Middle East dust as an important external driver of the Indian Ocean Dipole

Domain: physics
Published: 2026-01-29
Editorial outcome: major_revision

Abstract
The Indian Ocean Dipole (IOD) strongly influences Indo-Pacific climate extremes, yet its drivers remain incompletely understood. Using satellite observations, reanalysis, and climate model experiments, we identify Middle East dust emissions as a major external driver of IOD variability. Observational evidence shows that dust over the 1980-2020 period account for ~36% of interannual IOD variance, surpassing El Niño-Southern Oscillation as the main driver during boreal autumn. Climate simulations confirm that reduced dust enhances warming in the western Indian Ocean and induce easterly winds that shoal the eastern thermocline. These anomalies trigger Bjerknes and wind–evaporation–sea surface temperature feedbacks, amplifying a positive IOD pattern, and vice versa. Our findings reveal a powerful dust–IOD teleconnection, highlighting the need to incorporate aerosol forcing into seasonal forecasts and climate projections.

Introduction
The Indian Ocean Dipole (IOD) is the major mode of interannual climate variability in the Tropical Indian Ocean (TIO), marked by a seesaw pattern of sea surface temperature (SST) anomalies between the western and eastern TIO1,2. This anomalous SST pattern leads to changes in atmospheric circulation and rainfall patterns over the vast areas surrounding TIO, including Indonesia, India, Australia, and East Africa3–5. Positive IOD (pIOD) events bring warming to the western basin and cooling to the east, reshaping rainfall patterns across East Africa, India, Indonesia, and Australia, while negative phases (nIOD) have opposite effects. The extreme pIOD event in 2019 triggered unprecedented Australian bushfires and historic flooding along the Yangtze River6–8, underscoring the urgent need to understand the mechanism underlying IOD events. Most studies attribute IOD variability to external forcing4,9, internal air–sea interactions and remote El Niño-Southern Oscillation (ENSO) influences1,10,11, yet the role of aerosols remains largely unexplored. The TIO is located close to the world’s second largest dust source—the Middle East deserts. Previous studies have identified the far-reaching impacts of Middle East dust on the South Asian monsoon, Arabian Sea SST, and Indian summer monsoon rainfall12–14. Around 2010, both IOD behavior and Middle East dust emissions shifted markedly, with IOD transitioning from frequent negative to positive phases and Middle East dust emissions reaching historically low levels by 201915–18. This coincidence motivates our investigation into whether dust forcing influences IOD phases and quantifies its contribution to the variability of IOD.

Here, we show that variability in summertime Middle East dust (SMED) is a critical driver of autumn IOD events. Using model experiments, we establish a causal link where reduced SMED triggers a chain of atmosphere-ocean interactions in the tropical Indian Ocean. These interactions, primarily driven by wind-induced ocean dynamics and reinforced by cloud feedbacks, robustly promote the development of positive IOD conditions.

Results
ObservationallinkbetweendustandIOD
Here,weexaminevariationsintheIODandSMED,focusingontheirinteractionsduringtheirrespectivepeakseasons.Modern-EraRetrospectiveanalysisforResearchandApplications,Version2(MERRA-2)reanalysis(SupplementaryFig. 1) revealsthatdust loading over the Middle East and adjacent deserts peaks in boreal summer (June–August, JJA). In contrast, the IOD is seasonally phase-locked, typicallymaturingwithpronouncedSSTanomaliesinborealautumn(September-November, SON)9, when its statistical link to JJA dust activity is strongestasrevealed by the lagged seasonal correlations between dustobservations and the Dipole Mode Index (DMI) (SupplementaryFig.2).Thisseasonaloffsetisphysicallyplausiblegiventhemulti-monthatmosphericresidencetimeofdustaerosolsunderspecificconditions(strongupwardaircurrentsandlong-rangetransportforfine-grainedparticles)andthedelayedresponsethroughocean-atmospherefeedback19–22.Additionally,thedustactivityoverMiddleEastinSONhaslimiteddirectinfluenceontheIOD,asweakerautumndustactivitycoincideswiththeIOD’smaturephase(SupplementaryFig.1c),duringwhichcoupledocean-atmosphereprocessesdominateandtheroleofexternalforcingslikedustisdiminished.ModelsimulationsalsoconfirmthatSONistheseasonwhentheIODrespondsmoststronglytoreductionsindustforcing,therebyidentifyingSONastheoptimalperiodforanalysis(SupplementaryFig.3).

Thespatial distributionof SMED showswidespread emissions acrosstheMiddleEast,withfrequentdusttransportextendingoverthe northern Indian Ocean (Fig. 1a, b). Since around 2010, SMED, representedbyMulti-angleImagingSpectroRadiometer(MISR)nonspherical aerosol optical depth (AOD), has declined markedly (Fig. 1c). Other dust proxies also validate this declination (SupplementaryFig.4).Thisdeclinecoincideswithadecadalintensification of pIOD events23,24 (Supplementary Fig. 4), providing a strongmotivationtoinvestigatethephysicallinkagebetweenthesetwophenomena.

To quantify the dust-IOD connection, we regressed SON SST anomaliesoverthetropicalIndianOceanagainstSMEDandtookthenegativevaluesoftheregressioncoefficientstoindicatedustreduction.Theanalysisrevealsapronounceddipolepattern,withwarmanomaliesinthewesternandcoolanomaliesintheeasternTIO,closelyresemblingthecanonicalpIODSSTsignature(Fig. 1d)2. This dipole structureishighlyrobust,asconfirmedbymultipleindependentdustdatasets(SupplementaryFig.5).Theregressioncoefficientsfromthemulti-proxy analysis collectively suggest that a decrease in Middle Eastern dust loading is associated with a western Indian Ocean warmingofapproximately0.5Kperstandarddeviation,amagnitudecomparabletoSSTanomaliesobservedduringmoderateIODevents.Complementarysingularvaluedecomposition(SVD)betweenSMEDandSONSSTfurthersupportsthisrelationship,showingthattheleadingcoupled modecorrespondstonegativeSMEDsignalsand a pIOD-likeSSTpatternintheTIO(SupplementaryFig.6).Correlations exceed0.6forthemodesderivedfrombothMISRAOD,station-baseddustfrequencyobservations,anddustAODintheCESMsimulations,highlightingtheconsistencyoftheresults.

BecausebothIODandMiddleEastdustarealsoinfluencedbyEl Niño–Southern Oscillation (ENSO)10,12, we repeated the regression analysisafterremovingENSOsignalsfromboththeSMEDandSONSSTtimeseries.Theresultingdipolepatternremainsstrong,withregression coefficients stillreaching ~0.5K(Supplementary Fig. 7). Similar procedureisalsoappliedtoremovePDOsignalsfromboththeSMEDandSONSSTtimeseries(SupplementaryFig.8).The similardipolepatternstillremainsstrong(SupplementaryFig.8).Thispersistence suggests that the dust–IOD relationship is largely independent of ENSO-mediated and PDO-mediated climate variability, underscoring thedistinctroleofdustforcinginmodulatingIODbehavior.Moreover,the regression patterns derived from these different time periods exhibitconsiderableconsistency(SupplementaryFigs.7and8),furtherdemonstratingthatourfindingsarenotsensitivetothespecifictimeframeselectedbutarerobustacrossdifferentintervals.

The coupled response is not limited to SST anomalies but also involvescoherentatmosphericandoceanicprocesses.Oceanprofilesreveal subsurface heat transport from the eastern to western TIO, weakeneddownwellingintheeast,andadeepenedmixedlayerinthewest(Fig. 1e). These oceanic adjustments are dynamically linked to atmosphericanomalies,includingalow-pressurecenteroverthewesternTIO(Fig.1f).Theresultingzonalpressuregradientdrivesaclockwiseverticalcirculation(Fig.1g),manifestedasstrengthenedsurfaceeasterliesorweakenedwesterliesacrossthebasin.SuchcirculationanomaliesarecharacteristicofpIODevents,reinforcingthenotionthatdustreductionscansetfavorableconditionsfortheirdevelopment.

Collectively, observational evidence and modeling analyses demonstrate a robust negative SMED–IOD relationship. Reduced MiddleEastdustemissionsarestatisticallylinkedtoatmosphericandoceanicstatesresemblingpIODevents,suggestingthatdecadalvariations in dust loading likely exert an important, previously under-appreciatedinfluenceontropicalIndianOceanclimate.

Causalevidencefromclimatemodeling
AsignificantcorrelationbetweenSMEDandtheborealautumnDMI does not necessarily imply a causal relationship, as dust emission and transportarealsomodulatedbySSTvariability15,25.Additionally,reanalysis and satellite products often integrate signals from multiple climatemodes,complicatingeffortstoisolatethedustinfluence.To rigorouslyevaluatetheroleofMiddleEastdustinshapingTIOvariability, we performed coupled climate model simulations using the CommunityEarthSystemModel(CESM),whichexplicitlyincorporatesbothatmosphericforcing(solarradiation,greenhousegases,aerosols) andoceanfeedbacks.Themodelwasnudgedtowardrealitybyprescribing the observed evolution of Middle-East dust loading from 1980–2020.Specifically,thesummertime-meandustmassdensityinCESMwasscaledeachyearaccordingtothecorrespondingsummertimestation-baseddust-frequencyobservationsovertheMiddleEast,yielding a transient dust-mass timeseries thatfaithfullymirrorstheobservedvariability(seeMethods).Theprimaryreasonforfocusingon the summer season is that dust activity in the Middle East exhibits strong seasonality, with emissions and long-range transport being mostintenseandspatiallycoherentduringsummer(SupplementaryFig.1).ThismodelhaspreviouslybeenvalidatedforitscapabilitytocapturekeyfeaturesoftheIOD26.

TheCESMexperimentsrevealarobustdipoleSSTanomalypatternacrosstheTIOinresponsetoreduceddustforcing(seeMethod), closely mirroring the observed regression between dust and SST (Fig. 2a)despite somelatitudinaldisplacements partlyattributed to themodelbias.Thispatternischaracterizedbysignificantwarminginthewesternbasin(upto+0.5K)andcoolingintheeasternbasin(upto−0.5K),despiteminorspatialmismatches.Strikingly,theSSTresponsetoreducedSMEDforcingtranslatesintoaDMIamplitudeofroughly 1.0, comparable in magnitude to a moderate pIOD event. Both the spatialconfigurationandtheintensityoftheseanomaliessuggestthat SMEDcouldactasasignificantexternalforcingcapableofgenerating pronounced interannual variability in the TIO. Moreover, when the modelisdrivenbyobservedSMEDfrom1980to2020,itreproducesa statisticallysignificantfractionoftheIODvariabilityintheCESMhistoricalrunwithallforcing(SupplementaryFig.9a,R=0.61,p≤0.05). This correlation implies that approximately 36% (R2≈0.36) of the interannualvarianceinIODevolutioncanbelinearlyattributableto MiddleEastdustforcing,therebyprovidingadditionalobservational supportfortheSMED–IODteleconnection.WeappliedtheDMItimeseriesinthehistoricalruninsteadoftheobservation,asthemodel’s imperfectsimulationofIODevolutioncomparedwiththeobservation. ThesimulatedSMEDalsohashighcorrelationwithDMIseries(SupplementaryFig.9a,R=−0.58,p≤0.05),furthervalidatingtheirrelationship.Moreover,thisexplainedvarianceissimilartotheexplained varianceapplyingtheobservationaldataset(SupplementaryFig.9b, R=−0.64),corroboratingtherobustnessoftheidentifiedrelationship. Notably,apossiblepost-2010divergencebetweentheobservedand simulated SMED-DMI relationship may stem from the increasing influenceofotherclimateforcings(e.g.,CentralPacificElNiño,PDO) andlimitationsinherentintheobservationaldustrecord27,28.Nevertheless, the simulated correlation coefficient remains systematically lowerthanthatestimatedbyobservation.Thisattenuationisattributableto(i)therelianceofthemodelonstation-baseddustfrequencyto scale column-integrated dust-mass density, which introduces additionalsamplinguncertainty,and(ii)systematicbiasesinthemodel’s representationofIODdynamics(e.g.,inthethermoclinefeedbackand seasonal phase locking), which distort the precise evolution of the modeandcollectivelyamplifytheattenuationofthesimulatedcorrelationrelativetoobservations.

ThisSSTvariabilitydrivesasubstantialreorganizationofregional atmospheric and oceanic states. Ocean diagnostics reveal coherent subsurfaceheatredistribution,includingeast-to-westheattransport, reduced downwelling of warmed water in the eastern TIO, and a westward-propagating thermocline deepening accompanied by enhancedmixedlayerdepth(MLD)inthewesternbasin(Fig.2b).Itis noteworthy thata weakincrease in MLD is also simulated over the eastern tropical Indian Ocean (Fig. 2b), which may be attributed to internalclimatevariabilityratherthanadirectresponsetodustforcing.These oceanic adjustments induce a robust atmospheric anomaly response: enhanced convection over the warmed western TIO generatesaprominentlow-pressureanomaly(Fig.2c).Theresultingzonal pressuregradientestablishesathermallydirectcirculation,manifestingasaclockwiseverticalcell(Fig.2d)andsurfaceeasterlyanomalies that weaken the background westerlies across the basin (Fig. 2c). Althoughslightdiscrepanciesexistbetweensimulatedandobserved circulationfeatures (Figs.1f,g,2c,d),these biaseslikelystemfrom observationallimitations,theomissionofaerosol-cloudinteractionsintheCommunityAtmosphereModelversion4(CAM4)configuration, andintrinsicuncertaintiesinrepresentingcoupledatmosphere–ocean processes29,30.

TheTIOresponsedescribedaboveisfurthervalidatedthrough sensitivitysimulationsunderdustreductionscenarios(SupplementaryFig. 10). The TIOresponseto reduced dust forcing isquantified by comparing SONmeteorologicalfieldsbetweentwosimulations(seeMethodsfordetails):(1)abaselineexperimentusingthe1980–2020 climatologicalmeandustmixingratio,and(2)areduced-dustexperimentforcedbyaclimatologicaldustperturbation,eitherreducingor increasingdustlevelsbasedoninternalvariability(approximately20% changeindustlevels,SupplementaryFig.10;seeMethodsfordetails). Both simulations were carried out using the CESM model with the Parallel Ocean Program (POP2). This approach—contrasting a dust climatology against a systematically perturbed scenario—ensures temporalconsistencyandstrengthenscausalattributioncomparedtoconventionalhigh-versus-lowdustyearcomparisons,whichcouldconflatedust-driven signals with unrelated interannual variability. ThesesimulationsrevealarobustdipoleSSTanomalypatternoverthe TIOthatcloselymirrorstheregressionpatternbetweendustandSST observedinbothobservationaldataandmodelsimulations(SupplementaryFig.11a).Thispatternischaracterizedbysignificantwarming inthewesternbasin(upto+0.5K)andcoolingintheeasternregion (upto−0.5K),similartotheprevioussimulationdrivenbyobserved SMED from 1980 to 2020, despite minor spatial discrepancies. The dust-increasedscenarioalsoresultsinadipoleSSTpatternoverthe TIO,albeitwithanoppositesignal(SupplementaryFig.12a).

Mechanism:howdustshapestheIOD
Asnotedpreviously,thetransportofdustaerosolsfromtheMiddle East to the TIO reaches its highestintensity during boreal summer, largely driven by the strong and persistent northwesterly Shammal winds(SupplementaryFig.1b)15.Thesewindsactasanefficientconveyor,liftingandcarryingvastquantitiesofmineraldustfromthearid regionsoftheMiddleEastacrosstheArabianSeatowardthewestern TIO.Underthisdynamicwindregime,theinfluxofdustaerosolsis substantial,influencingbothradiativeandcloudprocessesoverthe region.Oncetransported,theseaerosolsexertastrongradiativeforcingbyabsorbingsolarradiation,whichheatstheuppertroposphere, while simultaneously reducing the amount of shortwave radiation reaching the ocean surface. The dust-induced heating rate profile alignswiththesimulatedverticaldistributionofdustextinction,which isprimarilyconfinedwithinthetroposphere.Thisverticalcoherence confirmsthattheradiativeheatingmaximacorrespondwellwiththe dustlayer’slocation,reinforcingthedirectthermodynamicimpactof dustradiativeabsorptionontheatmosphericcolumn.(Supplementary Fig. 13). This occurs through a combination of the direct radiative effect of dust and its indirect effects via interactions with cloud microphysics. As a result, the upper atmosphere experiences pronouncedwarming,whereasthesurfaceundergoescooling31,32.

Theregressionanalysisdemonstratesthatreduceddustloading shiftstheradiativebalance.Increasedsolarradiationpenetratestothe surface,asdemonstratedbybothcoupledclimatesimulations(Fig.3a) andatmosphere-onlysimulations(SupplementaryFig.14a),leadingto enhanced warming near the surface and reduced heating aloft. The translation of increased shortwave radiation into the initial SST anomaly involves the gradual heat accumulation within the ocean’s mixed layer. The observed ~2-month lag reflects the mixed-layer adjustment timescale, which is approximately 2 to 2.5 months for sustained thermal forcing in the TIO33. Given the relatively shallow mixedlayerinthewesternIndianOcean34–36,thispersistentradiative forcingcanefficientlywarmtheupperoceanwithinthistimeframe, overcomingdampingfrommixingandlatentheatloss.

ByemployingprescribedSSTs,thisatmosphere-onlysimulation inherentlyexcludesoceanfeedbacks,thusallowingaclearattribution oftheatmosphericresponsetothedustforcing.Thisverticalredistributionofenergyaltersatmosphericstability:theloweratmosphere warmsmorestrongly,whilediminishedwarmingintheupperatmosphereesultsinarelativecoolingthere(SupplementaryFig.15).These changessignificantlyaffectclouddynamics,drivingadecreaseinlowlevelcloudcover(blueshades)andanincreaseinhigh-levelclouds (brownshades)overthewesternTIO(Fig.3c,d).Thetransformationof cloudfieldsfurthermodifiestheradiativebudgetbyincreasingboth longwave and shortwave radiation, thereby amplifying the warming signaloverthewesternTIOinsynergywiththedirectradiativeeffectsofreduceddust(Fig.3a,b).Specifically,thedecreaseinlowclouds reducesthealbedo,allowingmoreshortwaveradiationtopenetrate and warm the ocean surface37,38. Concurrently, the increase in high cloudsenhancestheatmosphericgreenhouseeffectbyabsorbingand re-emittinglongwaveradiation,whichinhibitsradiativecoolingatthe surface37,39.Thesetwoeffectsactinsynergywiththedirectradiative effectofreduceddustloadingtoamplifythewarmingsignaloverthe westernTIO.Thiscloudfeedbackprocess,particularlythelow-cloud albedo feedback, is well-established as a key positive feedback mechanismmodulatingTIOvariability.Moreover,theinitialwarming and enhanced evaporation mightincreaseatmospheric water vapor overwesternTIO,whichstrengthensdownwardLWradiation(Fig.3b). Moreover,thedipolepatternofradiativefluxanomaliesovertheTIO arises from the anomalous zonal circulation (Fig. 2d) and zonal asymmetryincloud verticalstructure(Fig. 3c,d).Over the western basin, diminished low-level clouds enhance incoming shortwave radiation,while increased high-level clouds trap outgoing longwave radiation, leading to positive anomalies in both SW and LW fluxes. Conversely,theeasternbasinexhibitsoppositecloudadjustments— morelow-levelandfewerhigh-levelclouds—resultinginreducedsurfaceradiativefluxes.

The resulting surface warming over the western TIO enhances convective activity, triggering strong ascending air motions and establishingalowsealevelpressureanomalyinthisregion(Fig.2c). This anomaly creates a pressure gradient between the western and eastern TIO, which in turn drives an anomalous clockwise vertical circulationspanningthebasin(Fig.2d).Nearthesurface,thiscirculation anomaly is expressed as prevailing easterly wind anomalies (Fig.2c),oppositetotheclimatologicalwesterliesovertheTIO.These anomalouseasterliessuppresstheusualeastwardadvectionofwarm subsurfacewaterfromthewesterntotheeasternTIO,leadingtocold anomalies in the mixed layer of the eastern TIO (Fig. 2b). Consequently,theequatorialthermoclinetiltsupwardtowardtheeastand downward toward the west, reinforcing the temperature gradient acrossthebasin(Fig.2b).TheintensifiedzonalSSTgradientfurther strengthens the easterly wind anomalies through the well-known Bjerknesfeedback40.Additionally,theweakeningofwesterliesoverthe western TIO reduces surface evaporation, which in turn enhances warming there via the wind–evaporation–SST (WES) feedback mechanism41,42.Cloud-inducedradiativefeedbacksalsocontributeto thisdipolepattern,aschangesincloudcoverandtypestrengthenthe contrastbetween warm westernand cool eastern SSTs through the cloud–radiation–SSTfeedback43,44.

The differences between atmospheric-only and coupled model experimentsfurtherelucidatethemechanismsatplay.Thecoupled simulationsshowastrongerandmoreextensivesignalintheeastern TIO,withgreaterdecreasesinshort-andlongwaveradiation(Fig.3a, b),largerreductionsinhighclouds(Fig.3c),andanotableincreasein low cloud cover (largely absent in the atmospheric-only model, Fig.3d).ThesedifferencesunderscorethatchangesintheeasternTIO arepredominantlygovernedbyoceanicprocessesratherthanpurely atmosphericresponses.AdecompositionofSSTchanges(seeMethods)furtherconfirmsthisasymmetry:warminginthewesternTIOis primarily driven by enhanced shortwave radiation absorption (Supplementary Fig. 16a), whereas cooling in the eastern TIO is dominatedbyincreasedoceanheatuptake(SupplementaryFig.16b). Moreover, the atmospheric-only simulation exhibits a radiative responsethatisspatiallycoherentwiththedustaerosoldistribution (SupplementaryFig.14),whilethecoupledsimulationdisplaysadditionalspatialheterogeneityduetooceanicadjustments(SupplementaryFig.11).Thisredistributionlikelystemsfromocean-atmosphere coupling, wherein the initial radiative perturbation triggers atmospheric circulation adjustments and subsequent wind-evaporation- SST feedbacks, creating secondary radiative perturbations that partially obscure the direct dust–radiation signal, which is ultimately manifestedasateleconnectedresponse.Notably,thespatialpatternof theSWfluxresponseintheCAMsimulations,withitsmaximumover thenorthernandeasternArabianSearatherthanuniformlyacrossthe basin(SupplementaryFig.17a,c),suggeststhattheradiativeimpactof dust reduction initiates an atmospheric teleconnection. The initial perturbationmainlyoverthelandlikelyaltersregionalatmospheric heating and circulation patterns45–47, which in turnredistributes the energy downstream, setting the stage for the more extensive and evidentocean-atmospheresignalsandfeedbacksthatpropagatingto theTIOinthecoupledmodel.Nevertheless,bothexperimentsconsistently support the proposed physical framework linking Middle EasterndustforcingtoIODvariability.

Atmosphere-only model and observation comparisons further reinforcethesefindings.Changesinradiationfluxesandcloudfields simulatedbythecoupledmodelbroadlyagreewithreanalysisdatasets (Fig. 3; Supplementary Fig. 14), despite some discrepancies in the spatialdistributionandmagnitudeofsignals.Specifically,simulated radiationfluxresponsesareweakerthanthoseobserved(Fig.3a,b; Supplementary Fig. 14a–d), and the reduction in low cloud fraction exhibits lower statistical significance (Fig. 3d; Supplementary Fig. 14g–h). These mismatches likely reflect uncertainties in cloud parameterizations and theireffects on radiative fluxes,especiallyin capturing the complex microphysical processes controlling cloud dropletformationundervaryingaerosolconditions48,49.Despitethese limitations, the combined evidence from atmospheric and coupled simulations,supportedbyreanalysis,providesacoherentexplanation forthepositiveSSTdipolepatterntriggeredbyreducedMiddleEastern dust forcing. Furthermore, analysis of the month-to-month evolution of the IOD signal (Supplementary Fig. 2) shows that the anomaliespeakinbothamplitudeandstatisticalsignificanceduring SON,corroboratingtheselectionofSONastherepresentativeperiod forIOD-relatedanalysis.

Wefurtherextendtheanalysistoaparallelsensitivityexperiment thatimposesadust-increasescenario,whileotherwisemirroringthe preceding experimental design. In contrast to the reference simulation,thisperturbationyieldsanapproximatelyoppositeatmospheric response(SupplementaryFig.12).Elevateddustloadingsystematically reduces net surface shortwave radiation and suppresses high-level cloudcoveroverthewesternTIO(SupplementaryFig.12c,f).These cloud-radiative adjustments jointly diminish both longwave and shortwavefluxes,amplifyingthe directradiative cooling exerted by thedustitselfandgeneratingapronouncedsurfacecoolinganomaly in the western TIO (Supplementary Fig. 12). The resulting cooling establishesananomalouswestwardsurfacepressuregradientacross thebasin,drivingpersistentwesterlywindanomaliesatthesurfaceof thetropicalIndianOcean(SupplementaryFig.12b).Thesewesterlies pushsubsurfacewarmwatereastward,inducingpositivetemperature anomalieswithintheoceanicmixedlayeroftheeasternTIOandtilting theequatorialthermoclinedownwardfromwesttoeast(SupplementaryFig.12g).Allabovementionedprocessesarefavorabletothezonal SSTgradientoverTIOwithcoolerwesternpartandwarmereastern part(SupplementaryFig.12a).

TherelativecontributionofMiddleEastdusttoIOD
Prior work with a parsimonious IOD model quantified the roles of Indian-OceaninternaldynamicsandENSOinmodulatingIODintensity and asymmetry. To quantify the relative roles of dust, ENSO and intrinsicfeedbacksinshapingtheIOD,weconstructaparsimonious linearIODmodelbyapplyingleast-squaresregressiontomulti-source observations (Methods)50,51. The framework resolves the DMI variability into four additive terms: local ocean–atmosphere feedbacks, remoteENSOforcing,MiddleEastdustforcingandastochasticresidual(SupplementaryFig.18).

Acrossalldustproxies,themodelattributesthelargestshareof SONIODvariancetoMiddleEastdustforcing(SupplementaryFig.18, the absolute values of parameter over 0.40), exceeding even ENSO duringthematurephaseofthedipole.Thisdominancealignswiththe climatologicalpeakofIODamplitudeinSONandvalidatesourchoice ofatime-laggeddust–IODrelationship.Conversely,ENSOemergesas primarydriverduringtheprecedingborealwinterandspring(not shownhere,theabsolutevaluesofparameterover0.20),reflectingits ownpeakintheborealwinterandthefinitepropagationtimeofthe ENSO–IOD teleconnection. Intrinsic ocean–atmosphere feedbacks remainsecondarythroughouttheannualcycle(theabsolutevaluesof parameters over 0.08), accounting for a modest fraction of the explainedvariancerelativetothetwoexternalforcings.Thisdecompositionoutcome closely aligns with the model-based findings discussed earlier, thereby substantiating the predominant influence of Middle East dust in modulating IOD variability, particularly during boreal autumn.

Methods
Dustdatasets
Thedatasetscharacterizingdustincludevariousproxies,namelydust column mass density (DCMD, 2003–2023) from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2,2003–2023)55,non-sphericalaerosolopticaldepth(nsAOD, 2003–2023) from Multi-angle Imaging Spectro Radiometer (MISR) Level 356, dust optical depth (DOD, 2003–2023) from Moderate ResolutionImagingSpectroradiometer(MODIS)onboardtheAqua57, as well asdust observation fromsurface weather stations spanning from1980to202358.
Dustcolumnmassdensity(DCMD)fromMERRA-2tocharacterize dustactivity,withaspatialresolutionof0.5°×0.625°from2003to 2023. MERRA-2represents the mostrecent globalatmosphericreanalysisproducedbytheNationalAeronauticsandSpaceAdministration (NASA) Global Modeling and Assimilation Office (GMAO)55. MERRA-2 simulates dust aerosols through the Goddard Chemistry AerosolRadiationandTransport(GOCART)modelcoupledwiththe GoddardEarthObservingSystemModel,Version5(GEOS-5)system, consideringprocessessuchasdustemission,transport,anddeposition. The simulation incorporates data assimilation from various sources,includingground-basedAerosolRoboticNetwork(AERONET) AODobservationsandsatellitedatafromMODIS,AdvancedVeryHighResolution Radiometer (AVHRR), and MISR, with bias correction appliedtoreduceobservationalerrors.Globally,MERRA-2AODshows goodcorrelation(R>0.8)withAERONETmeasurement.
MODISAquadustopticaldepth(DOD)isbasedonacombination ofdustAODoverlandandocean57,59.DODisretrievedasacontinuous functionofAODandtheÅngströmexponenttodifferentiatedustfrom fineparticlesbasedonthesizedistributionofdusttowardsthecoarse range. In essence, MODIS DOD represents the optical depth of absorbing,coarse-modeaerosols,whichareoftendustparticlesover baregroundorsparselyvegetatedregions.Theretrievalprocessand estimatederrorsaredetailedinapreviousstudy57.
The non-spherical aerosol optical depth (nsAOD) derived from MISRLevel3dataisalsoconsideredasaproxyofdustactivity.BenefitingfromthesimultaneousobservationofsunlitEarthatninewidely spaced angles, MISR data enables the direct retrieval of AOD and particleproperties,particularlynsAOD56.Sincedustparticlesarepredominantlynon-spherical,thisopticaldepthservesasacrucialindicatorofdustactivities,particularlyindesertregionssuchasthearid andsemi-aridareasoftheMiddleEast60.Thisdatasetfeaturesaspatial resolution of 0.5°×0.5° and is used for the period between 2003 and2023.
Monthlydustfrequencyobservationsfromstationsareobtained from the hourly integrated surface dataset spanning 1980 to 2023 (available at https://www.ncei.noaa.gov/data/global-hourly/archive/ csv/).Thisdatasetincludesworldwidesurfaceweatherobservations andprovidesalong-termrecordofhourlysynopticweatherobservationsglobally.Ourmethodofprocessingstationdustobservationdata issimilartothepreviousstudy15,andtheannualstationdustactivity frequencyinsummeristhencalculatedbasedondailyobservation.We utilizedthisdatasetconsideringitslongertimespancomparedwith otherdatasetstomaximizethelengthofthetemporalseriesforanalyzingclimatevariabilityinthelongertimescale.
DustAerosolOpticalDepth(DAOD)retrievalsfromtheInfrared Atmospheric Sounding Interferometer (IASI) onboard the Metop-A satellitewasalsousedtoprovideanindependentvalidationofdust patterns61. IASI derives DAOD from thermal infrared spectral measurements, thereby providing 24-hour observational capability and addressingchallengesassociatedwithsurfacereflectanceoverbright desertsthataffectvisible-basedsensors(e.g.,MODIS)62.Theanalysis utilizestheIASI-Aproductfrom2008to2021,aperiodselectedforits consistent data availability to ensure a stable and homogeneous datasetforrobustcross-validation.

Meteorologicaldata,climatevariabilityindex,andoceandata
ThestrengthoftheIODisindicatedbythedifferenceintheabnormal SSTbetweenthewesternequatorialIndianOcean(50°E-70°Eand10°S- 10°N)andthesoutheasternequatorialIndianOcean(90°E-110°Eand 10°S-0°).ThistemperaturedifferenceistermedtheDipoleModeIndex (DMI,unit:K).WeappliedtheDMIcalculatedfromtheHadleyCenter Global Sea Ice and Sea Surface Temperature (HadISST) dataset (https://psl.noaa.gov/data/timeseries/month/DS/DMI/)63. It is generallyrequiredthattheDMIindexremainsaboveacertainthreshold (namely0.5)foraperiodoftime(usuallymorethanthreemonths)to be considered the positive Indian Ocean Dipole (pIOD) event, and whenitisnegative,itisknownasthenegativeIndianOceanDipole (nIOD)64.
The potential signal of other climate variability, such as ENSO, PacificDecadalOscillation(PDO)andMaddenJulienOscillation(MJO), werelargelyremovedfromouranalysis.Specifically,ENSOandPDO wereremovedbyregressingouttheNiño3.4index(https://psl.noaa. gov/data/timeseries/month/DS/Nino34/)63 as well as the PDO index (https://psl.noaa.gov/data/timeseries/month/DS/PDO/)fromboththe DMItimeseriesandSMEDtimeseries.TheMJOisprimarilymodulated bytheIODphaseratherthandirectlyinfluencingtheIODitself65,66,and we have effectively smoothed out its intra-seasonal variability by applyingalow-passfiltertoseasonalmeandata.
Wealsoemploythecloudpropertydataincludingcloudfraction of higher-level clouds and lower-level clouds from Satellite Application Facility on Climate Monitoring’s (CM SAF) cloud, albedoandsurfaceradiationproductfamilyEdition3(CLARA-A3) derived from AVHRR ranging from 2003 to 2023 to analyze the response of cloud properties to dust aerosols67. The MERRA-2 radiationdata(2003-2023)isalsoappliedinthisstudytodiagnose radiationfluxresponsestothedustactivity68.Tofurtherinvestigate theimpactofdustoncirculationpattern,wealsoutilizesealevel pressure(SLP),zonalandmeridionalwindat850hPa(UV850)from theMERRA-269.
Moreover,wealsoutilizedtheglobaloceanandsea-icereanalysis monthly mean data prepared by the European Center for Medium- RangeWeatherForecastsinordertodiagnosetheresponsesofocean vertical temperature and mixed layer depth to dust forcing in the observationaldatasets70.TheSSTdatafromtheCESMhistoricalrun rangingfrom1980to2014isalsoappliedinthisstudytocalculatethe contributionofdusttoIODevolution71,72.

Singularvaluedecompositionanalysis
The SingularValue Decomposition (SVD) is a multivariate statistical method used to identify coupled spatial-temporal relationships betweentwocovaryingfieldsbydecomposingtheircross-covariance matrix73,74.Thismethodhasbeenwidelyusedinthestatisticalanalysis ofclimatedata75,76.ForstandardizedanomalydatasetsX(representing SMEDwithMISRnon-sphericalAOD,stationdustfrequencydataset, andCESMdustAOD)andY(representingTIOSST),bothstructuredas space-timematrices(m×n),thecross-covariancematrixC XY=XYT is factorized into three components: C XY=UΣVT. Here, U and V are orthogonal matrices whose columns correspond to the dominant spatialpatternsofSMEDandTIOSST,respectively,whilethediagonal matrixΣcontainssingularvalues(σ k)thatquantifythemagnitudeof explained cross-covariance for each coupled mode. The temporal evolution of these modes, termed Principal Components (PCs), is derivedbyprojectingtheoriginaldatasetsontotheirrespectivesingular vectors (PC X=UTX, PC Y=VTY), which represent the normalizedtime-dependentamplitudesofthecoupledvariability.Thespatial loadings in U (Supplementary Fig. 6a, c, e) and V (Supplementary Fig.6b,d,f)highlightregionswheredustactivityandSSTanomalies exhibitthestrongestco-variability,withthefirstmodecapturingthe maximum shared variability and subsequent modes accounting for residualcovarianceunderorthogonalityconstraints.Tosumup,this approachcouldsuccessfullyisolatecoherentspatiotemporallinkages betweenSMEDandTIOSST.

Modelandexperimentaldesign
Numerousstudieshaveshownthatdustaerosolsplayasignificantrole intheclimatesystem.ThisstudyusestheCommunityEarthSystem Model (CESM) version 1.2, developed by the National Center for Atmospheric Research (NCAR), to investigate the impact of dust aerosolsontheIOD77.Themodelwasselectedforitsdemonstrated skillinaccuratelyreproducingboththetemporalevolutionandthe keyphysicalcharacteristicsoftheIOD26.Theatmosphericcomponent of CESM, the Community AtmosphereModel version 4.3 (CAM4.3), incorporates several major improvements in its physics parameterizations compared to previous CAM versions. We choose this versioninsteadofthemoreupdatedCommunityAtmosphereModel version5(CAM5)orCommunityAtmosphereModelversion6(CAM6) becausedustaerosolscanbeprescribedinthisversion,whileinlater versions it is computed online by meteorological variables and soil features, which cannot be easily controlled. Furthermore, we employedCESMversion1.2ratherthantheupdatedCESMversion2 becausethelatterisexclusivelycoupledwithCAM6.InCAM6,dust mixing ratios are computed interactively through a bidirectional couplingwithmeteorologicalconditions,whichagainlimitstheability toconstraindustconcentrationsforourexperimentaldesign.Forthe oceanic component, we apply the coupled ocean model with the ParallelOceanProgramversion2(POP2),similartotheCoupledModel IntercomparisonProject(CMIP),whichallowsfortheadjustmentof thewholeoceantotheaerosolforcing78.
TheCESMwasoperatedwitharesolutionof1.875°latitudeby2.5° longitudeand30verticallevels,usingthefinite-volumedynamicscore. Thedustentrainmentanddepositionmodelemitsdustinbothaccumulation (diameter: 0.1–1μm) and coarse (diameter: 1–10μm) modes79.Bothdryandwetdepositionprocessesarefullyrepresented inthemodel,aspartofthestandardaerosolmoduleinCESM80.
Inoursimulations,thedustaerosolforcingwasappliedbyperturbingthedustmixingratio—the3Dmassconcentrationfield.However,thedefaultdustmixingratioofCESMisnotsufficientlyaccurate to reproduce the spatiotemporal distribution of dustin the Middle East because of its inaccurate dust emission49,81–83. To address this issue,weusedtheratioofJJAMERRA-2dustAODtomodeleddustAOD toscaledustloadingineachmonth.Specifically,wefirstconducta controlrunusingthedefaultdustconcentrationfile.Then,wedivide themonthlydustAODresultofMERRA-2bythemonthlydustAODin the control run to obtain monthly proportionality coefficients. By multiplyingthesemonthlycoefficientswiththeoriginaldustmixing ratio,wecanbetterreproducethespatiotemporaldistributionofJJA Middle Eastern dust in the model. Results indicate that the model successfullyreproducesthespatialpatternandintensityofthedustin the Middle East (Supplementary Fig. 10a). Notably, the dustperturbationwasappliedexclusivelyovertheMiddleEastduringJJA.Beyond thisspatiotemporaldomain,themodelusesitsdefaultclimatological dustfields.
ToisolatetheimpactofMiddle Easterndust(10°S–30° N,35° E–70°E)oninterannualIODvariability,wedroveCESMwithobserved summertimedustmixing ratio over the Middle Eastfor1980–2020 anddiagnosedtheresultingIODindex.Specifically,thesummertime-meandustloadinginCESMwasrescaledeachyearusingtheregion’s summertimestationdustfrequencyobservations,generatingatransientdust-masstimeseriesthatfaithfullycapturesobservedvariability. Otheraerosolswereprescribedattheirdefault,present-daylevelsand not altered in the sensitivity simulations. Five ensemble members— generatedbyperturbinginitialtemperaturefieldsoverlandandocean —wereaveragedtosuppressinternalclimatenoise.Themagnitudeof thetemperatureperturbationis1×10−14K84.Thecoupledmodelwas alsoinitializedfromapre-industrialcontrolrunandsubsequentlyspun up for 5 years under present-day forcing conditions prior to the beginning of our formal experimental period (1980). This spin-up duration ensured the model’s ocean and atmosphere components reached a stable equilibrium under the prescribed forcing85. All the atmospheric variables and the IOD index were then regressed onto regionaldustloadingtoquantifythedust-drivenchangesfrom1980 to2020.
WefurtherconductedthesensitivityexperimentusingtheCESM to investigate the impact of decreasing dust forcing on SST. The experimental design involves two CESM simulations to investigate MiddleEastdustvariability.Thecontrolrunemploysthe1980–2020 climatologicalmeandustemissionsfromtheMiddleEastasboundary conditions. The forcing run is constructed through a multi-step approachderivedfromMERRA-2reanalysisdatainthesummertime: First,thearea-averageddustAODtimeseriesovertheMiddleEastis calculatedtorepresentregionalvariability.Thismeantimeseriesis thenusedasapredictorinamultivariateregressionagainstMERRA-2’s original dust AOD, generating a regression coefficient field that quantifies dust response patterns across the Middle East. Subsequently,thecoefficientfieldisscaledbyonestandarddeviation(1σ) oversummertimefrom1980to2020oftheregionalmeantimeseries todefinecharacteristicperturbationmagnitudes.Thefinalsummertimeforcing field is derived by subtracting this scaled perturbation fieldfromthe1980–2020climatologicalmean,ensuringcompatibility withCAM4’sinputrequirements.Additionally,duringtherestofthe year,dustemissions,alongwithallothermodelforcings,wereidenticaltothoseinthecontrolrun.Tominimizetheinfluenceofinternal variability on the results, we used 10 ensemble members for each experimentbyperturbingtheinitialconditionsfortemperaturefields. Thedifferenceoftheensemblemeansbetweentheforcingrunandthe controlrunisthenconsideredastheresponseofthemeteorological fieldstothedecreaseddustaerosolforcing.Allsimulationsareperformedfor5yearswiththeinitialfieldofthe1850historicalmean.Only thelast3yearsareanalyzed,andthefirsttwoyearsareusedasamodel spin-up.Thismethodologyexplicitlypreservestheobservedspatiotemporal structure of dust variability, where temporal fluctuations (captured through standard deviation scaling) and spatially heterogeneousresponses(encodedintheregressionpatterns)aresystematically integrated into the forcing field, thereby isolating the dynamicalimpacts of dustvariability withinthe 1980–2020 climate context.
We also conducted another sensitivity experiment using the CESM to investigate the impact of the increasing dust forcing, all configurationsaresimilartothepreviousone,butthefinaldustforcingfieldisderivedbyaddingthescaledperturbationfieldfromthe 1980–2020climatologicalmean.
Wethenconductedtheexperimentsimilartothefirstexperiment butwithprescribedSST(dataoceanmodel)tosolelyinvestigatethe atmospheric impact and exclude the influence of ocean processes (CAM).ThisconfigurationissimilartotheAtmosphericModelIntercomparisonProject(AMIP)withoutconsideringtheimpactofocean processes86.Alltheaboveexperimentconfigurationissummarizedin theSupplementaryTable1.

SimpleIODmodel
Toinvestigatethecontributionoflocalfeedbackprocesses,external ENSOforcing,dustforcing,andstochasticforcingtoIODgrowth,we usedasimplestochasticdeterministicmodelasfollows:
∂T/∂t=Feedbackm T(t)+ENSOm E(t)+Dustm D(t)+Stochasticm ξt (1)
whereTistheDMI,E(t)istheNiño3.4index,D(t)isthedustproxyover theMiddleEast,andξ t istheGaussianwhitenoisewithzeromeanand unit standard deviation. The subscript ‘m’ indicates the calendar month(m=1,2,…,12).TheparametersFeedback ,ENSO ,Dust and Stochastic are measures of the damping rate determined by local IndianOceanfeedbackprocesses,thesensitivityparameterofDMIto the external ENSO forcing, the sensitivity parameter of DMI to the MiddleEastexternaldustforcingandthestochasticforcing,respectively.Least-squaresfittingwasappliedtotheDMIandNiño3.4indices tocomputetheseparametersforeachcalendarmonth.Thus,these three parameters vary seasonally, and the methodology has been extensivelyvalidatedinpriorwork50,51.
Thesimplicityofthemodelusedinthisstudyhasadvantagesthat facilitatethephysicalunderstandingofthemodelresults,especiallyin determiningwhethertheIODamplitudechangeiscausedbyalocal processoraremoteinfluence.

Surfaceenergybudgetanalysis
Wediagnosethedriversoftheequatorialsurfacetemperaturechanges usingasurfaceenergybudgetdecompositionintheCESMsimulation similar to previous studies87,88. In detail, the tropical Indian SST responseisattributedtothefollowingfactors:changesinoceanheat uptake (T OHU), changes in shortwave radiation (T SW), changes in longwave radiation (T LW), changes in latent heat flux (T LH), and changesinsensibleheatflux(T SH).Similaritybetweenthesumofthe fiveterms(T SUM)andtherealresponse(T REAL)oftemperaturefurther verifiestheapplicabilityofthedecomposition.Neglectingthestorage term,themixedlayerenergybudgetinthequasi-equilibriumstatecan bewrittenas
Q SW+Q LW+Q LH+Q SH+Q OHU=0 (2)
whichstatesthatthesumofnetshortwaveradiationQ SW,netlongwaveradiationQ LW,latentheatfluxQ LH,andsensibleheatfluxQ SH is balancedbytheconvergenceofoceanheattransportQ OHU.Upward fluxesaredefinedaspositive,whichheattheatmosphericcolumn.
Followingthebulkformulaforevaporation,changesinthelatent heat flux related with Newtonian cooling can be expressed as Q LH=αQ LH T,whereα= Lv/R v T2,withL v beingthelatentheatofvaporization,R v beingthegasconstantformoistair,TbeingtheSST,and overbarsdenotingtheclimatologyoftheexperiment.Theremainder, Q LH,others=Q LH-Q LH,T, consists of latent heat flux changes due to changesinwind,changesinrelativehumidity,andchangesinstability. Then,wehaveanexpressionforSSTanomaliesas
T=(Q OHU-Q SW-Q LW-Q LH,others-Q SH)/αQ LH=T OHU +T SW +T LW +T LH +T SH (3)
Thus,wehave
T OHU =Q OHU/αQ LH
T SW = -Q SW/αQ LH
T LW = -Q LW/αQ LH
T LH,others = -Q LH,others/αQ LH
T SH = -Q SH/αQ LH
Here, Q can be obtained by subtracting it from two sensitive experiments,thus,wecancalculatethetemperaturedecomposition term,respectively.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Liu et al. estimated that dust aerosols from the Middle East regions act as a dominant external driver of the Indian Ocean Dipole. In particular, they show that summertime dust over the 1980-2020 period accounts for ~36% of interannual IOD variance, surpassing the El Niño-Southern Oscillation as the main driver during boreal autumn, using satellite observations and reanalysis. In addition, they perform CSEM v1.2 with the Parallel Ocean Program (POP2) and Community Atmosphere Model simulations (CAMS 4.3) to show that reduced summertime Middle East dust enhances warming in the western Indian Ocean during boreal autumn and induces easterly winds that shoal the eastern thermocline. This tilts of the thermocline, deepening it in the west and shoaling it in the east, and reinforces the SST gradient through the Bjerknes and wind–evaporation–sea surface temperature (WES) feedback, amplifying a positive IOD pattern, and vice versa.\nOverall, the central findings are scientifically interesting and clearly relevant to Indian Ocean climate variability, particularly the assessment that summertime Middle East dust explains a large fraction of IOD interannual variance, potentially surpassing ENSO’s influence during boreal autumn. However, characterizing Middle Eastern dust aerosols as the dominant external driver of IOD variability is currently overstated. A more explicit differentiation between the impacts of Middle Eastern dust and those of Central and East Asian dust emissions is required, as both may modulate the IOD. Furthermore, additional forcings and modes of variability, including anthropogenic aerosols, greenhouse gases, the Madden–Julian Oscillation (MJO), and the Pacific Decadal Oscillation (PDO), can also affect IOD variability during boreal autumn. These contributions should be carefully separated and quantified before making the strong claim that Middle East dust is the dominant external driver of the IOD.\nDust variability can both drive and respond to IOD variability. However, this bidirectional relationship does not yet appear to be well constrained in the authors’ analysis of dust–IOD interactions, partly because of substantial uncertainty in the representation of dust emissions in the global climate model CESM v1.2 (Kok et al., 2022; main text reference 18). Given this known bias, the authors should more clearly justify their choice of CESM v1.2 with CAMS 4.3 for this study and explain how this underestimate might affect their conclusions regarding dust–IOD interactions.\nPrior work has linked anthropogenic aerosols and greenhouse gases to Indian Ocean warming and IOD-like patterns, and has also shown that Middle East and West Asian dust influences the South Asian monsoon and Arabian Sea SST (see Banerjee and Kumar, 2016; Jin et al., 2014; Zheng and Hoteit, 2025). Also, It has been shown that Indian summer monsoon rainfall responds positively to Middle East dust (Jin and Yang, 2014). These findings from previous studies should be discussed explicitly in the main manuscript.\n-Banerjee, Priyanka, and S. Prasanna Kumar. \"ENSO modulation of interannual variability of dust aerosols over the northwest Indian Ocean.\" Journal of Climate 29, no. 4 (2016): 1287-1303.\n-Jin, Qinjian, Jiangfeng Wei, and Zong‐Liang Yang. \"Positive response of Indian summer rainfall to Middle East dust.\" Geophysical Research Letters 41, no. 11 (2014): 4068-4074.\n-Zheng, Yiling, and Ibrahim Hoteit. \"Asymmetric impacts of Indian Ocean Dipole on summer climate over Arabian Peninsula.\" Geophysical Research Letters 52, no. 20 (2025): e2025GL118195.\nGeneral comments:\n-Please use other reanalysis datasets (e.g., ERA5-CAMS, NAAPS) and satellite dust AOD products to check if the observed dust AOD pattern variability is reliable. In particular, since MODIS DOD has known bias, please cross-validate with IASI-DAOD or other independent datasets (such as CALIPSO).\n-Line #72: MISR only provides aerosol data from approximately year 2000 onward, whereas the caption of Fig. 1 states the MISR non-spherical aerosol optical depth data from 1980 to 2023. This should be corrected. If other datasets are combined for the pre-MISR period, this should be clearly mentioned in the figure caption.\n-In Fig. 1a, it is not clear whether the regression is performed on climatological means or on individual years. I assume the regression is currently done for individual years, comparing dust during JJA with other meteorological values during SON.\n-The rationale for using different time periods for the datasets is unclear, such as most satellite dust DOD cover 2003–2023, while dust station data spans 1980–2023. The manuscript should clarify the reason and discuss any resulting limitations and uncertainties in the analysis.\nLines 117-120: “Reduced Middle East dust emissions are statistically linked to atmospheric and oceanic states resembling pIOD events, suggesting that decadal variations in dust loading likely exert an important, previously underappreciated influence on tropical Indian Ocean climate.” It is not clear whether the authors remove ENSO-related variability from SST fields (in Fig. 1 and Extended Figs 1–5) before computing the IOD index. Also, there are several confounding factors that can also influence the IOD, such as the MJO and the PDO. The manuscript should detail how these confounding factors are accounted for or removed in the dust–IOD statistical analyses. Please cite that previous work here, especially when stated as “previously underappreciated influence on tropical Indian Ocean climate.”\nLines #31-35: The use of summertime station-based dust-frequency observations to scale annual-mean dust mass density in CESM v1.2 needs further justification. Could you please clarify why the other seasons were not considered? This could potentially underestimate the true annual mean dust mass density. The authors should also clarify this methodological choice and its possible implications.\nLine # 142- “Both the spatial configuration and the intensity of these anomalies strongly indicate that SMED acts as an external forcing capable of generating pronounced interannual variability in the TIO”. This conclusion is currently overstated, as the authors are not investigating the impact of other aerosols on IOD changes. How do PDO and MJO manifest in changes in IOD, since they are also affected by dust forcing, which in turn can affect the positive and negative phases of IOD?\nLines # 57-60, Supplementary Information (extended Fig. S6). Why, in the extended Fig. 6a, are they not including the SMED versus DMI sim? It would be good to plot the correlation coefficient and also the SVD analysis for this simulation run. As shown in Fig. S6b, the observations are quite off for the SMED and DMI patterns from 2010 onwards. What could be the reason for that?\nLine 474: Please cite the methodology and relevant references related to the implementation of Singular Value Decomposition Analysis.\nLines #212-215; “Once transported, these aerosols exert a strong radiative forcing by absorbing solar radiation, which heats the upper troposphere, while simultaneously reducing the amount of shortwave radiation reaching the ocean surface”.\n-It depends on the dust mineralogy as well; without constraining that over SMED, it is perhaps uncertain to quantify that. Authors should also consider providing the heating rate map rather than showing changes in surface radiative flux. That is important for assessing how much the radiative heating rate changes. To correctly determine vertical radiative heating rates, authors could consider the vertical profiles of dust extinction. Authors should also use the CALIPSO data or the CESM-based vertical profile of the dust extinctions to identify changes in heating rates.\n-Please explain why the TIO low cloud fraction is decreasing, and the high cloud fraction is increasing in the reduced-dust over SMED scenarios. How are authors constraining the other aerosols in the CESM v1.2 setup? Are authors only changing dust aerosol, or are they also changing other aerosols, such as sea salt aerosol in CESM v1.2 setup?\nLines #225-227 “These changes significantly affect cloud dynamics, driving a decrease in low-level cloud cover and an increase in high-level clouds over the western TIO (Fig. 3c, d).” Figure S8 and Figure 3 show otherwise. I see an enhanced low cloud fraction from the CSEM runs over the Western TIO. Please explain.\nLines #227-230: This needed a detailed investigation of the cloud feedback; the authors should cite past work for this statement here: “The transformation of cloud fields further modifies the radiative budget by increasing both longwave and shortwave radiation, thereby amplifying the warming signal over the western TIO in synergy with the direct radiative effects of reduced dust.”\nLines 232-237: Did the authors run the CESM v1.2 simulation from 1980 to 2020, or from 2010 to 2020?\nLine 288 and other places: “Furthermore, analysis of the month-to-month evolution of the IOD signal (figures omitted)”. The figure not shown does not look ideal. Please consider including all referenced figures in the supplementary materials.", "reviewer_2": "Please find my comments in the attachment.", "reviewer_3": "The manuscript investigates the role of mineral dust aerosols from the Middle East in modulating interannual variability of the Indian Ocean Dipole (IOD). Using a combination of satellite observations, reanalysis datasets, and climate model experiments, the authors demonstrate that variations in summer dust emissions between 1980 and 2020 explain approximately 36% of the observed interannual IOD variance—exceeding the influence of the El Niño–Southern Oscillation (ENSO) during boreal autumn. Overall, the paper identifies a previously underappreciated dust–IOD teleconnection, emphasizing the importance of aerosol forcing in seasonal prediction and long-term climate projections. Overall, the work is novel, and supported by comprehensive analysis. However, the manuscript is not always easy to follow, as several figure captions and axis labels lack clarity and may cause confusion for readers. I recommend publication after revision to address the points detailed below.\nMajor comments:\n1. The paper explains how dust emission reduction over Middle East causes a positive anomaly in solar radiation reaching to the western Indian ocean surface. However, it does not clearly discuss how this enhanced radiative energy translates into a positive SST anomaly, considering the ocean’s large heat capacity. Please expand on this physical mechanism. Is the ~2-month lag used in the analysis related to the ocean mixed-layer adjustment timescale?\n2. Please explain why there is a positive net LW flux at surface over the western Indian ocean in the case of dust deduction? Moreover, for both SW and LW, why the flux anomaly over eastern Indian ocean is always opposite to the western part?\nSpecific comments:\n3. Line 88, ‘reversing the regression coefficients to represent dust reduction’ is very confusing, I figured out its meaning when I looked at the figure caption of Fig. S3, it means you take the negative values of the regression coefficient. Please modify the sentence, and also clarify it in the caption of Fig.1.\n4. Fig S3, you mentioned ‘regression coefficient’ and ‘correlation coefficient’ in the figure caption, are they the same? Please clarify what you used and keep them consistent.\n5. In Fig1 (e), please clarify that the grey dashed line is the mixed layer depth anomaly, right? It is not just mixed layer depth. Also, please clarify the grey dashed line is corresponding to the y-axis on the right. Otherwise, it’s too confusing.\n6. Line 93, ‘0.5K per standard deviation of dust variability’, please clarify the meaning of this term. Authors use different dust proxies, e.g., dust AOD, column mass density, and dust frequency, what are their standard deviation of dust variability, respectively.\n7. Line 336, ‘suppress eastern upwelling’, should that be suppress eastern downwelling?", "reviewer_4": "Responsetothereviewof “MiddleEastDustasaDominantExternalDriverofthe\nIndianOceanDipole” (NCOMMS-25-75253-T).\nResponsestoReview01’scomments\nSummary:\nLiu et al. estimated that dust aerosols from the Middle East regions act as a dominant\nexternal driver of the Indian Ocean Dipole. In particular, they show that summertime\ndust over the 1980-2020 period accounts for ~36% of interannual IOD variance,\nsurpassing the El Niño-Southern Oscillation as the main driver during boreal autumn,\nusing satellite observations and reanalysis. In addition, they perform CSEM v1.2 with\nthe Parallel Ocean Program (POP2) and Community Atmosphere Model simulations\n(CAMS 4.3)toshow that reduced summertimeMiddleEast dust enhances warming in\nthe western Indian Ocean during boreal autumn and induces easterly winds that shoal\nthe eastern thermocline. This tilts of the thermocline, deepening it in the west and\nshoaling it in the east, and reinforces the SST gradient through the Bjerknes and\nwind–evaporation–sea surface temperature (WES) feedback, amplifying a positive\nIODpattern, and viceversa.\nOverall, the central findings are scientifically interesting and clearly relevant to Indian\nOcean climate variability, particularly the assessment that summertime Middle East\ndust explains a large fraction of IOD interannual variance, potentially surpassing\nENSO’s influence during boreal autumn. However, characterizing Middle Eastern\ndust aerosols as thedominant external driver of IOD variability is currently overstated.\nAmore explicit differentiation between the impacts of Middle Eastern dust and those\nof Central and East Asian dust emissions is required, as both may modulate the IOD.\nFurthermore, additional forcings and modes of variability, including anthropogenic\naerosols, greenhouse gases, the Madden–Julian Oscillation (MJO), and the Pacific\nDecadal Oscillation (PDO), can also affect IOD variability during boreal autumn.\nThese contributions should be carefully separated and quantified before making the\nstrong claim that MiddleEast dust isthedominant external driver oftheIOD.\nReply: We are grateful to the reviewer for his/her insightful and very valuable\nsuggestions, which have significantly contributed to the enhancement of our\nmanuscript. We have carefully addressed the comments and made the necessary\nrevisions, hoping that the updated version meets the journal's standards. Our\nresponses to each of the comments and suggestions are as follows. The referee’s\noriginal comments are shown in blue. Our replies are shown in black. The\ncorresponding changes in themanuscript are shown in Italicblack.\n1. Dust variability can both drive and respond to IOD variability. However, this\nbidirectional relationship does not yet appear to be well constrained in the authors’\nanalysis of dust–IOD interactions, partly because of substantial uncertainty in the\nrepresentation of dust emissions in the global climate model CESM v1.2 (Kok et al.,\n2022; main text reference 18). Given this known bias, the authors should more clearly\njustify their choice of CESM v1.2 with CAMS 4.3 for this study and explain how this\nunderestimatemight affect theirconclusions regarding dust–IOD interactions.\nReply: We thank the reviewer for this insightful comment regarding the bidirectional\nnature of dust-IOD interactions and the known uncertainties in modeled dust\nemissions.Weagree that thiscomplexity requires careful consideration.\nThe core reason for our model choice is to isolate the climatic impact of dust without\nthecomplication ofdust emission response to climate.Therefore, werequired amodel\nframework that allows dust aerosol concentrations to be prescribed, creating a\none-way forcing. Moreover, the prescribed dust field used in our study has been\nvalidated byprevious studies(Buchard et al., 2017;Che et al., 2022).\n(1) Choice of CAM version: We selected the Community Atmosphere Model version\n4.3 (CAM4.3) because it supports prescribed dust mixing ratios. This capability is\nessential for our experimental design. In contrast, later versions (CAM5 and CAM6)\nfeature more complex aerosol-climate interactions where dust is computed online\nbased on evolving meteorological and surface conditions, making it difficult to\nprescribefixed values ofdust loading.\n(2) Choice of CESM version: Consequently, we used CESM v1.2, which is\ncompatible with CAM4.3. The newer CESM v2 is exclusively coupled with CAM6,\nwhich employs the online dust scheme and the bidirectional dust-climate coupling we\nsought toavoid.\nThe additional explanation has been incorporated into the supplementary information\nofourrevised manuscript forclarity.Wealso cited ithere for convenience.\nThe atmospheric component of CESM, the Community Atmosphere Model version 4.3\n(CAM4.3), incorporates several major improvements in its physics parameterizations\ncompared to previous CAM versions. We choose this version instead of the more\nupdated Community Atmosphere Model version 5 (CAM5) or Community Atmosphere\nModel version 6 (CAM6) because dust aerosols can be prescribed in this version,\nwhile in later versions it is computed online by meteorological variables and soil\nfeatures, which cannot be easily controlled. Furthermore, we employed CESM version\n1.2 rather than the updated CESM version 2 because the latter is exclusively coupled\nwith CAM6. In CAM6, dust mixing ratios are computed interactively through a\nbidirectional coupling with meteorological conditions, which again limits the ability\ntoconstrain dust concentrationsfor our experimental design.\n2. Prior work has linked anthropogenic aerosols and greenhouse gases to Indian\nOcean warming and IOD-like patterns, and has also shown that Middle East and West\nAsian dust influences the South Asian monsoon and Arabian Sea SST (see Banerjee\nand Kumar, 2016; Jin et al., 2014; Zheng and Hoteit, 2025).Also, It has been shown\nthat Indian summer monsoon rainfall responds positively to Middle East dust (Jin and\nYang, 2014). These findings from previous studies should be discussed explicitly in\nthemain manuscript.\n-Banerjee, Priyanka, and S. Prasanna Kumar. \"ENSO modulation of interannual\nvariability of dust aerosols over the northwest Indian Ocean.\" Journal of Climate 29,\nno.4(2016): 1287-1303.\n-Jin, Qinjian, Jiangfeng Wei, and Zong‐Liang Yang. \"Positive response of Indian\nsummer rainfall toMiddle East dust.\" Geophysical Research Letters 41, no. 11(2014):\n4068-4074.\n-Zheng, Yiling, and Ibrahim Hoteit. \"Asymmetric impacts of Indian Ocean Dipole on\nsummer climate over Arabian Peninsula.\" Geophysical Research Letters 52, no. 20\n(2025): e2025GL118195.\nReply: Thanks for your helpful suggestions. We have now explicitly discussed the\nimportant findings from the cited studies in the revised manuscript. This discussion\nprovides a crucial context for our work by highlighting the established roles of\nanthropogenic forcings and regional dust in shaping the Indian Ocean climate system,\nthereby helping to better position our specific findings on the dust-IOD connection\nwithinthebroader literature.Wealso cited thediscussion here.\nMost studies attribute IOD variability to external forcing 4,9, internal air–sea\ninteractions and remote El Niño-Southern Oscillation (ENSO) influences 1,10,11, yet the\nrole of aerosols remains largely unexplored. The TIO is located close to the world’s\nsecond largest dust source – the Middle East deserts. Previous studies have identified\nthe far-reaching impacts of Middle East dust on the South Asian monsoon, Arabian\nSea SST,andIndian summer monsoon rainfall12-14.\n3. Please use other satellite dust AOD products to check if the observed dust AOD\npattern variability is reliable. In particular,since MODIS DOD has known bias, please\ncross-validatewith IASI-DAOD orotherindependent datasets (such as CALIPSO).\nReply: Thanks for your insightful and constructive suggestions. Due to its sparse\norbital tracks and significant data gaps caused by narrow swath width, CALIPSO\nlacks the continuous spatial coverage required for robust pattern analysis of dustAOD\nvariability. This sampling inadequacy is particularly problematic for dust studies,\nwhere episodic dust events and complex transport patterns require continuous spatial\nmonitoring to reliably characterize variability patterns. Additionally, frequent cloud\ncontamination further reduces data availability, compounding the spatial\nrepresentation issues. We therefore rely on IASI-DAOD and other independent\ndatasets for cross-validation and checking if the observed dust AOD pattern\nvariability isreliable.\nWe found that the SST dipole structure is highly robust, as confirmed by multiple\nindependent dust datasets, including MODIS dust optical depth, MERRA-2 dust\ncolumn mass density, station dust frequency, MISR non-spherical aerosol optical\ndepth, and IASI dust optical depth (Fig. R1 and Fig. R2, the same as Extended Data\nFig.5and Fig. 1din therevised manuscript).\nFig. R1. The regression coefficients of detrended SON SST on the detrended JJA\ndust activity proxies averaged overthe Middle East. These dust proxies include (a)\nMODIS dust optical depth (2003-2023), (b) MERRA-2 dust column mass density\n(2003-2023), (c) station dust frequency (1980-2023), and (d) IASI dust optical depth\n(2008-2021). Note that the regression coefficients in all figures are taken as their\nnegative values corresponding to dust reduction. The area with white dots passed the\nsignificance test ofp ≤ 0.05byStudent's t-test.\nFig. R2. Similar to Fig. R1, but for the detrended JJA MISR non-spherical\naerosoloptical depth averaged overthe MiddleEastfrom 2003to2023\nWe also incorporated additional introduction for the validating dataset and cited it\nhere.\nDust Aerosol Optical Depth (DAOD) retrievals from the Infrared Atmospheric\nSounding Interferometer (IASI) onboard the Metop-A satellite was also used to\nprovide an independent validation of dust patterns 56. IASI derives DAOD from\nthermal infrared spectral measurements, thereby providing 24-hour observational\ncapability and addressing challenges associated with surface reflectance over bright\ndeserts that affect visible-based sensors (e.g., MODIS) 57. The analysis utilizes the\nIASI-A product from 2008 to 2021, a period selected for its consistent data\navailabilitytoensureastableandhomogeneous dataset forrobustcross-validation.\n4. Line #72: MISR only provides aerosol data from approximately year 2000\nonward, whereas the caption of Fig. 1 states the MISR non-spherical aerosol optical\ndepth data from 1980 to 2023. This should be corrected. If other datasets are\ncombined for the pre-MISR period, this should be clearly mentioned in the figure\ncaption.\nReply: Thank you for catching this error. You are correct that MISR data are only\navailable from 2000 onward. We have corrected the caption for Figure 1 to reflect the\naccurate time range (2000–2023), and we apologize for the oversight. We also cited\nthecorrected figurecaption here for convenience.\nFig. 1. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on the\nMiddle East regional-averaged station dust frequency (averaged over the 10°S~30°N,\n35°E~70°E). (b) Similar to a, but for the JJA MISR non-spherical aerosol optical\ndepth (AOD). (c) Timeseries of standardized JJAstation dust frequency (the solid line)\nfrom 1980 to 2023, and JJA MISR non-spherical aerosol optical depth (the dashed\nline) from 2003 to 2023. (d-g) The regression coefficients of SON meteorological\nfields on the JJA MISR non-spherical aerosol optical depth averaged over the Middle\nEast. These meteorological fields include (d) sea surface temperature. (e) meridional\nmean (20°S-20°N) vertical cross section of temperature (shaded) and mixed layer\ndepth (the gray dashed line), (f) sea level pressure (shaded) with 850 hPa zonal and\nmeridional wind (UV, vectors, m/s), and (g) meridional mean (20°S-20°N) vertical\ncross section of geopotential height (GPH, shaded) with zonal and vertical wind (UW,\nvectors, m/s). The y-axes on the right in (e) corresponds to the response of mixed\nlayer depth. The regression is currentlydone for individual years. The area with white\ndots passed thesignificance test of p ≤ 0.05byStudent'st-test.\n5. In Fig. 1a, it is not clear whether the regression is performed on climatological\nmeans or on individual years. I assume the regression is currently done for individual\nyears, comparing dust during JJAwithother meteorological values during SON.\nReply: Sorry for the unclear explanation and you are right.The regression is currently\ndone for individual years, comparing dust during JJA with other meteorological\nvalues during SON. We have clarified it in the figure caption and cited it here for\nconvenience.\nFig. 1. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on the\nMiddle East regional-averaged station dust frequency (averaged over the 10°S~30°N,\n35°E~70°E). (b) Similar to a, but for the JJA MISR non-spherical aerosol optical\ndepth (AOD). (c) Time series of standardized JJA station dust frequency (the solid\nline), and JJA MISR non-spherical aerosol optical depth (the dashed line) from 1980\nto 2023. (d-g) The regression coefficients of SON meteorological fields on the JJA\nMISR non-spherical aerosol optical depth averaged over the Middle East. These\nmeteorological fields include (d) sea surface temperature. (e) meridional mean\n(20°S-20°N) vertical cross section of temperature(shaded) and mixed layer depth (the\ngray dashed line), (f) sea level pressure (shaded) with 850 hPa zonal and meridional\nwind (UV, vectors, m/s), and (g) meridional mean (20°S-20°N) vertical cross section\nof geopotential height (GPH, shaded) with zonal and vertical wind (UW,vectors, m/s).\nThe y-axes on the right in (e) corresponds to the response of mixed layer depth. The\nregression is currently done for individual years. The area with white dots passed the\nsignificancetest of p ≤ 0.05byStudent'st-test.\n6. The rationale for using different time periods for the datasets is unclear, such as\nmost satellite dust DOD cover 2003–2023, while dust station data spans 1980–2023.\nThe manuscript should clarify the reason and discuss any resulting limitations and\nuncertainties inthe analysis.\nReply: Thanks for pointing it out. We opted for different time periods because the\navailable data lengths vary across datasets. Our primary rationale for utilizing the\nlonger station record (1980-2023) was to maximize the length of the temporal series\nfor more robust results. Satellite data become available only since 2003, whereas\nstation data can extend back to 1980. To minimize errors arising from insufficient\nsample sizes, we utilized the longest possible time span for each dataset. Moreover,\nthe regression patterns derived from these different time periods exhibit considerable\nconsistency (Extended Data Fig. 7 & 8 in the revised manuscript), further\ndemonstrating that our findings are not sensitive to the specific time frame selected\nbut are robust across different intervals. We also added the supplementary discussion\ninthe revised manuscript and cited ithere.\nMoreover, the regression patterns derived from these different time periods exhibit\nconsiderable consistency (Extended Data Fig. 7 & 8), further demonstrating that our\nfindings are not sensitive to the specific time frame selected but are robust across\ndifferentintervals.\n7. Lines 117-120: “Reduced Middle East dust emissions are statistically linked to\natmospheric and oceanic states resembling pIOD events, suggesting that decadal\nvariations in dust loading likely exert an important, previously underappreciated\ninfluence on tropical Indian Ocean climate.” It is not clear whether the authors\nremove ENSO-related variability from SST fields (in Fig. 1 and Extended Figs 1–5)\nbefore computing the IOD index.Also, there are several confounding factors that can\nalso influence the IOD, such as the MJO and the PDO. The manuscript should detail\nhow these confounding factors are accounted for or removed in the dust–IOD\nstatistical analyses. Please cite that previous work here, especially when stated as\n“previously underappreciated influence ontropical Indian Ocean climate.”\nReply: We thank the reviewer for raising this important point. We have removed\nENSO-related variability from the SST fields by linear regression (Fig. 1 and\nExtended Figs 1–5 in the revised manuscript) prior to computing the IOD index, in\norder to isolatetheinfluence ofENSOonIOD variability.\nRegarding the Madden–Julian Oscillation (MJO), which is a dominant mode of\nintra-seasonal (30–100 days) variability, we note that its behavior is primarily\nmodulated by the phase of the IOD rather than directly influencing the IOD itself\n(Benedict et al., 2015; Chen et al., 2022). To minimize interference from\nintra-seasonal climate signals, we employ a seasonal mean, which smooths out these\nhigher-frequency variations.\nConcerning the Pacific Decadal Oscillation (PDO), we acknowledge its potential role\nin modulating IOD variability. However, in the present analysis, the PDO was not\nexplicitly removed due to its relatively weaker teleconnection with tropical Indian\nOcean climate on interannual timescales (Krishnamurthy and Krishnamurthy, 2016).\nTo isolate the independent influence of dust from that of the Pacific Decadal\nOscillation (PDO), we performed correlation analysis again but after regressing out\nthePDO signal, as shown in Fig. R3.The result confirms that dust forcing maintains a\npersistent and independent impact on the tropical Indian Ocean dipole pattern (Fig.\nR3).\nFig. R3. The regression coefficients of detrended SON SST on the detrended JJA\ndust activity proxies averaged over the Middle East by regressing out the\nimpacts of PDO. These dust proxies include (a) MODIS dust optical depth\n(2003-2023), (b) MERRA-2 dust column mass density (2003-2023), (c) MISR\nnon-spherical AOD (2003-2023), and (d) station dust frequency (1980-2023). Note\nthat the correlation coefficients in all figures are taken as their negative values\ncorresponding to dust reduction. The area with white dots passed the significance test\nof p ≤ 0.05 by Student's t-test. SST and dust proxies have been regressed out by\nPDOconsidering PDO’s considerate global impact.\nWe have now included a detailed description in the manuscript outlining how these\npotential confounding factors—including MJO and PDO—were addressed or\naccounted for in our dust–IOD statistical analyses. We also cited the corresponding\nexplanationin therevised manuscript here for convenience.\nThe potential signal of other climate variability, such as ENSO, Pacific Decadal\nOscillation (PDO) and Madden Julien Oscillation (MJO), were largely removed from\nour analysis. Specifically, ENSO and PDO were removed by regressing out the\nNiño3.4 index (https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/) 71as well as the\nPDO index (https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/) from both the DMI\ntime series and SMED time series. The MJO is primarily modulated by the IOD phase\nrather than directly influencing the IOD itself 72,73, and we have effectively smoothed\noutits intra-seasonal variabilityby applying alow-passfilter to seasonal mean data.\n8. Lines 131-135: The use of summertime station-based dust-frequency\nobservations to scale annual-mean dust mass density in CESM v1.2 needs further\njustification. Could you please clarify why the other seasons were not considered?\nThis could potentially underestimate the true annual mean dust mass density. The\nauthors should also clarify thismethodological choiceand itspossibleimplications.\nReply:Wethank thereviewer for this comment concerning our methodological useof\nsummertime observations and for identifying the inadvertent error. We have corrected\nit to the intended phrasing: \"Specifically, the summertime-mean dust mass density in\nCESM was scaled each year according to the corresponding summertime\nstation-based dust-frequency observations over theMiddleEast.\"\nThe primary reason for focusing on the summer (JJA) season is that dust activity in\nthe Middle East exhibits strong seasonality, with emissions and long-range transport\nbeing the most intense and spatially coherent during summer (Extended Data Fig. 1 in\nthe revised manuscript). This makes the summer season the most representative\nperiod for capturing the dominant dust patterns and their interannual variability that\nare central to our study.This seasonal focus is consistent with the approach of several\nprevious regional dust-climatestudies (Liu et al., 2023; Xiaet al., 2022).\nWe acknowledge the reviewer's concern that this approach might not fully capture the\nannual mean. To address this, we conducted a validation exercise by comparing our\nscaled annual-mean dust mass density against independent annual-mean data from the\nMERRA-2 reanalysis (Fig. R4). The results showed a reasonable consistency between\nthe two, increasing our confidence that the summertime-based scaling captures the\nkey spatial features oftheannual-mean dustdistributioneffectively.\nFig. R4. Distribution of annual-mean dust AOD over the Middle East. (a) the\nCESMmodel, (b) MERRA-2reanalysis, and (c) theirdifference (aminus b).\n9. Line # 142- “Both the spatial configuration and the intensity of these anomalies\nstrongly indicate that SMED acts as an external forcing capable of generating\npronounced interannual variability in the TIO”. This conclusion is currently\noverstated, as the authors are not investigating the impact of other aerosols on IOD\nchanges. How do PDO and MJO manifest in changes in IOD, since they are also\naffected by dust forcing, which in turn can affect the positive and negative phases of\nIOD?\nReply: We thank the reviewer for this insightful comment. We agree that stating\nSMED acts as an external forcing based solely on our current analysis may be too\nstrong, as we have not fully isolated its effect from other potential drivers like PDO\nand MJO. We acknowledge that these climate variabilities can co-vary with both dust\nemissions and IOD phases, and that future studies using targeted numerical\nexperiments are needed to fully disentangle their relative contributions and causal\npathways.\nIn response, we have toned down this specific conclusion in the manuscript to state\nthat our results suggest SMED can act as a significant external forcing, rather than\ndefinitively indicateit does.Wealso cited it here for convenience.\nBoth the spatial configurationand theintensity of these anomalies suggest that SMED\ncould act as a significant external forcing capable of generating pronounced\ninterannualvariabilityin theTIO.\nFurthermore, we have added a new paragraph to the discussion section to explicitly\naddress the potential confounding roles of PDO, MJO, and other aerosols, while also\nacknowledging the limitations of the current study that warrant further investigation\ninfuture work.\nWhile this study demonstrates a robust statistical linkage between SMED and IOD\nvariability, reinforced by sensitivity experiments that seek to isolate the dust influence\nfrom other factors (e.g., ENSO and PDO), we acknowledge that conclusively\nattributing this signal solely to dust remains challenging. Despite our efforts, the\npotential for complex, non-linear interactions with other aerosols or low-frequency\nclimate modes cannot be entirely ruled out. Therefore, we conclude that SMED likely\nacts as a significant external forcing on the IOD, but its independent contribution and\nthe precise pathways involved should be further validated through future idealized\nmodeling studies designed todefinitivelyseparate theseintertwinedinfluences.\n10. Lines # 57-60, Supplementary Information (extended Fig. S6). Why, in the\nextended Fig. 6a, are they not including theSMEDversus DMIsim? It would begood\nto plot the correlation coefficient and also the SVD analysis for this simulation run.\nAs shown in Fig. S6b, the observations are quite off for the SMED and DMI patterns\nfrom 2010onwards.What could bethe reason for that?\nReply: We thank the reviewer for these insightful questions regarding Extended Data\nFig.6.\n(1) The correlation and SVD for the SMED vs. DMI simulation: We agree with the\nreviewer that this is a valuable suggestion. In the revised manuscript, we have now\nincluded the spatial correlation coefficient between the simulated SMED and DMI\npatterns in the caption or directly on the panel for Fig. R5a here, same as Extended\nData Fig. 9a in the revised manuscript. Furthermore, we have conducted the\nrecommended SVD analysis between the dust emissions and SST fields from this\nspecific simulation. The results of this SVD analysis, which robustly capture the\ndominant dust-IOD coupling mode, have been added as a new supplementary figure\n(Fig. R6 here, same as Extended Data Fig. 6e-f in the revised manuscript) to\nstrengthen our mechanisticinterpretation.\nFig.R5. Thestandardized DMI andSMEDfrom 1980to 2020.The timeseries are(a)\nsimulated DMI forced by dust (solid cyan line), simulated DMI in the historical run\nwith all forcing (dashed cyan line), and simulated SMED (solid red line) and (b)\nobserved DMI andSMED.Allthetimeseries arestandardized for comparison\nFig. R6. The spatial pattern of the first SVD mode between dust proxies and\ntropical Indian ocean SST. The first mode of SVD between (a) detrended JJA MISR\nnon-spherical AOD in the Middle East, accounting for 64.2% of the covariance and\n(b) detrended SON tropical Indian Ocean SSTfrom 2003to 2023.(c-d) Similar to a-b,\nbut for the station dust frequency from 1980 to 2023. This mode account for 65.7% of\nthe covariance. (e-f) Similar to a-b, but for the dust AOD in the CESM simulation.\nThismode account for 71.2%of thecovariance.\nWealso supplemented corresponding discussion about thisfigure and cited ithere.\nComplementary singular value decomposition (SVD) between SMED and SON SST\nfurther supports this relationship, showing that theleading coupled mode corresponds\nto negative SMED signals and a pIOD-like SST pattern in the TIO (Extended Data\nFig. 6). Correlations exceed 0.6 for the modes derived from both MISR AOD,\nstation-based dust frequency observations, and dust AOD in the CESM simulations,\nhighlighting theconsistency of theresults.\n(2) The model-observation discrepancy post-2010: You are right that there might be a\ndivergence between the observed and simulated SMED-DMI relationship after\napproximately 2010. The primary factors likely include the increased influence of\nother climate forcings and limitations in the observed dust record. Specifically, the\nperiod post-2010 was characterized by strong and persistent climate anomalies,\nincluding a notable prevalence of Central Pacific El Niño events and a strong positive\nphase of the PDO (Freund et al., 2019; Lin et al., 2018). These modes exert a\npowerful influence on the IOD, which may have temporarily overshadowed or\nmodulated the dust-IOD linkage in the observations—a nuance not captured by the\nidealized \"SMED-only\" forcing simulation. Additionally, the observational dust data\nitself may contribute to the discrepancy, as this period encompasses the transition\nbetween satellite sensors and potential calibration drifts. Such technical factors can\nintroduce inhomogeneities in the long-term dust trend that are unrelated to true\nclimatevariability.\nWe have revised the text to explicitly acknowledge this period of discrepancy and\nprovide the above discussion on its potential causes, thereby presenting a more\ncompleteand critical assessment of ourresults.\nNotably, a possible post-2010 divergence between the observed and simulated\nSMED-DMI relationship may stem from the increasing influence of other climate\nforcings (e.g., Central Pacific El Niño, PDO) and limitations inherent in the\nstation-based dustrecord28,29.\n11. Line 474: Please cite the methodology and relevant references related to the\nimplementationof SingularValueDecompositionAnalysis.\nReply: Thanks for pointing this out. We have cited the methodology and relevant\nreferences.Wealsocited them here forconvenience.\nThe Singular Value Decomposition (SVD) is a multivariate statistical method used to\nidentify coupled spatial-temporal relationships between two covarying fields by\ndecomposing their cross-covariance matrix 81,82. This method has been widely used in\nthestatistical analysis ofclimate data 83,84.\n12. Lines #212-215; “Once transported, these aerosols exert a strong radiative forcing\nby absorbing solar radiation, which heats the upper troposphere, while simultaneously\nreducing theamount ofshortwave radiation reaching theocean surface”.\nIt depends on the dust mineralogy as well; without constraining that over SMED, it is\nperhaps uncertain to quantify that.Authors should also consider providing the heating\nrate map rather than showing changes in surface radiative flux. That is important for\nassessing how much the radiative heating rate changes. To correctly determine\nvertical radiative heating rates, authors could consider the vertical profiles of dust\nextinction. Authors should also use the CALIPSO data or the CESM-based vertical\nprofileof thedustextinctions to identify changes in heating rates.\nReply: We thank the reviewer for this insightful comment and for the constructive\nsuggestions to strengthen our analysis. We have addressed each point as detailed\nbelow.\n(1)Regarding dust mineralogy:\nWe agree with the reviewer that dust mineralogy is a key factor influencing radiative\nforcing. As our study did not employ a spatially resolved mineralogy scheme for the\nSMED region, we have now acknowledged this as a source of uncertainty in the\nrevised manuscript.Asentence has been added tothe Discussion section in therevised\nmanuscript andcited here for convenience:\nIt should also be noted that the magnitude of the dust radiative forcing is also\ndependent on dust mineralogy, which is not specifically constrained for the Middle\nEast region in our simulations, which may introduce some uncertainty in the\nquantitativeestimates.\n(2)Regarding theheating rateand vertical profiles:\nWe fully agree that analyzing the atmospheric heating rate provides a more direct\nmetric for assessing the impact of dust absorption on the atmospheric temperature\nstructure. In response to the reviewer's suggestion, we have added a new figure (Fig.\nR7b, the same as Extended Data Fig. 13b) showing the vertical heating rate profile in\nthe model. We also analyzed the spatial distribution of the dust-induced atmospheric\nheating rate (Fig. R7c, thesame asExtended Data Fig. 13c).\nFurthermore, to correctly interpret the vertical structure of this heating, we have\nfollowed the reviewer's advice and analyzed the vertical profile of dust extinction\nfrom our CESM simulations (Fig. R7a, the same as Extended Data Fig. 13a). The\nmodel results indicate that the majority of the transported dust aerosol is confined\nwithin the troposphere. This vertical profile supports the pattern of heating rates\nshown in our new figure, confirming that thedominant radiative heating occurs within\ntheatmospheric column, consistentwith thedust layer's location.\nFig. R7. Dustvertical profileand its corresponding heating rate forreduced dust.\n(a) The vertical profile of dust mixing ratio and (b) heating rate over the transported\narea (10°S~10°N, 35°E~70°E) in the model. (c) The responses of SON 700 hPa\nheating rate to reduced Middle East dust forcing in the CESM. The area with white\ndots passed thesignificance test ofp ≤ 0.05byStudent's t-test.\nWe also added the corresponding text in the revised manuscript to describe the added\nheating rate map and discusses the relationship between the simulated dust vertical\nprofileand theresulting radiativeheating.Wecited it here for convenience.\nOnce transported, these aerosols exert a strong radiative forcing by absorbing solar\nradiation, which heats the upper troposphere, while simultaneously reducing the\namount of shortwave radiation reaching the ocean surface. The dust-induced heating\nrate profile aligns with the simulated vertical distribution of dust extinction, which is\nprimarily confined within the troposphere. This vertical coherence confirms that the\nradiative heating maxima correspond well with the dust layer's location, reinforcing\nthe direct thermodynamic impact of dust radiative absorption on the atmospheric\ncolumn. (Extended Data Fig.13).\n13. Please explain why the TIO low cloud fraction is decreasing, and the high cloud\nfraction is increasing in the reduced-dust over SMED scenarios. How are authors\nconstraining the other aerosols in the CESM v1.2 setup? Are authors only changing\ndust aerosol, or are they also changing other aerosols, such as sea salt aerosol in\nCESMv1.2setup?\nReply: We thank the reviewer for raising these important points. The decrease in TIO\nlow-level cloud fraction and increase in high-level cloud fraction under reduced dust\ncan be attributed to dust-induced changes in atmospheric stability induced by changes\nin vertical temperature profile; this mechanism has been clarified in the revised\nmanuscript.Wealso cited it here for convenience.\nThis vertical redistribution of energy alters atmospheric stability: the lower\natmosphere warms more strongly, while diminished warming in the upper atmosphere\nresults in a relative cooling there (Extended Data Fig. 15). These changes\nsignificantly affect cloud dynamics, driving a decrease in low-level cloud cover and\nanincreaseinhigh-level clouds over thewestern TIO(Fig. 3c, d).\nRegarding the model setup, we confirm that only dust aerosol emissions were\nmodified in our CESM v1.2 experiments. Other aerosols, including sea salt, were\nprescribed at their default, present-day levels and not altered in the sensitivity\nsimulations. We also added supplementary explanation about the aerosol setup in the\nrevised manuscript and cited ithere.\nTo isolate the impact of Middle Eastern dust (10° S–30° N, 35° E–70° E) on\ninterannual IOD variability, we drove CESM with observed summertime dust mass\ndensity over the Middle East for 1980–2020 and diagnosed the resulting IOD index.\nSpecifically, the annual-mean dust loading in CESM was rescaled each year using the\nregion’s summertime station dust frequency observations, generating a transient\ndust-mass time series that faithfully captures observed variability. Other aerosols\nwere prescribed at their default, present-day levels and not altered in the sensitivity\nsimulations.\n14. Lines #225-227 “These changes significantly affect cloud dynamics, driving a\ndecrease in low-level cloud cover and an increase in high-level clouds over the\nwestern TIO (Fig. 3c, d).” Figure S8 and Figure 3 show otherwise. I see an enhanced\nlowcloud fraction from the CSEM runs over theWesternTIO. Pleaseexplain.\nReply: We thank the reviewer for this thoughtful observation, which has allowed us\nto improve the clarity of our figure. We acknowledge that the colormap in the original\nFigures 3 and S8 (now Figure S12) could have been more intuitively designed. To\nclarify, in these figures, the brown shades indeed indicate a decrease in cloud fraction,\nwhile the blue shades indicate an increase. The western TIO region is predominantly\nbrown, supporting our conclusion of a decreased low-cloud fraction there. We thus\nadded additional explanation in the revised manuscript to avoid misunderstanding and\ncited it here forconvenience.\nThese changes significantly affect cloud dynamics, driving a decrease in low-level\ncloud cover (blue shades) and an increase in high-level clouds (brown shades) over\nthewestern TIO(Fig. 3c, d).\n15. Lines #227-230: This needed a detailed investigation of the cloud feedback;\nthe authors should cite past work for this statement here: “The transformation of cloud\nfields further modifies the radiative budget by increasing both longwave and\nshortwave radiation, thereby amplifying the warming signal over the western TIO in\nsynergy withthe direct radiative effects ofreduced dust.”\nReply: We thank the reviewer for this suggestion. We have revised the manuscript to\nprovide a more detailed mechanistic explanation of the cloud feedback, supported by\nkey literature.\nThe text now explicitly states that the increase in shortwave radiation results from\nreduced low-level clouds (e.g., stratocumulus), which decreases planetary albedo.\nConversely, the increase in longwave radiation is linked to enhanced high-level cloud\ncover (e.g., cirrus), which amplifies thegreenhouse effect.\nThis explanation is now grounded in established cloud-radiative feedback theory,\nsupported by previous studies. The revised text presents a logically supported process\nrather than an assertion.\nWealso cited therevised texthere forconvenience.\nThe transformation of cloud fields—characterized by a reduction in low-level cloud\nfraction and an increase in high-level cloud fraction—further modifies the radiative\nbudget (Figs. 3a, b). Specifically, the decrease in low clouds reduces the albedo,\nallowing more shortwave radiation to penetrate and warm the ocean surface 37,38.\nConcurrently, the increase in high clouds enhances the atmospheric greenhouse effect\nby absorbing and re-emitting longwave radiation, which inhibits radiative cooling at\nthe surface 37,39. These two effects act in synergy with the direct radiative effect of\nreduced dust loading to amplify the warming signal over the western TIO. This cloud\nfeedback process, particularly the low-cloud albedo feedback, is well-established as a\nkey positivefeedback mechanism modulating TIOvariability.\n16. Lines 232-237: Did the authors run the CESM v1.2 simulation from 1980 to 2020,\norfrom 2010to 2020?\nReply: Sorry for the misunderstanding. We ran the CESMv1.2 simulation from 1980\nto 2020. The regression coefficients shown here were scaled by the magnitude of dust\ndecreasing trend from 2010to 2020intheCESM.\nWealso modifiedthe figure caption to clarify thisand cited ithere for convenience.\nFig. 3. The underlying mechanism accounting for the SMED-IOD teleconnection. The\nregression coefficients of SON meteorological fields onto JJA dust aerosol optical\ndepth (AOD) from 1980 to 2020. These coefficients were scaled by the magnitude of\ndust decreasing from 2010 to 2020 in the CESM. These meteorological fields include\n(a) net shortwave flux at surface, (b) net longwave flux at surface, (c) high cloud\nfraction, and (d) low cloud fraction. The area with white dots passed the significance\ntest ofp ≤ 0.05by Student'st-test.\n17. Line 288 and other places: “Furthermore, analysis of the month-to-month\nevolution of the IOD signal (figures omitted)”. The figure not shown does not look\nideal. Please considerincluding all referenced figures inthesupplementary materials.\nReply: Thanks for your helpful suggestions. We have included all figures in the\nsupplementary materials following your suggestions. We also cited it here for\nconvenience.\nFurthermore, analysis of the month-to-month evolution of the IOD signal (Extended\nData Fig. 2) shows that anomalies peak in both the amplitude and statistical\nsignificance during SON, corroborating the selection of SON as the representative\nseason for IOD-related analysis.\nFig. R8. Correlation coefficients between JJA dust proxies and DMI in different three\nmonths. These dust proxies include station dust frequency (the yellow line,\n1980-2023), MODIS dust optical depth (the orange line, 2003-2023), MISR\nnon-spherical AOD (the black line, 2003-2023), and MERRA-2 dust column mass\ndensity (the green line, 2003-2023). The dashed grey line indicates the position of\nzerocorrelation.\nReferences\nBenedict,J.J.,Pritchard,M.S.,andCollins,W.D.:SensitivityofMJOpropagationtoarobustpositive\nIndianOceandipoleeventinthesuperparameterizedCAM,JOURNALOFADVANCESIN\nMODELINGEARTHSYSTEMS,7,1901-1917,10.1002/2015MS000530,2015.\nBuchard,V.,Randles,C.A.,daSilva,A.M.,Darmenov,A.,Colarco,P.R.,Govindaraju,R.,Ferrare,R.,\nHair,J.,Beyersdorf,A.J.,Ziemba,L.D.,andYu,H.:TheMERRA-2AerosolReanalysis,1980\nOnward.PartII:EvaluationandCaseStudies,JOURNALOFCLIMATE,30,6851-6872,\n10.1175/JCLI-D-16-0613.1,2017.\nChe,Y.H.,Yu,B.F.,Parsons,K.,Desha,C.,andRamezani,M.:Evaluationandcomparisonof\nMERRA-2AODandDAODwithMODISDeepBlueandAERONETdatainAustralia,\nATMOSPHERICENVIRONMENT,277,10.1016/j.atmosenv.2022.119054,2022.\nChen,G.W.,Ling,J.,Zhang,R.W.,Xiao,Z.N.,andLi,C.Y.:TheMJOFromCMIP5toCMIP6:\nPerspectivesFromTrackingMJOPrecipitation,GEOPHYSICALRESEARCHLETTERS,49,\n10.1029/2021GL095241,2022.\nFreund,M.B.,Henley,B.J.,Karoly,D.J.,McGregor,H.V.,Abram,N.J.,andDommenget,D.:Higher\nfrequencyofCentralPacificElNinoeventsinrecentdecadesrelativetopastcenturies,Nature\nGeoscience,12,450-+,10.1038/s41561-019-0353-3,2019.\nKrishnamurthy,L.andKrishnamurthy,V.:DecadalandinterannualvariabilityoftheIndianOceanSST,\nCLIMATEDYNAMICS,46,57-70,10.1007/s00382-015-2568-3,2016.\nLin,R.P.,Zheng,F.,andDong,X.:ENSOFrequencyAsymmetryandthePacificDecadalOscillation\ninObservationsand19CMIP5Models,ADVANCESINATMOSPHERICSCIENCES,35,495-506,\n10.1007/s00376-017-7133-z,2018.\nLiu,G.,Li,J.,andYing,T.:TheshiftofdecadaltrendinMiddleEastdustactivitiesattributedtoNorth\nTropicalAtlanticvariability,ScienceBulletin,68,1439-1446,10.1016/j.scib.2023.05.031,2023.\nXia,W.W.,Wang,Y.,andWang,B.:DecreasingDustOvertheMiddleEastPartlyCausedbyIrrigation\nExpansion,EARTHSFUTURE,10,10.1029/2021EF002252,2022.\nBenedict,J.J.,Pritchard,M.S.,andCollins,W.D.:SensitivityofMJOpropagationtoarobustpositive\nIndianOceandipoleeventinthesuperparameterizedCAM,JOURNALOFADVANCESIN\nMODELINGEARTHSYSTEMS,7,1901-1917,10.1002/2015MS000530,2015.\nChen,G.W.,Ling,J.,Zhang,R.W.,Xiao,Z.N.,andLi,C.Y.:TheMJOFromCMIP5toCMIP6:\nPerspectivesFromTrackingMJOPrecipitation,GEOPHYSICALRESEARCHLETTERS,49,\n10.1029/2021GL095241,2022.\nFreund,M.B.,Henley,B.J.,Karoly,D.J.,McGregor,H.V.,Abram,N.J.,andDommenget,D.:Higher\nfrequencyofCentralPacificElNinoeventsinrecentdecadesrelativetopastcenturies,Nature\nGeoscience,12,450-+,10.1038/s41561-019-0353-3,2019.\nKrishnamurthy,L.andKrishnamurthy,V.:DecadalandinterannualvariabilityoftheIndianOceanSST,\nCLIMATEDYNAMICS,46,57-70,10.1007/s00382-015-2568-3,2016.\nLin,R.P.,Zheng,F.,andDong,X.:ENSOFrequencyAsymmetryandthePacificDecadalOscillation\ninObservationsand19CMIP5Models,ADVANCESINATMOSPHERICSCIENCES,35,495-506,\n10.1007/s00376-017-7133-z,2018.\nLiu,G.,Li,J.,andYing,T.:TheshiftofdecadaltrendinMiddleEastdustactivitiesattributedtoNorth\nTropicalAtlanticvariability,ScienceBulletin,68,1439-1446,10.1016/j.scib.2023.05.031,2023.\nXia,W.W.,Wang,Y.,andWang,B.:DecreasingDustOvertheMiddleEastPartlyCausedbyIrrigation\nExpansion,EARTHSFUTURE,10,10.1029/2021EF002252,2022.\nBenedict,J.J.,Pritchard,M.S.,andCollins,W.D.:SensitivityofMJOpropagationtoarobustpositive\nIndianOceandipoleeventinthesuperparameterizedCAM,JOURNALOFADVANCESIN\nMODELINGEARTHSYSTEMS,7,1901-1917,10.1002/2015MS000530,2015.\nChen,G.W.,Ling,J.,Zhang,R.W.,Xiao,Z.N.,andLi,C.Y.:TheMJOFromCMIP5toCMIP6:\nPerspectivesFromTrackingMJOPrecipitation,GEOPHYSICALRESEARCHLETTERS,49,\n10.1029/2021GL095241,2022.\nKrishnamurthy,L.andKrishnamurthy,V.:DecadalandinterannualvariabilityoftheIndianOceanSST,\nCLIMATEDYNAMICS,46,57-70,10.1007/s00382-015-2568-3,2016.\nLiu,G.,Li,J.,andYing,T.:TheshiftofdecadaltrendinMiddleEastdustactivitiesattributedtoNorth\nTropicalAtlanticvariability,ScienceBulletin,68,1439-1446,10.1016/j.scib.2023.05.031,2023.\nXia,W.W.,Wang,Y.,andWang,B.:DecreasingDustOvertheMiddleEastPartlyCausedbyIrrigation\nExpansion,EARTHSFUTURE,10,10.1029/2021EF002252,2022.\nBenedict,J.J.,Pritchard,M.S.,andCollins,W.D.:SensitivityofMJOpropagationtoarobustpositive\nIndianOceandipoleeventinthesuperparameterizedCAM,JOURNALOFADVANCESIN\nMODELINGEARTHSYSTEMS,7,1901-1917,10.1002/2015MS000530,2015.\nChen,G.W.,Ling,J.,Zhang,R.W.,Xiao,Z.N.,andLi,C.Y.:TheMJOFromCMIP5toCMIP6:\nPerspectivesFromTrackingMJOPrecipitation,GEOPHYSICALRESEARCHLETTERS,49,\n10.1029/2021GL095241,2022.\nKrishnamurthy,L.andKrishnamurthy,V.:DecadalandinterannualvariabilityoftheIndianOceanSST,\nCLIMATEDYNAMICS,46,57-70,10.1007/s00382-015-2568-3,2016.\nBenedict,J.J.,Pritchard,M.S.,andCollins,W.D.:SensitivityofMJOpropagationtoarobustpositive\nIndianOceandipoleeventinthesuperparameterizedCAM,JOURNALOFADVANCESIN\nMODELINGEARTHSYSTEMS,7,1901-1917,10.1002/2015MS000530,2015.\nChen,G.W.,Ling,J.,Zhang,R.W.,Xiao,Z.N.,andLi,C.Y.:TheMJOFromCMIP5toCMIP6:\nPerspectivesFromTrackingMJOPrecipitation,GEOPHYSICALRESEARCHLETTERS,49,\n10.1029/2021GL095241,2022.\nResponsetothereviewof “MiddleEastDustasaDominantExternalDriverofthe\nIndianOceanDipole” (NCOMMS-25-75253-T).\nResponsestoReview02’scomments\nSummary:\nThis study examined how mineral dust aerosols emitted from the Middle East a7ect\nthe Indian Ocean Dipole (IOD) using both observational data and the CESM model.\nWhile previous studies have examined how anthropogenic aerosols effect IOD, how\nMiddle Eastern dust aerosols affect the IOD is largely unclear. The study addresses a\nvery important question in the region, and the topic is very novel and inspiring. The\noverall analysis is quite comprehensive, however, some details in model setting, the\nfeedback link from subtropical dust loading totropical SSTs, and the magnitude ofthe\nradiative forcing need further clarification. Adding some discussion about the\nuncertainties indata and methodology also would strengthen thepaper.\nReply: We are grateful to the reviewer for his/her insightful and very valuable\nsuggestions, which have significantly contributed to the enhancement of our\nmanuscript. We have carefully addressed the comments and made the necessary\nrevisions, hoping that the updated version meets the journal's standards. Our\nresponses to each of the comments and suggestions are as follows. The referee’s\noriginal comments are shown in blue. Our replies are shown in black. The\ncorresponding changes in themanuscript are shown in Italicblack.\nMajorcomments\n1. Usually, the lifetime of dust aerosols in the atmosphere, especially in the lower\ntroposphere, is fairly short, from several days to a couple of weeks. The statement\n“multi-month atmospheric residence time of dust aerosols” (line 60) needs more\nevidence to support. I think it is important to understand whether the strong\nconnection between JJA Middle Eastern dust and SON IOD is primarily due to an\nusually long residence time of summertime Middle Eastern dust, or instead a delayed\nresponse through ocean-atmosphere feedback. It would also be helpful to clarify\nwhether MiddleEastern dust in SONhas any impact ontheIOD inSON.\nReply:Thank you foryour valuablecomments and insightful suggestions.\nWe agree that the near-surface residence time of dust aerosols is relatively short. The\noriginal statement referring to a \"multi-month atmospheric residence time\" has been\nrevised for greater precision, and supporting references have been added. The revised\ntext now emphasizes that under specific conditions—such as when dust is lifted by\nstrong upward air currents into the stratosphere or when fine-grained particles\nundergo long-range transport—certain fractions of dust aerosols may exhibit extended\nresidence times (e.g., exceeding one month). We have cited relevant studies on\ntransatlantic transport of Saharan dust and trans-Pacific transport of Asian dust (Kim\net al., 2019; Prospero et al., 2021; Yu et al., 2019a; Yu et al., 2019b), illustrating that\nunder favorable atmospheric circulation conditions, fine dust particles can remain in\ntheatmosphere from several weeks tomonths.\nMoreover, your distinction between the two possible mechanisms—long residence\ntime versus delayed ocean–atmosphere feedback—is indeed critical. The\nocean–atmosphere interaction is likely the dominant mechanism. Specifically, Middle\nEastern dust in summer may influence the radiative balance and thus affect sea\nsurface temperature patterns over the Arabian Sea and western Indian Ocean by the\ndelayed response through ocean-atmosphere feedback. These effects may\nsubsequently trigger an IOD event in the following season through air–sea\ninteractions.\nAdditionally, Middle Eastern dust in SON has limited direct influence on the IOD\nduring the same season as validated by various dust proxies (Fig. R1). This may be\nattributed to the generally weaker dust activity in the Middle East during autumn\n(Extended Data Fig. 1c in the revised manuscript), as well as the fact that the IOD is\nalready in its mature phase by SON, during which coupled ocean–atmosphere\nprocesses dominate its evolution and external forcings such as dust play a relatively\nminorrole.\nFig. R1. The regression coefficients of detrended SON SST on the detrended\nSON dust activity proxies averaged over the Middle East. These dust proxies\ninclude (a) MODIS dust optical depth (2003-2023), (b) MERRA-2 dust column mass\ndensity (2003-2023), (c) station dust frequency (1980-2023), and (d) IASI dust optical\ndepth (2008-2021). Note that the regression coefficients in all figures are taken as\ntheir negative values corresponding to dust reduction.The area with white dots passed\nthesignificance test ofp ≤ 0.05byStudent's t-test.\nWealso incorporated thesediscussions here for convenience.\nThis seasonal offset is physically plausible given the multi-month atmospheric\nresidence time of dust aerosols under specific conditions (strong upward air currents\nandlong-range transportfor fine-grained particles)andthe delayed responsethrough\nocean-atmosphere feedback 19-22. Additionally, the dust activity over Middle East in\nSON has limited direct influence on the IOD, as weaker autumn dust activity\ncoincides with the IOD's mature phase (Extended Data Fig. 1c), during which\ncoupled ocean-atmosphere processes dominate and the role of external forcings like\ndust isdiminished.\n2. The averaging area for the summertime Middle East Dust (SMED) index was not\nvery clear (e.g., in Fig, 1c). As shown in Figs. 1a and 1b, the domain only covers\nsouthern Middle East, and it also includes the Horn of Africa, which is often not\ngeographically considered part the Middle East. In the Methods section, there is a\ndefinition of Middle East dust (“10° S–30° N, 35° E–70° E”, line 522). But It’s\nunclear if this the averaging region used to calculate all the time series of SMED.And\nalso, the domain is a quite large, while most of the Middle East dust are located to the\nnorth of 10° N (supplementary Fig. 1).Are the results sensitive to the averaging area\nofSMED?Would thecorrelations change muchif onlyaveraging over theregion with\nhigh dust loading, e.g., tothe north of10° N?\nReply: We sincerely thank the reviewer for raising this critical point regarding the\nspatial definition of our Summertime Middle East Dust (SMED) index. The selected\ndomain (10°S–30°N, 35°E–70°E) was intentionally designed as an \"effective\ninfluence region\" to capture dust aerosol loading over the Indian Ocean that is\nrelevant to long-range transport and ocean-atmosphere interactions, rather than to\nstrictly adhere to geographical boundaries. This domain aims to include dust aerosols\nover the Arabian Sea and northwestern Indian Ocean—which are positioned to\ndirectly influence local sea surface temperatures via radiative effects—while\nexcluding dust from northern source regions that predominantly undergoes\nshort-range transport and may has limited reach over theocean.\nAdditionally, we have also performed a sensitivity analysis using a domain north of\n10°N (Fig. R2) and found that this domain selection also exhibits a very high\ncorrelation with the original (Extended Data Fig. 5 in the revised manuscript), and all\nkey correlations with the IOD remain nearly identical. We also plot two time series,\none derived from the region north of 10°N (10°N–30°N, 35°E–70°E) and the other\nfrom the region north of 10°S (10°S–30°N, 35°E–70°E). Both time series exhibit\nsimilar temporal variability across the entire study period (Fig. R3). The\nabove-mentioned results further confirm that our results are robust to the specific\nlatitudinal boundary. We have clarified this physical rationale and added a note on\nrobustness in therevised manuscript.\nFig. R2. Theregression coefficients ofdetrended SONSSTonthedetrended JJAdust\nactivity proxies averaged overthe MiddleEast (averaged overthe 10°N~30°N,\n35°E~70°E)byregressing out theimpacts of ENSO.These dust proxies include(A)\nMODIS dust optical depth(2003-2023), (B) MERRA-2 dust column mass density\n(2003-2023), (C)MISR non-sphericalAOD (2003-2023), and (D) stationdust\nfrequency (1980-2023). Notethat thecorrelation coefficients inall figures are taken\nas theirnegative values corresponding to dust reduction.Thearea withwhite dots\npassed the significance testof p ≤ 0.05byStudent's t-test. SSTand dust proxies\nhave been regressed outbyENSO considering ENSO’sconsiderate global impact.\nFig. R3. Timeseries of dustproxiesanalyzed in thisstudy.Solid linesdenote the\nstandardized June–July–August (JJA)stationdust frequency from 1980–2023,with\nblack and darkred lines representing theregions (10°S–30°N, 35°E–70°E)and\n(10°N–30°N, 35°E–70°E), respectively.Dashed linesindicate theJJAMISR\nnon-spherical aerosol optical depth from 2003–2023,corresponding tothesame two\ngeographicdomains.\n3. The radiative forcing of the dust aerosols are shown in both the coupled CESM\nsimulations and atmosphere-only model (CAM) runs. However, the location of\nradiative forcing directly from the dust aerosols. With reduced Middle Eastern dust\naerosols, one would expect to see enhanced shortwave (SW) fluxes (i.e., warming)\nat the surface extending from theArabian Peninsula southward toward theArabian Sea.\nHowever, this pattern is not clearly shown in the coupled simulation (Fig. S8c)--there\nis a cooling around 12 °N, while the warming single is located further south, round\n6 °N-6 °S, where the climatological dust loading is low. Is this due to a\nteleconnection?\nReply: Thank you for this insightful observation regarding the spatial pattern of\nsurface shortwave radiation response in our coupled model experiments. You have\ncorrectly identified a key nuance in how dust aerosol impacts manifest in coupled\nversus atmosphere-only simulations.\nYou rightly noted that reduced Middle Eastern dust should directly enhance surface\nshortwave fluxes over dust-laden regions from the Arabian Peninsula to the Arabian\nSea. This expected response is clearly present in our atmosphere-only (CAM)\nexperiments, as shown in Extended Data Fig. 14a.The different pattern in the coupled\n(CESM) simulation—with cooling near 12°N and warming displaced farther\nsouth—is indeed attributable to ocean-atmosphere coupling and the associated\nteleconnections as you suggested. The initial radiative perturbation triggers a specific\nchain of adjustments:\n(1) Circulation Adjustment: The surface warming over the western Tropical Indian\nOcean (TIO) enhances convection and establishes a low-pressure anomaly (Fig. 2c).\nThis sets up a basin-scale pressure gradient, driving an anomalous clockwise vertical\ncirculation and generating prevailing anomalous easterly winds at the surface over the\ncentralTIO, oppositetotheclimatological westerlies (Fig. 2c-d).\n(2) Mechanism for the Displaced Warming: These easterly anomalies suppress the\neastward advection of warm water and, through Bjerknes and wind-evaporation-SST\n(WES) feedbacks, help sustain and confine the core of the warming signal further\nsouth, near 6°N–6°S.\n(3) Mechanism for the Cooling near 12°N: Crucially, in the region near 12°N\n(Arabian Sea), the circulation adjustment leads to a strengthening of the prevailing\nclimatological winds (Fig. 2c, vectors). This increase in surface wind speed enhances\nlocal evaporation, which produces a net surface cooling via the WES feedback. This\nwind-driven cooling effectively counteracts and overshadows the direct radiative\nwarming from dust reduction, resulting in the net cooling anomaly observed in\nExtended Data Fig. 14c.\nIn summary, the coupling-induced atmospheric circulation adjustment—particularly\nthe wind speed changes and the subsequent WES feedback—redistributes the thermal\nresponse, explaining both the southward shift of the warming and the localized\ncooling around 12°N.\nTherefore, we have expanded the discussion in the revised manuscript to explicitly\naddress thismechanism.Wealso cited thediscussion here for convenience.\nMoreover, the atmospheric-only simulation exhibits a radiative response that is\nspatially coherent with the dust aerosol distribution (Extended Data Fig. 14), while\nthe coupled simulation displays additional spatial heterogeneity due to oceanic\nadjustments (Extended Data Fig. 11). This redistribution likely stems from\nocean-atmosphere coupling, wherein the initial radiative perturbation triggers\natmospheric circulation adjustments andsubsequent wind-evaporation-SSTfeedbacks,\ncreating secondary radiative perturbations that partially obscure the direct\ndust–radiation signal, which is ultimately manifested as a teleconnected response.\nNevertheless, both experiments consistently support the proposed physical framework\nlinkingMiddle Eastern dust forcing to IODvariability.\n4. In the CAM simulations, enhanced SW fluxes do appear from southern Arabian\nPeninsula towards the Arabian Sea (Figs. S13a, S10a), but mainly over the northern\nand easternArabian Sea.And the changes in SW flux over the western tropical Indian\nOcean are relatively weak. Does this imply a teleconnection that allow the local\nradiative impact of dust aerosols over theArabian Peninsulaand northernArabian Sea\ntopropagate to thetropical Indian Ocean?\nReply: Thank you for this perceptive question, which gets to the heart of the\ndynamical mechanisms at play. You are correct in observing that the enhanced SW\nfluxes in the CAM simulations are concentrated over the northern and easternArabian\nSea, with a weaker direct signal over the western tropical Indian Ocean. This spatial\nstructure indeed implies that the local radiative forcing from reduced dust does not\nremain confined, but rather initiates an atmospheric teleconnection that propagates its\ninfluence downstream.\nThemechanism isa three-step process:\n(1) Local Radiative Forcing & Atmospheric Adjustment: Reduced dust over the\nArabian Peninsula/northern Arabian Sea increases surface SW absorption, creating a\nstrong vertical heating gradient (surface warming, relative upper-level cooling;\nExtended Data Figs.11, 13a).Thismodifies local stabilityand circulation.\n(2) Atmospheric Teleconnection & Downstream Cloud Response: The adjusted\ncirculation and zonal cell (Fig. 2d) act as a teleconnection bridge, transporting the\nperturbation's influence to the western TIO. This alters the vertical thermodynamic\nprofile, driving a characteristic cloud reorganization there: decreased low-level and\nincreased high-level cloud cover (Fig. 3c, d). In the atmosphere-only (CAM) run, this\nteleconnected response is constrained by fixed SSTs, explaining the weaker direct\nmaritimesignal.\n(3) Amplification via Ocean-Atmosphere Coupling: This step explains the stronger\noceanic signal in the coupled model. The teleconnected cloud changes (Step 2) now\ninteract with the ocean. Reduced low cloud cover increases surface shortwave\nradiation, leading to SST warming. This warming then activates powerful positive\nfeedback loops: the cloud–SST feedback further suppressing low clouds (Lin et al.,\n2025; Wu et al., 2025; Wyant et al., 1997), the Bjerknes feedback and the\nwind‑evaporation‑SST (WES) feedback. These coupled feedbacks dramatically\namplify and extend theinitial atmospheric perturbation.\nTherefore, the CAM pattern confirms the dust reduction initiates an atmospheric\nteleconnection. The coupling with the ocean is essential to transform this initial\nadjustment into the stronger, basin-scale ocean-atmosphere feedbacks that effectively\ndrive the IOD development, as detailed in our manuscript. We have expanded the\ndiscussion in the revised manuscript to explicitly address this mechanism. We also\ncited thediscussion here for convenience.\nNotably, the spatial pattern of the SW flux response in the CAM simulations, with its\nmaximum over the northern and eastern Arabian Sea rather than uniformly across the\nbasin (Extended Data Fig. 17a &c), suggests that the radiative impact of dust\nreduction initiates an atmospheric teleconnection. The initial perturbation mainly\nover the land likely alters regional atmospheric heating and circulation patterns 46-48,\nwhich in turn redistributes the energy downstream, setting the stage for the more\nextensive and evident ocean-atmosphere signals and feedbacks that propagating to\ntheTIOin thecoupled model.\n5. Regarding the mechanisms of dust radiative forcing, it would be more convincing\nto include figures to show radiative fluxes at the TOA and also vertical heating\nprofiles intheregions withhigh dust loading.\nReply: Weappreciate the reviewer's insightful comment regarding the mechanisms of\ndust radiative forcing. In response, we have added the new figure (Fig. R4 and Fig.\nR5 here) in therevised manuscript (Extended Data Fig. 13and 17) as suggested.\nThese new results clearly illustrate the radiative forcing of reduced dust at the top of\natmosphere and its heating rate, which aligns well with the temperature response in\ntherevised manuscript (Extended Data Fig. 15).\nFig. R4. Dustvertical profileand its corresponding heating rate forreduced dust.\n(a) The vertical profile of dust mixing ratio and (b) heating rate over the transported\narea (10°S~10°N, 35°E~70°E) in the model. (c) The responses of SON 700 hPa\nheating rate to reduced Middle East dust forcing in the CESM. The area with white\ndots passed thesignificance test ofp ≤ 0.05byStudent's t-test.\nFig. R5. The responses of SON radiation fluxes to reduced Middle East dust forcing\nin the CAM. The radiation fluxes include (a) net shortwave flux at surface, (b) net\nlongwave flux at surface, and the net shortwave flux at the top of atmosphere (TOA).\nThearea with whitedotspassed thesignificance test ofp ≤ 0.05byStudent's t-test.\nWealso added thecorresponding discussionand cited it here.\nOnce transported, these aerosols exert a strong radiative forcing by absorbing solar\nradiation, which heats the upper troposphere, while simultaneously reducing the\namount of shortwave radiation reaching the ocean surface. The dust-induced heating\nrate profile aligns with the simulated vertical distribution of dust extinction, which is\nprimarily confined within the troposphere. This vertical coherence confirms that the\nradiative heating maxima correspond well with the dust layer's location, reinforcing\nthe direct thermodynamic impact of dust radiative absorption on the atmospheric\ncolumn. (Extended Data Fig.13).\n6. Some aspects of the model simulation setup are not very clear. In the CESM\ncoupled simulation from 1980 to 2020, besides SMED, what other forcings are\nincluded? Are anthropogenic aerosols and greenhouse gases included in the model?\nDo they vary over time?What’s thespin-up timefor thecoupled run?\nReply: We thank you for raising these important questions regarding our model\nconfiguration, which we are pleased to clarify. In our CESM1.2 coupled simulations,\nonly the emissions of dust aerosols were modified between the control and sensitivity\nexperiments. All other external forcings, including anthropogenic aerosols (e.g.,\nsulfate, black carbon, organic carbon) and greenhouse gases, were included in the\nmodel and prescribed at their default, present-day levels. These non-dust forcings\nwere held constant throughout the 1980–2020 simulation period and did not vary\ninterannually.\nThe coupled model was initialized from a pre-industrial control run and subsequently\nspun up for five years prior to the beginning of our formal experimental period (1980)\n(Xie et al., 2018). This spin-up duration ensured that the model's ocean and\natmosphere components reached astableequilibrium under theprescribed forcing.\nWe have added a detailed description of the experimental design, including the\naerosol and greenhouse gas forcing specifications and the spin-up procedure to the\nrevised manuscript for greater clarity.\nTo isolate the impact of Middle Eastern dust (10° S–30° N, 35° E–70° E) on\ninterannual IOD variability, we drove CESM with observed summertime dust mixing\nratio over the Middle East for 1980–2020 and diagnosed the resulting IOD index.\nSpecifically, the summertime-mean dust loading in CESM was rescaled each year\nusing the region’s summertime station dust frequency observations, generating a\ntransient dust-mass time series that faithfully captures observed variability. Other\naerosols were prescribed at their default, present-day levels and not altered in the\nsensitivity simulations. Five ensemble members—generated by perturbing initial\ntemperature fields over land and ocean—were averaged to suppress internal climate\nnoise. The magnitude of the temperature perturbation is 1×10-14 K 93. The coupled\nmodel was also initialized from a pre-industrial control run and subsequently spun up\nfor five years under present-day forcing conditions prior to the beginning of our\nformal experimental period (1980). This spin-up duration ensured the model's ocean\nand atmosphere components reached a stable equilibrium under the prescribed\nforcing 94. All atmospheric variables and the IOD index were then regressed onto\nregionaldust loadingto quantifythedust-driven changes from1980to 2020.\nMinorcomments\n7. It is mentioned that dust aerosols are prescribed in the model, however, it is not\nclear which dust variable was perturbed. Was it dustAOD, dust mass concentration at\neach pressure level, or emission fluxes? And is dust prescribed only the Middle East\n(leaving the rest of the world dust-free) or globally? It states that “we drove CESM\nwith observed summertime dust mass density “(line 523), but it is unclear which\nobservational dataset was used, and whether the forcing is applied only during the\nsummer months or throughout the year.Are both dry and wet deposition included in\nthemodel?\nReply: We sincerely thank the reviewer for raising these important points regarding\nthe dust aerosol prescription in our model. We have carefully revised the manuscript\ntoclarify the following aspects, as suggested.\n(1)Which dust variable was perturbed?\nIn our simulations, the dust aerosol forcing was implemented by perturbing the dust\nmixing ratio—that is, the three-dimensional field of dust mass concentration across\natmospheric pressure levels. This approach allows us to consistently represent the\nspatial and vertical distribution of dust aerosols. We have now explicitly stated this in\ntherevised textand cited ithere for convenience.\nIn our simulations, the dust aerosol forcing was applied by perturbing the dust mixing\nratio—the3Dmass concentration field.\n(2)Spatial andtemporal scope ofdust prescription\nThe dust perturbation was applied only over the Middle East region (10°S~30°N,\n35°E~70°E), as defined in the study, and only during the summer months\n(June–August). Outside this region and season, dust concentrations remain at their\nmodel climatological levels. The model is not dust-free elsewhere; rather, it uses the\ndefault climatological dust fields in other regions and seasons. We have clarified this\ninthe revised manuscript and cited ithere.\nNotably, the dust perturbation was applied exclusively over the Middle East during\nJJA. Beyond this spatiotemporal domain, the model uses its default climatological\ndust fields.\n(3)Observational dataset and scaling method\nThe dust mixing ratio used to constrain the model is based on the MERRA-2\nreanalysis dataset. To ensure consistency with observed dust aerosol optical depth\n(AOD), we scaled the default dust fields so that the simulated dustAOD matches the\nMERRA-2 dust AOD distribution over the study region. This scaling is applied only\nin summer and only over the Middle East. We have emphasized this in the revised\ntext.\nIn our simulations, the dust aerosol forcing was applied by perturbing the dust mixing\nratio—the 3D mass concentration field. However, the default dust mixing ratio of\nCESM is not sufficiently accurate to reproduce the spatiotemporal distribution of dust\nintheMiddle East because of itsinaccurate dust emission 50,89-91. Toaddress this issue,\nwe first conduct a control run using the default dust concentration file. Then, we\ndivide the monthly dust AOD result of MERRA-2 by the monthly dust AOD in the\ncontrol run to obtain monthly proportionality coefficients. By multiplying these\nmonthly coefficients with the original dust mixing ratio, we can better reproduce the\nspatiotemporal distribution of Middle Eastern dust in the model. Results indicate that\nthe model successfully reproduces the spatial pattern and intensity of the dust in the\nMiddle East (Extended Data Fig. 10a). Notably, the dust perturbation was applied\nexclusively over the Middle East during JJA. Beyond this spatiotemporal domain, the\nmodel uses its default climatologicaldust fields.\n(4)Inclusion ofdepositionprocesses\nBoth dry and wet deposition processes are fully represented in the model, as part of\nthe standard aerosol module in CESM. We have added a statement in the manuscript\ntoexplicitly confirm thisand cited ithere.\nBoth dry and wet deposition processes are fully represented in the model, as part of\nthestandardaerosolmodule inCESM89.\nWe hope these clarifications adequately address the reviewer’s concerns. Thank you\nonce again for theconstructivefeedback.\n8. For the sensitivity test with reduced dust loading, was the forcing applied only in\nsummertimeor all year round? It would be helpful to include a table summarizing key\nfeatures of each simulation.\nReply: Wethank you for this pertinent question regarding the experimental design. In\nthe dust reduction sensitivity experiment, the forcing (i.e., the reduction in dust\naerosol emissions) was applied exclusively during the summer (JJA) season. During\nthe rest of the year, dust emissions, along with all other model forcings, were identical\nto those in the control run. This setup was intentionally chosen to isolate the specific\nclimatic impact of summertime Middle Eastern dust variability. We also cited the\nadded explanation intherevised manuscript and cited ithere.\nThe final summertime forcing field is derived by subtracting this scaled perturbation\nfield from the 1980-2020 climatological mean, ensuring compatibility with CAM4’s\ninput requirements. Additionally, during the rest of the year, dust emissions, along\nwithallother model forcings, wereidentical tothose in thecontrolrun.\nFurthermore, we fully agree with your suggestion that a summary table would\nenhance the clarity of our experimental setup. In response, we have included a new\ntable (Table R1, namely Extended Data Table 1) in the revised manuscript. This table\nsystematically summarizes the key features of all simulations conducted in this study,\nincluding the specific forcing modifications, their temporal application, and the\nintegration periods for both theCESMand CAMexperiments.\nTableS1 Key features ofeach simulation.\nSimulation\nComponent Dust Forcing\nPeriod\nThe observed summertimedustmixing ratio overthe\n1980-2020\nMiddleEast for 1980–2020\n1980-2020 climatological mean dust mixingratio\nCESM\nreduced dustmixing ratio by~20%change\nincreased dustmixingratioby~20% change Five years\n1980-2020 climatological mean dust mixingratio\nCAM\nreduced dustmixing ratioby~20% change\n9. It is recommended to include some discussion about the uncertainties of the study.\nFor instance, in model simulations, dust content is prescribed, so any feedback\nfrom atmospheric circulation to the dust loading is not considered. In terms of\nstation data, what’s the uncertainty in measurements? Do the station location and\nnumbers change from 1980 to 2020? Does the uneven spatial distribution of the\nstations affect the results?\nReply: The reviewer for this valuable suggestion. We have added a\ndiscussion on the uncertainties in the revised manuscript. A key model uncertainty is\nthat prescribing dust aerosols neglects feedbacks from atmospheric circulation onto\ndust emissions and transport. For the station data, we only selected stations with\nlong-term records, so the location and number do not change from 1980 to 2020.\nWhile we used stations with long-term records to mitigate temporal inconsistencies,\nthe uneven coverage remains a limitation, which we acknowledge may influence the\nrepresentativeness of the observed regional trends. This discussion provides a more\nbalanced interpretation ofourresults.\nWe also cited the discussion about the uncertainties regarding the model and station\ndatahere.\nThis study has limitations regarding the model and station dust observations.\nSpecifically, the model experiments prescribe dust and thus neglect feedbacks of\natmospheric circulation on dust emissions. Additionally, the observational trends\ncould be influenced by the uneven spatial distribution of meteorological stations over\ntheanalysis period.\n10. Line 64,it may behelpful to showthe response thestrongestin SON.\nReply: We thank the reviewer for this valuable suggestion. We agree that explicitly\ndemonstrating the peak season of the SST response strengthens our argument.\nFollowing the reviewer's advice, we have conducted the additional analysis and\ncreated a figure that clearly shows the seasonal evolution of the SST response,\nconfirming that it is indeed strongest in the September-October-November (SON)\nseason.\nThis new figure has been included in the Supplementary Information as Fig. R6 (or\nExtended Data Fig. 3 in the revised manuscript), and we have added a citation to it in\ntherevised manuscript.Wealso cited ithere for convenience.\nModel simulations also confirm that SON is the season when the IOD responds most\nstrongly to reductions in dust forcing, thereby identifying SON as the optimal season\nfor analysis(Extended Data Fig.3).\nWe believe this addition significantly improves the clarity of our manuscript and are\ngrateful tothe reviewerfor theconstructive input.\nFig. R6. The SST responses in different seasons including (a) MAM, (b) JJA, (c)\nSON, and (d) DJF. The area with white dots passed the significance test of p ≤\n0.05byStudent's t-test.\n11. Lines 78-79, it is not clear what averaging area is used for the MISR\nnon-sphericalAOD.\nReply: Thanks for pointing it out. The averaging area for the MISR non-spherical\nAOD is the same as station dust record (averaged over the 10°S~30°N, 35°E~70°E).\nWehave added thissupplementary explanation inthe figure caption.\n12. Line 158, this seems to contradict the statement in lines 134-135. Could you\nplease clarify what thesystematicbiases are?\nReply: We sincerely thank the reviewer for this insightful comment, which highlights\na need for greater clarity in our discussion. The two statements address\ncomplementary rather than contradictory aspects of the model's performance, and we\nhave revised the text toeliminateany potential formisunderstanding.\nThe statement in Lines 134-135 emphasizes the model's success in reproducing the\nspatial pattern of the dust-IOD relationship—namely, the large-scale dipole structure\nover the tropical Indian Ocean. This demonstrates that the fundamental physical\nlinkage is captured bythemodel.\nIn contrast, the statement in Line 158 pertains to the attenuation of the temporal\ncorrelation coefficient (i.e., the simulated correlation is systematically lower than\nobserved). One key reason for this attenuation is the model's inherent systematic\nbiases in its representation of IOD dynamics. These include well-documented biases\ninclimate models, such as an overly strong thermocline feedback in theeastern Indian\nOcean, an exaggerated seasonal cycle, or an inaccurate mean state (Yao et al., 2016;\nXiao et al., 2022). Such biases do not necessarily prevent the model from simulating\nthe correct spatial pattern of the IOD's SST anomalies, but they can distort the\namplitude and precise timing of its evolution. This distortion in the dynamics\nintroduces \"noise\" into the simulated year-to-year relationship between dust (SMED)\nand theDipole Mode Index (DMI), thereby collectively amplifying thedeviation from\nobservations inthestrength of thetemporal correlation.\nTo clarify this, we have revised the relevant sentence in the manuscript and cited it\nhere.\nNevertheless, the simulated correlation coefficient remains systematically lower than\nthat estimated by observation. This attenuation is attributable to (i) the reliance of the\nmodel on station-based dust frequency to scale column-integrated dust-mass density,\nwhich introduces additional sampling uncertainty, and (ii) systematic biases in the\nmodel’s representation of IOD dynamics (e.g., in the thermocline feedback and\nseasonal phase locking), which distort the precise evolution of the mode and\ncollectively amplify the attenuation of the simulated correlation relative to\nobservations.\n13. Line 175, there is also a weak increase in the mixed layer depth over the eastern\ntropical Indian Ocean from 100°E to 108 °E (Fig. 2b). What could be the cause of\nthis?\nReply: We thank the reviewer for this observation. The weak increase in mixed layer\ndepth over the eastern tropical Indian Ocean is likely a result of internal climate\nvariability or model uncertainty in responding to the prescribed forcing. We have\nacknowledged this point inthe revised manuscript as part of the broader discussion on\ntheregional nuances ofthemodel response.Wealso cited it here forconvenience.\nIt is noteworthy that a weak increase in MLD is also simulated over the eastern\ntropical Indian Ocean (Fig. 2b), which may be attributed to internal climate\nvariabilityrather than adirect responsetodust forcing.\n14. Line 219, regression analysis is less straightforward than performing another\nsimulation without the variability of Middle Eastern dust and comparing the\ndifferences. Conducting a linear regression assumes that the relationships are linear,\nwhich may not necessarily be true.\nReply: Thank you for this important methodological point. We agree with the\nreviewer that a carefully designed sensitivity experiment provides a more direct and\nrobust demonstration of causality than regression analysis alone. In fact, we have\nconducted two additional sensitivity experiments as detailed in Extended DataTable 1:\none with increased Middle Eastern dust and another with decreased dust over this\nregion.\nIn our analysis, the regression analysis is mainly applied to the observational and\nreanalysis data to identify statistical relationships. In contrast, the changes shown in\nthe model experiments (with increased and decreased dust; Extended Data Figs. 9 &\n10)represent thedirect model response to theprescribed dust forcing.\nThe coherent alignment—in both pattern and sign—between this direct model\nresponse and the statistical relationships identified from observations strengthens the\nrobustness of the dust-IOD link, moving the evidence beyond the potential constraints\nofapurely linearstatistical framework.\n15. Line 220, “Increased solar radiation penetrates to the surface…” , this is not\nclearly shownin Fig. S8c, especially over theregion with reduced dust. Please clarify.\nReply: Thanks for pointing it out. You are correct that the region of increased net\nshortwave radiation at the surface in the fully coupled model experiment (Fig. S8c)\ndoes not perfectly align with the primary dust reduction area. This occurs precisely\nbecause the CESM is a fully coupled climate model. The initial radiative perturbation\nfrom dust reduction triggers a chain of ocean-atmosphere interactions, including\nadjustments in atmospheric circulation and wind-evaporation-SST feedbacks. These\nprocesses redistribute the energy downstream, generating secondary radiative\nanomalies that can partially mask the direct local signal. This energy redistribution is\na fundamental feature of the coupled climate system and is the key mechanism that\nestablishes theremote teleconnection pattern wedescribe.\nIn contrast, as the reviewer might anticipate, a parallel experiment conducted with an\natmosphere-only model (CAM, Extended Data Fig. 14c) does show a clear and\nlocalized increase in net shortwave radiation confined to the dust source region, as it\nlacks these ocean-mediated feedbacks. The comparison between these two\nexperiments robustly demonstrates how the direct radiative forcing from dust evolves\nintoa broader teleconnection within acoupled framework.\nWe have clarified this crucial point in the revised manuscript to better guide the\nreader's interpretation ofExtended Data Fig.11c.Wealso cited theclarification here.\nMoreover, the atmospheric-only simulation exhibits a radiative response that is\nspatially coherent with the dust aerosol distribution (Extended Data Fig. 14), while\nthe coupled simulation displays additional spatial heterogeneity due to oceanic\nadjustments (Extended Data Fig. 11). This redistribution likely stems from\nocean-atmosphere coupling, wherein the initial radiative perturbation triggers\natmospheric circulation adjustments andsubsequent wind-evaporation-SSTfeedbacks,\ncreating secondary radiative perturbations that partially obscure the direct\ndust–radiation signal, which is ultimately manifested as a teleconnected response.\nNevertheless, both experiments consistently support the proposed physical framework\nlinkingMiddle Eastern dust forcing to IODvariability.\n16. Lines 221-225, it's not clear why the coupled results are already available (line\n192) but then the analysis switches to an atmosphere-only model, and then returns to\nthecoupled simulation.\nReply: We thank the reviewer for raising this point regarding our experimental\nworkflow, which we agree requires clear justification. The sequential use of the\ncoupled model (CESM) and the atmosphere-only model (CAM) was a deliberate\ndesign to dissect theunderlying mechanisms.\nThe fully coupled simulation reveals the net effect of dust forcing, including all\nsubsequent ocean-atmosphere feedbacks and teleconnections. We then introduced the\natmosphere-only (CAM) experiments specifically to isolate the direct atmospheric\nresponse to the dust aerosol radiative forcing, devoid of any ocean feedbacks due to\nits use of prescribed SSTs. This approach allows us to cleanly attribute the initial\natmosphericperturbation to thedust forcing itself.\nFinally, returning to the coupled model results allows us to interpret the full, evolved\nclimate response—including the teleconnection patterns—as a consequence of the\ninitialforcing identified inCAM, nowamplified and modulated bycoupled processes.\nWe have clarified this rationale in the revised manuscript to guide the reader through\nouranalytical strategy.Wealso cited it here forconvenience.\nBy employing prescribed SSTs, this atmosphere-only simulation inherently excludes\nocean feedbacks, thus allowing a clear attribution of the atmospheric response to the\ndust forcing.\n17. Line 229-230 “the direct radiative effects of reduced dust”, is it possible to clarify\nthelocation ofthis effect in thefigure?\nReply: Thanks for your suggestion. We have clarified the location of this effect,\nwhich is over thewesternTIO and cited it here.\nThe transformation of cloud fields further modifies the radiative budget by increasing\nboth longwave and shortwave radiation, thereby amplifying the warming signal over\nthe western TIO in synergy with the direct radiative effects of reduced dust (Figs. 3a,\nb).\n18.Line 267, the response of shortwave fluxes is mainly over the land. How does the\nsignal propagate to theTIO?\nReply: Thank you for raising this question.The signal propagates from the land tothe\nTropical Indian Ocean (TIO) primarily through an atmospheric teleconnection. The\nmechanism is athree-step process:\n(1) Local Radiative Forcing & Atmospheric Adjustment: Reduced dust over the\nArabian Peninsula/northern Arabian Sea increases surface SW absorption, creating a\nstrong vertical heating gradient (surface warming, relative upper-level cooling;\nExtended Data Figs.11, 13a).Thismodifies local stabilityand circulation.\n(2) Atmospheric Teleconnection & Downstream Cloud Response: The adjusted\ncirculation and zonal cell (Fig. 2d) act as a teleconnection bridge, transporting the\nperturbation's influence to the western TIO. This alters the vertical thermodynamic\nprofile, driving a characteristic cloud reorganization there: decreased low-level and\nincreased high-level cloud cover (Fig. 3c, d). In the atmosphere-only (CAM) run, this\nteleconnected response is limited by fixed SSTs, explaining the weaker direct\nmaritimesignal.\n(3) Amplification via Ocean-Atmosphere Coupling: This step explains the stronger\noceanic signal in the coupled model. The teleconnected cloud changes (Step 2) now\ninteract with the ocean. Reduced low clouds enhance surface SW, warming the SST.\nThis warming then triggers powerful positive feedbacks: the cloud–SST feedback\nfurther suppressing low clouds (Lin et al., 2025; Wu et al., 2025; Wyant et al., 1997),\nthe Bjerknes feedbacks and wind-evaporation-SST (WES) feedbacks. These coupled\nfeedbacks dramatically amplify and extend theinitial atmosphericperturbation.\nTherefore, the CAM pattern confirms the dust reduction initiates an atmospheric\nteleconnection. The coupling with the ocean is essential to transform this initial\nadjustment into the stronger, basin-scale ocean-atmosphere feedbacks that effectively\ndrivetheIOD development, as detailed inourmanuscript.\nIn summary, the dust-induced radiative heating over the Middle East perturbs the\nregional atmospheric circulation, which in turn triggers a remote response that\ninfluences theTIO.\nWe have systematically elaborated this dynamical pathway in the revised manuscript\nwithin the \" Mechanism: how dust shapes the IOD\" section, detailing the key\nprocesses of (1) local radiative adjustment, (2) atmospheric bridge via zonal cell\ncirculation, and (3) amplification through air-sea feedbacks, and we refer the reader to\nthat discussionhere.\nWe have also clarified this dynamical pathway in the revised manuscript and cited it\nhere.\nThe initial perturbation mainly over the land likely alters regional atmospheric\nheating and circulation patterns 46-48, which in turn redistributes the energy\ndownstream, setting the stage for the more extensive and evident ocean-atmosphere\nsignalsandfeedbacks that propagating to theTIOinthe coupled model.\n19. Lines 275-276, “(Extended Data fig. 10)”, isn't the figure from the\natmosphere-only model run (as mentioned in line221)?\nReply: Thanks for pointing it out and you are right. We have modified the statement\nand cited it here.\nAtmosphere-onlymodel andobservation comparisons further reinforcethesefindings.\n20. Line 304,cooler western part?\nReply: Thanks for pointing it out and sorry for the typo. We have revised as\nsuggested.\n21. Line 355,doyou mean “reduced dust anomalies”\nReply: Thanks for pointing it out and you are right. We have incorporated this\nsuggestioninto therevised manuscript.\n22. Line 360,dust forcing in JJAorSON?\nReply: Sorry for the misunderstanding. It is indeed the dust forcing in JJA. We have\nclarified thisin therevised manuscript and cited it here.\n23. Lines 252-257, how is this done? Are you using the ratio of JJAMERRA-2 dust\nAODto modeled dustAODto scale dust loading ineach month?\nReply: Thanks for your suggestion and you are right. We are using the ratio of JJA\nMERRA-2 dustAOD to modeled dust AOD to scale dust loading in each month. We\nalso clarified thisprocedure in therevised manuscript and cited ithere.\nTo address this issue, we used the ratio of JJA MERRA-2 dust AOD to modeled dust\nAOD to scale dust loading in each month. Specifically, we first conduct a control run\nusing the default dust concentration file. Then, we divide the monthly dust AOD result\nof MERRA-2 by the monthly dust AOD in the control run to obtain monthly\nproportionality coefficients. By multiplying these monthly coefficients with the\noriginal dust mixing ratio, we can better reproduce the spatiotemporal distribution of\nJJAMiddleEastern dust in themodel.\n24. Line 258, “by perturbing initial temperature fields”, is it land or ocean surface\ntemperature? Howlarge are theperturbation?\nReply:Wethank thereviewer for thisquestion.The small,machine-level perturbation\n(1×10-14 K) was applied to the initial temperature fields of both the land and ocean\ncomponents to initiate the ensemble spread (Yeager et al., 2018). This detail has been\nclarified in therevised manuscript and cited here forconvenience.\nFive ensemble members—generated by perturbing initial temperature fields over land\nand ocean—were averaged to suppress internal climate noise. The magnitude of the\ntemperatureperturbationis 1×10-14K.\n25. Line 540,onestandard deviation over what timeperiod?\nReply: We thank the reviewer for pointing out the need for clarification. The one\nstandard deviation (1σ) is calculated from the summertime (June-July-August) data\nspanning the entire study period of 1980 to 2020, based on the regional mean time\nseries. This scaling is used to define the characteristic magnitude of the perturbations.\nWehave amended themanuscript to explicitlystatethis timeframe.\nSubsequently, the coefficient field is scaled by one standard deviation (1σ) over\nsummertime from 1980 to 2020 of the regional mean time series to define\ncharacteristicperturbation magnitudes.\n26. Fig. 1c, consider adding all the dust time series as a reference (e.g., including\nMODIS DOD and MERRA-2 dust column mass density). Is it possible to include\nDMI\ntime series as well and show the correlations between DMI and different Middle\nEasten dust indices from 1980 to 2023 and 2003 to 2023? Fig. 1d-g, can you please\nclarify in the figure caption the time period over which the regressions were\ncalculated? Was it 2003 to 2023? Were the data detrended before calculating the\nregressions?\nReply: Thank you for this helpful suggestion. We have included time series of\nadditional Middle Eastern dust indices and DMI time series in Extended Data Fig. 4\nin the revised manuscript (Fig. R7 here). This avoids overcrowding the figure while\nensuring all relevant data and statistical evidence are fully available to the reader in\nthe supplement. We also show the correlations between DMI and different Middle\nEastern dust indices from 1980 to 2023 and 2003 to 2023 in corresponding figures\n(Fig. 1 and Extended Data Fig. 4 in the revised manuscript). We cited this figure here\nforconvenience.\nFig. R7. Time series of standardized JJA MODIS dust optical depth (the black solid\nline), JJA MERRA-2 dust column mass density (the black dashed line), and SON DMI\nindex(the redsolid line)from2003to 2023.\nMoreover, we have clarified the figure caption the time period over which the\nregressions were calculated. The data has been detrended before calculating the\nregressions.Wehave modified the figure caption (Fig. 1 in the revised manuscript and\nFig.R8 here)as suggested and cited ithere.\nFig. R8. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on the\nMiddle East regional-averaged station dust frequency (averaged over the 10°S~30°N,\n35°E~70°E). (b) Similar to a, but for the JJA MISR non-spherical aerosol optical\ndepth (AOD, also averaged over the 10°S~30°N, 35°E~70°E). (c) Time series of\nstandardized JJA station dust frequency (the solid line) from 1980 to 2023, and JJA\nMISR non-spherical aerosol optical depth (the dashed line) from 2003 to 2023. (d-g)\nThe detrended regression coefficients of SON meteorological fields on the detrended\nJJA MISR non-spherical aerosol optical depth averaged over the Middle East from\n2003 to 2023. These meteorological fields include (d) sea surface temperature. (e)\nmeridional mean (20°S-20°N) vertical cross section of temperature (shaded) and\nmixed layer depth (the gray dashed line), (f) sea level pressure (shaded) with 850 hPa\nzonal and meridional wind (UV, vectors, m/s), and (g) meridional mean (20°S-20°N)\nvertical cross section of geopotential height (GPH, shaded) with zonal and vertical\nwind (UW, vectors, m/s). The y-axes on the right in (e) corresponds to the response of\nmixed layer depth. The regression is currently done for individual years. The area\nwithwhitedotspassed the significancetest of p ≤ 0.05byStudent'st-test.\n27. Fig. 2, are the fields derived from time averages or from regression? Fig. 2b, the\nlocation of the temperature anomalies appears at a higher altitude than in the\nobservation (Fig. 1). Is thisdueto model bias? Couldyou please comment onthis?\nReply:Wethank thereviewer for theseinsightful questions.\nFirstly, the fields in Fig. 2 are derived from regression analysis, not simple time\naveraging.Wehave clarified thisin thefigure caption of therevised manuscript.\nFig. 2. The regression coefficients of SON meteorological fields onto JJA dust aerosol\noptical depth (AOD) from 1980 to 2020 in the CESM coupled model simulations.\nThese coefficients were scaled by the magnitude of dust decreasing from 2010 to 2020\nin the CESM. These meteorological fields include (a) sea surface temperature. (b)\nmeridional mean (20°S-20°N) vertical cross section of temperature (shaded) and\nmixed layer depth (the gray dashed line), (c) sea level pressure(shaded) with 850 hPa\nzonal and meridional wind (UV, vectors, m/s), and (d) meridional mean (20°S-20°N)\nvertical cross section of geopotential height (GPH, shaded) with zonal and vertical\nwind (UW, vectors, m/s). The y-axes on the right in (b) corresponds to the response of\nmixed layer depth. The area with white dots passed the significance test of p ≤ 0.05\nbyStudent's t-test.\nSecondly, we agree that the displacement of temperature displacement is likely\nattributable to model bias. However, despite this bias in the exact altitude, the model\nsuccessfully captures the key physical process crucial for our proposed pathway. We\nhave added the additional discussion regarding the inconsistency of temperature\nresponse inthe manuscript andcited it here.\nThe CESM experiments reveal a robust dipole SST anomaly pattern across the TIO in\nresponse to reduced dust forcing (see Method), closely mirroring the observed\nregression between dust and SST(Fig. 2a) despite some latitudinal displacements that\narepartlyattributedto model biases.\n28.Fig.S2, please mark whether thecorrelations are statistically significant.\nReply: Thanks for your suggestions. We have the marked critical correlation\ncoefficients for statistical significance in Extended Data Fig. 2 in the revised\nmanuscript andcited ithere (Fig. R9).\nFig. R9. Correlation coefficients between JJA dust proxies and DMI in different three\nmonths. These dust proxies include station dust frequency (the yellow line,\n1980-2023), MODIS dust optical depth (the orange line, 2003-2023), MISR\nnon-spherical AOD (the black line, 2003-2023), and MERRA-2 dust column mass\ndensity (the green line, 2003-2023). The dashed grey line indicates the position of\nzero correlation. The dashed and solid red lines indicate the critical correlation\ncoefficients for statistical significance of station dust frequency and the other three\ndust proxies, respectively.\n29.Fig.S6a, could you please add thestimulated dustAODto it?\nReply: Thanks for your helpful suggestions and we have added the simulated dust\nAOD as suggested (Fig. R10 here, the same as the Extended Data Fig. 9 in the revised\nmanuscript).Wealso cited ithere forconvenience.\nFig. R10. The standardized DMI and SMED from 1980 to 2020. The time series are\n(a) simulated DMI forced by dust (solid cyan line), simulated DMI in the historical\nrun with all forcing (dashed cyan line), and simulated SMED (solid red line) and (b)\nobserved DMI andSMED.Allthetimeseries arestandardized for comparison\nWealso added theexplanation for thisfigure andalso cited ithere.\nThe simulated SMED also has high correlation with DMI series (Extended Data Fig.\n9a,R=-0.58, p ≤ 0.05), further validatingtheir relationship.\n30.Fig.S8a, is theobtained through regression?\nReply:Yes, you are right.Wehave clarified this inthe figure caption.\n31. Figs. S10b, d, are the flux date from MERRA-2?And what is the averaging area\nusedfor MISRAODintheregression analysis?\nReply: Indeed, you are correct. The flux data are sourced from MERRA-2. Regarding\nthe averaging area for MISR AOD, we confirm that it is identical to that specified in\nthemanuscript (10°S~30°N, 35°E~70°E)forconsistency.\n32. Fig. S11, which simulation does this refer to? Is it from the sensitivity simulation\northecoupled run from 1980to2020?\nReply: This simulation refers to the CESM coupled model simulations from 1980 to\n2020.Wehaveclarified this inthefigure caption and cited ithere.\nFig. S11. The responses of SON meridional mean (20°S-20°N) vertical cross section\nof temperature to reduced Middle East dust forcing from 1980 to 2020 in the CESM\ncoupled model simulations. The area with white dots passed the significance test of p\n≤ 0.05byStudent's t-test.\nReferences\nKim,D.,Chin,M.,Yu,H.B.,Pan,X.H.,Bian,H.S.,Tan,Q.,Kahn,R.A.,Tsigaridis,K.,Bauer,S.E.,\nTakemura,T.,Pozzoli,L.,Bellouin,N.,andSchulz,M.:AsianandTrans-PacificDust:AMultimodel\nandMultiremoteSensingObservationAnalysis,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,13534-13559,10.1029/2019JD030822,2019.\nLin,Y.J.,Cesana,G.,Proistosescu,C.,Zelinka,M.D.,andArmour,K.C.:TheRelativeImportanceof\nForcedandUnforcedTemperaturePatternsinDrivingtheTimeVariationofLow-CloudFeedback,\nJOURNALOFCLIMATE,38,513-529,10.1175/JCLI-D-24-0014.1,2025.\nProspero,J.M.,Delany,A.C.,Delany,A.C.,andCarlson,T.N.:TheDiscoveryofAfricanDust\nTransporttotheWesternHemisphereandtheSaharanAirLayer,BulletinoftheAmerican\nMeteorologicalSociety,102,E1239-E1260,10.1175/BAMS-D-19-0309.1,2021.\nWu,M.X.,Su,H.,andNeelin,J.D.:Multi-objectiveobservationalconstraintoftropicalAtlanticand\nPacificlow-cloudvariabilitynarrowsuncertaintyincloudfeedback,NatureCommunications,16,\n10.1038/s41467-024-53985-w,2025.\nWyant,M.C.,Bretherton,C.S.,Rand,H.A.,andStevens,D.E.:Numericalsimulationsanda\nconceptualmodelofthestratocumulustotradecumulustransition,JOURNALOFTHE\nATMOSPHERICSCIENCES,54,168-192,10.1175/1520-0469(1997)054<0168:NSAACM>2.0.CO;2,\n1997.\nXiao,Y.,Tang,Y.M.,Tan,X.X.,Wu,Y.L.,andYao,Z.X.:TheSST-WindCausalRelationshipduring\ntheDevelopmentoftheIODinObservationsandModelSimulations,RemoteSensing,14,\n10.3390/rs14051064,2022.\nXie,X.N.,Liu,X.D.,Che,H.Z.,Xie,X.X.,Wang,H.L.,Li,J.D.,Shi,Z.G.,andLiu,Y.G.:\nModelingEastAsianDustandItsRadiativeFeedbacksinCAM4-BAM,JOURNALOF\nGEOPHYSICALRESEARCH-ATMOSPHERES,123,1079-1096,10.1002/2017JD027343,2018.\nYao,Z.X.,Tang,Y.M.,Chen,D.K.,Zhou,L.,Li,X.J.,Lian,T.,andUlIslam,S.:Assessmentofthe\nsimulationofIndianOceanDipoleintheCESM-Impactsofatmosphericphysicsandmodelresolution,\nJOURNALOFADVANCESINMODELINGEARTHSYSTEMS,8,1932-1952,\n10.1002/2016MS000700,2016.\nYeager,S.G.,Danabasoglu,G.,Rosenbloom,N.A.,Strand,W.,Bates,S.C.,Meehl,G.A.,Karspeck,\nA.R.,Lindsay,K.,Long,M.C.,Teng,H.,andLovenduski,N.S.:PREDICTINGNEAR-TERM\nCHANGESINTHEEARTHSYSTEMALargeEnsembleofInitializedDecadalPrediction\nSimulationsUsingtheCommunityEarthSystemModel,BulletinoftheAmericanMeteorological\nSociety,99,1867-1886,10.1175/BAMS-D-17-0098.1,2018.\nYu,H.B.,Tan,Q.,Chin,M.,Remer,L.A.,Kahn,R.A.,Bian,H.S.,Kim,D.,Zhang,Z.B.,Yuan,T.L.,\nOmar,A.H.,Winker,D.M.,Levy,R.C.,Kalashnikova,O.,Crepeau,L.,Capelle,V.,andChédin,A.:\nEstimatesofAfricanDustDepositionAlongtheTrans-AtlanticTransitUsingtheDecadelongRecord\nofAerosolMeasurementsfromCALIOP,MODIS,MISR,andIASI,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,7975-7996,10.1029/2019JD030574,2019a.\nYu,Y.,Kalashnikova,O.V.,Garay,M.J.,andNotaro,M.:ClimatologyofAsiandustactivationand\ntransportpotentialbasedonMISRsatelliteobservationsandtrajectoryanalysis,Atmospheric\nChemistryandPhysics,19,363-378,10.5194/acp-19-363-2019,2019b.\nKim,D.,Chin,M.,Yu,H.B.,Pan,X.H.,Bian,H.S.,Tan,Q.,Kahn,R.A.,Tsigaridis,K.,Bauer,S.E.,\nTakemura,T.,Pozzoli,L.,Bellouin,N.,andSchulz,M.:AsianandTrans-PacificDust:AMultimodel\nandMultiremoteSensingObservationAnalysis,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,13534-13559,10.1029/2019JD030822,2019.\nProspero,J.M.,Delany,A.C.,Delany,A.C.,andCarlson,T.N.:TheDiscoveryofAfricanDust\nTransporttotheWesternHemisphereandtheSaharanAirLayer,BulletinoftheAmerican\nMeteorologicalSociety,102,E1239-E1260,10.1175/BAMS-D-19-0309.1,2021.\nXiao,Y.,Tang,Y.M.,Tan,X.X.,Wu,Y.L.,andYao,Z.X.:TheSST-WindCausalRelationshipduring\ntheDevelopmentoftheIODinObservationsandModelSimulations,RemoteSensing,14,\n10.3390/rs14051064,2022.\nXie,X.N.,Liu,X.D.,Che,H.Z.,Xie,X.X.,Wang,H.L.,Li,J.D.,Shi,Z.G.,andLiu,Y.G.:\nModelingEastAsianDustandItsRadiativeFeedbacksinCAM4-BAM,JOURNALOF\nGEOPHYSICALRESEARCH-ATMOSPHERES,123,1079-1096,10.1002/2017JD027343,2018.\nYao,Z.X.,Tang,Y.M.,Chen,D.K.,Zhou,L.,Li,X.J.,Lian,T.,andUlIslam,S.:Assessmentofthe\nsimulationofIndianOceanDipoleintheCESM-Impactsofatmosphericphysicsandmodelresolution,\nJOURNALOFADVANCESINMODELINGEARTHSYSTEMS,8,1932-1952,\n10.1002/2016MS000700,2016.\nYeager,S.G.,Danabasoglu,G.,Rosenbloom,N.A.,Strand,W.,Bates,S.C.,Meehl,G.A.,Karspeck,\nA.R.,Lindsay,K.,Long,M.C.,Teng,H.,andLovenduski,N.S.:PREDICTINGNEAR-TERM\nCHANGESINTHEEARTHSYSTEMALargeEnsembleofInitializedDecadalPrediction\nSimulationsUsingtheCommunityEarthSystemModel,BulletinoftheAmericanMeteorological\nSociety,99,1867-1886,10.1175/BAMS-D-17-0098.1,2018.\nYu,H.B.,Tan,Q.,Chin,M.,Remer,L.A.,Kahn,R.A.,Bian,H.S.,Kim,D.,Zhang,Z.B.,Yuan,T.L.,\nOmar,A.H.,Winker,D.M.,Levy,R.C.,Kalashnikova,O.,Crepeau,L.,Capelle,V.,andChédin,A.:\nEstimatesofAfricanDustDepositionAlongtheTrans-AtlanticTransitUsingtheDecadelongRecord\nofAerosolMeasurementsfromCALIOP,MODIS,MISR,andIASI,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,7975-7996,10.1029/2019JD030574,2019a.\nYu,Y.,Kalashnikova,O.V.,Garay,M.J.,andNotaro,M.:ClimatologyofAsiandustactivationand\ntransportpotentialbasedonMISRsatelliteobservationsandtrajectoryanalysis,Atmospheric\nChemistryandPhysics,19,363-378,10.5194/acp-19-363-2019,2019b.\nKim,D.,Chin,M.,Yu,H.B.,Pan,X.H.,Bian,H.S.,Tan,Q.,Kahn,R.A.,Tsigaridis,K.,Bauer,S.E.,\nTakemura,T.,Pozzoli,L.,Bellouin,N.,andSchulz,M.:AsianandTrans-PacificDust:AMultimodel\nandMultiremoteSensingObservationAnalysis,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,13534-13559,10.1029/2019JD030822,2019.\nProspero,J.M.,Delany,A.C.,Delany,A.C.,andCarlson,T.N.:TheDiscoveryofAfricanDust\nTransporttotheWesternHemisphereandtheSaharanAirLayer,BulletinoftheAmerican\nMeteorologicalSociety,102,E1239-E1260,10.1175/BAMS-D-19-0309.1,2021.\nXiao,Y.,Tang,Y.M.,Tan,X.X.,Wu,Y.L.,andYao,Z.X.:TheSST-WindCausalRelationshipduring\ntheDevelopmentoftheIODinObservationsandModelSimulations,RemoteSensing,14,\n10.3390/rs14051064,2022.\nXie,X.N.,Liu,X.D.,Che,H.Z.,Xie,X.X.,Wang,H.L.,Li,J.D.,Shi,Z.G.,andLiu,Y.G.:\nModelingEastAsianDustandItsRadiativeFeedbacksinCAM4-BAM,JOURNALOF\nGEOPHYSICALRESEARCH-ATMOSPHERES,123,1079-1096,10.1002/2017JD027343,2018.\nYao,Z.X.,Tang,Y.M.,Chen,D.K.,Zhou,L.,Li,X.J.,Lian,T.,andUlIslam,S.:Assessmentofthe\nsimulationofIndianOceanDipoleintheCESM-Impactsofatmosphericphysicsandmodelresolution,\nJOURNALOFADVANCESINMODELINGEARTHSYSTEMS,8,1932-1952,\n10.1002/2016MS000700,2016.\nYu,H.B.,Tan,Q.,Chin,M.,Remer,L.A.,Kahn,R.A.,Bian,H.S.,Kim,D.,Zhang,Z.B.,Yuan,T.L.,\nOmar,A.H.,Winker,D.M.,Levy,R.C.,Kalashnikova,O.,Crepeau,L.,Capelle,V.,andChédin,A.:\nEstimatesofAfricanDustDepositionAlongtheTrans-AtlanticTransitUsingtheDecadelongRecord\nofAerosolMeasurementsfromCALIOP,MODIS,MISR,andIASI,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,7975-7996,10.1029/2019JD030574,2019a.\nYu,Y.,Kalashnikova,O.V.,Garay,M.J.,andNotaro,M.:ClimatologyofAsiandustactivationand\ntransportpotentialbasedonMISRsatelliteobservationsandtrajectoryanalysis,Atmospheric\nChemistryandPhysics,19,363-378,10.5194/acp-19-363-2019,2019b.\nKim,D.,Chin,M.,Yu,H.B.,Pan,X.H.,Bian,H.S.,Tan,Q.,Kahn,R.A.,Tsigaridis,K.,Bauer,S.E.,\nTakemura,T.,Pozzoli,L.,Bellouin,N.,andSchulz,M.:AsianandTrans-PacificDust:AMultimodel\nandMultiremoteSensingObservationAnalysis,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,13534-13559,10.1029/2019JD030822,2019.\nProspero,J.M.,Delany,A.C.,Delany,A.C.,andCarlson,T.N.:TheDiscoveryofAfricanDust\nTransporttotheWesternHemisphereandtheSaharanAirLayer,BulletinoftheAmerican\nMeteorologicalSociety,102,E1239-E1260,10.1175/BAMS-D-19-0309.1,2021.\nXie,X.N.,Liu,X.D.,Che,H.Z.,Xie,X.X.,Wang,H.L.,Li,J.D.,Shi,Z.G.,andLiu,Y.G.:\nModelingEastAsianDustandItsRadiativeFeedbacksinCAM4-BAM,JOURNALOF\nGEOPHYSICALRESEARCH-ATMOSPHERES,123,1079-1096,10.1002/2017JD027343,2018.\nYu,H.B.,Tan,Q.,Chin,M.,Remer,L.A.,Kahn,R.A.,Bian,H.S.,Kim,D.,Zhang,Z.B.,Yuan,T.L.,\nOmar,A.H.,Winker,D.M.,Levy,R.C.,Kalashnikova,O.,Crepeau,L.,Capelle,V.,andChédin,A.:\nEstimatesofAfricanDustDepositionAlongtheTrans-AtlanticTransitUsingtheDecadelongRecord\nofAerosolMeasurementsfromCALIOP,MODIS,MISR,andIASI,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,7975-7996,10.1029/2019JD030574,2019a.\nYu,Y.,Kalashnikova,O.V.,Garay,M.J.,andNotaro,M.:ClimatologyofAsiandustactivationand\ntransportpotentialbasedonMISRsatelliteobservationsandtrajectoryanalysis,Atmospheric\nChemistryandPhysics,19,363-378,10.5194/acp-19-363-2019,2019b.\nKim,D.,Chin,M.,Yu,H.B.,Pan,X.H.,Bian,H.S.,Tan,Q.,Kahn,R.A.,Tsigaridis,K.,Bauer,S.E.,\nTakemura,T.,Pozzoli,L.,Bellouin,N.,andSchulz,M.:AsianandTrans-PacificDust:AMultimodel\nandMultiremoteSensingObservationAnalysis,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,13534-13559,10.1029/2019JD030822,2019.\nProspero,J.M.,Delany,A.C.,Delany,A.C.,andCarlson,T.N.:TheDiscoveryofAfricanDust\nTransporttotheWesternHemisphereandtheSaharanAirLayer,BulletinoftheAmerican\nMeteorologicalSociety,102,E1239-E1260,10.1175/BAMS-D-19-0309.1,2021.\nYu,H.B.,Tan,Q.,Chin,M.,Remer,L.A.,Kahn,R.A.,Bian,H.S.,Kim,D.,Zhang,Z.B.,Yuan,T.L.,\nOmar,A.H.,Winker,D.M.,Levy,R.C.,Kalashnikova,O.,Crepeau,L.,Capelle,V.,andChédin,A.:\nEstimatesofAfricanDustDepositionAlongtheTrans-AtlanticTransitUsingtheDecadelongRecord\nofAerosolMeasurementsfromCALIOP,MODIS,MISR,andIASI,JOURNALOFGEOPHYSICAL\nRESEARCH-ATMOSPHERES,124,7975-7996,10.1029/2019JD030574,2019a.\nYu,Y.,Kalashnikova,O.V.,Garay,M.J.,andNotaro,M.:ClimatologyofAsiandustactivationand\ntransportpotentialbasedonMISRsatelliteobservationsandtrajectoryanalysis,Atmospheric\nChemistryandPhysics,19,363-378,10.5194/acp-19-363-2019,2019b.\nResponsetothereviewof “MiddleEastDustasaDominantExternalDriverofthe\nIndianOceanDipole” (NCOMMS-25-75253-T).\nResponsestoReview03’scomments\nSummary:\nThe manuscript investigates the role of mineral dust aerosols from the Middle East in\nmodulating interannual variability of the Indian Ocean Dipole (IOD). Using a\ncombination of satellite observations, reanalysis datasets, and climate model\nexperiments, the authors demonstrate that variations in summer dust emissions\nbetween 1980 and 2020 explain approximately 36% of the observed interannual IOD\nvariance—exceeding the influence of the El Niño–Southern Oscillation (ENSO)\nduring boreal autumn. Overall, the paper identifies a previously underappreciated\ndust–IOD teleconnection, emphasizing the importance of aerosol forcing in seasonal\nprediction and long-term climate projections. Overall, the work is novel, and\nsupported by comprehensive analysis. However, the manuscript is not always easy to\nfollow, as several figure captions and axis labels lack clarity and may cause confusion\nfor readers. I recommend publication after revision to address the points detailed\nbelow.\nReply: We are grateful to the reviewer for his/her insightful and very valuable\nsuggestions, which have significantly contributed to the enhancement of our\nmanuscript. We have carefully addressed the comments and made the necessary\nrevisions, hoping that the updated version meets the journal's standards. Our\nresponses to each of the comments and suggestions are as follows. The referee’s\noriginal comments are shown in blue. Our replies are shown in black. The\ncorresponding changes in themanuscript are shown in Italicblack.\n1. The paper explains how dust emission reduction over Middle East causes a\npositive anomaly in solar radiation reaching to the western Indian ocean surface.\nHowever, it does not clearly discuss how this enhanced radiative energy translates\ninto a positive SST anomaly, considering the ocean’s large heat capacity. Please\nexpand on this physical mechanism. Is the ~2-month lag used in the analysis related\ntothe ocean mixed-layer adjustment timescale?\nReply: We thank the reviewer for raising this crucial point regarding the mechanism\nlinking enhanced solar radiation to the positive SST anomaly. We agree that a clearer\ndiscussion of this process is essential and have expanded upon it in the revised\nmanuscript.\nThe translation of increased net surface shortwave radiation into a positive SST\nanomaly occurs through the gradual heat accumulation withinthe ocean's mixed layer.\nThe ~2-month lag used in our analysis is indeed closely related to the mixed-layer\nadjustment timescale for the western Indian Ocean. Case studies in the tropical Indian\nOcean have shown that the thermodynamic response of the mixed layer to sustained\natmospheric forcing (such as radiative anomalies) can manifest over a period of\napproximately2to 2.5months (Azaneu, 2022).\nIn our case, the persistent positive anomaly in net surface heat flux (dominated by\nshortwave radiation) progressively heats the relatively shallow mixed layer typical of\nthe western tropical Indian Ocean. The observed lag represents the integrated time\nrequired for this accumulated heat to become a robust SST signal, consistent with the\nestablished mixed-layer response timescale for this region. We have incorporated this\nexplanationwith supporting references into therevised discussion section.\nThetranslation of increased shortwave radiation into the initial SSTanomaly involves\nthegradual heat accumulation withinthe ocean's mixed layer.Theobserved ~2-month\nlag reflects the mixed-layer adjustment timescale, which is approximately 2 to 2.5\nmonths for sustained thermal forcing in the TIO 34. Given the relatively shallow mixed\nlayer in the western Indian Ocean 35-37, this persistent radiative forcing can efficiently\nwarm the upper ocean within this timeframe, overcoming the damping from mixing\nandlatentheat loss.\n2. Please explain why there is a positive net LW flux at surface over the western\nIndian ocean in the case of dust deduction? Moreover, for both SW and LW, why the\nflux anomaly over eastern Indian ocean isalways oppositeto thewestern part?\nReply: We thank the reviewer for these insightful questions regarding the surface\nenergy balance.\n(1) Positive net LW flux anomaly over the western Indian Ocean: The positive\nanomaly in net surface longwave (LW) radiation is a result of coupled atmospheric\nadjustments. We propose two complementary mechanisms:First, as previously noted,\nthe initial warming and enhanced evaporation increase atmospheric water vapor,\nwhich strengthens downward LW radiation. Second, and importantly, the dust\nreduction triggers an atmospheric teleconnection that perturbs cloud fields. As\nelaborated below, this leads to increased high-level cloudiness over the western IO\nbasin. These high clouds are highly effective at emitting LW radiation downward,\nthereby significantly contributing to the observed positive net LWflux anomaly at the\nsurface.Wehave also incorporated theexplanation in therevised manuscript andcited\nithere.\nThe transformation of cloud fields further modifies the radiative budget by increasing\nboth longwave and shortwave radiation, thereby amplifying the warming signal over\nthe western TIO in synergy with increased SW and LW radiative fluxes over this\nregion of reduced dust (Figs. 3a, b). Moreover, the initial warming and enhanced\nevaporation might increase atmospheric water vapor over the western TIO, which\nstrengthensdownwardLWradiation(Fig. 3b).\n(2) Dipole pattern of flux anomalies: The opposing flux anomalies are a direct\nmanifestation of the zonal atmospheric circulation cell excited by the Middle East\ndust forcing. Over the western basin, the large-scale ascendance leads to reduced\nlow-level cloudiness, allowing more shortwave radiation to penetrate to the surface\n(positive SW anomaly). Concurrently, the circulation fosters increased high-level\ncloudiness, which traps longwave radiation (positive LW anomaly). Over the eastern\nbasin, the opposite vertical motion and cloud adjustments occur (increased low-cloud\nand decreased high-cloud) because of the anomalous zonal circulation, resulting in\nnegative SW and LW flux anomalies. We have incorporated this physical\ninterpretation into the revised manuscript to provide a more mechanistic explanation\nofthesurface flux changes and cited ithere.\nMoreover, the dipole pattern of radiative flux anomalies over the TIO arises from the\nanomalous zonal circulation (Fig. 2d) andzonal asymmetryin cloud vertical structure\n(Fig. 3c, d). Over the western basin, diminished low-level clouds enhance incoming\nshortwave radiation, while increased high-level clouds trap outgoing longwave\nradiation, leading to positive anomalies in both SW and LW fluxes. Conversely, the\neastern basin exhibits opposite cloud adjustments—more low-level and fewer\nhigh-level clouds—resultingin reduced surface radiativefluxes.\n3. Line 88, ‘reversing the regression coefficients to represent dust reduction’is very\nconfusing, I figured out its meaning when I looked at the figure caption of Fig. S3, it\nmeans you take the negative values of the regression coefficient. Please modify the\nsentence, andalso clarify it in thecaption of Fig.1.\nReply: We thank the reviewer for pointing out this lack of clarity. We have modified\nthe sentence and clarified the caption of Fig. 1 to explicitly state that the patterns for\ndust reduction were obtained by taking the negative values of the regression\ncoefficients.Wealso cited them here forconvenience.\nTo quantify the dust-IOD connection, we regressed SON SST anomalies over the\ntropical Indian Ocean against SMED and took the negative values of the regression\ncoefficients toindicatedust reduction.\n4. Fig S3, you mentioned ‘regression coefficient’and ‘correlation coefficient’in the\nfigure caption, are they the same? Please clarify what you used and keep them\nconsistent.\nReply: We thank the reviewer for highlighting this inconsistency. Indeed, the two\nterms are different and the analysis presented is based on the regression coefficient.\nWe have corrected the caption of Fig. S3 (and any other relevant figures) to\nconsistently use the term \"regression coefficient\" throughout the manuscript to avoid\nany confusion.\n5. In Fig1 (e), please clarify that the grey dashed line is the mixed layer depth\nanomaly, right? It is not just mixed layer depth. Also, please clarify the grey dashed\nlineiscorresponding to they-axis ontheright. Otherwise, it’s too confusing.\nReply: We thank the reviewer for this suggestion. The reviewer is correct. The grey\ndashed line in Fig. 1e represents the mixed layer depth anomaly. We have revised the\nfigure caption to explicitly state this and to clarify that it corresponds to the right\ny-axis,in order to eliminateany potential confusion.\nWealso cited it here forconvenience.\nFig. 1. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on its\nregional mean (averaged over the 10°S~30°N, 35°E~70°E). (b) Similar to a, but for\nthe JJA MISR non-spherical aerosol optical depth (AOD, also averaged over the\n10°S~30°N, 35°E~70°E). (c) Time series of standardized JJA station dust frequency\n(the solid line) from 1980 to 2023, and JJA MISR non-spherical aerosol optical depth\n(the dashed line) from 2003 to 2023. (d-g) The detrended regression coefficients of\nSON meteorological fields on the detrended JJA MISR non-spherical aerosol optical\ndepth averaged over the Middle East from 2003 to 2023. These meteorological fields\ninclude (d) sea surface temperature. (e) meridional mean (20°S-20°N) vertical cross\nsection of temperature (shaded) and mixed layer depth anomaly (the gray dashed\nline), (f) sea level pressure (shaded) with 850 hPa zonal and meridional wind (UV,\nvectors, m/s), and (g) meridional mean (20°S-20°N) vertical cross section of\ngeopotential height (GPH, shaded) with zonal and vertical wind (UW, vectors, m/s).\nThese coefficients were scaled by the magnitude of dust decreasing from 2010 to 2020\nin the JJA MISR non-spherical aerosol optical depth dataset. The right y-axis in (e)\ncorresponds to the mixed layer depth; the grey dashed line represents its zero value.\nThe regression is currently done for individual years. The area with white dots passed\nthesignificancetest of p ≤ 0.05byStudent'st-test.\n6. Line 93, ‘0.5K per standard deviation of dust variability’, please clarify the\nmeaning ofthis term.Authors usedifferent dust proxies, e.g., dustAOD, column mass\ndensity, and dust frequency, what are their standard deviation of dust variability,\nrespectively.\nReply: We thank the reviewer for this important clarification. The statement “~0.5 K\nper standard deviation of dust variability” should be interpreted as a statistical\nestimate derived from the composite of multiple dust proxies—not as a precise\none‑to‑one relationship for each individual proxy. In our analysis, a\none‑standard‑deviation decrease in different dust indicators (station dust frequency,\nMISR non‑sphericalAOD, MERRA‑2 dust column mass density) is associated with a\nwestern Indian Ocean warming of roughly 0.5 K. Because each proxy has its own\nmagnitude of variability, the exact warming per standard deviation slightly differs\nacross indicators; the value of ~0.5 K represents a central estimate from the\nmulti‑proxy analysis. We have revised the relevant text in the manuscript to clarify\nthispoint and to avoid any potentialmisinterpretation.\nThe regression coefficients from the multi-proxy analysis collectively suggest that a\ndecrease in Middle Eastern dust loading is associated with a western Indian Ocean\nwarming of approximately 0.5 K per standard deviation, a magnitude comparable to\nSSTanomalies observed duringmoderate IOD events.\n7. Line 336, ‘suppress eastern upwelling’, should that be suppress eastern\ndownwelling?\nReply:Thank you.Wehave revised as suggested.\nReferences\nAzaneu,M.,Matthews,A.,andBaranowski,D.:EddyadvectionandtheevolutionofMixedlayer\nproperties:acasestudyatSoutheastTropicalIndianOcean,EGUGeneralAssembly2022,Vienna,\nAustria2022.\nCarton,J.A.,Grodsky,S.A.,andLiu,H.:Variabilityoftheoceanicmixedlayer,1960-2004,\nJOURNALOFCLIMATE,21,1029-1047,10.1175/2007JCLI1798.1,2008.\nPrasad,T.G.andBahulayan,N.:MixedlayerdepthandthermoclineclimatologyoftheArabianSea\nandwesternequatorialIndianOcean,IndianJournalofMarineSciences,25,189-194,1996.\nSharma,R.,Agarwal,N.,Momin,I.M.,andAgarwal,V.K.:MixedLayerDepthanditsVariabilityin\ntheEasternEquatorialIndianOceanasRevealedbyObservationsandModelSimulations,Marine\nGeodesy,33,154-163,10.1080/01490419.2010.492281,2010.", "reviewer_5": "Responsetothereviewof “MiddleEastDustasaDominantExternalDriverofthe\nIndianOceanDipole” (NCOMMS-25-75253A).\nResponsestoReview01’scomments\nSummary:\nThe authors have addressed my major concerns, and the revised manuscript is\nsubstantially improved. I thank the authors for their careful and thorough revisions. I\nhave one remaining minor point, concerning consistency between the limitations now\nstated in the manuscript and the stronger framing used in the title and abstract. The\nnewly added limitations paragraph (Lines 465–481, beginning “While this study\ndemonstrates a robust statistical linkage between SMED and IOD variability…”) is a\nvaluableand welcome addition.\nIn light of these acknowledged uncertainties, the wording “dominant external driver”\nin the title appears stronger than what can be conclusively supported. I recommend\nsoftening this language to better align with the paper’s own caveats, for instance,\nremoving “dominant” from the title, abstract, and conclusion and replacing it with\nwording such as “important.” One possible revision to the title is “Middle East Dust\nas an Important External Driver of the Indian Ocean Dipole.” Related phrasing in the\nabstract and conclusion should be adjusted accordingly (e.g., Line 416: “exerts a\ndominantcontrol”).Withthisminorwording adjustment, Irecommend themanuscript\nforpublication.\nReply: Thank you for your positive assessment of our revisions and for your final\nrecommendation for publication. We are very pleased to hear that you find the\nmanuscript substantially improved.\nWe fully agree with your point regarding the need for consistency between the\ntitle/abstract framing and the nuanced limitations discussed in the text. Accordingly,\nwehave implemented your recommendation as follows:\n1. Title: Changed from “Middle East Dust as the Dominant External Driver of the\nIndian Ocean Dipole” to “Middle East Dust as an Important External Driver of the\nIndian Ocean Dipole.”\n2. Conclusion: Modified the phrase “exerts a dominant control” to “exerts an\nimportant control” (Line 407). Modified the phrase “the dominant driver” to “the\nimportant driver” (Line 435).\nWe believe these careful adjustments ensure that the manuscript’s claims are now\nfully consistent with its own caveats, strengthening the overall balance and precision\nofthepaper.\nReviewer #1(Remarks oncodeavailability):\nhttps://zenodo.org/records/15684742\nRegarding the post-processing Matlab code posted on Zenodo\n(https://zenodo.org/records/15684742): for accessibility and ease of use by\ninternational readers, I recommend that the in-code comments be provided in English\n(or at least include English translations alongside any non-English comments). This\nwould make the scripts much easier to follow and reproduce for the broader\ncommunity.Thank you.\nReply: Thank you for this helpful suggestion. We agree that English comments are\nessential for accessibility and have now updated all in-code comments in the Zenodo\nrepository accordingly.\nResponsestoReview02’scomments\nSummary: The authors did a great job in addressing my comments and questions. I\nhave nofurther suggestions.\nReply: Thank you for your positive feedback and for confirming that all your\ncomments have been addressed. We greatly appreciate your time and valuable\nsuggestions.\nResponsestoReview03’scomments\nSummary: The authors have addressed the reviewers’comments well. The responses\nare clear, and the revisions have improved the manuscript. I do not have any\nremaining major concerns, and I believe the paper is now suitable for publication in\nNature Communications. Irecommend acceptance in itscurrent form.\nReply: Thank you for your positive and constructive feedback. We are very pleased\nthat you find the revised manuscript suitable for publication. We sincerely appreciate\nyour timeand valuable contributions throughout thereview process.\nReview of “Middle East Dust as a Dominant External Driver of the Indian Ocean\nDipole” by Liu et al.\nThis study examined how mineral dust aerosols emitted from the Middle East a7ect the\nIndian Ocean Dipole (IOD) using both observational data and the CESM model. While\nprevious studies have examined how anthropogenic aerosols e7ect IOD, how Middle\nEastern dust aerosols a7ect the IOD is largely unclear. The study addresses a very\nimportant question in the region, and the topic is very novel and inspiring. The overall\nanalysis is quite comprehensive, however, some details in model setting, the feedback link\nfrom subtropical dust loading to tropical SSTs, and the magnitude of the radiative forcing\nneed further clarification. Adding some discussion about the uncertainties in data and\nmethodology also would strengthen the paper.\nMajor concerns:\n1. Usually, the lifetime of dust aerosols in the atmosphere, especially in the lower\ntroposphere, is fairly short, from several days to a couple of weeks. The statement\n“multi-month atmospheric residence time of dust aerosols” (line 60) needs more\nevidence to support. I think it is important to understand whether the strong\nconnection between JJA Middle Eastern dust and SON IOD is primarily due to an\nusually long residence time of summertime Middle Eastern dust, or instead a\ndelayed response through ocean-atmosphere feedback. It would also be helpful to\nclarify whether Middle Eastern dust in SON has any impact on the IOD in SON.\n2. The averaging area for the summertime Middle East Dust (SMED) index was not very\nclear (e.g., in Fig, 1c). As shown in Figs. 1a and 1b, the domain only covers southern\nMiddle East, and it also includes the Horn of Africa, which is often not\ngeographically considered part the Middle East. In the Methods section, there is a\ndefinition of Middle East dust (“10° S–30° N, 35° E–70° E”, line 522). But It’s unclear if\nthis the averaging region used to calculate all the time series of SMED. And also, the\ndomain is a quite large, while most of the Middle East dust are located to the north\nof 10° N (supplementary Fig. 1). Are the results sensitive to the averaging area of\nSMED? Would the correlations change much if only averaging over the region with\nhigh dust loading, e.g., to the north of 10° N?\n3. The radiative forcing of the dust aerosols are shown in both the coupled CESM\nsimulations and atmosphere-only model (CAM) runs. However, the location of\nradiative forcing directly from the dust aerosols. With reduced Middle Eastern dust\naerosols , one would expect to see enhanced shortwave (SW) fluxes (i.e., warming)\nat the surface extending from the Arabian Peninsula southward toward the Arabian\nSea. However, this pattern is not clearly shown in the coupled simulation (Fig. S8c)--\nthere is a cooling around 12 °N, while the warming single is located further south,\nround 6 °N-6 °S, where the climatological dust loading is low. Is this due to a\nteleconnection?\nPeninsula towards the Arabian Sea (Figs. S13a, S10a), but mainly over the northern\nand eastern Arabian Sea. And the changes in SW flux over the western tropical\nIndian Ocean are relatively weak. Does this imply a teleconnection that allow the\nlocal radiative impact of dust aerosols over the Arabian Peninsula and northern\nArabian Sea to propagate to the tropical Indican Ocean?\n4. Regarding the mechanisms of dust radiative forcing, it would be more convincing to\ninclude figures to show radiative fluxes at the TOA and also vertical heating profiles\nin the regions with high dust loading.\n5. Some aspects of the model simulation setup are not very clear. In the CESM\ncoupled simulation from 1980 to 2020, besides SMED, what other forcings are\nincluded? Are anthropogenic aerosols and greenhouse gases included in the\nmodel? Do they vary over time? What’s the spin-up time for the coupled run?\nIt is mentioned that dust aerosols are prescribed in the model, however, it is not\nclear which dust variable was perturbed. Was it dust AOD, dust mass concentration\nat each pressure level, or emission fluxes? And is dust prescribed only the Middle\nEast (leaving the rest of the world dust-free) or globally? It states that “we drove\nCESM with observed summertime dust mass density “(line 523), but it is unclear\nwhich observational dataset was used, and whether the forcing is applied only\nduring the summer months or throughout the year. Are both dry and wet deposition\nincluded in the model?\n6. For the sensitivity test with reduced dust loading, was the forcing applied only in\nsummertime or all year round? It would be helpful to include a table summarizing\nkey features of each simulation.\n7. It is recommended to include some discussion about the uncertainties of the study.\nFor instance, in model simulations, dust content is prescribed, so any feedback\nfrom atmospheric circulation to the dust loading is not considered. In terms of\nstation data, what’s the uncertainty in measurements? Do the station location and\nnumbers change from 1980 to 2020? Does the uneven spatial distribution of the\nstations a7ect the results?\nMinor comments:\n1. Line 64, it may be helpful to show the response the strongest in SON.\n2. Lines 78-79, it is not clear what averaging area is used for the MISR non-spherical\nAOD.\n3. Line 158, this seems to contradict the statement in lines 134-135. Could you please\nclarify what the systematic biases are?\n2\n4. Line 175, there is also a weak increase in the mixed layer depth over the eastern\ntropical Indian Ocean from 100°E to 108 °E (Fig. 2b). What could be the cause of\nthis?\n5. Line 219, regression analysis is less straightforward than performing another\nsimulation without the variability of Middle Eastern dust and comparing the\ndi7erences. Conducting a linear regression assumes that the relationships are\nlinear, which may not necessarily be true.\n6. Line 220, “Increased solar radiation penetrates to the surface…” , this is not clearly\nshown in Fig. S8c, especially over the region with reduced dust. Please clarify.\n7. Lines 221-225, it's not clear why the coupled results are already available (line 192) but\nthen the analysis switches to an atmosphere-only model, and then returns to the\ncoupled simulation.\n8. Line 229-230 “the direct radiative eMects of reduced dust”, is it possible to clarify the\nlocation of this eMect in the figure?\n9. Line 267, the response of shortwave fluxes is mainly over the land. How does the\nsignal propagate to the TIO?\n10. Lines 275-276, “(Extended Data fig. 10)”, isn't the figure from the atmosphere-only\nmodel run (as mentioned in line 221)?\n11. Line 304, cooler western part?\n12. Line 355, do you mean “reduced dust anomalies”\n13. Line 360, dust forcing in JJA or SON?\n14. Lines 252-257, how is this done? Are you using the ratio of JJA MERRA-2 dust AOD to\nmodeled dust AOD to scale dust loading in each month?\n15. Line 258, “by perturbing initial temperature fields”, is it land or ocean surface\ntemperature? How large are the perturbation?s\n16. Line 540, one standard deviation over what time period?\n17. Fig. 1c, consider adding all the dust time series as a reference (e.g., including\nMODIS DOD and MERRA-2 dust column mass density). Is it possible to include DMI\ntime series as well and show the correlations between DMI and di7erent Middle\nEasten dust indices from 1980 to 2023 and 2003 to 2023? Fig. 1d-g, can you please\n3\nclarify in the figure caption the time period over which the regressions were\ncalculated? Was it 2003 to 2023? Were the data detrended before calculatiing the\nregressions?\n18. Fig. 2, are the fields derived from time averages or from regression? Fig. 2b, the location\nof the temperature anomalies appears at a higher altitude than in the observation (Fig.\n1). Is this due to model bias? Could you please comment on this?\n19. Fig. 3, is it possible to include the corresponding figure of reduced dust loading\nhere? The location of the peak positive shortwave fluxes seems mismatch the\nlocation with high dust loading.\n20. Fig. S2, please mark whether the correlations are statistically significant.\n21. Fig. S6a, could you please add the stimulated dust AOD to it?\n22. Fig. S8a, is the obtained through regression?\n23. Figs. S10b, d, are the flux date from MERRA-2? Is it possible to add the MISR AOD\nreduction pattern? And what is the averaging area used for MISR AOD in the\nregression analysis?\n24. Fig. S11, which simulation does this refer to? Is it from the sensitivity simulation or\nthe coupled run from 1980 to 2020?\n4"}, "author_response": "Responsetothereviewof “MiddleEastDustasaDominantExternalDriverofthe\nIndianOceanDipole” (NCOMMS-25-75253-T).\nResponsestoReview01’scomments\nSummary:\nLiu et al. estimated that dust aerosols from the Middle East regions act as a dominant\nexternal driver of the Indian Ocean Dipole. In particular, they show that summertime\ndust over the 1980-2020 period accounts for ~36% of interannual IOD variance,\nsurpassing the El Niño-Southern Oscillation as the main driver during boreal autumn,\nusing satellite observations and reanalysis. In addition, they perform CSEM v1.2 with\nthe Parallel Ocean Program (POP2) and Community Atmosphere Model simulations\n(CAMS 4.3)toshow that reduced summertimeMiddleEast dust enhances warming in\nthe western Indian Ocean during boreal autumn and induces easterly winds that shoal\nthe eastern thermocline. This tilts of the thermocline, deepening it in the west and\nshoaling it in the east, and reinforces the SST gradient through the Bjerknes and\nwind–evaporation–sea surface temperature (WES) feedback, amplifying a positive\nIODpattern, and viceversa.\nOverall, the central findings are scientifically interesting and clearly relevant to Indian\nOcean climate variability, particularly the assessment that summertime Middle East\ndust explains a large fraction of IOD interannual variance, potentially surpassing\nENSO’s influence during boreal autumn. However, characterizing Middle Eastern\ndust aerosols as thedominant external driver of IOD variability is currently overstated.\nAmore explicit differentiation between the impacts of Middle Eastern dust and those\nof Central and East Asian dust emissions is required, as both may modulate the IOD.\nFurthermore, additional forcings and modes of variability, including anthropogenic\naerosols, greenhouse gases, the Madden–Julian Oscillation (MJO), and the Pacific\nDecadal Oscillation (PDO), can also affect IOD variability during boreal autumn.\nThese contributions should be carefully separated and quantified before making the\nstrong claim that MiddleEast dust isthedominant external driver oftheIOD.\nReply: We are grateful to the reviewer for his/her insightful and very valuable\nsuggestions, which have significantly contributed to the enhancement of our\nmanuscript. We have carefully addressed the comments and made the necessary\nrevisions, hoping that the updated version meets the journal's standards. Our\nresponses to each of the comments and suggestions are as follows. The referee’s\noriginal comments are shown in blue. Our replies are shown in black. The\ncorresponding changes in themanuscript are shown in Italicblack.\n1. Dust variability can both drive and respond to IOD variability. However, this\nbidirectional relationship does not yet appear to be well constrained in the authors’\nanalysis of dust–IOD interactions, partly because of substantial uncertainty in the\nrepresentation of dust emissions in the global climate model CESM v1.2 (Kok et al.,\n2022; main text reference 18). Given this known bias, the authors should more clearly\njustify their choice of CESM v1.2 with CAMS 4.3 for this study and explain how this\nunderestimatemight affect theirconclusions regarding dust–IOD interactions.\nReply: We thank the reviewer for this insightful comment regarding the bidirectional\nnature of dust-IOD interactions and the known uncertainties in modeled dust\nemissions.Weagree that thiscomplexity requires careful consideration.\nThe core reason for our model choice is to isolate the climatic impact of dust without\nthecomplication ofdust emission response to climate.Therefore, werequired amodel\nframework that allows dust aerosol concentrations to be prescribed, creating a\none-way forcing. Moreover, the prescribed dust field used in our study has been\nvalidated byprevious studies(Buchard et al., 2017;Che et al., 2022).\n(1) Choice of CAM version: We selected the Community Atmosphere Model version\n4.3 (CAM4.3) because it supports prescribed dust mixing ratios. This capability is\nessential for our experimental design. In contrast, later versions (CAM5 and CAM6)\nfeature more complex aerosol-climate interactions where dust is computed online\nbased on evolving meteorological and surface conditions, making it difficult to\nprescribefixed values ofdust loading.\n(2) Choice of CESM version: Consequently, we used CESM v1.2, which is\ncompatible with CAM4.3. The newer CESM v2 is exclusively coupled with CAM6,\nwhich employs the online dust scheme and the bidirectional dust-climate coupling we\nsought toavoid.\nThe additional explanation has been incorporated into the supplementary information\nofourrevised manuscript forclarity.Wealso cited ithere for convenience.\nThe atmospheric component of CESM, the Community Atmosphere Model version 4.3\n(CAM4.3), incorporates several major improvements in its physics parameterizations\ncompared to previous CAM versions. We choose this version instead of the more\nupdated Community Atmosphere Model version 5 (CAM5) or Community Atmosphere\nModel version 6 (CAM6) because dust aerosols can be prescribed in this version,\nwhile in later versions it is computed online by meteorological variables and soil\nfeatures, which cannot be easily controlled. Furthermore, we employed CESM version\n1.2 rather than the updated CESM version 2 because the latter is exclusively coupled\nwith CAM6. In CAM6, dust mixing ratios are computed interactively through a\nbidirectional coupling with meteorological conditions, which again limits the ability\ntoconstrain dust concentrationsfor our experimental design.\n2. Prior work has linked anthropogenic aerosols and greenhouse gases to Indian\nOcean warming and IOD-like patterns, and has also shown that Middle East and West\nAsian dust influences the South Asian monsoon and Arabian Sea SST (see Banerjee\nand Kumar, 2016; Jin et al., 2014; Zheng and Hoteit, 2025).Also, It has been shown\nthat Indian summer monsoon rainfall responds positively to Middle East dust (Jin and\nYang, 2014). These findings from previous studies should be discussed explicitly in\nthemain manuscript.\n-Banerjee, Priyanka, and S. Prasanna Kumar. \"ENSO modulation of interannual\nvariability of dust aerosols over the northwest Indian Ocean.\" Journal of Climate 29,\nno.4(2016): 1287-1303.\n-Jin, Qinjian, Jiangfeng Wei, and Zong‐Liang Yang. \"Positive response of Indian\nsummer rainfall toMiddle East dust.\" Geophysical Research Letters 41, no. 11(2014):\n4068-4074.\n-Zheng, Yiling, and Ibrahim Hoteit. \"Asymmetric impacts of Indian Ocean Dipole on\nsummer climate over Arabian Peninsula.\" Geophysical Research Letters 52, no. 20\n(2025): e2025GL118195.\nReply: Thanks for your helpful suggestions. We have now explicitly discussed the\nimportant findings from the cited studies in the revised manuscript. This discussion\nprovides a crucial context for our work by highlighting the established roles of\nanthropogenic forcings and regional dust in shaping the Indian Ocean climate system,\nthereby helping to better position our specific findings on the dust-IOD connection\nwithinthebroader literature.Wealso cited thediscussion here.\nMost studies attribute IOD variability to external forcing 4,9, internal air–sea\ninteractions and remote El Niño-Southern Oscillation (ENSO) influences 1,10,11, yet the\nrole of aerosols remains largely unexplored. The TIO is located close to the world’s\nsecond largest dust source – the Middle East deserts. Previous studies have identified\nthe far-reaching impacts of Middle East dust on the South Asian monsoon, Arabian\nSea SST,andIndian summer monsoon rainfall12-14.\n3. Please use other satellite dust AOD products to check if the observed dust AOD\npattern variability is reliable. In particular,since MODIS DOD has known bias, please\ncross-validatewith IASI-DAOD orotherindependent datasets (such as CALIPSO).\nReply: Thanks for your insightful and constructive suggestions. Due to its sparse\norbital tracks and significant data gaps caused by narrow swath width, CALIPSO\nlacks the continuous spatial coverage required for robust pattern analysis of dustAOD\nvariability. This sampling inadequacy is particularly problematic for dust studies,\nwhere episodic dust events and complex transport patterns require continuous spatial\nmonitoring to reliably characterize variability patterns. Additionally, frequent cloud\ncontamination further reduces data availability, compounding the spatial\nrepresentation issues. We therefore rely on IASI-DAOD and other independent\ndatasets for cross-validation and checking if the observed dust AOD pattern\nvariability isreliable.\nWe found that the SST dipole structure is highly robust, as confirmed by multiple\nindependent dust datasets, including MODIS dust optical depth, MERRA-2 dust\ncolumn mass density, station dust frequency, MISR non-spherical aerosol optical\ndepth, and IASI dust optical depth (Fig. R1 and Fig. R2, the same as Extended Data\nFig.5and Fig. 1din therevised manuscript).\nFig. R1. The regression coefficients of detrended SON SST on the detrended JJA\ndust activity proxies averaged overthe Middle East. These dust proxies include (a)\nMODIS dust optical depth (2003-2023), (b) MERRA-2 dust column mass density\n(2003-2023), (c) station dust frequency (1980-2023), and (d) IASI dust optical depth\n(2008-2021). Note that the regression coefficients in all figures are taken as their\nnegative values corresponding to dust reduction. The area with white dots passed the\nsignificance test ofp ≤ 0.05byStudent's t-test.\nFig. R2. Similar to Fig. R1, but for the detrended JJA MISR non-spherical\naerosoloptical depth averaged overthe MiddleEastfrom 2003to2023\nWe also incorporated additional introduction for the validating dataset and cited it\nhere.\nDust Aerosol Optical Depth (DAOD) retrievals from the Infrared Atmospheric\nSounding Interferometer (IASI) onboard the Metop-A satellite was also used to\nprovide an independent validation of dust patterns 56. IASI derives DAOD from\nthermal infrared spectral measurements, thereby providing 24-hour observational\ncapability and addressing challenges associated with surface reflectance over bright\ndeserts that affect visible-based sensors (e.g., MODIS) 57. The analysis utilizes the\nIASI-A product from 2008 to 2021, a period selected for its consistent data\navailabilitytoensureastableandhomogeneous dataset forrobustcross-validation.\n4. Line #72: MISR only provides aerosol data from approximately year 2000\nonward, whereas the caption of Fig. 1 states the MISR non-spherical aerosol optical\ndepth data from 1980 to 2023. This should be corrected. If other datasets are\ncombined for the pre-MISR period, this should be clearly mentioned in the figure\ncaption.\nReply: Thank you for catching this error. You are correct that MISR data are only\navailable from 2000 onward. We have corrected the caption for Figure 1 to reflect the\naccurate time range (2000–2023), and we apologize for the oversight. We also cited\nthecorrected figurecaption here for convenience.\nFig. 1. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on the\nMiddle East regional-averaged station dust frequency (averaged over the 10°S~30°N,\n35°E~70°E). (b) Similar to a, but for the JJA MISR non-spherical aerosol optical\ndepth (AOD). (c) Timeseries of standardized JJAstation dust frequency (the solid line)\nfrom 1980 to 2023, and JJA MISR non-spherical aerosol optical depth (the dashed\nline) from 2003 to 2023. (d-g) The regression coefficients of SON meteorological\nfields on the JJA MISR non-spherical aerosol optical depth averaged over the Middle\nEast. These meteorological fields include (d) sea surface temperature. (e) meridional\nmean (20°S-20°N) vertical cross section of temperature (shaded) and mixed layer\ndepth (the gray dashed line), (f) sea level pressure (shaded) with 850 hPa zonal and\nmeridional wind (UV, vectors, m/s), and (g) meridional mean (20°S-20°N) vertical\ncross section of geopotential height (GPH, shaded) with zonal and vertical wind (UW,\nvectors, m/s). The y-axes on the right in (e) corresponds to the response of mixed\nlayer depth. The regression is currentlydone for individual years. The area with white\ndots passed thesignificance test of p ≤ 0.05byStudent'st-test.\n5. In Fig. 1a, it is not clear whether the regression is performed on climatological\nmeans or on individual years. I assume the regression is currently done for individual\nyears, comparing dust during JJAwithother meteorological values during SON.\nReply: Sorry for the unclear explanation and you are right.The regression is currently\ndone for individual years, comparing dust during JJA with other meteorological\nvalues during SON. We have clarified it in the figure caption and cited it here for\nconvenience.\nFig. 1. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on the\nMiddle East regional-averaged station dust frequency (averaged over the 10°S~30°N,\n35°E~70°E). (b) Similar to a, but for the JJA MISR non-spherical aerosol optical\ndepth (AOD). (c) Time series of standardized JJA station dust frequency (the solid\nline), and JJA MISR non-spherical aerosol optical depth (the dashed line) from 1980\nto 2023. (d-g) The regression coefficients of SON meteorological fields on the JJA\nMISR non-spherical aerosol optical depth averaged over the Middle East. These\nmeteorological fields include (d) sea surface temperature. (e) meridional mean\n(20°S-20°N) vertical cross section of temperature(shaded) and mixed layer depth (the\ngray dashed line), (f) sea level pressure (shaded) with 850 hPa zonal and meridional\nwind (UV, vectors, m/s), and (g) meridional mean (20°S-20°N) vertical cross section\nof geopotential height (GPH, shaded) with zonal and vertical wind (UW,vectors, m/s).\nThe y-axes on the right in (e) corresponds to the response of mixed layer depth. The\nregression is currently done for individual years. The area with white dots passed the\nsignificancetest of p ≤ 0.05byStudent'st-test.\n6. The rationale for using different time periods for the datasets is unclear, such as\nmost satellite dust DOD cover 2003–2023, while dust station data spans 1980–2023.\nThe manuscript should clarify the reason and discuss any resulting limitations and\nuncertainties inthe analysis.\nReply: Thanks for pointing it out. We opted for different time periods because the\navailable data lengths vary across datasets. Our primary rationale for utilizing the\nlonger station record (1980-2023) was to maximize the length of the temporal series\nfor more robust results. Satellite data become available only since 2003, whereas\nstation data can extend back to 1980. To minimize errors arising from insufficient\nsample sizes, we utilized the longest possible time span for each dataset. Moreover,\nthe regression patterns derived from these different time periods exhibit considerable\nconsistency (Extended Data Fig. 7 & 8 in the revised manuscript), further\ndemonstrating that our findings are not sensitive to the specific time frame selected\nbut are robust across different intervals. We also added the supplementary discussion\ninthe revised manuscript and cited ithere.\nMoreover, the regression patterns derived from these different time periods exhibit\nconsiderable consistency (Extended Data Fig. 7 & 8), further demonstrating that our\nfindings are not sensitive to the specific time frame selected but are robust across\ndifferentintervals.\n7. Lines 117-120: “Reduced Middle East dust emissions are statistically linked to\natmospheric and oceanic states resembling pIOD events, suggesting that decadal\nvariations in dust loading likely exert an important, previously underappreciated\ninfluence on tropical Indian Ocean climate.” It is not clear whether the authors\nremove ENSO-related variability from SST fields (in Fig. 1 and Extended Figs 1–5)\nbefore computing the IOD index.Also, there are several confounding factors that can\nalso influence the IOD, such as the MJO and the PDO. The manuscript should detail\nhow these confounding factors are accounted for or removed in the dust–IOD\nstatistical analyses. Please cite that previous work here, especially when stated as\n“previously underappreciated influence ontropical Indian Ocean climate.”\nReply: We thank the reviewer for raising this important point. We have removed\nENSO-related variability from the SST fields by linear regression (Fig. 1 and\nExtended Figs 1–5 in the revised manuscript) prior to computing the IOD index, in\norder to isolatetheinfluence ofENSOonIOD variability.\nRegarding the Madden–Julian Oscillation (MJO), which is a dominant mode of\nintra-seasonal (30–100 days) variability, we note that its behavior is primarily\nmodulated by the phase of the IOD rather than directly influencing the IOD itself\n(Benedict et al., 2015; Chen et al., 2022). To minimize interference from\nintra-seasonal climate signals, we employ a seasonal mean, which smooths out these\nhigher-frequency variations.\nConcerning the Pacific Decadal Oscillation (PDO), we acknowledge its potential role\nin modulating IOD variability. However, in the present analysis, the PDO was not\nexplicitly removed due to its relatively weaker teleconnection with tropical Indian\nOcean climate on interannual timescales (Krishnamurthy and Krishnamurthy, 2016).\nTo isolate the independent influence of dust from that of the Pacific Decadal\nOscillation (PDO), we performed correlation analysis again but after regressing out\nthePDO signal, as shown in Fig. R3.The result confirms that dust forcing maintains a\npersistent and independent impact on the tropical Indian Ocean dipole pattern (Fig.\nR3).\nFig. R3. The regression coefficients of detrended SON SST on the detrended JJA\ndust activity proxies averaged over the Middle East by regressing out the\nimpacts of PDO. These dust proxies include (a) MODIS dust optical depth\n(2003-2023), (b) MERRA-2 dust column mass density (2003-2023), (c) MISR\nnon-spherical AOD (2003-2023), and (d) station dust frequency (1980-2023). Note\nthat the correlation coefficients in all figures are taken as their negative values\ncorresponding to dust reduction. The area with white dots passed the significance test\nof p ≤ 0.05 by Student's t-test. SST and dust proxies have been regressed out by\nPDOconsidering PDO’s considerate global impact.\nWe have now included a detailed description in the manuscript outlining how these\npotential confounding factors—including MJO and PDO—were addressed or\naccounted for in our dust–IOD statistical analyses. We also cited the corresponding\nexplanationin therevised manuscript here for convenience.\nThe potential signal of other climate variability, such as ENSO, Pacific Decadal\nOscillation (PDO) and Madden Julien Oscillation (MJO), were largely removed from\nour analysis. Specifically, ENSO and PDO were removed by regressing out the\nNiño3.4 index (https://psl.noaa.gov/gcos_wgsp/Timeseries/Nino34/) 71as well as the\nPDO index (https://psl.noaa.gov/gcos_wgsp/Timeseries/PDO/) from both the DMI\ntime series and SMED time series. The MJO is primarily modulated by the IOD phase\nrather than directly influencing the IOD itself 72,73, and we have effectively smoothed\noutits intra-seasonal variabilityby applying alow-passfilter to seasonal mean data.\n8. Lines 131-135: The use of summertime station-based dust-frequency\nobservations to scale annual-mean dust mass density in CESM v1.2 needs further\njustification. Could you please clarify why the other seasons were not considered?\nThis could potentially underestimate the true annual mean dust mass density. The\nauthors should also clarify thismethodological choiceand itspossibleimplications.\nReply:Wethank thereviewer for this comment concerning our methodological useof\nsummertime observations and for identifying the inadvertent error. We have corrected\nit to the intended phrasing: \"Specifically, the summertime-mean dust mass density in\nCESM was scaled each year according to the corresponding summertime\nstation-based dust-frequency observations over theMiddleEast.\"\nThe primary reason for focusing on the summer (JJA) season is that dust activity in\nthe Middle East exhibits strong seasonality, with emissions and long-range transport\nbeing the most intense and spatially coherent during summer (Extended Data Fig. 1 in\nthe revised manuscript). This makes the summer season the most representative\nperiod for capturing the dominant dust patterns and their interannual variability that\nare central to our study.This seasonal focus is consistent with the approach of several\nprevious regional dust-climatestudies (Liu et al., 2023; Xiaet al., 2022).\nWe acknowledge the reviewer's concern that this approach might not fully capture the\nannual mean. To address this, we conducted a validation exercise by comparing our\nscaled annual-mean dust mass density against independent annual-mean data from the\nMERRA-2 reanalysis (Fig. R4). The results showed a reasonable consistency between\nthe two, increasing our confidence that the summertime-based scaling captures the\nkey spatial features oftheannual-mean dustdistributioneffectively.\nFig. R4. Distribution of annual-mean dust AOD over the Middle East. (a) the\nCESMmodel, (b) MERRA-2reanalysis, and (c) theirdifference (aminus b).\n9. Line # 142- “Both the spatial configuration and the intensity of these anomalies\nstrongly indicate that SMED acts as an external forcing capable of generating\npronounced interannual variability in the TIO”. This conclusion is currently\noverstated, as the authors are not investigating the impact of other aerosols on IOD\nchanges. How do PDO and MJO manifest in changes in IOD, since they are also\naffected by dust forcing, which in turn can affect the positive and negative phases of\nIOD?\nReply: We thank the reviewer for this insightful comment. We agree that stating\nSMED acts as an external forcing based solely on our current analysis may be too\nstrong, as we have not fully isolated its effect from other potential drivers like PDO\nand MJO. We acknowledge that these climate variabilities can co-vary with both dust\nemissions and IOD phases, and that future studies using targeted numerical\nexperiments are needed to fully disentangle their relative contributions and causal\npathways.\nIn response, we have toned down this specific conclusion in the manuscript to state\nthat our results suggest SMED can act as a significant external forcing, rather than\ndefinitively indicateit does.Wealso cited it here for convenience.\nBoth the spatial configurationand theintensity of these anomalies suggest that SMED\ncould act as a significant external forcing capable of generating pronounced\ninterannualvariabilityin theTIO.\nFurthermore, we have added a new paragraph to the discussion section to explicitly\naddress the potential confounding roles of PDO, MJO, and other aerosols, while also\nacknowledging the limitations of the current study that warrant further investigation\ninfuture work.\nWhile this study demonstrates a robust statistical linkage between SMED and IOD\nvariability, reinforced by sensitivity experiments that seek to isolate the dust influence\nfrom other factors (e.g., ENSO and PDO), we acknowledge that conclusively\nattributing this signal solely to dust remains challenging. Despite our efforts, the\npotential for complex, non-linear interactions with other aerosols or low-frequency\nclimate modes cannot be entirely ruled out. Therefore, we conclude that SMED likely\nacts as a significant external forcing on the IOD, but its independent contribution and\nthe precise pathways involved should be further validated through future idealized\nmodeling studies designed todefinitivelyseparate theseintertwinedinfluences.\n10. Lines # 57-60, Supplementary Information (extended Fig. S6). Why, in the\nextended Fig. 6a, are they not including theSMEDversus DMIsim? It would begood\nto plot the correlation coefficient and also the SVD analysis for this simulation run.\nAs shown in Fig. S6b, the observations are quite off for the SMED and DMI patterns\nfrom 2010onwards.What could bethe reason for that?\nReply: We thank the reviewer for these insightful questions regarding Extended Data\nFig.6.\n(1) The correlation and SVD for the SMED vs. DMI simulation: We agree with the\nreviewer that this is a valuable suggestion. In the revised manuscript, we have now\nincluded the spatial correlation coefficient between the simulated SMED and DMI\npatterns in the caption or directly on the panel for Fig. R5a here, same as Extended\nData Fig. 9a in the revised manuscript. Furthermore, we have conducted the\nrecommended SVD analysis between the dust emissions and SST fields from this\nspecific simulation. The results of this SVD analysis, which robustly capture the\ndominant dust-IOD coupling mode, have been added as a new supplementary figure\n(Fig. R6 here, same as Extended Data Fig. 6e-f in the revised manuscript) to\nstrengthen our mechanisticinterpretation.\nFig.R5. Thestandardized DMI andSMEDfrom 1980to 2020.The timeseries are(a)\nsimulated DMI forced by dust (solid cyan line), simulated DMI in the historical run\nwith all forcing (dashed cyan line), and simulated SMED (solid red line) and (b)\nobserved DMI andSMED.Allthetimeseries arestandardized for comparison\nFig. R6. The spatial pattern of the first SVD mode between dust proxies and\ntropical Indian ocean SST. The first mode of SVD between (a) detrended JJA MISR\nnon-spherical AOD in the Middle East, accounting for 64.2% of the covariance and\n(b) detrended SON tropical Indian Ocean SSTfrom 2003to 2023.(c-d) Similar to a-b,\nbut for the station dust frequency from 1980 to 2023. This mode account for 65.7% of\nthe covariance. (e-f) Similar to a-b, but for the dust AOD in the CESM simulation.\nThismode account for 71.2%of thecovariance.\nWealso supplemented corresponding discussion about thisfigure and cited ithere.\nComplementary singular value decomposition (SVD) between SMED and SON SST\nfurther supports this relationship, showing that theleading coupled mode corresponds\nto negative SMED signals and a pIOD-like SST pattern in the TIO (Extended Data\nFig. 6). Correlations exceed 0.6 for the modes derived from both MISR AOD,\nstation-based dust frequency observations, and dust AOD in the CESM simulations,\nhighlighting theconsistency of theresults.\n(2) The model-observation discrepancy post-2010: You are right that there might be a\ndivergence between the observed and simulated SMED-DMI relationship after\napproximately 2010. The primary factors likely include the increased influence of\nother climate forcings and limitations in the observed dust record. Specifically, the\nperiod post-2010 was characterized by strong and persistent climate anomalies,\nincluding a notable prevalence of Central Pacific El Niño events and a strong positive\nphase of the PDO (Freund et al., 2019; Lin et al., 2018). These modes exert a\npowerful influence on the IOD, which may have temporarily overshadowed or\nmodulated the dust-IOD linkage in the observations—a nuance not captured by the\nidealized \"SMED-only\" forcing simulation. Additionally, the observational dust data\nitself may contribute to the discrepancy, as this period encompasses the transition\nbetween satellite sensors and potential calibration drifts. Such technical factors can\nintroduce inhomogeneities in the long-term dust trend that are unrelated to true\nclimatevariability.\nWe have revised the text to explicitly acknowledge this period of discrepancy and\nprovide the above discussion on its potential causes, thereby presenting a more\ncompleteand critical assessment of ourresults.\nNotably, a possible post-2010 divergence between the observed and simulated\nSMED-DMI relationship may stem from the increasing influence of other climate\nforcings (e.g., Central Pacific El Niño, PDO) and limitations inherent in the\nstation-based dustrecord28,29.\n11. Line 474: Please cite the methodology and relevant references related to the\nimplementationof SingularValueDecompositionAnalysis.\nReply: Thanks for pointing this out. We have cited the methodology and relevant\nreferences.Wealsocited them here forconvenience.\nThe Singular Value Decomposition (SVD) is a multivariate statistical method used to\nidentify coupled spatial-temporal relationships between two covarying fields by\ndecomposing their cross-covariance matrix 81,82. This method has been widely used in\nthestatistical analysis ofclimate data 83,84.\n12. Lines #212-215; “Once transported, these aerosols exert a strong radiative forcing\nby absorbing solar radiation, which heats the upper troposphere, while simultaneously\nreducing theamount ofshortwave radiation reaching theocean surface”.\nIt depends on the dust mineralogy as well; without constraining that over SMED, it is\nperhaps uncertain to quantify that.Authors should also consider providing the heating\nrate map rather than showing changes in surface radiative flux. That is important for\nassessing how much the radiative heating rate changes. To correctly determine\nvertical radiative heating rates, authors could consider the vertical profiles of dust\nextinction. Authors should also use the CALIPSO data or the CESM-based vertical\nprofileof thedustextinctions to identify changes in heating rates.\nReply: We thank the reviewer for this insightful comment and for the constructive\nsuggestions to strengthen our analysis. We have addressed each point as detailed\nbelow.\n(1)Regarding dust mineralogy:\nWe agree with the reviewer that dust mineralogy is a key factor influencing radiative\nforcing. As our study did not employ a spatially resolved mineralogy scheme for the\nSMED region, we have now acknowledged this as a source of uncertainty in the\nrevised manuscript.Asentence has been added tothe Discussion section in therevised\nmanuscript andcited here for convenience:\nIt should also be noted that the magnitude of the dust radiative forcing is also\ndependent on dust mineralogy, which is not specifically constrained for the Middle\nEast region in our simulations, which may introduce some uncertainty in the\nquantitativeestimates.\n(2)Regarding theheating rateand vertical profiles:\nWe fully agree that analyzing the atmospheric heating rate provides a more direct\nmetric for assessing the impact of dust absorption on the atmospheric temperature\nstructure. In response to the reviewer's suggestion, we have added a new figure (Fig.\nR7b, the same as Extended Data Fig. 13b) showing the vertical heating rate profile in\nthe model. We also analyzed the spatial distribution of the dust-induced atmospheric\nheating rate (Fig. R7c, thesame asExtended Data Fig. 13c).\nFurthermore, to correctly interpret the vertical structure of this heating, we have\nfollowed the reviewer's advice and analyzed the vertical profile of dust extinction\nfrom our CESM simulations (Fig. R7a, the same as Extended Data Fig. 13a). The\nmodel results indicate that the majority of the transported dust aerosol is confined\nwithin the troposphere. This vertical profile supports the pattern of heating rates\nshown in our new figure, confirming that thedominant radiative heating occurs within\ntheatmospheric column, consistentwith thedust layer's location.\nFig. R7. Dustvertical profileand its corresponding heating rate forreduced dust.\n(a) The vertical profile of dust mixing ratio and (b) heating rate over the transported\narea (10°S~10°N, 35°E~70°E) in the model. (c) The responses of SON 700 hPa\nheating rate to reduced Middle East dust forcing in the CESM. The area with white\ndots passed thesignificance test ofp ≤ 0.05byStudent's t-test.\nWe also added the corresponding text in the revised manuscript to describe the added\nheating rate map and discusses the relationship between the simulated dust vertical\nprofileand theresulting radiativeheating.Wecited it here for convenience.\nOnce transported, these aerosols exert a strong radiative forcing by absorbing solar\nradiation, which heats the upper troposphere, while simultaneously reducing the\namount of shortwave radiation reaching the ocean surface. The dust-induced heating\nrate profile aligns with the simulated vertical distribution of dust extinction, which is\nprimarily confined within the troposphere. This vertical coherence confirms that the\nradiative heating maxima correspond well with the dust layer's location, reinforcing\nthe direct thermodynamic impact of dust radiative absorption on the atmospheric\ncolumn. (Extended Data Fig.13).\n13. Please explain why the TIO low cloud fraction is decreasing, and the high cloud\nfraction is increasing in the reduced-dust over SMED scenarios. How are authors\nconstraining the other aerosols in the CESM v1.2 setup? Are authors only changing\ndust aerosol, or are they also changing other aerosols, such as sea salt aerosol in\nCESMv1.2setup?\nReply: We thank the reviewer for raising these important points. The decrease in TIO\nlow-level cloud fraction and increase in high-level cloud fraction under reduced dust\ncan be attributed to dust-induced changes in atmospheric stability induced by changes\nin vertical temperature profile; this mechanism has been clarified in the revised\nmanuscript.Wealso cited it here for convenience.\nThis vertical redistribution of energy alters atmospheric stability: the lower\natmosphere warms more strongly, while diminished warming in the upper atmosphere\nresults in a relative cooling there (Extended Data Fig. 15). These changes\nsignificantly affect cloud dynamics, driving a decrease in low-level cloud cover and\nanincreaseinhigh-level clouds over thewestern TIO(Fig. 3c, d).\nRegarding the model setup, we confirm that only dust aerosol emissions were\nmodified in our CESM v1.2 experiments. Other aerosols, including sea salt, were\nprescribed at their default, present-day levels and not altered in the sensitivity\nsimulations. We also added supplementary explanation about the aerosol setup in the\nrevised manuscript and cited ithere.\nTo isolate the impact of Middle Eastern dust (10° S–30° N, 35° E–70° E) on\ninterannual IOD variability, we drove CESM with observed summertime dust mass\ndensity over the Middle East for 1980–2020 and diagnosed the resulting IOD index.\nSpecifically, the annual-mean dust loading in CESM was rescaled each year using the\nregion’s summertime station dust frequency observations, generating a transient\ndust-mass time series that faithfully captures observed variability. Other aerosols\nwere prescribed at their default, present-day levels and not altered in the sensitivity\nsimulations.\n14. Lines #225-227 “These changes significantly affect cloud dynamics, driving a\ndecrease in low-level cloud cover and an increase in high-level clouds over the\nwestern TIO (Fig. 3c, d).” Figure S8 and Figure 3 show otherwise. I see an enhanced\nlowcloud fraction from the CSEM runs over theWesternTIO. Pleaseexplain.\nReply: We thank the reviewer for this thoughtful observation, which has allowed us\nto improve the clarity of our figure. We acknowledge that the colormap in the original\nFigures 3 and S8 (now Figure S12) could have been more intuitively designed. To\nclarify, in these figures, the brown shades indeed indicate a decrease in cloud fraction,\nwhile the blue shades indicate an increase. The western TIO region is predominantly\nbrown, supporting our conclusion of a decreased low-cloud fraction there. We thus\nadded additional explanation in the revised manuscript to avoid misunderstanding and\ncited it here forconvenience.\nThese changes significantly affect cloud dynamics, driving a decrease in low-level\ncloud cover (blue shades) and an increase in high-level clouds (brown shades) over\nthewestern TIO(Fig. 3c, d).\n15. Lines #227-230: This needed a detailed investigation of the cloud feedback;\nthe authors should cite past work for this statement here: “The transformation of cloud\nfields further modifies the radiative budget by increasing both longwave and\nshortwave radiation, thereby amplifying the warming signal over the western TIO in\nsynergy withthe direct radiative effects ofreduced dust.”\nReply: We thank the reviewer for this suggestion. We have revised the manuscript to\nprovide a more detailed mechanistic explanation of the cloud feedback, supported by\nkey literature.\nThe text now explicitly states that the increase in shortwave radiation results from\nreduced low-level clouds (e.g., stratocumulus), which decreases planetary albedo.\nConversely, the increase in longwave radiation is linked to enhanced high-level cloud\ncover (e.g., cirrus), which amplifies thegreenhouse effect.\nThis explanation is now grounded in established cloud-radiative feedback theory,\nsupported by previous studies. The revised text presents a logically supported process\nrather than an assertion.\nWealso cited therevised texthere forconvenience.\nThe transformation of cloud fields—characterized by a reduction in low-level cloud\nfraction and an increase in high-level cloud fraction—further modifies the radiative\nbudget (Figs. 3a, b). Specifically, the decrease in low clouds reduces the albedo,\nallowing more shortwave radiation to penetrate and warm the ocean surface 37,38.\nConcurrently, the increase in high clouds enhances the atmospheric greenhouse effect\nby absorbing and re-emitting longwave radiation, which inhibits radiative cooling at\nthe surface 37,39. These two effects act in synergy with the direct radiative effect of\nreduced dust loading to amplify the warming signal over the western TIO. This cloud\nfeedback process, particularly the low-cloud albedo feedback, is well-established as a\nkey positivefeedback mechanism modulating TIOvariability.\n16. Lines 232-237: Did the authors run the CESM v1.2 simulation from 1980 to 2020,\norfrom 2010to 2020?\nReply: Sorry for the misunderstanding. We ran the CESMv1.2 simulation from 1980\nto 2020. The regression coefficients shown here were scaled by the magnitude of dust\ndecreasing trend from 2010to 2020intheCESM.\nWealso modifiedthe figure caption to clarify thisand cited ithere for convenience.\nFig. 3. The underlying mechanism accounting for the SMED-IOD teleconnection. The\nregression coefficients of SON meteorological fields onto JJA dust aerosol optical\ndepth (AOD) from 1980 to 2020. These coefficients were scaled by the magnitude of\ndust decreasing from 2010 to 2020 in the CESM. These meteorological fields include\n(a) net shortwave flux at surface, (b) net longwave flux at surface, (c) high cloud\nfraction, and (d) low cloud fraction. The area with white dots passed the significance\ntest ofp ≤ 0.05by Student'st-test.\n17. Line 288 and other places: “Furthermore, analysis of the month-to-month\nevolution of the IOD signal (figures omitted)”. The figure not shown does not look\nideal. Please considerincluding all referenced figures inthesupplementary materials.\nReply: Thanks for your helpful suggestions. We have included all figures in the\nsupplementary materials following your suggestions. We also cited it here for\nconvenience.\nFurthermore, analysis of the month-to-month evolution of the IOD signal (Extended\nData Fig. 2) shows that anomalies peak in both the amplitude and statistical\nsignificance during SON, corroborating the selection of SON as the representative\nseason for IOD-related analysis.\nFig. R8. Correlation coefficients between JJA dust proxies and DMI in different three\nmonths. These dust proxies include station dust frequency (the yellow line,\n1980-2023), MODIS dust optical depth (the orange line, 2003-2023), MISR\nnon-spherical AOD (the black line, 2003-2023), and MERRA-2 dust column mass\ndensity (the green line, 2003-2023). The dashed grey line indicates the position 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“MiddleEastDustasaDominantExternalDriverofthe\nIndianOceanDipole” (NCOMMS-25-75253-T).\nResponsestoReview02’scomments\nSummary:\nThis study examined how mineral dust aerosols emitted from the Middle East a7ect\nthe Indian Ocean Dipole (IOD) using both observational data and the CESM model.\nWhile previous studies have examined how anthropogenic aerosols effect IOD, how\nMiddle Eastern dust aerosols affect the IOD is largely unclear. The study addresses a\nvery important question in the region, and the topic is very novel and inspiring. The\noverall analysis is quite comprehensive, however, some details in model setting, the\nfeedback link from subtropical dust loading totropical SSTs, and the magnitude ofthe\nradiative forcing need further clarification. Adding some discussion about the\nuncertainties indata and methodology also would strengthen thepaper.\nReply: We are grateful to the reviewer for his/her insightful and very valuable\nsuggestions, which have significantly contributed to the enhancement of our\nmanuscript. We have carefully addressed the comments and made the necessary\nrevisions, hoping that the updated version meets the journal's standards. Our\nresponses to each of the comments and suggestions are as follows. The referee’s\noriginal comments are shown in blue. Our replies are shown in black. The\ncorresponding changes in themanuscript are shown in Italicblack.\nMajorcomments\n1. Usually, the lifetime of dust aerosols in the atmosphere, especially in the lower\ntroposphere, is fairly short, from several days to a couple of weeks. The statement\n“multi-month atmospheric residence time of dust aerosols” (line 60) needs more\nevidence to support. I think it is important to understand whether the strong\nconnection between JJA Middle Eastern dust and SON IOD is primarily due to an\nusually long residence time of summertime Middle Eastern dust, or instead a delayed\nresponse through ocean-atmosphere feedback. It would also be helpful to clarify\nwhether MiddleEastern dust in SONhas any impact ontheIOD inSON.\nReply:Thank you foryour valuablecomments and insightful suggestions.\nWe agree that the near-surface residence time of dust aerosols is relatively short. The\noriginal statement referring to a \"multi-month atmospheric residence time\" has been\nrevised for greater precision, and supporting references have been added. The revised\ntext now emphasizes that under specific conditions—such as when dust is lifted by\nstrong upward air currents into the stratosphere or when fine-grained particles\nundergo long-range transport—certain fractions of dust aerosols may exhibit extended\nresidence times (e.g., exceeding one month). We have cited relevant studies on\ntransatlantic transport of Saharan dust and trans-Pacific transport of Asian dust (Kim\net al., 2019; Prospero et al., 2021; Yu et al., 2019a; Yu et al., 2019b), illustrating that\nunder favorable atmospheric circulation conditions, fine dust particles can remain in\ntheatmosphere from several weeks tomonths.\nMoreover, your distinction between the two possible mechanisms—long residence\ntime versus delayed ocean–atmosphere feedback—is indeed critical. The\nocean–atmosphere interaction is likely the dominant mechanism. Specifically, Middle\nEastern dust in summer may influence the radiative balance and thus affect sea\nsurface temperature patterns over the Arabian Sea and western Indian Ocean by the\ndelayed response through ocean-atmosphere feedback. These effects may\nsubsequently trigger an IOD event in the following season through air–sea\ninteractions.\nAdditionally, Middle Eastern dust in SON has limited direct influence on the IOD\nduring the same season as validated by various dust proxies (Fig. R1). This may be\nattributed to the generally weaker dust activity in the Middle East during autumn\n(Extended Data Fig. 1c in the revised manuscript), as well as the fact that the IOD is\nalready in its mature phase by SON, during which coupled ocean–atmosphere\nprocesses dominate its evolution and external forcings such as dust play a relatively\nminorrole.\nFig. R1. The regression coefficients of detrended SON SST on the detrended\nSON dust activity proxies averaged over the Middle East. These dust proxies\ninclude (a) MODIS dust optical depth (2003-2023), (b) MERRA-2 dust column mass\ndensity (2003-2023), (c) station dust frequency (1980-2023), and (d) IASI dust optical\ndepth (2008-2021). Note that the regression coefficients in all figures are taken as\ntheir negative values corresponding to dust reduction.The area with white dots passed\nthesignificance test ofp ≤ 0.05byStudent's t-test.\nWealso incorporated thesediscussions here for convenience.\nThis seasonal offset is physically plausible given the multi-month atmospheric\nresidence time of dust aerosols under specific conditions (strong upward air currents\nandlong-range transportfor fine-grained particles)andthe delayed responsethrough\nocean-atmosphere feedback 19-22. Additionally, the dust activity over Middle East in\nSON has limited direct influence on the IOD, as weaker autumn dust activity\ncoincides with the IOD's mature phase (Extended Data Fig. 1c), during which\ncoupled ocean-atmosphere processes dominate and the role of external forcings like\ndust isdiminished.\n2. The averaging area for the summertime Middle East Dust (SMED) index was not\nvery clear (e.g., in Fig, 1c). As shown in Figs. 1a and 1b, the domain only covers\nsouthern Middle East, and it also includes the Horn of Africa, which is often not\ngeographically considered part the Middle East. In the Methods section, there is a\ndefinition of Middle East dust (“10° S–30° N, 35° E–70° E”, line 522). But It’s\nunclear if this the averaging region used to calculate all the time series of SMED.And\nalso, the domain is a quite large, while most of the Middle East dust are located to the\nnorth of 10° N (supplementary Fig. 1).Are the results sensitive to the averaging area\nofSMED?Would thecorrelations change muchif onlyaveraging over theregion with\nhigh dust loading, e.g., tothe north of10° N?\nReply: We sincerely thank the reviewer for raising this critical point regarding the\nspatial definition of our Summertime Middle East Dust (SMED) index. The selected\ndomain (10°S–30°N, 35°E–70°E) was intentionally designed as an \"effective\ninfluence region\" to capture dust aerosol loading over the Indian Ocean that is\nrelevant to long-range transport and ocean-atmosphere interactions, rather than to\nstrictly adhere to geographical boundaries. This domain aims to include dust aerosols\nover the Arabian Sea and northwestern Indian Ocean—which are positioned to\ndirectly influence local sea surface temperatures via radiative effects—while\nexcluding dust from northern source regions that predominantly undergoes\nshort-range transport and may has limited reach over theocean.\nAdditionally, we have also performed a sensitivity analysis using a domain north of\n10°N (Fig. R2) and found that this domain selection also exhibits a very high\ncorrelation with the original (Extended Data Fig. 5 in the revised manuscript), and all\nkey correlations with the IOD remain nearly identical. We also plot two time series,\none derived from the region north of 10°N (10°N–30°N, 35°E–70°E) and the other\nfrom the region north of 10°S (10°S–30°N, 35°E–70°E). Both time series exhibit\nsimilar temporal variability across the entire study period (Fig. R3). The\nabove-mentioned results further confirm that our results are robust to the specific\nlatitudinal boundary. We have clarified this physical rationale and added a note on\nrobustness in therevised manuscript.\nFig. R2. Theregression coefficients ofdetrended SONSSTonthedetrended JJAdust\nactivity proxies averaged overthe MiddleEast (averaged overthe 10°N~30°N,\n35°E~70°E)byregressing out theimpacts of ENSO.These dust proxies include(A)\nMODIS dust optical depth(2003-2023), (B) MERRA-2 dust column mass density\n(2003-2023), (C)MISR non-sphericalAOD (2003-2023), and (D) stationdust\nfrequency (1980-2023). Notethat thecorrelation coefficients inall figures are taken\nas theirnegative values corresponding to dust reduction.Thearea withwhite dots\npassed the significance testof p ≤ 0.05byStudent's t-test. SSTand dust proxies\nhave been regressed outbyENSO considering ENSO’sconsiderate global impact.\nFig. R3. Timeseries of dustproxiesanalyzed in thisstudy.Solid linesdenote the\nstandardized June–July–August (JJA)stationdust frequency from 1980–2023,with\nblack and darkred lines representing theregions (10°S–30°N, 35°E–70°E)and\n(10°N–30°N, 35°E–70°E), respectively.Dashed linesindicate theJJAMISR\nnon-spherical aerosol optical depth from 2003–2023,corresponding tothesame two\ngeographicdomains.\n3. The radiative forcing of the dust aerosols are shown in both the coupled CESM\nsimulations and atmosphere-only model (CAM) runs. However, the location of\nradiative forcing directly from the dust aerosols. With reduced Middle Eastern dust\naerosols , one would expect to see enhanced shortwave (SW) fluxes (i.e., warming)\nat the surface extending from theArabian Peninsula southward toward theArabian Sea.\nHowever, this pattern is not clearly shown in the coupled simulation (Fig. S8c)--there\nis a cooling around 12 °N, while the warming single is located further south, round\n6 °N-6 °S, where the climatological dust loading is low. Is this due to a\nteleconnection?\nReply: Thank you for this insightful observation regarding the spatial pattern of\nsurface shortwave radiation response in our coupled model experiments. You have\ncorrectly identified a key nuance in how dust aerosol impacts manifest in coupled\nversus atmosphere-only simulations.\nYou rightly noted that reduced Middle Eastern dust should directly enhance surface\nshortwave fluxes over dust-laden regions from the Arabian Peninsula to the Arabian\nSea. This expected response is clearly present in our atmosphere-only (CAM)\nexperiments, as shown in Extended Data Fig. 14a.The different pattern in the coupled\n(CESM) simulation—with cooling near 12°N and warming displaced farther\nsouth—is indeed attributable to ocean-atmosphere coupling and the associated\nteleconnections as you suggested. The initial radiative perturbation triggers a specific\nchain of adjustments:\n(1) Circulation Adjustment: The surface warming over the western Tropical Indian\nOcean (TIO) enhances convection and establishes a low-pressure anomaly (Fig. 2c).\nThis sets up a basin-scale pressure gradient, driving an anomalous clockwise vertical\ncirculation and generating prevailing anomalous easterly winds at the surface over the\ncentralTIO, oppositetotheclimatological westerlies (Fig. 2c-d).\n(2) Mechanism for the Displaced Warming: These easterly anomalies suppress the\neastward advection of warm water and, through Bjerknes and wind-evaporation-SST\n(WES) feedbacks, help sustain and confine the core of the warming signal further\nsouth, near 6°N–6°S.\n(3) Mechanism for the Cooling near 12°N: Crucially, in the region near 12°N\n(Arabian Sea), the circulation adjustment leads to a strengthening of the prevailing\nclimatological winds (Fig. 2c, vectors). This increase in surface wind speed enhances\nlocal evaporation, which produces a net surface cooling via the WES feedback. This\nwind-driven cooling effectively counteracts and overshadows the direct radiative\nwarming from dust reduction, resulting in the net cooling anomaly observed in\nExtended Data Fig. 14c.\nIn summary, the coupling-induced atmospheric circulation adjustment—particularly\nthe wind speed changes and the subsequent WES feedback—redistributes the thermal\nresponse, explaining both the southward shift of the warming and the localized\ncooling around 12°N.\nTherefore, we have expanded the discussion in the revised manuscript to explicitly\naddress thismechanism.Wealso cited thediscussion here for convenience.\nMoreover, the atmospheric-only simulation exhibits a radiative response that is\nspatially coherent with the dust aerosol distribution (Extended Data Fig. 14), while\nthe coupled simulation displays additional spatial heterogeneity due to oceanic\nadjustments (Extended Data Fig. 11). This redistribution likely stems from\nocean-atmosphere coupling, wherein the initial radiative perturbation triggers\natmospheric circulation adjustments andsubsequent wind-evaporation-SSTfeedbacks,\ncreating secondary radiative perturbations that partially obscure the direct\ndust–radiation signal, which is ultimately manifested as a teleconnected response.\nNevertheless, both experiments consistently support the proposed physical framework\nlinkingMiddle Eastern dust forcing to IODvariability.\n4. In the CAM simulations, enhanced SW fluxes do appear from southern Arabian\nPeninsula towards the Arabian Sea (Figs. S13a, S10a), but mainly over the northern\nand easternArabian Sea.And the changes in SW flux over the western tropical Indian\nOcean are relatively weak. Does this imply a teleconnection that allow the local\nradiative impact of dust aerosols over theArabian Peninsulaand northernArabian Sea\ntopropagate to thetropical Indian Ocean?\nReply: Thank you for this perceptive question, which gets to the heart of the\ndynamical mechanisms at play. You are correct in observing that the enhanced SW\nfluxes in the CAM simulations are concentrated over the northern and easternArabian\nSea, with a weaker direct signal over the western tropical Indian Ocean. This spatial\nstructure indeed implies that the local radiative forcing from reduced dust does not\nremain confined, but rather initiates an atmospheric teleconnection that propagates its\ninfluence downstream.\nThemechanism isa three-step process:\n(1) Local Radiative Forcing & Atmospheric Adjustment: Reduced dust over the\nArabian Peninsula/northern Arabian Sea increases surface SW absorption, creating a\nstrong vertical heating gradient (surface warming, relative upper-level cooling;\nExtended Data Figs.11, 13a).Thismodifies local stabilityand circulation.\n(2) Atmospheric Teleconnection & Downstream Cloud Response: The adjusted\ncirculation and zonal cell (Fig. 2d) act as a teleconnection bridge, transporting the\nperturbation's influence to the western TIO. This alters the vertical thermodynamic\nprofile, driving a characteristic cloud reorganization there: decreased low-level and\nincreased high-level cloud cover (Fig. 3c, d). In the atmosphere-only (CAM) run, this\nteleconnected response is limited by fixed SSTs, explaining the weaker direct\nmaritimesignal.\n(3) Amplification via Ocean-Atmosphere Coupling: This step explains the stronger\noceanic signal in the coupled model. The teleconnected cloud changes (Step 2) now\ninteract with the ocean. Reduced low cloud cover increases surface shortwave\nradiation, leading to SST warming. This warming then activates powerful positive\nfeedback loops: the cloud–SST feedback further suppressing low clouds (Lin et al.,\n2025; Wu et al., 2025; Wyant et al., 1997), the Bjerknes feedback and the\nwind‑evaporation‑SST (WES) feedback. These coupled feedbacks dramatically\namplify and extend theinitial atmospheric perturbation.\nTherefore, the CAM pattern confirms the dust reduction initiates an atmospheric\nteleconnection. The coupling with the ocean is essential to transform this initial\nadjustment into the stronger, basin-scale ocean-atmosphere feedbacks that effectively\ndrive the IOD development, as detailed in our manuscript. We have expanded the\ndiscussion in the revised manuscript to explicitly address this mechanism. We also\ncited thediscussion here for convenience.\nNotably, the spatial pattern of the SW flux response in the CAM simulations, with its\nmaximum over the northern and eastern Arabian Sea rather than uniformly across the\nbasin (Extended Data Fig. 17a &c), suggests that the radiative impact of dust\nreduction initiates an atmospheric teleconnection. The initial perturbation mainly\nover the land likely alters regional atmospheric heating and circulation patterns 46-48,\nwhich in turn redistributes the energy downstream, setting the stage for the more\nextensive and evident ocean-atmosphere signals and feedbacks that propagating to\ntheTIOin thecoupled model.\n5. Regarding the mechanisms of dust radiative forcing, it would be more convincing\nto include figures to show radiative fluxes at the TOA and also vertical heating\nprofiles intheregions withhigh dust loading.\nReply: Weappreciate the reviewer's insightful comment regarding the mechanisms of\ndust radiative forcing. In response, we have added the new figure (Fig. R4 and Fig.\nR5 here) in therevised manuscript (Extended Data Fig. 13and 17) as suggested.\nThese new results clearly illustrate the radiative forcing of reduced dust at the top of\natmosphere and its heating rate, which aligns well with the temperature response in\ntherevised manuscript (Extended Data Fig. 15).\nFig. R4. Dustvertical profileand its corresponding heating rate forreduced dust.\n(a) The vertical profile of dust mixing ratio and (b) heating rate over the transported\narea (10°S~10°N, 35°E~70°E) in the model. (c) The responses of SON 700 hPa\nheating rate to reduced Middle East dust forcing in the CESM. The area with white\ndots passed thesignificance test ofp ≤ 0.05byStudent's t-test.\nFig. R5. The responses of SON radiation fluxes to reduced Middle East dust forcing\nin the CAM. The radiation fluxes include (a) net shortwave flux at surface, (b) net\nlongwave flux at surface, and the net shortwave flux at the top of atmosphere (TOA).\nThearea with whitedotspassed thesignificance test ofp ≤ 0.05byStudent's t-test.\nWealso added thecorresponding discussionand cited it here.\nOnce transported, these aerosols exert a strong radiative forcing by absorbing solar\nradiation, which heats the upper troposphere, while simultaneously reducing the\namount of shortwave radiation reaching the ocean surface. The dust-induced heating\nrate profile aligns with the simulated vertical distribution of dust extinction, which is\nprimarily confined within the troposphere. This vertical coherence confirms that the\nradiative heating maxima correspond well with the dust layer's location, reinforcing\nthe direct thermodynamic impact of dust radiative absorption on the atmospheric\ncolumn. (Extended Data Fig.13).\n6. Some aspects of the model simulation setup are not very clear. In the CESM\ncoupled simulation from 1980 to 2020, besides SMED, what other forcings are\nincluded? Are anthropogenic aerosols and greenhouse gases included in the model?\nDo they vary over time?What’s thespin-up timefor thecoupled run?\nReply: We thank you for raising these important questions regarding our model\nconfiguration, which we are pleased to clarify. In our CESM1.2 coupled simulations,\nonly the emissions of dust aerosols were modified between the control and sensitivity\nexperiments. All other external forcings, including anthropogenic aerosols (e.g.,\nsulfate, black carbon, organic carbon) and greenhouse gases, were included in the\nmodel and prescribed at their default, present-day levels. These non-dust forcings\nwere held constant throughout the 1980–2020 simulation period and did not vary\ninterannually.\nThe coupled model was initialized from a pre-industrial control run and subsequently\nspun up for five years prior to the beginning of our formal experimental period (1980)\n(Xie et al., 2018). This spin-up duration ensured that the model's ocean and\natmosphere components reached astableequilibrium under theprescribed forcing.\nWe have added a detailed description of the experimental design, including the\naerosol and greenhouse gas forcing specifications and the spin-up procedure to the\nrevised manuscript for greater clarity.\nTo isolate the impact of Middle Eastern dust (10° S–30° N, 35° E–70° E) on\ninterannual IOD variability, we drove CESM with observed summertime dust mixing\nratio over the Middle East for 1980–2020 and diagnosed the resulting IOD index.\nSpecifically, the summertime-mean dust loading in CESM was rescaled each year\nusing the region’s summertime station dust frequency observations, generating a\ntransient dust-mass time series that faithfully captures observed variability. Other\naerosols were prescribed at their default, present-day levels and not altered in the\nsensitivity simulations. Five ensemble members—generated by perturbing initial\ntemperature fields over land and ocean—were averaged to suppress internal climate\nnoise. The magnitude of the temperature perturbation is 1×10-14 K 93. The coupled\nmodel was also initialized from a pre-industrial control run and subsequently spun up\nfor five years under present-day forcing conditions prior to the beginning of our\nformal experimental period (1980). This spin-up duration ensured the model's ocean\nand atmosphere components reached a stable equilibrium under the prescribed\nforcing 94. All atmospheric variables and the IOD index were then regressed onto\nregionaldust loadingto quantifythedust-driven changes from1980to 2020.\nMinorcomments\n7. It is mentioned that dust aerosols are prescribed in the model, however, it is not\nclear which dust variable was perturbed. Was it dustAOD, dust mass concentration at\neach pressure level, or emission fluxes? And is dust prescribed only the Middle East\n(leaving the rest of the world dust-free) or globally? It states that “we drove CESM\nwith observed summertime dust mass density “(line 523), but it is unclear which\nobservational dataset was used, and whether the forcing is applied only during the\nsummer months or throughout the year.Are both dry and wet deposition included in\nthemodel?\nReply: We sincerely thank the reviewer for raising these important points regarding\nthe dust aerosol prescription in our model. We have carefully revised the manuscript\ntoclarify the following aspects, as suggested.\n(1)Which dust variable was perturbed?\nIn our simulations, the dust aerosol forcing was implemented by perturbing the dust\nmixing ratio—that is, the three-dimensional field of dust mass concentration across\natmospheric pressure levels. This approach allows us to consistently represent the\nspatial and vertical distribution of dust aerosols. We have now explicitly stated this in\ntherevised textand cited ithere for convenience.\nIn our simulations, the dust aerosol forcing was applied by perturbing the dust mixing\nratio—the3Dmass concentration field.\n(2)Spatial andtemporal scope ofdust prescription\nThe dust perturbation was applied only over the Middle East region (10°S~30°N,\n35°E~70°E), as defined in the study, and only during the summer months\n(June–August). Outside this region and season, dust concentrations remain at their\nmodel climatological levels. The model is not dust-free elsewhere; rather, it uses the\ndefault climatological dust fields in other regions and seasons. We have clarified this\ninthe revised manuscript and cited ithere.\nNotably, the dust perturbation was applied exclusively over the Middle East during\nJJA. Beyond this spatiotemporal domain, the model uses its default climatological\ndust fields.\n(3)Observational dataset and scaling method\nThe dust mixing ratio used to constrain the model is based on the MERRA-2\nreanalysis dataset. To ensure consistency with observed dust aerosol optical depth\n(AOD), we scaled the default dust fields so that the simulated dustAOD matches the\nMERRA-2 dust AOD distribution over the study region. This scaling is applied only\nin summer and only over the Middle East. We have emphasized this in the revised\ntext.\nIn our simulations, the dust aerosol forcing was applied by perturbing the dust mixing\nratio—the 3D mass concentration field. However, the default dust mixing ratio of\nCESM is not sufficiently accurate to reproduce the spatiotemporal distribution of dust\nintheMiddle East because of itsinaccurate dust emission 50,89-91. Toaddress this issue,\nwe first conduct a control run using the default dust concentration file. Then, we\ndivide the monthly dust AOD result of MERRA-2 by the monthly dust AOD in the\ncontrol run to obtain monthly proportionality coefficients. By multiplying these\nmonthly coefficients with the original dust mixing ratio, we can better reproduce the\nspatiotemporal distribution of Middle Eastern dust in the model. Results indicate that\nthe model successfully reproduces the spatial pattern and intensity of the dust in the\nMiddle East (Extended Data Fig. 10a). Notably, the dust perturbation was applied\nexclusively over the Middle East during JJA. Beyond this spatiotemporal domain, the\nmodel uses its default climatologicaldust fields.\n(4)Inclusion ofdepositionprocesses\nBoth dry and wet deposition processes are fully represented in the model, as part of\nthe standard aerosol module in CESM. We have added a statement in the manuscript\ntoexplicitly confirm thisand cited ithere.\nBoth dry and wet deposition processes are fully represented in the model, as part of\nthestandardaerosolmodule inCESM89.\nWe hope these clarifications adequately address the reviewer’s concerns. Thank you\nonce again for theconstructivefeedback.\n8. For the sensitivity test with reduced dust loading, was the forcing applied only in\nsummertimeor all year round? It would be helpful to include a table summarizing key\nfeatures of each simulation.\nReply: Wethank you for this pertinent question regarding the experimental design. In\nthe dust reduction sensitivity experiment, the forcing (i.e., the reduction in dust\naerosol emissions) was applied exclusively during the summer (JJA) season. During\nthe rest of the year, dust emissions, along with all other model forcings, were identical\nto those in the control run. This setup was intentionally chosen to isolate the specific\nclimatic impact of summertime Middle Eastern dust variability. We also cited the\nadded explanation intherevised manuscript and cited ithere.\nThe final summertime forcing field is derived by subtracting this scaled perturbation\nfield from the 1980-2020 climatological mean, ensuring compatibility with CAM4’s\ninput requirements. Additionally, during the rest of the year, dust emissions, along\nwithallother model forcings, wereidentical tothose in thecontrolrun.\nFurthermore, we fully agree with your suggestion that a summary table would\nenhance the clarity of our experimental setup. In response, we have included a new\ntable (Table R1, namely Extended Data Table 1) in the revised manuscript. This table\nsystematically summarizes the key features of all simulations conducted in this study,\nincluding the specific forcing modifications, their temporal application, and the\nintegration periods for both theCESMand CAMexperiments.\nTableS1 Key features ofeach simulation.\nSimulation\nComponent Dust Forcing\nPeriod\nThe observed summertimedustmixing ratio overthe\n1980-2020\nMiddleEast for 1980–2020\n1980-2020 climatological mean dust mixingratio\nCESM\nreduced dustmixing ratio by~20%change\nincreased dustmixingratioby~20% change Five years\n1980-2020 climatological mean dust mixingratio\nCAM\nreduced dustmixing ratioby~20% change\n9. It is recommended to include some discussion about the uncertainties of the study.\nFor instance, in model simulations, dust content is prescribed, so any feedback\nfrom atmospheric circulation to the dust loading is not considered. In terms of\nstation data, what’s the uncertainty in measurements? Do the station location and\nnumbers change from 1980 to 2020? Does the uneven spatial distribution of the\nstations affect the results?\nReply: The reviewer for this valuable suggestion. We have added a\ndiscussion on the uncertainties in the revised manuscript. A key model uncertainty is\nthat prescribing dust aerosols neglects feedbacks from atmospheric circulation onto\ndust emissions and transport. For the station data, we only selected stations with\nlong-term records, so the location and number do not change from 1980 to 2020.\nWhile we used stations with long-term records to mitigate temporal inconsistencies,\nthe uneven coverage remains a limitation, which we acknowledge may influence the\nrepresentativeness of the observed regional trends. This discussion provides a more\nbalanced interpretation ofourresults.\nWe also cited the discussion about the uncertainties regarding the model and station\ndatahere.\nThis study has limitations regarding the model and station dust observations.\nSpecifically, the model experiments prescribe dust and thus neglect feedbacks of\natmospheric circulation on dust emissions. Additionally, the observational trends\ncould be influenced by the uneven spatial distribution of meteorological stations over\ntheanalysis period.\n10. Line 64,it may behelpful to showthe response thestrongestin SON.\nReply: We thank the reviewer for this valuable suggestion. We agree that explicitly\ndemonstrating the peak season of the SST response strengthens our argument.\nFollowing the reviewer's advice, we have conducted the additional analysis and\ncreated a figure that clearly shows the seasonal evolution of the SST response,\nconfirming that it is indeed strongest in the September-October-November (SON)\nseason.\nThis new figure has been included in the Supplementary Information as Fig. R6 (or\nExtended Data Fig. 3 in the revised manuscript), and we have added a citation to it in\ntherevised manuscript.Wealso cited ithere for convenience.\nModel simulations also confirm that SON is the season when the IOD responds most\nstrongly to reductions in dust forcing, thereby identifying SON as the optimal season\nfor analysis(Extended Data Fig.3).\nWe believe this addition significantly improves the clarity of our manuscript and are\ngrateful tothe reviewerfor theconstructive input.\nFig. R6. The SST responses in different seasons including (a) MAM, (b) JJA, (c)\nSON, and (d) DJF. The area with white dots passed the significance test of p ≤\n0.05byStudent's t-test.\n11. Lines 78-79, it is not clear what averaging area is used for the MISR\nnon-sphericalAOD.\nReply: Thanks for pointing it out. The averaging area for the MISR non-spherical\nAOD is the same as station dust record (averaged over the 10°S~30°N, 35°E~70°E).\nWehave added thissupplementary explanation inthe figure caption.\n12. Line 158, this seems to contradict the statement in lines 134-135. Could you\nplease clarify what thesystematicbiases are?\nReply: We sincerely thank the reviewer for this insightful comment, which highlights\na need for greater clarity in our discussion. The two statements address\ncomplementary rather than contradictory aspects of the model's performance, and we\nhave revised the text toeliminateany potential formisunderstanding.\nThe statement in Lines 134-135 emphasizes the model's success in reproducing the\nspatial pattern of the dust-IOD relationship—namely, the large-scale dipole structure\nover the tropical Indian Ocean. This demonstrates that the fundamental physical\nlinkage is captured bythemodel.\nIn contrast, the statement in Line 158 pertains to the attenuation of the temporal\ncorrelation coefficient (i.e., the simulated correlation is systematically lower than\nobserved). One key reason for this attenuation is the model's inherent systematic\nbiases in its representation of IOD dynamics. These include well-documented biases\ninclimate models, such as an overly strong thermocline feedback in theeastern Indian\nOcean, an exaggerated seasonal cycle, or an inaccurate mean state (Yao et al., 2016;\nXiao et al., 2022). Such biases do not necessarily prevent the model from simulating\nthe correct spatial pattern of the IOD's SST anomalies, but they can distort the\namplitude and precise timing of its evolution. This distortion in the dynamics\nintroduces \"noise\" into the simulated year-to-year relationship between dust (SMED)\nand theDipole Mode Index (DMI), thereby collectively amplifying thedeviation from\nobservations inthestrength of thetemporal correlation.\nTo clarify this, we have revised the relevant sentence in the manuscript and cited it\nhere.\nNevertheless, the simulated correlation coefficient remains systematically lower than\nthat estimated by observation. This attenuation is attributable to (i) the reliance of the\nmodel on station-based dust frequency to scale column-integrated dust-mass density,\nwhich introduces additional sampling uncertainty, and (ii) systematic biases in the\nmodel’s representation of IOD dynamics (e.g., in the thermocline feedback and\nseasonal phase locking), which distort the precise evolution of the mode and\ncollectively amplify the attenuation of the simulated correlation relative to\nobservations.\n13. Line 175, there is also a weak increase in the mixed layer depth over the eastern\ntropical Indian Ocean from 100°E to 108 °E (Fig. 2b). What could be the cause of\nthis?\nReply: We thank the reviewer for this observation. The weak increase in mixed layer\ndepth over the eastern tropical Indian Ocean is likely a result of internal climate\nvariability or model uncertainty in responding to the prescribed forcing. We have\nacknowledged this point inthe revised manuscript as part of the broader discussion on\ntheregional nuances ofthemodel response.Wealso cited it here forconvenience.\nIt is noteworthy that a weak increase in MLD is also simulated over the eastern\ntropical Indian Ocean (Fig. 2b), which may be attributed to internal climate\nvariabilityrather than adirect responsetodust forcing.\n14. Line 219, regression analysis is less straightforward than performing another\nsimulation without the variability of Middle Eastern dust and comparing the\ndifferences. Conducting a linear regression assumes that the relationships are linear,\nwhich may not necessarily be true.\nReply: Thank you for this important methodological point. We agree with the\nreviewer that a carefully designed sensitivity experiment provides a more direct and\nrobust demonstration of causality than regression analysis alone. In fact, we have\nconducted two additional sensitivity experiments as detailed in Extended DataTable 1:\none with increased Middle Eastern dust and another with decreased dust over this\nregion.\nIn our analysis, the regression analysis is mainly applied to the observational and\nreanalysis data to identify statistical relationships. In contrast, the changes shown in\nthe model experiments (with increased and decreased dust; Extended Data Figs. 9 &\n10)represent thedirect model response to theprescribed dust forcing.\nThe coherent alignment—in both pattern and sign—between this direct model\nresponse and the statistical relationships identified from observations strengthens the\nrobustness of the dust-IOD link, moving the evidence beyond the potential constraints\nofapurely linearstatistical framework.\n15. Line 220, “Increased solar radiation penetrates to the surface…” , this is not\nclearly shownin Fig. S8c, especially over theregion with reduced dust. Please clarify.\nReply: Thanks for pointing it out. You are correct that the region of increased net\nshortwave radiation at the surface in the fully coupled model experiment (Fig. S8c)\ndoes not perfectly align with the primary dust reduction area. This occurs precisely\nbecause the CESM is a fully coupled climate model. The initial radiative perturbation\nfrom dust reduction triggers a chain of ocean-atmosphere interactions, including\nadjustments in atmospheric circulation and wind-evaporation-SST feedbacks. These\nprocesses redistribute the energy downstream, generating secondary radiative\nanomalies that can partially mask the direct local signal. This energy redistribution is\na fundamental feature of the coupled climate system and is the key mechanism that\nestablishes theremote teleconnection pattern wedescribe.\nIn contrast, as the reviewer might anticipate, a parallel experiment conducted with an\natmosphere-only model (CAM, Extended Data Fig. 14c) does show a clear and\nlocalized increase in net shortwave radiation confined to the dust source region, as it\nlacks these ocean-mediated feedbacks. The comparison between these two\nexperiments robustly demonstrates how the direct radiative forcing from dust evolves\nintoa broader teleconnection within acoupled framework.\nWe have clarified this crucial point in the revised manuscript to better guide the\nreader's interpretation ofExtended Data Fig.11c.Wealso cited theclarification here.\nMoreover, the atmospheric-only simulation exhibits a radiative response that is\nspatially coherent with the dust aerosol distribution (Extended Data Fig. 14), while\nthe coupled simulation displays additional spatial heterogeneity due to oceanic\nadjustments (Extended Data Fig. 11). This redistribution likely stems from\nocean-atmosphere coupling, wherein the initial radiative perturbation triggers\natmospheric circulation adjustments andsubsequent wind-evaporation-SSTfeedbacks,\ncreating secondary radiative perturbations that partially obscure the direct\ndust–radiation signal, which is ultimately manifested as a teleconnected response.\nNevertheless, both experiments consistently support the proposed physical framework\nlinkingMiddle Eastern dust forcing to IODvariability.\n16. Lines 221-225, it's not clear why the coupled results are already available (line\n192) but then the analysis switches to an atmosphere-only model, and then returns to\nthecoupled simulation.\nReply: We thank the reviewer for raising this point regarding our experimental\nworkflow, which we agree requires clear justification. The sequential use of the\ncoupled model (CESM) and the atmosphere-only model (CAM) was a deliberate\ndesign to dissect theunderlying mechanisms.\nThe fully coupled simulation reveals the net effect of dust forcing, including all\nsubsequent ocean-atmosphere feedbacks and teleconnections. We then introduced the\natmosphere-only (CAM) experiments specifically to isolate the direct atmospheric\nresponse to the dust aerosol radiative forcing, devoid of any ocean feedbacks due to\nits use of prescribed SSTs. This approach allows us to cleanly attribute the initial\natmosphericperturbation to thedust forcing itself.\nFinally, returning to the coupled model results allows us to interpret the full, evolved\nclimate response—including the teleconnection patterns—as a consequence of the\ninitialforcing identified inCAM, nowamplified and modulated bycoupled processes.\nWe have clarified this rationale in the revised manuscript to guide the reader through\nouranalytical strategy.Wealso cited it here forconvenience.\nBy employing prescribed SSTs, this atmosphere-only simulation inherently excludes\nocean feedbacks, thus allowing a clear attribution of the atmospheric response to the\ndust forcing.\n17. Line 229-230 “the direct radiative effects of reduced dust”, is it possible to clarify\nthelocation ofthis effect in thefigure?\nReply: Thanks for your suggestion. We have clarified the location of this effect,\nwhich is over thewesternTIO and cited it here.\nThe transformation of cloud fields further modifies the radiative budget by increasing\nboth longwave and shortwave radiation, thereby amplifying the warming signal over\nthe western TIO in synergy with the direct radiative effects of reduced dust (Figs. 3a,\nb).\n18.Line 267, the response of shortwave fluxes is mainly over the land. How does the\nsignal propagate to theTIO?\nReply: Thank you for raising this question.The signal propagates from the land tothe\nTropical Indian Ocean (TIO) primarily through an atmospheric teleconnection. The\nmechanism is athree-step process:\n(1) Local Radiative Forcing & Atmospheric Adjustment: Reduced dust over the\nArabian Peninsula/northern Arabian Sea increases surface SW absorption, creating a\nstrong vertical heating gradient (surface warming, relative upper-level cooling;\nExtended Data Figs.11, 13a).Thismodifies local stabilityand circulation.\n(2) Atmospheric Teleconnection & Downstream Cloud Response: The adjusted\ncirculation and zonal cell (Fig. 2d) act as a teleconnection bridge, transporting the\nperturbation's influence to the western TIO. This alters the vertical thermodynamic\nprofile, driving a characteristic cloud reorganization there: decreased low-level and\nincreased high-level cloud cover (Fig. 3c, d). In the atmosphere-only (CAM) run, this\nteleconnected response is limited by fixed SSTs, explaining the weaker direct\nmaritimesignal.\n(3) Amplification via Ocean-Atmosphere Coupling: This step explains the stronger\noceanic signal in the coupled model. The teleconnected cloud changes (Step 2) now\ninteract with the ocean. Reduced low clouds enhance surface SW, warming the SST.\nThis warming then triggers powerful positive feedbacks: the cloud–SST feedback\nfurther suppressing low clouds (Lin et al., 2025; Wu et al., 2025; Wyant et al., 1997),\nthe Bjerknes feedbacks and wind-evaporation-SST (WES) feedbacks. These coupled\nfeedbacks dramatically amplify and extend theinitial atmosphericperturbation.\nTherefore, the CAM pattern confirms the dust reduction initiates an atmospheric\nteleconnection. The coupling with the ocean is essential to transform this initial\nadjustment into the stronger, basin-scale ocean-atmosphere feedbacks that effectively\ndrivetheIOD development, as detailed inourmanuscript.\nIn summary, the dust-induced radiative heating over the Middle East perturbs the\nregional atmospheric circulation, which in turn triggers a remote response that\ninfluences theTIO.\nWe have systematically elaborated this dynamical pathway in the revised manuscript\nwithin the \" Mechanism: how dust shapes the IOD\" section, detailing the key\nprocesses of (1) local radiative adjustment, (2) atmospheric bridge via zonal cell\ncirculation, and (3) amplification through air-sea feedbacks, and we refer the reader to\nthat discussionhere.\nWe have also clarified this dynamical pathway in the revised manuscript and cited it\nhere.\nThe initial perturbation mainly over the land likely alters regional atmospheric\nheating and circulation patterns 46-48, which in turn redistributes the energy\ndownstream, setting the stage for the more extensive and evident ocean-atmosphere\nsignalsandfeedbacks that propagating to theTIOinthe coupled model.\n19. Lines 275-276, “(Extended Data fig. 10)”, isn't the figure from the\natmosphere-only model run (as mentioned in line221)?\nReply: Thanks for pointing it out and you are right. We have modified the statementnand cited it here.\nAtmosphere-onlymodel andobservation comparisons further reinforcethesefindings.\n20. Line 304,cooler western part?\nReply: Thanks for pointing it out and sorry for the typo. We have revised as\nsuggested.\n21. Line 355,doyou mean “reduced dust anomalies”\nReply: Thanks for pointing it out and you are right. We have incorporated this\nsuggestioninto therevised manuscript.\n22. Line 360,dust forcing in JJAorSON?\nReply: Sorry for the misunderstanding. It is indeed the dust forcing in JJA. We have\nclarified thisin therevised manuscript and cited it here.\n23. Lines 252-257, how is this done? Are you using the ratio of JJAMERRA-2 dust\nAODto modeled dustAODto scale dust loading ineach month?\nReply: Thanks for your suggestion and you are right. We are using the ratio of JJA\nMERRA-2 dustAOD to modeled dust AOD to scale dust loading in each month. We\nalso clarified thisprocedure in therevised manuscript and cited ithere.\nTo address this issue, we used the ratio of JJA MERRA-2 dust AOD to modeled dust\nAOD to scale dust loading in each month. Specifically, we first conduct a control run\nusing the default dust concentration file. Then, we divide the monthly dust AOD result\nof MERRA-2 by the monthly dust AOD in the control run to obtain monthly\nproportionality coefficients. By multiplying these monthly coefficients with the\noriginal dust mixing ratio, we can better reproduce the spatiotemporal distribution of\nJJAMiddleEastern dust in themodel.\n24. Line 258, “by perturbing initial temperature fields”, is it land or ocean surface\ntemperature? Howlarge are theperturbation?\nReply:Wethank thereviewer for thisquestion.The small,machine-level perturbation\n(1×10-14 K) was applied to the initial temperature fields of both the land and ocean\ncomponents to initiate the ensemble spread (Yeager et al., 2018). This detail has been\nclarified in therevised manuscript and cited here forconvenience.\nFive ensemble members—generated by perturbing initial temperature fields over land\nand ocean—were averaged to suppress internal climate noise. The magnitude of the\ntemperatureperturbationis 1×10-14K.\n25. Line 540,onestandard deviation over what timeperiod?\nReply: We thank the reviewer for pointing out the need for clarification. The one\nstandard deviation (1σ) is calculated from the summertime (June-July-August) data\nspanning the entire study period of 1980 to 2020, based on the regional mean time\nseries. This scaling is used to define the characteristic magnitude of the perturbations.\nWehave amended themanuscript to explicitlystatethis timeframe.\nSubsequently, the coefficient field is scaled by one standard deviation (1σ) over\nsummertime from 1980 to 2020 of the regional mean time series to define\ncharacteristicperturbation magnitudes.\n26. Fig. 1c, consider adding all the dust time series as a reference (e.g., including\nMODIS DOD and MERRA-2 dust column mass density). Is it possible to include\nDMI\ntime series as well and show the correlations between DMI and different Middle\nEasten dust indices from 1980 to 2023 and 2003 to 2023? Fig. 1d-g, can you please\nclarify in the figure caption the time period over which the regressions were\ncalculated? Was it 2003 to 2023? Were the data detrended before calculating the\nregressions?\nReply: Thank you for this helpful suggestion. We have included time series of\nadditional Middle Eastern dust indices and DMI time series in Extended Data Fig. 4\nin the revised manuscript (Fig. R7 here). This avoids overcrowding the figure while\nensuring all relevant data and statistical evidence are fully available to the reader in\nthe supplement. We also show the correlations between DMI and different Middle\nEastern dust indices from 1980 to 2023 and 2003 to 2023 in corresponding figures\n(Fig. 1 and Extended Data Fig. 4 in the revised manuscript). We cited this figure here\nforconvenience.\nFig. R7. Time series of standardized JJA MODIS dust optical depth (the black solid\nline), JJA MERRA-2 dust column mass density (the black dashed line), and SON DMI\nindex(the redsolid line)from2003to 2023.\nMoreover, we have clarified the figure caption the time period over which the\nregressions were calculated. The data has been detrended before calculating the\nregressions.Wehave modified the figure caption (Fig. 1 in the revised manuscript and\nFig.R8 here)as suggested and cited ithere.\nFig. R8. Dust proxies, sea surface temperature and meteorological field regressed on\ndust time series. (a) The regression coefficients of JJA station dust record on the\nMiddle East regional-averaged station dust frequency (averaged over the 10°S~30°N,\n35°E~70°E). (b) Similar to a, but for the JJA MISR non-spherical aerosol optical\ndepth (AOD, also averaged over the 10°S~30°N, 35°E~70°E). (c) Time series of\nstandardized JJA station dust frequency (the solid line) from 1980 to 2023, and JJA\nMISR non-spherical aerosol optical depth (the dashed line) from 2003 to 2023. (d-g)\nThe detrended regression coefficients of SON meteorological fields on the detrended\nJJA MISR non-spherical aerosol optical depth averaged over the Middle East from\n2003 to 2023. These meteorological fields include (d) sea surface temperature. (e)\nmeridional mean (20°S-20°N) vertical cross section of temperature (shaded) and\nmixed layer depth (the gray dashed line), (f) sea level pressure (shaded) with 850 hPa\nzonal and meridional wind (UV, vectors, m/s), and (g) meridional mean (20°S-20°N)\nvertical cross section of geopotential height (GPH, shaded) with zonal and vertical\nwind (UW, vectors, m/s). The y-axes on the right in (e) corresponds to the response of\nmixed layer depth. The regression is currently done for individual years. The area\nwithwhitedotspassed the significancetest of p ≤ 0.05byStudent'st-test.\n27. Fig. 2, are the fields derived from time averages or from regression? Fig. 2b, the\nlocation of the temperature anomalies appears at a higher altitude than in the\nobservation (Fig. 1). Is thisdueto model bias? Couldyou please comment onthis?\nReply:Wethank thereviewer for theseinsightful questions.\nFirstly, the fields in Fig. 2 are derived from regression analysis, not simple time\naveraging.Wehave clarified thisin thefigure caption of therevised manuscript.\nFig. 2. The regression coefficients of SON meteorological fields onto JJA dust aerosol\noptical depth (AOD) from 1980 to 2020 in the CESM coupled model simulations.\nThese coefficients were scaled by the magnitude of dust decreasing from 2010 to 2020\nin the CESM. These meteorological fields include (a) sea surface temperature. (b)\nmeridional mean (20°S-20°N) vertical cross section of temperature (shaded) and\nmixed layer depth (the gray dashed line), (c) sea level pressure(shaded) with 850 hPa\nzonal and meridional wind (UV, vectors, m/s), and (d) meridional mean (20°S-20°N)\nvertical cross section of geopotential height (GPH, shaded) with zonal and vertical\nwind (UW, vectors, m/s). The y-axes on the right in (b) corresponds to the response of\nmixed layer depth. The area with white dots passed the significance test of p ≤ 0.05\nbyStudent's t-test.\nSecondly, we agree that the displacement of temperature displacement is likely\nattributable to model bias. However, despite this bias in the exact altitude, the model\nsuccessfully captures the key physical process crucial for our proposed pathway. We\nhave added the additional discussion regarding the inconsistency of temperature\nresponse inthe manuscript andcited it here.\nThe CESM experiments reveal a robust dipole SST anomaly pattern across the TIO in\nresponse to reduced dust forcing (see Method), closely mirroring the observed\nregression between dust and SST(Fig. 2a) despite some latitudinal displacements that\narepartlyattributedto model biases.\n28.Fig.S2, please mark whether thecorrelations are statistically significant.\nReply: Thanks for your suggestions. We have the marked critical correlation\ncoefficients for statistical significance in Extended Data Fig. 2 in the revised\nmanuscript andcited ithere (Fig. R9).\nFig. R9. Correlation coefficients between JJA dust proxies and DMI in different three\nmonths. These dust proxies include station dust frequency (the yellow line,\n1980-2023), MODIS dust optical depth (the orange line, 2003-2023), MISR\nnon-spherical AOD (the black line, 2003-2023), and MERRA-2 dust column mass\ndensity (the green line, 2003-2023). The dashed grey line indicates the position of\nzero correlation. The dashed and solid red lines indicate the critical correlation\ncoefficients for statistical significance of station dust frequency and the other three\ndust proxies, respectively.\n29.Fig.S6a, could you please add thestimulated dustAODto it?\nReply: Thanks for your helpful suggestions and we have added the simulated dust\nAOD as suggested (Fig. R10 here, the same as the Extended Data Fig. 9 in the revised\nmanuscript).Wealso cited ithere forconvenience.\nFig. R10. The standardized DMI and SMED from 1980 to 2020. The time series are\n(a) simulated DMI forced by dust (solid cyan line), simulated DMI in the historical\nrun with all forcing (dashed cyan line), and simulated SMED (solid red line) and (b)\nobserved DMI andSMED.Allthetimeseries arestandardized for comparison\nWealso added theexplanation for thisfigure andalso cited ithere.\nThe simulated SMED also has high correlation with DMI series (Extended Data Fig.\n9a,R=-0.58, p ≤ 0.05), further validatingtheir relationship.\n30.Fig.S8a, is theobtained through regression?\nReply:Yes, you are right.Wehave clarified this inthe figure caption.\n31. Figs. S10b, d, are the flux date from MERRA-2?And what is the averaging area\nusedfor MISRAODintheregression analysis?\nReply: Indeed, you are correct. The flux data are sourced from MERRA-2. Regarding\nthe averaging area for MISR AOD, we confirm that it is identical to that specified in\nthemanuscript (10°S~30°N, 35°E~70°E)forconsistency.\n32. Fig. S11, which simulation does this refer to? Is it from the sensitivity simulation\northecoupled run from 1980to2020?\nReply: This simulation refers to the CESM coupled model simulations from 1980 to\n2020.Wehaveclarified this inthefigure caption and cited ithere.\nFig. S11. The responses of SON meridional mean (20°S-20°N) vertical cross section\nof temperature to reduced Middle East dust forcing from 1980 to 2020 in the CESM\ncoupled model simulations. The area with white dots passed the significance test of p\n≤ 0.05byStudent's 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