have focused on infrastructure damage risk and heatwave impact during the\ngrid recovery period.\n\nEntergy and Cleco have data on how the number of outages, peak outages, time to restore,\nnumber of hours each customer or service point was out of commission, poles, spans of wire,\ntransformers, substations, transmission lines. Can you calibrate your fragility models against the\naggregate Entergy and Cleco datasets? Or is this what you did? If this is not discussed within this\nmanuscript I presume it was within the references you have cited and I just didn't see it. But as a\nmodeler I'm interested in knowing how you validated your fragility functions and your model\noutputs.\nThank you for the comment. We incorporated the total number of pole failures into the\ncalibration process to ensure that the magnitude of damaged components in our simulation aligns\naccurately with reality. However, the primary focus during calibration was on county-level power\noutage data provided by the U.S. Department of Energy, Office of Cybersecurity, Energy\nSecurity, and Emergency Response, which was interpolated to an hourly resolution. As\nmentioned in Method “The parameters are estimated by the Markov chain Monte Carlo (MCMC)\nmethod to minimize the mean squared error between simulated and observed county-level power\noutages (1) under Hurricanes Laura and Ida with equal weight.”\nHurricane Response Record, Office of Cybersecurity, Energy Security, and Emergency\nResponse, U.S. Department of Energy (2021). (Reference 1)\n\nReviewer #3 (Remarks on code availability):\nThe code is very superficial, providing (very) simple scripts at the very last stage of the analysis,\nconverting final data output into figures for the manuscript. Much more useful would be the full\ncode base taking the user from the input data (e.g., synthetic storm tracks, heat index projections,\netc.) all the way through to the final data and figures. Unless I have some made some error in\ninterpretation, it would not be possible for me to reproduce the authors' results starting with the\nraw input data.\nThanks for checking. This is also the first time we use the new code platform provided by\nNature Communications. The current code platform is unable to fully execute our model because\nit relies on parallel computation settings that require both GPU and CPU interaction in MATLAB\nand some data processing relies on python. Additionally, the platform mandates that all results be\ndirectly compliable, while a complete run of our model would take several days to generate the\nfull risk profile. Thus, we only uploaded the results data and code to reproduce figures as what\nwe did for former papers.\nTo address these challenges and enhance the reproducibility of our results, we will\nupload the data and code and make a simplified calculation case available on GitHub upon\npublication. This case will represent the system, with geospatial information for distribution\nnodes blocked (as detailed distribution grid data cannot be published due to security and privacy\nrestrictions). It will also include a TC hazard input file that details the multi-hazard damage\nscenarios and recovery processes.\nWe have updated data and code statements."}, {"round": 2, "reviewer_comments": {"reviewer_2": "I have reviewed this revised manuscript, and find that the authors have done a good job addressing my concerns, as well as\naddressing the concerns of the other reviewers. As a result, I am now comfortable recommending publication of the\nmanuscript in its present form.\n(Remarks on code availability)\nThe code appears to be operable, but in any case is only code for generation of figures, since the code to generate the\nunderlying data was part of an earlier publication. I have not attempted to re-generate the figures for the study.", "reviewer_3": "The authors’ responses to my comments and suggestions are thoughtful, thorough, and well reasoned. I recommend\npublication of the piece without further revisions.\n(Remarks on code availability)"}, "author_response": "Response:\nThanks again for reviewing our paper.\n\nResponse:\nThank you for the comments. We have made an operatable case and put the data and\ncode on github.\n\nResponse:\nThanks again for reviewing our paper."}]

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