pith. sign in

arxiv: 1906.08298 · v1 · pith:MOMABZDDnew · submitted 2019-06-19 · ⚛️ physics.ao-ph · physics.geo-ph

Simulating the influence of sea-surface-temperature (SST) on tropical cyclones over South-West Indian ocean, using the UEMS-WRF regional climate model

Chibueze N. Oguejiofor , Babatunde J. Abiodun This is my paper

Pith reviewed 2026-05-25 19:33 UTC · model grok-4.3

classification ⚛️ physics.ao-ph physics.geo-ph
keywords tropical cyclonesea surface temperatureWRF modelSouth-West Indian Oceancyclone intensityprecipitationclimate simulation
0
0 comments X

The pith

Raising sea surface temperature by 2°C strengthens tropical cyclones over the South-West Indian Ocean.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper runs WRF model simulations of cyclone Enawo to test how uniform changes of plus or minus 2°C in sea surface temperature alter storm intensity. The model shows that warmer water produces stronger winds, heavier precipitation, lower central pressure, and sometimes a second low pressure system, while cooler water yields a modest weakening and smoother track. These results are compared against ERA5 reanalysis data for validation. The work implies that a global temperature rise near 2°C would increase cyclone damage in the South-West Indian Ocean region.

Core claim

The WRF simulations indicate that an increase in sea surface temperature by 2°C enhances the intensity of tropical cyclone Enawo, evident in higher maximum precipitation rates and wind speeds together with lower minimum pressure, and can trigger an additional low pressure system; a 2°C decrease produces a smaller reduction in intensity, a smoother track, lower precipitation and wind speeds, and higher surface pressure.

What carries the argument

Uniform ±2°C perturbations applied to sea surface temperature fields within UEMS-WRF regional model integrations driven by CFSR boundary conditions and validated against ERA5 reanalysis.

If this is right

  • Higher sea surface temperature increases cyclone intensity through elevated wind speeds and precipitation rates.
  • SST increase can produce secondary low pressure systems alongside the primary cyclone.
  • Lower sea surface temperature reduces intensity and yields smoother cyclone tracks.
  • A 2°C global temperature increase would produce more violent and less predictable cyclones in the South-West Indian Ocean.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same SST-intensity relationship may hold in other tropical cyclone basins if the underlying physics are comparable.
  • Regional risk assessments for Southern Africa and Madagascar could incorporate these intensity shifts when projecting future cyclone impacts.
  • Repeating the experiments with spatially varying rather than uniform SST perturbations would test the robustness of the reported effects.

Load-bearing premise

The WRF model setup with CFSR boundaries and standard physics schemes correctly reproduces how real tropical cyclones respond in intensity and structure to uniform sea surface temperature changes of 2°C.

What would settle it

A comparison showing that observed or independently modeled intensity metrics for Enawo or similar cyclones do not increase with +2°C SST anomalies, or do not decrease with -2°C anomalies, would contradict the central result.

Figures

Figures reproduced from arXiv: 1906.08298 by Babatunde J. Abiodun, Chibueze N. Oguejiofor.

Figure 1
Figure 1. Figure 1: Cross section of the structure of a cyclone showing the flow of air and the location of the cyclone eye and eye [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Global distribution of cyclone basin in the world and the statistics of average annual number of tropical storms [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Map showing the tracks of tropical systems in the South-West Indian ocean with a VMAX observation during [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Climatology of mean SSTs exceeding 22◦C in the meridional part of the SWIO basin for the active cyclone seasons from September to June during the 1999–2016 period. Temperatures are extracted from the 6-h-resolution global EI dataset at 0.75◦ latitude–longitude resolution [9]. Letters indicate the various islands or countries as in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Tropical cyclone Enawo at peak intensity just before it makes landfall over Madagascar on the 7th of March [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The track followed by tropical cyclone Enawo from it’s origin till it’s dissipation [21] [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Effect of climate change on the power of tropical cyclones as shown by four climate models [5] [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: shows the components of the WRF regional climate model and it’s data processing/forecasting workflow. The WRF framework includes components for dynamical solvers, initialization, WRF-Var, WRF-Chem amongst others. The two options for dynamical solvers in the WRF model includes: the Advanced Research WRF (ARW) solver developed primarily at National Center for Atmospheric Research (NCAR), and the Nonhydrostat… view at source ↗
Figure 9
Figure 9. Figure 9: Primary domain for WRF simulation [PITH_FULL_IMAGE:figures/full_fig_p010_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Experiment design and workflow for observing the effect of sea surface temperature (SST) changes in [PITH_FULL_IMAGE:figures/full_fig_p010_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: A comparison of the observed track (a) for cyclone Enawo with the simulated track (b) by the WRF model. [PITH_FULL_IMAGE:figures/full_fig_p011_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Validation of the windspeed vector generated by the WRF model (b) with the European Centre for Medium [PITH_FULL_IMAGE:figures/full_fig_p012_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Figure showing the track followed by cyclone Enawo from the 2nd of March to the 11th of march 2017. (a.) [PITH_FULL_IMAGE:figures/full_fig_p013_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: The total accumulated precipitation on the 6th of March 2017, for the cyclone location in (a) Control [PITH_FULL_IMAGE:figures/full_fig_p014_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Figure showing that (a) The maximum precipitation rate for the simulation with increased SST (black) is [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Vertical profiles showing variation in vertical windspeed of the tropical cyclone for: (a) The control [PITH_FULL_IMAGE:figures/full_fig_p017_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Vertical profiles showing variation in pressure of the tropical cyclone for: (a.) The control simulation, (b) [PITH_FULL_IMAGE:figures/full_fig_p018_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: (a) The velocity vector field for the control simulation (b) the velocity vector field for simulation with in [PITH_FULL_IMAGE:figures/full_fig_p019_18.png] view at source ↗
read the original abstract

Tropical cyclones remain a major threat to the lives, property and economy of communities around the South West Indian ocean (SWIO), notably Southern Africa and Madagascar. This study uses the weather research forecast (WRF) model to perform a series of simulations for tropical cyclone Enawo with the aim of investigating the effect of an increase or decrease (by 2$^{\circ}C$) in sea surface temperature (SST) on the intensity of the tropical cyclone (using windspeed, precipitation and pressure as measures of cyclone intensity). The experiment uses the data from European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 re-analysis dataset to validate the results of the WRF model which was ran using boundary conditions data from climate forecast system reanalysis (CFSR). The results indicate that the WRF model performs reasonably well in simulating the track and windspeed of the tropical cyclone, when compared to observational data. In simulating the tropical cyclone, the WRF model shows that an increase in the SST by 2$^{\circ}C$ generally increases the intensity of the tropical cyclone formed. This is evident in the increasing maximum precipitation rate as well as windspeed, and decreasing minimum pressure. An increase in SST also causes the emergence of a second low pressure system. On the other hand, a decrease in the SST by 2$^{\circ}C$ leads to a minute effect in the intensity but generally acts to decrease it. This results in a smoother track path for the tropical cyclone, a decrease in the maximum precipitation rate and windspeed, and an increase in the surface pressure. The results of this study have shown that increasing the global temperature by around 2$^{\circ}C$ - violation of the Paris Accord - would lead to more violent and unpredictable tropical cyclones within the SWIO, and hence more destruction and loss of lives.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper uses the UEMS-WRF regional model to simulate tropical cyclone Enawo (2017) in the South-West Indian Ocean, driven by CFSR boundary conditions and validated against ERA5 reanalysis. It performs sensitivity experiments with uniform SST perturbations of ±2°C and reports that +2°C SST increases maximum windspeed and precipitation rate, lowers minimum central pressure, and triggers a secondary low-pressure system, while −2°C SST produces smaller opposing changes. The authors conclude that a 2°C global warming (Paris Accord violation) would produce more violent and unpredictable tropical cyclones in the SWIO.

Significance. A robust demonstration that uniform +2°C SST perturbations intensify a representative SWIO cyclone would be relevant to regional climate-impact assessments. The present single-event design, however, supplies no quantitative validation metrics and no test of whether the reported sensitivity is representative of other genesis locations, tracks, or seasons, so the result remains a model-specific response rather than a general physical finding.

major comments (3)
  1. [Abstract / Conclusion] Abstract and Conclusion: the claim that +2°C SST 'generally increases the intensity of the tropical cyclone formed' and leads to 'more violent and unpredictable tropical cyclones within the SWIO' rests on a single-storm experiment (Enawo); no additional cyclones, ensemble members, or basin-wide statistics are shown to support extrapolation beyond this event.
  2. [Results] Results section: the statement that the WRF model 'performs reasonably well' is unsupported by any quantitative error statistics (track RMSE, intensity bias, RMSE for 10 m wind or central pressure) against ERA5 or best-track data; only qualitative agreement is described.
  3. [Methods] Methods: the sensitivity experiment applies a spatially uniform ±2°C SST perturbation to the entire domain; no test is reported of whether the intensity response depends on the spatial pattern of the anomaly or on the choice of physics schemes.
minor comments (1)
  1. [Abstract] The abstract refers to 'European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5' but the model is driven by CFSR; clarify the respective roles of the two datasets.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive comments. We respond point-by-point to the major comments below, indicating where we will revise the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Conclusion] Abstract and Conclusion: the claim that +2°C SST 'generally increases the intensity of the tropical cyclone formed' and leads to 'more violent and unpredictable tropical cyclones within the SWIO' rests on a single-storm experiment (Enawo); no additional cyclones, ensemble members, or basin-wide statistics are shown to support extrapolation beyond this event.

    Authors: We agree that the abstract and conclusion language suggests broader applicability than a single-event case study can support. The work examines cyclone Enawo specifically. We will revise both sections to state that the intensification response applies to this event and that generalization to the SWIO basin would require additional cyclones and statistics. revision: yes

  2. Referee: [Results] Results section: the statement that the WRF model 'performs reasonably well' is unsupported by any quantitative error statistics (track RMSE, intensity bias, RMSE for 10 m wind or central pressure) against ERA5 or best-track data; only qualitative agreement is described.

    Authors: The manuscript currently presents only qualitative comparisons. We will add quantitative validation statistics (track RMSE, intensity bias, and RMSE for 10 m wind and central pressure) against ERA5 and best-track data in the revised results section. revision: yes

  3. Referee: [Methods] Methods: the sensitivity experiment applies a spatially uniform ±2°C SST perturbation to the entire domain; no test is reported of whether the intensity response depends on the spatial pattern of the anomaly or on the choice of physics schemes.

    Authors: Uniform SST perturbations are a standard approach for isolating temperature sensitivity. We did not test spatially varying anomalies or alternative physics schemes. We will add an explicit statement in the methods or discussion section noting this limitation and that results are specific to the uniform perturbation and chosen configuration. revision: partial

standing simulated objections not resolved
  • Requests for additional cyclones, ensemble members, or basin-wide statistics to establish representativeness, as the study is designed and executed as a single-event case study of cyclone Enawo.

Circularity Check

0 steps flagged

No circularity: direct numerical sensitivity outputs from WRF runs

full rationale

The paper runs the WRF model under control and perturbed SST conditions for a single cyclone event, then compares track, windspeed, precipitation and pressure fields to ERA5 reanalysis and CFSR boundaries. No derivations, fitted parameters renamed as predictions, self-citations, or ansatzes appear in the reported chain; the intensity changes are simply the model outputs under the stated boundary perturbations, which is the explicit experimental design.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that the chosen WRF physics schemes and boundary conditions faithfully translate uniform SST changes into changes in cyclone intensity; this assumption is inherited from prior model development rather than tested here.

axioms (1)
  • domain assumption Standard WRF physics parameterizations capture the dominant processes governing tropical cyclone response to SST perturbations
    Invoked implicitly when the model is used to attribute intensity changes to the SST perturbation without additional validation of the parameterization suite for this sensitivity.

pith-pipeline@v0.9.0 · 5904 in / 1487 out tokens · 31799 ms · 2026-05-25T19:33:44.657753+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

34 extracted references · 34 canonical work pages

  1. [1]

    Weathering climate change: Insurance solutions for more resilient communities

    Swiss Re. Weathering climate change: Insurance solutions for more resilient communities. Swiss Reinsurance Company Ltd., Zurich, Switzerland , page 16, 2010

    work page 2010

  2. [2]

    Natural catastrophes 2009—analyses, assessments, positions, topics geo

    Munich Re. Natural catastrophes 2009—analyses, assessments, positions, topics geo. Munich Re, Munich, 2010

    work page 2009

  3. [3]

    Estimating the benefit of trmm tropical cyclone data in saving lives

    Robert F Adler. Estimating the benefit of trmm tropical cyclone data in saving lives. 2005

    work page 2005

  4. [4]

    Unicef zimbabwe cyclone idai situation

    UN Children’s Fund. Unicef zimbabwe cyclone idai situation. ReliefWeb Report No. 4, 2019

    work page 2019

  5. [5]

    Tropical cyclones and climate change

    Thomas R Knutson, John L McBride, Johnny Chan, Kerry Emanuel, Greg Holland, Chris Landsea, Isaac Held, James P Kossin, AK Srivastava, and Masato Sugi. Tropical cyclones and climate change. Nature geoscience, 3(3):157, 2010

    work page 2010

  6. [6]

    Global overview

    CJ Neumann. Global overview. in: Global guide to tropical cyclone forecasting, wmo/tc-no. 560. Report No., 1993

    work page 1993

  7. [7]

    Tropical cyclone motion: Environmental interaction plus a beta effect

    Greg J Holland. Tropical cyclone motion: Environmental interaction plus a beta effect. Journal of the Atmospheric Sciences, 40(2):328–342, 1983

    work page 1983

  8. [8]

    A climatology of southwest indian ocean tropical systems: Their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes

    Marie-Dominique Leroux, Julien Meister, Dominique Mekies, Annie-Laure Dorla, and Philippe Caroff. A climatology of southwest indian ocean tropical systems: Their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes. Journal of Applied Meteorology and Climatology , 57(4):1021–1041, 2018

    work page 2018

  9. [9]

    The era-interim reanalysis: Configuration and performance of the data assimilation system

    Dick P Dee, SM Uppala, AJ Simmons, Paul Berrisford, P Poli, S Kobayashi, U Andrae, MA Balmaseda, G Balsamo, d P Bauer, et al. The era-interim reanalysis: Configuration and performance of the data assimilation system. Quarterly Journal of the royal meteorological society , 137(656):553–597, 2011

    work page 2011

  10. [10]

    A climatology of southwest indian ocean tropical systems: their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes

    Marie-Dominique Leroux, Julien Meister, Dominique Mekies, Annie-Laure Dorla, and Philippe Caroff. A climatology of southwest indian ocean tropical systems: their number, tracks, impacts, sizes, empirical maximum potential intensity, and intensity changes. Journal of Applied Meteorology and Climatology , 57(4):1021–1041, 2018

    work page 2018

  11. [11]

    A preliminary climatology of extratropical transitions in the southwest indian ocean

    Kyle S Griffin. A preliminary climatology of extratropical transitions in the southwest indian ocean. 2010

    work page 2010

  12. [12]

    Climatology and landfall of tropical cyclones in the south-west indian ocean

    Alberto F Mavume, Lars Rydberg, Mathieu Rouault, and Johann RE Lutjeharms. Climatology and landfall of tropical cyclones in the south-west indian ocean. Western Indian Ocean Journal of Marine Science , 8(1), 2009

    work page 2009

  13. [13]

    Variability in tropical cyclone heat potential over the southwest indian ocean

    N Malan, CJC Reason, and BR Loveday. Variability in tropical cyclone heat potential over the southwest indian ocean. Journal of Geophysical Research: Oceans , 118(12):6734–6746, 2013

    work page 2013

  14. [14]

    A statistical hurricane intensity prediction scheme (ships) for the atlantic basin

    Mark DeMaria and John Kaplan. A statistical hurricane intensity prediction scheme (ships) for the atlantic basin. Weather and F orecasting, 9(2):209–220, 1994

    work page 1994

  15. [15]

    Tropical cyclone eline and its unusual penetration and impacts over the southern african mainland

    CJC Reason and A Keibel. Tropical cyclone eline and its unusual penetration and impacts over the southern african mainland. Weather and forecasting, 19(5):789–805, 2004

    work page 2004

  16. [16]

    On the peculiar storm track of tc favio during the 2006–2007 southwest indian ocean tropical cyclone season and relationships to enso

    MG Klinman and CJC Reason. On the peculiar storm track of tc favio during the 2006–2007 southwest indian ocean tropical cyclone season and relationships to enso. Meteorology and Atmospheric Physics, 100(1-4):233–242, 2008

    work page 2006

  17. [17]

    Satellite altimetry and the intensification of hurricane katrina

    Remko Scharroo, Walter HF Smith, and John L Lillibridge. Satellite altimetry and the intensification of hurricane katrina. Eos, Transactions American Geophysical Union , 86(40):366–366, 2005

    work page 2005

  18. [18]

    Warm ocean anomaly, air sea fluxes, and the rapid intensification of tropical cyclone nargis (2008)

    I-I Lin, Chi-Hong Chen, Iam-Fei Pun, W Timothy Liu, and Chun-Chieh Wu. Warm ocean anomaly, air sea fluxes, and the rapid intensification of tropical cyclone nargis (2008). Geophysical Research Letters, 36(3), 2009

    work page 2008

  19. [19]

    Tropical cyclone enawo post-event report

    Pamela Probst, Chiara Proietti, Alessandro Annunziato, Stefano Paris, and Annett Wania. Tropical cyclone enawo post-event report. Joint Research Centre (JRC), The European Commission’s Science and Knowledge Service: Brussels, Belgium, 2017

    work page 2017

  20. [20]

    https://www.nasa.gov/feature/goddard/2017/ enawo-southern-indian-ocean , 2017

    Nasa goddard modis rapid response team. https://www.nasa.gov/feature/goddard/2017/ enawo-southern-indian-ocean , 2017

    work page 2017

  21. [21]

    https://en.wikipedia.org/wiki/Cyclone_Enawo, 2017

    Wikipedia. https://en.wikipedia.org/wiki/Cyclone_Enawo, 2017

    work page 2017

  22. [22]

    Landsea C

    Rubiera J. Landsea C. W. Fernandez M. L. Pielke, R. A. J. and R. Klein. Hurricane vulnerability in latin america and the caribbean: Normalized damage and loss potentials. Nat. Hazards Rev. 4, pages 101–104, 2003

    work page 2003

  23. [23]

    Landsea C

    Rubiera J. Landsea C. W. Fernandez M. L. Pielke, R. A. J. and R. Klein. Normalized u.s. hurricane damage, 1925—1995. weath. forecast. 13. pages 621–631, 1995. 21 Simulating the influence of sea-surface-temperature (SST) on tropical cyclones over South-West Indian ocean, using the UEMS-WRF regional climate model A PREPRINT

    work page 1925

  24. [24]

    K. A. Emanuel. The contribution of tropical cyclones to the oceans’ meridional heat transport. j. geophys. res

    work page

  25. [25]

    pages 14771—-14782, 2001

    work page 2001

  26. [26]

    The dependence of hurricane intensity on climate

    Kerry A Emanuel. The dependence of hurricane intensity on climate. Nature, 326(6112):483, 1987

    work page 1987

  27. [27]

    Impact of co2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization

    Thomas R Knutson and Robert E Tuleya. Impact of co2-induced warming on simulated hurricane intensity and precipitation: Sensitivity to the choice of climate model and convective parameterization. Journal of climate , 17(18):3477–3495, 2004

    work page 2004

  28. [28]

    G. J. Holland. The maximum potential intensity of tropical cyclones. J. Atmos. Sci., 54:2519–2541, 1997

    work page 1997

  29. [29]

    Katsuyuki V . Ooyama. Conceptual evolution of the theory and modeling of the tropical cyclone. National Hurricane Research Laboratory, NOAA, pages 369–380, 1982

    work page 1982

  30. [30]

    Elsner J. B. Jagger, T. and X. Niu. et al. a dynamic probability model of hurricane winds in coastal counties of the united states. Appl. Stat. 40, pages 853 – 863, 2001

    work page 2001

  31. [31]

    Skerlj P. F. Vickery, P. J. and L. A. Twisdale. Simulation of hurricane risk in the us using an empirical track model. J. Struct. Engin. 126, page 1222–1237., 2000

    work page 2000

  32. [32]

    M. K. James and L. B. Mason. Synthetic tropical cyclone database. J. Wtrwy., Port, Coastal, and Oc. Engrg. 131 , page 181–192., 2005

    work page 2005

  33. [33]

    Skamarock WC, Klemp JB

    Dudhia J et al. Skamarock WC, Klemp JB. A description of the advanced research wrf version 3. 2008

    work page 2008

  34. [34]

    Prediction of tropical cyclone over north indian ocean using wrf model: sensitivity to scatterometer winds, atovs and atms radiances

    Venkata B Dodla, Desamsetti Srinivas, Hari Prasad Dasari, and Chinna Satyanarayana Gubbala. Prediction of tropical cyclone over north indian ocean using wrf model: sensitivity to scatterometer winds, atovs and atms radiances. In Remote Sensing and Modeling of the Atmosphere, Oceans, and Interactions VI , volume 9882, page 988213. International Society for...

    work page 2016