Moisture control of tropical cyclones in high-resolution simulations of paleoclimate and future climate

Domain: earth_science
Published: 2023-10-13
Editorial outcome: minor_revision

Abstract
The intensity of tropical cyclones (TCs) is expected to increase in response to greenhouse warming. However, how future climate change will affect TC frequencies and tracks is still under debate. Here, to further elucidate the underlying sensitivities and mechanisms, we study TCs response to different past and future climate forcings. Using a high-resolution TC-resolving global Earth system model with 1/4° atmosphere and 1/10° ocean resolution, we conducted a series of paleo-time-slice and future greenhouse warming simulations targeting the last interglacial (Marine Isotope Stage (MIS) 5e, 125 ka), glacial sub-stage MIS 5d (115 ka), present-day (PD), and CO2 doubling (2×CO2) conditions. Our analysis reveals that precessional forcing created an inter-hemispheric difference in simulated TC densities, whereas future CO2 forcing impacts both hemispheres in the same direction. In both cases, we find that TC genesis frequency, density, and intensity are primarily controlled by changes in tropospheric thermal and moisture structure, exhibiting a clear reduction in TC genesis density in warmer hemispheres.

Introduction
Landfalling tropical cyclones (TCs) can cause severe damage and impact human livelihoods and lives. Understanding what controls their frequency, tracks, and intensities is therefore of utmost importance. There is an emerging consensus between numerical models and theory suggesting that greenhouse warming will likely increase the intensity of TCs in the future 1–6. However, there is still a large uncertainty in model-based projections of TC frequencies. Part of this uncertainty can be attributed to sensitivities with respect to external forcings, climate model physics, and TC tracking algorithms 4,7–9. Due to a lack of theoretical understanding, it is unclear whether and where future TC frequencies will increase or decrease. In general, paleoclimate studies may provide an additional testbed for TC activity changes in future projections.

To gain a deeper understanding of how TCs respond to warm background climate states, several studies have examined synoptic and mesoscale atmospheric variability during the past warm climate episodes such as the Last interglacial period (LIG, ~125 thousand years before present, ka, global mean annual surface temperatures 0.8–0.13°C warmer than pre-industrial, and CO2 ~280 ppm) 10, the Eocene (56,000–33,900 ka, global mean surface temperatures 10.4–15.6°C warmer than later 20th century and CO2 ~1400 ppm), and the Pliocene (5300–2600 ka, global mean surface temperatures 1.8–3.6°C warmer than pre-industrial and CO2 ~400 ppm) 11. The LIG period was the latest time when global mean surface temperatures exceeded pre-industrial level; however, the CO2 levels were lower than other hothouse periods such as the Eocene and Pliocene, and the elevated temperatures during the LIG were likely caused by strong seasonal variations in the incoming solar radiation and corresponding climate rectifications 12.

Recent modeling studies 11,13,14 have documented that past hothouse boundary conditions allowed TCs to form at higher latitudes, as projected for future warmer conditions. However, whether any of these paleo situations can be regarded as a “qualitative analog” for the future is still an open question. This is because those TCs from paleoclimate simulations are not only affected by greenhouse gas forcing but also by orbital forcing, which incorporates seasonal variations in incoming solar radiation because of changes in Earth’s orbit and axis. Previous results on the effect of orbital forcing reveal that when the solar radiation is stronger in the Northern Hemisphere (NH), the NH becomes less favorable for TC activity, while the Southern Hemisphere (SH) becomes more favorable 15,16. Nevertheless, most of the studies are based on the idea that large-scale environments such as TC genesis potential indices (GPIs) can represent changes in TC activity. To obtain a detailed perspective on the relationship between large-scale climatic drivers and TC statistics, it is important to explicitly resolve TCs and their key mesoscale features in a climate model. This requires a relatively high atmospheric model resolution of at least 50 km 17 and it is also desirable to capture the coupling between atmosphere and ocean to account for cold wake effects and changes in ocean heat content, which may further influence the character and the statistics of TCs 18. To the best of our knowledge, there are no systematic paleo-time-slice simulations that explicitly resolve TCs using a fully coupled high-resolution Earth system model. In other words, TC statistics such as frequency and intensity under different background conditions have not been explored in detail yet. Therefore, our study aims to better comprehend TC statistics for past and future warmer climates by using high-resolution Earth system models that explicitly simulate TCs.

Apart from the question of “analog”, it is also important to elucidate how TCs are formed under different boundary conditions 15,16 to identify the underlying physical mechanisms and controlling factors. The LIG, also referred to as Marine Isotope Stage (MIS) 5e (~125 ka), and MIS 5d (the glacial sub-stage, ~115 ka), with large seasonal changes in solar radiation, serve as excellent test grounds. For consistency, we will refer to the LIG as MIS 5e hereafter. These two periods, MIS 5e and MIS 5d, represent extreme orbital conditions with low and high precession indexes, respectively, and large eccentricity. A low (high) precession corresponds to NH summer perihelion (aphelion) conditions and associated intensified (weakened) NH summer solar radiation. This effect is visible only for high values of Earth’s orbit eccentricity, and the interval from 125 to 115 ka represents one of these periods. In other words, contrary to the present-day (PD) orbital configuration, the MIS 5e is characterized by a warmer NH summer and a colder SH summer. The MIS 5d is characterized by a warmer SH summer and a colder NH summer. Understanding the TC response to an altered seasonal cycle amplitude may shed further light on the roles of interhemispheric forcings and seasonal changes in environmental factors such as sea surface temperature (SST), humidity, and vertical wind shear in TC genesis.

Here we set out to study the role of extreme orbital conditions and greenhouse gas forcing on TC frequencies and intensities using the high-resolution version 19 of the Community Earth System Model (CESM) 1.2.2 with 0.25° atmosphere and 0.1° ocean resolutions. To this end, we conducted two century-long time-slice simulations for MIS 5e (~125 ka) and MIS 5d (~115 ka) conditions (Fig. 1A) (“Methods”). The orbital forcing simulations are then compared with the experiments for PD climate and CO2 doubling conditions (2×CO2) 6,20,21. Compared to the previous study 6, which used the last 20 years of the simulation, this study detects TCs using the last 60 years of each simulation. We also employ two fundamentally different TC tracking algorithms (i.e., traditional vortex-based atmospheric conditions and phenomenon-based large-scale conditions; “Methods” text) to evaluate the sensitivity of the TC tracking schemes. To identify the linkage between large-scale and synoptic conditions, the results are then compared with the changes in the genesis potential index (GPI) and each of its controlling factors, also referred to as GPI component analysis. This study aims to determine whether there is a common link between large-scale environments and changes in TC characteristics from the past to the future. It also eventually seeks to improve the robustness of our physical understanding of the dominant drivers of TC frequency and intensity changes under various background conditions.

Results
Climate response to orbital and greenhouse gas forcings
MIS 5e and MIS 5d correspond approximately to the minimum and maximum phase of the precession index (Fig. 1A) during the past 130 ka. Atmospheric CO2 concentrations of ~275 ppm in both periods (Fig. 1B), as well as the corresponding mean tropical temperatures, were similar to pre-industrial levels (Table 1, Fig. 1C), as shown by a reconstruction of tropical SST 22 and a transient climate model simulation 23 conducted with a lower-resolution model version of CESM 1.2.2. Therefore, the comparison between the corresponding CESM 1.2.2 paleo simulations reveals mostly the impact of precessional variability and the associated effect of seasonal and hemispheric insolation asymmetry on the climate system.

Analyses of averaged tropical climate variables relevant for TC dynamics over the oceans in these simulations reveal the following: atmospheric responses to incoming solar radiation in the orbital forcing simulations during the NH summer and SH summer are out of phase (Fig. 2A). Due to the changing seasonal insolation by precession, there is increased solar radiation in the NH summer (July to September) and decreased radiation in the SH summer (January to March) during MIS 5e compared to MIS 5d (blue line in Fig. 2A). This anomalous distribution of solar insolation generates a warmer NH summer and autumn (as in the 2xCO2 forcing simulation at 200 hPa) and a colder SH summer and autumn (blue solid and dashed lines Fig. 2B). We observe increased temperatures with a lag of a few months both at lower and upper levels with a greater magnitude in the upper levels (Fig. 2B). The forced specific humidity variations are proportional to the temperature changes with higher values during the NH summer and lower values during the SH summer months in the MIS 5e in comparison with MIS 5d (Fig. 2C). As warm air can hold more moisture, the region with increased temperatures should, in general, correspond to reduced relative humidity (RH). Interestingly, the diagnosed changes in RH show a decrease during the NH summer and an increase during the SH summer in the MIS 5e compared with the MIS 5d simulation, however, the peak season is not exactly in phase with temperature (Fig. 2D). Especially, there is no apparent trend in decreasing RH in the NH summer due to regional variations in the moisture content associated with the regional-scale phenomenon (Supplementary Fig. S1).

In contrast to the orbital forcing simulations, which have clear seasonal (i.e., hemispheric) differences in the temperature and moisture responses, the 2×CO2 forcing simulation exhibits annually and hemispherically symmetric positive temperature anomalies relative to the PD climate conditions (red solid and dashed lines in Fig. 2B). The increased CO2 concentration in the atmosphere increases tropospheric temperatures with significantly higher values in the upper levels compared to the lower levels, due to the moist adiabat response (Fig. 2B) 13,24–26. The increased global air temperatures lead to year-round increased values of specific humidity and reduced RH in the lower troposphere across the tropical ocean basins (red line in Fig. 2C, D). Although there are variations in each month, it is interesting to observe that the NH summer climatic response to orbital forcing (i.e., MIS 5e minus MIS 5d) is qualitatively equivalent to that of the NH summer greenhouse warming response with higher temperatures. On the other hand, the SH summer response to orbital forcing is opposite to that of the CO2 doubling forcing (Fig. 2B).

These differences in temperatures and moisture content of the atmosphere lead to changes in the stability and the moist-entropy deficit parameters (Supplementary Fig. S2). It has previously been demonstrated that large-scale increases in atmospheric stability lead to a reduction in TC frequency 27–29. In addition, increased values of the moist-entropy deficit provide unfavorable conditions for TC formation, which reduces the extent of the sustained atmospheric convection during the genesis stage of a storm 30,31. The positive stability and moist-entropy deficit anomalies compared to the PD are most pronounced for the 2×CO2 simulation and they affect both hemispheres in their respective summer season. In contrast, in the MIS 5e simulation, we observe higher stability and moist energy deficit during the NH summer as compared to the MIS 5d simulation (Supplementary Fig. S2A, C); but weaker values during the SH summer (Supplementary Fig. S2B, D). Therefore, the environmental response to greenhouse gas forcing allows us to simply expect reduced TC frequencies in both hemispheres. In contrast, a hemispheric asymmetric response to orbital forcing is expected with reduced TC frequency in the NH summer and enhanced TC frequency in the SH summer. We will further test these hypotheses using the GPI analysis compared with two TC tracking schemes and explore whether the MIS 5e NH summer can be regarded as a qualitative analog to greenhouse warming.

Large-scale TC genesis environments
To understand the impact of orbital forcing and greenhouse gas forcing on TC genesis environments, we calculate the GPI (see Supplementary Fig. S3 and “Methods”) for different climate conditions from paleoclimate to future climate 16,31–36. We also evaluate the relative importance of each GPI component in relation to the corresponding change in the large-scale environment for TC genesis (i.e., GPI component analysis, Fig. 3 and Supplementary Fig. S4). As peak TC activity seasons vary by basins, we calculated GPI changes in the peak TC seasons for the respective basins (July to September (JAS) for the North Atlantic (NA), Eastern North Pacific (ENP), and Western North Pacific (WNP); October to December (OND) for the North Indian (NI); and January to March (JFM) for the Southern Indian (SI) and Southern Pacific (SP) basins), and combined them in one map (Fig. 3). In response to the low precession forcing, the total GPI is reduced in key regions of TC genesis such as the WNP, the northwestern NA, and equatorial side of the ENP, while is enhanced in the NI and SI, subtropical ENP, Caribbean Sea, and some areas in the SP (Fig. 3A). In the paleoclimate simulations, the thermodynamical conditions (i.e., moist-entropy deficit and maximum potential intensity) can explain most of the GPI changes except for the Caribbean Sea and north of WNP region (20°N–40°N) where dynamical conditions (absolute vorticity and vertical wind shear) largely contribute to the changes in GPI. Meanwhile, in the greenhouse gas forcing experiment, the total GPI is reduced over almost all the key TC genesis regions (Fig. 3F), and the moist-entropy deficit explains most of the changes in TC frequency (Fig. 3I). The percent contribution of each GPI term to the basin-averaged total GPI change is provided in Table 2. For both the orbital forcing and 2×CO2 simulations, the diagnosed changes in GPI are in first-order consistent with the changes in the thermodynamic variables (Table 2). Other GPI factors either play an important role on the regional scale or a secondary role in explaining the large-scale shifts in TC genesis potential GPI. In contrast, a recent paleo-study focusing on the Paleocene–Eocene thermal maximum (PETM) epoch (characterized by increased CO2 conditions and land-sea distribution) found that changes in vertical wind shear can explain the changes in the TC genesis under increased greenhouse gas conditions 13. In our orbital forcing simulations, the vertical wind shear is also not negligible. Their overall contribution to the entire basin, however, is less significant than thermodynamic variables because it is limited to the equatorial zone (Eq to 10°N) and out-of-phase in the equatorial WNP. Furthermore, the contribution of vertical wind shear in response to greenhouse gas forcing is not statistically significant except for ENP and NI.

To further test the robustness of our results, we also calculated the conventional GPI equation 37 (see “Methods”), which differs from Korty’s approach in two terms: (1) RH at 700 hPa instead of moist-entropy deficit and (2) vertical velocity at 500 hPa. The conventional GPI component analysis also shows that moisture-related variables, i.e., 700 hPa RH, can capture most of the changes in the GPI (Supplementary Fig. S4), thereby supporting our results from the other GPI equation.

Reduced values of RH and increased stability of the atmosphere due to increased upper-level temperatures act as unfavorable conditions for TC formation. The RH is an indicator for water vapor given the maximum amount of water the air can hold at a temperature. Therefore, it is proportional to water vapor in the atmosphere and approximately inversely proportional to atmospheric temperature. In differences between the two paleoclimate simulations, we observe a decrease in global mean RH during the NH warm season and an increase during its cold season, indicating that temperature plays a larger role in determining the sign of RH (Supplementary Fig. S5A–C). In the greenhouse warming experiment, the monthly global mean RH decreases with increasing temperatures (Supplementary Fig. S5D–F). The RH in the orbital forcing simulations shows regional differences (Supplementary Fig. S1). The changes in the temperature and moisture across the NA, WNP, and all the SH ocean basins agree with the global changes, but the ENP and NI basins have higher RH during the NH summer, irrespective of the higher air temperatures. We notice stronger low-level monsoon westerlies in the MIS 5e simulation (Supplementary Fig. S6D), which can be partly related to the enhanced land-sea thermal contrast 38. This circulation is associated with increased moisture transport into the Arabian Sea region as compared to the Bay of Bengal region (Supplementary Fig. S6B). In addition, across the ENP and NI basins, we observe an northward shift of the intertropical convergence zone (ITCZ) position in the MIS 5e simulation leading to favorable conditions for TC formation (Supplementary Fig. S7B, E).

Changes in detected TC genesis
The aforementioned changes in the large-scale conditions of different model simulations (i.e., orbital and CO2 forcing) can lead to changes in explicitly detected TCs as the storm tracking schemes use thresholds dependent on background large-scale environmental conditions. One of the advantages of high-resolution, fully coupled simulations presented here is that they explicitly resolve the structure of TCs and relevant air-sea interactions 6. As illustrated here for a category-4 storm (Saffir-Simpson) in the MIS 5e simulation (Supplementary Fig. S8), TCs have a pronounced eye in the center and an SST cold wake effect oftentimes larger than 3°C. It is also noted that the current version of the CESM under PD conditions can capture the observed TC genesis climatology with certain regional variations while underestimating TCs in the NA and WNP basins 6. To study the effect of paleoclimate and future forcings on TC tracks, we detected TCs using the last 60 years of each simulation (Supplementary Fig. S9, “Methods”).

Changes in TC genesis density during MIS 5e show an increased density in the SH summer across southwestern SI and SP ocean basins compared to the MIS 5d (Fig. 4A), mostly due to favorable TC forming conditions. Although not represented by the GPI, the tropical eastern Indian Ocean near Sumatra Island has a lower genesis density (Fig. 4A). In addition, during the MIS 5e period, the genesis density and track density differences (Fig. 4A, C) show a reduced number of detections in NA and WNP regions whereas the NI (Arabian Sea) and ENP basins show higher frequencies. The global and annual mean number of TCs in MIS 5e rises by 5% in comparison to that in MIS 5d as a result of a 3% reduction in the NH and an 18% increase in the SH (Supplementary Fig. S9E). We found no clear shift in the annual cycle (Supplementary Fig. S10).

The simulated changes in global and hemisphere mean TC genesis density (Fig. 4) agree with other paleoclimate simulations conducted with a coarser-resolution model that employs GPIs to study the variations of TC formations during the mid-Holocene period 15,16. However, these studies did not identify the inter-basin differences in genesis frequency and track density. Although there is a regional discrepancy between TC density and GPI in the SH summer during the MIS 5e, the general changes in TC genesis density during the NH summer are consistent with the changes in the GPI.

The future climate (i.e., greenhouse gas forcing) simulation shows reduced global annual TC genesis frequencies in both hemispheres compared to the PD (Fig. 4B and Supplementary Fig. S9) 6. The track densities indicate decreased densities in the tropics with a slightly increased track density in higher latitudes of the NH basins and in some locations of the South Pacific basin (Fig. 4D). In contrast to the MIS 5e climate that exhibits a clear hemispheric asymmetry in the genesis frequency, the future climate simulation shows an overall decrease in the mean annual TC frequency across all the ocean basins in both hemispheres. Previous studies demonstrated that changes in the Hadley circulation (i.e., weakening of rising branches of Hadley cells in both the hemispheres) along with unfavorable environmental conditions could suppress TC formation in both hemispheres in response to CO2 forcing 4,6,11,39. In addition, the changes in TC genesis locations and tracks are insensitive to the use of traditional and phenomenon-based tracking schemes both in the paleoclimate and future climate, thereby increasing the confidence of the simulated TC response (Supplementary Fig. S11 and “Methods”).

It is interesting to note that the changes in GPI are qualitatively similar to the detected changes in TC genesis that are explicitly simulated by the model using different tracking schemes in most of the ocean basins. Previous studies on the GPI analysis showed that there is some disagreement between the model-simulated TCs and GPI-estimated TC genesis frequency in future warmer climates 40. However, in the current study, the considerable agreement of these GPI results in most of the ocean basins using two GPI indices, including the paleoclimate, present, and future climates with the explicitly simulated TCs by the high-resolution model is encouraging. Additionally, unlike the response to a future warmer climate, the TC genesis density is not strongly reduced in the NH summer during the paleo-warmer period (MIS 5e).

We further compare the locations of TC genesis detected using TC tracking schemes to seasonal changes in the thermodynamical and dynamical conditions that influence TC formation. The seasonal variations in the 700 hPa RH closely align with the TC genesis frequency changes in most of the ocean basins showing that moisture may be a key controlling factor for the orbitally induced changes between MIS 5e and MIS 5d (Supplementary Fig. S6B). Similarly, another thermodynamical variable, maximum potential intensity, can capture the detected TC genesis frequency in most of the basins except ENP (Supplementary Fig. S6E). Moving on to changes in the dynamical variables, the development of the localized cyclonic vortex depends on large-scale absolute vorticity in the lower atmosphere. The seasonal fluctuations in the vorticity between the MIS 5e and MIS 5d are consistent with variations in the TC frequency, especially in SH basins. On the other hand, some regions of the NH basins, including the ENP, and NA basins show little linkage with simulated lower TC frequencies and higher low-level vorticity values (Supplementary Fig. S6C). Seasonal variations in the mid-level vertical velocity (Supplementary Fig. S6F) correspond with changes in relative humidity; the sustained convection that results from the greater mid-level moisture increases the mid-level upward motion. The lower values of the vertical wind shear between 850 and 200 hPa are important for the storms to maintain their strength, favoring the development of initial vortices to develop into storms and TCs. We further find that SH summer has higher shear in the MIS 5e compared to the MIS 5d in most of the ocean basins, which would oppose the simulated higher TC activities there (Supplementary Fig. S6G). In the paleoclimate simulations, the mismatch in the anomaly patterns of shear and TC frequency in some NH basins suggests that vertical wind shear plays only a minor role in describing global changes in TC frequency. This indicates that the variations in the thermodynamical variables, such as RH and MPI, are mainly responsible for the associated changes in TC formation across most of the global ocean basins in the paleoclimates, thereby outweighing the effects of other dynamical variables.

In response to rising CO2 levels, the reduced RH values (Supplementary Fig. S12B) cause a decrease in TC frequency in most of the global ocean basins (except NA and ENP). We similarly observed reduced mid-level vertical velocity and large-scale vorticity in the SH summer in the 2×CO2 experiment compared to the PD case (Supplementary Fig. S12C, F). The fluctuations of these conditions, however, exhibit basin asymmetry in the NH basins. Therefore, the low-level vorticity/mid-level vertical velocity variables cannot explain the TC formation differences in both hemispheres. In the 2×CO2 experiment, the vertical wind shear variations exhibit larger values in SH summer and asymmetric values in the NH ocean basins (Supplementary Fig. S12G). Hence, the underlying environmental conditions for a future warmer climate also suggest that moisture (RH) is a key factor influencing changes in the projected global TC frequencies.

Changes in TC intensity
The diagnosed changes in the environmental conditions might have an impact on the TC intensity as well. Here we have further examined the changes in the lifetime maximum 10m wind speeds and the mean sea level pressure of the storms in past, present, and future climate simulations (Supplementary Fig. S13). Although the model generally underestimates TC intensity, the model can capture the TC wind-pressure relationship in all the simulations and up to category-4 storms (Saffir-Simpson’s scale) of the present climate and category-5 storms of future climate (Fig. 5 and Supplementary Fig. S13). From the MIS 5d TC wind-pressure relationship, we can observe an increased frequency of intense storms compared to the MIS 5e simulation. There is a clear intensification of storms across the globe in 2×CO2 as compared to the PD climate. The frequency of storms stratified by intensity category shows that there are less intense storms in the NH during the MIS 5e, whereas more intense storms in the SH compared to the MIS 5d. (Fig. 5). In contrast to the orbital forcing simulation, which has hemispheric asymmetry in the frequency of the intense TCs, the greenhouse warming simulation shows a higher number of intense storms in both hemispheres. Furthermore, there is a reduced frequency (−20 to −470% across the category) of NH intense TC categories (Category-2 to Category-4 storms) during the MIS 5e and an increased frequency (+18 to +90%) in the SH during the MIS 5e (Category-1 to Category-4 storms) compared to the MIS 5d. In the 2×CO2 simulation, we observe a reduction in the weaker category storms (i.e., tropical storms (TS) to Category-2 storms) and an increase in the frequency of the stronger storms (Category-3 to Category-5) in both the hemispheres as compared to PD condition. As there is an overall reduction in the global TC frequency for future warmer climate conditions compared to the past warmer climates (Supplementary Fig. S10), we observe a more pronounced reduction in the weaker storms in future climates as compared to the paleoclimate time-slice experiments. Therefore, in addition to the changes in the TC frequency, we also observe a hemispherically asymmetric response of TC intensities for the paleoclimate simulation and asymmetric response in a future warmer climate.

To further understand what controls the TC intensity (maximum of 10m wind speeds), we examined one thermodynamical variable, the maximum potential intensity, which measures the convective instability of the atmosphere and gives the measure of maximum potential wind speeds a storm can attain under the background condition of the atmospheric temperature and moisture 1. The seasonal differences in the maximum potential intensity, in general, agree well with TC intensity change (Supplementary Figs. S6E and S12E). In paleoclimate simulations, the seasonally varying radiation influences both atmospheric temperature and SSTs. The atmospheric temperatures respond faster than the underlying SSTs (Supplementary Fig. S14) to the increases in radiation, leading to reduced maximum potential intensity (Supplementary Fig. S15) in the NH summer of MIS 5e as the upper troposphere is warmer while the surface layer is still not that heated yet. The NH autumn (October and November), by which time surface waters have several months to adjust, develops a warmer surface layer than the atmosphere above, with small differences leading to an increase in the maximum potential intensity (Supplementary Figs. S14 and S15). Therefore, maximum potential intensity values in the NH summer are greater in the MIS 5d simulation than those in the MIS 5e simulation, whereas maximum potential intensity in the SH summer is higher in the MIS 5e simulation than in the MIS 5d. These variations in the maximum potential intensity agree with earlier studies on paleoclimate that showed changes in TC intensity during the Holocene or Mid-Holocene periods 15,16. In contrast, the changes in maximum potential intensity for future climate simulation (2×CO2) follow the sign of SSTs throughout the year (Supplementary Figs. S14 and S15). Given these changes in the maximum potential intensity caused by orbital and CO2 forcings, the NH summer in MIS 5e cannot serve as an analog to the 2×CO2 simulation because the environmental response at lower and upper levels varies significantly to different forcings.

Methods
Model description
In the current study, we employ a high-resolution CESM 1.2.2 6 to perform fully coupled high-resolution global Earth system model simulations for paleoclimate, present and future climates. The atmospheric component of the model is CAM5 with a spectral dynamical core with a horizontal resolution of 0.25° and 30 vertical layers 44. The ocean component is POP2 which has a horizontal resolution of 0.1° (decreasing from the equator (11 km) to poles (2.5 km)) and 62 depth layers 45. The land model is the Community Land Model version 4 46, and the sea-ice component is the Community Ice Code version 4 47. The time variations of the carbon-nitrogen cycle are switched off in the current model simulations. Chu et al. 6 shows that the TC climatology in the PD simulation is quite realistic, with certain regional and seasonal differences in TC genesis frequencies (Supplementary Fig. S5 of Chu et al. 6) when compared with the observations (IBTrACS). They also show that the higher resolution reduces the mean SST bias (a common feature in some of the global climate models). The model can capture the localized air-sea interaction processes, mesoscale eddies, TC cold wakes, and convective features along the mountainous regions 6,19. The details of the greenhouse gases, vegetation, and orbital years of the model simulations are shown in Table 1. The PD simulation was initialized from a quasi-equilibrium state of the Small et al. 19 and simulated for 140 years, the 2×CO2 simulation was carried from the year 71 of the PD simulation with a doubling of atmospheric CO2 concentration and integrated for 100 years. Similarly, the two paleoclimate simulations (MIS 5e, MIS 5d), which were branched off from year 71 of the PD simulation, are integrated for 100 years. In the current study, we carry out the analysis for a period of the last 60 years to check the robustness of the results. The main difference between the two paleoclimate simulations is the precession configuration. In the MIS 5e, the Earth receives more solar insolation during the NH summer season and less during the SH summer season, and vice versa during the MIS 5d period.

Traditional TC detection and tracking scheme
Here we employ two different tracking schemes. The first is a traditional tracking scheme that uses certain circulation-based thresholds to identify and track initial vortices resembling TCs. This TC tracking scheme 6 is based on the idea of Bacmeister et al. 48, except that they use the 3-h interval data with a 200 km search radius for TC tracking and a 50 km radius for a 10m wind speed threshold. The steps involved are as follows:
(i) First, we identify the low-pressure systems using a threshold of surface pressure anomalies lower than −3 hPa;
(ii) The 10m wind speeds of the systems should exceed 10 ms−1 within a radius of 100 km from the low-pressure minima location otherwise, those lows are discarded;
(iii) These detected lows at 6-h intervals are tracked forward in time based on the nearest location in a similar instance of time, and the tracking is continued with a 400 km radius of the storm;
(iv) A track is chosen if the tracked storm has 17 ms−1 magnitude of 10m wind speeds during their lifetime and have a maximum duration of 2 days.
(v) To obtain the global TC counts close to observations, we impose a further threshold of 0.00145 ms−1 for the 850 hPa vorticity threshold.

Phenomenon-based Okubo-Weiss-Zeta parameter (OWZP) tracking scheme
For the second scheme, rather than detecting TC-like vortices, a phenomenon-based tracking scheme uses the thresholds of the large-scale climate variables to identify the recirculating regions with stronger vorticity and weaker deformation that have the potential for TC formations. The OWZP scheme 49 uses the thresholds of large-scale climate variables such as relative humidity, specific humidity, vertical wind shear, and OWZP, which is equivalent to vorticity but involves stretching and shearing deformations. This TC detection and tracking scheme shows better performance for different TC characteristics as assessed using the ERA-interim and ERA5 data 49,50 and involves two steps:
(i) In the first step, the “detection stage” involves identifying the grid points that satisfy the initial thresholds (see Supplementary Table S1 of the supporting information) and merging the nearby grid points into clumps;
(ii) In the next step, the “tracking stage,” the detected clumps are tracked forward in time until there exists no circulation by using the storm position determined by the 700 hPa steering wind. These clump positions along each of the detected tracks are further checked using the core thresholds of Supplementary Table S1 and given True (thresholds are satisfied) and False (not satisfied). A particular track is considered a TC track if the core thresholds are satisfied for 48 h consecutively. In 12-h data, there should be five consecutive True clumps along the track and the fifth True position is considered as the storm genesis location 50.

TC genesis potential index (GPI)
As TCs form under certain favorable environmental conditions, earlier studies developed certain empirical indices to identify the TC genesis locations. Emanuel and Nolan 51, Emanuel et al. 30, and Emanuel 31 developed GPIs with a set of environmental variables. These variables include (1) elevated values of large-scale absolute vorticity, which are required for the development of a strong localized TC-like vortex, and (2) higher mid-tropospheric moisture content, which is required for persistent convection through inflow from the moist boundary layer favoring deep convection. The mid-level moisture availability is measured through a magnitude of moist-entropy deficit estimated between the boundary layer and mid-troposphere and that changes with climate change even when the relative humidity does not change, (3) lower vertical wind shear which is necessary to maintain sustained vortex required for TC formation, (4) potential intensity which is the maximum attainable wind speed based on the available background thermodynamic environment of the atmospheric column (convective instability) involving the temperatures at the surface and outflow levels. Higher values of potential intensity indicate a more convectively unstable environment favoring the TC formation. Korty et al. 15 further developed this index with more sophisticated statistical analysis using similar environmental variables.

Earlier GPIs are based on the SSTs, absolute vorticity, relative humidity, and vertical wind shear variables. As the SSTs are not invariant with the background of changing climate conditions, this index cannot be employed in studies that focus on other climates 52. The inclusion of 500 hPa vertical velocity can capture the future changes in the TC genesis activity 37.

The above-discussed GPIs are evaluated in paleoclimate, present, and future climate studies 15,16,31,32,34–36. These studies note that the modified GPIs with the inclusion of moist-entropy deficit/vertical velocity can capture the TC climatology and seasonal cycle in different climate conditions. A recent study by Camargo et al. 36 using a high-resolution 0.25° model shows that TC climatology estimated using GPI does not have a strong relationship with the model’s explicitly simulated TC climatology. Most of the paleoclimate simulations are based on the changes in the GPI rather than TCs themselves, therefore in the current study, the comparison of the explicitly simulated TC climatology with the changes in the GPI can enhance our confidence in the paleoclimate TC variations and their associated atmospheric mechanisms.

GPI component analysis
To decipher the relative role of each GPI term, we adopt a variational GPI analysis technique 53. The equation below represents the changes in the TC genesis potential estimated using a GPI is equal to the sum of changes in each term of the GPI index by assuming that the non-linear (correlation) terms are negligible. Here the first term is the change in the variable between two simulations multiplied by the mean values of other terms of the GPI index. The summation of each of the terms on the right-hand side equals the overall changes in the GPI, that is, positive values correspond to the increasing GPI, and negative values reduce the GPI. In the current study, we conducted the GPI component analysis using both the GPIs.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors tried to give an idea of future tropical cyclone activity by comparing past and future\nclimate simulations. They used the MIS5e, MIS5d scenarios for the past climate and the PD, CO2\ndoubling scenarios for the future climate. By comparing the responses of tropical cyclone activity to\npast and future climate, they found that moisture plays a critical role in tropical cyclone activity. In\ngeneral, the manuscript is well organised. However, I have several concerns about its publication.\n1) I found that the authors have a deep knowledge of paleo-climate studies. Since the NCC is a letter,\nI understand that the authors have tried to shorten the length of the manuscript. However, this\nmakes it difficult for readers who are not experts in palaeo-climate to follow the significance of their\nfindings. Since the NCC is a letter to the non-expert in the field, I suggest that the authors add more\nexplanation and information about palaeoclimate studies.\nFor example, in lines 53-59, I don't know what is the limitation of the previous studies of warm\nclimate episodes. Please provide more information.\nAlso, what is the difference between the MIS5e and MIS5d simulations compared to the last\ninterglacial, the Eocene and the Pilocene simulations? Please explain and highlight the difference.\n2) According to the introduction, it seems to me that the authors have found striking evidence in the\nprojection of future tropical cyclone activity. However, their main finding is that only moisture is the\nmost important. In fact, this result can only be found with the result of PD, CO2 doubling\nexperiments. I don't know why the MIS5e and MIS5d experiments are essential to the authors'\nargument. Although the authors insist that the different experiments can reduce the uncertainty, I\nthink the authors still cannot avoid the uncertainty problem by using the single model, CESM. I think\nthe importance of this study is not to reduce the uncertainty, but rather a possible importance of\ntropospheric humidity.\n3) Lines 155-174: I can acknowledge that RH related variables are most similar to GPI. If the\nquantitative contribution of each factor is given in the table, it will be much easier to understand the\nresults.", "reviewer_2": "The following is a review of the manuscript submitted by Raavi et al entitled Moisture control of\ntropical cyclones in high-resolution simulations of paleoclimate and future climate. The results are\ninteresting and provides new insights on the paleoclimatology of tropical cyclones, which are timely\nand relevant particularly in the advent of discussions surrounding global environmental changes. I\nhave few comments, clarifications, and suggestions to the author. I believe the manuscript has\npotential for publication after the following comments have been adequately answered:\nL65-66 - the use of NH summer vis-a-vis boreal summer here is confusing\nL75 - What is MIS5d. It is not yet explained. why are they called extreme orbital conditions?\nL98 - consider putting this explanation in earlier parts to provided clarity in the discussion\nL148 - is it possible to put a timeseries of GPI in Fig. 1 to show such reduction in response to MIS5e-\nd, PDO, and 2xCO2? This is for ease of reading for readers that are universally adept to paleoclimate.\nL158 - \"TC genesis\"\nL161 - WNP typically covers until 100E while NI covers until 95E\nL167-168 - vertical wind shear in the WNP; how to determine the contribution of one term in GPI? is\nthere a way to determine significance?\nL171-173 - same with comments above. Looking at Fig. 3A and Fig S3C, there is a meridional\ncomponent of dGPI extending from south to north in the WNP, which is not captured in Fig 3D but is\ncaptured in Fig. 3E. Same can be be said in the Caribbean and Eastern Pacific where VWS seems to\naffect the dGPI.\nSimilarly, the filament of negative 850 hPa abs. vorticity in Fig 3B, resembles that in the eastern\nPacific indicating that atmospheric component (VWS and Abs. Vort) also affect dGPI. how to\ndetermine the contribution of one term in GPI? is there a way to determine significance?\nL176-181 - There is a need to characterize the term \"most\" here because it can be seen that VWS\nalso has considerable changes. This leads to my next question - does the blue mean decrease in\nVWS? If yes, does it mean more favorable TC genesis environment? Both sides remain to ask how to\ndetermine their significant contribution.\nL197-198: this is confusing because I though that the GPI in Fig. 3 denotes decrease in TC genesis.\nA northward ITCZ means favorable TC genesis environment because the moisture associated with the\nITCZ is found in the WNP.\nL215 - this is related to my previous comment. The mention of Fig S9 without relating to Fig. 4A first\nis confusing to the reader.\nL264 - where in the figure does it show the correlation? is it through eyeball only?\nL271 - what does it mean by global ocean changes in relation to VWS and TC frequency?\nL272 - the mismatch in the anomaly patterns vis-a-vis GPI in two simulations show that\nthermodynamic is more important that dynamic variable? In a paper by Sharmila and Walsh (2017;\nhttps://doi.org/10.1175/JCLI-D-16-0900.1), they mentioned that dynamic parameters are more\nimportant in the WNP and South Pacific.\nL295 - did you combine the peak season for each basin then plot the NH? Each basin has different\npeak season which has to be considered.\nI think there is a need to provide more explanation/analysis on the influence and contribution of\neach GPI terms to justify that thermodynamics is more important. This is particularly true for WNP\nsince it has the most TCs worldwide.\nL555 - explain these acronyms, are MIS5e and 5d the gray columns only? kindly explain in the\nfigures.\nL585 - what does the line plot mean? kindly explain it in the caption. there is no x-axis label in Fig.\n4A-B."}, "author_response": "Reply to Reviewer’s comments:\n“Moisture control of tropical cyclones in high-resolution simulations of paleoclimate and\nfuture climate” by Pavan Harika Raavi, Jung-Eun Chu, Axel Timmermann, Sun-Seon Lee, and\nKevin J. E. Walsh\nResponse to Reviewer 1:\nThank you very much for your valuable and helpful comments. We have answered your comments\ncarefully and have made corrections according to your comments. The main response to your\ncomments is as follows:\nGeneral comments\nThe authors tried to give an idea of future tropical cyclone activity by comparing past and\nfuture climate simulations. They used the MIS5e, MIS5d scenarios for the past climate and\nthe PD, CO2 doubling scenarios for the future climate. By comparing the responses of\ntropical cyclone activity to past and future climate, they found that moisture plays a critical\nrole in tropical cyclone activity. In general, the manuscript is well organised. However, I have\nseveral concerns about its publication.\nMajor comments\n1. I found that the authors have a deep knowledge of paleo-climate studies. Since the NCC is\na letter, I understand that the authors have tried to shorten the length of the manuscript.\nHowever, this makes it difficult for readers who are not experts in palaeo-climate to follow\nthe significance of their findings. Since the NCC is a letter to the non-expert in the field, I\nsuggest that the authors add more explanation and information about palaeoclimate studies.\nFor example, in lines 53-59, I don't know what is the limitation of the previous studies of\nwarm climate episodes. Please provide more information.\nResponse:\nThank you for your suggestion. We agree with the reviewer’s comment that the limitations of the\npaleoclimate studies were not adequately addressed. We have expanded into more details about\nthose paleoclimate studies in the introduction of the revised manuscript (Lines: 55-86).\nTo highlight the several sentences, most of the studies are based on low-resolution models and/or\nthe idea that large-scale environments such as an empirical TC genesis potential index (GPI) can\nrepresent changes in TC activity. To obtain a detailed perspective on the relationship between\nlarge-scale climatic drivers and TC statistics, it is important to explicitly resolve TCs and their key\nmesoscale features in a climate model. To the best of our knowledge, there are no systematic paleo-\ntimeslice studies that explicitly resolve TCs using a fully coupled high-resolution Earth system\nmodel. Especially, simulation for MIS5e (LIG) has not been conducted. In other words, TC\nstatistics such as frequency and intensity under different background conditions have not been\nfully tested.\nThis study is the first systematic approach to understand controlling factor of TC characteristics\nfor both orbital forcing (MIS5e vs MIS5d) and greenhouse gas forcing (2xCO2 vs PD) using fully\ncoupled high-resolution CESM simulations. In addition, to improve the robustness of our results,\nwe also included two tracking schemes, two GPI indices, and a relatively long TC tracking period\n(60 years) for each simulation.\nAlso, what is the difference between the MIS5e and MIS5d simulations compared to the last\ninterglacial, the Eocene and the Pilocene simulations? Please explain and highlight the\ndifference.\nResponse:\nThe last interglacial (LIG) was the most recent warm period that that took place at around 125ka\nand is also referred to as Marine Isotope Stage (MIS) 5e. Contrary to the present-day (PD) orbital\nconfiguration, the MIS5e was characterized by Northern Hemisphere (NH) summer perihelion\nconditions and warmer (colder) NH (SH) summers. The MIS5d is the glacial sub-stage\ncharacterized by Southern Hemisphere (SH) summer perihelion conditions and warmer (colder)\nSH (NH) summers. Stronger seasonal anomalies are caused by variations in the orbital forcing,\nwhich varies in the incoming solar radiation. A comparison of MIS5e and MIS5d helps us to\nunderstand the role of the orbital forcing on TC characteristics.\nDuring the early-Eocene the global mean annual surface temperatures were 13 + 2.6 degrees\nwarmer than the late 20th century temperatures and atmospheric CO was 1400 ppmv. The Mid-\n2\npliocene is the most recent period with the atmospheric CO concentration comparable to the\n2\npresent (400 ppmv), global mean annual surface temperatures are 1.8 to 3.6 degrees higher than\nthe pre-industrial temperatures.\nIn the LIG (MIS5e-MIS5d), global mean annual surface temperatures are 0.8-1.3 degrees warmer\nthan the pre-industrial average with CO levels of around 280 ppmv. The LIG period has lower\n2\ncaused by significant seasonal variations in the incoming solar radiation and climate rectifications.\nThe LIG period is the most recent time frame with higher global mean surface temperatures that\ncan serve as an analogue to the current interglacial period.\nThese details are included in the revised manuscript at lines 55-65 and 89-98.\n2. According to the introduction, it seems to me that the authors have found striking evidence\nin the projection of future tropical cyclone activity. However, their main finding is that only\nmoisture is the most important. In fact, this result can only be found with the result of PD,\nCO2 doubling experiments. I don't know why the MIS5e and MIS5d experiments are\nessential to the authors' argument. Although the authors insist that the different experiments\ncan reduce the uncertainty, I think the authors still cannot avoid the uncertainty problem by\nusing the single model, CESM. I think the importance of this study is not to reduce the\nuncertainty, but rather a possible importance of tropospheric humidity.\nResponse:\nWe totally understand the reviewer’s concern on uncertainty problem, and we appreciate this\ncomment. In the revised manuscript, we tried to emphasize our efforts to improve robustness of\nour results.\nCurrently, there is no consensus on changes in future TC frequency. This brought us an idea to\ndetermine whether there is a common link between the large-scale environments and changes in\nTC characteristics from the past to the future climates. As the reviewer stated, our result is simple\nthat moisture related variable or thermodynamical conditions are the dominating factors\ninfluencing the TC frequency both in the past and future warmer climates and we were happy to\nfind this key factor.\nIt is noted that moisture related variable or thermodynamical conditions are the dominating factors\ninfluencing the TC frequency both in the past and future warmer climates. We may infer from the\ncurrent study that one of the key contributing factors for TC frequency fluctuations related to\nvarious model climates (Past, present, and future) is moist entropy deficit.\nThe selection of MIS5e and MIS5d was made because these two periods represent the most recent\nextreme orbital conditions with low and high precession indexes respectively and large\neccentricity. A low (high) precession index corresponds to NH summer perihelion (aphelion)\ncondition and associated intensified (weakened) NH summer solar radiation. This effect is visible\nonly for high values of Earth’s orbit eccentricity and the intervals from 125-115 ka represent one\nof these periods. Therefore, MIS5e and MIS5d serve as excellent test grounds.\nOne of the limitations of this study is that we rely on a single model and a single ensemble.\nHowever, this model is among the highest TC-resolving scale fully coupled model and shows an\nexcellent performance in representing TC characteristics and their air-sea interaction (Chu et al.\n2020). To compensate for this limitation, we used a relatively long TC tracking period (60 years)\nfor each simulation compared to previous work which used the last 20 years only. In addition, to\nimprove the robustness of our results, we also included two fundamentally different tracking\nschemes and two different GPI indices. We hope you’ll find this response to be acceptable.\n3. Lines 155-174: I can acknowledge that RH related variables are most similar to GPI. If\nthe quantitative contribution of each factor is given in the table, it will be much easier to\nunderstand the results.\nResponse:\nThank you for your good suggestion. In the revised manuscript, we included Table 2 that describes\npercentage contribution of the different GPI terms to total GPI change (MIS5e – MIS5d; 2xCO2\n– PD). The table clearly shows that moist related variable is the largest contributor among others.\nResponse to Reviewer 2:\nWe wish to thank the reviewer for insightful comments and suggestions on our manuscript, which\nwere very useful to revise the first version. The followings are point-by-point responses to the\ncomments:\nGeneral comments\nThe following is a review of the manuscript submitted by Raavi et al entitled Moisture\ncontrol of tropical cyclones in high-resolution simulations of paleoclimate and future climate.\nThe results are interesting and provides new insights on the paleoclimatology of tropical\ncyclones, which are timely and relevant particularly in the advent of discussions surrounding\nglobal environmental changes. I have few comments, clarifications, and suggestions to the\nauthor. I believe the manuscript has potential for publication after the following comments\nhave been adequately answered:\nL65-66 - the use of NH summer vis-a-vis boreal summer here is confusing\nResponse:\nThank you for your suggestion. The boreal summer is replaced with NH summer and the austral\nsummer is replaced with SH summer.\nL75 - What is MIS5d. It is not yet explained. why are they called extreme orbital conditions?\nResponse:\nThank you for raising this point. Based on this question raised by two reviewers, we learn that the\nexplanation about the selection of MIS5e and MIS5d are not well addressed.\nThe MIS5d is a glacial sub-stage characterized by SH summer perihelion conditions and warmer\n(colder) SH (NH) summers. The selection of MIS5e and MIS5d was made because these two\nperiods represent the most recent extreme orbital conditions with low and high precession indexes\nrespectively and large eccentricity. The precession index is 𝑒 sin(𝜔 ), where 𝑒 is the eccentricity\ns\nand 𝜔 measures how close the sun is to the earth at midsummer. A low (high) precession index\ns\ncorresponds to NH summer perihelion (aphelion) condition and associated intensified (weakened)\nboreal summer solar radiation. This effect is visible only for high values of Earth’s orbit\neccentricity and the intervals from 125-115 ka represent one of these periods. Therefore, MIS5e\nand MIS5d serve as excellent test grounds.\nL98 - consider putting this explanation in earlier parts to provided clarity in the discussion\nResponse:\nThank you for your suggestion. The detailed explanation of MIS5e and MIS5d are now added in\nthe introduction part of the revised draft at lines 89-98.\nL148 - is it possible to put a timeseries of GPI in Fig. 1 to show such reduction in response to\nMIS5e-d, PDO, and 2xCO2? This is for ease of reading for readers that are universally adept\nto paleoclimate.\nResponse:\nThank you for your comment. We do agree that adding the GPI time series will ease the readers.\nHowever, our experiments (MIS5e, MIS5d, PD, and 2xCO2) are paleo and future timeslice\nsimulations using a high-resolution CESM model with 0.25° in the atmosphere and 0.1° in the\nocean, whereas the time series in Fig. 1 are from the long-term transient climate simulation using\na low-resolution CESM with the horizontal resolution of 3.75°×3.75° (Timmermann et al. 2022).\nTherefore, the experiments are fundamentally different. To avoid confusion from two different\nsimulations, we would like to keep Fig. 1 in its current form. We hope the reviewer understands\nour intention.\nL158 - \"TC genesis\"\nResponse:\nThank you for your comment. It is corrected in the revised manuscript.\nL161 - WNP typically covers until 100E while NI covers until 95E\nResponse:\nThank you for your comment. This is corrected in the revised manuscript for the respective figures.\nL167-168 - vertical wind shear in the WNP; how to determine the contribution of one term\nin GPI? is there a way to determine significance?\nResponse:\nThank you for your comment. In the revised manuscript, we included Table 2 that describes\npercentage contribution of the different GPI terms to total GPI change (MIS5e – MIS5d; 2xCO2\n– PD) with significant test. The table clearly shows that moisture related variable is the largest\ncontributor among others.\nL171-173 - same with comments above. Looking at Fig. 3A and Fig S3C, there is a meridional\ncomponent of dGPI extending from south to north in the WNP, which is not captured in Fig\n3D but is captured in Fig. 3E. Same can be said in the Caribbean and Eastern Pacific where\nVWS seems to affect the dGPI.\nResponse:\nThank you for pointing this out. As the reviewer stated, VWS plays an important role in\ndetermining dGPI over the western North Pacific, eastern Pacific, and Atlantic Ocean, especially\nin response to the orbital forcing (i.e., MIS5e-MIS5d). Therefore, their role is not negligible on the\nregional scale. However, the contribution of VWS is limited to the off-equatorial zone at around\n10°N, making its overall contribution over the entire NA and WNP less important than MED\n(Table 2). In addition, the contribution of VWS in response to greenhouse gas forcing is not\nstatistically significant (Table 2).\nSimilarly, the filament of negative 850 hPa abs. vorticity in Fig 3B, resembles that in the\neastern Pacific indicating that atmospheric component (VWS and Abs. Vort) also affect\ndGPI. how to determine the contribution of one term in GPI? is there a way to determine\nsignificance?\nResponse:\nOur response to this question will be similar to the above answer. Likewise in vertical wind shear,\nthe 850 hPa absolute vorticity seems to play an important role in the off-equatorial zone. But its\ncontribution over the entire basin is less important and is not statistically significant in both\nforcings. As this study aims to determine whether there is a common link between the large-scale\nenvironments and changes in TC characteristics that works both in the past to the future conditions,\nwe emphasized more on the moist-entropy deficit (MED). But as we mentioned, VWS and AVOR\nare also important in some areas.\nThe percent contribution in Table 2 is calculated by dividing the relative role of each GPI term\n(each term on the right-hand side of the GPI component analysis) by total changes in GPI (dGPI).\nBecause the GPI has no units, we expressed the percentage contribution rather than the actual dGPI\nvalue. The statistical significance of each term at individual grid points was tested by calculating\nGPIs by replacing only one term from each simulation. For example, the significance of the MED\ncontribution between MIS5e and MIS5d was calculated by comparing two GPIs: one with all terms\nin the MIS5e simulation and the other with all terms in the MIS5e simulation except for MED in\nthe MIS5d simulation.\nL176-181 - There is a need to characterize the term \"most\" here because it can be seen that\nVWS also has considerable changes. This leads to my next question - does the blue mean\ndecrease in VWS? If yes, does it mean more favorable TC genesis environment? Both sides\nremain to ask how to determine their significant contribution.\nResponse:\nWe apologize for any confusion caused due to insufficient explanation. Colors in Fig. 3 from\nindividual terms indicate their contribution to dGPI. For example, dGPI in Fig. 3A is the sum of\nFig. 3B to Fig. 3E. Red colors in each term indicate favorable TC genesis environment and blue\ncolors indicates unfavorable condition. For example, blue colors in vertical wind shear term (e.g.,\nFig. 3E) means decreases in Term4 (i.e., (25+ 𝑉 )−4) which is driven by increased vertical\nshear\nwind shear. Regarding the significant contribution, we believe the newly added Table 2 will help\nin understanding better.\nL197-198: this is confusing because I though that the GPI in Fig. 3 denotes decrease in TC\ngenesis. A northward ITCZ means favorable TC genesis environment because the moisture\nassociated with the ITCZ is found in the WNP.\nResponse:\nYou are right. In general, unfavorable conditions (decreases in TC generation) are produced in the\nNH summer due to changes in the thermodynamic environment in response to orbital forcing.\nHowever, a northward-shifted ITCZ partly offsets this effect, and we found that this influence is\nlimited to the NI and ENP regions, not in the WNP basin (Fig. R1).\nFig. R1. Differences in (a) GPI and (b) precipitation between MIS5e and MIS5d during the TC\npeak seasons across different ocean basins (i.e., JAS in North Pacific and North Atlantic basins;\nJFM in Southern Hemisphere basins; OND in North Indian Ocean).\nL215 - this is related to my previous comment. The mention of Fig S9 without relating to Fig.\n4A first is confusing to the reader.\nResponse:\nThank you for your valuable comment. We tried to increase the readability in the figure transition\nbetween Fig. 4 and Fig. S9 in lines 245-257.\nL264 - where in the figure does it show the correlation? is it through eyeball only?\nResponse:\nThank you for pointing out this inadequate word “correlate”. The sentence is modified in lines\n300-302 as below.\n“Seasonal variations in the mid-level vertical velocity (Fig. S6F) correspond with changes in\nrelative humidity;”\nL271 - what does it mean by global ocean changes in relation to VWS and TC frequency?\nResponse:\nThe “global ocean changes” is now corrected to “global changes” in line 308. Thank you.\nL272 - the mismatch in the anomaly patterns vis-a-vis GPI in two simulations show that\nthermodynamic is more important that dynamic variable? In a paper by Sharmila and\nWalsh (2017; https://doi.org/10.1175/JCLI-D-16-0900.1 ), they mentioned that dynamic\nparameters are more important in the WNP and South Pacific.\nResponse:\nThank you for your comment. The dynamical factors play a role in the orbital forcing simulations.\nHowever, we looked for a consistent or common limiting factor that applies to the majority of the\nglobal basins both in past and future warmer climates, and we emphasize that thermodynamic\nfactor is the major controlling factor.\nL295 - did you combine the peak season for each basin then plot the NH? Each basin has\ndifferent peak season which has to be considered. I think there is a need to provide more\nexplanation/analysis on the influence and contribution of each GPI terms to justify that\nthermodynamics is more important. This is particularly true for WNP since it has the most\nTCs worldwide.\nResponse:\nThank you for your comments. We have considered different peak seasons for different ocean\nbasins, including JAS for the NA, ENP, and WNP; OND for the NI; and JFM for the SI and SP\nbasins. As we have many components from two forcings, we wanted to exhibit them on one map\ninstead of showing all seasons in separate figures. In the revised manuscript, we have included\nTable 2 to quantitatively identify the governing factors associated with the TC frequency changes\nin both orbital and greenhouse warming experiments. We also tested that changing one month\nbefore and after the peak season in the WNP does not change our conclusions.\nL555 - explain these acronyms, are MIS5e and 5d the gray columns only? kindly explain in\nthe figures.\nResponse:\nThank you for your comments. The gray bars indicate the timing for four timeslice simulations.\nWe included this information in the Fig. 1 caption of the revised manuscript.\nL585 - what does the line plot mean? kindly explain it in the caption. there is no x-axis label\nin Fig. 4A-B.\nResponse:\nThank you for pointing out this missing information. The blue line plot shows the zonal mean\nlatitudinal distribution of TC frequency changes. We added this information to the figure caption\nand plotted the x-axis label in the updated revised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors' efforts to respond to my comments. All my comments are well addressed,\nbut there is just one point where I feel a bit uncomfortable.\nI think the importance of this study is to show that moisture is the most controlling factor for\ntropical cyclone frequency under global climate change, regardless of whether the origin of climate\nchange is greenhouse gases or other external forcings. I think this is a very interesting finding and\nmay provide clues to understanding future tropical cyclone activity. However, this is only true for the\nsingle model used in this study, which is the limitation of this study as the authors replied. In this\nrespect, I don't agree that this study contributes to reducing uncertainty about future changes in\ntropical cyclone activity. In the revised version of the manuscript, the related argument is weakened,\nso I don't have a strong disagreement against the revised manuscript. Nevertheless, I would\nrecommend that the authors avoid the statement about \"uncertainty\" in the introduction if they\ncan. This is because the statement may mislead readers into expecting that their study reduces\nuncertainty problems. Another alternative is for the authors to make the limitations of their study\nclearer in the conclusion.", "reviewer_2": "This is a review of the manuscript submitted by Raavi et al entitled Moisture\ncontrol of tropical cyclones in high-resolution simulations of paleoclimate and future climate.\nI believe that the results are interesting and provides new insights on the paleoclimatology of\ntropical\ncyclones. I believe that the authors have adequately replied to my comments in the previous\nrevision. I have minor editorial comments to which I defer to the editors for their discretion on such\ncomments. I would like to see bigger font/typeface size in ALL FIGURES (both main and\nsupplementary) as it is quite challenging to squint through the manuscript. Where it is possible, use\nbigger font sizes as some readers prefer printed copy rather than electronic copy. Make sure that\nthe color palette are also color blind-safe. The green in Fig. 5 is almost difficult to see. The contour in\nFig. S1 is too thick compared to the rest of the image. After all of these minor editorial comments\nhave been answered, I believe the manuscript can be considered for publication."}, "author_response": "Response to Reviewers\n“Moisture control of tropical cyclones in high-resolution simulations of paleoclimate and\nfuture climate” by Pavan Harika Raavi, Jung-Eun Chu, Axel Timmermann, Sun-Seon Lee, and\nKevin J. E. Walsh\nDear Editor:\nWe appreciate the opportunity to address the reviewers’ constructive suggestions and revise our\nmanuscript “Moisture control of tropical cyclones in high-resolution simulations of paleoclimate\nand future climate” once again. We appreciate the editor and the reviewers for their thorough\nreviews and useful comments that have been of great help in improving the manuscript. A response\nto the individual comments is provided below, with the reviewer’s comments in bold-faced text\nand our answers in the light-faced text. Finally, we thoroughly checked the editorial requests and\nrevised our figures and texts carefully.\nYours sincerely,\nPavan Harika Raavi and Jung-Eun Chu\nReviewer #1 (Remarks to the Author):\nI appreciate the authors' efforts to respond to my comments. All my comments are well\naddressed, but there is just one point where I feel a bit uncomfortable. I think the importance\nof this study is to show that moisture is the most controlling factor for tropical cyclone\nfrequency under global climate change, regardless of whether the origin of climate change is\ngreenhouse gases or other external forcings. I think this is a very interesting finding and may\nprovide clues to understanding future tropical cyclone activity. However, this is only true for\nthe single model used in this study, which is the limitation of this study as the authors replied.\nIn this respect, I don't agree that this study contributes to reducing uncertainty about future\nchanges in tropical cyclone activity. In the revised version of the manuscript, the related\nargument is weakened, so I don't have a strong disagreement against the revised manuscript.\nNevertheless, I would recommend that the authors avoid the statement about \"uncertainty\"\nin the introduction if they can. This is because the statement may mislead readers into\nexpecting that their study reduces uncertainty problems. Another alternative is for the\nauthors to make the limitations of their study clearer in the conclusion.\nThank you very much for your valuable comments. In the revised manuscript, we included a\nstatement regarding the limitation of the study which is based on single model usage (Line 416–\n418). In addition, to avoid overstating the uncertainty issue, we removed the statement about\nuncertainty reduction in the introduction of the revised manuscript. Thank you.\nReviewer #2 (Remarks to the Author):\nThis is a review of the manuscript submitted by Raavi et al entitled Moisture control of\ntropical cyclones in high-resolution simulations of paleoclimate and future climate. I believe\nthat the results are interesting and provides new insights on the paleoclimatology of tropical\ncyclones. I believe that the authors have adequately replied to my comments in the previous\nrevision. I have minor editorial comments to which I defer to the editors for their discretion\non such comments. I would like to see bigger font/typeface size in ALL FIGURES (both main\nand supplementary) as it is quite challenging to squint through the manuscript. Where it is\npossible, use bigger font sizes as some readers prefer printed copy rather than electronic\ncopy. Make sure that the color palette are also color blind-safe. The green in Fig. 5 is almost\ndifficult to see. The contour in Fig. S1 is too thick compared to the rest of the image. After all\nof these minor editorial comments have been answered, I believe the manuscript can be\nconsidered for publication.\nThank you for your considerate comments. To enhance the readability, we carefully checked the\nfont sizes, information, and blind-safe colors for all figures in the revised manuscript, including\nFig. 5 and Fig. S1 as you suggested. Thank you."}]

Decision letter