Weakened large-scale surface heat flux feedback at midlatitudes under global warming

Domain: earth_science
Published: 2024-11-19
Editorial outcome: major_revision

Abstract
The surface heat flux feedback, which refers to the response of surface heat flux anomaly to the underlying sea surface temperature anomaly (SSTA), is one of the key processes in air-sea interaction. It plays an important role in regulating various aspects of the climate system, ranging from local SSTA persistence to the global overturning circulation and major climate modes. Yet its change under greenhouse gas-induced warming remains unknown. Here, using an ensemble of global climate simulations under a high radiative forcing scenario, we demonstrate that the intensity of surface heat flux feedback for spatially large-scale SSTA at the midlatitudes is projected to halve by the end of the 21st century, compared to pre-industrial levels. Such weakening is primarily attributed to a more stabilized marine atmospheric boundary layer, which diminishes the air-sea thermal disequilibrium caused by SSTA. In a warming climate, the variance of midlatitude SSTA at large spatial scales is expected to be significantly enhanced in response to the weakened surface heat flux feedback.

Introduction
At the mid-latitudes, sea surface temperature anomaly (SSTA) at spatial scales of O(1000 km) or larger, referred to as the large-scale SSTA henceforth, is primarily driven by atmospheric internal variabilities1. Once generated, the SSTA induces surface heat flux anomaly (SHFA) that, in turn, act to damp itself. This process, known as the surface heat flux feedback, serves as a pivotal element in the coupled ocean-atmosphere system1–3. It not only regulates the amplitude, persistence and predictability of SSTA1,3–6 but also exerts broad effects on many aspects including the stability of global overturning circulation7–9, the variability of major climate modes10–14 and the climatic response to stratospheric ozone forcing15.

The intensity of the surface heat flux feedback α, defined as the surface heat flux change in response to 1 K SST change, varies considerably with space, season, and spatial scale5,6,8,16–22. Such variations reflect the complicated underlying dynamics of the surface heat flux feedback. On the one hand, the value of α depends on the mean-state SST due to the nonlinearity in the Clausius–Clapeyron relation (i.e., the nonlinear dependence of the saturated specific humidity on temperature)22 and on the near-surface atmosphere condition, especially the wind speed5,6,20,22. Specifically, higher mean-state SST and wind speed promote the SHFA induced by SSTA, increasing the value of α. On the other hand, the value of α is affected by the adjustment of the marine atmospheric boundary layer (MABL) to underlying SSTA6,19,20,22. The adjustment manifests in many aspects, including the near-surface air conditions5,6,22–30, the stability and thickness of MABL24,29,30 as well as the rainfall29. In particular, the adjustment of near-surface air temperature and humidity to underlying SSTA diminishes the air-sea thermal disequilibrium. This tends to weaken SHFA caused by SSTA, reducing the value of α.

Increased greenhouse gas emission has been exerting profound impacts on the midlatitude ocean and atmosphere, including warmer mean-state SST31,32, strengthened surface zonal wind33, and more stable MABL34. Such changes may reinforce or counteract each other in terms of the surface heat flux feedback, complicating its response to greenhouse gas-induced warming. So far, it remains elusive how the midlatitude surface heat flux feedback for large-scale SSTA will change in the future. Here, we address this question using outputs from an ensemble of global climate models in the Coupled Model Intercomparison Project Phase 6 (CMIP6)35.

Results
Simulated surface heat flux feedback in the CMIP6
We first validate the simulated midlatitude surface heat flux feedback for large-scale SSTA in the CMIP6 models (Supplementary Table 1) against that in the reanalysis dataset (‘CMIP6 models and reanalysis dataset’ and ‘Computation of large-scale SSTA and SHFA’ in Methods). Fig. 1 compares the time-mean (1950–1999) α and its decomposition into turbulent and radiative components (α T and α R) in the CMIP6 with their counterparts in the reanalysis dataset (‘Computation of surface heat flux feedback’ in “Methods”). The value of α in the reanalysis dataset is positive across the midlatitudes (20°–50°) and dominated by α T. It shows a general negative trend with the increasing latitude (Supplementary Fig. 1), with local enhancement along the major subtropical western boundary currents as well as their extensions (WBCEs). Such a spatial pattern of α is similar to that of mean-state SST and partially due to the nonlinearity in the Clausius–Clapeyron relation22,36.

The multi-model ensemble (MME) mean of CMIP6 reproduces the magnitude of α in the reanalysis dataset reasonably well (Fig. 1). The spatial mean α at the midlatitudes is 16.0 W m−2 K−1 in the CMIP6, very close to 16.1 W m−2 K−1 in the reanalysis dataset. The spatial pattern of α in the CMIP6 shows broad agreement with that in the reanalysis, qualitatively capturing the negative trend of α with the latitudes (Supplementary Fig. 1) and locally enhanced α along the subtropical WBCEs. However, the CMIP6 fails to faithfully reproduce some regional structures of α in the reanalysis dataset, leading to a moderate correlation coefficient (0.52) between the spatial variations of α in the CMIP6 and the reanalysis dataset. It should be noted that such regional differences of α are not necessarily attributed to the deficiencies of CMIP6 models but may partially result from the uncertainties of the reanalysis dataset itself, such as the lack of observational constraint37. In summary, the simulated α in the CMIP6 is qualitatively consistent with that in the reanalysis, whereas quantitative differences do exist in some regions. In view of these regional differences, we will mainly focus on the spatially overall change of α under greenhouse gas-induced warming.

Response of surface heat flux feedback to greenhouse gas-induced warming
Under a high radiative forcing scenario, the projected surface heat flux feedback for large-scale SSTA in the CMIP6 is universally weakened across the midlatitudes (Fig. 2d–f), compared to its pre-industrial level (Fig. 2a–c). The MME mean α in the CMIP6 averaged over 20°–50° during 2051–2100 is only about 52% of that in the pre-industrial control (PI-CTRL) simulation (Fig. 2g, h). This decrease of α is primarily attributed to the reduced α T with the change of α R playing a secondary role (Fig. 2g, h). We remark that the decrease of α under greenhouse gas-induced warming holds for not only the MME mean of CMIP6 but also its individual members (Supplementary Fig. 2). Such inter-model consistency lends strong support to the fidelity of the projected weakening of surface heat flux feedback for large-scale SSTA at midlatitudes.

To unravel the underlying mechanisms for the reduction α T under greenhouse gas-induced warming, we decompose α T into the components related to the background air-sea state αB T and adjustment of MABL to underlying SSTA αA T (‘Decomposition of surface turbulent heat flux feedback’ in “Methods”). The former assumes an atmosphere completely unaffected by the underlying SSTA, in other words, possessing an infinitely large heat capacity and ability to hold water vapor. The α T in the PI-CTRL simulation is systematically lower than αB T (Fig. 3a and Supplementary Fig. 3). This is because the MABL inevitably adjusts to SSTA in reality23,25,27,28. On the one hand, the adjustments of near-surface air temperature and specific humidity, referred to as the thermodynamic adjustments and denoted as αTA T, diminish the air-sea thermal disequilibrium caused by SSTA and thus weaken the surface heat flux feedback. The αTA T largely reconciles the difference between α T and αB T in the PI-CTRL simulation (Fig. 3a). On the other hand, the adjustment of near-surface wind speed, referred to as the dynamic adjustment and denoted as αDA T, contributes negligibly to the α T in the PI-CTRL simulation (Fig. 3a). Under greenhouse gas-induced warming, αB T is increased, whereas αTA T becomes more negative (Fig. 3d). The change of αTA T is larger in magnitude than that of αB T, accounting for the reduced α T under greenhouse gas-induced warming. Again, the change of αDA T plays a negligible role. In summary, the weakened midlatitude surface heat flux feedback for large-scale SSTA under greenhouse gas-induced warming is primarily attributed to the stronger thermodynamic adjustment of MABL to underlying SSTA.

The increased αB T under greenhouse gas-induced warming is mostly attributed to the latent heat flux component (Fig. 3d–f) and can be understood based on the nonlinearity in the Clausius–Clapeyron relation22,36. For a given magnitude of SSTA, the latent heat flux anomaly it induces is proportional to the derivative of saturated specific humidity with respect to the mean-state SST (‘Decomposition of surface turbulent heat flux feedback’ in “Methods”). As this derivative is an increasing function of mean-state SST due to the nonlinear Clausius–Clapeyron relation22,36 (Supplementary Fig. 4), the warmer mean-state SST under greenhouse gas-induced warming leads to a stronger response of latent heat flux anomaly to underlying SSTA, accounting for the increased αB T. This is argument is further supported by a positive trend of αB T over time in the ERA reanalysis (Supplementary Fig. 5), as the mean-state SST increase has already emerged during the past several decades38.

The stronger thermodynamic adjustment of MABL to underlying SSTA may result from the enhanced MABL stability that inhibits the removal of SSTA’s imprints on the near-surface air temperature and specific humidity via vertical mixing29,39–41. To test this conjecture, we analyze the inter-model relationship between the projected change of MABL stability and the change of thermodynamic adjustment of MABL to underlying SSTA. The MABL stability is measured as the air temperature at the 850 hPa minus that at the 1000 hPa. The thermodynamic adjustment of MABL to underlying SSTA is separately measured as the derivative of near-surface air temperature and specific humidity anomalies with respect to the underlying SSTA. Under greenhouse gas-induced warming, all the CMIP6 models consistently project a more stable MABL at midlatitudes (Fig. 4), probably due to the decreased moist-adiabatic lapse rate42. However, there are pronounced quantitative differences in the projected MABL stability changes among models. These inter-model differences are tightly correlated to the inter-model differences in the projected changes of thermodynamic adjustment of MABL to underlying SSTA (Fig. 4). The above results lend support that the enhanced MABL stability under greenhouse gas-induced warming is primarily responsible for the stronger thermodynamic adjustment of MABL to underlying SSTA.

Methods
CMIP6 models and reanalysis dataset
To analyze the response of surface heat flux feedback to greenhouse gas-induced warming, we use monthly outputs from a selected subset of CMIP6 models (Supplementary Table 1). Specifically, we select models that consists of a 500-year-long PI-CTRL simulation and a historical-and-future transient (HF-TNST) simulation from 1850 to 2100, outputting all the necessary quantities for the computation of surface heat flux feedback and its decomposition into different components (‘Decomposition of surface turbulent heat flux feedback’ in “Methods”).

The PI-CTRL simulation is driven by the pre-industrial natural variability forcing without impacts from anthropogenic greenhouse gas. The HF-TNST simulation, initialized from the PI-CTRL simulation, adopts the historical forcing during 1850–2014 and shared socioeconomic pathway 5–8.5 (SSP585) greenhouse gas forcing during 2015–210035. For the CMIP6 models containing an ensemble of PI-CTRL and HF-TNST simulation members, only the first ensemble member is used. The model outputs during the last 50 years in the PI-CTRL simulation and during the years 2051–2100 in the HF-TNST simulation are used to compute the surface heat flux feedback in the pre-industrial and high radiative forcing scenarios, respectively. As the CMIP6 models differ in their native grids, all the model outputs are interpolated onto a uniform 1°×1° grid to facilitate the computation of the MME mean of CMIP6. To evaluate the fidelity of CMIP6 models in simulating the surface heat flux feedback, we use the monthly ERA5 reanalysis dataset spanning from 1940–202037 and compare the surface heat flux feedback derived from the CMIP6 models and ERA5 reanalysis.

Computation of large-scale SSTA and SHFA
At midlatitudes, the air-sea interactions at oceanic mesoscales and large-scale are driven by oceanic and atmospheric internal variabilities, respectively53–56. Although understanding the anthropogenic change of mesoscale air-sea interaction is important for its own sake57, this study focuses on the response of large-scale air-sea interactions to greenhouse gas-induced warming. The large-scale SSTA and SHFA are computed according to the following steps. First, we select a period for analysis, e.g., the last 50 years in the PI-CTRL simulation or 2051–2100 in the HF-TNST simulation. Second, the monthly SST and SHF are subtracted by their respective monthly climatology over the selected period to obtain the anomalies (SSTA and SHFA). Third, a 4°×4° running mean is applied to the SSTA and SHFA to filter out the mesoscale signals. The cutoff wavelength of the 4°×4° running mean is around O(1000 km), comparable to the outer range of wavelength of oceanic mesoscale eddies at midlatitudes58. Fourth, linear trends and ENSO signals are removed from SSTA and SHFA at each grid59. We remove the ENSO’s imprints on the SSTA and SHFA as these imprints are characterized by strong air-sea coupling, making it difficult to disentangle SHFA induced by SSTA and atmospheric internal variabilities2,16,60. This is done by regressing SSTA and SHFA at each grid onto the first two principal components of SSTA in the tropical Pacific within 12.5°S-12.5°N and then subtracting the regression lines from SSTA and SHFA at each grid59. Imprints of ENSO signals on the other model outputs, such as near-surface air temperature, are removed in the same way. We remark that the removal of ENSO signals leads to a slight decrease in the variances of SSTA and SHFA at the midlatitudes, consistent with the global ENSO teleconnections61–63. Nevertheless, removing ENSO signals or not does not affect the major conclusions of this study, i.e., weakened surface heat flux feedback under greenhouse-gas-induced warming (Supplementary Fig. 7).

Supplementary Fig. 8b shows the spatial distribution of the correlation coefficient between large-scale SST tendency and turbulent heat flux anomaly (THFA) for the MME mean of CMIP6. Consistent with an atmosphere-driven regime, the correlation coefficient is highly positive. In addition, varying the filter size from 2° to 6° does not affect the correlation coefficient substantially (Supplementary Fig. 8). This is partially because oceanic mesoscale signals are largely suppressed in the monthly mean CMIP6 model output.

Computation of surface heat flux feedback
The SHFA at some locations can be decomposed as1,2,
Q0 = q0 − αT0 (1)
where the primes denote the anomalies, q0 is the SHFA component that is attributed to atmospheric internal variability and acts to generate SSTA (T0), and −αT0 the SHFA component induced by T0 with the feedback coefficient α. The value of α can be estimated by the cross-covariance between Q0 and T0, as1,6,19
R TQ (τ) = R Tq (τ) − αR TT (τ) (2)
where R TQ (τ) (R Tq (τ)) is the cross-covariance between T0 and Q0 (q0) when Q0 (q0) leads T0 by τ months and R TT (τ) is the auto-covariance of T0 at τ-month lag.
At the mid-latitudes, R Tq (τ) becomes negligible when T0 leads Q0 by a month or longer due to the short persistence of atmospheric internal variabilities1,2,16,19, in which case the value of α can be approximated as
α = − R TQ (τ) / R TT (τ) (3)
The value of α is computed by averaging the values of Eq. (3) at τ = −1, −2, −3 (Park et al.6). To avoid singularity caused by zero R TT (τ), we exclude samples with R TT (τ) insignificant from zero at the 95% confidence level before computing the average16. Once α is obtained, the SHFA component induced by T0 (the damping SHFA) is computed as −αT0 and the SHFA component attributed to atmospheric internal variability (the forcing SHFA) is computed as the difference between Q0 and −αT0 according to Eq. (1).

Decomposition of surface turbulent heat flux feedback
The surface heat flux Q is the sum of surface turbulent (Q T) and radiative heat flux (Q R)
Q = Q T + Q R, (4)
where Q T is the sum of sensible (Q S) and latent (Q L) components expressed as:
Q S = ρUC S C p (T a − T) (5)
Q L = ρUC L L (q − q sat) (6)
where ρ, T a, and q are the 2-m air density, temperature, and specific humidity, q sat is the saturated specific humidity at T, U is the 10-m wind speed, C p is the air specific heat capacity, L is the latent heat of vaporization, and C S and C L are the transfer coefficients for sensible and latent heat. Decompose the quantities in Eqs. (5) and (6) into the climatological mean values and anomalies (denoted with the overbars and primes, respectively). Linearizing Eqs. (5) and (6) with respect to anomalous quantities gives rise to
Q S0 ≈ ρU C S C p (T a0 − T0) + ρC S C p (T a − T) U0 (7)
Q L0 ≈ ρU C L L (q0 − dq sat / dT | T0) + ρC L L (q − q sat) U0 (8)
where we have approximated ρ, C p, L, C S and C L as their climatological mean values, and q sat = q sat (T). Taking the derivative of Eqs. (7) and (8) with respect to T0 and adding them up lead to the expression of α T
α T = ρU C S C p (dT a0 / dT0 − 1) + ρC S C p (T a − T) dU0 / dT0 + ρU C L L (dq0 / dT0 − dq sat / dT | T0) + ρC L L (q − q sat) dU0 / dT0 (9)
the first line of the right-hand side of Eq. (9) denote the sensible heat flux feedback (α S), with the first to third terms corresponding sequentially to the components related to the background air-sea state (αB S), thermodynamic (αTA S), and dynamic (αDA S) adjustments of MABL. Similar are the terms in the second line (denoted sequentially as αB L, αTA L and αDA L), but for the latent heat flux feedback. Note that αB L is linearly proportional to dq sat / dT that increases with T due to the nonlinearity in the Clausius–Clapeyron relation22,36. To evaluate the validity of decomposition, we compute α T directly as −dQ T / dT0. The difference between this direct computation of α T and that computed from Eq. (9) is negligible (Fig. 3).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Weakened Large-Scale Surface Heat Flux Feedback at Midlatitudes under Global Warming\nGeneral comments about the article\nThe authors investigate the weakened surface heat flux feedback under a high radiative forcing scenario. The manuscript is\nwell organized and written. The authors present an interesting article based on a subject that deserves attention from the\nscientific community. Their main conclusion is that the weakening is primarily attributed to a more stabilized marine\natmospheric boundary layer (MABL), which diminishes air-sea thermal disequilibrium caused by SSTA. The local MABL\nmodulation caused by SST might be more addressed in the introduction of this manuscript. Nevertheless, the authors either\nreview literature or support and compare their results mainly with authors from the North Hemisphere, focusing on that region\nand neglecting work developed in the South Hemisphere, e.g., ones by South Americans who have been studying\nmidlatitudes (e.g., Brazil-Malvinas Confluence region, BMC) scientifically for several years, exactly on this subject. Air-sea\ninteraction, surface turbulent heat fluxes feedback, MABL modulation, MABL stability, etc. There are several studies on the\nimportance of this region, as well as studies addressing the importance of the oceanic fronts and mesoscale of this region for\nboth oceanic and atmospheric issues. This is my major concern about this study. The scientific results are interesting, the\nmethodology is suitable to address the subject and the conclusions are shown by the analysis made. I see that this study\nhas the potential to be published after a minor review, according to the suggestions made in this review.\nSpecific comments to improve the manuscript.\n#62-64 Here the authors can facilitate the reader's life, saying what is the SST nonlinear Clausius–Clapeyron relation and\nlower atmosphere consequences explicitly, instead just citing the literature. This kind of situation is seen in many parts of the\npaper, especially when the author mention \"variability\". These kinds of affirmations are vague.\n#87-90 I can not see the argued negative trend. In the figure we only see positive values. These needs be corrected or better\nexplained.\n#96 same issues as above …\" capturing the negative trend of a \"\n#101-102 It is quite vague this affirmation. What kind of variability? Be more specific.\n#144-145 Here is one suggestion where the authors can include (along with 30 31) and compare with more recent literature\n(based on heat fluxes obtained through direct measurements, e.g. eddy covariance).\n#169-177 Again, be more specific about the Clausius–Clapeyron relation. Also, it can be mentioned/compared that this\nstronger response of latent heat flux to underlying sea surface temperature is also observed (the comment made in the\nreviewer's introduction) This is observation and not models only. This will give more reliability to the author's statement by\ncomparing it to observations.\n#178-194 Here it addresses both MABL mechanisms of stability. Static (e.g., vertical mixing and hydrostatic stability). Again,\nmany classical references must come in this section to sustain authors' findings.\n#316 or 325 or somewhere in the Methodology or together with decomposition of surface turbulent heat flux, I suggest\nexplicitly showing the full expression of Q and then explain the used terms. Including the radiation flux that either I didn't find,\nor I didn't see it.\n#578 Table 1 deserves a better explanation in the caption about the acronyms under the scenario column.\nSuggested reading:\nCabrera, M.; Santini, M.; Lima, L.; Carvalho, J.; Rosa, E.; Rodrigues, C.; Pezzi, L. The southwestern atlantic ocean\nmesoscale eddies: a review of their role in the air-sea interaction processes. Journal of Marine Systems, p. 103785, 2022.\nPezzi, L.P., de Souza, R.B., Santini, M.F. et al. Oceanic eddy-induced modifications to air–sea heat and CO2 fluxes in the\nBrazil-Malvinas Confluence. Sci Rep 11, 10648 (2021). https://doi.org/10.1038/s41598-021-89985-9\nPezzi, L. P. et al. Ocean-atmosphere in situ observations at the Brazil-Malvinas confluence region. Geophys. Res. Lett. 32,\n2–5. L22603, https://doi.org/10.1029/2005GL023866(2005).\nSantini, M. F., Souza, R. B., Pezzi, L. P. & Swart, S. Observations of air-sea heat fluxes in the Southwestern Atlantic under\nhigh- frequency ocean and atmospheric perturbations. Q. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.3905 . (2020).\nSouza, R.;Pezzi, L.;Swart, S.;Oliveira,F.;Santini,M.Air-Sea Interactions over Eddies in the Brazil-Malvinas Confluence.\nRemote Sens. 2021, 13, 1335. https://doi.org/10.3390/rs13071335\n(Remarks on code availability)\nI could not see the link https://zenodo.org/records/11500581?preview=1\nIt says I do not have permition.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "This work addresses a fundamental question of how large-scale surface heat flux feedback in natural variability at\nmidlatitudes will change under global warming, which has not been systematically investigated in previous studies.\nSpecifically, the authors examine the change of the feedback under high-emission scenarios by comparing the outputs of\nsimulations from 24 CMIP6 coupled climate models. The important result is that the large-scale surface heat flux feedback\nwill largely weaken by nearly 50% compared to its preindustrial level, which is primarily attributed to the strengthened\nstability in the air-sea boundary layer. Based on the CMIP6 results, the authors imply that the weakened feedback may exert\nprominent influences on global climate variability and ocean circulation. The results are important and interesting but there\nare issues requiring further revision or improving, which are listed below.\nMajor comments:\n1. The time scale of the large-scale surface heat flux feedback, as well as the so-called anomaly, is missed or not clearly\npresented in the title, abstract, and main text, which is important for readers to rapidly understand the topic of the present\nstudy. The authors may want to focus on the anomaly below the decadal time scale but not long-term climate change (based\non Fig. 5). But the large-scale surface heat flux feedback actually involves in varies time scales from daily to centennial and\nlonger, like the phenomena mentioned in L57-58 involved a wide-range and complicated time scales. Therefore, clarifying\nthe time scale of the study is necessary.\n2. The authors also need to well state why only focus on midlatitudes, and explain why the ENSO signal needs to be\nremoved in the so-called anomalies. This also raises a question of how the variance in SST and surface heat flux would\nchange if the ENSO signal is included and excluded. Actually, ENSO change is also a source of the midlatitudes climate\nvariability change.\n3. In terms of the major result, the increased SST variance may also be a result of the imperfect/incomplete removal of the\nwarming trend caused by external forcing given that the response of SST in historical and SSP5-8.5 simulations is not\nstrictly linear to the increase in external forcing. Therefore, the results may be sensitive to the time period (like starting from\n1850 or 1900, or 1950) and fit method (linear fit, Higher-order fitting, EOF1, EEMD/EMD, etc.) of for removing the external\nforcing induced-trend. The selected method may influence the magnitude of the percentage change of the feedback.\n4. The ocean mixed layer shoaling is tightly coupled with the SST change and surface heat flux change/anomaly, maybe it is\nnot possible to explicitly decompose the effects of MLD and surface heat flux on the changes in SST variance by diagnostic\nmethod.\n5. Method descriptions are largely missed for L138-140 and L224-226, and unclear for L 170-176, provide more detailed and\nconcise descriptions of the method part. Therefore, it is not easy to follow the argument proposed in the discussion part,\nespecially regarding the method to diagnose the role of MLD shoaling by decomposing the SHFA into forcing and damping\nparts like L226-228, and L230-236. Revisions are required to provide detailed information on the formulae and the\ncorresponding physical meaning of each term. It is not easy to understand in a logical and physical flow regarding this\ndecomposition. Moreover, is there any related literature utilizing a similar decomposition method?\n6. L238: In terms of the normalization of the SHFA by mixed layer heat capacity related to MLD, is the climatology MLD the\nsame in the normalization for the preindustrial period and future period? or the authors utilize climatology MLD at two\nrespective periods? If the normalization is conducted based on two different climatology MLD, the effect of the MLD shoaling\nis indeed included in the normalized results.\nMinor comments\n1. 24 models are not sufficient for the intermodel analysis given the availability of a large ensemble of CMIP6 simulations\n(more than 35 models?) at the current stage.\n2. L93-95: based on Figs.1a, d, the model-simulated alpha seems to be broadly smaller in CMIP6 MME than that in ERA5,\nespecially over the subtropics and western boundary currents regions and their extensions, but why the area-mean values\nare highly consistent.\n3. L104: why ‘given these regional differences‘?\n4. L152: confused or questioned expression of ‘the latter dominates the former’.\n5. L222: ‘greenhouse gas warming’ should be ‘greenhouse gas-induced warming’\n6. L233-234: Any supported evidence/literature/agreement regarding the weakened atmospheric internal variability?\n7. Fig. E1: Add multi-model ensemble mean as a black bar, with errobar indicating intermodel standard deviation.\nHow about the intermodel consistency regarding the spatial pattern of the changed alpha in each model?\n8. When interpreting the MME results, the signal-to-noise ratio, which is defined as the MME value divided by the intermodel\nstd value, should also be discussed to further support the significance of the change.\n9. How to calculate the MME pattern? MME pattern refers to ensemble-mean of patterns based on results calculated at each\nmodel’s anomalies or pattern based on results of multi-model ensemble-mean anomalies.\n10. It is inappropriate to interpolate all variables to a resolution of 0.25degree if the original oceanic resolution is only\n1degree.\n11. L61: ‘scale’ should be ‘time scale’\nL67: ‘additional’ is more appropriate than ‘further’\nL170: weird expression like ‘its induced’\nL329: ‘its’"}, "author_response": "Reply to the first reviewer\nWe are very grateful to you for your time in carefully reading our manuscript and\nproviding helpful comments that make our manuscript better. We have carefully\nconsidered each of your comments (in blue) and revised the manuscript accordingly.\nPlease find our response (in black) to your comments below.\nReviewer #1 (Remarks to the Author):\nGeneral comments about the article\nThe authors investigate the weakened surface heat flux feedback under a high radiative\nforcing scenario. The manuscript is well organized and written. The authors present an\ninteresting article based on a subject that deserves attention from the scientific\ncommunity. Their main conclusion is that the weakening is primarily attributed to a\nmore stabilized marine atmospheric boundary layer (MABL), which diminishes air-sea\nthermal disequilibrium caused by SSTA. The local MABL modulation caused by SST\nmight be more addressed in the introduction of this manuscript. Nevertheless, the\nauthors either review literature or support and compare their results mainly with authors\nfrom the North Hemisphere, focusing on that region and neglecting work developed in\nthe South Hemisphere, e.g., ones by South Americans who have been studying\nmidlatitudes (e.g., Brazil-Malvinas Confluence region, BMC) scientifically for several\nyears, exactly on this subject. Air-sea interaction, surface turbulent heat fluxes feedback,\nMABL modulation, MABL stability, etc. There are several studies on the importance\nof this region, as well as studies addressing the importance of the oceanic fronts and\nmesoscale of this region for both oceanic and atmospheric issues. This is my major\nconcern about this study. The scientific results are interesting, the methodology is\nsuitable to address the subject and the conclusions are shown by the analysis made. I\nsee that this study has the potential to be published after a minor review, according to\nthe suggestions made in this review.\nThanks for your advice. We have provided more discussion and cited more\nreferences on the MABL’s response of to SSTA in the introduction. Please see Line 69-\n73. In addition, we have cited the important studies addressing the air-sea interactions\nin the Southern Hemisphere. Please see the references 23-27.\nSpecific comments to improve the manuscript.\n#62-64 Here the authors can facilitate the reader's life, saying what is the SST nonlinear\nClausius–Clapeyron relation and lower atmosphere consequences explicitly, instead\njust citing the literature. This kind of situation is seen in many parts of the paper,\nespecially when the author mention \"variability\". These kinds of affirmations are vague.\nThank you for your suggestion. We have explicitly explained the nonlinearity in\nthe Clausius–Clapeyron and briefly stated the consequences of the nonlinear Clausius–\nClapeyron relation and near-surface atmospheric conditions on the of . Please see Line\n64-67. A detailed explanation on how the nonlinear Clausius–Clapeyron relation and\nnear-surface atmospheric conditions affect the value of  is provided in the result and\nmethod sections. Please see Line 183-194 and 385-394.\nWe are sorry for the general usage of the word ‘variability’. In the revised\nmanuscript, we have replaced ‘variability’ with more specific words wherever it is\npossible. Please see Line 29, 36 and 253.\n#87-90 I cannot see the argued negative trend. In the figure we only see positive values.\nThese needs be corrected or better explained.\nFig. R1 shows the zonally averaged  at the midlatitudes. There is a negative trend\nof  with the increasing latitude. We have provided Fig. R1 as Extended Data Fig.1 in\nthe revised manuscript to support our argument.\nFig. R1 | The zonally averaged time-mean surface heat flux feedback during 1950-\n1999. The blue and red lines show  estimated from the ERA5 reanalysis and MME\nmean of the CMIP6, respectively, with the shading denoting the 95% confidence\ninterval of the CMIP6 MME mean.\n#96 same issues as above …\" capturing the negative trend of a \"\nPlease see our reply to your above comment.\n#101-102 It is quite vague this affirmation. What kind of variability? Be more specific.\nThese lines do not contain the word ‘variability’. Do you mean the word ‘uncertainties’?\nThe uncertainties include lack of observational constraint (Hersbach et al. 2020). Please\nsee Line 116 in the revised manuscript.\n#144-145 Here is one suggestion where the authors can include (along with 30 31) and\ncompare with more recent literature (based on heat fluxes obtained through direct\nmeasurements, e.g. eddy covariance).\nThank you for your suggestion. Several recent literatures, especially those related\nto the eddy covariance method, have been added. Please see Line 158.\n#169-177 Again, be more specific about the Clausius–Clapeyron relation. Also, it can\nbe mentioned/compared that this stronger response of latent heat flux to underlying sea\nsurface temperature is also observed (the comment made in the reviewer's introduction)\nThis is observation and not models only. This will give more reliability to the author's\nstatement by comparing it to observations.\nThe nonlinearity in the Clausius–Clapeyron relation is explained in Line 64-65 and\n188-189 in the revised manuscript.\nThank you for your advice. The positive trend of 𝛼𝐵 with time is also observed in\n𝐿\nthe ERA5 reanalysis during the past half century (Fig. R2), which lends support to our\nconclusions. We have added the above analysis and provided Fig. R2 as Extended Data\nFig. 5 in the revised manuscript. Please see Line 192-194.\nFig. R2 | Time series of spatial mean 𝜶𝑩 during 1950-2010 estimated from the\n𝑳\nERA5 reanalysis. Here the mean-state SST in the computation of 𝛼𝐵 in some year is\n𝐿\ndefined as the time-mean SST over a 11-year-long period centered on that year.\n#178-194 Here it addresses both MABL mechanisms of stability. Static (e.g., vertical\nmixing and hydrostatic stability). Again, many classical references must come in this\nsection to sustain authors' findings.\nSeveral classical references about the MABL stability, MABL’s response to SSTA\nand underlying mechanisms have been added. Please see Line 197.\n#316 or 325 or somewhere in the Methodology or together with decomposition of\nsurface turbulent heat flux, I suggest explicitly showing the full expression of Q and\nthen explain the used terms. Including the radiation flux that either I didn't find,\nor I didn't see it.\nThank you for your suggestion. Full expression of Q has been added in the revised\nmanuscript. Please see Line 367-372.\n#578 Table 1 deserves a better explanation in the caption about the acronyms under the\nscenario column.\nExplanations to the acronyms have been added.\nReviewer #1 (Remarks on code availability):\nI could not see the link https://zenodo.org/records/11500581?preview=1\nIt says I do not have permission.\nSorry for the problem. Please try this new link:\nhttps://zenodo.org/records/13906937\nReply to the second and third reviewers\nWe are very grateful to you for your time in carefully reading our manuscript and\nproviding helpful comments that make our manuscript better. We have carefully\nconsidered your comments (in blue) and revised the manuscript accordingly. Please find\nour response (in black) to your comments below.\nReviewer #3 (Remarks to the Author):\nThis work addresses a fundamental question of how large-scale surface heat flux\nfeedback in natural variability at midlatitudes will change under global warming, which\nhas not been systematically investigated in previous studies. Specifically, the authors\nexamine the change of the feedback under high-emission scenarios by comparing the\noutputs of simulations from 24 CMIP6 coupled climate models. The important result is\nthat the large-scale surface heat flux feedback will largely weaken by nearly 50%\ncompared to its preindustrial level, which is primarily attributed to the strengthened\nstability in the air-sea boundary layer. Based on the CMIP6 results, the authors imply\nthat the weakened feedback may exert prominent influences on global climate\nvariability and ocean circulation. The results are important and interesting but there are\nissues requiring further revision or improving, which are listed below.\nThank you for your positive comment.\nMajor comments\n1. The time scale of the large-scale surface heat flux feedback, as well as the so-called\nanomaly, is missed or not clearly presented in the title, abstract, and main text, which\nis important for readers to rapidly understand the topic of the present study. The authors\nmay want to focus on the anomaly below the decadal time scale but not long-term\nclimate change (based on Fig. 5). But the large-scale surface heat flux feedback actually\ninvolves in varies time scales from daily to centennial and longer, like the phenomena\nmentioned in L57-58 involved a wide-range and complicated time scales. Therefore,\nclarifying the time scale of the study is necessary.\nWe are grateful to you for pointing out this important issue. The ‘large scale” in\nthis study is adopted from Frankignoul (1985), which means the large spatial scale.\nAccordingly, the large-scale SSTA and heat flux anomaly (SHFA) can contain\nvariabilities at a wide range of time scales. However, due to the temporal resolution and\ncoverage of the data we use and the way we compute anomalies, the large-scale SSTA\nand SHFA in this study only contain variabilities at monthly-to-decadal time scales.\nIn the revised manuscript, we have clarified that (1) the large scale means large\nspatial scale and (2) the time scale of anomalies ranges from months to decades. Please\nsee Line 33, 37, 51-52 and 82-87.\n2. The authors also need to well state why only focus on midlatitudes, and explain why\nthe ENSO signal needs to be removed in the so-called anomalies. This also raises a\nquestion of how the variance in SST and surface heat flux would change if the ENSO\nsignal is included and excluded. Actually, ENSO change is also a source of the\nmidlatitudes climate variability change.\n(1) Reply to your comment on the reasons for focusing on midlatitude\nIn this study, we focus on the midlatitudes, because the air-sea interactions differ\nsubstantially at midlatitudes and tropics3. In particular, the strong air-sea coupling (e.g.,\nthe ENSO) at tropics makes it difficult to disentangle the SHFAs induced by\natmospheric intrinsic variability and SSTA, and the formula (Eq. 3) for computing the\nsurface heat flux feedback becomes invalid (Frankignoul et al. 1998; Frankignoul and\nKestenare 2002).\nWe have included the above discussion in the revised manuscript. Please see Line\n87-90.\n(2) Reply to your comment on the reasons for removing the ENSO signals\nBecause of the global ENSO teleconnections, SHFA and SSTA at extra-tropical\nlatitudes may also be contaminated by the ENSO signals (Bjerknes 1969; Hoerling et\nal. 1997; Alexander et al. 2002; Yeh et al. 2017). As mentioned in our reply above, such\ncontamination may cause uncertainties for computing the surface heat flux feedback.\nTherefore, we remove the ENSO signal following Frankignoul and Kestenare (2002).\n(3) Reply to your comment on the effect of ENSO signals on SSTA and SHFA\nvariances\nTo address your comment, we compare the variances of SSTA and SHFA at\nmidlatitudes with the ENSO signals included to those with the ENSO signals excluded.\nRemoval of ENSO signals leads to a slight decrease in the variances of SSTA and SHFA\n(Fig. R1), consistent with the global ENSO teleconnections. Nevertheless, removing\nENSO signals or not does not affect the major conclusions of this study, i.e., weakened\nsurface heat flux feedback (Fig. R2) and enhanced SSTA variance (Fig. R1) under\nglobal warming.\nFig. R1 | Effects of ENSO on the variances of SSTA and SHFA at midlatitudes.\nSpatial mean variances of SSTA (a) and SHFA (b) at midlatitudes in the PI-CTRL (blue)\nand SSP585 (red) simulations. Here the ENSO signals are included in SSTA and SHFA.\nc,d, Same as a,b, but with ENSO signals excluded from SSTA and SHFA.\nFig. R2 | Projected changes of midlatitude large-scale surface heat flux feedback\nunder greenhouse gas-induced warming. Spatial distribution of time-mean surface\nheat flux feedback in the SSP585 simulation (2051-2100) minus that in the PI-CTRL\nsimulation, with the ENSO signals included in (a) and excluded from (b) the SSTA and\nSHFA.\n3. In terms of the major result, the increased SST variance may also be a result of the\nimperfect/incomplete removal of the warming trend caused by external forcing given\nthat the response of SST in historical and SSP5-8.5 simulations is not strictly linear to\nthe increase in external forcing. Therefore, the results may be sensitive to the time\nperiod (like starting from 1850 or 1900, or 1950) and fit method (linear fit, Higher-\norder fitting, EOF1, EEMD/EMD, etc.) of for removing the external forcing induced-\ntrend. The selected method may influence the magnitude of the percentage change of\nthe feedback.\nYour concern is important. In response to your concern, we use several different\nmethods to estimate the trends in the SSTA and SHFA caused by greenhouse gas forcing,\nincluding a linear fit, a quadratic fit, EMD and EOF. Using different methods for\ndetrending has minor influences on the change of surface heat flux feedback (Fig.R3)\nand SSTA variance (Fig.R4) under global warming. Therefore, the major conclusions\nof this study are qualitatively insensitive to the detrending methods.\nFig. R3 | Effect of detrending methods on the projected changes of midlatitude\nlarge-scale surface heat flux feedback under greenhouse gas-induced warming.\nSpatio-temporally mean surface heat flux feedback in the SSP585 simulation (2051-\n2100) minus that in the PI-CTRL simulation, computed based on the different\ndetrending methods, i.e., the linear fit (a), the quadratic fit (b), the EMD (c) and the\nEOF (d).\nFig. R4 | Effect of detrending methods on the projected changes of midlatitude\nlarge-scale SSTA variance under greenhouse gas-induced warming. Percentage\nchanges of frequency spectra of large-scale SSTA in the SSP585 simulation (2051-2100)\nrelative to the PI-CTRL simulation, with the SSTA computed based on the different\ndetrending methods, i.e., the linear fit (a), the quadratic fit (b), the EMD (c) and the\nEOF (d).\n4. The ocean mixed layer shoaling is tightly coupled with the SST change and surface\nheat flux change/anomaly, maybe it is not possible to explicitly decompose the effects\nof MLD and surface heat flux on the changes in SST variance by diagnostic method.\nThanks for your comments. In this study, the mixed layer depth (MLD) before and\nafter global warming is computed as the time-mean value during the last 50 year in the\nPI-CTRL simulation and 2051-2100 in the SSP585 simulation, respectively. In contrast,\nthe SSTA and SHFA are computed as the linearly detrended monthly anomalies in\nrespective periods. Therefore, the change of MLD under global warming reflects the\ntime-mean change of SST and surface heat flux, and is not coupled with the change of\nSSTA and SHFA variances.\n5. Method descriptions are largely missed for L138-140 and L224-226, and unclear for\nL170-176, provide more detailed and concise descriptions of the method part. Therefore,\nit is not easy to follow the argument proposed in the discussion part, especially\nregarding the method to diagnose the role of MLD shoaling by decomposing the SHFA\ninto forcing and damping parts like L226-228, and L230-236. Revisions are required to\nprovide detailed information on the formulae and the corresponding physical meaning\nof each term. It is not easy to understand in a logical and physical flow regarding this\ndecomposition. Moreover, is there any related literature utilizing a similar\ndecomposition method?\nWe apologize for the lack of detailed method descriptions, which makes the\nmanuscript difficult to follow. We have provided the detailed method descriptions in\nthe revised manuscript. Please see Line 343-366.\nOur methods are similar to those used by the previous studies (Park et al. 2005,\nHausmann et al. 2016 and Yuan et al. 2022). We have cited these studies in the revised\nmanuscript.\n6. L238: In terms of the normalization of the SHFA by mixed layer heat capacity related\nto MLD, is the climatology MLD the same in the normalization for the preindustrial\nperiod and future period? or the authors utilize climatology MLD at two respective\nperiods? If the normalization is conducted based on two different climatology MLD,\nthe effect of the MLD shoaling is indeed included in the normalized results.\nSorry for the unclarity. The SHFAs for the preindustrial period and future period\nare normalized by the climatology MLD at the two respective periods. We have made\nthis clear in the revised manuscript. Please see Line 248-249.\nMinus comments\n1. 24 models are not sufficient for the intermodel analysis given the availability of a\nlarge ensemble of CMIP6 simulations (more than 35 models?) at the current stage.\nWe use a subset (24) of CMIP6 models, because they are the only models\ncontaining all the necessary outputs used in the analysis of this study, e.g., the surface\nair variables in Eq. (9). We have explained why we choose a subset of CMIP6 models\nin the revised manuscript. Please see Line 289-296.\n2. L93-95: based on Figs.1a, d, the model-simulated alpha seems to be broadly smaller\nin CMIP6 MME than that in ERA5, especially over the subtropics and western\nboundary currents regions and their extensions, but why the area-mean values are\nhighly consistent.\nThanks for your question. We have double checked the computation. The result is\ncorrect.\n3. L104: why ‘given these regional differences’?\nIt has been revised as ‘In view of these regional differences’. Please see Line 118.\n4. L152: confused or questioned expression of ‘the latter dominates the former’.\nSorry for the ambiguity. We have revised this confusing statement as ‘the change\nof 𝛼𝑇𝐴 is larger in magnitude than that of 𝛼𝐵’. Please see Line 167-168.\n𝑇 𝑇\n5. L222: ‘greenhouse gas warming’ should be ‘greenhouse gas-induced warming’\nWe have replaced the ‘greenhouse gas warming’ with ‘greenhouse gas-induced\nwarming’ throughout the revised manuscript.\n6. L233-234: Any supported evidence/literature/agreement regarding the weakened\natmospheric internal variability?\nThanks for your question. We have not found any existing literature analyzing the\nresponse of atmospheric internal variability to global warming. To avoid overstatement,\nwe have changed this statement from\n‘This is because the atmospheric internal variability (i.e., the unnormalized forcing\nSHFA) weakens in a warming climate.’\nTo\n‘This is because the SHFA generated by atmospheric internal variability (i.e., the\nunnormalized forcing SHFA) weakens in a warming climate.’\nThe revised statement is supported by Extended Data Fig. 6b.\n7. Fig. E1: Add multi-model ensemble mean as a black bar, with errorbar indicating\ninter-model standard deviation.\nHow about the inter-model consistency regarding the spatial pattern of the changed\nalpha in each model?\nThank you for your suggestion. We have revised Extended Data Fig.1 following\nyour advice.\nWe have also examined the inter-model consistency regarding the spatial pattern\nof the 𝛼 change. Fig. R5 shows the zonally averaged 𝛼 change. All the models show\na universal decrease of 𝛼 across the midlatitudes.\nFig. R5 | Zonally averaged change of the surface heat flux feedback in the SSP585\nsimulation relative to the PI-CTRL simulation in the individual CMIP6 models.\n8. When interpreting the MME results, the signal-to-noise ratio, which is defined as the\nMME value divided by the intermodel std value, should also be discussed to further\nsupport the significance of the change.\nThank you for this suggestion. In the revised manuscript, we have provided the 95%\nconfidence interval for the MME mean in the revised manuscript. The lower and upper\nbounds of the 95% confidence interval have the same sign as the MME mean, lending\nsupport that the significant change of surface heat flux feedback under global warming.\n9. How to calculate the MME pattern? MME pattern refers to ensemble-mean of\npatterns based on results calculated at each model’s anomalies or pattern based on\nresults of multi-model ensemble-mean anomalies.\nThe MME pattern refers to the ensemble-mean of patterns based on results\ncalculated at each model’s anomalies. Take the MME mean pattern of 𝛼 as an example\n(Fig. 1d). We first compute 𝛼 based on the SHFA and SSTA in each model and then\naverage 𝛼 over all the models to obtain the MME mean 𝛼.\n10. It is inappropriate to interpolate all variables to a resolution of 0.25degree if the\noriginal oceanic resolution is only 1degree.\nWe are grateful to you for pointing out this issue. The results have been interpolated\nonto a 1°1° grid in the revised manuscript. Please see Line 306.\n11. L61: ‘scale’ should be ‘time scale’\nSorry for the unclarity. ‘Scale’ here refers to ‘spatial scale’ according to Frankignoul\n(1985). We have made this point clear in the revised manuscript. Please see Line 51-52\nand 62.\nL67: ‘additional’ is more appropriate than ‘further’\nWe have removed ‘further’ in the revised manuscript.\nL170: weird expression like ‘its induced’\nRevised as ‘the latent heat flux anomaly it induces’.\nL329: ‘its’\nRevised as ‘𝑞 is the saturated specific humidity at 𝑇’.\n𝑠𝑎𝑡\nReference list\n1. Hersbach, H. et al. The ERA5 global reanalysis. Quart J Royal Meteoro Soc 146,\n1999–2049 (2020).\n2. Frankignoul, C. Sea surface temperature anomalies, planetary waves, and air-sea\nfeedback in the middle latitudes. Rev. Geophys. 23, 357 (1985).\n3. Peng, Q., Xie, S.-P., Wang, D., Zheng, X.-T. & Zhang, H. Coupled ocean-atmosphere\ndynamics of the 2017 extreme coastal El Niño. Nat Commun 10, 298 (2019).4.\nFrankignoul, C., Czaja, A. & L’Heveder, B. Air–Sea Feedback in the North Atlantic\nand Surface Boundary Conditions for Ocean Models. J. Climate 11, 2310–2324\n(1998).\n5. C., F. & E., K. The surface heat flux feedback. Part I: estimates from observations in\nthe Atlantic and the North Pacific. Climate Dynamics 19, 633–647 (2002).\n6. Bjerknes, J. ATMOSPHERIC TELECONNECTIONS FROM THE EQUATORIAL\nPACIFIC. (1969).\n7. Hoerling, M. P., Kumar, A. & Zhong, M. El Niño, La Niña, and the Nonlinearity of\nTheir Teleconnections. Journal of Climate 10, 1769–1786 (1997).\n8. Yeh, S.-W. et al. ENSO Atmospheric Teleconnections and Their Response to\nGreenhouse Gas Forcing. doi:10.1002/2017RG000568.\n9. Alexander, M. A. et al. The atmospheric bridge:: The influence of ENSO\nteleconnections on air-sea interaction over the global oceans. J. Clim. 15, 2205–2231\n(2002).\n10. Park, S., Deser, C. & Alexander, M. A. Estimation of the Surface Heat Flux\nResponse to Sea Surface Temperature Anomalies over the Global Oceans. Journal\nof Climate 18, 4582–4599 (2005).\n11. Hausmann, U., Czaja, A. & Marshall, J. Mechanisms controlling the SST air-sea\nheat flux feedback and its dependence on spatial scale. Clim Dyn 48, 1297–1307\n(2017).\n12. Yuan, M., Li, F., Ma, X. & Yang, P. Spatio-temporal variability of surface\nturbulent heat flux feedback for mesoscale sea surface temperature anomaly in the\nglobal ocean. Front. Mar. Sci. 9, 957796 (2022)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have done a good job and revised the paper according to my revision suggestions. The authors have also responded and satisfactorily addressed my questions. Based on the evaluation of the responses received and the modifications made to the manuscript, I believe that the paper is ready for acceptance.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "I am glad that the authors have addressed the major concerns and nearly all comments reported in last review. There are several minor issues I think need to be clarified and then the manuscript can be accepted for publication.\nMinor comments:\n1. Given that the ENSO signal is excluded in the results, the authors also need to emphasize this in the conclusion that the\nweakened surface heat flux feedback in the midlatitudes is mainly point to the local feedback without remote effect from\nmajor climate mode like ENSO. In contrast, if including and excluding ENSO signal lead to nearly similar results, it seems to\ncontradict the statement in Lines 87-90.\n2. The reason of why the present study on the midlatitude is still not presented in the revision, for example, the\nimportance/significance and uniqueness of midlatitude feedback on regional climate prediction?\n3. The MLD shoaling effect was not well explained in the main text regarding the effect of the long-term shoaling of the mean\nMLD on changes in anomalous surface heat flux feedback. I think this issue requires more discussion description.\n4. It is not convinced that only 24 models provided all necessary variables under SSP5-8.5 given that surface variables are\noutputted by nearly all models, but it is possible that if the author haven’t tried all available data portals and thus can only get\nlimited model outputs as some model communities may shut down their server frequently. It is better to delete Line 296.\n5. Pay attention to the unit like in Line 107\n6. The color of the bar plot for Figs. 2, 3 … may need some adjustment to give readers a more comfortable vision. Actually,\ncolorbars of most figures may display a better vision if the authors select the colors more carefully (but the author may feel\nfree to follow this comment or not)."}, "author_response": "Reply to the first reviewer\nWe are very grateful to you for your time in carefully reading our manuscript and\nproviding helpful comments that make our manuscript better. We have carefully\nconsidered each of your comments (in blue) and revised the manuscript accordingly.\nPlease find our response (in black) to your comments below.\nReviewer #1 (Remarks to the Author):\nThe authors have done a good job and revised the paper according to my revision\nsuggestions. The authors have also responded and satisfactorily addressed my questions.\nBased on the evaluation of the responses received and the modifications made to the\nmanuscript, I believe that the paper is ready for acceptance.\nThanks for your positive comments and recognition to this study.\nReply to the second and third reviewers\nWe are very grateful to you for your time in carefully reading our manuscript and\nproviding helpful comments that make our manuscript better. We have carefully\nconsidered your comments (in blue) and revised the manuscript accordingly. Please find\nour response (in black) to your comments below.\nReviewer #3 (Remarks to the Author):\nI am glad that the authors have addressed the major concerns and nearly all comments\nreported in last review. There are several minor issues I think need to be clarified and\nthen the manuscript can be accepted for publication.\nThanks for your positive comments.\nMinor comments:\n1. Given that the ENSO signal is excluded in the results, the authors also need to\nemphasize this in the conclusion that the weakened surface heat flux feedback in the\nmidlatitudes is mainly point to the local feedback without remote effect from major\nclimate mode like ENSO. In contrast, if including and excluding ENSO signal lead to\nnearly similar results, it seems to contradict the statement in Lines 87-90.\nThank you for your comment. As demonstrated in the reply to your first-round\nreview, removing ENSO signals or not does not affect the major conclusion of this study,\ni.e., the weakened surface heat flux feedback (Fig. R1) at the midlatitudes under global\nwarming. In Lines 87-90 in our last manuscript, we state that ENSO plays an important\nrole in the surface heat flux feedback at the tropics. Therefore, there is no contradiction.\nIn the revised manuscript, we have provided Fig. R1 as Supplementary Fig. 7 and\nstated that removing ENSO signals or not does not affect the major conclusion of this\nstudy. Please see Line 283-287.\nFig. R1 | Projected changes of midlatitude large-scale surface heat flux feedback\nunder greenhouse gas-induced warming. Spatial distribution of time-mean surface\nheat flux feedback in the SSP585 simulation (2051-2100) minus that in the PI-CTRL\nsimulation, with the ENSO signals included in (a) and excluded from (b) the SSTA and\nSHFA.\n2. The reason of why the present study on the midlatitude is still not presented in the\nrevision, for example, the importance/significance and uniqueness of midlatitude\nfeedback on regional climate prediction?\nThanks for your comments. The primary reason why we focus on the midlatitude\nis the invalidation of our method (Eq. 3) at the tropics. Moreover, the surface heat flux\nfeedback at the midlatitudes does play an important role in the regional and global\nclimate variabilities. Please see Line 53-59 and 220-222.\n3. The MLD shoaling effect was not well explained in the main text regarding the effect\nof the long-term shoaling of the mean MLD on changes in anomalous surface heat flux\nfeedback. I think this issue requires more discussion description.\nThanks for your comment. We have added more discussion on the effect of shoaled\nmean-state mixed layer on the surface heat flux feedback and provided some references.\nPlease see Line 185-189.\n4. It is not convinced that only 24 models provided all necessary variables under SSP5-\n8.5 given that surface variables are outputted by nearly all models, but it is possible that\nif the author haven’t tried all available data portals and thus can only get limited model\noutputs as some model communities may shut down their server frequently. It is better\nto delete Line 296.\nThanks for your kind reminder. We have deleted this line.\n5. Pay attention to the unit like in Line 107\nRevised as W∙m−2 ∙K−1.\n6. The color of the bar plot for Figs. 2, 3 … may need some adjustment to give readers a\nmore comfortable vision. Actually, colorbars of most figures may display a better\nvision if the authors select the colors more carefully (but the author may feel free to\nfollow this comment or not).\nThanks for your suggestion. We choose these colors because we think they are easy\nto be distinguished from each other."}]

Decision letter