Effects of Turbidity on the Equatorial Circulation Using Numerical

NATIONAL & KAPODISTRIAN UNIVERSITY OF ATHENS

MSC IN ENVIRONMENTAL PHYSICS

Eﬀects of turbidity on the equatorial circulation using numerical simulations

Panagiotis Andriopoulos

supervised by Assist.Prof. Sarantis Sofianos

examined by Assist.Prof. Sarantis Sofianos Prof. George Kallos Assoc.Prof. Elena Flocas

Athens, June 2018 Abstract

Turbidity affects directly the physical properties of the global ocean through the absorption of solar radiation by the upper layers. The present thesis focuses on studying these effects, mainly on the equatorial region, by conducting sensitivity experiments with the use of an ocean circulation coupled with a biochemical numerical model (NEMO GCM-PISCES). Four experiments are performed, in which different parameterizations for the penetrative solar radiation correspond to different turbidity regimes. The first two experiments use globally constant values for the solar radiation penetration depth to represent clear and turbid waters respectively. In the third and fourth experiments the penetration depth depends on the concentration of the surface chlorophyll, where the third experiment uses remote sensing chlorophyll measurements and the fourth experiment uses chlorophyll produced by the biochemical model. Comparing the experiments showed that increasing turbidity produces SST increase everywhere but in the equatorial regions, where the open ocean upwelling is increased, bringing colder SSTs to the surface. Colder equatorial SSTs decrease the surface wind stress locally on the equator and while this tends to decrease divergence and upwelling, the results show that localized changes on the wind stress do not drive the increase in the upwelling and equatorial SST cooling

Key words : turbidity, numerical simulations, sensitivity,chlorophyll Acknowledgements

First of all, I would like to thank my supervisor, Sarantis Soﬁanos for giving me the opportunity to work with him and for his valuable contribution throughout the progress of this thesis. Furthermore, I want to thank Dr. Athanasia Papapostolou and Dr. Vasilios Vervatis for most helpful discussions and suggestions. I would also like to thank the examination committee for taking the time and eﬀort of reading this thesis. Last but not least, I want to thank my family and friends for their motivation and support during the last year.

2 Contents

1 Introduction 6 1.1 Motivation ...... 6 1.2 Theoretical background ...... 6

2 Methodology 9 2.1 Model description ...... 9 2.2 Experiments ...... 11

3 Results 14 3.1 Impacts of different turbidity parameterizations on biologically produced chlorophyll ...... 14 3.1.1 Differences in chlorophyll concentrations between experiments 15 3.2 Impacts of different turbidity parameterizations on temperature . . . 19 3.3 Effects of turbidity on wind stress and circulation...... 28 3.4 Changes in meridional overturning circulation...... 33

4 Summary and conclusions 36

A NOOA SST 37

3 List of Figures

2.1 Model bathymetry (m) ...... 10 2.2 Mean values of chlorophyll climatology...... 12 2.3 Time series for globally averaged kinetic energy (a) and potential temperature (b)...... 13

3.1 Seasonal PISCES chlorophyll for the RGB experiment. Each panel depicts a different season of the last year of simulations...... 15 3.2 Differences between biologically produced chlorophyll and satellite observations...... 16 3.3 Chlorophyll concentration differences between the experiments. These are the annual mean values of the last year of simulations (year 100). 17 3.4 Differences of ξ¯ between the RGB and COUPLED experiments. The values of ξ¯ where computed for the last year of simulations of both experiments...... 18 3.5 Time series for the last 30 years of simulations of the sea surface PISCES-chlorophyll concentrations in the (a) equatorial Atlantic (2.5oN- 2.5oS) and (b) equatorial Pacific (2.5oN−2.5oS)...... 19 3.6 Annual mean SST for the RGB experiment...... 20 3.7 SST differences between the experiments ...... 21 3.8 Time series of SST in the Atlantic (a) and Pacific (b) 2.5oS-2.5oN band for the last 30 years of simulations...... 23 3.9 Differences between the experiments of vertical velocities at 50m depth. 24 3.10 Temperature differences between the experiments in 25o W (Atlantic ocean), focused on the 15oN-15oS band, from the surface to 500m. . . 26 3.11 TTemperature differences between the experiments in 150o W (Pacific ocean), focused on the 15oN-15oS band, from the surface to 500m. . . 27 3.12 Wind stress magnitude of the RGB experiment for the last year of simulations...... 28 3.13 Differencesof wind stress between the experiments...... 29 3.14 Surface circulation differences between the four experiments. In (a) through (d) the colormap√ shows the magnitude of the circulation differences, calculated as | u2 + v2| ...... 31 3.15 Differences of meridional velocity for the Atlantic ocean...... 32 3.16 Differences of meridional velocity for the Pacific ocean...... 33 3.17 Meridional overturning circulation differences between the experiments close to the equator in the atlantic ocean...... 34 3.18 Meridional overturning circulation differences between the experiments close to the equator in the pacific ocean...... 35

4 A.1 Sea surface temperature from NOOA (https://www.nodc.noaa.gov/cgi- bin/OC5/woa13fv2/woa13fv2.pl) ...... 37

5 Chapter 1

Introduction

1.1 Motivation

Turbidity is an optical property of the ocean waters that is connected to the absorption of the incoming solar radiation, leading to modifications of the distribution of vertical heating in the upper layers of the ocean, directly affecting its thermal structure and dynamics. When turbidity increases, the fraction of radiative heating captured by the surface waters increases and less radiative heating penetrates to the deeper layers, resulting in a warming of the ocean surface (e.g., Lengaigne et al. 2007, Park et al. 2014). However, in the equatorial regions the opposite behavior occurs, since increased turbidity causes cooling of the sea surface temperature (Nakamoto et al. 2001, Manizza et al. 2005, Anderson et al. 2009, Park et al. 2014, Hernandez 2017). The mechanisms causing this opposite effect are still a matter of debate. The present thesis focuses on the feedback mechanism between different penetrative solar radiation parameterizations on the circulation and sea surface temperature of the equatorial Atlantic and Pacific oceans and the feedback between the sea surface temperature and circulation and the wind stress. The equatorial regions of the Indian ocean were not examined, since the dominant presence of monsoons introduce a seasonal bias to the annual mean of the ocean’s thermal structure and circulation. In order to study these feedback mechanisms, four experiments were performed, based on how the penetration of solar radiation is parameterized and their differences were examined.

1.2 Theoretical background

Turbidity is essentially an optical property of the ocean that modifies the absorption of the incoming solar radiation. In plain language, turbidity is a metric of how clear the water is. Downward solar irradiance is assumed to be decaying exponentially with water depth z: − z I(z) = I0 · e ξ (1.1) where, I(z) is the downward irradiance (energy per unit area per unit time), I0 is the incident less reflected and emergent irradiance at the surface and ξ is the attenuation length. It is defined as the depth at which the irradiance has reduced I I to 0 . The ratio 0 is also called e-folding depth. e e

6 The exponential decay of I0 expressed by 1.1 is a poor approximation in the upper 5m of the ocean, because of the selective absorption of the short and long wavelengths of the solar radiation by the water. Deeper than 10m however, 1.1 is a good assumption. Jerlov (1968) studied the vertical distribution of light in the upper ocean layers and proceeded to a classification of surface water types based on the absorption of ligth and therefore turbidity. He assumed that the attenuation coefficient of the downwelling irradiance Kd(λ) is a linear function of Kd at a reference wavelength (λ = 475nm), thus, the oceanic types I, IA, IB, II, and III are defined based on their Kd(475) values, which means that all the Kd(λ) values are fixed for a given type (Jerlov, 1976, Table 27). Paulson and Simpson (1977) suggested a different expression than 1.1, in which the solar radiation absorption is perceived between the visible and the infrared wave bands: h z z i − ξ − ξ I(z) = I0 · R · e 0 + (1 − R) · e 1 (1.2) where now R is a fraction of solar radiation that resides inthe almost non-penetrative wavebands. The first term on the left hand side of 1.2 refers to λ > 700nm and the second term to 400 ≤ λ ≤ 700nm and ξ0 and ξ1 are the attenuation lengths for the infrared and visible waveband respectively. ξ ξ Jerlov Type R 0 1 (m) (m) Type I 0.58 0.35 23 Type 1 (upper 50m) 0.68 1.2 28 Type IA 0.62 0.60 20 Type IB 0.67 1.0 17 Type II 0.77 1.5 14 Type III 0.78 1.4 7.9

Table 1.1: Values of R, ξ0 and ξ1 for each Jerlov type of water (Paulson and Simpson, 1977).

According to Paulson and Simpson (1977) the attenuation length has a constant global value, meaning that turbidity is everywhere the same. This is not a realistic assumption, therefore Morel (1988) suggested a new method where the ocean waters can be categorized in two classes: Case I waters, those waters for which phytoplankton plays a predominant role in determining their optical properties, and Case II waters, where the optical water properties are determined by the presence of sediments. Morel (1988) assumed that the radiation at depth z is given by: −K(λ)· Ez(λ) = E0(λ) · e (1.3) where λ is the wavelength of the radiation, Ez(λ) is the radiation at depth z, E0(λ) is the radiation at sea surface and e(λ) K(λ) = Kw(λ) + χc(λ) · C (1.4)

In 1.4 Kw(λ) represents the spectral values of the diﬀuse attenuation coeﬃcient for pure oceanic waters, χc(λ) and e(λ) depend on the wavelength and are tabulated in a table 61 values with the wavelengths ranged from 400 nm to 700 nm.

7 Lengaigne et al. (2007) have constructed a simplified version of this formulation in which visible light is split into three wavebands: blue (400-500 nm), green (500- 600 nm) and red (600-700nm) (RGB method). For each wave-band, the chlorophyll- dependent attenuation coefficient is fitted to the coefficients computed from the full spectral model of Morel and Maritorena (2001), that is a modified version of Morel’s 1988 model, assuming the following expression: − z 1 − R − z − z − z I(z) = I · R · e ξ0 + · e ξr + e ξg + e ξb (1.5) 0 3

The main advantage of Lengaigne’s method is that, in order to evaluate the penetration depth (here K = 1/ξ), it takes into account the chlorophyll concentration, which provides a more realistic approach, since the absorption of light by the chlorophyll contained in phytoplanktonic biomass plays a dominant role on determining the open ocean turbidity.

8 Chapter 2

Methodology

2.1 Model description

The model used in the present thesis is a global conﬁguration built from the oceanic component of Nucleus for European Modeling of the Ocean (NEMO, Madec et al. 1998; Madec 2008) coupled with the Pelagic Interaction Scheme for Carbon and Ecosystem Studies (PISCES) biogeochemical model (Aumont and Bopp 2006) and the Louvain-la-Neuve Sea Ice model (LIM). In the present thesis, the LIM3 version of LIM is used, since it mainly gives better results in the ice momentum conservation equation (Hunke and Dukowich, 1997). The model solves the three dimensional primitive equations discretized in an Arakawa C-grid, along with a nonlinear equation of state that couples salinity and temperature to the velocity of the ﬂuid assuming that hydrostatic approximation and incompressibility hold:

~ ∂Uh ~ ~ ~ 1 ~ 2 ~ ~ 1 ~ ~ U~ ~ U~ = − ∇ × U × U + ∇ U − fk × Uh − ∇p + D + F (2.1) ∂t 2 h ρ0 ∂p = −ρg (2.2) ∂z ∇~ · U~ = 0 (2.3) ∂T = −∇~ · (T U~ ) + DT + F T (2.4) ∂t ∂S = −∇~ · (SU~ ) + DS + F S (2.5) ∂t ρ = ρ(T, S, p) (2.6) When the solar radiation is let to penetrate in deeper layers, the following term is added to 2.4: 1 ∂I

ρ0 · Cp · e3 ∂K where ρ0 is the mean density, Cp is the heat capacity, e3 is the vertical discretization step and I the downward irradiance. The model uses the GLS turbulence closure scheme and a bilaplacian operator for the lateral diﬀusion. The resolution of the conﬁguration is 1o × 1o in the horizontal (362×332 grid points) and 75 unevenly distributed vertical levels and for surface heat

9 and momentum fluxes, CORE bulk formulae are used with 8 input data: u and v velocity components of wind speed at 2m, air temperature at 10m, specific humidity, short and longwave incoming solar radiation, total precipitation and (solid, liquid and snow) and river runoffs.. The model is forced with the DFS5.2 product, which is based on ERAinterim reanalysis data. The new configuration was developed based on the ORCA2 benchmark configuration and the ORCA025 configuration developed by UK Met Office.

Figure 2.1: Model bathymetry (m)

10 2.2 Experiments

For the purposes of the present thesis, four experiments were performed and their diﬀerences are examined. The present thesis focuses on the Atlantic and Paciﬁc regions. The equatorial regions of the Indian ocean are not examined, since the dominant presence of monsoons introduce a seasonal bias to the annual mean of the oceans thermal structure and circulation. Two of the experiments use Jerlov’s theory for calculating the penetrative and the other two use the RGB formulation, where it is assumed that chlorophyll concentration is spread uniformly in the vertical direction. The experiments are categorized as follows:

• RGB: the reference experiment, as it is the most realistic representation of the penetration of solar radiation. It uses as an input a chlorophyll 12 month climatology from satellite observations. This observed chlorophyll is used by the ocean circulation model to define ocean turbidity. Figure 2.2 shows 3-month mean values of the input chlorophyll observations, each panel representing a different season. The use of different month names to represent each season is chosen because the use of seasons’ names could create confusion when refer- ring to the northern or southern hemisphere. Instead, the name of the month that the 3 month average is centered around is used to refer to the different seasons. ”February” in Figure 2.2 refers to winter, ”May” to spring, ”August” to summer and ”November” to autumn and any figure that shows seasonal maps through the rest of the thesis uses the same notation. Figures 2.2.(a) and 2.2.(d) show high chlorophyll concentrations in the Antarctic region. This is because during these periods of the year, this region receives solar radiation permanently, so phytoplankton can photosynthesise and produce large amounts of chlorophyll. The same thing happens in May and August (Figures 2.2b,c) in the arctic region, for exactly the same reason. High chlorophyll concentrations are shown in the tropical Atlantic and Pacific ocean. This is because these are the equatorial upwelling regions, so nutrients from deeper layers of the ocean are brought to the surface and phytoplankton uses them to photosynthesise. In mid latitudes chlorophyll concentrations are smaller. In these regions, the sea surface temperature is high, thus the ocean is well stratified, so nutrients from deeper layers can’t be brought to the surface. Finally, high chlorophyll concentrations are shown in the near coast regions, where deeper water masses bring nutrients to the surface due to the coastal upwelling effect.

• COUPLED: in this experiment, the chlorophyll produced by PISCES is used as an input in order to compute ocean turbidity, through the same method used by the RGB experiment. This experiment can provide insights on possible positive or negative feedbacks, between turbidity changes, and parameter directly aﬀected by those changes namely temperature and the chlorophyll produced through a series of biogeochemical reactions simulated by PISCES.

• TYPE I: in this experiment, TYPE I water (clear water) is considered. Here, R = 0.58, ξ0 = 0.35m and ξ1 = 23m.

11 Figure 2.2: Mean values of chlorophyll climatology.

• TYPE III: in this experiment, TYPE III water (most turbid) is considered and R = 0.78, ξ0 = 1.4m and ξ1 = 7.9m.

TYPE III lets more infrared radiation to penetrate (RIII > RI ) in deeper layers, (ξ0III > ξ0I ) but absorbs larger amounts of the visible radiation (ξ1III < ξ1I ). This explains why TYPE III is more turbid than TYPE I. It should be noted that the biology-produced chlorophyll concentrations from PISCES (hereafter called PISCES-chlorophyll) in TYPE I and TYPE III experiments, are independent of any observations and depend only on the temperature ﬁelds produced by the ocean circulation component of the model. The PISCES-chlorophyll in the RGB experiment, is also independent of any chlorophyll observations, although the ocean circulation model in this experiment uses the satellite observations to calculate the penetration depth of the solar radiation. The PISCES chlorophyll in the COUPLED experiment is the same used by the ocean circulation model to calculate the penetration depth of the solar radiation using Lengaigne’s method (same as in the RGB experiment). This explains why while in all experiments PISCES is coupled with the ocean model, the name COUPLED is only used in that last mentioned experiment. The model simulated the RGB experiment for 70 years and then continued with the other three experiments and the RGB for 30 more years (100 years in total). In order to see whether the model has reached steady state, time series for global

12 volume averaged kinetic energy and potential temperature were examined.

(a) Kinetic energy.

(b) Potential temperature.

Figure 2.3: Time series for globally averaged kinetic energy (a) and potential temperature (b).

In Figures 2.3a,b it is shown that the trends in kinetic energy and potential temperature are signiﬁcantly small ( +1.02·10−7m2/s2/50years and +0.06oC/100years respectively) so it is safe to assume that the model has reached steady state.

13 Chapter 3

Results

3.1 Impacts of diﬀerent turbidity parameterizations on biologically produced chlorophyll

The present section examines how chlorophyll-dependent and chlorophyll-independent parameterizations of the penetration of solar radiation change the biology produced chlorophyll from the PISCES simulations (PISCES chlorophyll). Figure 3.1 shows the seasonal mean of the surface chlorophyll concentration for the RGB experiment for the last year of simulations. There is a tendency for increased chlorophyll concentrations at high latitudes,coastal areas, river outflow areas and equatorial upwelling regions. The spatial variation in the Atlantic ocean is poorly reproduced compared to the satellite chlorophyll observations (Fig. 2.2). The Mediterranean and Black seas are masked, because Si forcing had in those regions very high concentrations that affected the chlorophyll produced by diatoms, a type of phytoplankton. A very prominent feature of high chlorophyll concentrations is observed in the north Atlantic, along the path of the North Atlantic Drift. This shows that chlorophyll concentration is poorly diffused. An possible explanation is that north Atlantic is an area of enhanced mesoscale variability (Levy et al., 2014) and the relatively coarse resolution of the present model configuration could show limitations in accurately reproducing mesoscale features. Poor representation of chlorophyll concentration on the Atlantic is an additional reason why the present thesis focuses on the equatorial regions.

14 Figure 3.1: Seasonal PISCES chlorophyll for the RGB experiment. Each panel depicts a diﬀerent season of the last year of simulations.

Figure 3.2 shows the differences between PISCES-chlorophyll in the RGB experiment and satellite observations providing an assessment of how well PISCES simulates biogeochemical processes that affect chlorophyll concentrations. In the equatorial region, there is an overestimation in chlorophyll concentration values in the eastern equatorial Pacific close to the coast of Peru, and an underestimation in the eastern Atlantic equatorial region, close to the Gulf of Guinea. Although the equator is the area of interest here, in the polar regions where the presence of LIM increases complexity of tunning with OPA and PISCES, the chlorophyll concentration is poorly reproduced. There is a clear pattern in the differences between the two chlorophyll fields at the equatorial regions where there is river outflow (Congo, Senegal, Amazon).

3.1.1 Differences in chlorophyll concentrations between experiments The differences of the annual mean PISCES-chlorophyll concentrations of the last year of simulations between the four experiments are presented in Figure 3.3. Fig- ures 3.3a,b show more pronounced differences compared to 3.3c,d in the equatorial

15 Figure 3.2: Differences between biologically produced chlorophyll and satellite observations. regions. In figure 3.3a, the equatorial Pacific has increased values of chlorophyll concentrations, suggesting that increased turbidity in TYPE III increases chlorophyll concentration in the Pacific equatorial upwelling region. However, closer to the coast of Peru, opposite behavior is shown. There is a small tendency for the opposite behavior in equatorial Atlantic, where the more turbid TYPE III shows decreased chlorophyll concentration compared to the less turbid TYTPE I, although this negative difference between them is smaller in magnitude than the equivalent positive one in the Pacific ocean. Figures 3.3b,c show the same but smaller in magnitude pattern in the differences between the experiments. In figure 3.3d the differences between the RGB and COUPLED experiments are shown and reveal slightly positive but smaller in magnitude differences in both equatorial Pacific and Atlantic away from the coast. To better highlight the differences between the RGB-COUPLED PISCES-chlorophyll concentrations, the mean value of penetration depth (ξ¯) is computed. Based on equation 1.4 the mean value of ξ is:

− z 1 − z − z − z e ξ¯ = · (e ξb + e ξr + e ξg ) (3.1) 3 1 where K(λ) = ξ was estimated using Morel and Maritorena (2001) table for the

16 Figure 3.3: Chlorophyll concentration differences between the experiments. These are the annual mean values of the last year of simulations (year 100). wavelengths of 450 (blue), 550 (green) and 650nm (red). The depth value of 0.5m ¯ (first layer of the model) is used as z. On the equator, Figure 3.4 shows that ξRGB is ¯ larger than ξCOUP LED, meaning that in the COUPLED experiment, the equatorial Pacific and Atlantic are more turbid.

17 Figure 3.4: Diﬀerences of ξ¯ between the RGB and COUPLED experiments. The values of ξ¯ where computed for the last year of simulations of both experiments.

The increased equatorial turbidity in the COUPLED experiment compared to that of the RGB is also shown in the time series of sea surface chlorophyll concentration in the 2.5oN - 2.5oS band for both oceans (Figure 3.5a,b). While in the first 15 common years of the experiments (70-85 years of simulations) there is no clear difference between the RGB and COUPLED, in the last 15 years these differences show clearly that this equatorial band COUPLED is more turbid than RGB. Time series of PISCES-chlorophyll in the equatorial band show in the Atlantic ocean that TYPE I has more chlorophyll than TYPE III. In the equatorial Pacific ocean TYPE III has more chlorophyll than TYPE I apart from year 97 when both experiments had the same chlorophyll concentration.

18 (a) Atlantic ocean.

(b) Paciﬁc ocean.

Figure 3.5: Time series for the last 30 years of simulations of the sea surface PISCES- chlorophyll concentrations in the (a) equatorial Atlantic (2.5oN-2.5oS) and (b) equatorial Paciﬁc (2.5oN−2.5oS).

3.2 Impacts of diﬀerent turbidity parameterizations on temperature

Turbidity changes the penetration depth of solar radiation ξ in every experiment. When the penetration depth increases, the penetrative solar radiation also increases, thus the surface layer of the ocean gets warmer which in turn results to warmer sea surface temperatures (SSTs). This also aﬀects the stratiﬁcation of the ocean which also increases since the vertical temperature gradient closer to the surface is greater. Figure 3.6 shows the distribution of SST of the RGB experiment on the last year of simulations. SST has maximum values at mid latitudes and minimum at the

19 polar regions. Speciﬁcally, SST is ∼ 32oC on the equator, ∼ 25oC at mid latitudes and ∼ -1oC near the poles. The model reproduces well the SST structure, when compared with satellite observations (see appendix)

Figure 3.6: Annual mean SST for the RGB experiment.

20 The effect of turbidity changes on SST is shown by the differences of SST between the experiments (Figure 3.7). Differences between TYPE III and TYPE I show that when turbidity increases, SST values increase everywhere in the ocean except the equatorial upwelling regions (Figure 3.7a). Increasing turbidity by decreasing the penetration depth (TYPE III) should generally cause a warming of the ocean surface layers, which should lead to warmer SSTs and therefore increased stratification. In the equatorial Atlantic and Pacific the exact opposite of this expected behavior occurs. Figures 3,7(a,b,c) clearly show SST cooling with increased turbidity. This opposite behavior is connected to a feature that known as open ocean equatorial upwelling, consistent not only with a previous work that conducted the same experiments using the ORCA2 benchmark NEMO configurations (Theofilopoulos, 2017), but also with other previous studies (i.e. Park et al., 2014; Zhou, 2015; Hernandez et al., 2016). The temperature decrease along the equator with increased turbidity implies enhancement of the equatorial upwelling, that in turn is related to enhancement of the tropical cell circulation (Perez and Kessler 2009 and references therein). The comparison between RGB-COUPLED experiments (Figure 3.7d) shows SST differences of lower magnitude than the differences between the other three experiments (FIgure 3.7a,b,c) for the equator.

Figure 3.7: SST diﬀerences between the experiments

21 Focusing on the spatially averaged 2.5oN-2.5oS band time series in the Atlantic and Pacific oceans (Figure 3.8), the COUPLED and RGB experiments show almost no difference despite their differences in PISCES-chlorophyll (Figure 3.4, Figure 3.5). TYPE III-TYPE I from the start (year 70) show a very clear difference in SST with TYPE I being the warmest and interestingly enough, in both oceans, after year 85 that difference becomes almost systematic. After year 85, TYPE I is the warmest with the largest PISCES-chlorophyll concentration. Warmer SSTs imply less upwelling and this should result in less nutrients coming closer to the surface and less chlorophyll (Figure 3.9). The fact that PISCES-chlorophyll in TYPE I in the equatorial Atlantic ocean is largest can be explained by the way the temperature or other mechanisms besides nutrients affect the chlorophyll production in PISCES. The opposite occurs in the case of TYPE III in the equatorial Atlantic. In the equatorial Pacific, TYPE I is again the warmest, but also the one that has the lowest chlorophyll concentrations, that implies in both cases weaker upwelling than TYPE III (Figure 3.9).

22 (a) Atlantic ocean.

(b) Paciﬁc ocean.

Figure 3.8: Time series of SST in the Atlantic (a) and Paciﬁc (b) 2.5oS-2.5oN band for the last 30 years of simulations.

In order to identify the effects of turbidity on the vertical distribution of temperature, selective mid basin cross-sections at 25oW (Atlantic) and 150oW (Pacific) are examined (figures 3.10 and 3.11). Figure 3.10a shows that increased turbidity leads to the expected sea surface heating except the equatorial upwelling region. More importantly Figure 3.10a shows that in depths between 50 and 200m, there is a cooling of the ocean waters. This is explained since when turbidity increases, stratification of the upper layers also increases preventing upwelling. Exactly on the equatorial upwelling region, the temperature cooling from the surface to 50.m further showcases the increase in upwelling (Figure 3.9). The same vertical temperature distribution is shown in Figures 3.9b,c, although in a lower magnitude than this of Figure 3.9a. Figure 3.11d shows small but positive differences near the surface and as a consequence weakening in the upwelling (Figure 3.9d). In figure 3.11 the same cross-sections are presented for the Pacific ocean. Similar to the Atlantic

23 Figure 3.9: Diﬀerences between the experiments of vertical velocities at 50m depth.

24 ocean, heating of the surface layers and cooling of the subsurface layers occurs when turbidity increases, except for the equatorial upwelling region. The diﬀerences between the RGB-COUPLED experiments indicate a weakening of the upwelling in the equatorial Paciﬁc (Figure 3.9d). It should be noted that the cooling of the ocean waters as shown in Figure 3.11a,b extend in bigger depths than in the case of the Atlantic ocean.

25 Figure 3.10: Temperature diﬀerences between the experiments in 25o W (Atlantic ocean), focused on the 15oN-15oS band, from the surface to 500m.

26 Figure 3.11: TTemperature diﬀerences between the experiments in 150o W (Paciﬁc ocean), focused on the 15oN-15oS band, from the surface to 500m.

27 3.3 Eﬀects of turbidity on wind stress and circulation.

To investigate the mechanisms that modify the ocean temperature on the equator under diﬀerent turbidity regimes, possible feedbacks between SST changes and surface wind stress magnitude are also examined. In ﬁgure 3.12 the wind stress magnitude of the RGB experiment for the last year of simulations is presented. Surface wind stress exactly on the equator in both oceans, drives surface colder SSTs from upwelling to the west side of the equatorial basins. The wind stress is related mainly to the predominantly southeasterly trades, while the underlying surface current is the SEC that extends meridionally to both North and South sides of the equator.

Figure 3.12: Wind stress magnitude of the RGB experiment for the last year of simulations.

28 The wind stress magnitude differences between the experiments are shown in figure 3.13. Warmer SSTs will act to intensify the surface wind stress while colder SSTs will act to weaken the wind stress. Comparing figures 3.7 and 3.13, negative SST differences coincide with weakening of the magnitude of the wind stress on the equator. That is more pronounced in figures 3.13(a), and 3.13(b). Therefore, increased turbidity on the equator causes a decrease on the surface wind stress. In figure 3.13(a), when comparing the wind stress magnitude with the magnitude of the differences, it is shown that the differences are at most ∼ 10% of the actual values of the field on the equator.

Figure 3.13: Diﬀerencesof wind stress between the experiments.

29 Figure 3.14 shows the differences on surface circulation between the four experiments on the last year of simulations. In Figure 3.14a it is shown that, along the equatorial Pacific and Atlantic oceans, the surface circulation increases but exactly on the equator it appears to weaken. This implies that the wind stress decrease has a very localized effect on the equatorial surface circulation. The patterns shown in Figures 3.14b,c,d are basically consistent to the one of Figure 3.14.a. For the case of TYPE III-RGB, increased turbidity causes a decrease of the wind stress through an SST decrease, but the surface circulation between the two oceans re- sponds differently to the aforementioned change. In Figure 3.14b it is shown that a decrease of the wind stress causes a very localized decrease on the eastern equatorial Pacific zonal circulation, but the small decrease on the wind stress in equatorial Atlantic is not adequate to cause such an increase. In the RGB-TYPE I case, the magnitude of the differences is relatively small and in the equatorial Pacific and Atlantic circulation appears weakened. When comparing RGB-COUPLED, weaker wind stress magnitude negative differences on both equatorial Pacific and Atlantic are shown. But positive differences off the equator are relatively large when compared to the field wind stress values. At the same time, changes in wind stress in the RGB-COUPLED case can partially explain the circulation patterns (Figure 3.14d).

30 Figure 3.14: Surface circulation diﬀerences between the four experiments. In (a) through (d)√ the colormap shows the magnitude of the circulation diﬀerences, calculated as | u2 + v2|

31 In order to understand the effects of different turbidity parameterizations on the strength and vertical structure of the tropical cells, the relative differences of the meridional velocity between the experiments is examined. The ”relative differences” between the experiments are the differences divided each time by the sign of the subtracted experiment. This is a better way to understand strengthening and weakening of parameters like the meridional velocity, where their sign has a physical meaning. Figure 3.15 depicts relative differences in meridional velocity in the Atlantic ocean, where superimposed in black contours is the subtracted field to be used as a reference. In figure 3.15a a strengthening of poleward surface velocities is shown, as well as a strengthening of deeper currents returning to the equator. The surface-poleward and interior equatorward circulation is the same as the tropical cell circulation, concluding that the tropical cell circulation increases. Therefore, when turbidity increases tropical cells are strengthened in the equatorial Atlantic. The same pattern is shown in Figure 3.15b,c,d although less intense. Similar to the Atlantic ocean, the equatorial Pacific tropical cells appeared to also increase (Figure 3.16).

Figure 3.15: Diﬀerences of meridional velocity for the Atlantic ocean.

32 Figure 3.16: Diﬀerences of meridional velocity for the Paciﬁc ocean.

3.4 Changes in meridional overturning circulation.

MOC is defined as: Z z Z xw ψ(y, z) = − v(x, y, z)dxdz H xe The integral of this equation shows the strength of a large scale meridional mass transport. In Figures 3.17 and 3.18 the relative differences between the four experiments are presented. Figure 3.17a shows that when thurbidity increases, the poleward mass transport near the surface in the equatorial region increases significantly. The same pattern is present in the differences of the other three experiments. This indicates the increase of the tropical cells in the Atlantic ocean, as discussed in the previous section. Similarly to the Atlantic ocean, increased poleward mass transport is observed in the equatorial Pacific ocean (Figure 3.18). The magnitude of the differences in the equatorial Pacific is significantly larger compared to the magnitude of differences in the equatorial Atlantic. Although, in the RGB-COUPLED differences (Figure 3.18d), the magnitude of these differences is quite small. The strengthening of MOC on the Pacific ocean comes in agreement with the results of a previous study by Sweeney et al. (2005) who studied the effects of two different surface chlorophyll concentration dependent parameterizations of solar

33 irradiance. They showed that when the penetration depth of the incoming solar radiation increases, an SST warming occurs on the equatorial regions and that changes in mixed layer due to diﬀerent parameterizations of penetrative solar radiation oﬀ the equator result in a slowdown on the MOC.

Figure 3.17: Meridional overturning circulation diﬀerences between the experiments close to the equator in the atlantic ocean.

34 Figure 3.18: Meridional overturning circulation diﬀerences between the experiments close to the equator in the paciﬁc ocean.

35 Chapter 4

Summary and conclusions

In the present thesis, the possible feedback mechanisms between different penetrative solar parameterizations and the equatorial Atlantic and Pacific circulation and sea surface temperature and the feedback between the circulation and sea surface temperature and circulation and the wind stress are examined. To this end, four experiments were performed and the differences between them are examined. Their differences indicate that when turbidity increases the upper layers are more stabi- lized preventing upwelling everywhere except the equatorial regions. In the equatorial Pacific and Atlantic the tropical cells appear enhanced, leading to weakened stratification and SST cooling exactly on the equator. The SST cooling has a direct impact of the wind stress. SST cooling makes the above layer of the atmosphere more stable, leading to decreased wind stress magnitude. The decreased wind stress magnitude should result in a weakening of the surface divergence and upwelling, the results show that decreased wind stress magnitude causes a very localized decrease of the surface circulation exactly on the equator, while the surface circulation off the equator and the tropical cells increase in both the Atlantic and the Pacific oceans. This implies that differences on the local wind stress magnitude do not drive the increase in the upwelling and in the SST cooling on the equator. A future work could focus on the feedback that increased off-equatorial wind stress could have on the enhanced equatorial circulation. It is well known that the meridional circulation carries the off-equatorial changes of the thermocline temperature to tropical the mixed layer(Schneider and Zhu, 1997; Lin et al., 2008; Kara et al.,2003). Sweeney et al. (2005) showed that changes on the off-equatorial mixed layer depth directly affect the equatorial circulation. But still the effect of the wind stress in the subtropical regions is not yet thoroughly studied. Also, it would be very interesting to repeat the same experiments and examine their differences seasonally. In this way, the case of the equatorial Indian ocean could be studied and provide a better insight on how turbidity generally affects the equatorial circulation. A very interesting work could be conducting experiments vertical profile of chlorophyll. This could give an insight on how chlorophyll concentrations in deeper layers affect the ocean dynamics and thermodynamics. Finally, the possible feedback of the ocean biology to the earth’s atmosphere could be studied using coupled ocean-atmosphere simulations. Timmermann and Jin (2002) suggest that increased upwelling equatorial upwelling affect the temperature of the air masses just above the Pacific ocean leading to possible effects on the El Nino-Souther Equatorial Oscillation (ENSO).

36 Appendix A

NOOA SST

Figure A.1: Sea surface temperature from NOOA (https://www.nodc.noaa.gov/cgi- bin/OC5/woa13fv2/woa13fv2.pl)

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