Dynamics of Atmospheres and Oceans 60 (2013) 28– 45

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Dynamics of Atmospheresand Oceans

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Impact of the ocean diurnal cycle on the North Atlanticmean sea surface temperatures in a regionally coupledmodel

Virginie Guemasa,b,c,∗, David Salas-Méliab, Masa Kageyamac,Hervé Giordanib, Aurore Voldoireb

a Institut Català de Cienciès del Clima, Barcelona, Spainb Centre National de Recherches Météorologiques/Groupe d’Etude de l’Atmosphère Météorologique, Météo-France, CNRS, UMR 3589,Toulouse, Francec Laboratoire des Sciences du Climat et de l’Environnement, UMR 1572, CEA-CNRS-UVSQ, Gif-sur-Yvette, France

a r t i c l e i n f o

Article history:Received 28 November 2011Received in revised form 16 January 2013Accepted 17 January 2013

Available online 30 January 2013

Keywords:Mean climateOcean diurnal cycleHeat entrainmentVertical resolutionCoupling frequency

a b s t r a c t

This study investigates the mechanisms by which the ocean diurnalcycle can affect the ocean mean state in the North Atlantic region.We perform two ocean-atmosphere regionally coupled simulations(20◦N–80◦N, 80◦W–40◦E) using the CNRMOM1D ocean model cou-pled to the ARPEGE4 atmospheric model: one with a 1 h couplingfrequency (C1h) and another with a 24 h coupling frequency (C24h).The comparison between both experiments shows that accountingfor the ocean diurnal cycle tends to warm up the surface ocean athigh latitudes and cool it down in the subtropics during the borealsummer season (June–August). In the subtropics, the leading causefor the formation of the negative surface temperature anomalies isthe fact that the nocturnal entrainment heat flux overcompensatesthe diurnal absorption of solar heat flux. Both in the subtrop-ics and in the high latitudes, the surface temperature anomaliesare involved in a positive feedback loop: the cold (warm) surfaceanomalies favour a decrease (increase) in evaporation, a decrease(increase) in tropospheric humidity, a decrease (increase) in down-welling longwave radiative flux which in turn favours the surfacecooling (warming). Furthermore, the decrease in meridional seasurface temperature gradient affects the large-scale atmosphericcirculation by a decrease in the zonal mean flow.

© 2013 Elsevier B.V. All rights reserved.

∗ Corresponding author at: Institut Català de Cienciès del Clima, Carrer del Doctor Trueta, 203, 08005 Barcelona, Spain.E-mail addresses: vguemas@ic3.cat, virginie.guemas@ic3.cat (V. Guemas).

0377-0265/$ – see front matter © 2013 Elsevier B.V. All rights reserved.http://dx.doi.org/10.1016/j.dynatmoce.2013.01.001

V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45 29

1. Introduction

1.1. The diurnal cycle in the current generation of climate models

Reducing climate projection uncertainties is of primary concern for the climatologist community.One way to reach this objective is to improve global CGCMs (Coupled General Circulation Model)by correcting their biases. To do so, research efforts can target either the improvement of existingparameterizations for subgrid scale processes or the inclusion of previously non-resolved processes.This latter option comes with the burden of an increase in CGCM computational cost. Due to thelimitations in available computational resources, a crucial question is raised: which missing processesdo we really have to include? The answer requires an extensive understanding of their climate impacts.

Up to now, most CGCMs have not resolved the ocean diurnal cycle since, for years, it has been con-sidered to be negligible. However, recent satellite observations show that the ocean diurnal warmingcan reach 6–8 ◦C in the Atlantic Ocean during some days in summer (Merchant et al., 2008; Gentemannet al., 2008). Large ocean diurnal warmings can cover regions extending up to 1000 km (Gentemannet al., 2008). Could the representation of the ocean diurnal cycle significantly affect the simulated cli-mate in CGCMs? Before describing our experimental design to address this question, we give a shortintroduction on the dynamics of the ocean mixed layer and on previous studies tackling this problem.

1.2. The diurnal cycle of the ocean mixed layer (inspired by Bernie et al., 2007)

The diurnal variations in mixed layer depth result from the competition between stabilizing pro-cesses such as the absorption of solar heat flux and destabilizing processes such as convective andshear driven turbulent mixing. During the night, as no solar heat flux reaches the surface, the densityprofile is mixed over depth and the mixed layer reaches its greatest depth just before sunrise. Thesunrise causes a rapid shoaling of the mixed layer which accelerates the warming of the surface as theabsorbed heat is mixed on a shallower and shallower layer. The minimum in mixed layer depth occursat about midday and the maximum in sea surface temperature at around 3 pm. During the day, thepenetration of solar heat flux also contributes to build a stable stratification below the mixed layer. Asthe Sun goes down, the stabilizing effect of the solar heat flux decreases and the stratification belowthe mixed layer is slowly eroded. As the mixed layer deepens, its heat content is mixed downwardand the sea surface temperature decreases. The turbulent mixing intensifies during the night and thestratification built up during the day is more or less eroded depending on the intensity of the non solarheat flux and the wind stress.

According to Gentemann et al. (2008), most of the large diurnal warming events which peak around5 ◦C and 7 ◦C and which are spatially coherent over large areas, occur in the extra-tropics. Using satellitedata, Kawai and Wada (2007) show that the seasonally averaged ocean diurnal warming amplitude,computed from skin temperature, is about 0.75 ◦C year round in the Tropics and can also exceed 0.75 ◦Cin most of the mid-latitudes in summer (see their Fig. 5). The ocean diurnal warming can induce anincrease in the net surface heat flux towards the atmosphere of more than 50 W m−2 during the day,under clear sky and calm conditions (Fairall et al., 1996; Ward, 2006). Hence, the ocean diurnal cyclecan impact the atmosphere and take part in atmosphere-ocean coupling mechanisms. For instance, theSST (sea surface temperature) diurnal variations can affect the life cycle of tropical convective clouds(Chen and Houze, 1997; Woolnough et al., 2000; Dai and Trenberth, 2004) and the atmospheric profilesof heat, moisture and cloud properties (Clayson and Chen, 2002).

1.3. Impact of the ocean diurnal cycle on the mean climate

Recent studies show that resolving the SST variability on diurnal timescales can significantlymodulate the amplitude of SST variability on intraseasonal timescales (Shinoda and Hendon, 1998;Bernie et al., 2005, 2007; Shinoda, 2005; Bellanger, 2007; Guemas et al., 2011) and even on longertimescales (Bernie et al., 2008; Danabasoglu et al., 2006). It can improve the representation of ocean-atmosphere coupled modes of variability, such as the Madden–Julian Oscillation, by modifying its

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phase (Woolnough et al., 2007) and the amplitude of associated SST anomalies (Bernie et al., 2005,2008) or the ENSO by changing its frequency spectrum (Danabasoglu et al., 2006).

Shinoda and Hendon (1998) and Bernie et al. (2005) used 1-dimensional ocean models to showthat accounting for the ocean diurnal cycle is expected to increase the mean SST in the Tropics throughthe “rectification mechanism”. According to this mechanism, the diurnal absorption of solar heat fluxand the nocturnal entrainment heat flux compensate for each other. The thermodynamic profile andsurface temperature are the same during the night hours if the ocean diurnal cycle is accounted foror not. However, the diurnal peak in SST if accounting for the ocean diurnal cycle makes the dailymean SST larger than if this SST peak is not accounted for. Then, Danabasoglu et al. (2006) and Bernieet al. (2008) produced diagnostics from CGCM simulations with a 1 h and 3 h coupling time steprespectively, which they compared with control simulations coupled once a day. The main result ofthese studies is a warmer tropical band when the ocean diurnal cycle is simulated. In Bernie et al.(2008), this warming reaches 0.2 ◦C in the central and western Pacific and 0.3 ◦C in the eastern Pacific.This amplitude is consistent with the one expected by the “rectification mechanism”. In Danabasogluet al. (2006), the warming reaches 1 ◦C. Only a part of this signal is attributed to the “rectificationmechanism”, the other part is attributed to a positive atmospheric feedback. The amplification of thesignal is due to a decrease in cloud cover extent in response to the surface ocean warming triggeredby the “rectification” mechanism. However, no mechanism is suggested to explain how the surfaceocean warming causes the decrease in the cloud cover extent.

1.4. The use of a 1-dimensional ocean model

Despite a lack of agreement between previous studies on the atmospheric retroactions to the “rec-tification mechanism”, they seem to agree on the oceanic processes at stake when representing theocean diurnal variations. These processes are essentially 1-dimensional ocean processes. Accountingfor the ocean diurnal cycle tends to increase substantially the SST during the sunny hours throughthe positive retroaction between increased stratification and increased warming by solar absorption.Accounting for the ocean diurnal cycle barely affects though the SST during the night hours. Thishypothesis implies that the turbulent mixing is strong enough during the night to totally clear themixed layer memory from the previous day and the mixed layer is shallow enough during the day sothat the stratification is easily eroded during the following night. In this article, we aim at contributingto the investigation of these mechanisms with a focus on the North Atlantic region (NAE, 20◦N–80◦N,80◦W–40◦E). A way of validating the above suggested mechanism which relies on 1-dimensionalocean processes is to replace in an ocean general circulation model at each time step the advectiveterms computed from the thermodynamic state at this same time step by prescribed advective terms.This methodology allows for isolating the sensitivity of the 1-dimensional ocean processes to therepresentation of the ocean diurnal cycle. Here, we use an atmosphere general circulation model cou-pled in the North Atlantic region (NAE, 20◦N–80◦N, 80◦W–40◦E) to the fine resolution CNRMOM1D(Centre National de Recherches Météorologiques Ocean Model 1-dimensional) 1-dimensional oceanmodel to produce two 50-year simulations: one is coupled with a frequency of 1 h and resolves the1-dimensional processes linked to the ocean diurnal cycle while the other is coupled with a frequencyof 1 day as classically done in coupled ocean-atmosphere general circulation models and does notresolve these processes. The methodology cut any potential retroaction of the ocean general circula-tion on the initial 1-dimensional ocean response. At the same time, it contributes to the debate on theatmospheric response to this initial ocean mechanism. The success of this approach requires that theprescribed advective terms are realistic enough to produce stable simulations with a realistic climate.In this study, we use as an ocean model the CNRMOM1D (Guemas et al., 2008) 1-dimensional modelwith 124 vertical levels (1 m resolution near surface). This high vertical resolution optimizes the rep-resentation of turbulent processes and stratification in the ocean mixed layer, which constitute themain physical causes for SST changes on diurnal timescales. This high vertical resolution, combinedwith a coupling frequency of 1 h, constitutes a necessary condition for a good representation of the SSTdiurnal variations (Weller and Anderson, 1996; Sui et al., 1997; Shinoda and Hendon, 1998; Bernieet al., 2005). Daily climatologies of heat and freshwater trends, i.e. advective temperature and saltfluxes, are applied to account for the advective processes (see Fig. 1 in Guemas et al., 2008). As these

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climatologies are the same for both simulations, the potential impact of the ocean diurnal cycle onthe large-scale ocean circulation is not accounted for. The diurnal and intraseasonal ocean variabil-ity simulated by this ocean model has been extensively validated in Guemas et al. (2011). The paperis organized as follows. The coupled model components and the simulations run for these analysesare presented in Section 2. Section 3 focuses on the mechanism by which the mean climate may beaffected by the representation of the ocean diurnal cycle. Sections 4 and 5 are respectively dedicatedto some discussions and conclusions.

2. Model and simulations

The coupled climate model used in this study is based on the coupled core formed by the ARPEGE(Action de Recherche Petite Echelle Grande Echelle) version 4 AGCM (Atmosphere General CirculationModel; Déqué et al., 1999; Gibelin and Déqué, 2003) and the CNRMOM1D ocean model. The sea icemodel GELATO3 (Global Experimental Leads and sea ice model for ATmosphere Ocean; Salas-Mélia,2002), is embedded in the CNRMOM1D ocean model. These components run on the same horizontalgrid of 2.8◦ horizontal resolution. They run with different time steps and are coupled synchronously.These different components are described in the following sections.

2.1. The CNRMOM1D ocean model

The CNRMOM1D ocean model is based on the vertical mixing scheme developed by Bougeaultand Lacarrere (1989) for the atmospheric boundary layer and adapted to the ocean by Gaspar et al.(1990). In this formulation, the vertical mixing coefficients are based on the computation of two tur-bulent length scales representing the upward and downward conversions of turbulent kinetic energyinto potential energy. The second-order moments are expressed as a function of the turbulent kineticenergy, which is computed via a prognostic equation. This 1.5 turbulence closure parameterization hasbeen validated against observational data from buoys at diurnal to interannual timescales in variouslocations, for example at stations PAPA (50◦N, 145◦W), LOTUS (34◦N, 70◦W) and buoy Marisondelaunched at 156◦E and 1◦S during the TOGA-COARE campaign (Gaspar et al., 1990). This verticalmixing scheme has been incorporated under its original form in the SURFEX model (Le Moigne,2009) which is included into the ARPEGE version 5, ALADIN (Bubnová et al., 1995) and MESO-NH(http://mesonh.aero.obs-mip.fr/mesonh) atmospheric models. It has also been included in the NEMO(Blanke and Delecluse, 1993; Ethe et al., 2006; Madec, 2008) ocean model, though after a simplificationto reduce its computational cost. The vertical profile of solar absorption is also crucial in the repre-sentation of the stabilization processes in the upper ocean. Recently, Ohlmann (2003) developed anew parameterization of the solar transmission function depending on the chlorophyll concentration,which we implemented in the CNRMOM1D model. Ohlmann (2003)’s results show that the bias in thesolar transmission profile can be reduced by 50–90% compared to the Paulson and Simpson (1977)parameterization. The chlorophyll concentration was specified as a 12-month climatology derivedfrom satellite ocean colour measurements from the SEAWIFS project. The choice of the ocean verticalresolution is based on the results from Bernie et al. (2005): the simulated diurnal warming amplitudeincreases rapidly with the top layer vertical resolution up to a critical value in model level thicknessof 1 m and then it saturates (their Fig. 10). We obtain the same results with the CNRMOM1D model.We then chose a level thickness of 1 m down to 75 m depth which increases below 75 m up to a 500 mat the ocean bottom.

A validation of the CNRMOM1D ocean model based on a simulation forced with the ERA40 reanal-ysis data (Gibson et al., 1997; Uppala et al., 2004) is given in details in Guemas et al. (2008). Onaverage, the simulated Mixed Layer Depth (MLD) is too small compared to the observations fromde Boyer Montégut et al. (2004), by about 5–10 m in summer and about 10–50 m in winter, becauseof a too weak turbulent mixing. Nevertheless, the simulated large-scale patterns of MLD are similarto the observed ones (de Boyer Montégut et al., 2004) which reflects the realistic sensitivity of themixed layer to variations in surface forcings and the ability of the model to account for the main keymechanisms controlling the mixed layer heat content. This underestimation of the MLD is associatedwith a basinwide summer SST bias of about 0.5 ◦C compared to the PHC climatology (Polar science

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center Hydrographic Climatology; Steele et al., 2001). The intraseasonal SST variability simulated bythe CNRMOM1D ocean model is also realistic although along the American coast the bias in daily SSTanomalies can reach 0.3 ◦C (Guemas et al., 2011).

2.2. The GELATO3 sea ice model

In this study, GELATO3 is used with 4 different ice thickness categories: 0–0.3 m, 0.3–0.8 m, 0.8–3 mand over 3 m. Transitions or mergers between these categories may occur as ice thickness variesthermodynamically. Every slab of ice is evenly divided into 9 vertical layers and may be covered with1 layer of snow, for which snow ageing processes are considered. The heat diffusion equation is solvedalong the vertical (Salas-Mélia, 2002) through the entire slab.

2.3. The ARPEGE-Climat version 4 atmospheric model

ARPEGE-Climat is run at T63 truncation. The representation of most model variables is spectral.The grid has 31 levels in the vertical. The semi-Lagrangian advection scheme allows for a 30-m timestep. Stratiform clouds and deep convection are represented following Ricard and Royer (1993). TheISBA Soil–Vegetation–Atmosphere Transfer model, described by Mahfouf et al. (1995) is included. Soiland vegetation properties are prescribed from the global high-resolution ECOCLIMAP dataset (Massonet al., 2003). ARPEGE-Climat uses ocean temperature and albedo boundary conditions computed bythe CNRMOM1D-GELATO3 system and it provides surface fluxes to the ocean-sea ice model.

2.4. Design of C1h simulation

The C1h simulation is a 50-year-long simulation coupled every hour in the North Atlantic Euro-pean region (NAE, 20◦N–80◦N, 80◦W–40◦E). Outside the NAE region, the atmospheric model is forcedusing Reynolds et al. (2002) SST and Rayner et al. (2003) sea ice concentration climatologies as lowerboundary conditions. Between the coupling and the forcing domains, a linear interpolation of theboundary conditions is applied in a 10◦-transition zone between the surface conditions computedby CNRMOM1D and the observed ones. The CNRMOM1D is initialized from the PHC climatologyfor temperature and salinity and an ocean at rest. To obtain a realistic initial state for sea ice, theatmosphere-ocean-sea ice coupled model is run for 1 year with a sea ice extent initialized from theHadISST dataset of sea ice concentration (Rayner et al., 2003; http://badc.nerc.ac.uk/data/hadisst/)and ice thickness set at 3 m in the Arctic Ocean and 1 m around the Antarctic. During this 1-year longsimulation, the ocean state is relaxed towards the PHC climatology with an e-folding timescale of 1day. This year is thus considered as a spin-up year and is removed. Then, the sea ice model evolvesfreely.

As the advective and horizontal diffusive processes are not represented in the 1-dimensional oceanmodel, 3-dimensional heat and freshwater flux corrections are needed to obtain a stable mean state.To compute these flux corrections, a first 50-year atmosphere-ocean-sea ice coupled simulation isrun with a relaxation of ocean temperature and salinity towards PHC climatologies with an e-foldingtimescale of 1 day. Relaxation trends in temperature and salinity are saved at each time step. A dailyclimatology of these trends is computed from the last 49 years. The first year is considered as a spinup and removed to compute these heat and freshwater flux corrections. These 3-dimensional clima-tologies are then applied as 3-dimensional heat and freshwater flux corrections to obtain a second50-year coupled simulation which is the simulation studied here. The validation of this method forcomputing the heat and freshwater correction fluxes is further developed in Guemas et al. (2008, inparticular Fig. 1). Using this method, we obtain a stable simulation named C1h.

2.5. Validation of C1h simulation

The C1h large-scale patterns of winter (December–February) and summer (June–August) MLD,defined as the depth at which the density difference from the sea surface is 0.03 kg m−3 (Fig. 1a and b)are similar to the observations from de Boyer Montégut et al. (2004; see Fig. 2 in Guemas et al., 2008).

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Fig. 1. Seasonal mean MLD (Mixed Layer Depth) in C1h experiment: (a) in boreal summer (June–August), (b) in boreal winter(December–January). Contour interval: 10 below 50 m, 50 m between 50 m and 300 m, 100 m under 300 m depth. Seasonal meandifferences between C1h SST (sea surface temperature) and the PHC data (Steele et al., 2001): (c) in summer (May–September),(d) in winter (November–March). Contour interval: 0.5 ◦C.

The winter MLD maxima, corresponding to deep convection sites, are properly reproduced by theocean model apart from small shifts in locations, a smaller extent and a lower maximum depth. Thestorm track crossing the North Atlantic Ocean affects the turbulent mixing at mid-to-high latitudes inthe model as well as in the observations. As in the ocean forced simulation (Guemas et al., 2008), thesimulated MLD is underestimated and this underestimation reaches 30% in summer. Furthermore, asin the ocean forced simulation, we can see a summer mean basinwide SST bias in C1h (Fig. 1c and d)but this bias is amplified by the coupling with the ARPEGE atmosphere model: it is around 1–1.5 ◦Cover the entire North Atlantic Basin and reaches a maximum of 4 ◦C east of NewFoundland. A cold biasoccurs in winter in the high latitudes together with a too large sea ice extent according to the PHCdata.

The major imprint of the ocean diurnal cycle in C1h consists of a sea surface diurnal warming whichhas a summer mean (June–August) amplitude of about 0.5 ◦C between 20◦N and 40◦N and whichdecreases with latitude north of 40◦N. The diurnal warming of each day is defined as the differencebetween the absolute maximum occurring between 10h30 and 18h00 local time and the preceding

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Fig. 2. Probability density functions of the simulated summer (June–August) SST diurnal warmings, in ◦C, in four latitude bands.These PDFs were built from histograms with 0.02 ◦C-width bins divided by the total number of diurnal warming values and by0.02 ◦C. The integral of each PDF amounts to 1. For more details about PDFs, see Von Storch and Zwiers (1999).

absolute minimum occurring between 18h00 and 10h30. In case there is no local extremum in thesetime windows, the diurnal warming is set to zero. Fig. 2 shows the probability density functions(PDF) of the simulated summer diurnal warming amplitudes for four latitudinal bands (20◦N–30◦N,30◦N–40◦N, 40◦N–50◦N and 50◦N–60◦N) in the North Atlantic Ocean. These PDF consist of a peak at0 ◦C and a heavy-tailed mode. A wide variety of atmospheric conditions prevent from any diurnalwarming of the sea surface, as for example a dense cloud cover or the occurrence of wind gusts, hencethe presence of the peak at 0 ◦C in the PDFs. The simulated diurnal warmings range between 0 ◦Cand 1.9 ◦C. The heavy tailed mode is similar to the one obtained by Gentemann et al. (2008, Fig. 1)using satellite data except that they observe larger extreme diurnal warmings, by a factor 2–3. Thesummer mean amplitude of about 0.5 ◦C in the subtropics is consistent with the climatology obtainedby Kennedy et al. (2007) using 25 cm depth temperatures given by drifting buoys. However, it is abouttwice as low as the one given by Kawai and Wada (2007)’s climatology produced from satellite data. Wecan explain this discrepancy by the fact that in our model, we define the SST as the bulk temperature ofthe first ocean level (of thickness 1 m), while satellite observations such as the ones used by Kawai andWada (2007) measure the skin temperature. Indeed, a sharp temperature gradient can be observedthrough the uppermost ocean metre during the daily peak in SST (Yokoyama et al., 1995; Soloviev andLukas, 1997; Ward, 2006; Kawai and Wada, 2007). Ward (2006) estimated that the difference betweenthe surface temperature and the 1-m depth temperature can reach 2.7 ◦C. In winter, the ocean diurnalwarming (not shown) is close to zero in the model as in the observations, due to lower solar heat fluxand stronger winds than in summer.

3. Impact of the ocean diurnal cycle: comparison between C1h and C24h

To assess the impact of the ocean diurnal cycle on the North Atlantic mean SSTs, the C1h simulationis compared to a second simulation, named C24h, for which the ARPEGE-Climat atmosphere model, theCNRMOM1D ocean model and the GELATO3 sea ice model exchange data only once a day, without anychanges of the other parameters with respect to the C1h simulation. Thus, the ocean diurnal cycle isnot represented in the C24h simulation, as opposed to the C1h simulation. The 3-dimensional heat andfreshwater flux corrections computed for the C1h simulation are also applied to the C24h simulation.Using this method, we obtain a stable C24h simulation and we ensure that the potential differences inocean and atmosphere mean states between C1h and C24h simulations are the initial 1-dimensional

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Fig. 3. (a) Monthly mean SST differences between C1h and C24h experiments. Continuous (dashed) line corresponds to thearea-averaged SST in the Atlantic Ocean between 20◦N (50◦N) and 40◦N (80◦N). Summer (June–August) mean differences in: (b)SST and (c) sea ice concentration between C1h and C24h experiments. The red and blue contours circle respectively negativeand positive anomalies which are significant to the 95% level, according to a student T-test (Von Storch and Zwiers, 1999).Contour interval: (b) 0.3 ◦C and (c) 10%. (For interpretation of the references to colour in this figure legend, the reader is referredto the web version of this article.)

consequences of the representation of the ocean diurnal cycle in C1h, rather than coming from anocean general circulation retroaction.

3.1. Anomalies in mean SST induced by the ocean diurnal cycle

In Section 2.5, we showed that the summer (June–August) mean ocean diurnal warming in C1hhas an amplitude of about 0.5 ◦C in the subtropics and that this amplitude decreases with latitude.Following the theory of Bernie et al. (2005), Shinoda (2005) and Bernie et al. (2007), such a diurnalwarming would lead to a higher averaged SST by about 0.1–0.3 ◦C in C1h than in C24h. This subtropicalSST difference could be amplified by the mechanism suggested by Danabasoglu et al. (2006).

Comparing the area-averaged SST between C1h and C24h for each month (Fig. 3a), we observe thatthe ocean diurnal cycle induces a cooling in the subtropics and a warming in the mid- to high latitudesduring boreal summer (June–August). The anomaly in area-averaged SST between 20◦N and 40◦N inC1h with respect to C24h is about −1 ◦C from June to August while the anomaly between 50◦N and80◦N is about 1 ◦C during the same period.

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The comparison between C1h and C24h patterns of summer mean SST (Fig. 3b) shows that thepositive SST anomalies in the mid- to high latitudes are confined to the northern high latitudes, inthe areas which are sparsely covered by summer sea ice packs. These anomalies reach about 5 ◦Cin the Labrador Sea. On the contrary, the negative anomalies spread between 20◦N and 40◦N andreach a minimum of −2 ◦C in the southeastern part of the North Atlantic Basin. The positive feedbackbetween sea ice melting (Fig. 3c) and surface warming explains the larger absolute anomalies in thehigh latitudes than in the subtropics. The anomalies in sea ice concentration reach locally −100% whichcorresponds to a total melting of sea ice.

While we expected a warming in the subtropics following the theory suggested by Bernie et al.(2005), Shinoda (2005) and Danabasoglu et al. (2006), we obtain a cooling in this region using theARPEGE4/CNRMOM1D/GELATO3 coupled model. The subtropical (polar) cooling (warming) occurseach summer and disappears each winter. From the comparison between C1h and C24h, we concludethat taking into account the ocean diurnal cycle affects the amplitude of the seasonal cycle althoughboth C1h and C24h are stable.

3.2. Anomalies in mean SST trend induced by the ocean diurnal cycle

As we are interested in the amplitude of the seasonal cycle, we compute here the monthly SSTtrends in both C1h and C24h as the trend in SST between the beginning of each month and the endof the same month. Comparing the long-term mean area-averaged SST trend between C1h and C24hfor each month (Fig. 4a), we observe that the cooling in the subtropics and the warming in the mid-to high latitudes begin each May. The anomaly in area-averaged SST trend between 20◦N and 40◦Nin C1h with respect to C24h ranges between −0.3 ◦C/month and −0.5 ◦C/month during May and Junewhile the anomaly between 50◦N and 80◦N ranges between 0.6 ◦C/month and 0.8 ◦C/month during thesame period. The anomalies in SST trend vanish during July and change of sign from August onwards.

The trend in Mixed Layer Temperature (MLT) is computed at each time step during the simulation.The pattern of mean MLT trend anomalies in C1h with respect to C24h (Fig. 4b) during May and Juneis consistent with the pattern of summer SST anomalies. The anomalies in MLT trend are negativebetween 20◦N and 40◦N with amplitude of about −1 ◦C/month. In the Greenland and Labrador Seas,positive anomalies in MLT trend appear during May and June with amplitude of about 2.5 ◦C/month.To understand how the summer SST anomalies form each year, we now perform heat budgets.

3.3. What is the leading cause for the formation of the boreal summer SST and sea ice concentrationanomalies?

3.3.1. MethodologyAs the same advective terms are prescribed in C1h and C24h, the MLT trend anomalies (Fig. 4b)

cannot be explained by ocean heat transport anomalies but rather by the anomalies in:

- non solar surface heat flux (sum of the downwelling and upwelling longwave radiative fluxes andthe latent and sensible heat fluxes)

- solar heat flux which is absorbed within the mixed layer- turbulent heat flux through the base of the mixed layer

These three fluxes have been computed on-line, which allows closing the mixed layer heat budget:the sum of the flux anomalies gives the pattern of MLT trend anomalies shown in Fig. 4b. All thesefluxes averaged over May and June and between 20◦N and 40◦N are given in Table 1 together with thetotal MLT trend anomaly. A positive flux contributes to increasing the MLT.

The four components of the non solar heat flux (downwelling and upwelling longwave radiativefluxes, latent and sensible heat fluxes) could not be converted on-line from surface heat fluxes, inW/m2, into heat flux applied to the MLD, in ◦C/month, as the ocean model receives their sum. Thus, toestimate the contribution of each of these four components in terms of heat flux applied to the MLD,

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Fig. 4. (a) Differences in long-term mean monthly SST trend between C1h and C24h experiments. Continuous (dashed) line corresponds to the area-averaged SST trend in the AtlanticOcean between 20◦N (50◦N) and 40◦N (80◦N). (b) May–June mean differences in MLT (Mixed Layer Temperature) trend between C1h and C24h experiments. Contour interval: 0.5 ◦C/month.

38 V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45

Table 1Average over the 20◦N–40◦N area of the heat fluxes contributing to the May–June MLT trend anomalies shown in Fig. 3. Apositive heat flux contributes to warming the mixed layer.

C1h–C24h (May–June) (◦C/month)

Online entrainment heat flux −1.52Online non solar heat flux −0.15Online solar heat flux absorbed in the mixed layer 1.27

Sum −0.4

Offline latent heat flux 0.62Offline sensible heat flux 0.02Offline downwelling longwave radiative flux −2.61Offline upwelling longwave radiative flux 2.70

Error on the offline non solar heat flux 0.88

Clear sky downwelling longwave radiative flux −2.45Cloud induced downwelling longwave radiative flux −0.16

we use the daily surface fluxes provided by the atmospheric model and the daily MLD given by theocean model:

Q iMLD = Fi

SURFACE� Cph

; i = LW↑, LW↓, LH, SH

where FiSURFACE is the daily surface flux (in W/m2), � is the water density (in kg/m3), Cp is the water

specific heat (in J/kg/◦C), h is the daily MLD (in m), Q iMLD is the daily heat flux applied to the MLD (in

◦C/month), LW↑ and LW↓ are the upwelling and downwelling longwave radiative fluxes respectivelyand LH and SH are the latent and sensible heat fluxes, respectively. This computation creates an errorassociated with the sub-diurnal variations which cannot be taken into account because hourly outputswould have been necessary. However, the minor terms of this offline budget, namely the sensible andlatent heat flux contributions, would remain minor even if this error was entirely attributed to any oneof them. Similarly, the major contributors, namely the upwelling and downwelling longwave radiativefluxes, would remain the major contributors even if the error was entirely attributed to any one ofthem. These four components averaged over May and June and between 20◦N and 40◦N are also givenin Table 1 together with the difference between the non solar heat flux anomaly computed on-lineand the sum of the anomalies of each of its components which are computed using daily surface fluxesand MLDs. This off-line computation allows an assessment of the relative contribution of each of thenon solar heat flux components.

In the area partially covered with sea ice, we perform a surface heat budget on the ocean/sea icesystem rather than on the ocean system alone. The mean surface heat fluxes during May and June over

Table 2May–June mean differences in surface heat flux between C1h and C24h experiments in the area partially covered with sea ice(Labrador sea, Davis Strait and Greenland Sea). A positive heat flux contributes to warming the ocean and sea ice.

C1h–C24h (May–June) (W/m2)

Downwelling solar heat flux −9.71Upwelling solar heat flux 23.77Latent heat flux −3.02Sensible heat flux −1.07Downwelling longwave radiative flux 7.75Upwelling longwave radiative flux −9.17

Sum 8.56

Clear sky downwelling longwave radiative flux 4.59Cloud induced downwelling longwave radiative flux 3.16

V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45 39

the area where summer sea ice persists are given in Table 2. A positive flux contributes to melting thesea ice and warming the sea surface.

3.3.2. Heat flux through the base of the mixed layerThe C1h–C24h anomaly in heat flux through the base of the mixed layer between 20◦N and 40◦N

is negative with amplitude of −1.52 ◦C/month (Table 1). As we explained in detail in Section 1.2, therepresentation of the ocean diurnal cycle allows for a deepening of the mixed layer during the nightand the entrainment of cold waters from below the mixed layer. It also allows for a shallowing andwarming of the mixed layer during the day. Such a large negative contribution of the entrainmentheat flux to the MLT trend anomaly (C1h–C24h) indicates that the nocturnal entrainment of coldwater overcompensates the diurnal warming of the shallow mixed layer. A small part of this sig-nal can correspond to an amplification of the cooling by the non solar heat flux which amounts to−0.15 ◦C/month: the surface cooling by the surface fluxes causes a deepening of the mixed layer andthe entrainment of cold waters from below the mixed layer. But the anomaly in entrainment heat fluxis the largest contributor to the mixed layer heat budget and cannot solely constitute an amplificationof the cooling by surface heat fluxes. Furthermore, a weakening of the atmospheric zonal flow occursin the North Atlantic Ocean, which we will discuss in Section 3.4. As a consequence, the entrainmentof cold waters is very unlikely to correspond to a dynamic entrainment due to an increase in surfacewind. The nocturnal entrainment of cold water constitutes a major contribution to the MLT trendanomaly.

3.3.3. Solar heat fluxThe anomaly in solar heat flux which is absorbed within the mixed layer between 20◦N and 40◦N

is positive with amplitude of 1.27 ◦C/month (Table 1). This signal is mainly due to the absorption ofthe input solar heat flux in a shallower mixed layer during the day in C1h than in C24h. The solar heatflux absorption therefore tends to counteract the cooling of the mixed layer in C1h compared to C24h.

In the area partially covered with sea ice, the anomalies in downwelling and upwelling surfacesolar heat flux are −9.71 W/m2 and 23.77 W/m2, respectively (Table 2), which correspond to weakerdownwelling and upwelling surface solar heat flux in C1h than in C24h. The anomaly in downwellingsolar heat flux tends to counteract the surface warming and sea ice melting while the anomaly inupwelling solar heat flux favours the warming and melting. The anomaly in upwelling solar heatflux results from the albedo change associated with the sea ice melting. Sea ice melting and surfacewarming are linked through a positive feedback loop involving albedo changes. Although the anomalyin upwelling surface solar heat flux is the main term in the ocean/sea ice heat budget, it cannot be thecause for the initiation of this melting and warming but it only amplifies it.

3.3.4. Longwave radiative fluxBetween 20◦N and 40◦N, the downwelling longwave radiative flux (Table 1) contributes to the cool-

ing with amplitude of −2.61 ◦C/month. It constitutes a major contribution to the MLT trend anomaly.However, the anomaly in downwelling longwave radiative flux is nearly compensated for by theanomalies in upwelling longwave, latent and sensible heat fluxes.

In the area partially covered with sea ice (Table 2), the area-averaged anomaly in downwellinglongwave radiative flux reaches 7.75 W/m2. Thus, the anomalies in downwelling longwave radiativeflux contribute both to the sea ice melting and surface warming in the high latitudes and to the surfacecooling south of 40◦N.

These anomalies in downwelling longwave radiative fluxes can be explained either by a change inthe tropospheric humidity, since the water vapour is a greenhouse gas, or by a change in the cloudcover extent. To assess each of their contribution, we diagnose the anomalies in clear sky downwellinglongwave radiative fluxes. Table 1 gives its average over the 20◦N–40◦N area during May and June:−2.45 ◦C/month. The anomalies in downwelling longwave radiative fluxes are mainly due to anomaliesin tropospheric humidity in this area. In the area partially covered with sea ice (Table 2), the anomaliesin downwelling longwave radiative flux due to atmospheric humidity and cloud cover extent amountto 60% and 40% of the total anomalies, respectively. The contribution of clouds is mainly due to lowclouds which fraction increases by +15% around Greenland in C1h with respect to C24h (not shown).

40 V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45

From the surface up to the mid-troposphere, the specific humidity is weaker (larger) South (North) of40◦N in C1h than in C24h by about −1 g/kg (+0.5 g/kg) (not shown). These anomalies can result fromthe anomalies in evaporation. Indeed, the latent heat flux anomalies (Tables 1 and 2) correspond to aweaker evaporation in C1h than in C24h south of 40◦N and a larger one above the area partially coveredwith sea ice. These evaporation anomalies constitute a response to the surface temperature anomalies.The surface temperature anomalies also contribute to decreasing the atmospheric humidity throughthe Clausius–Clapeyron effect since the SST anomalies are transmitted to the low troposphere.

Hence, to sum up, we have shown that:

(1) The downwelling longwave radiative flux contributes to the formation of the surface temperatureanomalies and the sea ice melting.

(2) The anomalies in downwelling longwave radiative flux are caused by the anomalies in atmospherichumidity.

(3) These anomalies in atmospheric humidity seem to be a consequence of the surface temperatureanomalies.

A positive feedback loop is thus involved in the apparition of the boreal summer SST anomaliesthat reach amplitudes of −2 ◦C in the subtropics and 5 ◦C in the high latitudes. This feedback loop setsin both regions to amplify the initial anomaly towards more positive values in the high latitudes andmore negative ones in the subtropics.

3.3.5. ConclusionThe mixed layer heat budget (Table 1) shows that the summer (June–August) SST anomalies (Fig. 3b)

in the subtropics are mainly due to the anomalies in nocturnal entrainment heat flux during May andJune (Section 3.3.2). The anomalies in downwelling longwave radiative flux also contribute to theirformation (Section 3.3.4) but they are nearly compensated for by the anomalies in upwelling longwaveradiative flux (non solar heat flux: −0.15 ◦C/month). The surface cooling by the entrainment heat fluxtriggers the positive feedback loop involving the atmospheric humidity and the downwelling radiativeflux. In the high latitudes, Table 2 shows that the sea ice melting and surface warming are mainly dueto the positive anomaly in downwelling longwave radiative flux. A positive feedback loop also occursin the high latitudes. The triggering mechanism for this positive feedback loop will be discussed inSection 4.2.

3.4. Impact of the SST anomalies on the large-scale atmospheric circulation

The summer SST anomalies in the C1h experiment with respect to the C24h experiment are associ-ated with a weaker meridional SST gradient in C1h than in C24h (Fig. 3b). Following Bjerknes (1964)’sequilibrium and the thermal wind relationship, the large-scale atmospheric circulation responds tothe negative anomaly in meridional SST gradient by a decrease in the zonal flow. The Z500 anomalies(Fig. 5) reach +25 m above Greenland.

3.5. Damping of the surface temperature and sea ice concentration anomalies before winter

In Section 3.2, we compared the mean monthly SST trends between C1h and C24h (Fig. 4a) andwe observed that the anomalies in SST trend vanish during July and change of sign from August. Thesummer SST and sea ice concentration anomalies disappear before each winter. The area-averaged SSTtrend between 20◦N and 40◦N exceeds 0.6 ◦C/month during August and September. The decrease in SSTanomalies in the high latitudes is slower: the area-averaged SST trend reaches about −0.3 ◦C/monthfrom August to November. To understand how the spring and summer trends in surface temperatureand sea ice concentrations change of sign, we perform a mixed layer heat budget between 20◦Nand 40◦N during August and September and a surface heat flux budget on the ocean/sea ice systemin the high latitudes during August to November. The mixed layer heat budget during August andSeptember (not shown) shows that the inversion of the SST trend between 20◦N and 40◦N is mainlydue to a decrease in the entrainment heat flux anomaly. Indeed, as the solar heat flux decreases during

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Fig. 5. (a) Summer (June–August) mean differences in geopotential height at 500 hPa between C1h and C24h experiments. The red and blue contours circle respectively negative andpositive anomalies which are significant to the 95% level, according to a student T-test (Von Storch and Zwiers, 1999). Contour interval: 2.5 m. (b) Summer (June–August) mean geopotentialheight at 500 hPa in the C24h experiment. Contour interval: 50 m. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of thisarticle.)

42 V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45

autumn, the amplitude of the ocean diurnal cycle decreases, and the nocturnal mixed layer deepeningreduces. In the area partially covered with ice, the ocean/sea ice heat budget (not shown) shows that theinversion of the surface temperature and sea ice concentration trends is mainly due to the increase inupwelling longwave radiative flux and in heat loss by latent and sensible heat exchanges as a responseto the positive surface temperature anomalies. The triggering of the inversion of the positive feedbackloop in the high latitudes will be discussed in Section 4.2. By winter, the sea surface temperature andsea ice concentration anomalies have disappeared in both subtropical and polar regions and they buildagain during the following spring.

4. Discussion

4.1. On the use of a 1-dimensional ocean model

The SST anomalies in C1h with respect to C24h reach 5 ◦C in the high latitudes and −2 ◦C in thesubtropics. However, by prescribing the same heat and freshwater advective terms (see Section 2.4) inboth simulations, i.e. the same climatological heat and freshwater transport across the boundaries ofthe NAE region and within the NAE region, we do not account for the impact of the ocean diurnal cycleon the ocean general circulation. The decrease in meridional SST gradient due to the representation ofthe ocean diurnal cycle could cause a weakening of the thermohaline circulation if the thermohalinecirculation is able to respond on such short timescales (a few months). Such a weakening wouldpartially compensate for the SST anomalies and reduce the impact of the ocean diurnal cycle on thelarge-scale atmospheric circulation. Thus, our study can overestimate the impact of the ocean diurnalcycle on the North Atlantic European mean climate. However, on the short timescales on which theSST anomalies develop, the advective processes are very unlikely to play a major role. Hence ourpotential overestimation of the impact of the ocean diurnal cycle should be only marginal. Foremost,our simplified context is limited to a qualitative analysis of the mechanism by which the ocean diurnalcycle affects the ocean and atmosphere summer mean states.

4.2. How does the feedback loop start in the high latitudes?

The triggering mechanism for the positive feedback loop by the nocturnal entrainment heat flux isonly valid in the subtropics, not in the high latitudes as the ocean diurnal cycle is negligible there (seeFig. 5 of Kawai and Wada, 2007). However, the positive feedback loop is the leading cause for the seaice melting and surface warming in the high latitudes. Two hypothetical sources could be involved inthe triggering mechanism: (1) a non linear response of the sea ice melting to the high frequency solarheat flux, and (2) a teleconnection with the subtropics. An investigation of the first hypothesis wouldrequire additional sensitivity experiments in which the sea ice would be coupled to the atmosphereeither once a day or once an hour while the ocean and the atmosphere would be coupled once a day.The second hypothesis could be tested by isolating the high latitude response with a regional ArcticOcean model in which the boundary conditions are kept constant while coupling the atmosphere andocean-sea ice system either once a day or once an hour. Both sets of experiments require additionaldevelopments.

4.3. Differences with the mechanisms presented by Bernie et al. (2008) and Danabasoglu et al. (2006)

Bernie et al. (2008) and Danabasoglu et al. (2006) used different atmospheric models than in ourstudy. However, our heat budget points at oceanic processes as the leading cause for the develop-ment of the SST anomalies, just as their analyses do. The different mechanisms obtained might ratheroriginate from the different ocean models used. The positive SST anomalies obtained by Bernie et al.(2008) and Danabasoglu et al. (2006) in the subtropics are explained by the “rectification mechanism”at first order. According to this mechanism, the diurnal absorption of solar heat flux and the nocturnalentrainment heat flux compensate for each other. In our case, the nocturnal entrainment heat fluxovercompensates the diurnal absorption of solar heat flux: the mixed layer deepens and the surfacecools downs. The differences in climate response between our study and theirs rely on differences in

V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45 43

sensitivity of the ocean turbulence parameterization at first order. Danabasoglu et al. (2006) used aKPP (K-Profile Parameterization, Large et al., 1994) scheme for the turbulent mixing processes whileBernie et al. (2008) used a different TKE (turbulent kinetic energy) scheme from ours. The differentsensitivity of these different schemes highlights an obvious need to reduce the uncertainties in theocean turbulent mixing processes.

In our study, we also obtain a warming of the mid- to high latitudes when resolving the oceandiurnal cycle which was not seen in Bernie et al. (2008) and Danabasoglu et al. (2006). The impact ofthe ocean diurnal cycle on the mean state is dependent on the latitude according to our results, with anocturnal cooling effect dominating in the subtropics but not in the subpolar gyre. This dependence ofthe net balance on the latitude might be related to the dependence of the stratification on the latitude.Indeed, the density profile is more stable at low latitudes, hence more sensitive to a strengthening ofthe turbulent mixing than a well-mixed density profile as in the mid- to high latitudes.

4.4. Sensitivity to the solar absorption profile

The solar absorption has been represented here following Ohlmann (2003)’s parameterizationwhich depends on the vertically integrated chlorophyll concentration. We specified the chlorophyllconcentration as a 12-month climatology due to the sparse data coverage over our simulated period.In this way, the large-scale and low frequency impacts of the phytoplankton on the solar absorp-tion are reasonably taken into account. However, the ocean diurnal cycle might have an impact onthe chlorophyll concentration and a retroaction could take place hence modulating the impact of theocean diurnal cycle on the mean state. This effect is not investigated here. A more robust assessmentwould require a coupling to a biogeochemical model which is currently undertaken by most researchinstitutes.

5. Conclusion

This study investigates the mechanism by which the ocean diurnal cycle can modify the ocean andatmosphere mean states. The analyses are based on a comparison between two coupled simulationsin the North Atlantic European region (20◦N–80◦N, 80◦W–40◦E) between the ARPEGE-Climat atmo-sphere model, the CNRMOM1D 1-dimensional ocean model and the GELATO3 sea ice model. The twosimulations differ only by the coupling frequency: 1 h in C1h simulation and 1day in C24h simulation.

A comparison between the monthly mean SSTs in C1h and C24h shows that significant warm(cold) anomalies in the high latitudes (subtropics) appear during each boreal summer (June–August)and disappear during each boreal winter. The high latitude SST anomalies are associated with seaice melting. A comparison between the monthly mean SST trends shows that these SST and sea iceconcentration anomalies form during May and June and decay during August and September in thesubtropics and August to November in the high latitudes. A mixed layer heat budget in the 20◦N–40◦Nregion and an ocean-sea ice surface heat budget in the area where summer sea ice persists allowto investigate the mechanism by which these SST and sea ice concentration anomalies appear anddisappear each year. The summer decrease in meridional SST gradient induced by the ocean diurnalcycle is responsible for changes in the large scale atmospheric circulation, with a decrease in the zonalmean flow.

In the subtropics, the nocturnal entrainment heat flux is the leading cause for the formation of theboreal summer SST anomalies. Both in the subtropics and in the high latitudes, the surface temper-ature and sea ice concentration anomalies are involved in a positive feedback loop: the cold (warm)surface anomalies favour a decrease (increase) in tropospheric water content, a decrease (increase) indownwelling longwave radiative flux which in turn favours the surface cooling (warming).

In our study as well as in Bernie et al. (2008) and Danabasoglu et al. (2006)’ ones, the impact ofthe ocean diurnal cycle on the mean climate is large enough to consider its inclusion in ocean generalcirculation models to be of high interest. Whichever study is considered, the dominant mechanismexplaining the impact of the ocean diurnal cycle relies on the non-linear response of the mixed layerdepth to the diurnal variations in surface solar flux. The proposal from Danabasoglu et al. (2006) to

44 V. Guemas et al. / Dynamics of Atmospheres and Oceans 60 (2013) 28– 45

interpolate the solar heat flux to a 1 h frequency in OGCMs seems then perfectly suitable to reproducethe impact of the ocean diurnal cycle on the mean climate.

Acknowledgments

This work formed part of Virginie Guemas’s PhD Thesis at the Centre National de RecherchesMétéorologiques, Toulouse, funded by Météo-France and the Commissariat à l’Energie Atomique(CEA). The authors are grateful for helpful discussions with Eric Guilyardi and wish to thank Pascal Ter-ray and Eric Maisonnave for the availability of their statistical package STATPACK. The two anonymousreviewers are thoroughly acknowledged for their fruitful comments. This study was supported by theEuropean’s Commission 6th framework Programme (ENSEMBLES, contract GOCE-CT-2005-505539)and by the ANR Blanc CHAMPION.

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