Evolution of the Arabian Sea Upwelling from the Last Millennium to the Future As Simulated by Earth System Models

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Article Evolution of the Arabian Sea Upwelling from the Last Millennium to the Future as Simulated by Earth System Models

Xing Yi *, Birgit Hünicke and Eduardo Zorita

Institute of Coastal Systems Analysis and Modeling, Helmholtz-Zentrum Hereon, Max-Planck-Str.1, 21502 Geesthacht, Germany; [email protected] (B.H.); [email protected] (E.Z.) * Correspondence: [email protected]

Abstract: Arabian Sea upwelling in the past has been generally studied based on the sediment records. We apply two earth system models and analyze the simulated water vertical velocity to investigate coastal upwelling in the western Arabian Sea over the last millennium. In addition, two models with slightly different configurations are also employed to study the upwelling in the 21st century under the strongest and the weakest greenhouse gas emission scenarios. With a negative long-term trend caused by the orbital forcing of the models, the upwelling over the last millennium is found to be closely correlated with the sea surface temperature, the Indian summer Monsoon and the sediment records. The future upwelling under the Representative Concentration Pathway (RCP) 8.5 scenario reveals a negative trend, in contrast with the positive trend displayed by the upwelling favorable along-shore winds. Therefore, it is likely that other factors, like water stratification in the upper ocean layers caused by the stronger surface warming, overrides the effect from the upwelling favorable wind. No significant trend is found for the upwelling under the RCP2.6 scenario, which is likely due to a compensation between the opposing effects of the increase in upwelling favorable winds and the water stratification.

Citation: Yi, X.; Hünicke, B.; Zorita, Keywords: Arabian Sea upwelling; earth system models; last millennium E. Evolution of the Arabian Sea Upwelling from the Last Millennium to the Future as Simulated by Earth System Models. Climate 2021, 9, 72. 1. Introduction https://doi.org/10.3390/cli9050072 Upwelling lifts the cold nutrient-rich water from deeper ocean layers to the surface, which cools the surface water and provides the biologically active layers with nutrients. It Academic Editor: Salvatore Magazù has a great impact on human activities. For instance, about 20% of the total marine ﬁsh catches originate from upwelling regions that cover only 2% of the entire ocean area [1]. Received: 18 March 2021 Upwelling also affects the climate by cooling the sea surface temperature (SST) and by Accepted: 27 April 2021 Published: 29 April 2021 inﬂuencing the air–sea interactions associated with the variation of SST [2]. It has been suggested that as one of the climatic effects, under the global warming scenario, coastal

Publisher’s Note: MDPI stays neutral upwelling at global scale would be intensified as the upwelling favorable wind-stress with regard to jurisdictional claims in would be strengthened due to the enhanced air–sea temperature gradient [3]. Numerous published maps and institutional affil- studies have provided insights into the upwelling variations over the last few decades. iations. Most of these studies focus on the four major eastern boundary upwelling systems (EBUSs), namely, the California [4], Canary [5], Humboldt [6] and Benguela [7] upwelling systems. In support of the upwelling intensification hypothesis by Bakun [3], Narayan et al. [8] found positive trends over the late 20th century in all these four coastal upwelling regions and Sydeman et al. [9] also reported upwelling intensification in the major EBUSs after Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. synthesizing the results from previous studies. This article is an open access article In addition to the major EBUSs, the Arabian Sea is one of the most productive regions distributed under the terms and in the world [10]. The coastal upwelling in the western Arabian Sea is mainly driven by the conditions of the Creative Commons southwest (SW) wind-stress, which is induced by the Indian summer Monsoon (ISM) [11]. Attribution (CC BY) license (https:// Since the link between the ISM and the upwelling is very pronounced, many studies have creativecommons.org/licenses/by/ used the ISM as an indicator of the upwelling, and vice versa [12–14]. However, direct 4.0/). observations of upwelling in terms of water mass vertical velocity over a long time period

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are rare, thus, alternative upwelling proxies such as SST, surface wind-stress and sediment records have been generally applied. An alternative tool to analyze upwelling variability is provided by model simulations. The knowledge of upwelling evolution in the past is crucial to understand and model the upwelling variations at present and to predict them in the future. A widespread approach to study upwelling in the past is through sediment records where the abundance of G. bulloides is stored. G. bulloides belongs to the phylum foraminifera and is very sensitive to upwelling variations so it is generally used as an upwelling proxy. Over the last millennium, the Arabian Sea upwelling is reported to exhibit a slight decrease until approximately 1600 and an abrupt increase afterwards [15–17]. As for the future, whereas a study focused on the Arabian Sea upwelling is not yet available, Wang et al. [18] used the Coupled Model Intercomparison Project Phase 5 (CMIP5) [19] model outputs to conduct an analysis on the 21st century upwelling under the future scenario of Representative Concentration Pathway (RCP) 8.5. They indicated that the upwelling intensity and duration are both increased at high latitudes in most of the major EBUSs except California. The RCP8.5, along with the RCP6.0, RCP4.5 and RCP2.6 are labeled by the aimed radiative forcing in the year 2100, namely 8.5, 6.0, 4.5, and 2.6 W·m−2 [20]. The RCP scenarios imply several variables for the next centuries such as concentration of greenhouse gases, coverage of land use and chemically active gases. It is known that the external climate forcings are different over the last millennium and in the future. The responses to these forcings are also different among the major EBUSs [21]. Therefore, in the present study we analyze the impact of the external climate forcings on the Arabian Sea upwelling system and whether its response to the past can be linked with that to the future. We investigate here the Arabian Sea upwelling variation over the last millennium by analyzing model simulations of water vertical velocity and by comparing the model results with the observational sediment records to determine the existence of the long-term trends. The evolution of future upwelling in the Arabian Sea is also investigated by using models with slightly different conﬁgurations from the last millennium simulations. We compare the future upwelling modeled under the RCP8.5 and RCP2.6 scenarios to gain the information on how the greenhouse gas emission level could affect the variation of upwelling.

2. Model and Data Earth system models, in a way similarly to weather prediction models, include a numerical representation of the Earth’s atmosphere, ocean, land, sea-ice, and their physical and geochemical interactions. This numerical representation is to a large extent based on the known physical and chemical laws of momentum, energy and matter transfer, but also includes heuristic representations of small-scale processes (such as turbulence) that cannot be realistically resolved due to the still coarse spatial resolution of these models of about 100 km. The interested reader is referred to Edwards [22]. In this study, we selected four earth system models from the CMIP5 model pool, which have the highest horizontal resolution to study the Arabian Sea upwelling. The outputs from two of these models are used for the last millennium study and the other two, which are similar to the previous ones but with slightly different configurations, were applied for the future scenarios. For the analysis over the last millennium, we used the paleo configuration of the Earth System Model of Max-Planck Institute for Meteorology (MPI-ESM-P) [23] and the Last Millennium Ensemble (LME) project [24] of the Community Atmosphere Model Version 5 from CESM (CESM-CAM5) [25]. To estimate the upwelling variabilities in the future, we applied the low resolution configuration of the MPI-ESM (MPI- ESM-LR) and the Community Climate System Model (CCSM4), which is the predecessor of the CESM. Note that the CCSM4 uses a different version of atmosphere model (CAM4) than the CESM (CAM5). The MPI-ESM-P and the MPI-ESM-LR share the same horizontal resolutions of about 2 degrees for the atmosphere (192 × 96) and about 1 degree for the ocean (256 × 220). Their vertical resolutions are the same as well with 47 levels for the atmosphere and Climate 2021, 9, 72 3 of 19

40 levels for the ocean. In spite of this, the MPI-ESM-P is available for the last millennium (850–1849) and the MPI-ESM-LR covers the future period (2006–2100) simulated under the greenhouse gas emission scenarios. The CESM-CAM5 and the CCSM4 also have identical horizontal resolutions for the ocean, which is about 1 degree on the longitude and 0.5 degrees on the latitude (320 × 384). For the atmosphere, the CESM-CAM5 has a horizontal resolution of 2.5 degrees on the longitude and 1.875 degrees on the latitude (144 × 96), which is four times coarser than the CCSM4 (288 × 192). On the vertical, they share the same resolutions with 60 levels in the ocean and 30 levels in the atmosphere. The CESM-CAM5 covers the last millennium period (850–1849) and the CCSM4 covers the 21st century (2005–2100). In the last millennium models, the external forcings are prescribed by a CMIP5 project deﬁned standard protocol [26], which consists of solar irradiance variability [27], orbital forcing [28], volcanic activity [29,30], land-use changes [31] and greenhouse gases concentration [32–34]. For the future simulations, only the anthropogenic forcing is changed based on the RCP scenarios. The MPI-ESM-P and the MPI-ESM-LR each have an ensemble of three simulations while the CESM-CAM5 and the CCSM4 have ensembles of 13 simulations. Thus, we selected the ﬁrst three simulations from each of them to compare with the MPI-ESM models. The differences between simulations within each ensemble provide an estimation of the amplitude of the effect of internal climate variability, whereas the signal shared by all simulations will approximate the response to the imposed external forcing [21]. We investigated several variables that are modeled by the simulations, including upwelling velocity, sea surface temperature (SST), surface wind-stress, zonal wind speed, and sea level pressure (SLP). The upwelling velocity is the “vertical velocity” in the CESM ensembles but has to be calculated from the “vertical water mass transport” for the MPI- ESM ensembles. Since coastal upwelling in the western Arabian Sea occurs from 200 m below the ocean surface [35], we averaged the upper 200 m of the data. We used the monthly data from the models and because the upwelling season starts in May and ends in September [36], only the summer months June, July and August (JJA) were selected. One exception is that for the SST we chose July, August and September (JAS) due to the lag in the response of SST to the upwelling [37]. In addition to the modeled data, we also applied the sediment records used by Anderson et al. [15] to compare with our results.

3. Results 3.1. Arabian Sea Upwelling in the Last Millennium The mean summer upwelling velocities in the Arabian Sea modeled by MPI-ESM-P and CESM-CAM5 for the last millennium are presented in Figure1. To avoid duplication, we only show the results of one simulation from each of these two simulation ensembles since the three simulations in each ensemble share very similar spatial patterns. The models present similar patterns of the summer upwelling in the Arabian Sea where strong upwelling occurs along the coast especially in the western Arabian Sea, induced by the southwest wind-stress (Figure1a,b). Coastal upwelling in the western Arabian Sea is more intense in the MPI-ESM-P simulation, where the velocity can reach 1.5 m/day, than in CESM-CAM5 where the maximum velocity is around 1 m/day. The magnitude of these velocities is reasonable as it matches the estimated order of magnitude suggested from studies focused on present time period [37–39]. In addition, weak downwelling in the central Arabian Sea is found in both models. Downwelling here results from the convergence zone generated by the wind-stress curl [10,40]. Thus, the spatial distribution of the vertical velocity is consistent with the Ekman pumping effect [41]. Climate 2021, 9, 72 4 of 19 Climate 2021, 9, x FOR PEER REVIEW 4 of 20

FigureFigure 1.1.Spatial Spatial patternspatterns ofof JJAJJA meanmean ArabianArabian SeaSea upwellingupwelling velocityvelocity modeledmodeled byby ((aa)) MPI-ESM-PMPI-ESM-P andand ((bb)) CESM-CAM5.CESM-CAM5. TimeTime series series of of JJA JJA coastalcoastal upwellingupwelling velocityvelocity inin thethe westernwestern ArabianArabian SeaSea modeledmodeled byby ((cc)) MPI-ESM-PMPI-ESM-P andand ((d)) CESM-CAM5.CESM-CAM5. TheThe areaarea selectedselected for calculating the variable variable time time series series is is shown shown in in (a (,ab,b) within) within the the green green line. line. The The plotted plotted time time series series are aresmoothed smoothed by a by 31-year a 31-year moving moving mean mean window window and and r1, r2, r1, r3 r2, repr r3 representesent the thethree three simulations. simulations. t1, t2, t1, t3 t2, are t3 the are trends the trends (per 1000 years) of each simulation with p1, p2 and p3 indicating their p-values, respectively, and the star symbols mark the (per 1000 years) of each simulation with p1, p2 and p3 indicating their p-values, respectively, and the star symbols mark the simulations that pass the significance test at the 95% level. simulations that pass the signiﬁcance test at the 95% level.

WeWe averagedaveraged thethe upwellingupwelling alongalong thethe coastcoast andand calculatecalculate thethe upwellingupwelling velocityvelocity timetime seriesseries ofof thisthis areaarea (Figure(Figure1 c,d).1c,d). This This selected selected area area spans spans about about 5 5 degrees degrees (500 (500 km) km) from from thethe coastlinecoastline to the the open open ocean ocean (shown (shown within within the the green green line line in inFigure Figure 1a,b).1a,b). Although Although the thecoastal coastal upwelling upwelling in this in region this region is reported is reported to extend to extend about 100 about km 100 from km the from coast the towards coast towardsoffshore offshore[37], such [37 a], narrow such a narrowselection selection is not possible is not possible in our case in our due case to the due coarse to the resolu- coarse resolutionstions of the of models. the models. In our In selected our selected study study area, area,the upward the upward vertical vertical velocity velocity actually actually pre- presentssents upwelling upwelling induced induced by by the the combination combination of of the the SW SW wind-stress wind-stress and the wind-stresswind-stress curl.curl. Therefore,Therefore, whenwhen usingusing thethe simulatedsimulated upwellingupwelling velocityvelocity inin thethe studystudy area,area, thethe effectseffects ofof EkmanEkman transporttransport andand EkmanEkman suctionsuction areare notnot separated.separated. ThisThis isis alsoalso reasonablereasonable becausebecause thethe along-shorealong-shore SW SW wind-stress wind-stress is highlyis highly correlated correlated with with the off-shorethe off-shore wind-stress wind-stress curl since curl theysince are they both are driven both driven by the by land–sea the land–sea pressure pressure gradient. gradient. In general, In general, the time the series time showseries thatshow on that average, on average, the mean the upwelling mean upwelling velocity ve modeledlocity modeled by MPI-ESM-P by MPI-ESM-P is larger is than larger the than one simulatedthe one simulated by CESM-CAM5 by CESM-CAM5 by around by 0.3 around m/day. 0.3 The m/day. multidecadal The multidecadal variation is,variation however, is, largerhowever, in CESM-CAM5. larger in CESM-CAM5. All six simulations All six simu by thelations two models by the revealtwo models negative reveal trends negative of the upwellingtrends of the velocity. upwelling A detailed velocity. interpretation A detailed inte ofrpretation these trends of these and theirtrends signiﬁcance and their signif- level willicance be level presented will be in presented Section 3.3 in. Section 3.3. SinceSince upwellingupwelling lifts lifts the the cold cold water water from from the the deeper deeper layers layers to to the the ocean ocean surface surface during dur- theing upwellingthe upwelling season, season, the the SST SST is oftenis often anticorrelated anticorrelated with with upwelling upwelling in in the the upwelling upwelling region.region. This This relationship relationship is is well well captured captured in inboth both of ofthe the models models (Figure (Figure 2a,b).2a,b). Strong Strong neg- negativeative correlations correlations are areshown shown along along the thecoastal coastal upwelling upwelling regions regions and andeven even greater greater (r < (r−0.8) < − values0.8) values are found are found at some at somelocations locations in the in western the western Arabian Arabian Sea in Seathe inCESM-CAM5 the CESM- model. We also notice the positive correlation areas in the central Arabian Sea where weak

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downwellingCAM5 model. is Werevealed also notice as shown the positive in Figure correlation 1a,b. Downwelling areas in the is central normally Arabian associated Sea withwhere convergence weak downwelling zones at is the revealed surface as and shown weaker in Figure (stronger)1a,b. Downwelling downwelling is is normally related to associated with convergence zones at the surface and weaker (stronger) downwelling is cooler (warmer) surface water in the same region. In terms of vertical velocity as it is in related to cooler (warmer) surface water in the same region. In terms of vertical velocity as our case, downwelling is represented by the negative values of vertical velocities, thus, it is in our case, downwelling is represented by the negative values of vertical velocities, larger (smaller) velocities indicate weaker (stronger) downwelling. In principle, the SST thus, larger (smaller) velocities indicate weaker (stronger) downwelling. In principle, the should be negatively correlated with the vertical velocities in both upwelling and down- SST should be negatively correlated with the vertical velocities in both upwelling and wellingdownwelling regions. regions. However, However, Figure Figure 2a,b 2showa,b showss positive positive correlations correlations between between SST SST and and ver- ticalvertical velocity velocity in the in thedownwelling downwelling region region of central of central Arabian Arabian Sea. Sea. This This is likely is likely due due to tothe effectthe effect of horizontal of horizontal advection advection [42,43], [42,43 which], which transports transports the the cold cold water water mass mass raised raised by bythe coastalthe coastal upwelling upwelling from from the western the western to the to central the central Arabian Arabian Sea. Sea.This Thiscold coldwater water mass mass dom- inatesdominates the SST the SSTvariability variability in the in the central central Arabian Arabian Sea Sea because because the the influence inﬂuence ofof thethe weak weak downwellingdownwelling on the local SSTSST isis veryvery small.small.

FigureFigure 2. 2. CorrelationCorrelation coefficient coefficient between the SST and the upwellingupwelling velocityvelocity modeledmodeled byby (a(a)) MPI-ESM-P MPI-ESM-P and and ( b(b)) CESM- CESM- CAM5CAM5 with with significance significance level greater than 95% ((pp-value-value << 0.05).0.05). CorrelationCorrelation coefficientcoefficient between between the theG. G. bulloides bulloidesabundance abundance andand the the upwelling upwelling velocity velocity simulated by (c) MPI-ESM-PMPI-ESM-P andand ((dd)) CESM-CAM5.CESM-CAM5. TheThe green green point point marks marks the the location location of of the the sedimentsediment core core where where the the G. bulloides abundance is recorded.recorded. TheThe regionsregions surroundedsurrounded byby the the green green contour contour lines lines present present significantsignificant correlations thatthat areare at at a a higher higher level level than than 90% 90% (p-value (p-value < 0.1). < 0.1). All theAll correlationthe correlation coefficients coefficients are calculated are calculated from from the detrended time series. the detrended time series.

AsAs a a comparison comparison with the sediment records,records,which which is is considered considered as as the the observational observational data,data, FigureFigure2 c,d2c,d shows shows the the correlations correlation betweens between Arabian Arabian Sea upwelling Sea upwelling and the G.and bulloides the G. bulloidesabundance abundance retrieved retrieved from the from sediment the sediment cores RC2730 cores andRC2730 RC2735 and [RC273515]. This [15]. record This has rec- ordbeen has interpreted been interpreted as an indicator as an ofindicator upwelling of upwelling in this region in [this44]. region We only [44]. mark We the only location mark theof RC2730location onof RC2730 the maps on becausethe maps these because two th coresese two are cores located are very located close very to close each to other. each other.The calculation The calculation of the of correlation the correlation is performed is performed on the on 50-year the 50-year averaged averaged data duringdata during the theoverlapping overlapping years years (1050–1849) (1050–1849) of ourof ou modeledr modeled upwelling upwelling velocity velocity and and the theG. G. bulloides bulloides abundance.abundance. To To account for autocorrelation,autocorrelation, wewe appliedapplied thethe nonparametric nonparametric resampling resampling method suggested by Ebisuzaki [45] to estimate the significance level of the correlations. These filtered series presumably reflect the variations in the external forcing, or at least

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method suggested by Ebisuzaki [45] to estimate the significance level of the correlations. These filtered series presumably reflect the variations in the external forcing, or at least the externally forced component of the variability should be large. Since the external forcing should be ideally the same in the observations and in the simulations, a positive correlation between both records should be expected. However, the records from only two cores located at very close positions might not represent the upwelling in a broader area. In spite of this, the maps present significant correlations along the coast all the way to the northern Arabian Sea, especially for the MPI-ESM-P model. Such correlations indicate that the models reasonably reproduce the variability of upwelling velocities at time scales that are presumably driven by the external climate forcing. The simulated records are thus comparable to the sediment records. It has to be noted that the comparison between the modeled upwelling and the G. bulloides abundance does not aim to validate the model but to reveal the effect of external forcing. The correlations vary largely among the simulations as shown in Table1 and are not significant everywhere in the study area (Figure2), which raises uncertainty of the results. However, as mentioned before, the limitation of the G. bulloides abundance sample size and the sampling location exist. In spite of that, these results reflect the effects of external forcing on both the model outputs and the observation.

Table 1. Correlation coefficient between the coastal upwelling velocity in the western Arabian Sea and the Indian Monsoon Index (IMI), the upwelling PCs *, the SLP PC2, and the G. bulloides abundance for all the six simulations from the two models. All the correlation coefficients are calculated from the detrended time series and are significant at the 95% significance level except for the G. bulloides where only the second simulation of MPI-ESM-P presents the significant correlation.

MPI-ESM-P Upwelling TS CESM-CAM5 Upwelling TS r1 r2 r3 r1 r2 r3 IMI 0.64 0.61 0.60 0.64 0.66 0.65 Upwelling PCs * 0.79 0.85 0.83 0.92 0.93 0.93 SLP PC2 0.61 0.50 0.55 0.84 0.87 0.83 G. bulloides 0.14 0.66 −0.06 −0.27 0.07 0.21 * Upwelling PC2 is used for correlations within the MPI-ESM-P simulations but upwelling PC1 is used for CESM-CAM5.

3.2. EOF Analysis of Upwelling We performed an empirical orthogonal function (EOF) analysis [46] on the Arabian Sea upwelling to identify its main spatial variation patterns and the corresponding temporal evolutions. EOF analysis can identify the spatial patterns, which describe the data variance, by generating the EOF modes and their corresponding principal component (PC) time series, where each mode is ranked by its explained proportion of the total variance. The leading two modes from the EOF analysis of the upwelling are given in Figure3 . The ranks of the first two modes are switched between MPI-ESM-P and CESM-CAM5. The first mode from CESM-CAM5 (Figure3b) accounts for 41% of the total variance and reveals similar spatial patterns to the second mode of the MPI-ESM-P simulation (Figure3c ), which accounts for 24% of the total variance. We find that these EOF modes are related to the interannual variations as they capture the spatial patterns of the mean state of the upwelling (Figure1a,b) where the different signs between coastal and central Arabian Seas show the contrast between these regions. Their PC time series (Figure4b,c) also have a strong positive correlation with the time series averaged from the coastal upwelling in the western Arabian Sea (Figure1c,d) for all the simulations, respectively (Table1). Climate 2021, 9, 72 7 of 19 Climate 2021,, 99, ,x x FOR FOR PEER PEER REVIEW REVIEW 7 of 20 7 of 20

FigureFigure 3. First 3. EOFFirst mode EOF of mode JJA Arabian of JJA Sea Arabian upwelling Sea velocity upwelling modeledvelocity by (a) MPI-ESM-P modeled and by (b () aCESM-CAM5) MPI-ESM-P and and second EOF mode from (c) MPI-ESM-P and (d) CESM-CAM5 with their explained variance in brackets. (b) CESM-CAM5 and second EOF mode from (c) MPI-ESM-P and (d) CESM-CAM5 with their Figure 3. First EOF mode of JJA Arabian Sea upwelling velocity modeled by (a) MPI-ESM-P and (b) CESM-CAM5 and secondexplained EOF mode variance from (c in) MPI-ESM-P brackets. and (d) CESM-CAM5 with their explained variance in brackets.

Figure 4. First principal component (PC) time series corresponding to the ﬁrst EOF mode for all the simulation in (a) MPI-ESM-P and (b) CESM-CAM5 and second PC time series of (c) MPI-ESM-P and (d) CESM-CAM5. The plotted PCs are smoothed by a 31-year moving mean window and r1, r2, r3 represent the three simulations. t1, t2, t3 are the trends (per 1000 years) of each simulation with p1, p2 and p3 indicating their p-values, respectively, and the star symbols mark the simulations that pass the signiﬁcance test at the 95% level. Climate 2021, 9, x FOR PEER REVIEW 8 of 20

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Figure 4. First principal component (PC) time series corresponding to the first EOF mode for all the simulation in (a) MPI- ESM-P and (b) CESM-CAM5 and second PC time series of (c) MPI-ESM-P and (d) CESM-CAM5. The plotted PCs are smoothed by a 31-year moving mean window and r1, r2, r3 represent the three simulations. t1, t2, t3 are the trends (per 1000 years)Thus, of each the simulation intensity with ofp1, the p2 and coastal p3 indicating upwelling their p-values, in the respectively, western and Arabian the star Seasymbols isin mark phase the simulationswith the that intensity pass the significance of the upwelling test at the 95% inthe level. rest of the coastal areas and also with the intensity of the downwelling inThus, the central the intensity Arabian of the Sea.coastal This upwelling result in is the supported western Arabian by the Sea correlation is in phase with between upwellingthe and intensity the Indian of the upwelling Monsoon in Indexthe rest (IMI).of the coastal We calculated areas and also the with IMI the from intensity the of model derived U850the winddownwelling data based in the central on the Arabian definition Sea. This of Wangresult isand supported Fan [ 47by ].the The correlation IMI is be- significantly correlatedtween toupwelling our upwelling and the Indian time seriesMonsoon in Index all the (IMI). simulations We calculated (Table the1 IMI). from the It is shown inmodel Figure derived5a,b U850 that wind the IMIdata isbased also on correlated the definition to of the Wang upwelling and Fan [47]. velocity The IMI is significantly correlated to our upwelling time series in all the simulations (Table 1). in the rest of the coastalIt is shown areas in in Figure the Arabian 5a,b that the Sea IMI and is also the correlated negative to the correlation upwelling invelocity the in central downwellingthe regionrest of the indicates coastal areas that in the the IMIArabian contributes Sea and the to negative the intensification correlation in the of thecentral downwelling as well.downwelling Since the region IMI isindicates calculated that the based IMI contributes on the wind to the field, intensification which is of caused the down- by the sea level pressurewelling (SLP)as well. gradient, Since the IMI we is also calculated applied based the on EOF the wind analysis field, which to the is SLPcaused field. by the We find that the timesea serieslevel pressure of the coastal(SLP) gradient, upwelling we also in applied the western the EOF Arabian analysis to Sea the from SLP field. both We find that the time series of the coastal upwelling in the western Arabian Sea from both of of the models are alsothe models significantly are also correlatedsignificantly withcorrelated the PCwith time the PC series time ofseries the of second the second mode mode from the EOF analysisfrom ofthe the EOF SLP analysis (Table of the1). SLP (Table 1).

FigureFigure 5. Correlation 5. Correlation coefficient coefficient between the between upwelling the velocity upwelling and the velocity IMI modeled and by the (a) IMI MPI-ESM-P modeled and by (b)( aCESM-) MPI- CAM5. The IMI is calculated by subtracting the U850 wind averaged in box2 from that in box1 (boxes shown in c, d). All the ESM-P and (b) CESM-CAM5. The IMI is calculated by subtracting the U850 wind averaged in correlation coefficients are calculated from the detrended time series and are significant at the 95% significance level. The secondbox2 EOF from mode that of inJJA box1 SLP modeled (boxes by shown (c) MPI-ESM-P in c,d). Alland the(d) CESM-CAM5 correlation with coefficients their explained are calculated variance in frombrackets. the Thedetrended green lines demonstrate time series approximately and are significant the simplified at thebounda 95%ries significance that divide the level.positiveThe and negative second EOF EOF phases. mode of JJA SLP modeled by (c) MPI-ESM-P and (d) CESM-CAM5 with their explained variance in brackets. This EOF mode of SLP displays a clear spatial pattern representing the contrast be- The green lines demonstratetween Africa approximately and Indo-Asia the (Figure simplified 5c,d). boundariesIn general, the that patterns divide revealed the positive from the and two negative EOF phases.models are very similar. However, the boundary line, which separates the positive and

This EOF mode of SLP displays a clear spatial pattern representing the contrast between Africa and Indo-Asia (Figure5c,d). In general, the patterns revealed from the two models are very similar. However, the boundary line, which separates the positive and negative EOF phases, rotates anticlockwise in CESM-CAM5 comparing to that in MPI-ESM-P. This tilt might be responsible for the 8% more variance captured by the second EOF mode of SLP from CESM-CAM5 than that from MPI-ESM-P as well as the higher correlation between the SLP PC2 and the coastal upwelling time series derived from CESM- CAM5 than that from MPI-ESM-P (Table1). An explanation might be that, with this tilt, the second EOF mode revealed from CESM-CAM5 captures a contrast of the spatial patterns in the southern Arabian Sea that contributes to the variance representing the SW Climate 2021, 9, 72 9 of 19

wind-stress, which highly correlates with the time series of the coastal upwelling in the western Arabian Sea.

3.3. Upwelling Trends In order to understand the millennial scale variability of the Arabian Sea upwelling, we calculated the long-term linear trends of the vertical velocity and determined their significance level using the Mann–Kendall test. Figure6a,b shows the upwelling trends derived from the two simulations. Both models reveal negative trends in the northern Arabian Sea and along the coast where the intense upwelling occurs. On the contrary, the central and eastern Arabian Seas display positive trends. Note that the central Arabian Sea is dominated by downwelling, and so the positive trends in this region actually indicate a weakening of downwelling, whereas in the eastern Arabian Sea the upwelling is slightly strengthened. However, in the western Arabian Sea, the region of our main focus, the upwelling velocity decreased over the last millennium, whereby the MPI-ESM-P model displays a more significant reduction than the CESM-CAM5 model. The upwelling velocity trends share very similar spatial patterns with the first mode of MPI-ESM-P (Figure3a) and the second mode of CESM-CAM5 (Figure3d) from the EOF analysis. In addition, their PC time series (Figure4a,d) also confirm the trends revealed in the upwelling velocity time series (Figure1c,d). The different signs in the EOF spatial patterns and the trends in the PC time series indicate the weakening of the western Arabian Sea coastal upwelling. The trends revealed from the time series are small in terms of their amplitudes over a thousand years compared to the mean upwelling velocity. However, negative trends of western Arabian Sea upwelling are shown consistently in all the six simulations by the two models despite the different internal conditions used for the simulations. Thus, the overall picture of these trends is highly statistically significant, which is determined by applying a classical sign test [46]. The sign test is usually applied in statistical climatology, for instance, Tebaldi et al. [48] employed this method to test the significance level of the precipitation trends simulated in an ensemble of climate simulations. In our case, the fact that all the six simulations reveal negative trends leads to a strong overall significance, not to mention that the trends are not only all negative, but some are also statistically significant at the individual p = 0.05 level. Therefore, the weakening of upwelling in this region is very likely a robust feature in the simulations. These trends are likely induced by the external forcing used to drive the models. Among all the external forcings, only the orbital forcing displays the long-term millennial scale trend, so that the identified upwelling trends can presumably be attributed to the orbital forcing. The change in orbital forcing is a significant cause of the SST trends revealed in the tropical and subtropical regions [49]. It contributes to the increased land–sea thermal contrast, which enhances the SLP gradient between land and ocean. As a result, the monsoon was strengthened during the Mid-Holocene [50]. More recently, Gill et al. [51] have shown negative trends of the Arabian Sea upwelling and the Indian Monsoon from past to present, which is also linked to the orbital forcing. To confirm the effect of the orbital forcing, we calculated the trends of the SLP (Figure6c,d ) and the SW wind-stress (Figure6e,f) since the upwelling is mainly forced by the SW wind-stress, which is generated from the SLP gradient. The spatial patterns of the SLP trends show clearly that in mid latitudes the SLP tends to increase and in low latitudes the SLP tends to decrease. These patterns are quite likely resulting from the effect of orbital forcing, as similar long-term changes are also derived from the differences between equilibrium mid-Holocene and present climate simulations [52]. For the last millennium, Lücke et al. [53] have recently found that the trends in the climate models can be attributed to the orbital forcing. Climate 2021, 9, 72 10 of 19

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FigureFigure 6. Long-term 6. Long-term trends of trends JJA upwelling of JJA velocity upwelling modeled velocity by (a) MPI-ESM-P modeledby and ( a(b)) MPI-ESM-PCESM-CAM5. andLong-term (b) CESM- trends of CAM5.JJA SLP modeled Long-term by (c) trends MPI-ESM-P of JJA and SLP (d) CESM-CAM5. modeled by Long-term (c) MPI-ESM-P trends of and JJA (SWd) CESM-CAM5.wind-stress in the Long-termArabian Sea modeled by (e) MPI-ESM-P and (f) CESM-CAM5. The regions surrounded by the green contour lines present significant trendstrends that ofare JJA at a SWhigher wind-stress level than 90% in the(p-value Arabian < 0.1). Sea modeled by (e) MPI-ESM-P and (f) CESM-CAM5. The regions surrounded by the green contour lines present signiﬁcant trends that are at a higher level than 90% (p-value < 0.1). These trends are likely induced by the external forcing used to drive the models. Among all the external forcings, only the orbital forcing displays the long-term millennial Moreover, wescale also trend, compared so that the our identified results upwelling with those trends from can presumably the individual be attributed forcing to the experiments (orbital-forcing-onlyorbital forcing. The and change solar-forcing-only) in orbital forcing is of a the significant CESM-CAM5 cause of the model. SST trends It is revealed in the tropical and subtropical regions [49]. It contributes to the increased land–sea found that both upwelling and SLP in the orbital-forcing-only simulations present similar thermal contrast, which enhances the SLP gradient between land and ocean. As a result, trend patterns as whatthe monsoon is shown was in Figurestrengthened6 but during they are the not Mid-Holocene revealed from [50]. theMore solar-forcing- recently, Gill et al. only simulations. This[51] have further shown ascertains negative trends the impact of the Ar ofabian the Sea orbital upwelling forcing. and Withthe Indian positive Monsoon trends in the midfrom latitudes past to and present, negative which is trends also linked in theto the low orbital latitudes, forcing. the SLP contrast between these areas is reduced,To confirm whichthe effect further of the orbital affects forcing, the wind we calculated in the Arabian the trends Sea. of the During SLP (Figure the summer upwelling6c,d) and season, the SW this wind-stress SLP contrast (Figure 6e,f) drives since the the SWupwelling wind is somainly when forced the by SLP the SW wind-stress, which is generated from the SLP gradient. The spatial patterns of the SLP contrast is reduced, the SW wind is also weakened. Figure6e,f presents the negative trends of SW wind-stress as expected. In general, the trend from MPI-ESM-P is larger than that from CESM-CAM5. Since the western Arabian Sea coastal upwelling is signiﬁcantly linked

to the SW wind-stress in the Arabian Sea, the negative trends of SW wind-stress can cause the decrease in the upwelling velocity (Figure6a,b). A larger trend of SW wind-stress in the case of the MPI-ESM-P model than in the CESM-CAM5 model also results in stronger trend of upwelling. Therefore, the weakening of the coastal upwelling in the western Arabian Sea is induced by the reduction of the SW wind-stress, which results from the long-term change of the SLP contrast between mid and low latitudes due to the orbital forcing. Climate 2021, 9, 72 11 of 19

3.4. Future Scenarios In addition to the analyses of the last millennium simulations, we studied the trends in the future scenarios as well. Figure7 presents the time series of the SST, the SW wind-stress and the upwelling velocity modeled by MPI-ESM-LR and CCSM4 under the scenario Climate 2021RCP8.5., 9, x FORIn PEER order REVIEW to be consistent with the last millennium study, these time series were12 of 20

calculated from the same selected area shown in Figure1.

FigureFigure 7. Time 7. Time series of series (a) SST, of (c ()a SW) SST, wind-stress (c) SW and wind-stress (e) upwelling and velocity (e) upwelling modeled by MPI-ESM-LR velocity modeled under the by scenario MPI- of RCP8.5 for the 21st century. (b), (d) and (f) are the same variables respectively modeled by CCSM4. r1, r2, r3 represent theESM-LR three simulations under the of each scenario model. of t1,RCP8.5 t2, t3 are forthe trends the 21st (per century. 100 years) (b of,d each,f) are simulation the same with variables p1, p2 and respectively p3 indicating theirmodeled p-values, by respectively, CCSM4. r1,andr2, the r3star represent symbols mark the threethe simula simulationstions that pass of each the significance model. t1, test t2, at t3 the are 95% the level. trends (per 100 years) of each simulation with p1, p2 and p3 indicating their p-values, respectively, and the All the simulations by the two models are quite consistent regarding the results for the star symbols mark theRCP8.5 simulations scenario that by showing pass the positive signiﬁcance trends testin SST at theand 95%SW wind-stress level. but negative trends in upwelling velocity. The SST (Figure 7a,b) increases significantly under the effect of the All the simulationsgreenhouse by the gas twoemission. models In the are MPI-ESM-LR quite consistent model (Figure regarding 7a) the SST the begins results with for lower the RCP8.5 scenariovalues by showing than that in positive the CCSM4 trends model in (Figure SST and 7b) but SW they wind-stress rise to similar but values negative by the end trends in upwellingof velocity. the simulations, The SST so the (Figure SST modeled7a,b) increases by the MPI-ESM-LR signiﬁcantly has under a larger the trend effect than the of the greenhouse gasCCSM4. emission. The SW In wind-stress the MPI-ESM-LR (Figure 7c,d) model is also (Figurestrengthened7a) the dueSST to the begins enhanced with contrast lower values than thatbetween in thethe surface CCSM4 heating model over (Figure the land7b) and but the theysea. This rise finding to similar of enhanced values upwelling by favorable winds is consistent with the result of Menon et al. [54]. They presented that the the end of the simulations, so the SST modeled by the MPI-ESM-LR has a larger trend than the CCSM4. The SW wind-stress (Figure7c,d) is also strengthened due to the enhanced

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contrast between the surface heating over the land and the sea. This finding of enhanced upwelling favorable winds is consistent with the result of Menon et al. [54]. They presented that the simulated changes in the Monsoon circulation are quite robust across the CMIP model ensemble and consistent with the changes simulated by the MPI-ESM and CESM models, including the strengthening of the upwelling favorable winds in the future under RCP8.5 scenario. Although all the simulations by both models show positive trends, the CCSM4 simulations (Figure7d) reveal more distinct and significant trends than the MPI- ESM-LR (Figure7c). As a result of the intensification of the wind-stress, the upwelling velocity is expected to increase as well. However, negative trends of upwelling velocity are found in all the simulations (Figure7e,f) especially for the MPI-ESM-LR (Figure7e). In addition, we also examined the simulated upwelling in the CESM model under the RCP8.5 scenario in the 21st century and it shows similar results as in the CCSM4 model with significant trends at the same order, despite the differences between two models. Therefore, an interesting question arises as to what is causing the negative trend in upwelling velocity given that the upwelling favorable wind-stress is projected to increase. Figure7 indicates that with stronger trends in SST and weaker trends in SW wind-stress, the MPI-ESM- LR simulations present a stronger decrease in upwelling whereas the CCSM4 simulated upwelling decreases less as the SST reveals weaker trends and the SW wind-stress shows stronger trends in CCSM4. Thus, it is likely that the increase in surface water stratification caused by warming of the upper ocean layers in the RCP8.5 scenario could override the effect of the upwelling favorable wind. The upwelling weakens more when the warming is stronger or the wind is less intensified, and vice versa. This hypothesis is confirmed by the results simulated under the RCP2.6 scenario where both models show weaker warming of surface water than the RCP8.5 simulations but no consistent trend in the SW wind-stress or the upwelling. That is, without the strong increase in SST the water stratification in the upper layers is less intense so the decrease in upwelling is weaker and is compromised by the effect of the SW wind-stress. Note that the correlations between the upwelling and the SW wind-stress are similar in RCP8.5 and RCP2.6 scenarios (Table2) so the influences of the wind on the upwelling are at the same level.

Table 2. Correlation coefficient between the upwelling velocity and the SW wind-stress modeled by MPI-ESM-LR and CCSM4 for the RCP8.5 and RCP2.6 scenarios. All the correlation coefficients are calculated from the detrended time series and are significant at the 95% significance level.

MPI-ESM-LR CCSM4 r1 r2 r3 r1 r2 r3 RCP8.5 0.5457 0.4787 0.5420 0.4844 0.5500 0.5321 RCP2.6 0.4270 0.4259 0.4752 0.7569 0.6391 0.6879

In addition, Figure8 presents the trends of upwelling velocity and water temperature at all levels in the upper 200 m, which further supports that the upwelling reduction under RCP8.5 scenario is caused by the surface water stratiﬁcation due to the strong warming. It is very clear, especially in the CCSM4 plots (Figure8c,d), that in the upper 50–100 m the upwelling velocity shows larger negative trends when the trends of the water temperature increase. At around 50 m depth, the temperature trends reach their peaks and the upwelling trends also get to the lowest values (in the surface layer above 100 m). The upwelling is actually most intense at this layer, but it is most reduced as well, which causes the drop in the upwelling time series shown in Figure7. Therefore, in spite of the positive correlation between the SW wind-stress and the upwelling, the warming of the upper ocean layers under the RCP8.5 scenario reduces the upwelling velocity more efﬁciently. Climate 2021, 9, 72 13 of 19 Climate 2021, 9, x FOR PEER REVIEW 14 of 20

FigureFigure 8.8.Vertical Vertical trends trends of of (a ()a water) water temperature temperature and and (b )(b upwelling) upwelling velocity velocity in thein the upper upper 200 200 m modeledm modeled by MPI-ESM-LR.by MPI-ESM- (LR.c,d) ( arec) and the ( samed) are but the bysame CCSM4. but by Solid CCSM4. lines Solid are the lines results are the for result the RCP8.5s for the scenario RCP8.5 andscenario dotted and lines dotted are lines for the are RCP2.6 for the RCP2.6 scenario. The colored lines show the trends of r1 (blue), r2 (red), and r3 (green) in each model. The black lines on scenario. The colored lines show the trends of r1 (blue), r2 (red), and r3 (green) in each model. The black lines on the right the right of each panel present the mean value of the current variable. of each panel present the mean value of the current variable.

4.4. DiscussionDiscussion OneOne limitationlimitation of of this this study study is thatis that it relies it re tolies a greatto a great extent extent on the on realism the realism of the models of the employed,models employed, which makes which it makes very difficult it very todifficul validatet to thevalidate results the as results there are as nothere available are no directavailable observations direct observations on upwelling on upwelling over the time over span. the time However, span. weHowever, do find awe connection do find a betweenconnection our between modeled our upwelling modeled velocityupwelling and velo thecity observational and the observational sediment records. sediment They rec- presentords. They a significant present correlationa significant (Figure correlation2), which (Figure indicates 2), which the effect indicates of the externalthe effect forcing. of the Inexternal addition, forcing. they alsoIn addition, have similar they patterns also have in thesimilar time patterns series in in terms the oftime the series trends. in Itterms can be of noticedthe trends. that It the can long-term be noticed trend that revealed the long from-term the trend upwelling revealed time from series the (Figure upwelling1) tends time to flip,series especially (Figure in1) thetends CESM-CAM5 to flip, especially model, in at the around CESM-CAM5 the year 1550. model, This at flip around was reportedthe year by1550. Anderson This flip et al.was [15 reported] where theyby Anderson reconstructed et al. the [15] upwelling where indexthey (monsoonreconstructed winds) the fromupwelling the sediment index (monsoon records over winds) the lastfrom millennium. the sediment They records found over a slightly the last negative millennium. trend fromThey 1000found to 1500a slightly and a negative positive trendtrend thereafter, from 1000 which to 1500 is very and similara positive to our trend finding. thereafter, Thus, wewhich divided is very our similar upwelling to our time finding. series Thus, into two we periodsdivided based our upwelling on the flip time and series calculated into two the trendsperiods separately based on (Tablethe flip3). and calculated the trends separately (Table 3).

TableTable 3.3. Upwelling trends (m/day (m/day per per 1000 1000 years) years) calcul calculatedated from from the the two two models models for fordifferent different timetime periods:periods: thethe entireentire timetime periodperiod (850–1849),(850–1849), thethe ﬁrstfirst 700700 yearsyears (850–1549),(850–1549), and and the the last last 300 300 years (1550–1849).years (1550–1849).

MPI-ESM-PMPI-ESM-P Upwelling Upwelling Trend Trend CESM-CAM5 CESM-CAM5 Upwelling Upwelling Trend Trend r1r1 r2r2 r3r3 r1r1 r2r2 r3r3 850–1849850–1849− 0.0187− *0.0187− *0.0062 −0.0062− 0.0164−0.0164 * * −0.0013−0.0013 −0.0129−0.0129 −−0.01380.0138 850–1549850–1549 −0.0120−0.0120− 0.0052−0.0052− 0.0105−0.0105 −0.0005−0.0005 −0.0028−0.0028 −−0.03430.0343 * * 1550–18491550–1849 0.05670.0567 0.0862 0.0862 * * 0.00620.0062 −0.0100−0.0100 0.14840.1484 * * 0.09050.0905 ** Trends Trends that that are are significant significant at the at 95% the level.95% level. It is very clear that five out of six of the simulations indicate a flip in the long-term It is very clear that five out of six of the simulations indicate a flip in the long-term trend at 1550 as the signs of the trends change from negative to positive after this point (excepttrend at for 1550 the as CESM-CAM5 the signs of r1), the which trends is change at about from the samenegative time to as positive the G. bulloides after thisrecords. point This(except is also for the shown CESM-CAM5 when comparing r1), which the is upwelling at about the time same series time with as the the G. replot bulloides of the records. data fromThis is Anderson also shown et al. when [15] presentedcomparing in the Figure upwelling9. time series with the replot of the data from Anderson et al. [15] presented in Figure 9.

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Figure 9.Figure Upwelling 9. Upwelling velocity modeled velocity by MPI-ESM-P modeled by (blue) MPI-ESM-P and CESM-CAM5 (blue) and (red) CESM-CAM5 for the last millennium (red) for (upper) the last and replottedmillennium using data of (upper) Anderson and et replotted al. (2002) usingfor the dataG. bulloides of Anderson abundance et al. (lower). (2002) The for theblueG. and bulloides red markersabundance show the data from(lower). two sediment The blue records and redlocated markers very closely show thein the data western from Arabian two sediment Sea, RC2730 records and located RC2735, very respectively. closely in The green line is the average of all the records in a block of 50 years. The dashed black lines show the period where the “flip” is detected.the western Arabian Sea, RC2730 and RC2735, respectively. The green line is the average of all the records in a block of 50 years. The dashed black lines show the period where the “flip” is detected. To explore the flip in the modeled upwelling time series, we selected a range of years To explorefrom the flip 1550 in to the 1650 modeled when the upwelling flip might time happe series,n. We we considered selected aeach range of the of yearsyears within from 1550 to 1650this when range the as a flip flip might point happen.and calculated We considered the trends eachbefore of and the after years this within point, this that is, the range as a flip pointtrend andfrom calculated 850 to the theselected trends year before and the and trend after from this the point, selected that year is, the to trend1850. We ap- from 850 to theplied selected this yearto all and the six the simulations trend from of the the selected MPI-ESM-P year and to 1850. the CESM-CAM5 We applied models this and to all the six simulationscombined the of theresults. MPI-ESM-P The histograms and the in Figure CESM-CAM5 10 show that models 95% andof the combined trends before the the results. Theflip histograms point are innegative Figure and 10 73%show of thatthe trends 95% of after the the trends flip point before are the positive. flip point Among all are negative andthe 73% trends of thebefore trends and after the the flip flip poin pointt, the are percentage positive. of Among the significant all the trends(p-value < 0.1) before and afterones the are flip 35% point, and the 25%, percentage respectively. of theMore significant importantly, (p -valuethe significant < 0.1) ones trends are are consistent on the signs in both cases (negative before flip and positive after flip). Therefore, 35% and 25%, respectively. More importantly, the significant trends are consistent on the the models produce realistic upwelling time series that agree with the observational sed- signs in both casesiment (negative records. However, before flip uncertainties and positive remain after as flip).the exact Therefore, time when the the models trends change produce realisticsigns upwelling varies from time simulation series that to agree simulation, with thewhich observational might be caused sediment by the records.different initial However, uncertaintiesconditions remain applied as for the each exact simulation. time when the trends change signs varies from simulation to simulation, which might be caused by the different initial conditions applied for each simulation. Model resolution also has an impact on the realism of the results [55,56]. However, it is still unclear what resolution is most suitable to model the Arabian Sea coastal upwelling system. In our study, the MPI-ESM uses a coarser horizontal resolution (256 × 220) than the CESM (320 × 384) and produces the upwelling velocity with larger means but less variability, as well as larger trends. The upwelling velocity simulated by the CESM, however, has stronger correlation with the wind and the wind based IMI. Whether these differences are caused by the different resolutions is uncertain, but this comparison might shed light on how the modeled Arabian Sea upwelling responds to the change in the model resolutions.

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Figure 10. Histograms of (a) trends and (b) p-values for the period before the selected flip point. Histograms of (c) trends Figure 10. Histograms of (a) trends and (b) p-values for the period before the selected flip point. Histograms of (c) trends and ((dd)) pp-values-values for for the the period period after after the the selected selected flip flip point. point. (e) Trends(e) Trends before before (dot) (dot) and after and (plus)after (plus) each flip each point flip from point 1550 from to1550 1650. to 1650. The colored The colored markers markers show sh significantow significant trends trends with p -valuewith p-value < 0.1. < 0.1.

A recent study found a reduction in the future upwelling at low latitudes in most Model resolution also has an impact on the realism of the results [55,56]. However, it of the four EBUSs [18]. In our analysis for the future scenario RCP8.5, the upwelling is still unclear what resolution is most suitable to model the Arabian Sea coastal upwelling velocity also reveals a negative trend whereas the SW wind-stress presents a positive trend. system. In our study, the MPI-ESM uses a coarser horizontal resolution (256 × 220) than Thus, the reduction of the upwelling is not caused by the wind-stress, although they are the CESM (320 × 384) and produces the upwelling velocity with larger means but less still significantly correlated. The analysis for the RCP2.6 scenario does not show a trend variability, as well as larger trends. The upwelling velocity simulated by the CESM, how- in either the upwelling or the SW wind-stress, but it shows strong correlation between upwellingever, has stronger and the wind-stresscorrelation with as well the as wind the RCP8.5 and the scenario. wind based Wang IMI. et al.Whether [18] suggested these dif- severalferences mechanisms are caused that by the could different lead to aresolution reductions inis theuncertain, upwelling but at this low comparison latitudes such might as strengthenedshed light on downwelling how the modeled favorable Arabian winds, Sea weakened upwelling upwelling responds favorable to the change winds, andin the enhancedmodel resolutions. water stratification due to the greenhouse warming. More specifically, Roxy et al.A [57 recent] looked study into found the simulated a reduction chlorophyll in the future concentrations upwelling andat low found latitudes out a in drop most of of marinethe four phytoplankton EBUSs [18]. In duringour analysis the last for 60 the years future due scenario to the enhanced RCP8.5, the ocean upwelling stratification, velocity whichalso reveals shows a a negative similar mechanism trend whereas to our the case. SW wind-stress The stratified presents water under a positive the RCP8.5 trend. sce-Thus, nariothe reduction reduces the of upwelling,the upwelling however, is not this caused is not by shown the wind-stress, in the RCP2.6 al scenario.though they Although are still bothsignificantly models used correlated. in the present The an studyalysis show for the the RCP2.6 overriding scenario effect ofdoes the not water show stratification a trend in foreither the RCP8.5the upwelling scenario, or their the future SW wind-stress, decadal trends but are it notshows mutually strong consistent correlation (Figure between 11). Whereasupwelling this and effect the existswind-stress for all theas well simulations as the RCP8.5 of the MPI-ESM-LR scenario. Wang model et al. since [18] the suggested begin- ningseveral of themechanisms 21st century that and could reaches lead maxima to a reduction during 2044in the to upwelling 2081, the CCSM4at low latitudes model does such notas strengthened show the same downwelling results. Therefore, favorable it remains winds, for weakened further study upwelling to investigate favorable at whatwinds, leveland enhanced of future emissionswater stratification between the due strongest to the andgreenhouse the weakest warming. warming More scenarios specifically, the waterRoxy et stratification al. [57] looked and into the effectthe simulated of wind-stress chlorophyll approximately concentrations balance. and found out a drop of marine phytoplankton during the last 60 years due to the enhanced ocean stratification, which shows a similar mechanism to our case. The stratified water under the RCP8.5 scenario reduces the upwelling, however, this is not shown in the RCP2.6 scenario. Although both models used in the present study show the overriding effect of the water stratification for the RCP8.5 scenario, their future decadal trends are not mutually consistent (Fig- ure 11). Whereas this effect exists for all the simulations of the MPI-ESM-LR model since the beginning of the 21st century and reaches maxima during 2044 to 2081, the CCSM4

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Climate 2021, 9, 72 model does not show the same results. Therefore, it remains for further study16 of to 19 investigate at what level of future emissions between the strongest and the weakest warming scenarios the water stratification and the effect of wind-stress approximately balance.

FigureFigure 11. Decadal 11. Decadal trends trends for the forfuture the RCP future 8.5 scenario RCP 8.5 in scenario the MPI-ESM-LR in the MPI-ESM-LR (upper) and CCSM4 (upper) (lower) and CCSM4models for upwelling (left) and SST (right). P1 to P5 represent the 19-year periods from 2006 to 2100. (lower) models for upwelling (left) and SST (right). P1 to P5 represent the 19-year periods from 2006 to 2100. 5. Conclusions 5. Conclusions The variability of the Arabian Sea upwelling over the last millennium and in the 21st century was investigated in this study. We statistically analyzed and compared the outputs The variabilityof offour the earth Arabian system model Sea upwelling simulation ense overmbles. the lastOver millenniumthe last millennium, and in the the modeled 21st century was investigatedupwelling velocity in this has study.a strong Wecorrelation statistically with the analyzed SST and the and IMIcompared as well as the the observed outputs of four earthG.bulloides system abundance. model simulation This indicates ensembles. a close connection Over theamong last the millennium, Arabian Sea upwelling, the modeled upwellingthe velocity Indian summer has a strong Monsoon correlation and the sediment with the records. SST and A negative the IMI long-term as well trend as the is found in the upwelling velocity over the last millennium, which is resulting from the orbital forc- observed G.bulloides abundance. This indicates a close connection among the Arabian Sea ing of the models. In the last 300 years, however, the upwelling reveals a positive trend that upwelling, the Indianmatches summer the observations Monsoon in and the sediment the sediment records. records. As for the A future negative scenarios, long-term the correlation trend is found in thebetween upwelling upwelling velocity and the over SW wind-stress the last millennium, remains at the which same level is resulting under the from RCP8.5 and the orbital forcingRCP2.6 of the scenarios models. but In the the stronger last 300 greenhouse years, however, gas emission the scenario upwelling leads to reveals opposite a trends positive trend thatin matches the SW wind-stress the observations and the upwelling in the sedimentvelocity. The records. positive trend As for in the the SW future wind-stress scenarios, the correlationis caused between by the intensified upwelling SLP gradient and the between SW wind-stress land and ocean remains due to at the the warming same effect. level under the RCP8.5The upwelling and RCP2.6 velocity scenarios shows a butnegative the strongertrend because greenhouse under the gasstronger emission scenario, the warming of the sea water tends to stabilize the surface layer, which overrides the effect of scenario leads to oppositethe SW wind-stress trends in and the interrupts SW wind-stress the upwelling. and the upwelling velocity. The positive trend in the SW wind-stress is caused by the intensiﬁed SLP gradient between land and ocean dueAuthor to the Contributions: warming effect. Conceptualization, The upwelling X.Y., B.H. velocity and E.Z.; shows methodology, a negative X.Y. and trend E.Z.; valida- because under thetion, stronger X.Y. and scenario, B.H.; formal the analysis, warming X.Y.; ofinvestig theation, sea water X.Y.; writing—original tends to stabilize draft preparation, the surface layer, which overrides the effect of the SW wind-stress and interrupts the upwelling.

Author Contributions: Conceptualization, X.Y., B.H. and E.Z.; methodology, X.Y. and E.Z.; validation, X.Y. and B.H.; formal analysis, X.Y.; investigation, X.Y.; writing—original draft preparation, X.Y.; writing—review and editing, X.Y., B.H. and E.Z.; visualization, X.Y.; supervision, B.H. and E.Z. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Data Availability Statement: The model outputs are within the CMIP5 model pool and are available from https://pcmdi.llnl.gov/mips/cmip5/data-portal.html (accessed on 17 March 2021). The G. bulloides records from Anderson et al. [15] can be accessed at ftp://ftp.ncdc.noaa.gov/pub/data/ paleo/contributions_by_author/anderson2002/anderson2002.txt (accessed on 17 March 2021). Climate 2021, 9, 72 17 of 19

Acknowledgments: This paper is a contribution to the Cluster of Excellence “Climate, Climatic Change, and Society” (CLICCS). We thank the Max-Planck-Institute for Meteorology for providing the MPI-ESM model data. All the other publicly available data used in this study are gratefully acknowledged. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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