Why Is The Mediterranean A Climate Change Hot Spot?

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VOLUME 33JOURNAL OF CLIMATE15 JULY 2020Why Is the Mediterranean a Climate Change Hot Spot?A. TUEL AND E. A. B. ELTAHIRRalph M. Parsons Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts(Manuscript received 5 December 2019, in final form 20 April 2020)ABSTRACTHigher precipitation is expected over most of the world’s continents under climate change, except for a fewspecific regions where models project robust declines. Among these, the Mediterranean stands out as a resultof the magnitude and significance of its winter precipitation decline. Locally, up to 40% of winter precipitationcould be lost, setting strong limits on water resources that will constrain the ability of the region to developand grow food, affecting millions of already water-stressed people and threatening the stability of this tenseand complex area. To this day, however, a theory explaining the special nature of this region as a climatechange hot spot is still lacking. Regional circulation changes, dominated by the development of a stronganomalous ridge, are thought to drive the winter precipitation decline, but their origins and potential contributions to regional hydroclimate change remain elusive. Here, we show how wintertime Mediterraneancirculation trends can be seen as the combined response to two independent forcings: robust changes in largescale, upper-tropospheric flow and the reduction in the regional land–sea temperature gradient that ischaracteristic of this region. In addition, we discuss how the circulation change can account for the magnitudeand spatial structure of the drying. Our findings pave the way for better understanding and improved modeling of the future Mediterranean hydroclimate.1. IntroductionLocated at the border between the arid subtropics andthe temperate midlatitudes, the Mediterranean Basin ischaracterized by low annual precipitation totals andhigh interannual variability, which impose a state ofsemipermanent water stress across much of NorthAfrica and the Middle East. Summers are warm and dry,dominated to the east by the influence of subtropicalremote forcing triggered by the Indian monsoon, whichcauses intense subsidence across the region (Rodwelland Hoskins 1996), and to the west by the subtropicalhigh. In winter, however, the Mediterranean Sea regionis largely outside the influence of such tropical teleconnections, and storms and rain are brought by midlatitude westerlies. Consequently, winter precipitationis key to the region’s agriculture and economy, withits future of paramount importance for the basin’scountries. Regional- and local-scale processes, such asSupplemental information related to this paper is available atthe Journals Online website: ding author: A. Tuel, atuel@mit.eduland–sea circulations, also play a significant part inshaping Mediterranean climate variability and climatechange (Bolle 2003).The Mediterranean has long stood out in successivegenerations of global climate models (GCMs) as beingparticularly sensitive to rising concentrations of greenhouse gases. Models overwhelmingly project, across allscenarios, a large reduction in precipitation, more thanin other land regions in relative terms (Fig. 1b) (Giorgiand Lionello 2008; Planton et al. 2012). A large partof that decline occurs during winter, south of 408N(Fig. 1d), with enhanced drying over northwesternAfrica [from 230% to 240% in December–February(DJF) precipitation] and the eastern Mediterranean(from 220% to 225%). In summer, significant warming and drying is also projected for the northernMediterranean (Brogli et al. 2019). While the largeinterannual variability in wintertime Mediterraneanclimate makes the significance of past trends hard toestablish (Kelley et al. 2012), observations and reanalysis products are in general consistent with historical simulations and projections for the upcomingcentury (Fig. 2b). The Mediterranean has experiencedsubstantial drying over the last century, part of whichcannot be explained by simple internal variabilityDOI: 10.1175/JCLI-D-19-0910.1Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS CopyrightPolicy (www.ametsoc.org/PUBSReuseLicenses).5829

5830JOURNAL OF CLIMATEVOLUME 33FIG. 1. CMIP5 winter (DJF) multimodel mean projected change in (a) worldwide SLP, (c) Mediterranean SLPand 850-hPa winds, and (d) Mediterranean precipitation under RCP8.5 (2071–2100 minus 1976–2005). HistoricalDJF SLP contours are also shown in (c) (from 1010 to 1024 hPa every 2 hPa; the closed contour over North Africa is1024 hPa). Dots in (a) and (d) indicate that more than 80% of models agree on the sign of the change. Also shown is(b) median (black dots) and 90% intermodel spread (gray whiskers) of relative change in annual precipitation inCMIP5 models under RCP8.5 (2071–2100 minus 1976–2005), against annual mean historical (1976–2005) precipitation, for 25 land regions covering the whole globe (Giorgi and Bi 2005). The Mediterranean is highlighted with ared dot. In (a)–(d), projections have been renormalized by each model’s global projected temperature change.(Hoerling et al. 2012; Kelley et al. 2012). An unprecedented drought in the eastern Mediterranean exacerbated the already tense situation in Syria that led to theoutbreak of a civil war (Kelley et al. 2015). Furtherdrying will inevitably exacerbate social and geopoliticaltensions in this severely water-stressed region.Changes in the regional low-level circulation havelong been suspected to play a dominant role in thewinter Mediterranean drying (e.g., Seager et al. 2014;Zappa and Shepherd 2017; Tuel and Eltahir 2018; Brogliet al. 2019), in particular through the development of ananomalous surface anticyclone over the MediterraneanBasin (Seager et al. 2019), a striking feature of global climateprojections (Figs. 1a,c). This anomalous Mediterraneanridge extends from roughly November to April and hasbeen consistently present in successive generations ofmodels (Giorgi and Lionello 2008). Its magnitude, andthat of the associated wind field, is strongly correlated tothe regional precipitation decline across models (Fig. S2in the online supplemental material) (Zappa et al.2015b). Observed trends have up to now been consistentwith model projections (Fig. 2a). Giorgi and Lionello(2008) suggested that the anomalous high may drive theprojected drying by increasing atmospheric stability inthe region and suppressing Mediterranean cyclones,as discussed in later studies (e.g., Rojas et al. 2013;Zappa et al. 2015a). Subsequently, based on a threedimensional analysis of the moisture budget, Seageret al. (2014) concluded that the Mediterranean precipitation decline was due to increased moisturedivergence by the time-mean flow, due to anomalousanticyclonic circulation in the region. Zappa et al.(2015b) additionally showed that the intermodel spreadin precipitation change could be well captured by a

15 JULY 2020TUEL AND ELTAHIRFIG. 2. Five-year smoothed median (black line) and range (grayshading) of CMIP5 model simulations of DJF Mediterranean(a) SLP (08–308E, 328–488N) and (b) precipitation (208W–408E,308–458N) anomalies (map on Fig. S1 of the online supplementalmaterial), alongside observations/reanalysis data from HadSLP2,NOAA 20CR, and ERA-20C [in (a)] and GPCP and CRU TS4 v2[in (b)].simple wind index reflective of the regional circulation,supporting the idea that future Mediterranean hydroclimate trends are primarily driven by changes in theregional atmospheric circulation (Fig. S2).Why the anomalous high pressure develops in the firstplace, however, and how it connects to the robust pattern of the precipitation response remains unclear.Changes in the large-scale circulation, notably the expansion of the Hadley circulation (Lu et al. 2007) andthe corresponding poleward shift of the North Atlanticstorm track (Yin 2005; Scheff and Frierson 2012; Woollingset al. 2012), the weakening of the Mediterranean stormtrack, and changes in regional flow regimes (Zappa et al.2015a; Rojas et al. 2013), have previously been suggestedas driving mechanisms behind Mediterranean circulationtrends. More recently, the role of shifts in NorthernHemisphere stationary waves was shown to modulateprecipitation projections over California (Simpson et al.2016) and suspected to impact the Mediterranean as well(Seager et al. 2019). Tuel and Eltahir (2018) also firstsuggested that the regional warming contrast between landand sea could play a role. Still, the specific contributions of5831those mechanisms to projected Mediterranean climatetrends and their spread has not been clearly quantified.Additionally, the role of Hadley cell and storm-track shiftshas been challenged as they were found to occur, in abruptCO2 experiments, on a much faster time scale than theMediterranean drying (He and Soden 2017). The zonalsymmetry in Hadley cell shifts is also at odds with theamplified Mediterranean sea level pressure (SLP) signal(Fig. 1a), so that it is not obvious why the Mediterraneanwould be particularly sensitive in climate projections.Therefore, while much attention has been given to thisregion, a comprehensive theory for the wintertimeMediterranean precipitation decline is still lacking. Inthis study, we seek to understand what drives projectedwintertime low-level circulation and precipitation trendsin the region, with winter defined as the DJF period.Based on results of simulations from phase 5 of theCoupled Model Intercomparison Project (CMIP5) andidealized simulations with the Massachusetts Institute ofTechnology (MIT) Regional Climate Model, we showthat global-scale circulation changes originating near thetropopause and the independent response to changesin the sea–land surface temperature gradient over theMediterranean Basin greatly contribute to future trendsin the Mediterranean hydroclimate. In addition, we discuss the physical connections between circulation andprecipitation projections in the western and easternMediterranean. Our primary focus is the CMIP5 multimodel mean, but intermodel spread is also discussedin the light of the two proposed mechanisms.2. DataWe analyze recent surface temperature, pressure, andprecipitation trends in the Mediterranean region usingvarious datasets. Observed surface temperatures aretaken from the NOAA Merged Land–Ocean SurfaceTemperature Analysis (MLOST; Vose et al. 2012) (landand ocean; 1850–2018), and the CRU TS4.02 dataset(Harris et al. 2014) (land only; 1901–2018). Monthlyprecipitation over land from CRU TS4.02 is also used,and, for ocean coverage, we consider data from theGlobal Precipitation Climatology Project, version 2.3(Adler et al. 2003), as well (1979–2018). Sea surfacetemperature (SST) data are taken from HadISST(Rayner et al. 2003) and ERSST v5 (Huang et al. 2017).SLP from the HadSLP2 dataset (1850–2018) is used(Allan and Ansell 2006); for purposes of comparison,we also look at SLP in the NCEP–NCAR (Kalnay et al.1996), NOAA 20CR (Compo et al. 2011), and ERAtwentieth-century (20c; Poli et al. 2016) reanalyses.Future climate trends are analyzed using 30 GCMsimulations from CMIP5 (Taylor et al. 2012), under the

5832JOURNAL OF CLIMATEVOLUME 33FIG. 3. CMIP5 winter (DJF) multimodel mean projected change in Northern Hemisphere200-hPa (a) zonal and (b) meridional winds. Dots indicate that more than 80% of models agreeon the sign of the change. For each model, projections have been renormalized by that model’sglobal projected temperature change.historical and representative concentration pathway8.5 (RCP8.5) scenarios. We used all models that provided SLP, precipitation, surface temperature, andspecific humidity, temperature, winds, and pressurevelocity on pressure levels at monthly resolution forour reference (1976–2005) and future (2071–2100) periods. A detailed list of the 30 selected models can befound in Table S1 in the online supplemental material(expansions of model acronyms are available at https://www.ametsoc.org/PubsAcronymList). For each model,only the r1i1p1 ensemble member is used. All modeloutput is regridded to a common 18 3 18 grid. Unlessspecified, all changes under RCP8.5 are defined as the2071–2100 minus 1976–2005 average, and all anomalies(of SLP, precipitation, etc.) are defined with respect tothe 1976–2005 reference period.3. Quantifying the SLP response to upper-levelcirculation changeCMIP5 GCMs robustly agree on the pattern ofNorthern Hemisphere upper-tropospheric circulationchange under continued anthropogenic forcing: astrengthening of the midlatitude jet (Barnes andPolvani 2013) and associated shift in the pattern ofquasi-stationary waves (Brandefelt and Krnich 2008)(Fig. 3). A direct consequence of these changes is thedevelopment of an upper-tropospheric, anomalousanticyclonic circulation over the Mediterranean. Becauseof the generally equivalent barotropic character ofwinter stationary waves (Held et al. 2002), it is expected that such a change in upper-level flow wouldtranslate into anticyclonic circulation and higher pressure at low levels.a. MethodsTo quantify the impact of trends in upper-troposphericflow on Mediterranean low-level circulation, we apply toeach selected GCM an analog-based ‘‘dynamical adjustment’’ model. We give here a brief overview of themethod presented in Deser et al. (2016) and refer totheir paper for further mathematical details. We consider two physical fields, a predictor field X 2 Rp, and apredictand field Y 2 Rq, linked by some physical relationship (variability in X influences variability in Y); pand q refer to the space dimension. We assume that a‘‘training’’ series of concurrent values of X and Y, oflength n, is available, which we note (Xi)0#i#n and(Yi)0#i#n. The goal of dynamical adjustment is to estimate the amplitude of the field Yt associated with thepredictor field Xt, observed at a time t . n. To that end,we select the N 5 50 closest analogs of field Xt among

15 JULY 2020TUEL AND ELTAHIRthe (Xi)0#i#n, with Euclidean distance used as themetric. Among these N fields, we randomly select asubsample of M 5 30 fields, which are assembled in ap 3 M matrix Xc. The corresponding Y values aresimilarly put in matrix Yc. Then, optimal linear combination weights b are estimated such thatXt ’ bXc ;(1)bXc represents a ‘‘constructed analog’’ of Xt. The sameweights b are also applied to Yc to estimate the com t 5 bYc . Theponent of Yt induced by predictor Xt: Yprocedure is then repeated 1000 times, each time with anew random subsample of 30 analogs. The purpose ofthis subsampling is to increase robustness of the resultsand better quantify the variability in the reconstructedestimates (Deser et al. 2016). The results are not particularly sensitive to the specific choice of N and M overthe range 30 # N # 60 and M ’ 2N/3.The method is applied to each GCM separately, usingfor X the DJF meridional wind field anomalies at200 hPa in the 208–808N latitude range, and for Y theNorthern Hemisphere DJF SLP anomaly field. Seasonalanomalies are computed by subtracting from each fieldits 1976–2005 mean. The historical (1850–2005) simulations are used as ‘‘training’’ series (thus n 5 155 winters),and we calculate constructed analogs for all wintersbetween 2070 and 2100. Our choice of predictor is motivated by the results of Simpson et al. (2016), whichimplied that projected upper-level meridional windanomalies were reflective of the shifts in mean stationary wave structure. Results are not significantlydifferent when using the zonal wind component, oreven the streamfunction, since upper-tropospheric flowis essentially nondivergent at seasonal time scales.An important assumption of this approach is that, atfirst order at least, the surface circulation response toupper-level wind pattern change is linear. To test thathypothesis, we also reconstruct annual SLP anomalies inthe historical runs: for each of the 155 winters in 1850–2005, the other 154 winters are used to look for analogs.This helps determine whether interannual variability ofMediterranean SLP is correctly reconstructed in bothhistorical and future simulations.b. ResultsMost of the year-to-year variability in DJF MediterraneanSLP is well reconstructed based on upper-troposphericflow anomalies, in both historical and RCP8.5 runs(Fig. 4c). The model-mean squared correlation coefficientis about 0.75 for each scenario (model range is 0.55–0.85),and the average root-mean-square error is 1.2 hPa (modelrange 0.95–1.5 hPa). SLP anomalies are correctly5833approximated over the whole range of interannual variability; only very low SLP values seem to be slightlyoverestimated. Despite the mean shift toward positiveSLP anomalies in future projections, the range of valuesof RCP8.5 anomalies is not substantially different fromhistorical ones. This increases our confidence that themethod will correctly capture shifts in the distribution ofSLP anomalies connected to projected changes inupper-level wind fields. Looking ahead, we find thatfuture upper-tropospheric wind patterns are consistentwith an amplified SLP response over the Mediterraneanand also east of Japan (Figs. 4a,b). They also account for80% of intermodel spread in Mediterranean projections(Fig. 4d). The decrease in SLP over the North Pacificis also well reproduced, but not so much over NorthAmerica, the North Atlantic, and Siberia, where otherfactors, notably linked to thermodynamic forcing [e.g.,Arctic amplification and the North Atlantic warminghole (NAWH)] likely play a role (Gervais et al. 2019).However, only 40% of the mean GCM response is accounted for (0.7 hPa as compared with 1.7 hPa in themultimodel mean). In some models, this dynamicallyinduced SLP change is even of the opposite sign of thetotal simulated response by that same model (Fig. 4d).Similarly, during the ‘‘extended’’ winter season, outsideDJF, future upper-tropospheric circulation anomalies arestill connected to an increase in SLP over the Mediterraneanbut explain only ;30%–40% of the whole signal (Fig. S3 inthe online supplemental material).4. Surface thermodynamical forcingThe geography of the Mediterranean Basin is unique,characterized by the existence of a large sea enclosedby continents on almost all sides. During winter, because of water’s larger thermal capacity, the Mediterranean Sea is on average warmer than the surroundingland. However, as a result of enhanced warming overland under climate change (Sutton et al. 2007; Byrneand O’Gorman 2018), future climate projections exhibit a robust and gradual relative cooling of theMediterranean Sea with respect to surrounding land(Fig. 5a). We define a simple sea–land temperature difference index as the difference between mean DJF temperatures over sea and over land in the Mediterraneanregion (58W–388E, 288–488N; Fig. 5a). In historicalCMIP5 simulations and ERA-Interim, that differencereaches about 58C; by 2100, RCP8.5 simulations project a0.58C decrease, with little spread across models (Fig. 5b).The magnitude of the projected index change is similarto its historical interannual standard deviation, suggestinga potentially important role of sea–land temperaturechange in shaping future regional circulation. Up to now,

5834JOURNAL OF CLIMATEVOLUME 33FIG. 4. Northern Hemisphere CMIP5 multimodel mean change in DJF SLP (a) estimated by dynamical adjustment and (b) projected by CMIP5 models. Stippling shows agreement on the sign of the change by 80% ofmodels. Numbers indicate average value within the dash-outlined box. (c) Model vs dynamically reconstructed DJFMediterranean (08–308E, 328–488N) SLP anomalies in historical (black dots) and RCP8.5 (red dots) simulations, forall 30 CMIP5 models. (d) Mediterranean (08–308E, 328–488N) CMIP5 intermodel change in DJF SLP estimated bythe dynamical adjustment model along with 90% confidence intervals. The two models used in Brogli et al. (2019)are highlighted with red dots.the projected decrease

shaping Mediterranean climate variability and climate change (Bolle 2003). The Mediterranean has long stood out in successive . We give here a brief overview of the method presented in Deser et al. (2016) and refer to their paper for further mathematical details. We con-

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