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UNIVERSITÉ DU QUÉBEC À MONTRÉAL

ÉTUDE DE L'ÎLOT DE CHALEUR URBAIN DANS LE CUMA T COURANT ET FUTUR POUR LE CAS DE L'ÎLE DE MONTRÉAL

MÉMOIRE

PRÉSENTÉ

COMME EXIGENCE PARTIELLE

DE LA MAÎTRISE EN SCIENCES DE L'ATMOSPHÈRE

PAR

FRANÇOIS ROBERGE

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Service des bibliothèques

Avertissement

La diffusion de ce mémoire se fait dans le respect des droits de son auteur, qui a signé le formulaire Autorisation de reproduire et de diffuser un travail de recherche de cycles supérieurs (SDU-522 - Rév.01-2006}. Cette autorisation stipule que «conformément

à

l'article 11 du Règlement no 8 des études de cycles supérieurs, [l'auteur] concède

à

l'Université du Québec

à

Montréal une licence non exclusive d'utilisation et de publication de la totalité ou d'une partie importante de [son] travail de recherche pour des fins pédagogiques et non commerciales. Plus précisément, [l'auteur] autorise l'Université du Québec à Montréal

à

reproduire, diffuser, prêter, distribuer ou vendre des copies de [son] travail de recherche

à

des fins non commerciales sur quelque support que ce soit, y compris l'Internet. Cette licence et cette autorisation n'entraînent pas une renonciation de [la] part [de l'auteur]

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[ses] droits moraux ni

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[ses] droits de propriété intellectuelle. Sauf entente contraire, [l'auteur] conserve la liberté de diffuser et de commercialiser ou non ce travail dont [il] possède un exemplaire.»

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REMERCIEMENTS

Je tiens à remercier ma directrice de recherche Laxmi Sushama et mon codirecteur Pierre Gauthier pour leurs soutien, aide et conseils.

Je tiens également à remercier mes collègues Oleksandr Huziy, Gulilat Tefera Diro, Bernardo Stephan Teufel et Gemechu Fanta Garuma pour les discussions intéressantes concernant le climat près de la surface ainsi que le milieu urbain.

Je tiens à remercier spécialement mon épouse Rosamina sans qui ce projet aurait été impossible. La science est remplie d'obstacles et d'embûches. Cependant, elle a su me donner le soutien et le courage nécessaire pour rester sur la route qui mène au succès.

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LISTE DES FIGURES ... v

LISTE DES TABLEAUX ... vii

LISTE DES ABRÉVIATIONS, DES SIGLES ET DES ACRONYMES ... viii

RÉSUMÉ ... x

ABSTRACT ... xi

INTRODUCTION ... 12

0.1 Organisation du mémoire ... 16

CHAPITRE 1 URBAN HEA T ISLAND IN FUTURE AND CURRENT CUMA TES FOR THE ISLAND OF MONTREAL ... 18

1.1 Introduction ... 19

1.2 Models and Methods ... 23

1.2.1 Models ... 23

1.2.2 Methodology ... 24

1.3 Mode! validation and analysis in current climate ... 26

1.4 Projected changes ... 30

1.5 Summary and conclusion ... 32

CONCLUSION ... 34

ANNEXE A FIGURES ... 37

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IV

ANNEXEB

TABLEAUX ... 50

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Figure Page

Figure 1.1. (a) Total urban fraction, (b) building fraction, and (c) pavement fraction at 0.0025° resolution for the entire experimental domain covering the island of Montreal. Values are shawn only for grid cells with urban fraction above or equal to 50%. Water bodies within the domain are shawn in white ... 38

Figure 1.2. (a) Land surface temperatures from MODIS (top row), CLASS+TEB (middle row) and CLASS (bottom row) for JJA (left two colurnns) and SON (right two columns) at 12h and 23h (local time) for the 2001-2010 period. CLASS+TEB and CLASS simulations are driven by CRCM-ERA. (b) Diurnal cycle of the land surface temperature (LST) for the urban (gridcells with urban fractions greater than 50%) and nonurban (gridcells with urban fractions smaller than 1 %) regions and surface urban beat island intensity (UHI) for JJA and SON from CLASS+TEB simulation. The right Y -axis corresponds to surface UHI. ... 39

Figure 1.3. (a) Mean daily 2m temperature from Daymet (first row) and ANUSPLIN (second row) observation datasets and CLASS+TEB simulation (third row) for the 1981-2010 period, (b) Land surface temperature differences between urban and non-urban fractions of the same gridcell for CLASS+ TEB simulation for the 1981-2010 period. ( c) Mean daily 2m temperature differences between CLASS+ TEB and CLASS simulations driven by CRCMS-ERA, for the 1981-2010 period. The left column corresponds to summer (JJA) and the right column is for fall (SON) ... 40

Figure 1.4. (a) Mean daily summer and fall precipitation from driving CRCMS-ERA for the 1981-2010 period. Sensible (SHF) and latent beat (LHF) flux differences between (b) urban and nonurban fractions in CLASS+TEB simulation, (c) CLASS+ TEB and CLASS simulations, for the 1981-2010 period. ( d) Differences in surface runoffbetween CLASS+TEB and CLASS simulations for summer and fall for the 1981-2010 period. CLASS+TEB and CLASS simulations are driven by CRCMS-ERA ... 42

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VI

Figure 1.5. Difference in the number of hot days between CLASS+TEB and CLASS simulations driven by CRCM5-ERA for the 1981-2010 period based on the 90th percentile threshold from CLASS only simulation ... 44

Figure 1.6. (a) Land surface temperatures from MODIS (top row), CLASS+TEB (middle row) and CLASS (bottom row) for JJA (left two columns) and SON (right two columns) at 12h and 23h (local time) for the 2001-2010 period. CLASS+TEB and CLASS simulations are driven by CRCM5-CanESM2. (b) Diurnal cycle of the land surface temperature (LST) for the urban (gridcells with urban fractions greater than 50%) and nonurban (gridcells with urban fractions smaller than 1 %) regions and surface urban heat island intensity (UHI) for JJA and SON from CLASS+ TEB simulation. The right Y -axis corresponds to surface UHI.. ... .45

Figure 1. 7. Land surface temperatures for the current 1981-2010 (first row) and future 2071-2100 (second row) periods and their projected changes (third row) for summer (left column) and fall (right column) for CLASS+TEB simulations driven by CRCM5-CanESM2 ... 46

Figure 1.8. Projected changes to sensible (~SHF) and latent (~LHF) heat fluxes for the urban fractions (first column) and non-urban fractions (second column) for summer (JJA) and fall (SON), for the period 2071-2100 in comparison to 1981-2100, based on CLASS+TEB simulations driven by CRCM5-CanESM2 ... .47

Figure 1.9. Time series of mean 2m temperature (T2m) for urban (red) and nonurban (blue) regions and UHI intensity (black) for current 1981-2010 (solid lin es) and future 2071-2100 (dashed lines) periods for sumrner (left) and fall (right) from CLASS+TEB simulations driven by CRCM5-CanESM2. The right Y-axis corresponds to UHI. ... 48

Figure 1.1 O. Projected changes to the number of hot da ys, for summer (left) and fall (right), for the future 2071-2100 period with respect to the current 1981-2010 period, for CLASS+TEB (first row) and CLASS (second row) simulations, driven by CRCM5-CanESM2. The difference between the projected changes from CLASS+ TEB and CLASS simulations are shown in the third row ... .49

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Tableau Page

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LISTE DES ABRÉVIATIONS, DES SIGLES ET DES ACRONYMES

ANUSPLIN Australian National University Splines

Can Vec Canadian V ector Data

CanESM2 Canadian Earth System Model, version 2

CCI LC Climate Change Initiative Land Cover

CCSM4 Community Climate System Model, version 4

CLASS Canadian Land Surface Scheme

C02 Carbon Dioxide

CRCM5 Canadian Regional Climate Model, version 5

CRCM5-CanESM2 CRCM5 simulations driven by CanESM2

CRCM5-ERA CRCM5 simulation driven by ERA-Interim

Daymet Daily Meteorology

ECMWF European Centre Medium-Range Weather Forecasts

EDT Eastern Daylight Time

ERA-Interim Third generation reanalysis of ECMWF

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ICU IPCC LAI LST MODIS MO RUSES MRCC5 NASA RCP SRES SRTM TEB UCM UHI WRF

Îlot de chaleur urbain

Intergovernmental Panel on Climate Change Leaf Area Index

Land Surface Temperature

Moderate-Resolution lmaging Spectroradiometer

Met Office Reading Urban Surface Exchange Unified Mode! Modèle régional canadien du climat, version 5

National Aeronautics and Space Administration Representative Concentration Pathway

Special Report on Emissions Scenarios Shuttle Radar Topography Mission Town Energy Balance mode! Urban Canopy Mode! Urban heat island

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RÉSUMÉ

Les régions urbaines avec leurs caractéristiques distinctes de la surface modifient Je partitionnement de l'énergie et de l'eau, ce qui cause généralement des températures plus grandes en comparant avec les régions adjacentes non urbaines. Ce phénomène, appelé 1 'îlot de chaleur urbain (ICU), est étudié pour Je cas de la ville de Montréal situé dans Je centre-est du Canada dans la province du Québec, pour le climat courant et futur, pour l'été et l'automne. L'objectif est aussi de quantifier la modulation urbaine des changements projetés au climat près de la surface incluant les extrêmes de température pour la région considérée. Les changements projetés de l'ICU sont étudiés en utilisant des simulations hors-ligne à haute résolution (250 rn) réalisées avec un modèle de surface, Je Canadian Land Surface Scheme (CLASS), avec ou sans représentation urbaine, pour Je scénario RCP 8.5. Le modèle Town Energy Balance (TEB) est utilisé dans les simulations avec représentation urbaine. Une comparaison de la température de surface et à 2m de la simulation avec représentation urbaine avec des ensembles de données disponibles indique que Je modèle capture 1 'ICU qui dans Je climat courant est d'à peu près 2°C en été et de 1

o

c

en automne. Les résultats démontrent également que les régions urbaines n'augmentent pas seulement la température, mais aussi les extrêmes de température comme Je nombre de jours chauds. Les changements projetés de la température de surface et à 2m suggèrent des hausses importantes de température dans les régions urbaines et non urbaines avec une faible hausse de l'intensité de 1 'ICU. La hausse de I'ICU de surface en été est principalement due à une hausse de température de surface légèrement plus basse dans les régions non urbaines dues à une hausse du flux de chaleur latente associée à une hausse de précipitation dans les données de forçage. En automne, elle est plutôt due à une hausse de température légèrement plus élevée dans les températures de surfaces urbaines comparativement avec les régions non urbaines associée avec une hausse et une baisse de flux de chaleur sensible et latente respectivement pour les régions urbaines. De plus, 1' analyse des changements projetés du nombre de jours chauds suggère une hausse de 5-8 jours en été et de 2-5 jours en automne due à la représentation urbaine. Cette étude a également permis de quantifier Je rôle des régions urbaines sur le climat de surface et les extrêmes de températures ainsi que leurs impacts sur les changements projetés pour la région considérée. Cette infonnation est cmciale pour les études concernant l'impact des changements climatiques et les mesures d'adaptation possibles pour plusieurs secteurs. Mots clés: îlot de chaleur urbain, changement climatique, climat près de la surface, jours chauds

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Urban regions with their distinct surface characteristics modify energy and water partitioning, leading to generally higher temperatures in comparison to the adjoining non-urban or rural regions. This phenomenon, referred to as the Urban Heat Island (UHI), is studied for the city of Montreal situated in central-eastern Canada in the province of Quebec, for current and future climates, for the summer and fall seasons. lt is a Iso the ai rn to quantify the urban modulation of projected changes to near-surface

climate, including temperature extremes, for the region. Projected changes to the UHI

for the region are studied using offline high-resolution (250 rn) simulations performed

with a state-of-the-art land surface scheme, the Canadian Land Surface Scheme

(CLASS), with and without an urban representation, for the Representative

Concentration Pathway (RCP) 8.5 scenario. The Town Energy Balance (TEB) mode)

is used in the simulation with urban representation. Comparison of the land surface and 2m air temperatures from the offline simulation with improved urban representation with available datasets indicate that the mode) is able to capture features such as the UHI, which in current climate is around 2°C in summer and 1

o

c

in fall. Results also demonstrate that urban regions augment not only the mean temperature, but also temperature extremes such as the number of hot days. Projected changes to the surface

and 2m temperature suggest significant increases for both urban and nonurban regions,

with small increases in the UHI intensity for the study region. Increase in the summer surface UHI is primarily due to the slightly lower increases in surface temperature for the nonurban regions, due to increases in latent heat flux associated with increased precipitation in the driving data. The small increase in UHI intensity in fall is due to

the slightly higher increase in urban surface temperatures compared with nonurban

regions, associated with increase and decrease in sensible and latent heat fluxes, respective) y, for the urban regions. Furthermore, analysis of the projected changes to the number of hot days suggests significant increases, with urban regions augmenting the increases by 5-8 days in summer and 2-5 days in fall. This study enabled to quantify the role of urban regions on the surface elima te and selected temperature extremes and also its impact on projected changes for the study region. This information is crucial for climate change-related impact and adaptation studies for many sectors.

Keywords : Urban heat island, climate change, near-surface climate, temperature

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INTRODUCTION

La modélisation numérique du climat urbain devient de plus en plus nécessaire pour quantifier 1' impact de 1 'urbanisation sur le climat tant à l'échelle mondiale que régionale. Selon le rapport des Nations Unies sur l'urbanisation à l'échelle mondiale (United Nations, 2014), en Amérique du Nord, 82% de la population est en zones urbaines. Au niveau mondial, cela est de 54% et ce nombre devrait atteindre 65% en 2050. La ville de Montréal n'est pas une exception à ces chiffres. La population est constamment en hausse. En 1996, la population était de 1,8 million et en 2013, elle était de 1,95 million. De plus, ce nombre ne comprend pas la population vivant en banlieues. Une hausse de population peut mener à une hausse future dans l'effet d'îlots de chaleur. Il est donc primordial de s'intéresser à la modélisation de la surface urbaine. Il n'y a pas de cela très longtemps, les régions urbaines n'étaient pas adéquatement représentées dans les modèles climatiques dus, entre autres, à la résolution plus grossière de ces modèles informatiques. Toutefois, avec la hausse des ressources informationnelles, il devient de plus en plus possible de faire de la modélisation du climat régional à des résolutions plus fines afin de mieux comprendre les interactions et réponses climatiques.

Afin de bien comprendre le contexte du traitement de la surface urbaine, il est important de s'intéresser en premier lieu à l'évolution des modèles de surface. Dans les modèles numériques du climat, les modèles de surface sont aussi importants que les autres

composantes comme l'atmosphère. Ceux-ci s'occupent du traitement du bilan

énergétique et hydrique de la surface. Sellers et al (1997) et Pitman (2003) ont classifié les modèles de surface en trois générations. Les modèles de la première génération sont

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des modèles simples basés sur le principe «bucket». Un très bon exemple de modèle de cette génération est celui proposé par Manabe (1969). Étant donné que ce dernier ne considère pas la variation diurne de 1' insolation, celui-ci ne considère pas le transfert de chaleur dans le sol. La rayonnement net dépend uniquement du rayonnement infrarouge absorbé ainsi que solaire absorbé. L'équation énergétique utilisée est très simple:

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où RL est le rayonnement infrarouge net, R5 est le rayonnement solaire net. Q5 est le flux de chaleur sensible et QL est le flux de chaleur latente.

Une importante limitation dans ces modèles est que l'évaporation ne prend pas en compte les caractéristiques des plantes. De plus dans ces modèles, le sol est limité à une certaine quantité d'eau. Lorsque ce seuil est atteint, il y a évaporation.

Les modèles de la seconde génération traitent maintenant le sol sur deux couches et la végétation comme une couche à part entière. Le modèle de Deardoff ( 1978) considère certaines caractéristiques biophysiques de la végétation comme la résistance stomatique ou l'eau retenue sur les plantes. L'équation du bilan énergétique de surface est:

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G est le flux de chaleur vers le sol qui se calcule comme ceci :

G =-À

(

aT

)

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14

il. est la conductivité thermique du sol,

T

est la température du sol et

z

est la profondeur.

Une grande différence avec les modèles de la génération précédente vient aussi du • calcul du bilan hydrique dans lequel le ruissellement ne dépend plus directement de la quantité d'eau dans le sol. La quantité d'eau disponible dans le sol est bel et bien définie à partir des caractéristiques de ce dernier en se basant sur la loi de Darcy (voir Hillel (2008)).

De plus certains modèles de la deuxième génération considèrent le système racinaire

et leur profondeur maximale comme un facteur contrôlant la quantité d'eau disponible

pour l'évaporation (Sellers et al., 1997).

La troisième génération de modèle de surface se distingue de la précédente végétation par son incorporation du cycle du carbone et la réponse végétative. Contrairement aux modèles de deuxième génération, la végétation n'est pas fixe. Elle permet de prendre en compte l'influence de la végétation sur le climat et l'influence du climat sur la végétation. Ces modèles considèrent trois facteurs limitants à la photosynthèse : la limitation de la lumière, la limitation due à l'enzyme photosynthétique et la limitation due à la capacité de l'utilisation (Collatz et al., 1991 ). L'équation du bilan énergétique de surface devient de plus en plus compliquée afin de bien représenter les processus de surface et incorpore maintenant le bilan d'énergie de surface de la canopée végétative. Selon le modèle proposé par Niu et al. (20 11), elle est :

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Toutefois, les modèles de surface tels que décrits précédemment sont limités dans le traitement des surfaces urbaines. Certains modèles comme CLASS (Verseghy, 1991) ou encore MOSES (Cox et al., 1999) considèrent les régions urbaines de la même façon que la végétation dans les modèles de surface de seconde génération c'est-à-dire

comme une canopée avec des caractéristiques correspondantes aux matériaux urbains.

Par exemple, CLASS considère le milieu urbain comme du sol nu avec une certaine longueur de rugosité. Utiliser des modèles de surface modifiés comme ceci peut donner des résultats acceptables à 1 'échelle mondiale (McCarthy, 201 0). Cependant, lors de simulations à échelles fines, une meilleure représentation des processus est nécessaire pour bien représenter la morphologie du milieu urbain (Masson, 2005; Lee, 2011). Ces modèles urbains viennent souvent remplacer le travail du modèle de surface dans les zones urbaines afin de calculer les termes du bilan hydrique et énergétique. Ces modèles comme Town Energy Balance (TEB) (Masson, 2000) ou encore Weather Research and Forecasting Urban Canopy Model (WRF/UCM) (Kusaka, 2001) se basent sur la représentation du canyon urbain décrite par Oke (1982). De plus, ces modèles permettent de prendre en compte des facteurs humains comme les flux de chaleur et d'humidité issus du chauffage, de la climatisation, des industries et du trafic automobile. Cela permet donc d'étudier l'influence du milieu urbain sur le climat et particulièrement dans Je cas présent l'effet d'îlots de chaleur urbain. Toutefois, l'étude du milieu urbain ne peut se faire sans 1 'apport des modèles de surfaces décrits ci-haut dû entre autres à la présence de zones végétatives dans les villes.

Dans le cas de la présente étude, une grande attention a été portée à l'implémentation de TEB dans le Surface Prediction System (Carrera et al, 201 0) qui est une plateforme permettant une modélisation complète de la surface; la performance de CLASS seul, qui est un modèle de surface de deuxième génération, et de l'utilisation de l'ensemble composé de CLASS pour la fraction non urbaine et de TEB pour la fraction urbaine a été vérifié afin de savoir quelle configuration semble mieux reproduire l'effet d'îlots de chaleur urbaine. Une longue série de tests sur différents domaines et sur différentes

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16 périodes a permis, tout en validant avec les images sateiiites de MODIS, de conclure que CLASS+TEB semble mieux reproduire l'effet d'îlots de chaleur urbains que CLASS.

L'objectif principal de cette étude est d'étudier et de quantifier les changements projetés de 1 'ICU de surface et près de la surface ainsi que la modulation urbaine sur les extrêmes de températures. L'ICU de surface et près de la surface sont définis selon la température de surface et à 2m respectivement. L'étude se concentre sur le cas de la viiie de Montréal et ses alentours. Le modèle de surface CLASS ainsi que le modèle urbain TEB seront utilisés afin de faire des simulations hors-ligne à long-terme pour le climat présent ( 1981-201 0) et futur (2071-21 00). Les simulations seront réalisées à une résolution de 250m. Les simulations seront forcées par les sorties à 0.11 o du MRCC5. Le modèle régional est lui-même forcé parERA-Interim à 0.75° pour la validation des sorties de modèles et par CanESM2 à 2.81 o pour le scénario RCP 8.5 pour l'étude des changements projetés à la température, à l'intensité de I'ICU, aux flux turbulents et au nombre de jours chauds. Les scénarios RCP sont un ensemble de trajectoire de concentration de gaz à effets de serre développés pour supporter la recherche sur les changements climatiques et le scénario 8.5 correspond à un forçage radiatif de 8.5 W/m2 à la fin du 21

e siècle comparativement aux valeurs préindustrieiies (Intergovernmental Panel on Climate Change, 20 13). Étant le scénario le plus extrême, il a été décidé de le considérer dans la présente étude. L'effet de 1 'urbanisation sur les futurs extrêmes de température sera en autres étudiée en détail. L'étude est décrite en détail dans le chapitre 1.

0.1 Organisation du mémoire

Pour faire suite à cette introduction en guise de premier chapitre, un article scientifique rédigé en anglais correspond au chapitre 1 de ce mémoire. L'article comprend 1 'introduction (section 1.1 ), les modèles utilisés ainsi que la méthodologie (section 1.2),

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la validation du modèle et l'analyse pour le climat courant (section 1.3), les

changements projetés (section 1.4) ainsi qu'une récapitulation et conclusion (section

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CHAPITRE I

URBAN HEAT ISLAND IN FUTURE AND CURRENT CLIMATES FOR THE ISLAND OF MONTREAL

by

François Roberge1, Laxmi Sushama1•2

1Centre ESCER (Étude et Simulation du Climat à l'Échelle Régionale), Université du

Québec à Montréal, Montréal, Canada

2Department of Civil Engineering and A pp lied Mechanics, Trottier Institute in Sustainability in Engineering and Design, McGill University, Montréal, Canada

Submitted to Sustainable Cities and Society

Corresponding author address: François Roberge, B.Sc., Centre ESCER, Université du

Québec à Montréal, 201 Ave. Président-Kennedy, Montréal, Québec H2X 3Y7,

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1.1 Introduction

The well-known phenomenon of urban beat island (UHI), defined as the difference in

temperature between urban and adjoining non-urban regions, is crucial information for

a number of sectors including health and energy. Temperatures are generally higher in

urban areas due to the higher absorptivity of buildings and pavements and also because

of the reduced vegetation in urban regions, which reduces evaporation and therefore

evaporative cooling. Urban regions through its impact on energy and water partitioning

at the surface because of the different thermal, radiative, moisture and aerodynamic

properties impact the near-surface climate. Furthermore, the urban canyons can Jead to

wind channelling and impact wind speed. Other anthropogenic factors that can further

enhance UHI include automobile emissions, heating and air conditioning systems

(Landsberg, 1981 ). Y et another factor to be considered is the increasing urban

population. According to the United Nations report on world scale urbanization (United

Nations, 2014), in North America, 82% of the population lives in urban areas. At the

world leve!, it is 54% and this is expected togo up to 65% by 2050. The city ofMontreal

is no exception, with the population having risen to 1.95 million in 2013 from 1.8 million in 1996. This number doesn 't take into account the suburban population. An increase in population can further enhance the urban beat island effect, without careful integrated planning considering ali interactions and feedbacks, including climate change.

The numerical modelling of urban climate is becoming more and more necessary to quantify the impact of urbanization on the local climate. Urban regions are not

adequately represented in many climate models, primarily because of their coarser

resolution. With increasing computing resources, it is now becoming possible to

perform high-resolution regional climate mode! (RCM) simulations with improved parameterizations for the urban regions, which would facilitate better understanding of urban-climate interactions and feedbacks (Lemonsu et al., 2015). Though offline

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20 studies with urban models are useful in understanding urban surface processes, coup led

simulations are required to capture the two-way interactions between the surface and

the atmosphere.

Lemonsu et al. (20 12) for example, studied the evolution of the urban climate for the

city of Paris using offline simulations performed with the Town Energy Balance (TEB)

urban mode! for the IPCC SRES A2 and A 1 B scenarios. The ir results suggest higher

increases in the mean 2m temperatures for non-urban regions, compared to that for

urban regions, for ali seasons and scenarios. The higher increase for non-urban regions

is mostly due to the drier soi! conditions in future climate, which leads to reduced latent

beat flux and increased sensible beat flux. For the study region, in summer, the

frequency of the noctumal UHI grea ter than 2°C is projected to decrease from 74% to

66% from present to future climate. For diurnal UHis, the projected decreases are even

higher, from 74% to 50%.

Offline simulations have also been used extensively to support climate change

mitigation studies. For example, Kaloustian et al. (20 16), using the same urban mode!

TEB for the city of Beirut, demonstrated that light-coloured pavements may con tri bute to augmenting the UHI compared to dark-coloured pavements, contrary to what is generally expected, due to multiple reflectances in the urban canyon. However, their study showed that increasing the albedo of the rooftops is effective in decreasing the

UHI intensity.

Studies with coupled urban-climate models are on the rise and they are being applied

to genera te transient climate change simulations to study the impacts of urban regions

on projected changes. The climate change simulations are available for different RCP

scenarios. The RCPs are a set of greenhouse gas concentration trajectories designed to support research on the impacts of climate change (lntergovernmental Panel on Climate Change, 2013 ). Oleson et al. (20 12) studied the response of urban and rural regions to

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climate change by analyzing the Coupled Madel Intercomparison Project phase 5 (CMIP5) simulations of the Earth system madel Community Climate System Mode] version 4 (CCSM4), for RCP 2.6, 4.5 and 8.5 scenarios. In CCSM4, the canyon type urban madel has the following five components: roof, sunlit wall, shaded wall, and pervious and impervious canyon floor. The pervious floor is representative of residential lawns and parks, while, impervious floor represents roads, parking lots and sidewalks. Oleson et al. (20 12) report that the average UHI at the end of the twenty-first century will be similar to that of present day for RCPs 2.6 and 4.5, but noted decreases for RCP8.5. Bath the daytime and nocturnal UHis are projected to decrease in RCP8.5, with the decrease in the daytime UHI being ]ai·ger and more uniform across regions and seasons than for the nocturnal UHI. Warming of the rural surface due to changes in evaporation was reported to be the main driver for this enhanced warming for rural regions compared to urban regions.

RCMs, due to their higher spatial resolution compared to the Global Climate Models, are particularly interesting in the study of urban regions and the ir climate interactions, as they can represent better the urban morphology (Lemonsu et al., 20 15). High resolution RCMs can be employed at city-leve] to provide useful infonnation required by many climate change adaptation and mitigation studies. Chen et al. (20 15) studied the impact of urbanization on projected changes for the RCP 4.5 scenario for China, using simulations performed with the regional climate mode] WRF (Weather Research and Forecasting). They suggest a possible warming in the mean annual temperatures of the arder of 0.6-0.8°C by the 2050s due exclusive] y to urbanization. Bohnenstengel et al. (2011), using coupled simulations with the Met Office Unified madel version 6.1 with the newly developed urban scheme MORUSES, studied UHI for London. They found UHI values as high as 5°C over regions with higher urban fractions and also that the introduction of green areas did not change significantly the UHI for their study regiOn.

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The main objective of this study is to assess projected changes to the surface and near-surface UHI- defined based on the surface and 2m air temperature, respectively - for the city of Montreal situated in central-eastern Canada in the province of Que bec. It is also the aim to quantify the urban modulation of projected changes to near-surface climate, including temperature extremes, for the region. The study region, as discussed

in Oke et al. (1971 ), is complex due to the influence of topography and the presence of water bodies. The surrounding water bodies can bring higher relative humidity and breezes, which can cause higher latent heat flux than in surrounding cities. lt is,

however, important to have fully coupled urban-land-atmosphere models to capture these complex phenomena. Previous UHI focussed studies for the region have looked at links between city size and UHI intensity (Oke, 1973), diurnal cycle of UHI (Touchaei et al., 2015), surface radiation and energy exchanges during the winter-spring transition periods (Lem on su et al., 201 0), application of the Canadian urban and

land surface external modelling system GEM-SURF at very high resolution for the spring-summer transition period (Leroyer et al. 2011) and solutions to redu ce city temperatures (Wang and Akbari, 2016). No studies have been undertaken so far to assess projected changes to the UHI for the region at the spatial and temporal scales considered here. This study, therefore, will contribute key information to support better planning and adaptation in various sectors.

This paper is organized as follows. A brief description of the models used and the methodology is presented in section 1.2. Mode] validation and analysis in current climate are presented in section 1.3, followed by the impact of urban regions on projected climates in section 1.4. Summary and conclusions are provided in section 1.5.

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1.2 Models and Methods

1.2.1 Mode1s

The offline simulations considered in this study are performed over a domain covering

the Montreal island at 0.0025° (-250 m) resolution (Figure 1.1), using the Canadian

Land Surface Scheme (CLASS) [Verseghy, 1991; Verseghy et al., 1993]. In the

standard configuration of CLASS, urban fractions are considered as bare soi1 with

reduced perrneability. CLASS simulations with improved representation of urban

regions use the TEB urban mode! (Masson, 2000). Details of these models are

discussed below.

1.2.1.1 Canadian Land Surface Scheme (CLASS)

CLASS di vides the land fraction of each grid cell into a maximum of four sub-areas:

bare soi!, vegetation, snow over bare soi! and snow with vegetation. CLASS recognizes

four main vegetation categories: needleleaf trees, broadleaf trees, crops and grasses. It

considers spatially varying structural attributes and physiological properties for each vegetation type. Vegetation is static, but seasonal variations are considered as variations in the Leaf Area Index (LAI). Using a pseudomosaic approach, it computes, for each sub-area, the radiative and turbulent heat and moisture fluxes by integrating

the energy and water balances of the land surface. The hydrological budget is calculated only for the layers above the bedrock, which vary with gridcell. Urban regions are treated as bare soi!. In this study CLASS is configured with 16 layers reaching a total

depth of 10 m.

1.2.1.2 Town Energy Balance (TEB)

TEB is a physically-based scheme for urban energy budget based on a generalization

of local canyon geometry (Masson, 2000). It is a single-layer urban canopy mode!.

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- -- - - -- - - -- - -- - -- -

-24 structure. The length of the roads are considered to be far grea ter than the ir widths and any road orientation is possible, and ali exist with the same probability. The buildings are assumed to have the same height and width. The urban morphology in TEB is defined in terrns of the building and road fractions, building height, building and canyon aspect ratio and roughness length. The radiative characteristics such as albedos and emissivity for roofs, roads and walls and thermal properties such as thermal conductivity and heat capacity of each roof, road and wall layers are required fields.

1.2.2 Methodology

The aim of the study is to assess UHI evolution, and the impact of urban regions on projected changes to near-surface climate, including temperature extremes, for the Montreal region. To this end, land surface simulations, with and without an urban model/parameterization, are performed, for both current and future climates. These two mode) configurations will be referred to as CLASS and CLASS+TEB, respectively,

hereafter. Details of the simulations are provided below. The analysis presented in this paper focuses on summer (JJA) and fall (SON) seasons as the UHI intensity is higher during these seasons.

For mode) validation, CLASS and CLASS+TEB simulations spanning the current 1981-2010 period are performed over a domain covering the island of Montreal (Fig. 1.1) at 250 rn resolution, driven by the Canadian Regional Climate Mode) (CRCM5) simulation outputs at 0.11

°,

which in turn is driven by ERA-Interim. This driving data will be referred to as CRCM5-ERA hereafter. We decided to use CRCM5-ERA as the driving data as no other high-resolution dataset with the atmospheric fields required to run CLASS and CLASS+TEB were available. To assess projected changes, both CLASS and CLASS+ TEB simulations are perfom1ed for the current 1981-2100 and future 2071-2100 periods over the same domain, driven by CRCM5 simulation, which is driven by the Canadian Earth System Mode) (CanESM2) at the lateral boundaries.

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corresponds to RCP 8.5 scenario. Projected changes are assessed by comparing the future 2071-2100 period with the current 1981-2010 period. Table 1.1 provides a

summary of experiments performed.

The geophysical fields required for setting up the simulations are obtained at 0.0025°

resolution from the Climate Change Initiative Land Cover (CCI LC) database for land-mask and vegetation, Canadian Digital Elevation Data 1 :250000 and NASA Shuttle Radar Topography Mission (SRTM) v3.0 datasets for topography, Global Soi! Dataset

for Earth System Modeling (GSDE) for soi! characteristics and Canadian Vector Data

(CanVec) v.9.0 dataset for urban characteristics.

Comparing CLASS+ TEB and CLASS simulations, particularly surface temperature,

fluxes and temperature extremes su ch as hot da ys, will help assess the impact of urban

regions on near-surface climate. On the other band, comparison between the urban and

non-urban fractions of a given grid cell in CLASS+ TEB will help quantify the difference between these fractions due to the difference in surface characteristics alone

as these are offline simulations and do not mode! temperature advection.

The UHI, in this study, is defined based on the surface and 2m air temperatures, and are referred to as surface UHI and near-surface UHI, respectively, hereafter. The UHI based on CLASS+TEB simulation is the difference in mean surface or 2m temperature for grid cells with higher urban fractions (i.e., higher than 50%) and those located

outside of the main city with generally reduced urban fractions (i.e., Jess than 1 %). Furthermore, a hot day in this study is defined as a day with temperature above the 90th

percentile threshold of the respective grid-cell and day of the year.

Validation of the oftline simulations is done by comparing CRCMS-ERA simulated

land surface temperature (LST) with MODIS dataset, which is available at 0.05° over North America (Wan et al., 2015) for the 2001-2010 period. The datais available for

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26 12h and 23h (local time) over the study domain. MODIS LST is interpolated to the current grid at 0.002S0

using bilinear interpolation. We also validate the mean daily summer and fall 2m temperatures with Daymet and ANUSPLIN datasets. The gridded ANUSPLIN daily 2m temperature dataset (Hopkinson et al., 2011) covers the Canadian land-mass south of60°N. lt is available at 0.1 o resolution and is interpolated from daily observations at Environment Canada climate stations, using a thin plate smoothing spline surface fitting method (Hutchinson et al., 2009). The Daymet gridded daily dataset (Thorn ton et al., 20 17) covers North America, notth of 2S0

parallel, and is available at 0.01 o resolution. lt is generated by applying a truncated Gaussian fil ter from daily observations from NOAA National Centers for Environmental Information's Global Historical Climatology Network (NOAA-GHCN) and the

Servicio Meteoro/6gico Nacional of Mexico (Thomton et al., 1997).

1.3 Mode! validation and analysis in current climate

Figure 1.2a shows the spatial distribution of LST for MODIS, CLASS+TEB and CLASS simulations at 12h and 23h local time. The surface urban heat island effect can be clearly seen in the MODIS dataset, with a temperature difference of up to

s

o

c

between the urban and the surrounding non-urban areas in summer (JJA) at 12h. This difference is much reduced for 23h. The patterns are very similar for fall (SON). However, the differences in temperature between the urban centre and surrounding non-urban regions are 3°C and 2°C respectively for 12h and 23h.

The CLASS+TEB simulation captures the surface UHI effect as in MODIS for summer and fall (second row of Fig. 1.2a). More spatial structure is visible in the simulation due to the higher resolution used here. However, the simulated LSTs are warmer than for MODIS by up to

s

o

c.

This could be partly explained by the small positive temperature bias in the driving CRCMS-ERA data. Furthermore, it must be noted that, since Montreal island is surrounded by water bodies, LST interpolations of MODIS

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data might bring in sorne errors, particularly near the shoreline. CLASS simulation

(bottom row of Fig. 1.2a) fails to capture the surface UHI effect due to the absence of

good urban representation.

To understand better the diurnal cycle of the UHI, the mean diurnal cycle of the urban and non-urban surface temperatures and UHI are plotted for summer and fall in Fig. 1.2b. The diurnal variation of surface UHI in summer is in the 0.5 to 11

o

c

range, with maximum UHI occurring close to 14h. It is interesting to note that the maximum temperature for the non-urban region is delayed compared to that for the urban region. This could be due to many reasons, including the high rate of evaporation from rural

regions close to 12h (figure not shawn), which slightly cools the surface and also

possibly due to the high heat capacity of the sail matrix with its Iiquid water content.

The features are very similar for fall, however, with a very much attenuated diurnal

UHI cycle varying between 0 and 4.5°C.

Figure 1.3a shows the mean daily 2m temperature for Daymet, ANUSPLIN and

CLASS+ TEB simulation. The 2m temperature in CLASS+ TEB is higher by about

2.5°C than that of bath datasets. The near-surface UHI is clearly visible in the

CLASS+ TEB simulation, but to a lesser extent in the datasets. The coarser resolution

of Daymet and ANUSPLIN compared to the simulation, along with the fewer number

of stations considered in the generation of these datasets, particularly over the study

domain, explain the Jack of details. The datasets therefore may not be providing

representative values at the 250 rn resolution of the simulation making the validation

difficult.

To understand the contribution of urban fractions to the mean gridded temperature

values, Figure 1.3b shows the surface temperature differences between the urban and

non-urban fractions for grid cells with more than 50% urban fraction for CLASS+TEB

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to 4

o

c.

The differences between the two seasons is primarily due to the seasonal

differences in solar radiation received at the surface and also due to the presence of snow on the ground during Iate faii. As mentioned earlier, since the simulations

considered here are offline, there is no temperature advection, and therefore these differences are entirely due to the differences in surface properties. In a coupled simulation these differences between the urban and non-urban fractions of a grid-cell

might be significantly reduced.

Figure 1.3c shows the differences in the 2m air temperature between CLASS+ TEB and

CLASS simulations, which reflects the aggregated effect of urban regions on the

grid-averaged 2m air temperature. The differences are higher where the urban fractions are

higher (cf. Fig. 1.1 a). The differences are even higher where the pavement fraction is higher than the building fraction since pavements have Iower albedo values than buildings. The differences are generaiiy Jess than 0.5°C for grid ceiis with urban

fractions Jess than 30%.

We now look at the differences in turbulent fluxes between the urban and non-urban

fractions of a grid celland also between the CLASS+TEB and CLASS only simulations,

which reflect the aggregated effect of urban regions on the considered fluxes. To

explain the seasonal differences, we start by Iooking at the driving CRCM5-ERA mean precipitation over the domain for the 1981-2010 period (Fig. 1.4a). For summer,

average precipitation over the domain is 3.75 mm/day, with higher values to the

north-west and south-east of the island. For faii, precipitation bas the same pattern, with an average value of 3.55 mm/day.

Figure 1.4b shows the sensible and latent heat flux differences between the urban and

non urban fractions of a grid cell in the CLASS+ TEB simulation, while Figure 1.4c

shows the same, but between the aggregated grid cell values from CLASS+TEB and CLASS simulations. In Fig. 1.4b, the sensible beat fluxes, as expected, are higher for

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the urban fractions than the non-urban fractions, as reflected in the positive values, for

both seasons for CLASS+TEB simulation. Differences of up to 100 W/m2 are noted

for summer, while for fall, the differences reach up to 40 W /m2. Latent heat fluxes are

lower for the urban fraction than the non-urban fractions, which can be explained by the impervious nature of urban fractions, which leads to reduced water retention and

therefore evaporation. The differences are in the -40 to -100 W /m2 range for summer.

The magnitude of the differences are reduced in fall, generally in the -20 to -40 W /m2

range, since there is Jess energy available for evaporation and also Jess precipitation. These are in agreement with the higher LST values for the urban fractions compared to

the non-urban fractions of the grid cells shown in Fig. 1.3b. The differences in the aggregated values of sensible heat fluxes between CLASS+TEB and CLASS simulations are in the 40 to 80 W/m2 range for highly urbanized regions, and diminish

towards the city outskirts due to reduced urban fractions for both summer and fall. For latent heat flux it is in the, -60 to -80 W/m2 range in summer. The magnitude of the

differences is slightly reduced in fall. It is in the -20 to -40 W/m2 range.

Figure 1.4d shows the differences in the grid cell leve! aggregated values of total surface runoff between CLASS+ TEB and CLASS simulations. As expected, surface

runoff is higher in CLASS+ TEB simulation, where urban fractions are higher, by up to

3 mm/day. The surface runoff differences are higher in fall than in summer. This is because surface runoff is reduced for non-urban fractions due to higher infiltration associated with reduced precipitation, while for urban regions the small differences in precipitation from summer to fall have very little effect on surface runoff.

Figure 1.5 shows the differences in the number of hot days derived from CLASS+TEB and CLASS simulations based on the 90th percentile from CLASS only simulation for summer and fall. We can see here that the differences in the number of hot days due to

the urban effect can be as high as 15 days in summer and 7 days in fall over high urban

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others. Improper or Jack of representation of urban regions in climate and weather

models can lead to the underestimation of hot days and therefore hot spells/heat waves

which can have significant implications.

1.4 Projected changes

Before looking at projected changes for the period 2071-2100 with respect to the

1981-2010 period, it is useful to compare the CLASS-TEB and CLASS simulations driven by CRCM5-CanESM2 with MODIS dataset (Fig. 1.6a) for current climate, to see the

impact of the new driving data on the surface UHI. Figure 1.6a shows that the

CLASS+TEB simulation captures the UHI spatial patterns. However, the temperatures

are higher than that for CRCM5-ERA driven simulations and is due to the higher

positive bias in CRCM5-CanESM2 (Dira et al., 20 17). Figure 1.6b shows the mean

diurnal cycle ofLST for CLASS+TEB simulation driven by CRCM5-CanESM2. The

general pattern, though very similar to that for CRCM5-ERA driven, has higher

temperature values, due to the higher positive bias discussed above.

We now look at the projected changes to the driving data before assessing projected

changes based on CLASS and CLASS+ TEB simulations. The projected changes to the

driving CRCM5-CanESM2 40m air temperature are in the 6 to 7°C range for summer

and 4 to 6°C in fall. For precipitation, the driving CRCM5-CanESM2 suggests an

increase of up to 0.1 mm/day over the who le domain in summer, and a decrease of up

to 0.25 mm/day in fall (figure not shown).

Projected changes to the land surface temperature for the future 2071-2100 period with

respect to the 1981-2010 period in the CLASS+ TEB simulation driven by CanESM2

is in the 5.5°- 7 .5°C range in summer, with the largest changes in the regions of hjgher

urban fractions (Fig. 1. 7), suggesting a small increase in the surface UHI intensity by

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for the non-urban regions, we look at the projected changes to the turbulent fluxes. As shown in Fig. 1.8, for summer, while projected changes to both sensible and latent heat fluxes are almost negligible for the urban fractions, the sensible heat flux is found to

decrease by about 4 W/m2 and latent heat flux to increase by about 11 W/m2 for the non-urban fractions, both ofwhich contribute to reduced surface temperatures for these regions compared with the central urban region. The increase in the latent heat flux for

the non-urban fractions could be explained by the increased precipitation in the driving data in future climate.

For fall, projected changes to sensible and latent heat fluxes for the urban regions suggest slight increase and decrease, respectively. For the non-urban fractions, both projected changes are positive, but Jess th an 5W /m2. Sorne decreases in latent heat flux can be noted for the nonurban fractions to the southwest of the domain, due to decrease in precipitation. The projected increase and decrease in sensible and latent heat fluxes Jead to slight increase in the surface UHI for fall over the high urban fraction regions

as shown in Fig. 1.7.

Analysis of the projected changes to the near-surface UHI, estimated based on the 2m

temperature show similar patterns as for the surface UHI, with even smaller changes to

UHI. Figure 1.9 shows the time series of mean 2m temperatures for urban and non-urban regions for current and future climates for summer and fall. The interannual

variability of the urban and non-urban temperatures appears higher in future climate compared to current climate. Figure 1. 9 demonstrates th at the overall temperature

increase for urban and non-urban regions is much larger than the increase in the UHI

effect. Lauweat et al. (20 15) in the ir study of 8 different cities located on three different

continents reported no appreciable changes to the UHI intensity in future climate.

We now look at the impact of urban regions on the projected changes to the number of

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temperature above the 90th percentile based on the CLASS simulation for current climate. For both seasons, projected changes to the number of hot days are higher for CLASS+TEB for regions within the city. The differences in the projected changes to the number of hot da ys between CLASS+ TEB and CLASS show cl earl y the impact of urban regions on projected changes to the number of hot days, which is around 5-8 days in summer and 2-5 days in fall. Results suggest that urban regions modulate the frequency of occurrence of temperature extremes in future climate.

1.5 Summary and conclusion

This study focussed on the UHI for the Montreal island in current and future climates using offline land surface mode! simulations with and without improved parameterization for urban fractions. The impact of urban regions on temperature extremes was also considered in this study. The offline simulations driven by the Canadian Regional Climate Mode! driven by ERA-Interim were compared against the MO DIS dataset, by comparing the land surface temperatures for the 2001-2010 period.

The simulation with improved representation ofurban regions (CLASS+TEB) captures weil the surface UHI compared to the CLASS only simulation. Comparison of the 2m temperature values in CLASS+TEB with available datasets such as Daymet and ANUSPLIN demonstrated that the mode! is warrner compared with both datasets. lt could be that the model-simulated temperatures are more realistic compared to the observed datasets as not many stations were considered over the study region while producing these datasets. This highlights the need for more station observations and/or high-resolution gridded datasets to support proper mode! validation.

Projected changes to the UHI is assessed by comparing the future 2071-2100 period with the current 1981-2010 period of the CLASS+ TEB simulation, driven by CRCM5 driven by CanESM2 for RCP8.5 scenario. Though large increases in the surface temperatures are noted for the urban and non-urban regions, small increases to the UHI

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intensity are noted in future climate, for both summer and fall. The small increase in

surface UHI for summer is primarily due to slightly lower increase in surface

temperature for the non-urban regions, which is associated with projected decreases in

sensible beat flux and increases in latent beat flux due to increase in precipitation. Furthermore, the increase in surface UHI for fall is due to the projected increase in

sensible beat flux and decrease in latent beat flux for the urban regions. The projected

increase in surface UHI is smaller for fall compared with summer. The impact of urban

regions on projected changes to the number of hot days were assessed by comparing

projected changes to the number of hot days in CLASS and CLASS+TEB simulations.

Results suggest differences on the order of 5-8 days for summer and 2-5 days for fall,

highlighting the need for improved representation of urban regions in climate mode! s.

The simulations considered here did not take into account future urban growth, which may enhance the UHI in future climate. As discussed in Oleson et al. (2012), the UHI

intensity is sensitive to the height to width ratio of buildings, which is one measure of

urban density. More comprehensive study using fully coupled climate mode!

simulations are required to simulate the two-way feedbacks between land and urban

surface and atmosphere. Canada-wide studies of changes to hot spell characteristics

( e.g. Jeong et al. 20 16) suggest spatial differences in changes to characteristics of

temperature extremes. To better understand the changes at city-scale, it is important to

perform high-resolution climate simulations focussed on various Canadian cities using

a fully coupled system as discussed above. Furthermore, a multi-model ensemble

involving severa! high resolution regional climate models driven by different GCMs

for different RCP scenarios are required to better quantify the uncertainties.

Nevertheless, this study provides useful information on the impact of urban regions on

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CONCLUSION

L'objectif principal de cette étude était d'analyser le phénomène d'îlot de chaleur urbain dans Je cas de l'île de Montréal dans le climat courant et futur en utilisant un modèle de surface avec et sans paramétrisation de la surface urbaine. Plusieurs étapes ont été réalisées afin d'arriver à ce but. En tout premier lieu, une attention toute particulière lors de cette étude a été portée dans 1 'implémentation de TEB dans Je Surface Prediction System. Afin de vérifier si l'ajout d'une meilleure paramétrisation urbaine permet de mieux reproduire l'effet d'îlots de chaleur urbains dans le cas de la ville de Montréal, des simulations hors-ligne ont été faites avec CLASS+TEB et CLASS à 0.0025° (250 rn) de résolution. Comme CLASS, un modèle de surface de deuxième génération considère la surface urbaine comme étant du sol nu, l'ajout d'un modèle de surface urbaine devrait en principe permettre d'obtenir de meilleurs résultats et ce spécialement à des résolutions très fines dues au fait que les caractéristiques de la surface urbaine peuvent énormément varier énormément dans 1' espace.

Quelques embûches ont été rencontrées dans la validation des sorties du modèle. Bien que cette étude se concentre sur des simulations hors-ligne, des données de forçages ont été utilisées. Pour la validation des sorties du modèle, une simulation à 0.11 o du MRCC5 elle-même forcée parERA-Interim à 0.75° a été utilisée. Lors de l'analyse de cette simulation, il a été remarqué que celle-ci avait un biais positif de température ce qui est confirmé par Lucas-Picher et al. (2016). Afin d'atténuer l'impact du traitement de la surface urbaine par CLASS dans Je MRCC5, il a été choisi de forcer CLASS et TEB par le plus bas niveau pronostique du modèle. Par la suite, les sorties de la température de surface de CLASS+TEB et de CLASS ont été comparés avec les données satellitaires de MODIS à 0.05° (Wan et al, 2015) pour la période 2001-2010.

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Bien qu'ayant un biais positif de température, il a été montré que CLASS+ TEB reproduit mieux 1' effet d'îlot de chaleur urbain de surface que CLASS seul. Par la suite, les sorties de la température à 2m de CLASS+ TEB ont été comparées avec les ensembles de données d'observations Daymet et ANUSPLIN à 1 et 10 km de résolution respectivement. Bien que ces ensembles de données montrent un effet d'îlots de chaleur urbain, les températures de ces dernières étaient plus basses que les moyennes climatologiques obtenues avec CLASS+TEB. Cela peut s'expliquer par plusieurs facteurs. Premièrement, l'advection de température et plusieurs autres phénomènes ne sont pas considérés dans les simulations hors-ligne. Deuxièmement, les données d'observations utilisées dans ces ensembles de données viennent de stations météorologiques. Or, l'emplacement de ces stations météorologiques que ce soit dans un aéroport où à l'extérieur fait l'objet de recommandation quant à leur emplacement comme la distance par rapport à des édifices afin d'éliminer tout biais possible (Peterson, 2003). Toutefois, dans le contexte actuel, c'est en quelque sorte ce biais issu des villes qui est recherché. Troisièmement, ces mêmes stations sont peu nombreuses sur 1 'île de Montréal et leurs emplacements ont varié durant la période considérée dans la présente étude. Il a été déterminé que ces données d'observations ne pouvaient pas être utilisées comme validation. Comme, cette étude se concentre sur des simulations climatiques, il aurait été impossible de mettre en place des points d'observations comme dans Lemonsu et al. (20 1 0) où divers points de mesures avaient été installées pour une période de quelques jours.

Par la suite, les changements futurs de 1 'effet d'îlots de chaleur urbains à Montréal ont été analysés en comparant deux séries de simulations avec CLASS+TEB et CLASS, forcées par le MRCCS à 0.11 °, forcé par le modèle global CanESM2, pour le scénario RCP8.5 pour le climat futur (2071-2100) et le climat présent (1981-2010). Il a été trouvé que bien qu'il y ait une augmentation significative de la température de la surface et celle à 2m associée à une augmentation uniforme du rayonnement infrarouge descendant, l'intensité de 1 'effet d'îlots de chaleur urbains n'augmente que très peu. En

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36

été, cette augmentation minime de l'intensité de l'ICU de surface s'explique par un

réchauffement des régions non urbaines légèrement moins important que celui des régions urbaines dues à la réaction des régions non urbaines à une augmentation de la précipitation associée à une augmentation du flux de chaleur latente ainsi gu 'une baisse du flux de chaleur sensible. En automne, l'augmentation de l'ICU de surface est due à une augmentation projetée du flux de chaleur sensible et une baisse du flux de chaleur

latente associée à une baisse de précipitation. Par la suite, l'augmentation projetée du nombre de jours chauds dû à l'urbanisation a été investiguée en se basant sur la

simulation contrôle avec CLASS seulement pour le climat courant en se basant sur le

90e pourcentile de température moyenne quotidienne à 2m. Pour arriver à cette fin,

l'augmentation projetée du nombre de jours chauds par CLASS+TEB a été comparée à celle prévue par CLASS seulement. La différence entre les deux indique donc l'impact de l'urbanisation sur le nombre de jours chauds projetés. Les résultats indiquent donc un impact de 5-8 jours en été et de 2-5 jours en automne ce qui pourrait

avoir un impact important sur le stress dû à la chaleur.

Les simulations utilisées dans les présentes études n'ont pas pris en compte divers phénomènes comme l'augmentation dans l'espace occupé par la ville. Cependant,

celles-ci indiquent une nécessité d'inclure une modélisation urbaine dans les modèles

climatiques. Bien que les résultats indiquent que 1' intensité de 1 'ICU ne change que très

peu, la température dans les villes continuera évidemment d'augmenter ce qui pourrait

avoir des conséquences sur la santé publique comme une hausse de la mortalité lors des périodes de grandes chaleurs. Cela démontre d'un grand besoin en simulation

urbaine à résolution fine. Toutefois, des simulations couplées sont évidemment

nécessaires afin d'évaluer 1' impact des régions urbaines sur les changements projetés du climat.

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FIGURES

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(a) Pavement fraction

(c) Total urban fraction

0 20 40 60 (b) Building fraction 80 % 100 38

Figure 1.1. (a) Total urban fraction, (b) building fraction, and (c) pavement fraction at

0.0025° resolution for the en tire experimental domain covering the island of Montreal.

Values are shown only for grid cells with urban fraction above or equal to 50%. Water

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MODIS CLASS+TEB CLASS (a) LST comparison 12h 10 15 JJA 20 23h ' L - .. . .

. •

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yi

~~

-

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.

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..

oc

25 30 35 SON 12h

,-

·

"'

1

~·'v'

~.

5 10 (b) Diurnal cycle 40

Mean LST urban 12 40 - Mean LST urban

û - Mean LST non urban

2

35 - Mean LST non urban ~ 35 - Surface UHI • 10 - Surface UHI

1"

1

~ . 8 9 3 l'? 30 Cl) E-25 6 Ï ::;) E-25 Cl) ~ Cl) :;; 20' 4 ~ 20

~

151 "' t: 2 Vl ::> ~ 15 '0 '0 § 10 0 :5 10 ..J 5

-

-2 5

-0 4 8 12 16 20 24 0 4 8 12

Local ume Local t1me

23h

o

c

15 12 10 8 9 6 Ï ::;) Cl) 4 u "' t: ::> 2 Vl 0 ·2 16 20 24

Figure 1.2. (a) Land surface temperatures from MODIS (top row), CLASS+TEB

(middle row) and CLASS (bottom row) for JJA (left two columns) and SON (right two

columns) at 12h and 23h (local ti me) for the 2001-2010 period. CLASS+ TEB and

CLASS simulations are driven by CRCM-ERA. (b) Diurnal cycle of the land surface

temperature (LST) for the urban (gridcells with urban fractions greater than 50%) and

nonurban (gridcells with urban fractions smaller than 1 %) regions and surface urban

heat island intensity (URT) for JJA and SON from CLASS+TEB simulation. The right

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--- -~-Daymet 1 km ANUSPLIN 10 km CLASS+TEB (250 m) <'1LST (urban-nonurban) CLASS+TEB JJA (a) ·c 23 22 5 22 21.5 21 205 20 19 5 19 ·c 23 225 22 21.5 21 205 20 195 19 ·c 23 22.5 22 21.5 21 20,5 20 19 5 19 (b) 7.5 6.5 4.5 - - - -- - --

-40

SON

·c 11 105 10 95 8 5 ·c 23 22 5 22 21 5 21 205 20 19 5 19 ·c 11 10.5 10 95 85 ·c 3 5 25 1 5 os

Figure 1.3. (a) Mean dai1y 2m temperature from Daymet (first row) and ANUSPLIN

(second row) observation datasets and CLASS+TEB simulation (third row) for the

1981-2010 period, (b) Land surface temperature differences between urban and non-urban fractions of the same gridcell for CLASS+ TEB simulation for the 1981-201 0 period. (c) Mean daily 2m temperature differences between CLASS+TEB and CLASS

simulations driven by CRCM5-ERA, for the 1981-2010 period. The left column

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.0-.T2m (CLASS+ TEB)-CLASS Figure 1.3. (continued) JJA ·c SON (c) ·c

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42 JJA

SON

(a) Precipitation mm/day 30

"

PR

37

-

-'

1

. \.;__:-

.

'-'· 35

(b) Turbulent fluxes (Urban-nonurban) wtm' 100 80 60

SHF

40 20 20 40

LHF

60 ·80 100 (c) Turbulent fluxes (CLASS+TEB)-CLASS wtm'

SHF

LHF

Figure 1.4. (a) Mean daily summer and fall precipitation from driving CRCM5-ERA

for the

1981

-

2010

period. Sensible (SHF) and latent heat (LHF) flux differences

between (b) urban and non urban fractions in CLASS+ TEB simulation, ( c)

CLASS+ TEB and CLASS simulations, for the

1981

-

2010

period. ( d) Differences in

surface runoff between CLASS+ TEB and CLASS simulations for summer and fa

li for

the

1981

-

2010

period. CLASS+TEB and CLASS simulations are driven by

(44)

JJA SON (d) Surface runoff(CLASS+TEB)-CLASS

Figure 1.4. (continued)

mm/day

"

l

"

(45)

44

JJA SON hot days 15 hot da:;s 6 10 5 4 3 5 2 1 0 0

Figure 1.5. Difference in the number of hot days between CLASS+TEB and CLASS simulations driven by CRCM5-ERA for the 1981-201 0 period based on the 90th

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(a) LST comparison JJA SON 12h 23h 12h 23h MO DIS CLASS+TEB CLASS

o

c

o

c

10 15 20 25 30 35 5 10 15 (b) Diurnal cycle 40

]- Mean LST urban 1 12 40 - Mean LST urban 12

G - Mean LST nonurban

!... 35 - Surface UHI - 10 û !... 35 -- Mean LSurface UHI ST nonurban 10 Cil ~ 30 ~ 3 30 . 8 9 8

t

~ "' Cil ài E-25 ·6 Ï E-25 6 Ï :::> :::> Cil ~ Cil ~ ~ 20 ·4 ~ 20 4 u "' u "' ~ 15 't: "' 't: •2 "' ::> ~ 15 2 "' ::> "0 "0 po 0 § 10 0 ...J 5 ·2 5 ·2 0 4 8 12 16 20 24 0 4 8 12 16 20 24

Local time Local t1me

Figure 1.6. (a) Land surface temperatures from MODIS (top row), CLASS+TEB (middle row) and CLASS (bottom row) for JJA (left two columns) and SON (right two columns) at 12h and 23h (local time) for the 2001-2010 period. CLASS+TEB and CLASS simulations are driven by CRCM5-CanESM2. (b) Diurnal cycle of the land surface temperature (LST) for the urban (gridcells with urban fractions greater than 50%) and nonurban (gridcells with urban fractions smaller than 1 %) regions and surface urban heat island intensity (UHI) for JJA and SON from CLASS+ TEB simulation. The right Y-axis conesponds to surface UHI.

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46

JJA SON

Current

Future

Future-Current

Figure 1 . 7. Land surface temperatures for the current 1981-201 0 (first row) and future

2071-2100 (second row) periods and their projected changes (third row) for summer

(left column) and fall (right column) for CLASS+TEB simulations driven

by

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JJA SON

Urban fraction Nonurban fraction Urban fraction Nonurban fraction

6SHF

L1LHF

-10 -5 0 5 10 15 20

Figure 1.8. Projected changes to sensible (L1SHF) and latent (L1LHF) heat fluxes for the urban fractions (first column) and non-urban fractions (second colurnn) for summer (JJA) and fa li (SON), for the period 2071-21 00 in comparison to 1981-21 00, based on CLASS+TEB simulations driven by CRCM5-CanESM2.

Figure

Figure  1.1.  (a)  Total  urban  fraction,  (b)  building  fraction,  and (c)  pavement  fraction  at
Figure  1.2 .  (a)  Land  surface  temperatures  from  MODIS  (top  row) ,  CLASS + TEB
Figure  1 . 3.  (a)  Mean  dai1y  2m  temperature  from  Daymet  (first  row)  and  ANUSPLIN
Figure  1.4 . (a)  Mean  daily  summer and  fall  precipitation  from  driving  CRCM5-ERA
+7

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