projections of heat waves events in the intra-americas region...

Research ArticleProjections of Heat Waves Events in the Intra-Americas RegionUsing Multimodel Ensemble

Moises Angeles-Malaspina 1 Jorge E Gonzaacutelez-Cruz 2 and Nazario Ramiacuterez-Beltran3

1Mechanical Engineering Department Polytechnic University of Puerto Rico San Juan PR 00918 USA2NOAA-CREST City College of New York New York NY 1031 USA3Department of Industrial Engineering University of Puerto Rico Mayaguez PR 00680 USA

Correspondence should be addressed to Jorge E Gonzalez-Cruz jgonzalezcruzccnycunyedu

Received 22 June 2017 Revised 11 December 2017 Accepted 18 December 2017 Published 22 January 2018

Academic Editor Annalisa Cherchi

Copyright copy 2018 Moises Angeles-Malaspina et al This is an open access article distributed under the Creative CommonsAttribution License which permits unrestricted use distribution and reproduction in any medium provided the original work isproperly cited

Significant accelerated warming of the Sea Surface Temperature of 015∘C per decade (1982ndash2012) was recently detected whichmotivated the research for the present consequences and future projections on the heat index and heat waves in the intra-Americasregion Present records every six hours are retrieved fromNCEP reanalysis (1948ndash2015) to calculate heat waves changes Heat indexintensification has been detected in the region since 1998 and driven by surface pressure changes sinking air enhancement andwarmweaker cold advectionThis regional warmer atmosphere leads to heat waves intensification with changes in both frequencyand maximum amplitude distribution Future projections using a multimodel ensemble mean for five global circulation modelswere used to project heat waves in the future under two scenarios RCP45 and RCP85 Massive heat waves events were projectedat the end of the 21st century particularly in the RCP85 scenario Consequently the regional climate change in the current timeand in the future will require special attention to mitigate the more intense and frequent heat waves impacts on human healthcountriesrsquo economies and energy demands in the IAR

1 Introduction

The intra-American region encompasses Northern SouthAmerica Central America Gulf of Mexico the Caribbeanregion and the Western Atlantic [1ndash3] where a complexinteraction among synoptic atmosphericoceanic patternsdrives the rainfall activity in the region [4ndash7] (see Figure 1(a))

In tropical regions the Sea Surface Temperature (SST) isone of the variables that drive rainfall with a weak impactwhen SST is less than 26∘C and a stronger effect when SST isbetween 26∘C and 295∘C The relationship between SST andrainfall is not linear so precipitation tends to decrease whenSST is greater than 295∘C [8] Consequently SST modulatesthe intra-Americas region (IAR) rainfall development fromMay to July while in the next months the vertical windshear (VWS) impacts the thermal vertical convection andcyclogenesis activity [5 9 10] An additional variable in theIAR is the Bermuda-Azores high-pressure system (one pole ofthe North Atlantic Oscillation Index NAO) which interacts

with the Caribbean low-level jet causing a downward dryair hindering precipitation and warming the surface [411 12] A direct consequence of the high-pressure systemoscillation is the development of strongerweaker easterlywinds causing coolerwarmer SST leading to a drierwetterregion [4 13 14] This relationship is known as the wind-evaporation-SST feedback pointing out a direct relationshipamong wind speed evaporation and sea level pressure (SLP)[15] Furthermore a deep subsidence from Central Americato the Caribbean region could play an additional role in theIAR rainfall activity [4 16] Moreover Saharan dust episodesfrom June to August are likely to generate a rainfall deficitdue to aerosol interaction with cloud condensation nucleiand dry air coming from the northwest African desert [17]These complex relationships lead to a bimodal rainfall patternin the IAR during the rainy season [4 5 18ndash21] This rainyseason is divided into the Early Rainfall Season (ERS) definedfrom April to July (which contains the first rainfall peak) andthe Late Rainfall Season (LRS) from August to November

HindawiAdvances in MeteorologyVolume 2018 Article ID 7827984 16 pageshttpsdoiorg10115520187827984

2 Advances in Meteorology

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

LongitudeIntra-American region

Gulf of Mexico

CentralAmerica

Caribbean SeaSouth America MDR

Lesser Antilles

Greater Antilles0

50

100

200

500 800

900

1000

1500

2000

2500

300

0(a)

200

150

100

50

Rain

(mm

)

Jan Mar May Jul Sep NovMonthClimatology 1979ndash2015

IAR climatology

Dry rainfallseason

Early rainfallseason

Late rainfallseason

(b)

Figure 1 (a)Major areas in the IAR (b) IAR rainfall bimodal nature showing theDS ERS and LRSThe dry season is defined fromDecemberto March corresponding to the season with the lowest rainfall intensity events

holding the second rainfall peak In addition a dry season(DS) is defined between December and March where thelowest rainfall intensity events are observed (see Figure 1(b))

IAR warming was recently detected with an annual SSTincrease of 0016∘C per year in the ERS while in the LRSan SST increase of 0021∘C per year was observed [6] Thisregional warming could disrupt the synoptic atmosphericand oceanic patterns A warmer region increases the near-surface air temperature and intensify evaporation from thesea enhancing moisture content in the atmosphere and driv-ing the relative humidity (RH) change In these conditionsextreme events such as HWs could intensify

Air temperature and RH are key variables to calculatethe heat index (HI) which is used to define HWs eventsIn addition different regions across the world have a uniquetemperature and RH climatology causing several heat waves(HWs) definitions [22] such as in Europe where a heat waveis defined as at least six consecutive days with dailymaximumair temperature greater than the relative threshold of the 90thpercentile [23] A more complex definition was applied towhole Europe and the United States (US) using at least threeconsecutive days with daily maximum temperature and aver-age maximum temperature above the 975th percentile [24]The heat waves were estimated using the National Centersfor Environmental Prediction (NCEP) reanalysis data for thepresent time and the Parallel Climate Model for future pro-jections using the business-as-usual scenario Percentile wasapplied to daily data from 1961 to 1990 for reanalysis data and2080ndash2099 for GCMs outputs [24] On the other hand a heatwave magnitude index (HWMI) was developed to analyzeHWs across Earth regions including Europe United StatesGreece and Russia [25 26]These researchers selected NCEPreanalysis and ERA-Interim (ERA-I) reanalysis datasetsupscaling ERA-I to the NCEP resolution Additionally theydefined a HWMI as the maximum magnitude of heat waves

in a year where the heat wave was defined as at leastthree consecutive days with daily maximum temperaturegreater than the 90th percentile and centered in a 31-daymoving window [25 26] On the other hand other HWdefinitions were developed as at least two consecutive dayswith daily mean temperature greater than the 95th percentile(calculated with daily data for 28 years) providing the highestHWs occurrence frequency in the Northwest United States[27 28] Furthermore in Southern US a heat wave wasdefined as at least two consecutive days with daily maximumand minimum HI above the relative threshold of the 99thpercentile [29] Percentile was obtained from 39 years of dailydata Moreover the US National Weather Service (NWS) hasdefined the heat waves events as at least three consecutivedays with daily maximum temperature greater than theabsolute threshold of 32∘C [30] Although this criterion iswidely used it does not capture the HWs events at differentregions This way in the US regions with high RH (northeastregion) a heat wave is defined as at least three consecutivedays with temperature at or above 322∘C [22]

During the past decade a connection between climatechange and heat waves intensity-frequency alterations wasidentified even causing more death than all other extremeevents together in the US such as hurricanes floods andtornadoes In the Eastern and Western US a 20 HWsnumber increase was observed from 1949 to 1995 whilefrom 1999 to 2003 688 deaths per year were registeredprimarily due to extreme hot day events [31] In the city ofChicago HWs impact on human health caused 800 deathsin the year 1995 [30] These hot day events were determinedusing the NWS definition The mortality risks in 43 UScities were investigated from 1987 to 2005 A heat wavemortality risk increase of 249 for every 1∘F heat waveintensity and 038 for every one day where the heat waveduration increases was found [27] In this case the HW was

Advances in Meteorology 3

defined using mean temperature and the 95th percentile asthreshold

Additional factors contributing to heat waves events arefast urban growth and the urban heat island (UHI) whichincrease the daytime air temperature (1∘Cndash6∘C) as well asthe nighttime temperature [31] As a consequence the UHIintensifies heat waves events affecting elderly population andcommunities without air conditioning systems [32 33]

Heat waves analyses in the tropical region and particu-larly in the IAR region are very scarce One of the few casesstudied is the two hottest summers registered in the Island ofPuerto Rico (2012 and 2013) In these events a strong surfacehigh-pressure system was located in the Northeast AtlanticOcean causing high mortality related to high air temperatureand heat stroke [34 35] In addition heat index vulnerabilitywas developed for San Juan Bay estuary in Puerto Ricowhere high vulnerability areas correspond to urban zoneswith a high concentration of elderly and unemployed pop-ulations [36] Furthermore a research agenda for the IARwas proposed to mitigate the extreme heat impact on thecommunity residents The development of a study programis expected in the near future to mitigate and adapt to climatechanges [37] On the other hand a recent work pointed outa heat index increasing trend of 005∘Cyr in Mesoamericaand theCaribbean Sea (MAC) and theCaribbean IslandsTheauthors used NCEP reanalysis and weather station data from1980 to 2014 Additionally they found 45 heat wave events inthe period of study with 82 of these events with a durationof 2ndash25 days [22]

Future global warming due to anthropogenic activitiesis likely to enhance HW events becoming more intenseand frequent and with longer duration [24 38] SeveralGeneral Circulation Models (GCMs) were used to calculatea multimodel ensemble for daily maximum air temperaturealong the 21st century A global mean HWMI increase wasprojected by these models Furthermore they show intenseheat wave magnitude in Southern US Central America andNorthern South America especially in the end of the 21stcentury but without capturing events in the Caribbean [26]In addition heat waves projection was performed in otherregions For instance for Australia an annual death increaseof 50 was projected due to heat stroke in the year 2050[33] Chicagomay experience increases of 25 ofHWeventswhile Paris may experience a 31 increase in the last 20 yearsof the 21st centuryThese projections were obtained using theParallel Climate Model (PCM) under the business-as-usualscenario [24] In addition Los Angeles forecastrsquos future HWsnumber will increase (12 to 70) during the climate period2070ndash2099 [31] while fast heat waves duration and frequencychange were detected in North America in the early decadesof the 21st century [39] In Europe HW events were projectedunder the A1B Intergovernmental Panel on Climate Change(IPCC) scenario showing a frequency increase to 40 days(2075ndash2094) and amplitude intensification up to 7∘C per day[40] On the other hand heat waves projection in the IARand the Caribbean region is very scarce but it is expected tobe as vulnerable as high latitudes regions due to intense hotday events [32]

Based on the literature a changing climate may lead toextreme events such as heat waves consequently this workattempts to assess the likely repercussions of IARwarming onHWs frequency and intensity in the present and in the futurefollowing the IPCC Representative Concentration Pathway(RCP) 45 and RCP85 scenarios Hence the fundamentalquestion this work attempts to answer is how extreme heatwaves eventsmay change in a warmer 21st century in the IAR

In the next sections this article is organized as followsReanalysis data and the GCMs description will be presentedalong with the methodology to calculate HI and HWsPosteriorly the current climatology and climate disruptionare analyzed In the next section heat waves eventsrsquo changesin frequency and intensity are related to the regional warm-ing Finally future projections using GCMs are studied toaccomplish the main goals of this research

2 Methodology

In this section the observed reanalysis data and GCMsused to project heat waves along the 21st century aredescribed Posteriorly heat index and heat wave definitionsare explained The nonparametric Mann-Kendall test waschosen to assess trends in both current and future time seriesprojections Furthermore the IPCC scenarios are describedas the baseline for numerical experiments

21 Reanalysis Data The National Centers for Environmen-tal Prediction (NCEP) reanalysis data from 1948 to 2015 andat 25 degrees of resolution was selected to calculate theIAR climatology and to determine changes in extreme eventssuch as HWs This dataset has been widely used to analyzeatmospheric events at synoptic scales In the Caribbeanregion the NCEP data was used to analyze the North AtlanticOscillation Index and the ENSO impact on the rainfallgeneration [9 18 19 41ndash43] In a similar way NCEP datawas used to study the low-level winddivergent anomalies thevertical wind shear the sea level pressure the circulation beltbetween Central America and the Greater Antilles and thehigh-pressure system relationship with summer dry eventsin this region (low rainfall in the month of July) [4 5 9 1013 19 44] Furthermore NCEP reanalysis was the baseline tostudy the HWMI across Europe Greece and Russia amongother regions [26] Consequently this dataset was selected tobe representative of the whole intra-Americas region and tolink heat waves events with large-scale phenomena to identifypossible synoptic atmospheric variables driving these hot dayevents

The relevant NCEP atmospheric fields in this work com-prise the surface air temperature (∘C) and relative humidity() at 0 6 12 and 18 hours in the universal time coordinate(UTC) In the Caribbean region at this time interval themaximum air temperature could correspond to 18 UTCThismaximum air temperature is relevant in this work because itprovides themaximumHI per day used in theHWdefinitionFor instance in Puerto Rico the 18 UTC time correspondsto 2 pm while 12 UTC time is far away from the expectedmaximum temperature Consequently one maximum HIvalue per day is calculated at 18 UTC


In addition monthly surface air temperature and merid-ional and zonal winds at 850mbar level (ms) are retrieved tocompute warmcold advection This advection is calculatedas the dot product between the air temperature gradient(997888rarrnabla119879) and the wind velocity (997888rarr119881) expressed in the equation120597119879120597119905 +

997888rarr119881 ∙997888rarrnabla119879 = 0 in cylindrical coordinates with an

Earth radius of 637times 106meters Vertical Omega velocity andRH from 1000 to 300mbar meridionally averaged between85∘N and 16∘N are used to determine vertical atmospherestructures This vertical air motion is expressed by meansof the pressure rate of change for an air parcel in Pas andrepresents the vertical velocity at a synoptic scale assuminghydrostatic equilibrium Positive values represent sinking airwhile negative valuesmean rising air In addition sinking dryair could play a significant role in surface warming On theother hand the dataset is divided into two climate periodsThe first one is defined from 1948 to 1998 and the secondclimate period is defined from 1999 to 2015 The year 1998was selected because this year depicts the beginning of anaccelerated climate change in the IAR region as it will beexplained in Section 4 of this article

22 IPCC Scenarios The third and fifth reports of theIPCC have related the global warming to the CO

2increase

attributed mostly to the burning of fossil fuels [38 45] Fur-thermore rawinsonde and satellites measurements showedthat the troposphere and the Earth surface have been warm-ing [45] In consequence the IPCC has developed a seriesof scenarios to project likely future impacts of the humanactivity on the global warming The old scenarios IS92and SRES [46] have been updated to the third generationof scenarios called representative concentration pathwaysThe RCPs are composed of four different CO

2emissions

and radiative forcing The lowest forcing scenario RCP26projects an anthropogenic total CO

2emission of 997 PgCyr

in 2020 and a radiative forcing peak of 490CO2-eq by

midcentury to decline later The intermedia scenario RCP45represents a maximum total CO

2emission in the year 2050

(1115 PgC yrndash1) with a shorter radiative forcing stabilizationafter 2100 (650CO

2-eq) while the scenario RCP60 stabilizes

shortly after 2100 with 850 CO2-eq and a maximum CO

2

emission in the year 2080 (1707 PgC yrndash1) The business-as-usual scenario RCP85 describes a nonstop CO

2emission

and a permanent radiative forcing increase with a value of1370 CO

2-eq in 2100 [45 47] The scenarios RCP45 and

RCP85 were selected in this research to quantify the impactof the likelihood of the global average surface temperaturesto exceed between 2 and 4 degrees Celsius at the end of the21st century

23 General Circulation Models CMIP5 Project The fifthphase of the Coupled Model Intercomparison Project(CMPI5) affords a set of climate simulations to evaluate pastand future climate change where long-term simulations aremade available for climate responses to changing atmosphericcomposition and land cover [48 49] In this study five GCMswere selected to calculate future daily maximum HI GCMs

provide atmospheric data every 3 hours at 0 3 6 9 12 15 18and 21UTC and overlapping with NCEP dataset at 6 12 and18UTC In consequence in this study GCMs outputs wereresampled to the same NCEP time resolution (0 h 6 h 12 hand 18 h) to have the air temperature and relative humidityat the same matched time period The maximum air temper-ature used to calculate the daily maximum HI correspondsto 18UTC which for instance for the Island of Puerto Ricocorresponds to 200 pm We consider it as representative ofthe region On the other hand GCMs with three-hourly dataoutputs are very scarce but we found five models They arethe Community Climate System Model version 4 (CCSM4)with a resolution of 09 times 125 degrees in latitude andlongitude respectively the Centre National de RecherchesMeteorologiques Coupled Models version 5 (CNRM-CM5)with 14 times 14 degrees of resolution NOAAGeophysical FluidDynamics LaboratoryClimateModel version 3 (GFDL-CM3)with 20 times 25 degrees Model for Interdisciplinary Researchon Climate-Earth System Model (MIROC-ESM) with 279times 281 degrees and the Meteorological Research Institute-Coupled Atmosphere-Ocean GCM (MRI-CGCM3) at 111times 1125 degrees of resolution Currently CCSM4 is one ofthe state-of-the-art global numerical models with the finestresolution in latitude while the model MRI-CGCM3 hasthe finest resolution in longitude Additional models werechosen because they provide 3-hourly output data On theother hand mesoscale models such as the Regional Atmo-spheric Model System (RAMS) or the Weather Research andForecasting Model (WRF) afford high resolution but with avery expensive computational cost (there is no output datasetavailable in the IAR)

These GCM models do not provide RH data therebythe RH is calculated every 3 hours using the surface airtemperature surface pressure and specific humidity (SH)The SH allows calculating the vapor mass mixing ratio whilethe surface pressure allows calculating the vapor pressureFinally the RH is obtained using the ClausiusndashClapeyronequation for saturated vapor pressure [50ndash52] In additionthese GCM data are distributed into two climate periods thefirst one is called the ldquofirst future climate periodrdquo from 2026to 2045 while the second dataset is called the ldquosecond futureclimate periodrdquo from 2081 to 2100

24 Heat Index and Heat Waves Heat index is an apparenttemperature in function of air temperature and RH estimatedfrom a complex bodyrsquos heat transfer balance [53] and repre-sents a measure of heat-stress danger [54] The HI is calcu-lated using the methodology developed by NOAArsquos NationalWeather Service and using Rothfuszrsquos regression with anerror of 13∘F [55] This regression is adjusted according tothe temperature and RH ranges [56 57] Furthermore anexcessive environmental heat is ruled by days with extremelyhigh HI This event is defined as HWs when a prolongedperiod of high-pressure system leads to excessively hotterdays with high humidity

In humid regions heat waves calculated with air temper-ature underestimate the HW intensity making it essential touse an apparent temperature to account for the heat wave


amplification due to high humidity [58] The intra-Americasregion particularly the Caribbean is characterized by intenseRH as well as high air temperature along the entire yearConsequently in this region the heat index is a good alter-native to account for humidity impact on heat wave intensityIn addition peoplersquos adaptation to this warm environmentrequires defining a relative threshold instead of an absolutethreshold [59] This relative threshold is defined as thepercentile calculated for daily data recorded in a long periodof time [29 60] In our work NCEP provides data everysix hours so that daily maximum air temperature and itscorresponding RH are taken at 18 UTC to calculate the dailymaximum HI from 1948 to 2015 Consequently combiningthe heat wave NWS definition for regions with high RH interms of HWs duration [22] and the definition for a verywarm environment (SouthernUS) relative to a threshold [22]leads to the definition used in this work Accordingly here aheat wave is defined as at least three consecutive days withdaily maximumHI greater than the 995th percentile A littlehigher threshold is considered here to account for Caribbeanpeoplersquos adaptation to warm and humid environments

In order to compare the two climate periods (1948ndash1998and 1999ndash2015) a unique threshold was used to identify heatwaves First the threshold was calculated using the 995thpercentile using daily maximum heat index from 1948 to2015 and used for both climate periods This threshold wascalculated according to the common methodology foundin the literature which takes a time period record of manyyears to calculate the percentile [24 27 29 30] Posteriorlya second threshold is computed from the first climate period(1948ndash1998) and used as a threshold for the second period(1999ndash2015) to determine the impact of the threshold cal-culation in the heat waves number frequency Results showthat there are no relevant changes in the heat waves numberprobability particularly for events with high probabilities ofoccurrence Consequently the percentile calculated in thefirst climate period is selected in this work to account forheat waves development in the second climate period Onthe other hand GCMs dataset was obtained for two periods2026ndash2045 and 2081ndash2100 Daily heat index was calculatedfor both periods and the 995th percentile was estimatedin the first 20 years to get the threshold This threshold isalso used to evaluate the heat waves numbers at the secondperiod (2081ndash2100)Thiswaywe can compare both periods toidentify future changes in the heatwave number and intensity

Statistical significance for temporal trends in time seriesis evaluated using theMann-Kendall testThe parametric testis a commonly used method used to detect trends in climatedata but it requires independent and normally distributeddata Likewise strong temporal autocorrelated data couldgenerate artificial trends without statistical significance Analternative is the nonparametric Mann-Kendall test which isa distribution-free test capable of handling weakly autocor-related time series [61 62] A positivenegative test statistic119885 value represents increasingdecreasing trend in data If thenull hypothesis is not rejected the variablersquos values in the timeseries will fluctuate around its average instead of presentinga statistically significant linear trend The rate of change ortrend is estimated using theTheil-Sen approach [61 63]

3 Current IAR Climatology

Intense HI could develop extreme HW events with a highimpact on humanhealth [27 31 35 64 65] aswell as in energydemand for air conditioning systems [66] An early warningsystem is relevant to mitigate possible heat strokesrsquo impacton the populationrsquos health This warning system requiresidentifying possible synoptic atmospheric drivers of extremeheat waves events In this manner this section analyzes theHI climatology and these possible drivers

The IAR climatology evolves from a low SST in thedry season with 257∘C in average to more intense oceansurface warming in the ERS (seasonal average of 273∘C)In the LRS the SST reaches its maximum value in themonth of August with 283∘C (Figure 2(a)) This seasonalocean warming is translated into a warm pool spreadingup to the Greater Antilles and even further This seasonalwarm pool enhances evaporation increasing moisture con-tent in the atmosphere with the largest values in the LRSA second effect of a warmer ocean is the near-air surfacetemperature increase due to sensible heat flux exchange Themaximum air temperature peak is developed in the LRSspecifically in August (27∘C) In addition the combinationof the air temperature and RH allows calculating the HIwhich follows the SST and air temperature tendency witha maximum peak of 297∘C in August (Figure 2(a)) Con-sequently current IAR ocean warming could lead to dayswith higher HI and likely a region with more intense HWevents

According to the IAR climatology the LRS correspondsto the warmer season where a high caution area for heatstroke spans further the Greater Antilles following the warmpool spread The Greater and Lesser Antilles Yucatan inMexico and the Southern American Caribbean coastline areareas with high caution because of HI ranges between 29 and32∘C The HI between 26 and 29∘C denotes a low cautionarea for Central America and Mexico coastlines along withinland South America In a similar way Southern US depictsa low caution area with the exception of southern Floridawhere a heat strokersquos high caution predominates (Figure 2(b))In this season the Bermuda-Azores high-pressure systemis weakening (maximum intensity is observed in the ERS)and moving back to the Atlantic Ocean causing a SLPdecrease in the IAR This seasonal high-pressure systemvariation corresponds with the high caution area spreadAccording to the wind-evaporation-SST feedback process[15] during the LRS the lower SLP in the region (withrespect to the ERS) corresponds to a less intense wind speedand higher SST with a more stratified surface ocean [15]which impacts the HIrsquos strength Consequently there is aninverse relationship between the HI and the SLP seasonalevolution The high caution area boundary corresponds toa SLP between 1011mbar along Mexico Central Americaand South America coastline and 1016mbar further than theGreater Antilles (Figure 2(b)) On the other hand the verticalpressure velocity averaged from 600 to 400mbar points outan IAR dominated by an upward air motion with high SSTand hence a more buoyant atmosphere The Greater andLesser Antilles depict an Omega vertical velocity between


(∘C)

30

29

28

27

26

25

24Jan Mar May Jul Sep Nov

MonthClimatology 1948ndash2015

HI

SSTTＣＬ

(a)

Heat index climatology1948ndash2015 (∘C)

20 26 29 32

Safe Lowcaution

Highcaution

Extremecaution

10121011

10141013

10151016

1017

1012

1011

1014

0

00

0

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

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95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

minus001minus001

002

minus002

minus002

minus003 minus008minus006

minus004

(b)100

200

300

400

500

600

700

800

900

1000

Omega-RH LRS climatology1948ndash2015 85ndash16∘N 5

times10minus2 ０s

minus35 minus3

minus25

minus2

minus15

minus1

minus05 0

05

45 45

45

55

55

55

70

7060

6050

50

5050

40

4040 40

35

65

65

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7580

Leve

l

100∘ W

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90∘ W

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80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Longitude

(c)

Warm advection climatology850Ｇbar 1948ndash2015 10

minus12

minus09

minus06

minus03 0

03

06

09

12

times10minus5∘Cs

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

(d)

Figure 2 IAR climatology for (a) SST air temperature and HI (b) Heat index in ∘C (color shades) SLP in mbar (dotted black contour line)vertical velocity in Pas (blue contour line) in the LRS (c) vertical velocity in color shades (times10minus2Pas) relative humidity in (black contourline) and wind vector in the LRS (d) Warmcold advection in color shades (times10minus5∘Cs) and wind vector at 850mbar in the LRS

minus001 andminus002 Paswhile Central America and the SouthernAmerican Caribbean coastline are controlled by minus002 andminus003 Pas (Figure 2(b)) In a similar way the wind velocityvertical cross section averaged in the meridional area 85to 10∘N denotes a region dominated by rising air in theLRS with RH between 50 and 40 in the atmosphere atthe levels 600ndash400mbar (Figure 2(c)) A regional circulationbelt is developing from Central America to the westernMain Developed Region (MDR) which enlarges in theLRS generating a wider rising air area (85∘Wndash55∘W) Thisatmospheric circulation corresponds with the high cautionarearsquos western border where the sinking air is observed atthe longitude 55119900W (Figures 2(b) and 2(c)) The warmcoldadvection at 850mbar is weakening season by season with

the weaker cold advection in the LRS and covering theGreater Antilles (0 to minus03 times 10minus5∘Cs) Mexico (minus03 times 10minus5 tominus06times 10minus5∘Cs) andNorthern SouthAmerica (minus03times 10minus5 tominus06 times 10minus5∘Cs) In this season and in the Caribbean regioncold advection does not convey true cold air because thetemperature gradients are very small and hence it does notreduce drastically the surface air temperature According toFigure 2(d) a warm advection is noticed on Central America(03 times 10minus5 to 12 times 10minus5∘Cs) Gulf of Mexico (0 to 03 times10minus5∘Cs) and Southern US and Northern Cuba (0 to 03times 10minus5∘Cs) Consequently warm-weaker cold advection incombination with lower SLP and higher SST leads to HIintensification and turns the IAR in a high caution area forheat strokeexhaustion


28

278

276

274

272

27

268

266

2641948 1958 1968 1978 1988 20081998

Years 1948ndash2015Input daily maximum

0006∘C per yearp value = 002

003∘Cper year

p value = 001

TＣＬ

(∘C)

Annual max TＣＬ IAR

(a)

19981948 1958 1968 1978 1988 2008


315

31

305

30

295

29

285

28

Hea

t ind

ex (∘

C)

Annual max HI IAR


007∘Cper year

p value = 0003

(b)

Figure 3 Climate disruption acceleration since 1998 on the IAR using (a) the maximum annual temperature and (b) the maximum annualheat index

4 Climate Disruption

Near-surface air temperatures at synoptic scale are relatedto the SST in the IAR following the seasonal warm poolspread In addition evaporation intensification followingthe wind-evaporation-SST feedback modifies the moisturecontent in the atmosphere as well as the RH The HI iscalculated using the air temperature and the relative humidityand hence is affected by changes in the SST Accordinglythe air temperature and HI could be suitable indicatorsof regional climate disruption The annual maximum airtemperature obtained from the monthly average of dailymaximum air temperature shows an air temperature rate ofincrease of 0006∘C per year (1948ndash1998) with good statisticalsignificance (p value = 002) Since 1998 an accelerated airtemperature increase has been observed with an annual trendof 003∘C (Figure 3(a)) and proper statistical significance (pvalue = 001) In a similar way the annual maximum HIpossesses a tendency to increase from 1948 to 1998 with a rateof 0015∘Cper year but since 1998 theHI has been rising in anaccelerated manner (007∘C per year) with suitable statisticalsignificance (Figure 3(b)) Therefore the year 1998 is selectedas the baseline to compare two climate periods and to identifythe regional climate disruption effects on theHWs frequency

The climate difference between 1999ndash2015 and 1948ndash1998depicts atmospheric warming in all IAR seasons The lowestchange is observed in the DS with HI disruption of 016∘Cand a maximum change of 074∘C in the LRS The SLPchange follows an inverse relationship with the HI showing apositive change in the DS (03mbar) and negative differencein the LRS (minus017mbar) where the SLP is decreasing andthe HI obtains its maximum peak (Figure 4(a)) In thislast rainy season a positive HI change spans across thewhole IAR with the highest disruption in the SouthernGreater Antilles including Puerto Rico and the westernMDR(15ndash20∘C) The second more relevant change is observed

in the Northern Greater Antilles (Cuba and DominicanRepublic) Gulf of Mexico Florida and the South AmericanCaribbean coastline (09ndash15∘C) while the lowest disruptionis detected in Mexico Central America and South Americainland (03ndash06∘C) In contrast a small area covering partof Guyana and Suriname in Southern America has a HIdecrease between 0 and minus03∘C (Figure 4(b)) This regionalwarming and tendency to intensify the heat index in theregion happens at the same time with the SLP decreaseThe higher SLP difference ranges from minus03 to minus05mbaroverlapping with the highest HI change area while thelowest negative or even zero SLP change corresponds to thelowest HI climate difference encompassing Mexico CentralAmerica and South America (Figure 4(b)) According to theSLP anomaly time series in the IAR (1948ndash2015) in the LRSthe maximum anomaly is 09mbar while the lowest one isrepresented by minus086mbar In addition the average negativeSLP anomaly holds a low value (minus016mbar) Consequentlythe SLP climate difference between the two selected climateperiods represents a significant regional climate disturbanceIn a similar way the vertical air motion change impacts theregional atmospheric warming In the LRS positive Omegavelocity averaged from 600mbar to 400mbar dominates theCaribbean region and the MDR (001ndash002 Pas) showing atendency to enhance sinking dry air and to warm up thesurface (Figure 4(b)) Furthermore the Omega velocity ver-tical cross section points out a sinking air dry intensificationfrom the upper atmosphere (ΔRH = minus14 at 300mbar) tothe surface (Figure 4(c)) Warm advection is strengtheningacross the Greater Antilles (02 times 10minus5∘Cs) Lesser Antilles(03 times 10minus5ndash05 times 10minus5∘Cs) Mexico and Southern US with02 times 10minus5∘Cs (see Figure 4(d)) Weaker cold advection withrespect to the ERS brings less cold air to the region whichcomes together with positive HI change Consequently a SLPdecrease sinking dry air intensification and warm and coldadvection weakening converge to intensify the HI in the IAR


1

05

0

minus05Jan Mar May Jul Sep Nov

MonthClimate differenceIAR-NCEP data

ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

LongitudeHeat index LRS climate diff1948ndash1998 1999ndash2015

0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000

Omega-RH LRS climate diff1948ndash1998 1999ndash2015 5

times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Longitude Lat 85ndash16∘N

(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

LongitudeWarm advection LRS climate diff850Ｇbar 1948ndash1998 1999ndash2015 10

minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)

Figure 4 (a) Climate difference between 1999ndash2015 and 1948ndash1998 for HI and SLP (b) LRS heat index change in ∘C (color shades) SLPchange (dotted black contour line) and vertical velocity change in Pas (blue contour line) (c) LRS vertical velocity change in color shades(times10minus2Pas) relative humidity in (black contour line) and wind vector (d) LRS warmcold advection change in color shades (times10minus5∘Cs)and wind vector at 850mbar

5 Current Climate Heat Waves

Annual HWs events were calculated in every grid pointacross the IAR to later accumulate themThis approach allowsidentifying the total HWs number time evolution duringthe past 68 years Low HWs number is observed in thefirst 51 years with 28 events on average over the IAR Since1999 HWs number has increased in an accelerated mannerwith an average value of 84 events (Figure 5(a)) This HWsnumber growth is related to the HI strengthening due to theconjunction of SLP decrease warm-weaker cold advectionand sinking air intensification

HWs number time series is disaggregated to observe hotday events distribution across the IAR Low HWs numberis concentrated in Southern Central America and Caribbean

coast of South America (2ndash6 events) during the first climateperiod (1948ndash1998) while a higher number of events wereidentified in Mexico (10ndash14 events) and Northern CentralAmerica (10ndash16 events) as shown in Figure 5(b) Further-more the Caribbean is characterized as a region withoutheat waves events On the other hand the second climateperiod (1999ndash2015) is compared against the first climate todetect possible HWs disruption The highest HWs numberchange is focalized in the Greater and Lesser Antilles with anincrease between 8 and 14 events The second most impactedarea covers Guyana Suriname and French Guiana in SouthAmerica (2ndash10 eventsrsquo increase) In contrast Central Americaand Mexico represent a region with HWs activity decreasewith a HWs number reduction between minus12 and zero events(see Figure 5(c))


Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

LongitudeClimate period 1948ndash1998

0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

LongitudeClimate diff 1999ndash2015 1948ndash1998

minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)

Figure 5 (a) Annual HWs number accumulated across the IAR for the period 1948ndash2015 (b) Heat waves number in the LRS in the firstclimate period 1948ndash1998 (c) Heat waves number change in the LRS between the climate periods 1999ndash2015 and 1948ndash1998

Heat waves number frequency was calculated in the twoclimate periods to detect any change in the region Therelative frequency from 1948 to 1998 points out a probabilityof 043 to develop three or fewer HWs events per month inthewhole IAR LargerHWsnumbers have a lower probabilityto develop so that an interval between 6 and 15 eventsholds an occurrence probability of 019 In addition greaterevents than 15 and less than 102 represent a low accumulatedprobability (013) In the second climate period there is ashift in the HWs occurrence probability (relative frequencydistribution) In contrast with the first period three or fewerHWs eventsrsquo development per month holds a probability of022 while a larger number of HWs events increase its prob-ability HWs events between 6 and 15 represent a probabilityof 033 which translates to almost twice the probability of thefirst climate period (Figure 6(a)) Furthermore HWs eventsbetween 15 and 102 have a higher probability which is morethan twice the probability in the preceding period

During a HW event the HI evolves day by day untilreaching the highest value This maximum HI defines themaximum HW amplitude In the first climate period themaximum HI during a HW event converges between 34and 36∘C with a probability of 029 Events with hotterdays have lower occurrence probability such as HI rangingbetween 36 and 38∘C with a probability of 016 Additionallythe statistical parameters skewness and kurtosis definea maximum amplitude distribution left skewed with lowsymmetry (skewness = minus026) and with a close shape toGaussian distribution (kurtosis = minus002) In the next 17 yearsthere is a clear shift in the maximum HW amplitude with amaximum HI peak between 36 and 38∘C and an occurrenceprobability of 042 (Figure 6(b)) Furthermore a distributionshape change is detected A more intense left skewed tailis characterized in this second climate period (skewness= minus058) while a sharper peak (kurtosis = 128) depicts adeviation from the Gaussian distribution observed in the first


05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42

Heat waves max amplitude (∘C)

(b)

Figure 6 (a) Heat waves frequency of the whole region (b) Regional average of heat waves maximum amplitude

climate period (Figure 6(b)) Therefore the IAR warmingexpressed by changes in the SST air temperature and HI iscausing HWs events intensification in both events numberand amplitude

6 Heat Waves Projection Using GeneralCirculation Models

Heat index computed from GCMs was validated recentlyby the authors using NCEP reanalysis data Low errorswere obtained ranging from 10 to 5 [66] Heat indextrend analysis was performed using a multimodel ensemblemean Two scenarios RCP45 and RCP85 were selectedto represent the regional warming likelihood along the 21stcentury Positive HI trend at a rate of 0041∘C per yearwas projected in the RCP45 scenario during the period2026ndash2045 Using the Mann-Kendall test this trend hassuitable statistical significance with 119901 value close to zero Incontrast in the last two decades (2081ndash2100) HI did notshow a trend but rather a variability around the mean HIvalue of 291∘C (Figure 7(a)) A fast HI increase is pointedout in the RCP85 scenario and in the first future climateperiod 2026ndash2045 with a rate of increase of 006∘C per yearIn the second future climate period (2081ndash2100) acceleratedHI intensification is projected at a rate of 012∘C per year(Figure 7(b)) In this scenario trends have proper statisticalsignificance with 119901 values close to zero

On the other hand HI climatology calculated from 2081to 2100 was compared against the first future climate Theclimate difference between these two future climate periodsdepicts a maximum HI change of 2∘C in August (RCP45)while the scenario RCP85 stands for higher HI change withan increment of 64∘C (Figure 7(c)) This RCP85 scenarioprojects the largest climate change in every season due togreater greenhouse gas release and concentration in the

atmosphere causing awarmer atmosphere and the likelihoodto develop stronger extreme events such as HWs

Positive HI change in both scenarios RCP45 and RCP85translates intoHWs events increase and intensification In thefirst scenario RCP45 the future climate period 2026ndash2045denotes a small monthly average HWs number (14 events)while the annual average events are 124 In the same scenariothe time series of the average heat waves number remarksan evident increase in the second future climate period(2081ndash2100) with 158 events per month and 1848 eventsper year on average across the whole IAR (Figure 8(a) andTable 1) This HWs time evolution corresponding to thescenario RCP45 is disaggregated into a spatial distributionto show regions with HWs number difference between thetwo future climate periods Low HWs events were projectedin the Caribbean region (2ndash8 events) during the first futureclimate Greater Antilles show HWs activities between 2and 5 events while Central America and Northern SouthAmerica hold some spots without HWs Substantial HWsactivities are simulated in the second future climate par-ticularly in the Caribbean region with the second highestevents increase with respect to the first future climate TheGreater Antilles including Cuba Dominican Republic andPuerto Rico point out HWs number increase between 80and 100 events Central America Honduras and Guatemaladepict a positive difference in the number of events between40 and 60 while the third highest HW events increasesare concentrated in Nicaragua Costa Rica and Panama(60ndash100 events) Northern South America follows the sametendency as the southern Central America with CentralColombia ranging between 30 and 80 eventsrsquo increase withits Caribbean coastline pointing out the highest events(around 100ndash120 more events than the first future climateperiod) Furthermore Venezuela denotes 60ndash100 eventsrsquoincrease in the last two decades of the 21st century (Fig-ure 8(b))


Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR

0041∘C per yearp value = 14e minus 07

2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)

Jan Mar May Jul Sep NovMonthClimatology difference

RCP45RCP85

(c)

Figure 7Multimodel ensemblemean for (a) annualmeanHI time series RCP45 scenario (b) AnnualmeanHI time series RCP85 scenario(c) HI climate difference for RCP45 and RCP85

Following with the analysis in the first scenario RCP45the heat waves number distribution indicates an occurrenceprobability of 039 to develop 1 to 10 heat waves and 023for 10 and 20 events in the period 2026ndash2045 In the last 20years of the 21st century the probability distribution changednoticeablywith a higher probability to develop a large amountof heat waves with 035 for 10 and 80 events and 026 forevents between 100 and 200 (Figure 8(c)) On the other handHWs maximum amplitude probability distribution showshigh asymmetry in the first climate period with a left skewedtail (skewness of minus14) and a sharper peak heavily tailed(kurtosis equal to 27) Hence this probability distributiondoes not follow a Gaussian distribution where hot day eventsmostly tend to have maximumHI between 30 and 36∘C witha probability of 041There is no shift in the distribution shapeof themaximumHWs duration during the period 2081ndash2100with skewness and kurtosis of minus114 and 11 respectivelyFurthermore the probability to developmaximumheat indexbetween 30 and 36∘C increased to 048 (Figure 8(d))

The second scenario the RCP85 represents a moreactive IAR in terms of HWs development The HWs number

increased remarkably in this scenario when the hot dayevents are identified using the same threshold as for thescenario RCP45 The monthly average HWs number inRCP85 corresponds to 34 events in the first future climatewhile the annual average increased by almost three times(356 events) the RCP45 scenario The second future climatedenotes a relevant intensification with a monthly average of372 events and an annual average increase by two and a halftimes with respect to the scenario RCP45 (Figure 9(a) andTable 1) The HWs change corresponding to the scenarioRCP85 is shown as a spatial distribution to identify regionswith highlow hot day events In the period 2026ndash2045 mostof the HWs are developing in the Caribbean region withlarger events in the Greater Antilles (21ndash30 events) includingthe Dominican Republic (12ndash18 events) and Puerto Rico(12ndash15 events) In the second future climate period massiveheat waves activities are projected across the whole regionparticularly in Central America and Mexico with 110 to 180eventsrsquo increase in the last twodecades of the 21st centurywithrespect to the first future climate In addition the Northern


Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)

Heat waves numberClimate diff 2081ndash2100 2026ndash2045

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)

0 100 200 300 400 Heat waves number

500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

Figure 8 Multimodel ensemble mean for the RCP45 scenario (a) annual time series (b) HWs number change between the climate periods2081ndash2100 and 2026ndash2045 (c) HWs frequency of the whole IAR including the ocean area and (d) regional average of HWs maximumamplitude

South America denotes even higher events with an increasebetween 140 and 360 events (Figure 9(b)) In this scenario theCaribbean represents an areawith lower heat stroke activitieswith Cuba projecting HWs rise between 80 and 100 eventsand the Dominican Republic between 80 and 140 eventsand Puerto Rico depicts future events intensification rangingfrom 80 to 100 events

On the other hand according to the scenario RCP85there is a substantial change in HWs number probabilitydistribution when both climate periods are compared In thesecond period (2081ndash2100) a higher probability to developHWs events between 204 and 420 is observed with a proba-bility of 054 (Figure 9(c)) Additionally the HWs maximumamplitude has a probability of 049 to develop events withmaximum HI between 34 and 36∘C during the first futureclimate A left skewed distribution (skewness of minus091) and

sharper peak (kurtosis of 144) are a characteristic of thisperiod In the last two decades of the 21st century HWsmaximum amplitude distribution changes to a Gaussiandistribution with skewness and kurtosis of minus00472 andminus04989 respectively Furthermore there is a clear shift in themaximum HI during HWs events The maximum amplitudeis concentrated now between 36 and 38∘C with a probabilityof 041 (Figure 9(d))

These heat wavesrsquo increase could cause heat-related illnessintensification with more frequent heat exhaustion and evenheat stroke [34 35 64 65 67 68] A high populationmortality could be recurrent in the future primarily in elderlypopulations and people with preexisting medical conditionsandwithout air conditioning systems In addition tomitigatethe harmful impact on health [31 32] more energy will berequired to cool down room environments


Table 1 Total annual heat waves number for the whole IAR

Years Heat waves number IARRCP45 RCP85 Years RCP45 RCP85

2026 50 63 2081 1787 45402027 25 136 2082 1736 45992028 11 89 2083 1991 47242029 54 195 2084 1981 45512030 28 132 2085 1812 46352031 28 42 2086 1543 43162032 81 27 2087 1761 45082033 9 109 2088 1759 45672034 35 154 2089 1843 45352035 78 89 2090 1365 45142036 82 549 2091 1630 44132037 124 262 2092 2869 44422038 158 374 2093 1469 43812039 71 849 2094 1443 46122040 138 541 2095 1722 45392041 204 257 2096 1870 42102042 536 487 2097 1839 43532043 62 561 2098 1809 44682044 569 846 2099 2067 39502045 142 1348 2100 2664 4402Annual avg 124 356 Annual avg 1848 4463Monthly avg 14 34 Monthly avg 158 372

7 Discussion and Conclusions

This article explores current HI trends and HWs change dueto regional warming as well as future projection in the intra-Americas region under the RCP45 and RCP85 scenarios

The IAR climatology denotes a SST increase season byseason causing an air temperature peak in the month ofAugust which translates together with the RH intomaximumhuman discomfort expressed by high HI peak in this monthThe warmest season is characterized by a high caution areafor heat stress encompassing the Caribbean region as wellas Yucatan in Mexico and the Caribbean coast of SouthAmerica This large area is in agreement with the warm poolseason expansion the regional circulation belt stretchingdeep in the western MDR and the SLP decrease followingthe wind-evaporation-SST feedback process

A significant climate disruption was identified in the year1998 with an accelerated air temperature and HI increasesince this year with a positive rate of 003∘C and 007∘C peryear respectively Two climate periods were selected to iden-tify changes in HI and feasible variables driving this regionalatmospheric warming The climate difference between1999ndash2015 and 1948ndash1998 pointed out HI intensification of074∘C in the LRS as an average across the IAR A clear inverserelationship between HI and SLP change was detectedFurthermore sinking dry air intensification together withwarm advection strengthening and weaker cold advectiontendency can increase the regional atmospheric warming

The accelerated HI rise conducts changes in HWs activ-ities across the IAR during the LRS In the second climateperiod HWs activities intensification was detected mainlydue to negative SLP change sinking dry air enhancementand warm-weaker cold advection with respect to the ERSFurthermore there is a clear change in HWs number fre-quency in the last 17 years (1999ndash2015) as well as in the HWsmaximum amplitude distribution

The multimodel ensemble mean for future HI and HWsshows a future IAR warmer atmosphere projecting a largeamount of HWs particularly in the last two decades of the21st century where higher HI values are simulated In thescenario RCP45 substantial HWs activities are predictedmainly in the Caribbean region From 2081 to 2100 HWsnumber probability distribution changed noticeably whilethe maximum amplitude distribution did not show a distri-bution shape variation Despite this there is a probabilityincrease to develop a maximum HI between 30 and 36∘CIn the business-as-usual scenario (RCP85) massive HWsevents are projected across the whole region particularlyin Central America Mexico and Northern South AmericaAdditionally there is a variation in both HWs number andmaximum amplitude distribution shape with the maximumHI shifted to the interval 36ndash38∘CThe socioeconomic impli-cations of these projections for the region may be significantin terms of human health and energy demand projections asthe authors recently reported [66]


Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

LongitudeHeat waves numberClimate diff 2081ndash2100 2026ndash2045

20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)

Figure 9 Multimodel ensemble mean for the RCP85 scenario (a) annual time series (b) HWs number change between the climate periods2081ndash2100 and 2026ndash2045 (c) HWs frequency of the whole region including the ocean area and (d) regional average of HWs maximumamplitude

Conflicts of Interest

The authors declare that there are no conflicts of interestregarding the publication of this article

Acknowledgments

The analysis process was conducted at the high performancecomputing facilities at City College of New York Thiswork was sponsored by the National Oceanic and Atmo-spheric Administration (NOAA) Office of Education Part-nership ProgramAwardNA11SEC4810004 and by theUnitedStates Agency for InternationalDevelopment (USAID) underCooperative Agreement no AID-517A15-00002 and the USNational Foundation Grant no CBET-1438324

References

[1] J A Amador ldquoThe Intra-Americas Sea low-level jet Overviewand future researchrdquo Annals of the New York Academy ofSciences vol 1146 pp 153ndash188 2008

[2] S Curtis ldquoDaily precipitation distributions over the intra-Americas sea and their interannual variabilityrdquo Atmosfera vol26 no 2 pp 243ndash259 2013

[3] A MMestas-Nunez D B Enfield and C Zhang ldquoWater vaporfluxes over the Intra-Americas Sea Seasonal and interannualvariability and associations with rainfallrdquo Journal of Climatevol 20 no 9 pp 1910ndash1922 2007

[4] D W Gamble and S Curtis ldquoCaribbean precipitation reviewmodel and prospectrdquo Progress in Physical Geography vol 32 no3 pp 265ndash276 2008

[5] M E Angeles J E Gonzalez N D Ramırez-Beltran CA Tepley and D E Comarazamy ldquoOrigins of the Caribean


rainfall bimodal behaviorrdquo Journal of Geophysical ResearchAtmospheres vol 115 Article ID D11106 2010

[6] E Glenn D Comarazamy J E Gonzalez and T SmithldquoDetection of recent regional sea surface temperature warmingin theCaribbean and surrounding regionrdquoGeophysical ResearchLetters vol 42 no 16 pp 6785ndash6792 2015

[7] N Hosannah J Gonzalez R Rodriguez-Solis et al ldquoTheconvection aerosol and synoptic-effects in the tropics (cast)experimentrdquo BAMS pp 1593ndash1600 2017

[8] I Folkins and C Braun ldquoTropical rainfall and boundary layermoist entropyrdquo Journal of Climate vol 16 no 11 pp 1807ndash18202003

[9] M Taylor D Enfield and A Chen ldquoInfluence of the tropicalatlantic versus the tropical pacific on caribbean rainfallrdquo Journalof Geophysical Research Atmosphere vol 107 no C9 pp 10ndash142002

[10] M E Angeles J E Gonzalez D J Erickson III and JL Hernandez ldquoPredictions of future climate change in thecaribbean region using global general circulation modelsrdquoInternational Journal of Climatology vol 27 no 5 pp 555ndash5692007

[11] J Moran Online Weather Studies American MeteorologicalAssociation Boston Mass USA 2002

[12] C Wang ldquoVariability of the Caribbean Low-Level Jet and itsrelations to climaterdquo Climate Dynamics vol 29 no 4 pp 411ndash422 2007

[13] A Giannini Y Kushnir and M A Cane ldquoInterannual vari-ability of Caribbean rainfall ENSO and the Atlantic OceanrdquoJournal of Climate vol 13 no 2 pp 297ndash311 2000

[14] B E Mapes P Liu and N Buenning ldquoIndian monsoon onsetand the Americas midsummer drought Out-of-equilibriumresponses to smooth seasonal forcingrdquo Journal of Climate vol18 no 7 pp 1109ndash1115 2005

[15] B Qu A Gabric J Zhu D Lin F Qian and M ZhaoldquoCorrelation between sea surface temperature and wind speedin greenland sea and their relationships with nao variabilityrdquoWater Science and Engineering Journal vol 5 no 3 pp 304ndash3152012

[16] V Magana and E Caetano ldquoTemporal evolution of summerconvective activity over the Americas warm poolsrdquoGeophysicalResearch Letters vol 32 no 2 pp 1ndash4 2005

[17] F Chouza O Reitebuch A Benedetti and B WeinzierlldquoSaharan dust long-range transport across the Atlantic studiedby an airborne Doppler wind lidar and the MACC modelrdquoAtmospheric Chemistry and Physics vol 16 no 18 pp 11581ndash11600 2016

[18] A A Chen and M A Taylor ldquoInvestigating the link betweenearly season Caribbean rainfall and the El Nino+1 yearrdquo Inter-national Journal of Climatology vol 22 no 1 pp 87ndash106 2002

[19] M A Taylor T S Stephenson A Owino A A Chen and J DCampbell ldquoTropical gradient influences on Caribbean rainfallrdquoJournal of Geophysical Research Atmospheres vol 116 no 18Article ID D00Q08 2011

[20] M E Angeles J E Gonzalez D J Erickson III and J LHernandez ldquoThe impacts of climate changes on the renewableenergy resources in the Caribbean regionrdquo Journal of SolarEnergy Engineering vol 132 no 3 pp 0310091ndash03100913 2010

[21] V Moron R Frelat P K Jean-Jeune and C GaucherelldquoInterannual and intra-annual variability of rainfall in Haiti(1905ndash2005)rdquo Climate Dynamics vol 45 no 3-4 pp 915ndash9322015

[22] N Ramirez J Gonzalez J Castro M Angeles E Harmsenand C Salazar ldquoAnalysis of the heat index in the mesoamericaand caribbean regionrdquo Journal of Applied Climatology andMeteorology vol 56 pp 2905ndash2925 2017

[23] M Beniston D B Stephenson O B Christensen et al ldquoFutureextreme events in European climate an exploration of regionalclimate model projectionsrdquo Climatic Change vol 81 no 1 pp71ndash95 2007

[24] G A Meehl and C Tebaldi ldquoMore intense more frequent andlonger lasting heat waves in the 21st centuryrdquo Science vol 305no 5686 pp 994ndash997 2004

[25] M Zampieri S Russo S di Sabatino M Michetti E Scoc-cimarro and S Gualdi ldquoGlobal assessment of heat wavemagnitudes from 1901 to 2010 and implications for the riverdischarge of the Alpsrdquo Science of the Total Environment vol 571pp 1330ndash1339 2016

[26] S RussoADosio RGGraversen et al ldquoMagnitude of extremeheat waves in present climate and their projection in a warmingworldrdquo Journal of Geophysical Research Atmospheres vol 119no 22 pp 12500ndash12512 2014

[27] G Brooke Anderson and M L Bell ldquoHeat waves in the UnitedStates Mortality risk during heat waves and effect modificationby heat wave characteristics in 43 US communitiesrdquo Environ-mental Health Perspectives vol 119 no 2 pp 210ndash218 2011

[28] T T Smith B F Zaitchik and J M Gohlke ldquoHeat waves inthe United States Definitions patterns and trendsrdquo ClimaticChange vol 118 no 3-4 pp 811ndash825 2013

[29] P J Robinson ldquoOn the definition of a heat waverdquo Journal ofApplied Meteorology and Climatology vol 40 no 4 pp 762ndash775 2001

[30] K Hayhoe S Sheridan L Kalkstein and S Greene ldquoClimatechange heat waves and mortality projections for ChicagordquoJournal of Great Lakes Research vol 36 no 2 pp 65ndash73 2010

[31] G Luber and M McGeehin ldquoClimate change and extreme heateventsrdquo American Journal of Preventive Medicine vol 35 no 5pp 429ndash435 2008

[32] J A Patz D Campbell-Lendrum T Holloway and J A FoleyldquoImpact of regional climate change on human healthrdquo Naturevol 438 no 7066 pp 310ndash317 2005

[33] A J McMichael R E Woodruff and S Hales ldquoClimate changeand human health present and future risksrdquo The Lancet vol367 no 9513 pp 859ndash869 2006

[34] P Mendez-Lazaro O Martınez-Sanchez R Mendez-TejedaE Rodrıguez and N Schmitt-Cortijo ldquoExtreme heat eventsin san juan puerto rico trends and variability of unusual hotweather and its possible effects on ecology and societyrdquo Journalof Climatology and Weather Forecasting vol 3 no 2 pp 1ndash72015

[35] P A Mendez-Lazaro C M Perez-Cardona E Rodrıguezet al ldquoClimate change heat and mortality in the tropicalurban area of San Juan Puerto Ricordquo International Journal ofBiometerology pp 1ndash9 2016

[36] P Mendez-Lazaro F E Muller-Karger D Otis M J McCarthyand E Rodrıguez ldquoA heat vulnerability index to improveurban public health management in San Juan Puerto RicordquoInternational Journal of Biometerology pp 1ndash14 2017

[37] J Gonzalez M Georgescu M Lemos N Hosannah and DNiyogi ldquoClimate changersquos pulse in central america and theCaribbeanrdquo EoS vol 98 2017

[38] IPCC Climate Change 2001 The Scientific Basis Contributionof Working Group I to the Third Assessment Report of the Inter-governmental Panel on Climate Change Cambridge University


Press Cambridge United Kingdom and New York NY USA2001

[39] N-C Lau and M J Nath ldquoA model study of heat waves overNorth America Meteorological aspects and projections for thetwenty-first centuryrdquo Journal of Climate vol 25 no 14 pp 4761ndash4764 2012

[40] A Amengual V Homar R Romero et al ldquoProjections of heatwaveswith high impact on humanhealth in EuroperdquoGlobal andPlanetary Change vol 119 pp 71ndash84 2014

[41] D Birgmark El Nino-Southern Oscillation and North AtlanticOscillation Induced Sea Surface Temperature Variability in theCaribbean Sea University of Gothenburg Department of EarthSciences 2014

[42] A Giannini M A Cane and Y Kushnir ldquoInterdecadal changesin the ENSO Teleconnection to the Caribbean Region and theNorth Atlantic Oscillationrdquo Journal of Climate vol 14 no 13pp 2867ndash2879 2001

[43] A Giannini J C H Chiang M A Cane Y Kushnir andR Seager ldquoThe ENSO teleconnection to the Tropical AtlanticOcean Contributions of the remote and local SSTs to rainfallvariability in the Tropical Americasrdquo Journal of Climate vol 14no 24 pp 4530ndash4544 2001

[44] V Magana J A Amador and S Medina ldquoThe midsummerdrought over Mexico and Central Americardquo Journal of Climatevol 12 no 6 pp 1577ndash1588 1999

[45] IPCC Climate Change 2013 The Physical Science Basis Con-tribution of Working Group I to the Fifth Assessment Reportof the Intergovernmental Panel on Climate Change CambridgeUniversity Press Cambridge United Kingdom and New YorkNY USA 2013

[46] IPCC pecial Report on Emissions Scenarios A Special Report ofWorking Group III of the Intergovernmental Panel on ClimateChange Cambridge University Press Cambridge United King-dom and New York NY USA 2000

[47] R Moss M Babiker S Brinkman et al ldquoTowards New Scenar-ios for Analysis of Emissions Climate Change Impacts andResponse Strategiesrdquo Technical Summary IntergovernmentalPanel on Climate Change Geneva Switzerland 2008

[48] K E Taylor R J Stouffer and G A Meehl ldquoAn overview ofCMIP5 and the experiment designrdquo Bulletin of the AmericanMeteorological Society vol 93 no 4 pp 485ndash498 2012

[49] G A Meehl L Goddard J Murphy et al ldquoDecadal predictioncan it be skillfulrdquo Bulletin of the American MeteorologicalSociety vol 90 no 10 pp 1467ndash1485 2009

[50] J Holmboe G Forsythe andW Gustin Dynamic MeteorologyJohn Wiley and Sons Inc New York NY USA 1945

[51] J Wallace and P Hobbs Atmospheric Science An IntroductorySurvey Academic Press New York NY USA 2006

[52] M Jacobson Fundamentals of Atmospheric Modeling Cam-bridge University Press Cambridge UK 2005

[53] R G Steadman ldquoThe assessment of sultriness Part I Atemperature-humidity index based on human physiology andclothing sciencerdquo Journal of Applied Meteorology and Climatol-ogy vol 18 no 7 pp 861ndash873 1979

[54] R Stull Meteorology for Scientists and Engineers BrooksColeBelmont Calif USA 2000

[55] NWS The Heat Index Equation Natl Weather ServWeather Predict Cent httpwwwwpcncepnoaagovhtmlheatindex equationshtml

[56] G Brooke Anderson M L Bell and R D Peng ldquoMethods tocalculate the heat index as an exposuremetric in environmental

health researchrdquo Environmental Health Perspectives vol 121 no10 pp 1111ndash1119 2013

[57] A L Hass K N Ellis L R Mason J M Hathaway and DA Howe ldquoHeat and humidity in the city Neighborhood heatindex variability in a mid-sized city in the Southeastern UnitedStatesrdquo International Journal of Environmental Research andPublic Health vol 13 no 1 article no 117 2016

[58] S Russo J Sillman andA Sterl ldquoHumidHeatWaves atDiferentWarming Levelsrdquo Science vol 7 pp 1ndash7 2017

[59] M Mohan A Gupta and S Bhati ldquoA modified approachto analyze thermal comfort classificationrdquo Atmospheric andClimate Sciences vol 4 no 1 pp 7ndash19 2014

[60] M Nitschke G R Tucker A L Hansen S Williams Y Zhangand P Bi ldquoImpact of two recent extreme heat episodes onmorbidity and mortality in Adelaide South Australia A case-series analysisrdquo Environmental Health A Global Access ScienceSource vol 10 no 1 article no 42 2011

[61] A M Carmona and G Poveda ldquoDetection of long-termtrends in monthly hydro-climatic series of Colombia throughEmpirical Mode Decompositionrdquo Climatic Change vol 123 no2 pp 301ndash313 2014

[62] K H Hamed ldquoTrend detection in hydrologic data the Mann-Kendall trend test under the scaling hypothesisrdquo Journal ofHydrology vol 349 no 3-4 pp 350ndash363 2008

[63] Q Zhang V P Singh P Sun X Chen Z Zhang and JLi ldquoPrecipitation and streamflow changes in China Changingpatterns causes and implicationsrdquo Journal of Hydrology vol410 no 3-4 pp 204ndash216 2011

[64] E Scoccimarro P Giuseppe and S Gualdi ldquoThe Role ofHumidity in Determining Scenarios of Perceived TemperatureExtremes in EuroperdquoEnvironmental Research Letters vol 12 no11 pp 1ndash8 2017

[65] C C Macpherson and M Akpinar-Elci ldquoCaribbean heatthreatens health well-being and the future of humanityrdquo PublicHealth Ethics vol 8 no 2 pp 196ndash208 2015

[66] M Angeles J Gonzalez and N Ramirez ldquoImpacts of climatechange on building energy demands in the Intra-AmericasRegionrdquo Journal of Theoretical and Applied Climatology pp 1ndash14 2017

[67] G Xie Y Guo S Tong and L Ma ldquoCalculate excess mortalityduring heatwaves using Hilbert-Huang transform algorithmrdquoBMCMedical ResearchMethodology vol 14 no 1 article no 352014

[68] K Zhang T-H Chen and C E Begley ldquoImpact of the 2011heat wave on mortality and emergency department visits inHouston Texasrdquo Environmental health a global access sciencesource vol 14 p 11 2015

Hindawiwwwhindawicom Volume 2018

Journal of

ChemistryArchaeaHindawiwwwhindawicom Volume 2018

Marine BiologyJournal of


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EcologyInternational Journal of


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Applied ampEnvironmentalSoil Science

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Forestry ResearchInternational Journal of



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Geophysics

Environmental and Public Health

Journal of




Microbiology


Public Health Advances in

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MeteorologyAdvances in

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018Hindawiwwwhindawicom Volume 2018

ChemistryAdvances in

ScienticaHindawiwwwhindawicom Volume 2018


Geological ResearchJournal of

Analytical ChemistryInternational Journal of


Submit your manuscripts atwwwhindawicom


33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

LongitudeIntra-American region

Gulf of Mexico

CentralAmerica

Caribbean SeaSouth America MDR

Lesser Antilles

Greater Antilles0

50

100

200

500 800

900

1000

1500

2000

2500

300

0(a)

200

150

100

50

Rain

(mm

)

Jan Mar May Jul Sep NovMonthClimatology 1979ndash2015

IAR climatology

Dry rainfallseason

Early rainfallseason

Late rainfallseason

(b)

Figure 1 (a)Major areas in the IAR (b) IAR rainfall bimodal nature showing theDS ERS and LRSThe dry season is defined fromDecemberto March corresponding to the season with the lowest rainfall intensity events

holding the second rainfall peak In addition a dry season(DS) is defined between December and March where thelowest rainfall intensity events are observed (see Figure 1(b))

IAR warming was recently detected with an annual SSTincrease of 0016∘C per year in the ERS while in the LRSan SST increase of 0021∘C per year was observed [6] Thisregional warming could disrupt the synoptic atmosphericand oceanic patterns A warmer region increases the near-surface air temperature and intensify evaporation from thesea enhancing moisture content in the atmosphere and driv-ing the relative humidity (RH) change In these conditionsextreme events such as HWs could intensify

Air temperature and RH are key variables to calculatethe heat index (HI) which is used to define HWs eventsIn addition different regions across the world have a uniquetemperature and RH climatology causing several heat waves(HWs) definitions [22] such as in Europe where a heat waveis defined as at least six consecutive days with dailymaximumair temperature greater than the relative threshold of the 90thpercentile [23] A more complex definition was applied towhole Europe and the United States (US) using at least threeconsecutive days with daily maximum temperature and aver-age maximum temperature above the 975th percentile [24]The heat waves were estimated using the National Centersfor Environmental Prediction (NCEP) reanalysis data for thepresent time and the Parallel Climate Model for future pro-jections using the business-as-usual scenario Percentile wasapplied to daily data from 1961 to 1990 for reanalysis data and2080ndash2099 for GCMs outputs [24] On the other hand a heatwave magnitude index (HWMI) was developed to analyzeHWs across Earth regions including Europe United StatesGreece and Russia [25 26]These researchers selected NCEPreanalysis and ERA-Interim (ERA-I) reanalysis datasetsupscaling ERA-I to the NCEP resolution Additionally theydefined a HWMI as the maximum magnitude of heat waves

in a year where the heat wave was defined as at leastthree consecutive days with daily maximum temperaturegreater than the 90th percentile and centered in a 31-daymoving window [25 26] On the other hand other HWdefinitions were developed as at least two consecutive dayswith daily mean temperature greater than the 95th percentile(calculated with daily data for 28 years) providing the highestHWs occurrence frequency in the Northwest United States[27 28] Furthermore in Southern US a heat wave wasdefined as at least two consecutive days with daily maximumand minimum HI above the relative threshold of the 99thpercentile [29] Percentile was obtained from 39 years of dailydata Moreover the US National Weather Service (NWS) hasdefined the heat waves events as at least three consecutivedays with daily maximum temperature greater than theabsolute threshold of 32∘C [30] Although this criterion iswidely used it does not capture the HWs events at differentregions This way in the US regions with high RH (northeastregion) a heat wave is defined as at least three consecutivedays with temperature at or above 322∘C [22]

During the past decade a connection between climatechange and heat waves intensity-frequency alterations wasidentified even causing more death than all other extremeevents together in the US such as hurricanes floods andtornadoes In the Eastern and Western US a 20 HWsnumber increase was observed from 1949 to 1995 whilefrom 1999 to 2003 688 deaths per year were registeredprimarily due to extreme hot day events [31] In the city ofChicago HWs impact on human health caused 800 deathsin the year 1995 [30] These hot day events were determinedusing the NWS definition The mortality risks in 43 UScities were investigated from 1987 to 2005 A heat wavemortality risk increase of 249 for every 1∘F heat waveintensity and 038 for every one day where the heat waveduration increases was found [27] In this case the HW was








2 Methodology









2increase


2emissions









2


2emission


















(∘C)

30

29

28

27

26

25



HI

SSTTＣＬ

(a)


20 26 29 32

Safe Lowcaution

Highcaution

Extremecaution

10121011

10141013

10151016

1017

1012

1011

1014

0

00

0

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

minus001minus001

002

minus002

minus002


minus004

(b)100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus35 minus3

minus25

minus2

minus15

minus1

minus05 0

05

45 45

45

55

55

55

70

7060

6050

50

5050

40

4040 40

35

65

65

75

7580

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Longitude

(c)


minus12

minus09

minus06

minus03 0

03

06

09

12

times10minus5∘Cs

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

(d)





28

278

276

274

272

27

268

266

2641948 1958 1968 1978 1988 20081998



003∘Cper year

p value = 001

TＣＬ

(∘C)


(a)

19981948 1958 1968 1978 1988 2008


315

31

305

30

295

29

285

28

Hea

t ind

ex (∘

C)

Annual max HI IAR


007∘Cper year

p value = 0003

(b)







1

05

0



ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)







Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy






















2 Methodology









2increase


2emissions









2


2emission


















(∘C)

30

29

28

27

26

25



HI

SSTTＣＬ

(a)


20 26 29 32

Safe Lowcaution

Highcaution

Extremecaution

10121011

10141013

10151016

1017

1012

1011

1014

0

00

0

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

minus001minus001

002

minus002

minus002


minus004

(b)100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus35 minus3

minus25

minus2

minus15

minus1

minus05 0

05

45 45

45

55

55

55

70

7060

6050

50

5050

40

4040 40

35

65

65

75

7580

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Longitude

(c)


minus12

minus09

minus06

minus03 0

03

06

09

12

times10minus5∘Cs

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

(d)





28

278

276

274

272

27

268

266

2641948 1958 1968 1978 1988 20081998



003∘Cper year

p value = 001

TＣＬ

(∘C)


(a)

19981948 1958 1968 1978 1988 2008


315

31

305

30

295

29

285

28

Hea

t ind

ex (∘

C)

Annual max HI IAR


007∘Cper year

p value = 0003

(b)







1

05

0



ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)







Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy




















2increase


2emissions









2


2emission


















(∘C)

30

29

28

27

26

25



HI

SSTTＣＬ

(a)


20 26 29 32

Safe Lowcaution

Highcaution

Extremecaution

10121011

10141013

10151016

1017

1012

1011

1014

0

00

0

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

minus001minus001

002

minus002

minus002


minus004

(b)100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus35 minus3

minus25

minus2

minus15

minus1

minus05 0

05

45 45

45

55

55

55

70

7060

6050

50

5050

40

4040 40

35

65

65

75

7580

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Longitude

(c)


minus12

minus09

minus06

minus03 0

03

06

09

12

times10minus5∘Cs

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

(d)





28

278

276

274

272

27

268

266

2641948 1958 1968 1978 1988 20081998



003∘Cper year

p value = 001

TＣＬ

(∘C)


(a)

19981948 1958 1968 1978 1988 2008


315

31

305

30

295

29

285

28

Hea

t ind

ex (∘

C)

Annual max HI IAR


007∘Cper year

p value = 0003

(b)







1

05

0



ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)







Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
























(∘C)

30

29

28

27

26

25



HI

SSTTＣＬ

(a)


20 26 29 32

Safe Lowcaution

Highcaution

Extremecaution

10121011

10141013

10151016

1017

1012

1011

1014

0

00

0

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

minus001minus001

002

minus002

minus002


minus004

(b)100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus35 minus3

minus25

minus2

minus15

minus1

minus05 0

05

45 45

45

55

55

55

70

7060

6050

50

5050

40

4040 40

35

65

65

75

7580

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Longitude

(c)


minus12

minus09

minus06

minus03 0

03

06

09

12

times10minus5∘Cs

33∘N30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘NEQ

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude

Longitude

(d)





28

278

276

274

272

27

268

266

2641948 1958 1968 1978 1988 20081998



003∘Cper year

p value = 001

TＣＬ

(∘C)


(a)

19981948 1958 1968 1978 1988 2008


315

31

305

30

295

29

285

28

Hea

t ind

ex (∘

C)

Annual max HI IAR


007∘Cper year

p value = 0003

(b)







1

05

0



ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)







Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















28

278

276

274

272

27

268

266

2641948 1958 1968 1978 1988 20081998



003∘Cper year

p value = 001

TＣＬ

(∘C)


(a)

19981948 1958 1968 1978 1988 2008


315

31

305

30

295

29

285

28

Hea

t ind

ex (∘

C)

Annual max HI IAR


007∘Cper year

p value = 0003

(b)







1

05

0



ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)







Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















1

05

0



ΔHI ∘CΔSLP mbar

(a)

minus04

minus01

002

002

minus002minus002

0

00

0

0

00

00

0

0

0minus0

minus01 minus02

minus02

minus02

minus03

minus03

minus04

minus04

minus05

minus05

minus05

minus06minus07

minus07

minus03 0

03

06

09

12

15 2

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0

(∘C)

(b)

100

200

300

400

500

600

700

800

900

1000


times10minus2 ０s

minus3

minus2minus2

minus2

minus2

minus14

minus4

minus4

minus6minus8minus10

minus10minus12

minus2

minus15

minus1

minus05 0

05 1

15 2

Leve

l

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


(c)

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

Latit

ude

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W


minus05

minus03

minus02 0

02

03

04

05

times10minus5∘Cs

(d)







Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















Years1950 1960 1970 1980 1990 2000 2010

Hea

t wav

es n

umbe

r

0

50

100

150

200

250

300

350

(a)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


0 2 4 6 8 10 12 14 16 18

(b)

Heat waves number

EQ

30∘N33∘N

27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

55∘ W

50∘ W

45∘ W

40∘ W

Latit

ude


minus15

minus12 minus9

minus6

minus3 0 2 4 6 8 10 12 14 16

(c)





05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

0 20 40 60 80 100 120

Heat waves number

(a)

05

04

03

02

01

0

Rela

tive f

requ

ency

1948ndash19981999ndash2015

30 32 34 36 38 40 42


(b)









Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















Years 2026ndash21002020 2030 2040 2050 2060 2070 2080 2090 2100

27

28

29

30

IAR


2912∘C

Mea

n H

I (∘ C)

(a)

Years 2026ndash21002020 2040 2060 2080 2100

26

28

30

32

34

36

IAR



Mea

n H

I (∘ C)

(b)

6

5

4

3

2

Δm

ean

HI (

∘ C)


RCP45RCP85

(c)






Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy
















Years2030 2040 2050 2060 2070 2080 2090 2100

Num

ber o

f hea

t wav

es

0

500

1000

1500

2000

2500

3000

(a)


30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude

Longitude

20 30

40

50

60

70 80 90

100

120

160

200

300

(b)


500 600

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100

(c)

30 32 34 36 38 40 42 44

Rela

tive f

requ

ency

0

01

02

03

04

05

2026ndash20452081ndash2100


(d)

















Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy


























Years2030 2040 2050 2060 2070 2080 2090 2100

Hea

t wav

es n

umbe

r

0

1000

2000

3000

4000

(a)

30∘N27∘N24∘N21∘N18∘N15∘N12∘N9∘N6∘N3∘N

100∘ W

95∘ W

90∘ W

85∘ W

80∘ W

75∘ W

70∘ W

65∘ W

60∘ W

Latit

ude


20 30

40

50

60

70

80

90

100

120

160

200

300

(b)

05

04

03

02

01

0

Rela

tive f

requ

ency

0 100 200 300 400 500 600 700 800

Heat waves number

2026ndash20452081ndash2100

(c)

030 32 34 36 38 40 42 44


2026ndash20412081ndash2100

05

04

03

02

01

Rela

tive f

requ

ency

(d)




Acknowledgments


References











































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy




















































































Journal of










Volume 2018





Geophysics


Journal of




Microbiology





Agronomy















projections of heat waves events in the intra-americas region...

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