Adolfo Chamorro

Keywords: Modélisation atmosphérique régionale, Downscaling dynamique, Vent côtier, El Niño, Changement climatique Coastal winds dynamics in the Peruvian upwelling Regional atmospheric modelling, Dynamical downscaling, Coastal wind, El Niño, Climate change

This thesis work is dedicated to my wife, Evelyn, who has been a constant source of support and encouragement during academic challenges and of the life. This work is also dedicated to my parents, Vicente and Francisca, and to my sisters (Yeni, Tatiana, Enma, Selma

Chapter 1 Introduction 1.1 The Peruvian upwelling system

Upwelling systems are regions of the ocean where cooler and nutrient rich waters from the deep ocean are transported to the surface under the action of surface winds. The nutrient rich upwelled water stimulates phytoplankton blooms that create areas with high fisheries resources [START_REF] Ryther | Photosynthesis and fish production in the sea[END_REF]; [START_REF] Cushing | Plankton production and year class strength in fish populations: an update of the match/mismatch hypothesis[END_REF] on one hand and, on the other hand, it has a strong influence upon the regional climate due to the presence of cool surface waters [START_REF] Mechoso | Ocean-cloud-atmosphere-land interactions in the southeastern pacific[END_REF][START_REF] Bretherton | The epic 2001 stratocumulus study[END_REF].

Regions of upwelling are located in equatorial and coastal regions of the ocean. The upwelling systems in the eastern boundaries of the oceans, also known as Eastern Boundary Upwelling Systems (EBUS), are the most productive regions of the world ocean due to their important coastal biological productivity which support large commercial fisheries, up to 20% of the global fish catch [START_REF] Pauly | Primary production required to sustain global fisheries[END_REF]. The four main EBUS are the Benguela upwelling system situated off southwestern Africa, the Canary upwelling system off northwestern Africa, the California upwelling system off western North America, and the Humboldt upwelling system off western South America. Due to the biomass of phytoplankton in these regions, upwelling zones can be identified by high concentrations of Chlorophyll-a (Fig. 1.1). Fig. 1.1 Chlorophyll-a concentration from SeaWIFS satellite for the 1998-2007 period and EBUS location [START_REF] Fréon | Eastern Boundary Upwelling Ecosystems: Integrative and comparative approaches[END_REF].

Chapter 1. Introduction

The Peruvian upwelling system is part of the Humboldt upwelling system and it is located aproximately between 5 • S and 20 • S along the coast of Peru. It is the most productive region among the EBUS [START_REF] Chavez | The northern Humboldt Current System: Brief history, present status and a view towards the future[END_REF]. It is of fundamental importance because of its immense fishery resources which support a large part of the regional economy (∼ 10% of the world's fish catch; FAO Fisheries and Aquaculture Statistics, http://www.fao.org/fishery/statistics/).

Upwelling regions are located on the eastern flanks of the large-scale subtropical anticyclones, where winds parallel to the coast directed towards the equator induce the upwelling processes. In particular, the Peruvian upwelling system is located on the eastern flank of the South Pacific Anticyclone (SPA) and the upwelling is induced by southerly alongshore winds associated with the southeast trade winds (Fig. 1.2).

Regional climatic features

The southeast Pacific climate system, where Peruvian upwelling system is located, together with strong ocean coastal upwelling, is characterized by an extensive stratocumulus deck at the top of the marine boundary layer and the longest continental mountain chain in the world named the Andes Cordillera (Fig. 1.3). The stratocumulus deck plays an important role in the regional climate by reflecting incoming solar radiation. The Andes Cordillera reaches altitudes above 4 km, it is located along the west coast of the continent from equator to about 40 • and it represents an obstacle for tropospheric flow [START_REF] Richter | Orographic influences on subtropical stratocumulus[END_REF][START_REF] Takahashi | Processes controlling the mean tropical pacific precipitation pattern. Part I: The Andes and the eastern Pacific ITCZ[END_REF].

Over the tropical ocean at approximately 8 • N, the Intertropical Convergence Zone (ITCZ) is located where the northeast and southeast trade winds converge and warm air rises creating a low pressure zone. The rising air produces high cloudiness, frequent The subtropical subsidence over the southeast Pacific ocean produces a temperature inversion in the PBL [START_REF] Bretherton | Southeast pacific stratocumulus clouds, precipitation and boundary layer structure sampled along 20s during VOCALS-REx[END_REF] several meters above the surface. In a temperature inversion, a warm air layer (of ∼600 m thick at 30 • S) is trapped between cooler layers above and below. The PBL is often topped by a shallow layer of stratocumulus cloud. In fact, the SEP is covered by the largest and most persistent deck of stratocumulus in the world [START_REF] Klein | Expansion of the Headley cell under global warming[END_REF].

A special aspect of the planetary boundary layer is its turbulent nature, which has an effect on the vertical profiles of wind, temperature and moisture within the boundary layer.

b) The oceanic boundary layer

It is the upper layer of the ocean. In the coastal region, upwelling is the main physical process, which is primarily a response of the ocean to the atmospheric forcing. In the Peruvian upwelling system, it is sustained by a permanent moderate wind blowing equatorward. The alongshore wind pushes the surface water away from the coast inducing the rise of cold water to the surface. The wind-driven oceanic flow is confined to the surface in the Ekman layer. In the Ekman layer the frictional force is balanced by the Coriolis force. The net horizontal motion averaged in depth is perpendicular to the wind and is referred as the Ekman transport. The Ekman transport is to the left of the wind in the Southern Hemisphere. On the other hand, the wind intensity, increasing offshore, creates divergence of the Ekman transport which also drives vertical motions and enhances the upwelling. This process is known as Ekman pumping. The profile of the wind decrease towards the coast [START_REF] Capet | Upwelling response to coastal wind profiles[END_REF], called wind drop-off, is affected by orography and air-sea interactions [START_REF] Boé | What shapes mesoscale wind anomalies in coastal upwelling zones?[END_REF][START_REF] Renault | Orographic shaping of us west coast wind profiles during the upwelling season[END_REF], but it has not been extensively studied for the Peru region. (see red arrows in Fig. 1.4). Zonal and meridional compo-nents of Ekman transport T x and T y , and Ekman pumping W Ek can be computed using the following definitions:

T x = τ y ρf T y = - τ x ρf and W ek = 1 ρf ∂τ y ∂x - ∂τ x ∂y
where τ x and τ y are the zonal and meridional components of the surface wind stress, ρ is the seawater density and f is the Coriolis parameter.

The El Niño -Southern Oscillation (ENSO) 1.2.1 Large scale characteristics

The ENSO is a irregular oscillation of the ocean-atmosphere climate system that occurs across the equatorial Pacific ocean with a period of 2 to 7 years, caused by recurring redistributions of heat and atmospheric momentum [START_REF] Mcphaden | Genesis and Evolution of the 1997-98 El Niño[END_REF]. ENSO perturbs the east-west atmospheric circulation pattern along the equator, known as the Walker Circulation. ENSO consists of a warm phase, El Niño, and a cold phase, La Niña. El Niño event, the warm phase of the ENSO, is characterized by unusually warm ocean temperatures in the central and eastern equatorial Pacific, higher sea-level pressures in the Western Pacific and weakening of the Trade Winds leading to a flattening of the thermocline associated with a depression of the thermocline in the eastern Pacific, and an elevation of the thermocline in the west (top right Fig. 1.5). According to the locations of sea surface temperature (SST) anomalies, El Niño can be divided into two types: eastern Pacific El Niño (EP El Niño) and central Pacific El Niño (CP El Niño) [START_REF] Capotondi | Understanding ENSO diversity[END_REF]; U.S. CLIVAR Report, 2013). La Niña, the cold phase of the ENSO which is the opposite of the El Niño, is characterized by stronger than normal trade winds over the Pacific ocean, and extensive cooling of the central and eastern Pacific ocean and the thermocline moves closer to the surface (bottom right Fig. 1.5).

The Southern Oscillation is the atmospheric component of ENSO. It involves a fluctuaction of the surface pressure accross the equatorial Pacific and occurs in concert with El Niño. They both rely on and modulate each other (Fig. 1.6). [START_REF] Bjerknes | Atmospheric teleconnections from the equatorial pacific[END_REF] showed that an anomalous positive difference of SST between the West and East Pacific enhances the easterly wind stress in the equatorial Pacific, which in turns amplifies the anomalous positive SST difference (the La Niña phase). The opposite conditions, an anomalous negative difference of SST between the West and East Pacific, would lead to El Niño. This positive feedback between atmosphere and ocean is called the Bjerknes feedback.

El Niño is characterized by anomalous westerly winds in the West-Central Pacific, which cause the generation of eastward propagating oceanic Kelvin waves and westwardmoving Rossby waves. Kelvin waves lead to the accumulation of warm water and the deepening of the thermocline in the eastern Pacific [START_REF] Wyrtki | El Niño-The Dynamic Response of the Equatorial Pacific Oceanto Atmospheric Forcing[END_REF]. Some scientists (Suarez

The impact of El Niño on the Peruvian upwelling system

A unique aspect of the Peruvian upwelling system is its proximity to the equator in the Eastern Pacific, which places it directly under the influence at interannual time scales of El Niño events.

During El Niño events, SST off the coast of Peru rises up strongly above normal (for example 3 • C during EN 1997EN -1998;;[START_REF] Picaut | Mechanisms of the 1997-1998 El Niño-La Niña, as inferred from space-based observations[END_REF]), the thermocline and nutricline deepen significantly which reduce the productivity [START_REF] Barber | Biological consequences of El Niño[END_REF]; Espinoza-Morriberón et al., 2017), while the upwelling favorable winds increase. Due to the deeper thermocline, upwelling brings up warm water with few nutrients, causing serious economic consequences like the reduction of the Peruvian anchoveta fisheries, populations of fish and seabirds (Fig. 1.7). In addition to the fishing industry, agriculture is another of the main sectors of the economy that is severely affected by the El Niño events due to heavy rains, floodings or extremely hot or cold weather [START_REF] Bayer | The 1997-1998 El Niño as an unforgettable phenomenon in northern Peru: a qualitative study[END_REF].

The observations showed that during El Niño events while on one hand the coastal wind is strongly increased [START_REF] Enfield | Thermally driven wind variability in the planetary Boundary layer above Lima, Peru[END_REF][START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF]Fig. 1.8), on the other hand, the upwelling of cool water is cut off by the poleward advection of tropical waters along the coast [START_REF] Wyrtki | El Niño-The Dynamic Response of the Equatorial Pacific Oceanto Atmospheric Forcing[END_REF][START_REF] Colas | 1997-1998 El Niño off Peru: A numerical study[END_REF]. Note that there is still upwelling during El Niño, but of warmer water because of the deepening of the thermocline. While the occurence of upwelling-favorable winds off the coast of Peru during El Niño events led to realize that coastal warming off Peru must be remotely forced by Kelvin waves [START_REF] Wyrtki | El Niño-The Dynamic Response of the Equatorial Pacific Oceanto Atmospheric Forcing[END_REF], the processes that drive the nearshore wind increase off Peru during El Niño remain unclear. Some studies suggested that the wind intensification off the coast of Peru during El Niño could be driven by the strengthening of the cross-shore pressure gradient owing to an enhanced cross-shore thermal contrast between land and sea, which should support a geostrophic wind [START_REF] Enfield | Thermally driven wind variability in the planetary Boundary layer above Lima, Peru[END_REF]; [START_REF] Bakun | The marine ecosystem off Peru: What are the secrets of its fishery productivity and what might its future hold?[END_REF]; Fig. 1.9a). On the other hand, [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF] suggested that anomalous SST gradients that appear along the Peruvian coasts (due to the fact that SST anomalies are largest in the North coast than in the south during El Niño) could drive a strengthening of the alongshore pressure gradient and so produce the wind acceleration (Fig. 1.9b), but the mechanisms were not studied. In addition, a warm SST anomalies could enhance the turbulent mixing in the PBL and increase the transfer of vertical momentum from upper layers to the ocean surface, causing surface wind intensification (e.

Global climate change

Climate change refers to the change in the mean state or the variability of the climate system over long periods of time (typically decades or longer), due to natural variability or as a result of human activity (IPCC AR4 WG1, 2007).

Greenhouse effect

Solar radiation that passes through the atmosphere warms the surface of the Earth and then some of the radiations go back into space. However, due to the presence of greenhouse gasses in the atmosphere (H 2 O, CO 2 , CH 4 , O 3 , etc.) a part of the outgoing radiation is trapped and reemitted in all directions. This so-called natural "greenhouse effect" balances the average temperature on Earth (Fig. 1.10). Without greenhouse gases, the average surface temperature would be very low (around -18 • C rather than the about 14.5 • C found today) to support life as we know it.

However, as greenhouse gases have increased rapidly in the last decades due to industrial activities, the natural greenhouse effect has intensified which have led to an unusually ever-rising average global temperature (Fig. 1.11).

Climate change scenarios

Climate change scenarios or socioeconomic scenarios are projections of future greenhouse gases (GHG) emissions used by analysts to assess future vulnerability to climate change [START_REF] Carter | Developing and applying scenarios. climate change 2001: Impacts, adaptation, and vulnerability[END_REF]. The scientific community, led by the Intergovernmental Panel on Climate Change (IPCC), has developed climate change scenarios which are based on possible

a) Idealized scenarios

These are simple scenarios based on the assumption of an progressive increase (1% yr-1) of the atmospheric CO 2 concentration until a doubling (2CO 2 scenario) or quadrupling (4CO 2 scenario) of its initial value [START_REF] Covey | An overview of results from the Coupled Model Intercomparison Project[END_REF]. The scenario of reference is called "preindustrial" scenario with CO 2 concentration fixed to its preindustrial level (280 ppm; Fig. 1.12).

b) Special Report on Emissions Scenarios (SRES) scenarios

These are a group of 40 scenarios developed by the IPCC in the year 2000. Each scenario is divided into one of four families (A1, A2, B1, B2), each with common themes (e.g: Population changes, energy sources, economic development, etc.). B1 scenario is the more optimistic and A2 is the more pesimistic. Political or legislative actions have no effect on the development of these scenarios, for example commitments made under the Kyoto protocol. The SRES scenarios were used in the IPCC Third Assessment Report (TAR), published in 2001, and in the IPCC Fourth Assessment Report (AR4), published in 2007 (Fig. 1.12).

c) Representative Concentration Pathways (RCPs) scenarios

The RCPs is a set of four new pathways developed for the climate modeling community as a basis for climate change modeling experiments [START_REF] Van Vuuren | The representative concentration pathways: An overview[END_REF]. It have been adopted by the IPCC for its fifth Assessment Report (AR5) in 2014 and it supersedes the SRES scenarios projections published in 2000. The four RCPs cover a wide range of possible radiative forcing (relative to pre-industrial values) found in the scientific literature published for the year 2100, i.e. from 2.6 to 8.5 W/m2. So, each RCP is based on possible emission scenarios already published. Total radiative forcing is determinated by positive forcing from greenhouse gases and negative forcing from aerosols, where the CO 2 forcing is the most dominant factor (Fig. 1.13).

Global climate change projections

The Coupled Model Intercomparison Project (CMIP) was established as a standard experimental protocol for studying the climate change simulations of different coupled atmosphere-ocean general circulation models (AOGCMs). The research based on the phase five of CMIP (CMIP5) dataset, which involve 20 climate modeling groups from around the world (with 65 different model configurations), provided much of the new material underlying the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5).

CMIP5 provide projections of future climate change on two time scales, the so-called near term (out to about 2035) and long term (out to 2100 and beyond). Results from CMIP5 models show that global surface temperature change for the end of the 21st century is likely to exceed 1.5 • C relative to 1850 to 1900 for all RCP scenarios except RCP2.6. It is likely to exceed 2 • C for RCP6.0 and RCP8.5, and more likely than not to exceed 2 • C for RCP4.5. Surface temperatures increase at a greater rate over land than ocean due to the larger evaporation and heat capacity of oceans waters. Precipitation spatial variability increases, with reduction in the subtropics and increase at high latitudes and low tropical levels (Fig. 1.14).

b) Regional modelling studies on other upwelling systems

There are few references about the impact of climate change on coastal wind in other upwelling systems. [START_REF] Miranda | Climate change and upwelling: Response of Iberian upwelling to atmospheric forcing in a regional climate scenario[END_REF] analized outputs from climate change atmospheric simulations (under the IPCC A2 scenario, and the periods 1961-1990 and 2071-2100) with the RACMO regional model at 50 km of horizontal resolution and found an intensification of upwelling favorable wind at three west Iberian capes (Finisterre, Carvoeiro and St. Vincent) of the northern part of the Canary upwelling system. They were associated with an increase of the land-sea thermal contrast and a decrease of the sea level pressure over land, in agreement with Bakun's hypothesis. [START_REF] Snyder | Future climate change and upwelling in the California Current[END_REF] used the RegCM2.5 regional climate atmospheric model (at 40 km of horizontal resolution) to estimate changes in wind-driven upwelling under increased CO 2 concentration (from 338-369 ppm to 635-686 ppm) in the California upwelling system. They compared the period 1980-1999 with CO 2 concentrations from 338 ppm to 369 ppm against the period 2080-2099 with CO 2 concentration from 635 ppm to 686 ppm. They found an increase of the upwelling-driven wind stress curl off northern California during warmest months (June-September) which produces an intensified upwelling season, and a temporal displacement of one month of the peak of the upwelling season. The land-ocean temperature gradient increased during summer months asociated with the intensification of the wind stress curl, in agreement with the hypothesis of [START_REF] Bakun | Global climate change and intensification of coastal ocean upwelling[END_REF].

Research questions

This thesis aims to improve the knowledge of the key mechanisms governing the coastal wind changes off the Peru coast during warming conditions, namely El Niño situation and future climate change.

Coastal winds during El Niño events

Coastal winds off Peru during El Niño events are usually upwelling favorable which contrasts with the observed SST warming. The physical processes producing these wind anomalies have not been studied so far. This thesis addressed this issue through the following questions:

• What are the main forces that induce the alongshore wind anomalies off Peru during El Niño events?

• What is the impact of anomalously warm surface ocean on the low atmosphere conditions during El Niño?

• What are the respective roles of the large scale and local SST forcing on the alongshore wind anomalies?

• What is the role of the South Pacific Anticyclone on alongshore winds off Peru during El Niño event?

• Are the same dynamical processes active during different El Niño events?

Research questions

Effects of climate change on coastal winds

The initial analysis of the Peruvian studies of the projected change for the upwelling favorable winds off Peru needs further refinement, and its results need to be confirmed by using more recent climate scenarios. This thesis addressed this issue through the following questions:

• What are the projected coastal wind changes in the Peruvian area by performing a dynamical downscaling of the multi model mean of an ensemble of global models?

• Are the projected coastal wind changes off Peru under older idealized scenarios different from those under the new scenarios RCP ?

• Are the projected coastal wind changes sensitive to the resolution of the regional model?

• What are the mechanisms responsible for possible projected wind changes? Are they the same as during El Niño events?

Chapter 2

Data and methods

In this chapter, we present the regional atmospheric model configured to address the research questions about El Niño processes and climate change in the Peru region. First, we describe the data used to build the lateral boundary and initial conditions, as well as the data obtained from observations used to calibrate and validate the model simulations.

Then, we provide a brief description of the model. And finally, we show the steps carried out in order to configurate the model and to improve the simulations in our region of interest.

Data

Global atmospheric reanalysis data

Atmospheric reanalysis data from three database were used to build the initial and lateral boundary conditions for our simulations:

CMIP5 Models

The outputs from thirty one (31) CMIP5 models, for the historical runs from 1989 to 2009 and RCP8.5 scenario from 2080 to 2100, were assembled for the climate change study. Information about each model is listed in Table 2.1. The variables used to build the boundary conditions and the initial state (humidity, air temperature, zonal and meridional wind component, atmospheric pressure and sea surface temperature) were interpolated to a common 1 2011)) in situ measurements of wind, temperature and air temperature were also used to validated the simulations. The data were collected during oceanographic campaigns that took place in the Southeast Pacific between October and November 2008. We used radiosonde data collected by 216 launches (with altitude increments of 10 m) along a zonal section at 20 • S.

Shortwave radiation at ocean surface from the International Satellite Cloud Climatology Project (ISCCP; Zhang et al. ( 2004)) onto 1 • × 1 • and daily resolution used by the OAFlux project (http://oaflux.whoi.edu) were used to evaluate the model solution heat fluxes and cloud cover. Data sets of air specific humidity at surface were also obtained from the OAFLUX project.

The Weather Research and Forecasting (WRF) model

The Weather Research and Forecasting (WRF) model is a numerical weather prediction (NWP) and atmospheric simulation system designed for both research and operational applications [START_REF] Skamarock | A Description of the Advanced Research WRF Version 3[END_REF]. It is an open-source model which development is led by the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), the Air Force Weather Agency (AFWA), the Naval Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration (FAA). It is suitable for a broad span of applications including real-time NWP, data assimilation development and studies, parameterized-physics research, regional climate simulations, air quality modeling, atmosphere-ocean coupling, and idealized simulations.

Figure 2.1 shows the principal components of the WRF system. It has three main components, the WRF Pre-processing System (WPS), the WRF model and the Post-Processing & Vizualization component. WPS is used for setting initial and boundary conditions (i.e. simulation area, terrain, land use, soil type, etc) on the simulation domain using external data sources. The WRF model contains two dynamical solvers, referred to as the ARW (Advanced Research WRF) core and the NMM (Nonhydrostatic Mesoscale Model) core. The ARW core was used in our simulations. The dynamical core runs the model simulations with selected physical parameterizations, time step, etc. The Post-

Primitive equations

The so-called primitive equations are those that govern the evolution of the atmospheric motions. They consist of the following main sets of balance equations.

Equation of state:

p = ρR d T Conservation of mass: ∂ρ ∂t + ∂U ∂x + ∂V ∂y + ∂W ∂z = 0
Conservation of momemtum:

∂U ∂t = -U ∂U ∂x -V ∂U ∂y -W ∂U ∂z - 1 ρ ∂P ∂x + f V + U m ∂V ∂t = -U ∂V ∂x -V ∂V ∂y -W ∂V ∂z - 1 ρ ∂P ∂y -f U + V m ∂W ∂t = -U ∂W ∂x -V ∂W ∂y -W ∂W ∂z - 1 ρ ∂P ∂z -g + W m
Conservation of energy:

∂Θ ∂t + ∂U θ ∂x + ∂V θ ∂y + ∂W θ ∂z = ρQ
where U = ρu, V = ρv, W = ρw and Θ = ρθ. v = (u, v, w) are the velocity components in the (x,y,z) directions respectively, θ is the potential temperature, ρ is the air density, f is the Coriolis parameter, P is the atmospheric pressure, U m , V m , W m include vertical and horizontal diffusion, Q is the heat flow per unit of mass, R d is the gas constant for dry air, and T is the absolute temperature.

The WRF model solves the primitive equations written in flux form using a terrainfollowing hydrostatic pressure vertical coordinate, which is denoted by η and defined as:

η = (p h -p ht )/µ
where µ = p hs -p ht , p h is the hydrostatic component of the pressure, and p hs and p ht refers to values along the surface and top boundaries, respectively. η varies from a value of 1 at the surface to 0 at the upper boundary of the model domain (Fig 2 .2).

Model discretization a) Temporal discretization

The model uses a third order Runge Kutta (RK) time integration scheme (i.e. of three substeps) with a user-defined large time step ∆t, and with a small step ∆τ for acoustic and gravity wave modes.

Defining the prognostic variables in the WRF-ARW solver as Φ = (U, V, W, Θ, φ ′ , µ ′ , Q) and the model equations as Φ t = R(Φ), the Runge Kutta 3rd order time integration takes the form of 3 steps to advance a solution Φ(t) a Φ(t + ∆t):

Φ * = Φ t + ∆t 3 R(Φ t ) Φ * * = Φ t + ∆t 2 R(Φ * ) Φ t+∆t = Φ t + ∆tR(Φ * * ) b) Spatial discretization
The spatial discretization in the WRF-ARW solver (which is used for our simulations) uses the Arakawa C-grid staggering for the variables as shown in Fig. 2.3. That is, variables from WRF can be located at one of 4 possible points within each grid cell: at the center of the cell (θ-point), at the center of the left face (u-points), at the center of the front face (v-points), and at the center of the bottom face (w-points). Scalar variables (e.g. temperature, pressure, etc.) generally live at θ-points. One exception is the geopotential, which lives at w-points. This grid and variable definitions define the spatial discretization for the WRF-ARW solver.

Model configuration

We calibrated the WRF model version 3.3.1 in terms of the sensitivity to physical parameterizations and to the model domain configuration. It was used in our study about El Niño. For our study on climate change, we used the same physical parameterizations and a similar domain configuration nested in other bigger domain (see section 4.2). In addition, we also examined the sensitivity of the model results to the lateral boundary conditions from different databases.

On the other hand, we also present a set of modifications to the WRF code that was done to add some capabilities to the model, and the machine configuration used to run our simulations.

Sensitivity to physical parameterizations

As first step, the model performance with six differents set of physical parameterizations were tested on the domain shown in Fig.

.2 -Parameterization schemes for physical processes for each of WRF testing experiments

The following parameterizations were used:

• Shortwave radiation:

-The Dudhia (1989) scheme, a simple downward integration, allowing efficiently for clouds and clear-sky absorption and scattering.

-Goddard shortwave (Chou and Suarez, 1994), a two-stream multi-band scheme with ozone from climatology and cloud effects.

Chapter 2. Data and methods

-RRTMG shortwave [START_REF] Iacono | Radiative forcing by long-lived greenhouse gases: Calculations with the aer radiative transfer models[END_REF], a new shortwave scheme with the MCICA method of random cloud overlap.

• Planetary Boundary Layer:

-Yonsei University scheme (YSU; Hong and Lim ( 2006)), a non-local-K scheme with explicit entrainment layer and parabolic K profile in unstable mixed layer.

-Mellor-Yamada Nakanishi and Niino Level 2.5 PBL (MYNN2; Nakanishi and Niino ( 2009)), predicts sub-grid TKE terms.

-Total-Energy Mass Flux (TEMF; Angevine, Jiang and Mauriten ,2010) scheme, a sub-grid total energy prognostic variable, plus mass-flux type shallow convection.

• Surface Layer:

-MM5 similarity [START_REF] Zhang | A high-resolution model of the planetary boundary layer sensitivity tests and comparisons with SESAME -79 data[END_REF], based on Monin-Obukhov with Carslon-Boland viscous sub-layer and standard similarity functions from lookup tables.

-MYNN surface layer, Nakanishi and Niino PBL's surface layer scheme.

-TEMF surface layer, Total Energy-Mass Flux surface layer scheme.

• Longwave radiation: RRTM scheme [START_REF] Mlawer | Radiative transfer for inhomogeneous atmosphere: RRTM, a validated correlated-k model for the longwave[END_REF].

• Microphysics: WRF single-moment 6-class scheme [START_REF] Hong | The wrf single-moment 6-class microphysics scheme (wsm6)[END_REF]).

• Cumulus: Betts-Miller-Janjic scheme [START_REF] Janjic | The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes[END_REF].

• Land surface: Noah land surface model [START_REF] Chen | Coupling an advanced land surface hydrology model with the penn state-NCAR MM5 modeling system. Part II: Preliminary model validation[END_REF].

a) Evaluation of near-surface variables

Model-simulated winds, humidity and shortwave flux on the sea surface are validated against satellite observations. Results are presented for the six sets of physical parameterizations (see Table 2.2). Figure 2.5 shows the mean sea surface wind for the year 2008 from QuikSCAT and the WRF sensitivity experiments. All WRF experiments reproduce the regional pattern of the surface winds in agreement with QuikSCAT observations. They show that winds off the Peru coast are strongest offshore between 6-20 • S and blow predominantly from the northwest direction. They decrease in the nearshore region and are oriented alongshore. The WRF6 experiment shows a marked underestimation of the wind intensity almost everywhere. All the other WRF experiments show a wind speed positive bias nearshore off northern Chile and off the Peru coast (Fig. 2.6), showing that, the model does not reproduce properly the nearshore wind drop-off. It will be shown later (in section 4) that it is an issue partly related to the model horizontal resolution.

The model-simulated shortwave flux is examined. Note that surface meteorological variables and ocean surface dynamics are very sensitive to this variable. 160-180 W m 2 , located around 600 km offshore northern Chile and off the coast, north of the equator, respectively. Both minimums are reproduced by WRF1, WRF2, WRF3, WRF4 sensitivity experiments. WRF3 shows a large positive bias everywhere, but WRF5 and WRF6 are worse, since they are not able to reproduce the regional shortwave flux pattern.

Simulated humidity was also evaluated.

b) Impact of schemes on vertical profiles

Vertical profiles of wind, temperature and humidity from WRF1 and WRF4 experiments were compared with the VOCALS-REx observations. Figure 2.10 shows cross-shore vertical sections at 20 • S of the mean meridional and zonal wind and temperature of WRF1 and WRF4 sensitivity experiments and VOCALS-REx observations between 28 October and 3 November 2008. Both WRF experiments reproduce the zonal and meridional vertical structure of the winds in agreement with observations. They show that meridional winds flow southward at high levels (above ∼600 hPa) and northward near the sea surface, with a maximum intensity offshore at ∼900 hPa in observations and ∼950 hPa in the WRF experiments. The meridional wind changes its direction at height of ∼500 hPa at 85 • W both in model and observations. The height of the wind reversal decreases toward the shore reaching ∼960 hPa in observations and ∼890 hPa at 72 • W in WRF. The vertical structure of temperature shows a temperature inversion between ∼800-950 hPa both in observations and WRF (magenta contours in Figs. 2.10a,b,c). Zonal winds flow eastward at high levels (above ∼700 hPa) and westward near the sea surface. The height of the wind reversal decreases towards the coast, from ∼700 hPa at 85 • W (both observations and WRF experiments) to ∼950 hPa in observations and ∼880 hPa in WRF experiments at 73 • W.

Figure 2.11 shows the vertical profiles of mean potential temperature and humidity at 20 • S,85 • W from WRF1, WRF4 experiments and VOCALS-REx observations between 25 and 27 October 2008. Note that some additional profiles were also calculated for dates close to those of the measurements, which allow to obtain a representation of the high frequency variation of the simulated profile. Vertical profiles of potential temperature are Chapter 2. Data and methods well reproduced by both WRF experiments near surface and in upper layers. On the other hand, the vertical profile of specific humidity is sligthly better reproduced by WRF4, but it is significantly underestimated by WRF1 between 870 and 920 hPa. The analysis carried out in this subsection indicates that the set of physical parameterizations of WRF4 (see Table 2.2) generates better results compared to the other sets of parametrizations, thus this set is used in the experiments hereafter.

Sensitivity to the domain configuration and to the lateral boundary conditions

As a second step, the small domain in Fig 2 .4 was nested inside a bigger domain (see Fig. 2.12) to investigate the model sensitivity to the nested domain configuration. Note that the large domain has a resolution of 0.75 • and encompasses the South East Pacific and the main part of South America. The number of vertical levels was set to 60 in both domains, defining 21 levels in the first 1000 m, which allows to better represent the complex topography of the Andes mountain.

In order to assess the model sensitivity to the lateral boundary conditions, we forced our simulation by two differents databases: the ERA-Interim and CFSR reanalysis. Simulations were done for the period 1997-1998. Fig. 2.12 WRF domains used to evaluate the sensitivity to nested domain configuration and to the lateral boundary conditions Figure 2.13 shows the mean surface wind for the period 1997-1998 from WRF simulations with three different configurations. The first of them was configured using a single domain (in Fig. 2.4) and forced by the ERA-Interim reanalysis. The second was configured using the nested domains configuration (in Fig. 2.12) and also forced by the ERA-Interim reanalysis. And the third was also configured using the nested domains but forced by the CFSR reanalysis. In the inner part of the domain, the simulated wind spatial pattern is almost the same for the single and nested configurations. There are major differences in the offshore part of the domain, mainly near its western border. Differences between simulations with different lateral boundary conditions (ERAI and CFSR) are relatively small. Similar differences are observed at high levels in wind and air temperature (Fig. 2.14), that is, small differences in the inner part and major differences in the outer part.

These analyses show that a nested domain can be useful to obtain clean solutions (without lateral boundary efffects) of the model around the border of its domain. Moreover, it should be useful in a future work when the WRF atmospheric fields produced in our simulations will be used to force a regional oceanographic model. On the other hand, the sensitivity of model results to the change of the database used to build the lateral boundary conditions is weak, especially in the nearshore region. Thus, the nested domain configuration was used in our simulations hereafter.

WRF code modifications

A set of modifications to the WRF code was performed (following Oerder (2016)) in order to add some capacities to the model : (1) to compute the turbulence shear stress (2) to extract the individual horizontal momentum tendency terms and (3) to record the mean cumulative of the momemtum terms. In the PBL scheme used in our simulations (i.e MYNN), momemtum mixing is parameterized by a Reynolds turbulent stress ( τ ), which is defined as:

τ = ρK M ∂ ν ∂z
with ρ the air density, K M the momentum vertical diffusion coefficient, and ∂ ν ∂z the vertical shear of horizontal velocity.

It is not calculated by default by the WRF model. For this reason, functions of the MYNN PBL scheme were modified to record the values of the momentum vertical diffusion coefficient (K M ). Then, the τ term was calculated and recorded in the WRF outputs.

b) Extracting horizontal momentum tendency terms

Wind horizontal velocity time variations are driven by the following momemtum balance:

∂ ν ∂t = ν • ∇ ν + ∂ ∂z τ ρ -f k × ν - 1 ρ ∇P (2.8)
with f the Coriolis parameter, P the pressure and k the vertical unitary vector. The right-hand terms of equation represent the wind advection, the effect of turbulent vertical mixing of momentum, the Coriolis and the pressure gradient forces, respectively. The standard WRF configuration only allows the user to record the output of total zonal and meridional components of horizontal momentum tendency (first term in equation (2.8)), which are recorded over each user-defined large time step. However, additional variables had to be introduced within the dynamical solver to record the output of the others momentum terms (the right-hand terms of equation (2.8)) to make them available for history output.

c) Recording the mean cumulative

A simple time average of the momentum equation (2.8) relates the forcing time average to the difference between the final and initial wind but not to the average wind V . They showed that V is related to the momentum terms by the following formula:

V -V (t o ) ∆t = Fn∈f orces [F n ]
where V (t o ) is the initial velocity at the beginning of the time interval, ∆t the time step, and F n the momentum terms. The bracket ([]) is the double time averaging operator defined as:

[F n ] = 1 N + 1 N p=0 ( p k=1 F n )
with N the number of time steps during the time interval.

For this reason, the WRF code was modified to record the double time average of each momentum terms at each model time step, following Oerder et al. (2016).

Model run optimization

The simulations were mainly performed on the supercomputer Curie from the GENCI at the CEA (projects x2014011140, x2015011140 and x2016011140) in France and some of them on the cluster of the Institute of the Sea of Peru (IMARPE). Curie machine has 10080 eight-core processors, Intel R Xeon R Next Generation (2.7 GHz), total of 80640 cores, for MPI parallel codes. The IMARPE cluster has 12 BLADE DELL servers (Xeon E5-2680v3 2.5GHz), total of 288 Cores. Both of them use the Linux operating system.

The WRF model was built to run on both machines utilizing MPI Intel compilers version 14. After doing tests, we chose 200 processors to execute the WRF model, which guarantees optimal use of the computational resources (see graph 2.15). Each year of simulation requires aproximately 6000 hours of time computing.

Chapter 3

Coastal winds dynamics during El Niño events 3.1 Introduction

The Peruvian upwelling system is strongly impacted by El Niño events (see chapter 1). During the so-called "canonical" El Niño events, there is an apparent contradiction between the coastal SST warm anomalies and the coastal wind intensification off the Peru coast [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF]. The purpose of this chapter is to study this apparent paradox, using the WRF regional atmospheric model to simulate the surface winds response to El Niño SST anomalies. The specific aims are:

• to study the physical mechanisms responsible for the intensification of alongshore coastal winds in the Peruvian upwelling system during the 1997-1998 El Niño event by analysing the momentum budget of the atmospheric boundary layer.

• to study the roles of the large scale signal and SST local forcing on the alongshore wind anomalies in order to disentangle the main drivers of wind intensification during the 1997-1998 event.

The interpretation is extended to other El Niño conditions during the 1979-2016 period using data from Reanalysis.

Summary

The WRF regional atmospheric model was used to study the physical processes driving the wind intensification off the Peru coast during the intense 1997-1998 El Niño event. As anomalously warm waters accumulated near the coast, the equatorward coastal wind increased by ∼1-1.5 m s -1 during 5-6 months (up to ∼40 % increase with respect to the climatological mean over the 1994-2000 period) as observed by the ERS satellite.

A simulation performed for the period 1994-2000 reproduced the coastal wind response to local sea surface temperature (SST) forcing and large scale atmospheric conditions during El Niño 1997-1998. The model, evaluated against satellite data, represented well the intensity, seasonal and interannual variability of alongshore winds. However, the wind decrease towards the coast (the so-called wind drop-off; Capet et al. ( 2004)) was poorly simulated, especially between 10-16 • S.

A momentum balance analysis showed that the coastal wind intensification was mainly driven by the enhancement of the alongshore pressure gradient. Vertical mixing tended to counterbalance the alongshore pressure gradient, leading to a quasi-equilibrium between the alongshore pressure gradient and the frictional force. Moreover, a quasi-linear relation between surface wind and pressure gradient anomalies was found. Our results are consistent with previous modeling studies in the region [START_REF] Muñoz | Dynamics of the Low-Level Jet off the West Coast of Subtropical South America[END_REF][START_REF] Belmadani | What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile?[END_REF].

The enhancement of the alongshore pressure gradient occured because the atmospheric pressure decreased more north (∼6 • S) than south (14 • S), in association with the larger increase of SST, air temperature and humidity off northern Peru (i.e associated with the inhomogeneous warming along the coast during El Niño event).

Surface warming induced an increase of the height of the PBL of up to two and a half times and of the vertical turbulent mixing coefficient (K z = τ /(∂V /∂z)) of up to three times their values in mean climatological conditions. However vertical mixing of momentum (∂τ /∂z) remained negative and was stronger (in absolute value) during El Niño than in mean climatological conditions. Thus, vertical profiles of wind, mixing coefficient, and momentum trends showed that the surface wind intensification was not caused by the increase of turbulence in the planetary boundary layer. Moreover, the temperature inversion in the vertical mitigated the development of pressure gradient due to air convection during part of the event. We concluded that it is likely that two conditions, a SST anomaly alongshore gradient and a weak (or absent) temperature vertical inversion, would be necessary to drive a strong wind anomaly in the coastal region. The SST gradient drives the pressure gradient, and the weak temperature inversion allows shallow convection to develop without activate any "back-pressure effect" (a mechanism compensating the surface pressure gradient; [START_REF] Hashizume | Direct observations of atmospheric boundary layer response to SST variations associated with tropical instability waves over the eastern equatorial Pacific[END_REF]).

In addition, two sensitivity experiments were done to isolate the respective impacts of the large scale atmospheric signal (called BRY-EN experiment, forced by 1997-1998 El Niño boundary conditions and neutral SST forcing from the years 1994-1995) and of the SST local forcing (called SST-EN experiment, forced by El Niño SST forcing and 1994-1995 neutral boundary conditions) on the coastal wind intensification. Analysis of simulations show that the wind intensification was primarily driven by the local SST forcing whereas large scale variability associated with the South Pacific Anticyclone modulated its effects.

Due to the model computational cost, the simulations were performed for a relatively short time period (1994)(1995)(1996)(1997)(1998)(1999)(2000) including only one El Niño event. In order to evaluate if the same dynamical processes were active during other El Niño events, we performed similar diagnostics using the ERA-Interim reanalysis data of 0.75 • (∼80 km) over the period 1979-2016. They confirmed that intensifications of alongshore wind off Peru were associated with SST alongshore gradient anomalies, as during the 1997-1998 event.

Article published in Climate Dynamics

Our results are detailed in the following article.

Reference

Additionnal results and discussion (not included in the paper)

Here, we extend our results and discussion about the wind drop-off in the model validation. We also extend our results on shortwave flux at air-sea interface and air temperature on land surface, which were useful to extend the discussion referring on land sea thermal contrast.

The wind drop-off

The wind decrease towards the coast (i.e. the wind drop-off) was poorly simulated by the model. The WRF winds are too strong at the coast in comparison with the satellite winds (Fig. 3.1). [START_REF] Capet | Upwelling response to coastal wind profiles[END_REF] showed that increasing the spatial resolution (from 27 km to 3 km) in the COAMPS model increased the wind drop-off at the coast of California. However the range of spatial resolution in Capet et al. ( 2004) is higher than in our case (25 km) and the region is different (higher latitude, lower mountain range). Oerder et al. (2016) show that the simulated WRF wind drop-off (near 20 • S off South Peru) is in better agreement with satellite wind and rawinsonde data, when the model resolution is higher (∼9 km). This is likely due to a better representation of the coastal orography, among other effects. It will be verified in the next chapter. Besides, the wind drop-off can not be measured accurately with satellite wind measurements within 50 km from the coast. We will address this aspect of the wind in another chapter (section 4.3.1). The large differences between QSCAT and ERS offshore are probably due to differences in the algorithms that retrieve surface wind from the wind stress which are nearly directly measured by the scatterometers scaterrometers.

Shortwave flux at the air-sea interface

The spatial distribution of the mean downward shortwave flux at the surface is not well reproduced by the model (specially over land) compared with data from two satellites (SRB and ISCCP; Fig. 3.2a, b andc). It is related with the difficulty to simulate the cloud cover in this region [START_REF] Wyant | The PreVOCA experiment: Modeling the lower troposphere in the Southeast Pacific[END_REF]. Satellite data shows two minimum shortwave flux patterns (∼175 W m -2 ) over sea located off the continental coast between 12-30 • S and between 0-8 • N in the Panama Bight region. Note that the shortwave flux measured by SRB is stronger than ISCCP data. The shortwave flux along the Peru-Chile coast is overestimated by the model by 13% off Peru and 20% off Chile with respect to ISCCP (less with respect to SRB). Moreover, the shortwave flux modelled is strongly underestimated over land on the steep slopes of the Andes and is overestimated in the Amazon basin rainforest.

On the other hand, eventhough the mean shortwave flux is poorly reproduced, the shortwave anomalies simulated by the model during 1997-1998 El Niño are of the correct magnitude (Figs. 3.2d, e and f)). Model and observations show strong shortwave positive anomalies (∼50-70 W m -2 ) between 0-8 • N and off the coast of northern Chile (∼25 W m -2 ).

Figure 3.3 shows the mean annual cycle of the shortwave flux from the model and observations in the coastal domain over sea and over land. Over sea the WRF downward shortwave flux annual cycle is in agreement with observations. Over land the WRF shortwave flux is strongly underestimated, particularly in summer with 250 W m -2 in the observations against 180 W m -2 in WRF (-28%). We may underestimate the landsea thermal gradient due to this shortwave bias. Note that the poor representation of downward shortwave radiation and associated cloud cover in models of the Southeast Pacific lower troposphere is a well-known problem (e.g. Wyant et al. (2010)).

Air temperature on land surface

Despite the shortwave bias, the model reproduces reasonably well the surface air temperature distribution and, more importantly for the purpose of the present study, the air temperature anomalies at surface during EN, in agreement with reanalysis data (see Fig. 3.4). It shows that the air temperature increases more over sea than over land associated with the strong SST warming during El Niño, thus reducing the land-sea thermal contrast.

However, the land-sea thermal contrast is believed to have an impact on the wind at a smaller spatial scale, as suggested by [START_REF] Enfield | Thermally driven wind variability in the planetary Boundary layer above Lima, Peru[END_REF] in the region of Lima. To evaluate the skill of the model to reproduce the local air temperature conditions, we computed the mean annual cycle and the monthly averaged air temperature above land at 12 • S (Lima location) in the model. We found a temperature inversion in winter (see Fig. 3.5). These model results are in agreement with those computed by [START_REF] Enfield | Thermally driven wind variability in the planetary Boundary layer above Lima, Peru[END_REF] using rawinsonde data for the period 1963-1977 (see his Figure 4). We conclude that a high resolution modelling will be necessary to test the Enfield's conclusions. Chapter 4

Climate change impact on the Peruvian coastal winds 4.1 Introduction

Global Climate Models (GCMs) do not simulate realistically the wind and sea surface temperature in nearshore regions because of their coarse spatial resolution (∼100-200 km) and/or their regional biases [START_REF] Belmadani | What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile?[END_REF][START_REF] Wang | A global perspective on CMIP5 climate model biases[END_REF]. Hence, there is a great need to achieve the downscaling of GCMs climate change projections to obtain finer-scale projections with realistic representation of the regional conditions.

As mentioned in chapter 1, there are few regional studies about the impact of the climate change on the upwelling-favorable wind on the Peruvian upwelling system. Using scatterometer gridded winds (0.5 • × 0.5 • ) from QuikSCAT, Goubanova et al. (2011) applied a statistical downscaling method to obtain surface wind at ∼50 km of resolution from the outputs of the IPSL-CM4 global model. They found that surface alongshore winds off Peru show a significant weakening (5-10 %) in summer over the 2CO 2 and 4CO 2 scenarios. It was associated with a decrease of intraseasonal variability of the coastal winds off Peru. Belmadani et al. (2014) used the LMDz atmospheric global model with progressively refined horizontal resolution of 0.5 • × 0.5 • in the Peru-Chile upwelling region. They found that downscaled winds show a weakening off Peru in both summer (20-30 %) and winter (5-10 %). They were associated with the SPA southward shift and the increase of precipitation over the tropics associated with convective anomalies.

The limitations of these regional wind projections are the models relatively low spatial resolution (∼50-100 km), the downscaling of a single global model, and the idealized CMIP3 scenarios (2CO 2 and 4CO 2 ) that were used. In this chapter we assess the regional impact of climate change on coastal winds off Peru using a high resolution atmospheric model, the multi-model mean (MMM) of 31 CMIP5 models, and the new RCP8.5 pessimistic scenario. We also study the mechanisms governing local wind changes and relate them to previous findings from other authors.

Model simulations

First, we performed two simulations on the largest domain (WRF 105 km) independently: a control simulation over the period 1994-2003, using 6-hourly lateral boundaries conditions from the NCEP2 reanalysis and SST forcing from the OISST database [START_REF] Reynolds | Daily high-resolution-blended analyses for sea surface temperature[END_REF]); and a climate change simulation where anomalies of the seasonal cycle from the CMIP5 multi-model mean (CMIP5 MMM) for the RCP8.5 scenario (period 2086-2095) are added to the 6-hourly NCEP2 lateral boundary conditions and to the OISST forcing, following the same methodology as in [START_REF] Dutheil | Impact of surface temperature biases on climate change projections of the South Pacific Convergence Zone[END_REF].

Then, two simulations (control and climate change) were performed on the nested domain of 21 km of resolution using the coarse solutions from the WRF 105 km simulations

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in one-way nesting mode. Finally, high resolution simulations (7 km) were performed on the innermost domain using the solutions from the WRF 21 km simulations, control and climate change respectively.

Anomalies of the seasonal cycle from the CMIP5 MMM were generated from 31 CMIP5 climate models (see subsection 2.1.2). They are monthly differences of the future climate from 2080 to 2100 (RCP8.5 scenario) with the present climate from 1989 to 2009. It means that the interannual variability at the boundaries is prescribed in the future climate and is identical to that of the present climate. The anomalies are added to the NCEP2 temperature, water vapour mixing ratio, geopotential height, and wind at each vertical model level, and to the OISST sea surface temperature to build the boundary conditions, the initial state and the surface (SST) forcing of the climate change simulation. This methodology approach to assess future climate change, called the pseudo global warming method (PGW), allows to reduce the model bias of the CGCMs projection by the use of the climate difference estimated by the CGCMs [START_REF] Rasmussen | High-resolution coupled climate runoff simulations of seasonal snowfall over colorado: A process study of current and warmer climate[END_REF].

For example, Figure 4.2a shows the annual mean SST ensemble averaged for 31 global models over the period 1989-2009. It shows the well-known SST pattern in the tropical Pacific, warm pool in the west and a cold tongue in the east. This pattern is not well reproduced by the models compared to observations [START_REF] Reynolds | Daily high-resolution-blended analyses for sea surface temperature[END_REF] given that they exhibit a quite robust and strong positive bias of ∼2-3 • C off the West coast of South America (Fig. 4.2b). This bias is well known and has been attributed to different processes: lack of low-altitude clouds, lack of upwelling of cool water, abscence of surface oceanic eddies [START_REF] Richter | Climate model biases in the eastern tropical oceans: Causes, impacts and ways forward[END_REF]. To build the SST forcing for the climate change simulation, we computed the monthly climatological differences in SST from the CMIP5 MMM between the period 2080-2100 (RCP8. This allows to filter the bias shown in Figure 4.2b. However it assumes (and it is a strong assumption) that the strong SST bias in the CMIP models has no impact on the amplitude of the CMIP SST anomalies, which is impossible to verify. A similar formula is used for other variables for the boundary conditions.

Note that the patterns of the SST climate anomalies (future-present) in summer and winter differ from each other off Peru (Fig. 4.3a, b), and the SST climate anomalies are stronger in June and are weaker in January-February in the nearshore area (100-km coastal band; Fig. 4.3c).

Results

Surface wind validation

We start with the evaluation of the simulated surface winds in the present period. To show the impact of the model resolution on modeled nearshore winds, the annual mean surface wind from WRF 105 km, WRF 21 km and WRF 7 km (here after referred to as WRF105, 2004)) averaged between 7-13 • S obtained from the three model configurations and QuikSCAT observations. It shows that WRF105 produces a strong wind decrease shoreward (the so-called wind drop-off) but it cannot resolve the nearshore zone due to its lower resolution. WRF21 produces a weak wind drop-off in the first 200 km off the coast, and WRF7 reproduces reasonably well the wind drop-off in the coastal zone in agreement with observations. Note that the QuikSCAT satellite have a "blind zone" near the coast of approximately 50 km where no data are available [START_REF] Bentamy | Gridded surface wind fields from Metop / ASCAT measurements[END_REF].

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wind speed compared to QuikSCAT, except in summer where modelled wind speed is overestimated by about 1 m s -1 at 100 km offshore. Unfortunately there are no in situ observations such as the VOCALS data base to evaluate the nearshore wind in summer.

-500 -400 -300 -200 -100 0

Cross-shore distance (km)

Future changes in the mean and seasonal cycle of the surface wind

The mean surface winds in the period 2086-2095 under the scenario RCP8. ). We found weak changes in the mean state of the surface winds. Wind change in the offshore region is positive, about 0.2 m s -1 at 400-km offshore and it decreases shoreward. In the nearshore region, wind change is slightly negative off the northern and southern coast of Peru, and is slightly positive off the central coast. Positive change in the nearshore region between 11-14 • S is greater in WRF7 than in WRF21. Concerning the change in the seasonal cycle of the alongshore winds, they tend to weaken in spring and summer (by ∼5 %) off the northern and southern coast, and alongshore winds tend to intensify in fall and winter off the central coast by ∼5 % in WRF21 and ∼10 % in WRF7.

Wind differences driven by resolution will be discussed later (section 4.4). Figure 4.8 shows that the offshore wind changes can be different from the nearshore wind changes, for example the offshore wind speed changes are always positive, whereas the nearshore wind changes are negative in summer and positive in winter. Something peculiar happens between 11 • S and 14 • S near the coast (north of the Paracas peninsula, where Pisco bay is located), where wind change is always positive. This will be discussed in more details in next section.

Climate-change induced modifications of the momentum budget

First, in order to investigate the spatial patterns of the forces which drive the surface winds off Peru under historical and future conditions, we computed the annual mean meridional component of the forces in the first layer of the model for the whole domain (Figs. 4.9 and 4.10). Second, to examine the coastal wind change dynamics, we computed the summer and winter mean of the wind and the alongshore forces in a coastal band of 100 km along the Peru coast (Figs. [START_REF] Capet | Upwelling response to coastal wind profiles[END_REF].11 and 4.12). Figure 4.9 shows the spatial structure of the different forces driving the meridional surface wind in present conditions (period 1994-2003). These terms are computed in the model surface layer in contact with the ocean (see subsection 2.3.3). Note that winds off Peru are mostly oriented South-North. Figure 4.9a shows the advection term (V_ADV). In most of the domain the magnitude of this term is nearly zero, except in the nearshore region north of 14 • S off the Pisco bay. There, this term shows a local positive maximum, showing that it accelerates the meridional wind locally. In order to analyze the dynamical processes associated with the upwelling-favorable winds off the Peru coast, an alongshore momentum budget is performed in a 100-km In future conditions during summer (January-March), the pressure gradient decreases along the coast for the most part (Fig. 4.11c) similarly as the alongshore winds (Fig. 4.11a). The pressure gradient decrease is stronger between 14-17 • S where the alongshore wind decrease is about -0.5 m s -1 . It will be shown in section 4.3.5 that the decrease in pressure gradient and alongshore winds during summer is in association with the poleward displacement of the South Pacific Anticyclone.

On the other hand, the alongshore wind increases along the coast between 8-15 • S in winter (July-September) under future conditions (Fig. 4.12a). As in summer, the pressure gradient and vertical mixing are the dominant forces driving the wind along the coast, except between 12-14 • S where advection plays an important role (Fig. 4.12b). In future conditions, the pressure gradient tends to increase relatively strongly (∼16 %) between 13.5-15.5 • S and moderately (∼13 %) between 9-12.5 • S, while the advection term tends to increase moderately (∼10 %) between 7-14 Fig. 4.12 Same as Fig. 4.11 but for winter (July-September)

Advection terms of the momentum budget and local wind intensification

We found that the advection term of the momentum budget plays a major role in the coastal wind local intensification observed between 12-14 • S in the Paracas-Pisco bay (Fig. 4.12c) during May-August. In order to investigate the zonal, meridional and vertical advection of momentum, we computed these terms offline from the climatological velocities. We used the following formula:

V _ADV = -U ∂V ∂x -V ∂V ∂y -W ∂V ∂z
where V _ADV is the total advection of meridional momentum, -U ∂V /∂x is the zonal advection term (V _ADV x ), -V ∂V /∂y is the meridional advection term (V _ADV y ), and -W ∂V /∂z is the vertical advection term (V _ADV z ) of the meridional momentum. Figure 4.13 shows the mean total advection term of meridional momentum and their respective zonal, meridional and vertical contributions between May and August in present conditions in the Paracas-Pisco bay area. It shows that downwind of the peninsula, the advection accelerate the meridional wind component V (Fig. 4.13a). This has been documented in previous studies of expansion fans near capes (e.g. [START_REF] Perlin | Coastal Atmospheric Circulation around an Idealized Cape during Wind-Driven Upwelling Studied from a Coupled Ocean-Atmosphere Model[END_REF]; [START_REF] Soares | The North African coastal low level wind jet: a high resolution view[END_REF]). This wind acceleration is mainly due to the meridional advection term that largely dominates (Fig. 4.13c). The zonal advection is weak (even slightly negative near the cape; Fig. 4.13c), with the exception of the inner bay where it is of comparable intensity than the meridional advection. The contribution of the vertical advection term is negligible, except around the cape (positive downwind and negative upwind of the peninsula; Fig. 4.13d).

Figure 4.14 shows the change in total advection and the change in the zonal, meridional and vertical terms over May-August under future conditions. The total advection

Future changes in the vertical structure of the PBL

Here we examine the changes in the mean vertical structure of the PBL (turbulent vertical mixing coefficient (K z ), temperature, and momentum vertical mixing term) between present and future conditions. Spatio-temporal averages are computed between 7-15 • S and within the first 100 km from the coast.

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The vertical profiles of alongshore wind, turbulent vertical mixing coefficient, and momentum terms show very weak changes between the surface and 1600 m in summer (Fig. 4.17). On the other hand, while the mixing coefficient almost does not change in winter (Fig. 4.18), the wind intensification in winter occurs between the surface and ∼700 m with a maximun change at ∼300 m which produces a slight increase of the wind shear below ∼300 m.

The contribution of the wind shear increase to the turbulent stress (τ = K z .dV /dz) and the unchanged vertical mixing coefficient (K z ) act such that the momentum vertical mixing is reduced in winter in warm conditions (Fig. 4.18a; blue line in Fig. 4.18b). This means that a less efficient downward vertical flux of momentum from the upper layers to the surface layer occurs in winter under warm conditions. It is balanced by the increase of pressure gradient and advection terms (green and red lines respectively in Fig. 4.18b). ADV COR PGF MIX Fig. 4.17 Mean summer vertical profile of a) alongshore wind (in m s -1 ) and turbulent vertical mixing coefficient (K z in m 2 s -1 ) and b) alongshore forces (in m s -1 ). Average were computed between 7-15 • S, and within 100 km from the coast. Full and dashed lines correspond to future and historical conditions, respectively.

Role of the large scale atmospheric forcing and local forcing

In this subsection, we analyze the possible mechanisms which could govern the coastal wind changes.

a) The South Pacific Anticyclone (SPA)

Changes in alongshore winds may be driven by changes in the large scale atmospheric circulation, particularly the strength and location of the South Pacific Anticyclone (SPA). Figure 4.19 displays the changes in surface atmospheric pressure during the summer and winter seasons. .17 but for winter.

In summer, the SPA is located at its southernmost position and is less intense (white lines in Fig. 4.19a). It moves towards southwest (away from the Peru coast) and the pressure at its center increases by ∼2 hPa under future conditions with respect to present conditions (black lines in Fig. 4.19a). It results that the surface atmospheric pressure increases at subtropical latitudes off Chile and decreases at tropical latitudes off the Peru coast (shading colors in Fig. 4.19a). This likely induces the decrease in alongshore pressure gradient (Fig. 4.11c) that induces a general weakening of alongshore winds off Peru coast in summer (Fig. 4.11a).

In winter, the SPA is located at its northernmost position and is more intense than summer (white lines in Fig. 4.19b). It almost does not change its position but the pressure at its center increases in ∼2 hPa under future conditions (black lines in Fig. 4.19b). This produces an intensification of surface atmospheric pressure off Peru and Chile, strong at subtropical latitudes and weak at tropical latitudes (shading colors in Fig. 4.19b). This likely increases the pressure gradient off the Peru coast and, consequently, may produce the moderate wind increase.

Monthly variations of the SPA (intensity, latitude and longitude of the maximum pressure within the SPA) are shown in Figure 4.20. Under present conditions, the seasonal variation of the intensity shows a maximum in September-October (∼1026 hPa) and minimum in May (∼1022 hPa, blue line in Fig. 4.20a). Moreover, the center of the SPA is located at its southernmost position in February (∼34.5 • S) and at its northernmost position in June (∼28.5 • S, blue line in Fig. 4.20b). It is located at its westernmost position in January (∼110 • W) and at its easternmost position in September-November (∼94 • W, blue line in Fig. 4.20c). Under future conditions, the intensity of the center of the SPA increases around the year by ∼2 hPa, with a maximum value in September and minimum value in May (red line in 4.20a). Regarding its position, except in July the SPA center moves towards the south (red line in 4.20b) and except in January, February, July and August, the SPA center moves towards the west (red line in 4.20c). Note that the center of the SPA was determinated by the maximum monthly value of sea level pressure data in the 120-80 • W,20-45 • S area. These displacements and intensifications of the SPA likely have an impact on the alongshore pressure gradient and may influence the coastal winds. b) The land-sea thermal contrast [START_REF] Bakun | Global climate change and intensification of coastal ocean upwelling[END_REF] proposed that the nearshore winds in upwelling regions may increase in the future due to the increase of land-sea temperature difference. He suggested that under global warming, due to the increase of the greenhouse gases, land temperature will rise faster than ocean temperature and the pressure will decrease stronger on land, driving an enhancement of the cross-shore pressure gradient and thereby increasing the upwelling 112 Chapter 4. Climate change impact on the Peruvian coastal winds favorable winds. As Bakun says the land surface temperature increase more than sea surface temperature in our simulations (Fig. 4.21a), and the land sea temperature contrast increase in both summer and winter seasons (Fig. 4. 21b andc). However, the sea level pressure increases on land (by 4-6 hPa; Fig. 4.22) rather than diminish, in disagreement with Bakun's hypothesis. Moreover, in spite of a decrease of winds in summer, the land-sea gradient increases, also invalidating Bakun's hypothesis in the summertime.

Note that García-Reyes et al. (2013) using reanalysis database showed that the variability in upwelling-favorable winds is dominated by oceanic atmospheric pressure systems, and only weakly influenced by terrestrial atmospheric pressure systems.

113

to changes in convection in the North of Peru. Using a vorticity balance in the surface layer, they found that:

βV ≈ f ∂W ∂z + curl(f riction)
with β = ∂f /∂y (f Coriolis parameter)>0 They showed that the changes of vorticity in the 4CO 2 climate scenario were dominated by changes in the planetary vorticity term (f ∂W/∂z), thus:

∆V ≈ f ∆( ∂W ∂z )
where ∆(X) = X 4CO 2 -X P I .

As f < 0 and ∆(∂W/∂z) > 0 because convection is enhanced by the warming, the ∆V is negative, thus V will decrease.

Our simulations show an increase of the precipitation (associated with a convection enhanced) in summer, but it occurs mainly near the equatorial Pacific and far from the Peru coast (Fig. To better understand these differences, we compared the alongshore wind anomalies from both simulations (Fig.

Results

115

In order to evaluate if the temporal displacement and increase of the 1997-1998 El Niño wind anomalies peaks observed in the high resolution WRF 7 km climate change simulation is a robust characteristic of El Niño events under future conditions, we performed two WRF simulations at 105 km of horizontal resolution for a relatively long time periods (1982-2016 for current conditions and 2074-2108 for the RCP8.5 scenario). Figure 4.26 shows time series of monthly wind anomalies averaged in a 160-km-wide coastal band and between 7-11 • C for present and future conditions (black and red lines respectively in Fig. 4.26) for five El Niño periods.

For the 1982-1983 El Niño period, the wind anomaly maximum peak in May 1982 occurs one month later (in June) under future condtions (there is also a peak intensification of ∼0.5 m s -1 ). For the 1987-1988 El Niño period, the peak in February 1987 increases by ∼0.25 m s -1 and the maximum peak in April 1987 occurs one month later (in May) under future conditions. While the maximum peaks for the El Niño 1991-1992 and El Niño 1997-1998 (in February 1992 and January 1998 respectively) occur two months later (in April) under future conditions. For the 2015-2016 El Niño period, the maximun peak occurs in July 2015, while the maximun peak occurs in September under future conditions. Despite the peaks displacement, there is a good correlation between future and present wind anomalies, suggesting that the interannual variability of coastal winds off Peru is mainly driven by the SST interannual variability (as we showed in chapter 3), which is exactly similar in the future and current conditions by construction in our simulation framework (see subsection 4.2.2). The differences observed between future and current El Niño wind anomalies are probably associated to differences in the regional atmospheric conditions (position of the SPA, etc.). To investigate further this aspect, running ensemble simulations to suppress the impact of the atmospheric variability could be useful.

In addition, we compared El Niño wind anomalies from WRF7, WRF21 and WRF105 to evaluate the influence of the model resolution. We found that the spatial patterns of surface wind anomalies during El Niño conditions are quite similar, with strong wind anomalies alongshore and off the North coast (Fig. 4.27). It shows that model resolution used for our study about El Niño winds (1/4 • ) in the previous chapter 3 was adequate for that task and the results are independent of the model resolution.

Discussion and conclusions

A high resolution regional atmospheric model is used to investigate the impact of climate change on upwelling favorable winds off the Peru coast. The WRF model is applied using three nested domains with 105, 21 and 7 km of horizontal resolution. We performed a control simulation in the period 1994-2003 forced by the NCEP2 reanalysis, and a climate change simulation where mean anomalies of the seasonal cycle from 31 CMIP5 global models are added to the NCEP2 lateral boundary conditions. The high resolution atmospheric simulation (WRF7), evaluated with satellite data, reproduced well the intensity, seasonal and interannual variability of alongshore winds, as well as the spatial variability of the mean wind drop-off near the coast. We found weak changes in the mean state of upwelling favorable winds off Peru for period 2086-2095 under the RCP8.5 scenario with respect to the control simulation. In overall, winds tend to decrease in spring-summer (∼0-5 %) and to increase in fall-winter (∼5-10 %), so that there is a slight intensification of the seasonal cycle. In particular, there is a local strong intensification of the nearshore winds in the Paracas-Pisco bay area between ∼12-14 • S during winter.

A momentum balance shows that in the whole region the pressure gradient is the dominant force driving the surface wind acceleration. Under the climate change scenario, the pressure gradient decreases in summer and increases in winter, driving the weakening and/or intensification of the winds off the coast respectively. For the nearshore region between ∼12-14 • S in the Paracas-Pisco bay area which is protected by an orographic obstacle, the advection term plays a crucial role on the wind dynamics. In this region, the total advection term increase under future conditions, mainly related to the enhancement of the meridional advection.

On the other hand, air temperature and humidity are uniformily increased in the planetary boundary layer (PBL), so that temperature inversion characteristics do not change. The turbulent vertical mixing coefficient increases in summer, but the change of the vertical mixing of momentum is negative, showing that Wallace's mechanism (enhancement of the vertical transport of momentum) may not be important at this scale in the Peru region.

The wind changes are mainly associated to changes in the intensity and position of the South Pacific Anticyclone. The decrease of the coastal winds off the Peru coast during summer under future conditions seems to be associated to the SPA displacement towards the south despite of its intensification. In contrast the coastal winds intensification during winter seems to be linked with the SPA intensification. It suggest that changes in coastal winds off Peru are very sensitive to the changes in the position and intensity of the SPA under future conditions. 0n the other hand, model results show that the land-sea temperature contrast, which has been hypothesized to be a potential local factor to drive a coastal wind intensification in upwelling systems [START_REF] Bakun | Global climate change and intensification of coastal ocean upwelling[END_REF], increases in spite of a decrease of coastal winds in summer, invalidating Bakun's Hypothesis in the summertime for the Peru region. In addition, our simulations show a increase of the precipitation in summer, which could contribute to the weakening of the wind during this season as was suggested by [START_REF] Belmadani | What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile?[END_REF], but it occurs far from the Peru coast so that its contribution on the coastal winds decrease in summer should not be too strong, as in the case of [START_REF] Belmadani | What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile?[END_REF]. Finally, we confirm that interannual variability of the coastal winds off Peru are mainly driven by the interannual variability of the SST alongshore gradient under present and future conditions.

Our results confirm the weakening of coastal winds off Peru in summer found in previous studies [START_REF] Goubanova | Statistical downscaling of sea-surface wind over the Peru-Chile upwelling region: Diagnosing the impact of climate change from the IPSL-CM4 model[END_REF][START_REF] Belmadani | What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile?[END_REF], but contrast with the annual mean (Table 4.1). Note that the wind decrease in summer is important for biogeochemistry because the summer season is the most productive season and the availability of nutrients controls the intensity of surface productivity [START_REF] Echevin | The seasonal cycle of surface chlorophyll in the peruvian upwelling system: A modelling study[END_REF]). Thus regional climate change may induce a less productive summer season. The discrepancies among these studies may come from specific differences in the projected change of the position and intensity of the SPA. A potential limitation of our results is that anomalies of the SST seasonal cycle from the CMIP5 multi-model mean at lower resolution (∼1 degree) were linearly extrapolated in the nearshore region to force our high resolution atmospheric climate change simulation. It could introduce unrealistic SST patterns that could impact the coastal wind [START_REF] Oerder | Peru-Chile upwelling dynamics under climate change[END_REF]. Moreover, another potential limitation of our study is that our forced atmospheric simulations cannot take into account the retroaction of the atmosphere on the ocean, which could modify the nearshore surface temperature. So, ocean-atmosphere coupled simulations could be needed to study with better precision the impact of climate change in nearshore regions.

Chapter 5

Conclusions and perspectives

This thesis examined the coastal wind dynamics in the Peruvian upwelling system under two very different warming conditions: interannual El niño event and long term warming in a climate change scenario. It allowed to improve the knowledge of the key mechanisms governing the coastal wind dynamics during El Niño events and under current and future climate scenarios. For this purpose, the Weather Research and Forecasting (WRF) model was configured for the Peru region as a regional climate model in terms of domain size, physical parameterization and lateral boundary conditions (Chapter 2).

In this chapter of the thesis, a summary of the main conclusions is provided for both studies on El Niño and climate change, as well as some pespectives for future works.

Coastal winds during El Niño events

In the first part of this thesis (Chapter 3), we studied the physical processes driving the coastal wind intensification observed during El Niño 1997-1998 in the Peruvian upwelling system. We performed a WRF simulation at a resolution of 1/4 • in the period 1994-2000.

It allowed to reproduce the alongshore wind anomalies observed off the Peru coast during 1997-1998 El Niño event. Our analysis showed that:

• The alongshore pressure gradient anomalies drove the alongshore wind anomalies during El Niño period. They were mainly balanced by the vertical turbulent mixing anomalies (friction at the surface).

• The alongshore pressure gradient anomalies were generated by the stronger heating of the air column in the north than in the south, which was forced by the inhomogeneous warming of the SST along the Peru coast during El Niño event.

• A mechanism compensating the surface pressure gradients (so-called "back-pressure effect") was strong in the case of a marked temperature inversion above the PBL during part of the event. In contrast, a weak temperature inversion allowed shallow convection to develop without triggering any "back-pressure effect" in the rest of the event.

• It is likely that two conditions are needed to drive a strong wind anomalies during El Niño in the Peru coastal region: a SST anomaly alongshore gradient and a weak or absent temperature vertical inversion.

• Our results suggest that an increase of the land-sea thermal gradient may not play a strong dynamical role for the alongshore wind during El Niño, in contrast to the so-called "Bakun's hypothesis".

In addition, we performed two sensitivity experiments to study the respective roles of large scale atmospheric signal and of the SST local forcing in driving the wind anomalies during El Niño. They confirmed that the coastal wind intensification was primarily driven by the local SST forcing, whereas large scale variability associated with the South Pacific Anticyclone modulated its effects.

Examination of other El Niño events over the period 1979-2016 using reanalysis data confirmed that intensifications of alongshore wind off Peru were associated with SST alongshore gradient anomalies, as during the 1997-1998 event.

El Niño winds feedback on ocean dynamics

As [START_REF] Kessler | The circulation of the eastern tropical Pacific: A review[END_REF] suggested, El Niño events are an example of winds forcing the ocean in one place (the westerly wind bursts in the western Pacific Ocean), oceanic Kelvin waves carrying the signal in thermocline depth across thousands of kilometers (eastward to the eastern equatorial Pacific), thus changing the SST remotely (anomalous SST gradient northward), which then feeds back to modify the winds in the distant location (winds off the Peru coast). In addition, the nearshore wind increase off Peru during an El Niño event produces a strong Ekman pumping in the ocean (i.e. downward velocity; Fig. 5.1) which contributes to deepen the thermocline and enhances the ocean warming in addition to the one produced by the downwelling Kelvin wave (Thomas et al., 2001). It would be interesting to study the response of the ocean forced by the El Niño-related wind anomalies. We could examine the upwelling response by evaluating the role of the wind spatial modifications during El Niño (e.g. [START_REF] Halpern | Offshore Ekman transport and Ekman pumping off Peru during the 1997-1998 El Niño[END_REF]) and the role of the horizontal advection due to El Niño poleward currents near the coast. To do so, we could use a regional ocean model (ROMS) forced by the WRF wind fields produced in chapter 3.

The "coastal El Niño" dynamics

Coastal El Niño events are defined as such when warm conditions are encountered near the coasts of Peru whereas cold-to-neutral conditions are found in the central equatorial pacific. Such events are scarce but have been encountered in 1891, 1925 and recently in summer 2017 [START_REF] Takahashi | The very strong El Niño in 1925 in the far-eastern Pacific[END_REF][START_REF] Garreaud | Short Communication A plausible atmospheric trigger for the 2017 coastal El Niño[END_REF][START_REF] Echevin | Forcings and evolution of the 2017 coastal El Niño off Northern Peru and Ecuador[END_REF]. The behaviour of the ocean atmosphere system during the local warming "coastal El Niño" is different from "canonical" El Niño events. Indeed, northerly wind anomalies were observed during these events (Fig. 5.2,Fig 5.3a), in contrast to the well known upwelling-favorable southerly wind anomalies observed during the 'canonical" El Niño events. As in our previous study of the 1997-1998 El Niño, we could use the WRF model to study the wind dynamics during the local warming "coastal El Niño" and then use it to isolate the impact of the different effects (wind decrease, wind stress curl, etc.) on the ocean warming.

Climate change impact on coastal winds

123

• The land-sea thermal contrast increases under future conditions due to greater heat capacity of the ocean than the land. However, the wind decreases in summer, invalidating Bakun's hypothesis in the summertime.

• Model simulation showed an increase of precipitation off northern Peru in summer. This could also contribute to the weakening of the southerly wind in summer, as suggested by [START_REF] Belmadani | What dynamics drive future wind scenarios for coastal upwelling off Peru and Chile?[END_REF].

Pespectives on additional regional downscaling

Given that the change in nearshore winds off Peru could be very sensitive to the change in position and intensity of the SPA, it should be taken into account when the wind projection of individual global models are examined. For example while coastal wind is projected to decrease in the IPSL-CM5 and CNRM-CM5 models (∼10-15 %), no change is projected with the GFDL-ESM2M model (Fig. 5.4). It suggests that additional regional downscaling of different large grid-scale climate models could be needed to improve the consensus on the projections of the wind in the Peruvian upwelling system for the end of the 21st century. In addition, it would be interesting to study the regional impact of climate change on coastal winds off Peru in a near future (e.g. the period 2020-2030).

Regional oceanic climate change scenarios

One motivation for this study was the possibility to produce high resolution atmospheric forcing to run regional oceanic models simulations. It will allow to produce regional oceanography climate change scenarios to estimate the impact of the global warming on the upwelling and regional circulation in the Peruvian upwelling system. Similarly, it would be interesting to study the impact of the future winds downscaled using WRF on the ocean circulation. More precisely we suggest to do two ocean simulations: one forced by ocean boundary conditions from some reanalysis product (e.g. SODA; Carton and Giese (2008)) over the period 1994-2003 (same as our control atmospheric run) and by the WRF 7 km control run (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003), and other forced by renalysis ocean boundary conditions plus ocean boundaries anomalies from CMIP5 multi model mean and by the WRF 7 km RCP8.5 simulation. By following this protocol, we would be fully consistent with the regional atmospheric simulations. Furthermore, it would be intersting to see how these simulations would contrast with the ocean simulations of

High resolution atmosphere and ocean climate modelling

Understanding of the ocean dynamics in shallow regions of the Peruvian shelf and coastal embayments is still limited. As scatterometer winds have a blind zone near the coast, atmospheric high resolution outputs from WRF simulations for the present conditions could also be used to force ocean models to study the oceanic circulation at much smaller scale. For example, this could be done in the highly productive Paracas-Pisco bay (Fig.

Fig. 1 . 2

 12 Fig. 1.2 Monthly average sea level pressure and surface winds for July, using the 1959-1997 climatological base period of the NCEP Reanalysis project. The red line denotes the Inter Tropical Convergence Zone location. (Source of image: Climate Lab Section of the Environmental Change Research Group, Department of Geography, University of Oregon)

Fig. 1 .

 1 Fig. 1.4 A scheme of the main physical processes (courtesy of V. Echevin)

Fig. 1 . 5

 15 Fig. 1.5 Schematic diagrams of El Niño, Normal and La Niña conditions. Source: https: // www. pmel. noaa. gov/ elnino/ schematic-diagrams

Fig. 1 . 8

 18 Fig. 1.8 Coastal wind observations off Peru during El Niño events from anemometer and radiosonde wind data at Lima-Callao airport (left panel; Enfield (1981)) and from ERS satellite (right panel; Kessler (2006)).

  g. Wallace et al. (1989); Fig. 1.9c).

Fig. 1 . 9 A

 19 Fig. 1.9 A scheme of some hypothesis proposed to explain the wind intensification off the Peru coast during El Niño (courtesy of F. Colas)

Fig. 1 .

 1 Fig. 1.10 Schematic of greenhouse effect. Adapted from https: // www. eartheclipse. com

Fig. 1 .

 1 Fig. 1.11 a) Observed global mean combined land and ocean surface temperature anomalies, from 1850 to 2012 from three data sets. Top panel: annual mean values. Bottom panel: decadal mean values including the estimate of uncertainty for one dataset (black). Anomalies are relative to the mean of 1961-1990. b) Map of the observed surface temperature change from 1901 to 2012 derived from temperature trends determined by linear regression from one dataset (orange line in panel a). Source: IPCC, 2013: Summary for Policymakers.

Fig. 1 .

 1 Fig. 1.14 Maps depicting the annual mean surface temperature change (in • C) and the average change (in %) in annual mean precipitation for the RCP2.6 and RCP8.5 scenarios for the time period between 2081 and 2100 (source: IPCC 2013 SPM)

Fig. 2 . 1

 21 Fig. 2.1 WRF software infrastructure. It has three main components: the WRF Pre-processing System (WPS), the WRF model and the Post-Processing & Vizualization component. Source: http: // www2. mmm. ucar. edu/ wrf/ users/ model. html .

Fig. 2 . 2

 22 Fig. 2.2 WRF vertical coordinate η (Skamarock et al. 2008).

Fig. 2 . 3

 23 Fig. 2.3 Horizontal and vertical grids of the WRF-ARW solver (Skamarock et al. 2008)

2

 2

. 4 .Fig. 2 . 4

 424 Fig. 2.4 WRF domain used to evaluate the sensitivity to physical parameterizations.

  Figure 2.7 shows the mean net shortwave flux at the ocean surface for the year 2008 from ISCCP observations and WRF. Observations show two regions of minimum values of about 180-200 and

Fig. 2 . 5 Fig. 2 . 6

 2526 Fig. 2.5 Mean surface wind speed (in m s -1 , colours) and direction (arrows) for the year 2008 from a) QuikSCAT satellite and b)-g) WRF sensitivity experiments.

Figure 2 .

 2 8 shows the mean water vapor mixing ratio for the year 2008 from OAFLUX observations and WRF sensivity experiments. Observations show that humidity spatial distribution is almost zonally uniform with high values in the north and low values in the south of the domain. It shows values around 9-11 g kg -1 off the coast of southern Peru and around 11-15 g kg -1 off the northern coast of Peru. All WRF sensitivy experiments reproduce relatively well the regional spatial distribution of humidity, but show the largest biases nearshore off southern Peru and northern Chile (Fig. 2.9). WRF1, WRF5 and WRF6 experiments show a strong positive bias almost everywhere in the model domain.

Fig. 2 . 7

 27 Fig. 2.7 Mean net shortwave flux at ground surface for the year 2008 (in W m -2 ) from a) ISCCP observations and b)-g) WRF sensitivity experiments.

Fig. 2 . 8

 28 Fig. 2.8 Mean water vapor mixing ratio for the year 2008 (in g kg -1 ) from a) OAFLUX observations and b)-g) WRF sensivity experiments.

Fig. 2 . 9

 29 Fig. 2.9 Mean water vapor mixing ratio bias (in g kg -1 ) from WRF sensitivity experiments with respect to OAFLUX observations for the year 2008.

Fig. 2 .Fig. 2 .

 22 Fig. 2.10 Cross-shore vertical sections at 20 • S of the mean meridional wind (shading, in m s -1 ) and air temperature (magenta contours, in • C) from a)-b) WRF1 and WRF4 sensitivity experiments and c) VOCALS-REx observations, and zonal meridional wind (shading, in m s -1 ) from d)-e) WRF1 and WRF4 sensitivity experiments and f) VOCALS-REx observations. Data averaged between 28 October and 3 November 2008. Black contours indicate zero meridional wind speed.

Fig. 2 .

 2 Fig. 2.13 Mean surface wind speed (in m s -1 )) and direction (arrows) for the period 1997-1998 from WRF simulations with three different configurations: a) Single domain forced with ERA-Interim data, b) Nested domain forced with ERA-Interim data, and c) Nested domain forced with CFSR data.

Fig. 2 .

 2 Fig. 2.14 Cross-shore vertical sections at 15 • S of the mean meridional wind (shading, in m s -1 ) and air temperature (magenta contours, in • C) from three WRF simulations with different configurations (same as Fig. 2.13).

Fig. 2 .

 2 Fig. 2.15 Execution time of the WRF model (1 month of simulation) when varying the number of CPU cores on the IMARPE cluster.

Figure 5 Jan

 5 Figure 1

Figure 9 JanFigure 16 3. 4

 9164 Figure 9

Fig. 3 . 1

 31 Fig. 3.1 Mean zonal profiles of the wind speed in the year 2000, averaged between 7 • S and 13 • S, from WRF model, and the scatterometers QSCAT and ERS.

Fig. 3 . 2 Fig. 3 . 3

 3233 Fig. 3.2 Mean shortwave flux at the surface (W m -2 ) for the period 1994-2000 from a) WRF, b) SRB and c) ISCCP. d-f) Respective anomalies during El Niño period November 1997-January 1998.

Fig. 3 . 4

 34 Fig. 3.4 Air temperature at 2 meters over land from a) WRF model and b) ERA-interim. Anomalies of the air temperature at 2 meters from the surface during November 1997-January 1998 (Niño conditions) from c) WRF and d) ERA-interim. Units are in • C.

Fig. 3 .

 3 Fig. 3.5 a) Mean annual cycle of air temperature over Lima (12 • S) and b) monthly average for the period 1994-2000.

96 Chapter 4 .

 4 Climate change impact on the Peruvian coastal winds 4.2 Model and data 4.2.1 WRF model configuration We used the WRF model version 3.7.1 with three nested domains (Fig. 4.1a). The largest domain encompasses the tropical Pacific region with a horizontal resolution of 105 km. It was also used to carry out downscaling of climate change projections of the South Pacific Convergence Zone (see Dutheil et al. (2018)). The second domain covers the Peru and Northern Chile regions (30 • S-12 • N) with a horizontal resolution of 21 km. The innermost domain covers nearly all of the Peru coast (17 • S-5 • S) with a horizontal grid spacing of 7 km. The high resolution of the innermost domain improves the representation of the orography and shape of the coastline (Fig. 4.1b). A total of 32 vertical levels were used in the parent domain, and 60 vertical levels in the second and innermost domain, with 21 levels in the first ∼1000 m which allows to represent better the coastal terrain located between the Andes and the ocean (Oerder, 2016). The physical parameterizations used in this study are described in section 2.3.

Fig. 4 .

 4 Fig. 4.1 a) The three model domains used in the WRF simulations: d01, d02 and d03, with horizontal resolution of 105, 21 and 7 km respectively b) Model orography (in meters) above sea level for the innermost domain.

  5 scenario) and 1989-2009 (Fig. 4.2c). They are added to the SST forcing from NCEP2 (OISST), using the following equation: SST (W RF RCP 8.5 ) = SST (N CEP 2, daily)+SST clim (CM IP 5 RCP 8.5 , 2080-2100, monthly) -SST clim (CM IP 5 historical , 1989 -2009, monthly)

Fig. 4 .

 4 Fig. 4.2 a)The annual ensemble mean SST for 31 global models (period 1989-2009). b) The annual ensemble mean SST bias calculated by the difference between the model SST and the AVHRR SST observations. The dots denote where 80% of the 31 models have the same sign for the SST bias. c) Differences in SST between the periods 2080-2100 (RCP8.5 scenario) and 1989-2009 (historical simulation) from 31 CMIP5 global climate models.

Fig. 4 . 3

 43 Fig. 4.3 SST climate anomalies (future-present) in a) summer and b) winter in the Peru region. c) Seasonal cycle of the SST climate anomalies averaged in a 100 km coastal band between 7-15 • S. Data from the CMIP5 MMM.

Figure 4 .

 4 5 shows the climatological mean annual cycle of the alongshore wind off the Peru coast from WRF7 and QuikSCAT over the period 2000-2003. Overall, modelled and observed alongshore wind climatologies are in agreement on the seasonal cycle. The strongest winds (<7 m s -1 ) are found during austral winter (July-September) around 15 • S, where there is a well-marked seasonal cycle. It is well reproduced by the model. On the other hand, the alongshore wind is weaker during austral summer (January-March), especially between 5-12 • S. It is overestimated by the model by about 1 m s -1 (∼ 25% error).

Figure 4 .Fig. 4 . 4 Fig. 4 . 5

 44445 Fig. 4.4 Mean surface wind for the period 2000-2003 from a) WRF105, b) WRF21, c) WRF7 simulations, and d) QuikSCAT satellite observations. e) Mean cross-shore surface wind speed between 7-13 • S.

Fig. 4 . 6

 46 Fig. 4.6 Mean cross-shore surface wind speed (between 7-13 • S) in a) summer, b) autumn, c) winter, and d) spring, for the period 2000-2003, from WRF7 (red lines) and QuikSCAT (black lines).

  5 from WRF21 and WRF7 are shown in Figures 4.7a and 4.7d respectively. Their differences with the period 1994-2003 are shown in Figures 4.7b and 4.7e. Also shown is the relative change (in %) in the mean seasonal cycle of alongshore winds off the Peru coast (Figures 4.7cand 4.7f

Fig. 4 . 7

 47 Fig. 4.7 Mean surface wind in the period 2086-2095 under the RCP8.5 scenario from a) WRF21 and d) WRF 7km. Change in mean surface wind (2086-2095 average minus 1994-2003 average) from b) WRF21 and e) WRF7. Relative change (in %) in the mean seasonal cycle of alongshore winds (in a coastal band of ∼100 km) for the period 2085-2095 relative to the period 1994-2003 from c) WRF21 and f) WRF7.

Figure 4 .

 4 Fig. 4.8 Change in mean surface wind in a) summer, b) autumn, c) winter, and d) spring, from WRF7 simulation.

  4.9d). They show strongly negative values off the South coast that decrease to the north showing some similar structures to those seen in the pressure gradient term.Comparison of advection, pressure gradient, Coriolis and vertical mixing terms shows that in general the pressure gradient is the dominant force (Fig.4.9b), which is mainly compensated by the Coriolis force and vertical mixing (Figs. 4.9c and 4.9d), except in the nearshore region north of 14 • S off the Pisco bay where advection plays an important role in balancing the negative pressure gradient. On average over the time period, trends in the wind (DV /dt) are much weaker than the other terms of the momentum balance (not shown).

Figure 4 .

 4 10 shows the momentum anomalies due to climate change, i.e. the difference between the RCP8.5 (period 2086-2095) and the present time (period 1994-2003). Comparison of change in advection, pressure gradient, Coriolis and vertical mixing shows that at large scale pressure gradient and vertical mixing are the dominant forces (Figs. 4.10b and 4.10d). The pressure gradient change shows relatively strong positive anomalies (∼150 m s -1 ) in the South-West region that decrease shoreward. The vertical mixing change is negative and opposed to the pressure gradient change. The two terms almost balance each other everywhere in the domain, except in the nearshore region north of 14 • S off the Pisco bay where advection change is positive and relatively strong (Fig. 4.10a) and pressure gradient change is negative. The Coriolis term change has smaller values (Fig. 4.10c).

Fig. 4 . 9

 49 Fig. 4.9 Mean annual meridional component of the surface forces (in m s -1 ) in the period 1994-2003: a) advection (V_ADV), b) pressure gradient (V_PGF), c) Coriolis force (V_COR) and d) turbulent vertical mixing (V_MIX).

4. 3 105 Fig. 4 .

 31054 Fig. 4.10 Change in the mean annual meridional component of the surface forces (in m s -1 ) between the period 2086-2095 under the scenario RCP8.5 and the period 1994-2003: a) advection (V_ADV), b) pressure gradient (V_PGF), c) Coriolis force (V_COR) and d) turbulent vertical mixing (V_MIX).

Fig. 4 .

 4 Fig. 4.11 a) Mean alongshore wind along the Peru coast in summer (January-March) for present (1994-2003) and future (2086-2095) conditions. b) Mean alongshore momentum budget along the Peru coast for present conditions. c) Projected change in the mean alongshore momentum budget.

Fig. 4 .

 4 Fig. 4.13 a) Total advection of meridional momentum over May-August under present conditions and their respectives b) zonal, b) meridional and vertical contributions in the Paracas-Pisco bay area. Arrows in a) indicate surface winds.

Fig. 4 .

 4 Fig. 4.14 a) Change in total advection of meridional momentum over May-August under future conditions. Change of the b) zonal, b) meridional and vertical advection contributions.

Figures 4 .

 4 15a-b show the vertical sections of the annual mean air temperature along the Peru coast for future and present conditions, respectively. The air temperature shows an almost homogeneous increase of 3-4 • C between the surface and 1600 m. There remains a well marked temperature inversion in winter and not in summer (Fig. 4.15c). Similarly, the air humidity in the vertical section increases almost uniformly by 2-3 g kg -1 between the surface and 1600 m (Fig. 4.16a-b), both during summer and winter (Fig. 4.16c).

Fig. 4 .

 4 Fig. 4.15 Alongshore vertical section of the annual mean of air temperature for a) future and b) present conditions. c) Air temperature vertical profile for summer and winter seasons in future conditions. Doted lines show the air temperature in present conditions.

Fig. 4 .

 4 Fig. 4.16 Alongshore vertical section of the annual mean of air humidity for a) future and b) present conditions. c) Air humidity vertical profile for summer and winter seasons in future conditions. Doted lines show the air humidity in present conditions.

Fig. 4 .

 4 Fig.4.18 Same as Fig.4.17 but for winter.

Fig. 4 .

 4 Fig. 4.19 Mean sea level pressure in present and future conditions (white and black contours respectively) and sea level pressure change (shading) for a) summer (January-March) and b) winter (July-September). Blue lines indicate zero sea level pressure changes. Outputs from WRF105 simulations.

Fig. 4 .

 4 Fig. 4.20 Climatology of the a) intensity, b) latitudinal position, and c) longitudinal position of the South Pacific Anticyclone (SPA) for present and future conditions (blue and red lines respectively).

Fig. 4 .

 4 Fig. 4.21 a) Change in land and sea surface temperature. b) Land sea temperature contrast in summer (January-March) for present and future conditions. c) Same as b) but in winter (July-September).

Fig. 4 .

 4 Fig. 4.22 Change in the mean sea level pressure (in hPa, colors) and wind (in m s -1 , vectors)

  4.23a). So, its contribution on the weakening of the coastal wind in summer should not be too strong, as in the case of Belmadani et al. (2014) simulations. Note that simulations with the IPSL global model used by Belmadani et al. (2014) tipically show the overestimation of precipitation in regimes of atmospheric subsidence (Dufresne et al., 2013).

Fig. 4 .

 4 Fig. 4.23 Change in precipitation (in mm d -1 , colors) and wind (in m s -1 , vectors) for a) summer and b) winter from WRF 21 km.

4. 3 . 6

 36 El Niño wind anomalies under future conditions In order to evaluate the impact of climate change on El Niño wind anomalies, we compared wind anomalies during the November 1997-February 1998 from WRF7 control simulation against the wind anomalies during the November 2089-February 2090 from WRF7 climate change simulation (Fig. 4.24). Note that SST anomalies are the same in both cases by construction of our future SST forcing (see section 4.2.2). Positive wind anomalies are stronger near the coast and decrease offshore under present and future conditions (Figs. 4.24a and b respectively). The major differencens between both spatial patterns are located off the North coast where wind anomalies under future conditions are less intense that under present conditions (Figs. 4.24c).

  4.25). They show a temporal displacement and intensification of the wind anomaly peak in the Northern coast from February to April (from ∼1 m s -1 under present conditions to ∼1.5 m s -1 under future conditions) as a effect of climate change (Figs. 4.25a, b).

Fig. 4 .

 4 Fig. 4.24 Mean surface wind anomalies (in m s -1 ) from WRF7 over El Niño conditions: a) November 1997-February 1998 and b) November 2089-February 2090 (RCP8.5 scenario). c) Differences between b and a).

Fig. 4 .

 4 Fig. 4.25 Time-latitude diagrams of alongshore wind anomalies from WRF7.

Fig. 4 .

 4 Fig.[START_REF] Capet | Upwelling response to coastal wind profiles[END_REF].26 Time evolution of alongshore wind anomalies (in m s -1 ) for WRF 105 km in future (red line) and present (black line) conditions for five El Niño events. Anomalies were averaged in a 160-km-wide coastal band and between 7-11 • S.

4. 4 Fig. 4 .

 44 Fig. 4.27 Mean surface wind anomalies (in m s -1 ) over February 2090-May 2090 (Niño conditions) from a) WRF7, b) WRF21 and c) WRF105.

Fig. 5 . 1

 51 Fig. 5.1 Monthly mean anomalies of a) alongshore wind stress, b) Ekman pumping/suction, and c) sea surface temperature (Halpern, 2002).

Fig. 5 . 2

 52 Fig. 5.2 Monthly sea surface temperature (shading, • C) and surface wind (m s -1 ) anomalies from ICOADS (Takahashi and Martínez, 2017).

Fig. 5 . 4

 54 Fig. 5.4 Mean wind stress intensity in a coastal box off Peru from IPSL-CM5A, CNRM-CM5 and GFDL-ESM2M global models (Gévaudan et al., 2018)-.

  A few studies (Echevin et al., 2012; Oerder et al., 2015; Gévaudan et al., 2018) on dynamical downscaling of ocean climate scenarios from some global models (IPSL-CM4, IPSL-CM5, CNRM-CM5, GFDL-ESM2M) were performed using the ROMS regional ocean circulation model for the Peru upwelling system with different types of wind products. Echevin et al. (2012) used surface winds statistically downscaled from outputs of the global model IPSL-CM4 performed by Goubanova et al. (2011) under quadrupling CO 2 scenario. Oerder et al. (2015) used downscaled surface winds from the LMDz model with increased horizontal resolution in the Peru-Chile upwelling system performed by Belmadani et al. (2014) under 2CO 2 and 4CO 4 scenario. Gévaudan et al. (2018) used QuikSCAT satellite winds perturbed by CMIP5 models without downscaling. They found a surface ocean warming of 3-4 • C (quite similar to the SST anomalies in the global model run, see Figure 5.5) and a reduction in upwelling intensity off Peru associated with the reduction of the alongshore winds, as well as an intensification of the poleward coastal undercurrent (PCUC), a shoaling of the equatorward surface coastal Peru-Chile current (PCC) and the strengthening of the mesoscale eddy activity when regional warming increases.

  Echevin et al. (2012), Oerder et al. (2015) and Gévaudan et al. (2018).

Fig. 5 . 5

 55 Fig. 5.5 SST change between the periods 2091-2100 and 2006-2015 from a) CNRM-CM5 model and b) ROMS model (Gévaudan et al., 2018).

  Fig. 5.7 Surface wind stress mesoscale anomalies (10 -2 N m -2 , colors) and sea surface temperature anomalies ( • C, contours). Fields are from a WRF-NEMO simulation and time-averaged over July 2007 (Oerder et al., 2016).

2.1.3 Observational data

  • × 1 • grid before the multimodel mean was calculated.

	Wind observations from two different satellite-borne scatterometers (ERS and QuikSCAT)
	were used to evaluate the realism of the modelled surface winds over the sea surface.
	The two products were processed by CERSAT (2002a, b) and downloaded from http:
	//www.ifremer.fr/cersat. Wind data from ERS are provided at 1 • × 1 • resolution over
	the period 1992-2000 at a weekly frequency, and QuikSCAT wind fields at 1/4 • × 1/4 •
	resolution over the period 2000-2008 at a daily frequency.
		The daily Optimum Interpolation Sea Surface Temperature (OISST) at 1/4 • (Reynolds
	et al., 2007) was used as SST forcing for the atmospheric model. It is commonly known
	as the Reynolds SST and it is available from https://www.esrl.noaa.gov/psd/data/
	Number gridded/data.noaa.oisst.v2.html. Model 1 ACCESS1-0 CSIRO-BOM (Australia) Institution VOCALS-REx (VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment; Resolution (in degrees) 1.875 × 1.25 2 ACCESS1-3 CSIRO-BOM (Australia) 1.875 × 1.25 Wood et al. (
	3	bcc-csm1-1	BCC (China)	1 × 1.33
	4	BNU-ESM	GCESS (China)	2.8 × 2.8
	5	CanESM2	CCCma (Canada)	2.8125 × 2.8125
	6	CCSM4	NCAR (USA)	0.9 × 1.25
	7	CESM1-BGC	NSF-DOE-NCAR (USA)	0.9 × 1.25
	8	CESM1-CAM5	NSF-DOE-NCAR (USA)	0.9 × 1.25
	9	CMCC-CM	CMCC (Italy)	0.75 × 0.75 (L31)
		CMCC-CMS	CMCC (Italy)	0.75 × 0.75 (L95)
		CNRM-CM5	CNRM-CERFACS (France)	1.5 × 1.5
		CSIRO-Mk3-6-0	CSIRO-QCCCE (Australia)	1.875 × 1.875
		FGOALS-g2	LASG-CESS (China)	3 × 2.8
		FIO_ESM	FIO (China)	2.8 × 2.8
		GFDL-CM3	NOAA GFDL (USA)	2 × 2.5
		GFDL-ESM2G	NOAA GFDL (USA)	2 × 2.5
		GFDL-ESM2M	NOAA GFDL (USA)	2 × 2.5
		HadGEM2-CC	MOHC (UK)	1.25 × 1.875
		HadGEM2-ES	MOHC (UK)	1.25 × 1.875
		inmcm4	INM (Russia)	1.5 × 2.0
		IPSL-CM5A-LR	IPSL (France)	1.875 × 3.75
		IPSL-CM5A-MR	IPSL (France)	1.25 × 2.5
		IPSL-CM5B-LR	IPSL (France)	1.875 × 3.75
		MIROC5	MIROC (Japan)	2.8 × 2.8
		MIROC-ESM-CHEM	MIROC (Japan)	2.8 × 2.8
		MIROC-ESM	MIROC (Japan)	2.8 × 2.8
		MPI-ESM-LR	MPI (Germany)	1.875 × 1.875 (L47)
		MPI-ESM-MR	MPI (Germany)	1.875 × 1.875 (L95)
		MRI-CGCM3	MRI (Japan)	1.125 × 1.125
		NorESM1-M	NCC (Norway)	1.875 × 2.5
		NorESM1-ME	NCC (Norway)	1.875 × 2.5
		Table 2.1 -List of CMIP5 Models used

  • S (Figs. 4.7c).

	a) Alongshore wind (JAS)			b) Momentum budget		c) Change in momentum budget
	6 o S			Present Future		6 o S			ADV PGF		6 o S				ADV' PGF'
									COR					COR'
	8 o S					8 o S			MIX		8 o S				MIX'
	10 o S					10 o S				10 o S
	12 o S					12 o S				12 o S
	14 o S					14 o S				14 o S
	16 o S					16 o S				16 o S
	2	4	6	8	10	-1000	-500	0	500	1000	-150 -100	-50	0	50	100	150
			m/s

Table 4 .

 4 1 -Characteristics and results of previous climate change studies of coastal winds off Peru.

Processing & Vizualization component refers to the graphics and verification tools used to process the output data.

Acknowledgements

This research was funded by a PhD fellowship from CIENCIACTIVA/CONCYTEC-PERU to study at the University Pierre and Marie Curie (UPMC) of Paris, France. It is also a contribution to the cooperative agreement between the Instituto del Mar del Perú (IMARPE) and the Institut de Recherche pour le Developpement (IRD) I would like to specially acknowledge to my advisors, Drs. Vincent Echevin and François Colas from IRD, for their invaluable academic support and patience in the development of this thesis. Also, thanks to Drs. Jorge Tam and Dimitri Gutiérrez, my supervisors at IMARPE. I would also like to acknowledge to all my lab colleagues from LMOECC (Laboratorio de Modelado Oceanográfico, Ecosistémico y del Cambio Climático) at IMARPE and LOCEAN (Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques) at UPMC who have greatly contributed towards my evolution and supported me during the development of this thesis.