Download The role of local atmospheric forcing on the

The role of local atmospheric forcing on the modulation of ocean mixed layer in
reanalysis and coupled single column ocean model
Byju Pookkandy, Dietmar Dommenget, Nicholas Klingaman, Scott Wales, Christine
Chung, Claudia Frauen, Holger Wolff***
Abstract:
The role of local atmospheric forcing on ocean mixed layer depth (MLD) over the
global oceans are studied using ocean data assimilation products and a single-column
ocean model coupled with an atmospheric general circulation model. We focus on
how the annual mean and the seasonal cycle of the MLD relate to the different forcing
characteristic at different parts of the world oceans. We further analysis how
anomalous variations in the monthly mean MLD relate to anomalous atmospheric
forcings. In the combination of the analysis of the ocean reanalysis data and the single
column ocean model we identify regions with different dominant forcings and
different mean and variability characteristic of the MLD. Much of the global oceans
MLD characteristics appear to be directly linked to the different atmospheric forcing
characteristics at different locations. Here heating and wind input are the main driver
and at some, mostly coastal, regions the atmospheric salinity forcing. The annual
mean MLD is more closely relate to the annual mean wind stress and the seasonality
is more closely related to the seasonality in the heating. The single column ocean
model, however, also points out that the MLD characteristics over most global ocean
regions, and in particular in the tropics and subtropics, cannot be maintained by local
atmospheric forcings only, but are also a result of oceanic dynamics that are not
simulated in a single column ocean model. Thus lateral ocean dynamics are essential
in maintaining observed MLD.
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1. Introduction
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The processes occurring in the upper ocean are predominantly forced by the atmosphere.
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Heating and cooling at daily to seasonal scale, wind-stress, evaporation and precipitation are
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the dominant physical processes that act and interact with the upper layer of the ocean. The
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vigorous turbulent mixing near the surface by these forces results in a layer of homogeneous
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temperature and salinity (and thus density) with depth, which is defined as the ocean mixed
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layer depth (MLD).
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Understanding the physics of MLD is important for climate dynamics, because the thickness
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of the mixed layer modulates its heat capacity and hence its ability to store excess heat from
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the atmosphere (Godfrey and Lindstrom 1989; Maykut and McPhee 1995; Swenson and
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Hansen 1999; Peter et al. 2006; Montégut et al. 2007; Dong et al. 2007). In addition the
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variations of the MLD, over the seasonal cycle or in anomalies from it, is one of the main
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processes in exchanging heat between the atmosphere and the deeper oceans. Proper
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quantification of the heat budget is important as it governs the evolution of the sea surface
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temperature (SST) (Chen et al. 1994; Alexander et al. 2000; Dommenget and Latif 2002; Qui
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et al. 2004; Dong et al. 2008). In a study using a simple stochastic model Dommenget and
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Latif (2002) speculated that the low frequency (say decadal) variability in SST is an
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integration of the heat stored in the mixed layer and the sensitivity of SST to MLD peaks at
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midlatitudes. Therefor the seasonal and interannual variability in SST can be simulated by the
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local air-sea interactions and proper representation of MLD variability. Mixed layer
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dynamics is also important for the ocean biological productivity(Fasham 1995; Polovina et al.
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1995; Narvekar and Kumar 2006) and acts as a medium for exchange of trace gases between
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the ocean and atmosphere (Takahashi et al. 1997; Bates 2001; Sabine et al. 2004).
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A number of studies have examined the mixed layer dynamics and demonstrated the variation
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in MLD at different spatial and temporal scales (Montery and Levitus, 1997; Kara et al.
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2003, 2000; Cronin and Kessler 2002; Montégut et al. 2004). Most of such studies were
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limited to either a small region or at point locations where in-situ data is available and then
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extrapolated over a wider domain using some specific techniques. Since the advent of Argo
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data re-analysis methods has produced much more reliable source of representation of the
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state of the ocean at global scale. It has been understood that the MLD in summer is
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dominated by the entrainment due to wind induced mixing and surface buoyancy force in
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winter (Alexander et al. 2000). These theories are generalised concept on MLD variability in
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the global ocean, however, the relative role of these forcing vary in time and space. This
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uneven distribution of MLD forcing on seasonal time scale is not known or understood in
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detail over the global ocean. Carton et al. (2008) and Lorbacher et al. (2006) examined the
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variability in MLD at global scale from observation, but it provides little information about
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the Southern ocean. Numerous attempts have been made to understand the ocean surface
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boundary layer and its sensitivity to atmospheric forcing (Adamec et al, 1984; Large and
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Crawford, 1995; Kantha and Clayson, 1994 etc.). Most of the studies of MLD variability
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have been limited at one location or in small ocean basin.
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The focus of our study is to examine the mean, seasonal cycle and variability of the MLD
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over the whole global oceans and how it relates to the different atmospheric forcings and the
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upper ocean stratifications. The aim is to understand what are the driving mechanisms of the
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MLD in different regions and to what extent can these be understood by the local air-sea
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interactions. In this we will focus on open ocean regions and do not discuss sea ice regions or
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shallow coastal ocean regions. We will therefore analyse the MLD and the atmospheric
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forcing terms in ocean reanalysis data and in a single column ocean mixed layer model
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coupled to an atmospheric general circulation model (GCM). The latter model simulation
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allows us to understand to what extent the MLD characteristics can be simulated just by local
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air-sea interactions. It also allows to diagnose the limitations of local air-sea interactions
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assumption by analysing the model flux correction terms used in the model to maintain a
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density profiles close to the observed mean profile.
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The paper is organized as follows: A detailed description of the reanalysis data, coupled
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model and the method of estimation of MLD is presented in the following section. The focus
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of the section 3 is on the observed characteristics of MLD estimated from the reanalysis
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observation. In section 4 we repeat most of the analysis for the single column model
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simulation. The results are summarized and discussed in section 5.
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2. Data, Model and Methods
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We use reanalysis and a single column ocean model data to understand the characteristics of
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global ocean mixed layer depth and their relation to local atmospheric forcing. Details of the
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datasets and methods used in this study are described bellow.
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a. Reanalysis data set
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The characteristics of mixed layer presented here are, partly, based on the temperature and
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salinity vertical profiles from German contribution to Estimating the Circulation and Climate
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of the Ocean system (GECCO2) for the period 1948-2011. The data has a spatial resolution
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of 1x1 degree in longitude and latitude and comprises 50 vertical levels. The synthesis used
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model assimilation with available hydrographic and satellite data. A complete description of
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this
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ftp://ftp.icdc.zmaw.de/EASYInit/GECCO2/.
dataset
is
given
by
Kohl
(2014);
and
the
data
is
available
at
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b. The coupled single column ocean model (ACCESS-KPP)
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A 50-year simulation of a coupled atmosphere-ocean single column model is used to study
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the MLD variability over the global ocean. The atmospheric component of the coupled model
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consists of the Australian Community Climate and Earth-System Simulator (ACCESS
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version 1.3) atmospheric GCM, which is similar to that of the UK Met Office’s Global
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Atmosphere version 1.0 and some modification included by the Centre for Australian
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Weather and Climate Research (CAWCR). The horizontal resolution of 3.75º longitude by
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2.5º latitude (referred to as N48) and 38 levels in vertical is applied in this model setup. A
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detailed description of the atmospheric model is given in Bi et al. (2013).
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The ocean component of the coupled model used for this study is the single column first-
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order nonlocal K-Profile Parameterisation (KPP) mixed layer model (Klingaman et al, 2009)
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by Large et al. (1994). It comprises 40 vertical levels with the layer thickness increasing
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exponentially from the surface to 1000m deep. The single column ocean model is coupled
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globally to the atmospheric model with a spatial resolution of 3.75ºx2.5º. The atmospheric
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model provides the near surface heat flux, wind-stress and freshwater flux (EMP) to the
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ocean at each time step. The mixed layer model does not present processes such as horizontal
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advection or upwelling from bellow the limited vertical domain. Mixing in the interior is
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governed by shear instability, which is modelled as a function of local gradient Richardson
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number. A boundary layer depth is determined at each grid point, based on the critical value
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of the turbulent processes parameterised by a bulk Richardson number. Vertical diffusivity
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coefficients due to turbulent shear are estimated in the diagnosed boundary layer depth.
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Mixing is strongly enhanced in the boundary layer in both convective and wind driven
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situations enabling boundary layer properties to penetrate well into the thermocline. Readers
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are referred to Large et al. (1994) for detailed description of the first order nonlocal KPP
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fundamentals. Ocean model is embedded with varying sea-ice concentration at the higher
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latitudes. The sea ice model is a simple thermodynamical model of melting and freezing of
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sea ice by local forcings. However, regions with sea ice will not be discussed in this study. A
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flux correction is applied to the temperature and salinity tendency in order to reduce the
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climate drift, which is described in the following sub-section..
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c. Flux corrections
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The ACCESS-KPP model computes the diffusivity profiles at each grid point and then
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estimates the temperature and salinity profile. Since lateral ocean dynamics are absent in the
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model, the temperature and salinity drifts away from the reference values for a longer (many
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decades) run. So it is necessary to correct the fluxes in order to prevent climate drift. The flux
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correction values are computed such that the mean seasonal cycle of temperature and salinity
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closely follow the climatology from WOA09 (Antonov et al, 2010; Locarnini et al, 2010).
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The temperature and salt flux corrections are obtained from a number of iterations, where the
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ACCESS-KPP model temperature and salinity are free to evolve. Bias between the model
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variables and the climatological values are then computed as the flux correction. These
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values are then added to the tendency equation as a forcing term in the model simulation. The
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flux corrections vary at each grid and month of year but are state independent and remain the
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same from one year to the next. Characteristics of the flux correction terms are discussed in
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the analysis section.
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d. Method for MLD estimation
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A wide range of criteria exists in defining MLD from the vertical profiles of temperature,
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salinity or density. Generally, all these criteria fall into two categories: the difference criteria
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and the gradient criteria. In the difference criteria MLD is defined as the depth where the
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oceanic property has changed by a critical value from a reference depth near to the surface
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(Kara et al. 2000; Montégut et al. 2004). The gradient criteria detect the shallowest depth
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where the vertical gradient of the oceanic tracers exceeds a given value (Brainerd and Gregg
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1995; Lorbacher et al. 2006). The former one largely depends on the difference value chosen
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and produces miss-representation of MLD in the regions of weekly-stratified surface layers.
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The gradient criteria also depend on the threshold value of the derivative, but since the
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gradient is expected to be large at the base of the MLD, it gives a better accuracy for
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estimation of MLD. Lorbacher et al. (2006) further developed the gradient criteria by adding
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the standard deviation threshold to assume the shallowest extreme curvature. A better
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estimation of MLD is done by exponential interpolation method (for thick layers) and hence
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the Lorbacher et al. (2006) criteria is used to estimate the mixed layer from the density
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profiles for all the data used in this study.
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3. Observed MLD
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We start the analysis with a first look at the seasonal mean MLDs; see Fig. 1. The seasonal
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mean MLDs shown in Figure 1 are largely consistent with those found in previous studies
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(Kara et al. 2003, Montégut et al. 2004 or Lorbacher et al. 2006). Fig. 2 simplifies the
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seasonal mean MLDs to the annual mean and the relative strength of the seasonal cycle. They
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show a number of well-known characteristics, such as the strong seasonal cycle in the extra-
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tropical regions or the shallow MLDs in the upwelling regions along coastlines or the
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equatorial Pacific. Overall the structure of the global oceans seasonal mean MLDs is fairly
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complex. To simplify the following discussions and analysis it is useful to define some basic
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MLD regimes, based on the annual mean MLD and the relative strength of the seasonal
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cycle; see Fig. 2. We can split the world ocean into the following 3 regimes:
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•
Extra-tropical-seasonal: Regions where the seasonal cycle standard deviation is
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larger than 60% of the annual mean MLD (Fig. 3a). These are regions with a very
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shallow warm season MLD and a deep cold season MLD. This is mostly a band along
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30-40 degrees with some regions extending further to higher latitudes. The most
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extreme seasonality is seen in the Southern Ocean in the Indian and Pacific sections,
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which are within the Antarctic Circumpolar Current (ACC) (see also Dong et al.
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2008; Sallée et al. 2006). The northern North Atlantic is also a region of extreme
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seasonality.
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•
Constant-deep: Regions with an annual mean MLD > 30m and the seasonal cycle
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standard deviation is less than 60% of the annual mean MLD (Fig. 3b). These are
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mostly the off equatorial tropical and subtropical regions, but are also large fractions
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of the Southern Ocean and parts of the northern North Pacific.
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•
Constant-shallow: Regions with an annual mean MLD < 30m and the seasonal cycle
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standard deviation is less than 60% of the annual mean MLD (Fig. 3c). These mark
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mostly coastal regions and in particular upwelling regions along major continents and
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equatorial regions. But also coast lines in the north-western North Atlantic and West
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Australia. Also most of the North Indian Ocean is in this shallow regime.
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In following analysis we focus on how these different mean MLD regimes relate to the local
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atmospheric forcing and the stratification of the upper ocean.
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3.1 The annual mean MLD and its relation to local forcing
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The external forces that act on the MLD are the heat, momentum and evaporation-
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precipitation difference (EMP) fluxes. All these forces have to interact with the upper ocean
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stratification. Strongly stratified ocean needs strong forcing to break the stratification and to
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promote the growth of MLD. By comparing the annual mean values of the atmospheric
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forcings and the upper ocean stratifications (Fig. 4) with the annual mean MLD (Fig. 2a) we
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get some idea of the relative importance of the different forcings. Note that the shading in
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Fig. 4 is coded in such a way that bluish colours indicate forcings or stratifications that would
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support deeper mixed layers and reddish colours indicate forcings or stratifications
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supporting shallow MLD.
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The annual mean Net Heat Flux (NHF) in the higher latitudes (>40º) are mostly negative,
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which implies that the ocean is losing heat (Fig 4a). Heat-lose at the surface makes the upper
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ocean statically unstable, which tends to deepen the MLD. Upwelling regions along
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coastlines and the equator, and tropical Indian Ocean are marked by annual mean heat gain,
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which tends to support shallow MLD. Overall we find only a moderate match between annual
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mean NHF forcing and MLD (spatial correlation of 0.3). Most of the constant-shallow MLD
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regime seems to be associated with annual mean heat gain. Also much of the extra-tropical
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regions with deeper annual mean MLD are associated with supporting NHF heat loss.
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However, many regions with shallower than global mean MLD are associated with annual
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mean heat loss, which does not support the observed shallow MLD (e.g. coastal regions in
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the western North Pacific and Atlantic; southern subtropical regions in the Indian and Pacific
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Oceans). Also regions with deeper than global mean MLD are associated with annual mean
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heat gain, which does not support the observed deep MLD (e.g. off-equatorial tropical
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Pacific; parts of the Southern Ocean).
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The annual mean wind stress forcing shows a slightly better match to the annual mean MLD
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than NHF (spatial correlation of 0.4). In particular higher latitudes regions with deep annual
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mean MLD also are associated with strong mean wind stress and equatorial regions with
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shallow annual mean MLD are associated with weak mean wind stress. However, we also see
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large regions where the annual mean MLD is deep despite fairly moderate annual mean wind
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stress (e.g. latitude bands around 30 degrees on both hemispheres) and regions where the
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annual mean MLD is shallow despite fairly strong annual mean wind stress (e.g. subtropical
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South Indian Ocean; Arabian Sea). We can also note that for most regions NHF and wind
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stress have similar tendencies on the MLD, with both either supporting deeper MLD (e.g.
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most extra-tropical regions) or supporting shallower MLD (e.g. equatorial regions). Notable
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exceptions here are parts of the subtropical regions, parts of the Southern Ocean, northern
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North Pacific, western North Atlantic and the Arabian Sea.
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The Precipitation and evaporation (EMP) plays in general a minor role in mixing processes in
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the upper ocean by changing the density structure (Dong et al, 2009), which is also
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highlighted here by the mismatch of the annual mean EMP tendencies with the annual mean
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MLD for most regions (spatial correlation of 0.0). The mean EMP forcing tendencies are also
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mostly unrelated to the NHF and wind stress forcings. In some regions, however, where both
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NHF and wind stress tendencies do not match the MLD, the EMP forcing does indeed seem
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to match the MLD. These appear to be mostly smaller and coastal regions (e.g. coastal
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regions of Greenland; some parts of the western boundary of the North Pacific and Atlantic).
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Remarkable are the shallower MLD regions in the subtropical Indian and western Pacific
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Oceans. Here all three atmospheric forcing would support deeper annual mean MLD. The
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shallower MLD here seem to match the stronger upper ocean stratifications. Although, upper
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ocean stratifications is partly a reflection of the atmospheric forcings, it is also a reflection of
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oceanic process. Since the annual mean atmospheric forcings seem to mismatch the mean
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MLD in this regions, it suggest that oceanic processes independent of the local atmospheric
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forcing contribute to the stratification of the upper ocean and thus a shallower MLD.
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3.2 The MLD seasonal cycle and its relation to local forcing
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The extra-tropical-seasonal MLD regime largely represents the world ocean regions with
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strong mean MLD seasonal cycle (Fig. 2b). These regions are fairly well matched with the
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strength of the NHF seasonal cycle (Fig. 5a). Although the wind stress forcings in the
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Northern Hemisphere support the extra-tropical-seasonal MLD regime, they do not show
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strong seasonality in the Southern Hemisphere. Thus we find that the extra-tropical-seasonal
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MLD regime is mostly a result of seasonally varying tendencies of heating and cooling at the
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surface. This is consistent with previous findings (Monterey and Levitus, 1997; Kantha and
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Clayson, 2000; Kara et al. 2003; Montégut et al. 2004). However, we again find some regions
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where this simple picture is not supported. In particular the western boundaries of the North
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Pacific and Atlantic have very strong seasonal cycle in NHF, but not in the MLD. Also the
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very strong seasonality in the Indian and Pacific Ocean ACC regions are not directly matched
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by the seasonality in neither of the atmospheric forcings.
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An interesting aspect of the combined effect of annual mean forcing and the strength of the
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seasonality in the forcings can be seen along the zonal bands in the northern midlatitudes, see
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Fig. 6. In the zonal band at around 30oN in the North Pacific the MLD is deeper on the
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western side and it has a stronger seasonality. Both mean and seasonality decrease to the east.
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This is supported by the behaviour of the mean heat flux and its seasonality. In the west we
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have an annual mean cooling and strong seasonality supporting the MLD behaviour and to
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the east the mean cooling turns into a mean warming and the seasonality in the heat is
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decreasing again supporting the MLD behaviour. This reflects the change in atmospheric
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forcing from strong continental characteristics in the west (e.g. strong cooling in winter and
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strong seasonality) and more in balance with the ocean state in the east, due to the preferred
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westerly wind conditions that tend to transport characteristics from the west to the east. This
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is somewhat similar in the North Atlantic, but the relationship is broken near the western
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boundary region.
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3.3 Anomaly variability and its relation to local forcing
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We next consider the MLD variability beyond the seasonal cycle. The second column of
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Fig.1 shows the standard deviation (stdv) of monthly mean anomalies MLD estimated for
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each season. We can notice that the patterns of strong and weak MLD stdv match very well
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the patterns of deep and shallow mean MLD (first column in Fig. 1). Thus MLD variability is
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stronger when the mean MLD is deep and MLD variability is weak, when mean MLD is
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shallow. This pattern even holds when we consider the coefficient of variation (CV; the ratio
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of stdv divided by the mean); see last column in Fig. 1. The equatorial Pacific and Atlantic,
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however, mark regions where the MLD variability is strong even though the mean MLD is
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fairly shallow, which is consistent with the studies by Lorbacher et al. (2006). These mark
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regions where the concept of MLD may not be so useful, as these regions are more
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dominated by lateral ocean wave dynamics in the thermocline. It is also remarkable to note
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that CV > 100% are observed in the spring and winter seasons in the higher latitudes of North
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Atlantic and Pacific and in the Southern Ocean. This suggests a substantial amount of MLD
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variability. Even for most other regions the CV values is larger than 30%, indicating
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significant MLD variability for most regions in most seasons. Keerthi et al. (2012) have also
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shown large CV values (>40%) at western and eastern equatorial Indian Ocean during boreal
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summer.
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Fig. 7 shows the cross-correlation between monthly mean MLD and NHF anomalies for in-
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phase and for one month lead-time for the NHF for different seasons. Negative in-phase
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correlations dominate in all seasons and most regions, consistent with cooling (negative NHF
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anomalies) at the surface ocean leading to increased convective mixing and, hence, a deeper
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MLD; and vice versa for positive NHF anomalies. Equatorial regions in the central Pacific
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and western Atlantic show, however, negligible correlation between NHF and MLD
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anomalies. We can further note that in both hemispheres the in-phase correlation is stronger
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in fall and winter and fairly weak in spring and summer. The one month MLD lag cross-
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correlation to NHF is largest in the fall season and weakest in the spring season in both
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hemispheres. This relation is consistent with the idea that deeper MLD evolves more slowly
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and therefore has a longer lag-time relation between forcing (NHF) and MLD anomaly.
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The wind stress forcing is in most regions positively correlated with MLD, consistent with
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stronger than normal wind stress anomalies leading to deeper MLDs; and vice versa for
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negative wind stress anomalies (Fig. 8). In the mid to higher latitudes this relationship tends
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to be stronger in spring and summer. We can also note that stronger correlations are in
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essentially two regions: the off-equatorial tropical and subtropical regions with a tendency
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for stronger correlations in the warmer season, and in the extra-tropical higher latitudes in
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Southern Ocean and a bit less in the northern North Pacific and Atlantic. The lag one month
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correlation overall appears to be strongest in the subtropical regions and in the fall season in
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the Southern Ocean.
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Both the cross-correlations of MLD with NHF and wind stress are on a similar level, with
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some more wide spread and clearer correlation for the NHF with MLD. Cross-correlations of
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MLD with EMP are in general weaker than those with NHF and wind stress, but are mostly
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positive, which is consistent with increased evaporation (saltiness forcing) leading to deeper
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MLDs (see SFig.1). This relationship is strongest in fall and winter seasons on both
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hemispheres.
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4. The ACCESS-KPP model
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We now take a look at the MLD and its relation to the atmospheric forcings in the ACCESS-
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KPP model with a single column ocean mixed layer model. Thus in this simulation the MLD
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is purely a result of local atmospheric forcing, vertical mixing and a background state
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independent heat and salinity flux correction to maintain the mean density profile in the
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ocean columns.
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A comparison of the patterns of the seasonal mean MLD and its variability between observed
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(Fig.1) and the ACCESS-KPP model (Fig. 9) already illustrates that the model does a very
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good representation of the mean MLD characteristics. In the following we will discuss the
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same MLD characteristics as discussed above for the observations and we will have a look at
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the role of the flux corrections in maintaining the realistic upper ocean density profiles.
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4.1 The annual MLD and its relation to local forcing
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Figures 10 and 11 show the same atmospheric forcings and upper ocean stratification values
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as in Fig. 4 and 5, but for the ACCESS-KPP model. First of all we can note that the annual
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mean and seasonal cycle atmospheric forcings in the ACCESS-KPP model are very similar to
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the observed values. This suggests that we can analysis the MLD characteristics in the
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ACCESS-KPP model for similar atmospheric forcings conditions. Some mismatches in the
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annual man NHF exist in the northern Indian Ocean where the observed NHF is positive, but
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the ACCESS-KPP model values are negative.
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The regional distribution of the annual mean MLD and the three main MLD regimes in the
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ACCESS-KPP model are fairly similar to the observed (compare Figs. 2 and 3 with 12 and
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13). The ACCESS-KPP model has a very similar regional distribution of the extra-tropical-
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seasonal MLD regime, it also simulates the constant-deep MLD regime with a subtropical
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and southern ocean region and it simulates parts of the constant-shallow MLD regime in the
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coastal upwelling regions. However, some significant differences between the ACCESS-KPP
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model and the observations can be noted: first, the overall mean MLD in the ACCESS-KPP
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model is larger than observed (63m and 41m, respectively). It misses several regions within
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the constant-shallow MLD regime: the equatorial cold tongues, the tropical Indian Ocean and
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the northern oceans coastal regions. The extra-tropical-seasonal MLD regime is also a bit
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more wide spread in the northern oceans than it is observed. The ACCESS-KPP model is
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further simulating fairly deep MLD in the eastern subtropical Pacific and subtropical Indian
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Ocean, which is much shallower in the observations. The deeper MLD in these regions seem
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to fit to fairly strong annual mean local atmospheric heat and wind forcings. It thus seem to
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indicate that the observed shallow MLD in these regions does not result from the local
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forcing, but may involve lateral ocean dynamics not represented in the single column
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ACCESS-KPP model.
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The relation between the atmospheric forcings and the MLD in ACCESS-KPP model is also
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similar to the observed (compare Figs. 4 and 10). Again the wind stress forcing tends to be
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more strongly related to the annual mean MLD than the heat flux forcing and again the EMP
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forcing has no relation to the annual mean MLD.
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4.2 The seasonal cycle of MLD and its relation to local forcing
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As we said in the previous section, the model strives to produce the observed seasonal
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changes reasonably well (compare Figures 2 and 12): it essentially captures the zonal bands
336"
of strong extra-tropical-seasonal MLD regime in the midlatitudes, weaker in the tropics and
337"
subtropics and also weaker in parts of the southern ocean. As also indicated in the previous
338"
section the ACCESS-KPP model tends to over estimate the seasonality in the eastern North
339"
Pacific and western costal area in the North Atlantic. The model is good at representing the
340"
mean summer MLD, but overestimates MLD during fall and spring. This however, cannot be
341"
due to stronger seasonality in the atmospheric forcings, as they appear to be similar or weaker
342"
than observed. This suggests that the ACCESS-KPP model is missing some dynamics to
343"
capture these features.
344"
The seasonal cycle of the atmospheric forcings are well captured by the ACCESS-KPP model
345"
and again the relation between the heating and wind stress forcing and the MLD are mostly
346"
as observed (compare Figures 5 and 11). Thus the seasonality in the heat is the main forcing
347"
for the seasonal cycle of the MLD in ACCESS-KPP model.
348"
349"
4.3 Anomaly variability and its relation to local forcing
350"
The over all strength and seasonal distribution of the anomaly variability of the MLD in the
351"
ACCESS-KPP model is very similar to the observed (compare Figs. 1 and 9). The stdv of
352"
MLD also mostly strong where the mean MLD is deep, but it follows the mean MLD even
353"
more closely. Thus the coefficient of variability is more homogenous in the ACCESS-KPP
354"
model.
355"
The correlation between heat flux and wind stress forcings and the MLD are similar in all
356"
seasons, but are overall much stronger than observed (compare Figs. 7 and 8 with 14 and 15).
357"
This could be related to both errors in the observations and to the simplified dynamics of the
358"
ACCESS-KPP model. The observations include atmospheric heat fluxes, ocean observations
359"
and the model simulation of the reanalysis model. The errors in these factors will lead to
360"
inconsistencies between the forcings and the MLD. Subsequently we have to assume that the
361"
correlation between forcings and the MLD is decreased in the observations. On the other
362"
hand the ACCESS-KPP model has simplified dynamics that only include local forcings and
363"
vertical mixing; neglecting all other lateral ocean dynamics. Subsequently we have to assume
364"
that the correlation between local forcings and the MLD is artificially enhanced.
365"
As in the observations the effect of the anomalous wind stress forcings is stronger in the
366"
warmer, shallower MLD seasons. The lag-1 correlation of the heat flux is also stronger in the
367"
cold, deep MLD seasons as in the observations. The EMP also shows higher correlations with
368"
MLD (see SFig.1 and 2) than observed, but still much weaker than the correlations with heat
369"
flux and wind stress, suggesting that the EMP forcing is not as important.
370"
371"
4.4 Missing dynamics and the role of flux corrections
372"
Although the ACCESS-KPP model does a fairly good representation of the seasonal mean
373"
MLD and its variability by only simulating the local atmospheric forcing and the vertical
374"
single column dynamics, it does indeed have substantial limitations in simulating the MLD.
375"
A key aspect of the ACCESS-KPP model is that additional heat and salinity flux correction
376"
terms exist on all ocean model levels. These flux corrections are essential in many regions to
377"
maintain the overall stratification in the density profile of the ocean, see Fig. 16. Without the
378"
heat flux corrections, in particular (salinity corrections not shown), the ocean stratification in
379"
the tropics and subtropics would collapse eventually (over periods longer than 10yrs) and the
380"
warm surface waters would mix all the way to the bottom layer of the KPP model (1000m
381"
depth). This is indicated by the significant surface warming and subsurface cooling of the
382"
heat flux correction over wide spread regions in the tropics and subtropics. This effectively
383"
stabilizes the density profile and thus maintains the ocean stratifications. Since the flux
384"
correction mimic the mean effect of the missing ocean dynamics, it suggests that lateral and
385"
other ocean dynamics not simulated in the ACCESS-KPP model are indeed important in
386"
simulating the main MLD characteristics.
387"
388"
"
389"
5. Summary and Discussion
390"
In this study, we described the spatial and temporal relation between the local atmospheric
391"
forcing and the mean, seasonal cycle and variability of the MLD using a recent ocean
392"
reanalysis product (GECCO2) and single column ocean model (KPP) coupled to an
393"
atmospheric model (ACCESS1.3) over the global ocean. The aim of this study was to
394"
understand what are the driving mechanisms of the MLD in different regions and to what
395"
extent can these be understood by the local air-sea interactions. We focussed on the MLD
396"
characteristics of the annual mean, the relative seasonal cycle strength and the variability
397"
anomalous from the seasonal cycle. To further simplify the fairly complex characteristics we
398"
introduced three main regimes based on the characteristics of the annual mean and the
399"
relative seasonal cycle strength.
400"
First of all we can summaries our findings about the annual mean and the relative seasonal
401"
cycle strength of the MLD:
402"
403"
•
The annual mean MLD over most open ocean regions (away from sea ice or shallow
coastal regions) follows the wind and heat forcing. Regions with stronger mean wind
404"
forcing tend to have larger annual mean MLD and regions with annual mean net
405"
heating tend to have shallower annual mean MLD. The annual mean wind forcing
406"
strength appears to be most strongly related to the annual mean MLD and stronger
407"
than the net heat forcing. Both of these forcings are, however, only weakly correlated
408"
with the global pattern of the annual mean MLD indicating that the relationship is
409"
more complex. The annual mean EMP forcing is for most regions not related to the
410"
annual mean MLD suggesting it is not a dominating forcing. However, in some
411"
regions where both annual mean NHF and wind stress tendencies do not match the
412"
MLD, the EMP forcing does indeed seem to match the annual mean MLD (e.g.
413"
coastal regions of Greenland; some parts of the western boundary of the North Pacific
414"
and Atlantic).
415"
•
The relative seasonal cycle strength of the MLD is also mostly related to the relative
416"
seasonal cycle strength of the net heating and wind stress, but here the relationship
417"
with the net heating is stronger than with the wind forcing. Thus relative seasonal
418"
cycle strength of the MLD mostly follows the strength of the seasonal cycle in the net
419"
heating.
420"
•
Remarkable are a few regions in which neither of the atmospheric forcings seem to
421"
follow the annual mean or seasonal cycle characteristics of the MLD. The shallower
422"
MLD regions in the subtropical Indian and western Pacific Oceans does not seem to
423"
relate to any of three atmospheric forcing. The western boundaries of the North
424"
Pacific and Atlantic have very strong seasonal cycle in NHF, but not in the MLD.
425"
Also the very strong seasonality in the Indian and Pacific Ocean ACC regions are not
426"
directly matched by the seasonality in neither of the atmospheric forcings. Since the
427"
atmospheric forcings seem to mismatch the MLD characteristics in these regions and
428"
the single column ocean model is also not capable in simulating these characteristics,
429"
it suggests that oceanic processes independent of the local atmospheric forcing
430"
contribute to the stratification of the upper ocean and thus the MLD in significant
431"
ways.
432"
433"
Alternatively or in addition we can summarize the processes that dominate the three different
434"
MLD regimes:
435"
436"
•
Extra-tropical-seasonal: This regime of the MLD in the midlatitudes is primarily a
result of the strong seasonal cycle in the heating. The seasonal cycle in heating is
437"
indeed strongest in the midlaitudes and matches the strongest relative seasonal cycle
438"
strength of the MLD fairly well. This, however, is also modulated by oceanic fronts,
439"
strong boundary currents and atmospheric storm track positions.
440"
•
Constant-deep: The regime where the MLD is fairly deep (>30m) through out the
441"
year with no strong seasonal cycle mostly are in the subtropics and some high
442"
latitudes. These are regions with no strong annual mean heating nor cooling, no strong
443"
seasonal cycle in heating and the winds tend to be slightly below the global mean.
444"
•
Constant-shallow: Shallow MLD without strong seasonal cycles are mostly coastal
445"
regions, equatorial regions and the tropical Indian Ocean. Some of these regions are
446"
fairly well related to the local forcings, such as coastal and some equatorial upwelling
447"
regions. However, much of this constant-shallow MLD regime is not well matched
448"
with the local forcings and is not well simulated in the ACCESS-KPP simulation,
449"
suggesting that local forcings and processes are not sufficient to explain these MLD
450"
characteristics. In particular in the higher latitudes coastal regions, the central ocean
451"
basin equatorial regions and in the Indian Ocean are the constant-shallow MLD
452"
regimes not matched to local forcings nor processes.
453"
454"
The analysis of the anomalous variability of MLD and how it relates to the local forcing can
455"
be summarized to the following main points:
456"
•
The overall strength of the MLD variability is in general proportional to the mean
457"
variability, with stronger variability when the mean MLD is deep (e.g. cold season).
458"
The MLD variability appears to quite significant with values of 30% to over 100% of
459"
the seasonal mean MLD values.
460"
•
Most of this is well captured by the ACCESS-KPP model, but in some regions this is
461"
not well simulated by the model. This includes the equatorial Pacific and in higher
462"
latitudes the simulation does not simulate as strong MLD variability relative to the
463"
seasonal mean MLD as observed.
464"
•
MLD variability is mostly negatively correlated with heat flux variability and
465"
positively with wind stress variability. In the mid to higher latitudes the relationship
466"
to heating tends to be stronger in the cold (deep MLD) seasons and the relationship to
467"
wind forcing tends to be stronger in the shallower MLD (warm) seasons. In seasons
468"
with deeper mean MLD the relationship to the atmospheric forcings tends to become
469"
stronger when the atmospheric forcings leads by about one month, which seem to be
470"
consistent with a larger inertia of the MLD when it is deeper.
471"
•
The ACCESS-KPP model is consistent with these observed relationships between the
472"
local atmospheric forcings and the MLD, but the relationships are significantly
473"
stronger in the model simulations. This may to some part reflect the simplification of
474"
the model to only local vertical mixing processes and neglecting other ocean
475"
dynamics. Thus over estimate the role of local forcings on the MLD variability. But it
476"
may also point to observational errors that lead to inconsistencies between
477"
atmospheric forcings and the ocean state that artificially degrade the relationship
478"
between local forcing and the MLD.
479"
While overall the local forcing perspective does a fairly good representation of most of the
480"
MLD characteristics in the global oceans, it does have some limitations as mentioned above.
481"
This is in particular highlighted by the fact that the ACCESS-KPP model does require
482"
significant flux correction in temperature (most importantly) and also in salinity. These are
483"
important to maintain upper ocean stratification close to the observed. Without such terms in
484"
the ACCESS-KPP model the upper ocean stratification in most subtropical regions would
485"
collapse after about 10yrs and the MLD would go all the way down to the deepest layer of
486"
the ACCESS-KPP model (1000m).
487"
The analysis has shown that the MLD characteristics are a complex interaction between the
488"
local forcings, ocean stratifications and potentially lateral ocean dynamics. Most of the
489"
results discussed here result from comparisons of the overall statistics in observed MLD and
490"
local forcings and in the ACCESS-KPP simulation. To further untangle the interactions and
491"
the relative contribution of different forcings and different oceanic process it would require to
492"
do sensitivity experiments with the ACCESS-KPP or similar model simulations, in which
493"
forcings or elements of the forcings are turned ‘off’ or in which processes are altered or
494"
turned ‘off’. However, this is beyond this study and is left here for future analysis.
495"
496"
497"
498"
499"
Acknowledgments
500"
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Figure Captions:-
638"
Fig" 1:" Seasonal" statistics" of" mixed" layer" depth" from GECCO2 averaged over January-
639"
March (a,b,c), April-June (d,e,f), July-September (g,h,i) and October-December (j,k,l) . Left
640"
side panel of the figure shows the long-term monthly mean averaged over the seasonal
641"
months of the year (a,d,g,j). The standard deviations of MLD in each month for the years
642"
1948-2011 averaged then to the seasonal months are shown in the middle panel (b,e,h,k). On
643"
the right panel (c,f,i,l) we plotted the standard deviation relative to the corresponding
644"
monthly mean MLD (i.e., the Coefficient of Variance).
645"
646"
Fig"2:"(a) Annual mean MLD estimated from GECCO2 density profiles, and (b) Seasonal
647"
amplitude (computed as the standard deviation of monthly climatology) of MLD relative to
648"
annual mean MLD."
649"
650"
Fig"3:"Different"MLD"regimes"derived"from"GECCO2"dataset."(a)"ExtraLtropicalLseasonal,"
651"
shows"the"regions"where"seasonal"amplitude"change"is">60%"of"the"annual"mean"MLD."
652"
(b)"ConstantLseep,"where"MLD">30m"and"seasonal"change"is"<60%."(c)"ConstantL
653"
shallow,"shows"the"regions"with"<60%"relative"seasonal"change"with"MLD"<30m."
654"
655"
Fig"4:"Annual mean fields from GECCO2. (a) net heat flux (NHF); positive/negative sign
656"
indicate the ocean is gaining/losing heat, (b) wind-stress, (c) density gradient between surface
657"
and 100m depth, and (d) Evaporation minus Precipitation (E-P). Colour code is in such a way
658"
that blue shading represents favourable for deep MLD and red shading for shallow mixed
659"
layers. Spatial correlation (r) between the forcing variable and annual mean MLD (Fig 2a) is
660"
shown at top-left corner of the corresponding figure. "
661"
662"
Fig" 5: Seasonal amplitude of GECCO2 variables (computed as the standard deviation of
663"
monthly climatology). (a) net heat flux (NHF), (b) wind-stress, (c) density gradient between
664"
surface and 100m depth, and (d) Evaporation minus Precipitation (E-P). Spatial correlation
665"
(r) between seasonal change for forcing variable and MLD (relative to annual mean, in Fig
666"
2b) is shown at top-left corner of the corresponding figure.
667"
668"
Fig"6:"The annual mean MLD, net heat flux (NHF) and wind-stress (from GECCO2)
669"
averaged along (a) 30º-32ºN (North Pacific) and (b) 33º-35ºN (North Atlantic). The
670"
positive/negative heat flux (NHF) values show the ocean is losing/gaining heat. The colour
671"
shading is the corresponding seasonal amplitude change at each grid point; light-red for NHF,
672"
light-blue for MLD and light-yellow is for wind-stress."
673"
674"
Fig"7:"Cross"correlation"of"Net"heat"flux"(NHF)"and"MLD"anomalies"in"different"seasons"
675"
from"GECCO2."Left"panel"shows"concurrent"correlations"and"the"right"panel"represents"
676"
corresponding"oneLmonth"lag"correlations,"where"MLD"lags"behind"oneLmonth"of"the"
677"
forcing."Figure"aLb"shows"the"lag"correlation"during"JanuaryLMarch"(JFM),"cLd"
678"
represents"AprilLJune"(AMJ),"eLf"shows"the"lag"correlation"during"JulyL"September"(JAS),"
679"
and"gLh"corresponds"to"OctoberLNovember"(OND).""
680"
681"
Fig 8: Cross correlation of wind-stress (TAU) and MLD anomalies in different seasons from
682"
GECCO2. Left panel shows concurrent correlations and the right panel represents
683"
corresponding one-month lag correlations, where MLD lags behind one-month of the forcing.
684"
Figure"aLb"shows"the"lag"correlation"during"JanuaryLMarch"(JFM),"cLd"represents"AprilL
685"
June"(AMJ),"eLf"shows"the"lag"correlation"during"JulyL"September"(JAS),"and"gLh"
686"
corresponds"to"OctoberLNovember"(OND).""
687"
688"
Fig 9:"Seasonal"statistics"of"mixed"layer"depth,"same"as"in"Fig"1"but"for ACCESS-KPP
689"
coupled model.
690"
691"
Fig"10:"Annual mean fields from ACCESS-KPP coupled model. (a) net heat flux (NHF);
692"
positive/negative sign indicate the ocean is gaining/losing heat, (b) wind-stress, (c) density
693"
gradient between surface and 100m depth, and (d) Evaporation minus Precipitation (E-P).
694"
Colour code is in such a way that blue shading represents favourable for deep MLD and red
695"
shading for shallow mixed layers. Spatial correlation (r) between the forcing variable and
696"
annual mean MLD (Fig 12a) is shown at top-left corner of the corresponding figure.
697"
698"
Fig 11: Seasonal amplitude of ACCESS-KPP forcing variables (computed as the standard
699"
deviation of monthly climatology). (a) net heat flux (NHF), (b) wind-stress, (c) density
700"
gradient between surface and 100m depth, and (d) Evaporation minus Precipitation (E-P).
701"
Spatial correlation (r) between seasonal change for forcing variable and MLD (relative to
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annual mean, in Fig 12b) is shown at top-left corner of the corresponding figure.
703"
"
704"
Fig"12:"(a) Annual mean MLD estimated from ACCESS-KPP simulated density profiles, and
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(b) Seasonal amplitude of MLD relative to annual mean MLD (same as in Fig 2).
706"
707"
Fig"13:"Different"MLD"regimes"derived"from"ACCESSLKPP"simulation."(a)"ExtraLtropicalL
708"
seasonal,"shows"the"regions"where"seasonal"amplitude"change"is">60%"of"the"annual"
709"
mean"MLD."(b)"ConstantLseep,"where"MLD">30m"and"seasonal"change"is"<60%."(c)"
710"
ConstantLshallow,"shows"the"regions"with"<60%"relative"seasonal"change"with"MLD"
711"
<30m."
712"
713"
Fig"14:"Cross"correlation"of"Net"heat"flux"(NHF)"and"MLD"anomalies"in"different"seasons"
714"
from"ACCESSLKPP"model."Left"panel"shows"concurrent"correlations"and"the"right"panel"
715"
represents"corresponding"oneLmonth"lag"correlations,"where"MLD"lags"oneLmonth"of"
716"
the"forcing."Figure"aLb"shows"the"lag"correlation"during"JanuaryLMarch"(JFM),"cLd"
717"
represents"AprilLJune"(AMJ),"eLf"shows"the"lag"correlation"during"JulyL"September"(JAS),"
718"
and"gLh"corresponds"to"OctoberLNovember"(OND).""
719"
720"
Fig 15: Cross correlation of wind-stress (TAU) and MLD anomalies in different seasons
721"
from"ACCESSLKPP"model. Left panel shows concurrent correlations and the right panel
722"
represents corresponding one-month lag correlations, where MLD lags one-month of the
723"
forcing. Figure"aLb"shows"the"lag"correlation"during"JanuaryLMarch"(JFM),"cLd"represents"
724"
AprilLJune"(AMJ),"eLf"shows"the"lag"correlation"during"JulyL"September"(JAS),"and"gLh"
725"
corresponds"to"OctoberLNovember"(OND)."
726"
"
727"
Fig"16:"Annual"mean"Heat"flux"correction,"(a)"surface"to"50m"depth"and"(b)"200"to"300m"
728"
depth,"applied"to"the"temperature"tendency"equation"in"the"model."Flux"correction"is"
729"
applied"in"order"to"reduce"the"drift"in"temperature"values"for"long"simulation.""
730"
731"
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Fig! 1:! Seasonal! statistics! of! mixed! layer! depth! from GECCO2 averaged over JanuaryMarch (a,b,c), April-June (d,e,f), July-September (g,h,i) and October-December (j,k,l) . Left
side panel of the figure shows the long-term monthly mean averaged over the seasonal
months of the year (a,d,g,j). The standard deviations of MLD in each month for the years
1948-2011 averaged then to the seasonal months are shown in the middle panel (b,e,h,k). On
the right panel (c,f,i,l) we plotted the standard deviation relative to the corresponding monthly
mean MLD (i.e., the Coefficient of Variance).
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Fig!2:!(a) Annual mean MLD estimated from GECCO2 density profiles, and (b) Seasonal
amplitude (computed as the standard deviation of monthly climatology) of MLD relative to
annual mean MLD
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Fig!3:!Different!MLD!regimes!derived!from!GECCO2!dataset.!(a)!ExtraEtropicalEseasonal,!
shows!the!regions!where!seasonal!amplitude!change!is!>60%!of!the!annual!mean!MLD.!
(b)!ConstantEseep,!where!MLD!>30m!and!seasonal!change!is!<60%.!(c)!ConstantE
shallow,!shows!the!regions!with!<60%!relative!seasonal!change!with!MLD!<30m.!
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Fig!4:!Annual mean fields from GECCO2. (a) net heat flux (NHF); positive/negative sign
indicate the ocean is gaining/losing heat, (b) wind-stress, (c) density gradient between surface
and 100m depth, and (d) Evaporation minus Precipitation (E-P). Colour code is in such a way
that blue shading represents favourable for deep MLD and red shading for shallow mixed
layers. Spatial correlation (r) between the forcing variable and annual mean MLD (Fig 2a) is
shown at top-left corner of the corresponding figure. !
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Fig!5: Seasonal amplitude of GECCO2 variables (computed as the standard deviation of
monthly climatology). (a) net heat flux (NHF), (b) wind-stress, (c) density gradient between
surface and 100m depth, and (d) Evaporation minus Precipitation (E-P). Spatial correlation (r)
between seasonal change for forcing variable and MLD (relative to annual mean, in Fig 2b) is
shown at top-left corner of the corresponding figure. !
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(a) North Pacific
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(b) North Atlantic
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Fig!6:!The annual mean MLD, net heat flux (NHF) and wind-stress (from GECCO2) averaged
along (a) 30º-32ºN (North Pacific) and (b) 33º-35ºN (North Atlantic). The positive/negative
heat flux (NHF) values show the ocean is losing/gaining heat. The colour shading is the
corresponding seasonal amplitude change at each grid point; light-red for NHF, light-blue for
MLD and light-yellow is for wind-stress.!
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Fig!7:!Cross!correlation!of!Net!heat!flux!(NHF)!and!MLD!anomalies!in!different!seasons!
from!GECCO2.!Left!panel!shows!concurrent!correlations!and!the!right!panel!represents!
corresponding!oneEmonth!lag!correlations,!where!MLD!lags!behind!oneEmonth!of!the!
forcing.!Figure!aEb!shows!the!lag!correlation!during!JanuaryEMarch!(JFM),!cEd!represents!
AprilEJune!(AMJ),!eEf!shows!the!lag!correlation!during!JulyE!September!(JAS),!and!gEh!
corresponds!to!OctoberENovember!(OND).!!
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Fig 8: Cross correlation of wind-stress (TAU) and MLD anomalies in different seasons from
GECCO2. Left panel shows concurrent correlations and the right panel represents
corresponding one-month lag correlations, where MLD lags behind one-month of the forcing.
Figure!aEb!shows!the!lag!correlation!during!JanuaryEMarch!(JFM),!cEd!represents!AprilE
June!(AMJ),!eEf!shows!the!lag!correlation!during!JulyE!September!(JAS),!and!gEh!
corresponds!to!OctoberENovember!(OND).!!
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Fig 9:! Seasonal! statistics! of! mixed! layer! depth,! same! as! in! Fig! 1! but! for ACCESS-KPP
coupled model. !
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Fig!10:!Annual mean fields from ACCESS-KPP coupled model. (a) net heat flux (NHF);
positive/negative sign indicate the ocean is gaining/losing heat, (b) wind-stress, (c) density
gradient between surface and 100m depth, and (d) Evaporation minus Precipitation (E-P).
Colour code is in such a way that blue shading represents favourable for deep MLD and red
shading for shallow mixed layers. Spatial correlation (r) between the forcing variable and
annual mean MLD (Fig 12a) is shown at top-left corner of the corresponding figure.
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Fig 11: Seasonal amplitude of ACCESS-KPP forcing variables (computed as the standard
deviation of monthly climatology). (a) net heat flux (NHF), (b) wind-stress, (c) density
gradient between surface and 100m depth, and (d) Evaporation minus Precipitation (E-P).
Spatial correlation (r) between seasonal change for forcing variable and MLD (relative to
annual mean, in Fig 12b) is shown at top-left corner of the corresponding figure.
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Fig!12:!(a) Annual mean MLD estimated from ACCESS-KPP simulated density profiles, and
(b) Seasonal amplitude of MLD relative to annual mean MLD (same as in Fig 2).
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Fig!13:!Different!MLD!regimes!derived!from!ACCESSEKPP!simulation.!(a)!ExtraEtropicalE
seasonal,!shows!the!regions!where!seasonal!amplitude!change!is!>60%!of!the!annual!
mean!MLD.!(b)!ConstantEseep,!where!MLD!>30m!and!seasonal!change!is!<60%.!(c)!
ConstantEshallow,!shows!the!regions!with!<60%!relative!seasonal!change!with!MLD!
<30m.!
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Fig!14:!Cross!correlation!of!Net!heat!flux!(NHF)!and!MLD!anomalies!in!different!seasons!
from!ACCESSEKPP!model.!Left!panel!shows!concurrent!correlations!and!the!right!panel!
represents!corresponding!oneEmonth!lag!correlations,!where!MLD!lags!oneEmonth!of!the!
forcing.!Figure!aEb!shows!the!lag!correlation!during!JanuaryEMarch!(JFM),!cEd!represents!
AprilEJune!(AMJ),!eEf!shows!the!lag!correlation!during!JulyE!September!(JAS),!and!gEh!
corresponds!to!OctoberENovember!(OND).!!
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Fig 15: Cross correlation of wind-stress (TAU) and MLD anomalies in different seasons from!
ACCESSEKPP!model. Left panel shows concurrent correlations and the right panel represents
corresponding one-month lag correlations, where MLD lags one-month of the forcing. Figure!
aEb!shows!the!lag!correlation!during!JanuaryEMarch!(JFM),!cEd!represents!AprilEJune!
(AMJ),!eEf!shows!the!lag!correlation!during!JulyE!September!(JAS),!and!gEh!corresponds!
to!OctoberENovember!(OND).!!
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Fig!16:!Annual!mean!Heat!flux!correction,!(a)!surface!to!50m!depth!and!(b)!200!to!300m!
depth,!applied!to!the!temperature!tendency!equation!in!the!model.!Flux!correction!is!
applied!in!order!to!reduce!the!drift!in!temperature!values!for!long!simulation.!!
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Supplementary,figures,
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SFig!1:!Cross!correlation!of!EMP!and!MLD!anomalies!in!different!seasons!from!GECCO2.!
Left!panel!shows!concurrent!correlations!and!the!right!panel!represents!corresponding!
oneEmonth!lag!correlations,!where!MLD!lags!behind!oneEmonth!of!the!forcing.!Figure!aEb!
shows!the!lag!correlation!during!JanuaryEMarch!(JFM),!cEd!represents!AprilEJune!(AMJ),!
eEf!shows!the!lag!correlation!during!JulyE!September!(JAS),!and!gEh!corresponds!to!
OctoberENovember!(OND).!
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SFig!2:!Cross!correlation!of!EMP!and!MLD!anomalies!in!different!seasons!from!ACCESSE
KPP!model.!Left!panel!shows!concurrent!correlations!and!the!right!panel!represents!
corresponding!oneEmonth!lag!correlations,!where!MLD!lags!behind!oneEmonth!of!the!
forcing.!Figure!aEb!shows!the!lag!correlation!during!JanuaryEMarch!(JFM),!cEd!represents!
AprilEJune!(AMJ),!eEf!shows!the!lag!correlation!during!JulyE!September!(JAS),!and!gEh!
corresponds!to!OctoberENovember!(OND).!