Available online at www.sciencedirect.com

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Journal of Marine System

SST sensitivity of a global ocean–atmosphere coupled system to theparameterization of boundary layer clouds

João Teixeira a,b,⁎,1, Paul May c, Maria Flatau a, Timothy F. Hogan a

a Naval Research Laboratory, Monterey, California, USAb NATO Undersea Research Centre, La Spezia, Italy

c CSC, Monterey, California, USA

Received 13 December 2005; accepted 25 October 2006Available online 16 February 2007

Abstract

In this note we report on the impact of a more realistic low cloud cover parameterization on the Sea Surface Temperature (SST)predicted by an ocean–atmosphere coupled system. In particular, it is shown that the atmospheric model with more realisticboundary layer clouds leads to a more realistic distribution of the SST in the ocean's tropics, subtropics and mid-latitudes. TypicalSST biases of up to 5 °C in regions under frequently observed stratus and stratocumulus are substantially reduced to unbiasedvalues at places. This represents a substantial SST forecast improvement in terms of operational coupled systems used in thecontext of Maritime Rapid Environmental Assessment (MREA).© 2007 NATO Undersea Research Center (NURC). Published by Elsevier B.V. All rights reserved.

Keywords: Ocean–atmosphere coupled systems; Global models; Sea Surface Temperature; Boundary layer cloud parameterizations

1. Introduction

Although the subtropical boundary layer has been asource of fascination for meteorologists for manydecades and its importance to the atmospheric scienceshas not escaped their attention, it has been only rel-atively recently (e.g. Randall et al., 1985; Hartmannet al., 1992; Klein and Hartmann, 1993; Albrecht et al.,1995; Bechtold et al., 1995; Ma et al., 1996; Philanderet al., 1996; Bretherton, 1997) that the atmospheric andoceanic communities have become aware of just howimportant subtropical boundary layer clouds are to the

⁎ Corresponding author. NATO Undersea Research Centre, VialeSan Bartolomeo 400, 19138 La Spezia, Italy.

E-mail address: teixeira@nurc.nato.int (J. Teixeira).1 UCAR/VSP while at the Naval Research Laboratory.

0924-7963/$ - see front matter © 2007 NATO Undersea Research Center (doi:10.1016/j.jmarsys.2007.02.012

overall climate system. The radiative forcing of cloudsover the globe is the largest in the subtropical regionsadjacent to the west coast of continents (e.g. Hartmannet al., 1992; Hartmann, 1994), where persistent sheets ofstratocumulus clouds reflect a substantial portion of thedownwelling short-wave radiation. A change of cloudcover in these regions will deeply affect the incomingshort-wave radiation (the albedo of stratocumulus ismuch larger than that of the sea surface), but will notdramatically change the outgoing long-wave radiation(OLR).

Although much progress has been achieved in termsof the representation of clouds in global atmosphericmodels (e.g. Tiedtke, 1993; Fowler et al., 1996; DelGenio et al., 1996; Rasch and Kristjánsson, 1998;Chaboureau and Bechtold, 2002), stratocumulus cloudsare still often severely underestimated even in the most

NURC). Published by Elsevier B.V. All rights reserved.

30 J. Teixeira et al. / Journal of Marine Systems 69 (2008) 29–36

state-of-the-art weather and climate prediction models(e.g. Jakob, 1999; Duynkerke and Teixeira, 2001;Siebesma et al., 2004). In coupled ocean–atmospheremodels, often used for seasonal and climate prediction,this stratocumulus underestimation leads to severe SSTbiases, and although there are studies of such effects inthe literature (e.g. Ma et al., 1996; Philander et al., 1996;Stockdale et al., 1998; Li and Hogan, 1999), detailedresults of the impact of realistic stratocumulus cloudparameterizations in coupled experiments are rare.

The impact of boundary layer clouds on the SeaSurface Temperature (SST) prediction in the context ofocean–atmosphere coupled systems is also importantfor short and medium forecast ranges. A particularlyrelevant issue is that stratocumulus clouds and the SSTare part of an important positive feedback: an increase(decrease) of stratocumulus clouds leads to lower(higher) SST which in turn often leads to more (less)stratocumulus. This feedback makes the simulation ofthis entire coupled process a rather unstable one andcontributes to poor forecasts of both clouds and SST,which constitutes a major problem in terms of MaritimeRapid Environmental Assessment (MREA).

In coastal regions this type of problem is amplifieddue not only to the high prevalence of upwelling events,but also due to the complex three-dimensional structuresand feedbacks that characterize the ocean/atmospheredynamics and physics in the coastal areas. Recent high-resolution two-way coupled studies (Pullen et al., 2006)with the Coupled Ocean/Atmosphere Mesoscale Pre-diction System (COAMPS®) have highlighted not onlythe complexity of these issues but also the benefits offully taking into account the two-way interaction be-tween the ocean and the atmosphere.

In Teixeira and Hogan (2002) a new cloud para-meterization was proposed and shown to producerealistic subtropical boundary layer clouds in the NavyOperational Global Atmospheric Prediction System(NOGAPS). In particular, the stratocumulus cloudcover was shown to reproduce the climatologicalmeans with great accuracy and the seasonal and diurnalcycles were within observational ranges. In this notewe report on the impact of the new cloud parameter-ization on ocean–atmosphere coupled simulations.Here we analyze the effect of the new cloud parame-terization on the SST in the ocean's tropics and sub-tropics. The cloud parameterization, the ocean modeland the atmospheric model are briefly described inSection 2. The main impacts of the new parameteriza-tion on the coupled ocean–atmosphere simulations arepresented in Section 3, and in Section 4, some con-clusions are discussed.

2. Models and cloud parameterization

TheUSNavy global coupled system consists of tightlyintegrated global circulation models of the atmosphere(NOGAPS) and of the ocean (Parallel Ocean Program—POP) together with atmospheric and oceanic dataassimilation components.

2.1. Atmospheric model — NOGAPS

The Navy Operational Global Atmospheric PredictionSystem — NOGAPS (Hogan and Rosmond, 1991) is aglobal spectral model in the horizontal with energy-conserving finite differences in the vertical. The dynamicvariables are vorticity and divergence, virtual potentialtemperature, specific humidity and terrain pressure. Themodel is centered in time with a semi-implicit treatment ofgravity wave propagation. The parameterizations include aRichardson number boundary layer parameterization(Louis et al., 1982), surface flux parameterization (Louis,1979), gravitywavedrag (Palmer et al., 1986), shallow anddeep cumulus parameterization (Peng et al., 2004), andshort-wave and long-wave radiation (Harshvardhan et al.,1987). In the control version, the Slingo (1987) cloudparameterization is used for deep convective clouds, basedon convective precipitation, and for boundary layer clouds,based on the boundary layer inversion strength.

To parameterize the interaction between clouds andlong-wave radiation, NOGAPS uses the broadband fluxemissivity method (e.g. Stephens, 1984) where the liq-uid water path is calculated based on an in-cloud liquidwater content proportional to the saturation specifichumidity, with a constant of proportionality of 0.0079for stable clouds and 0.056 for cumulus clouds. For iceclouds the ice water content is estimated based on atemperature dependence formulation from Heymsfieldand Platt (1984). For the mixed phase a linear mixture ofice and liquid from −40 to 0 °C is assumed. The short-wave optical thickness is calculated using the sameliquid/ice water path as described for the long-wave anda mean effective radius of 5 μm for stable clouds and23 μm for the convective clouds.

2.2. Ocean model — POP

The ocean circulation model POP is a descendant ofthe Geophysical Fluid Dynamics Laboratory (GFDL)Bryan–Cox–Semtner model family (Semtner, 1997)that was developed at Los Alamos National Laboratory(Smith et al., 1992). The ocean mixed layer parameter-ization in POP is based on the k-profile approach ofLarge et al. (1994). The coupling or flux exchange

31J. Teixeira et al. / Journal of Marine Systems 69 (2008) 29–36

between atmosphere and ocean occurs every 3 h withPOP supplying the Sea Surface Temperature to theatmosphere, and NOGAPS providing momentum, heat,and moisture fluxes directly to the ocean model.

For the coupled experiments described in this paperNOGAPS is used with a horizontal resolution of T159and 30 layers in the vertical. In the coupled configura-tion POP is used on a ½° resolution Mercator grid with36 vertical levels.

2.3. Cloud parameterization

The new cloud parameterization is a combination oftwo independent diagnostic cloud fraction equations forcumulus and stratocumulus (Teixeira and Hogan, 2002),as described below.

2.3.1. Cumulus cloud parameterizationIt has been shown (Teixeira, 2000) that, for cumulus,

the prognostic cloud fraction equation of Tiedtke (1993)is typically dominated by the production of clouds bydetrainment from shallow convection and the erosion ofclouds by turbulent mixing with the environment. Thisrelationship can be written approximately as:

@a@t

¼ D 1−að Þ− alcK qs−qð Þ ð1Þ

where a is the cloud fraction, D is the detrainment rate,lc is the mean in-cloud liquid water content, q is themean specific humidity, qs is the mean saturation spe-cific humidity and K is an erosion coefficient. Moreover,in typical shallow convection situations the two termson the rhs are of similar magnitude and the cloudfraction tendency is negligible in comparison. In thiscase, a simple diagnostic relation (Teixeira, 2001;Teixeira and Hogan, 2002) to calculate cloud fractionas a function of relative humidity, detrainment and ero-sion can be written as

a ¼ D

Dþ Kb 1−RHð Þ ð2Þ

where RH is the mean relative humidity, and we assumelc=βqs (e.g. ECMWF, 1991). In NOGAPS we useD=5.10−6 s−1 and K=5.10−6 s−1, which have beenshown to produce realistic results for shallow cumuluscloud cover. With RH=0.8 and β=0.01, for example, thiscorresponds to an e-folding time for cumulus clouddissipation of about 3 h. At each time-step, the cloudscheme uses information from the convection parameter-ization to set the height of the cloud base and cloud top. Itwas decided to use constant values of D, as opposed to

Tiedtke (1993) where only the erosion coefficient is takenas constant because it was found (Teixeira, 2001) that it ismore important tomaintain a realistic balance between thecreation of clouds due to detrainment and destruction dueto the erosion, than to use the value of detrainmentestimated by the convection scheme at each time-step. Inthis way the clouds are coupled to the convection schemebut are independent of the details of the convectionparameterization. From the previous equation it isstraightforward to see that cumulus cloud fractionincreases with β and the detrainment rate and decreaseswith the erosion coefficient.

2.3.2. Stratocumulus cloud parameterizationWe follow the Large Eddy Simulation (LES) studies of

Cuijpers and Bechtold (1995) (hereafter CB95) in order todevelop a simplified version of a Probability DensityFunction (PDF) based cloud parameterization. In CB95 itis suggested that cloud fraction can be diagnosed as:

a ¼ 0:5þ aarctanðgQÞ ð3Þwhere α=1/π, γ=1.55, Q ¼ qt−qs

r and qt= l+q is themean total water content (with l being the mean liquidwater content). We assume that the standard deviation σcan be determined as r ¼ k

ffiffiffiffiffiffiffiffiffiqt Vqt V

q¼ g

ffiffiffiffiffiffiffih Vh V

p, where λ and

η are constants of proportionality and θ is the potentialtemperature. The variance of potential temperature isobtained from the steady state version (neglecting thetransport term) of the prognostic variance equation (e.g.Stull, 1989) assuming that an eddy-diffusivity approachfor the flux leads to

r2 ¼ B2k@h@z

� �2

ð4Þ

where B is a constant and the turbulent diffusioncoefficient is simplified to k=(1−a)kd+akc, where kdand kc are constants and are respectively the turbulentdiffusion coefficients for the dry and the cloudy parts.Taking this into account and assuming that qt=alc+q=aβqs+q, the following iterative equation to diagnosecloud fraction is obtained

aiþ1 ¼ 0:5

þaarctan gqsðbai−ð1−RHÞÞ

Bffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffið1−aiÞkd þ aikc

pj @h@z j

!:

ð5Þ

This iterative equation converges rapidly, normally inless than 10 iterations, for realistic values of the terms on

Fig. 1. Monthly mean low cloud cover — LCC (%) for August 2002 from (A) the ISCCP data set, (B) the control version of the coupled simulationand (C) the new version of the coupled simulation.

32 J. Teixeira et al. / Journal of Marine Systems 69 (2008) 29–36

the rhs (Teixeira and Hogan, 2002). The equation is usedat every vertical level below the boundary layerinversion but the vertical gradient of potential temper-

ature considered is only the one at the boundary layerinversion. The values used for the iterative Eq. (5) are:kc=25 m

2 s−1, kd=0.25 m2 s−1 (e.g. Van Meijgaard and

33J. Teixeira et al. / Journal of Marine Systems 69 (2008) 29–36

van Ulden, 1998) and B=2.10−3 s½ K−1. By imposingthe condition kc≫kd, we are assuming that, whenthere is a cloud, turbulence is mostly produced with-in the cloud. The liquid/ice water and the cloud opticalproperties are diagnosed as described at the end ofSection 2.1. Note that, as opposed to the cumulus para-meterization where a prognostic approach is simplifiedto a diagnostic one, in the stratocumulus scheme theoriginal parameterization (Cuijpers and Bechtold, 1995)is already diagnostic.

Teixeira and Hogan (2002) show that in this scheme,stronger inversions lead to smaller values of cloudfraction for high relative humidity, and to larger values ofcloud fraction below that. This behaviour is consistentwith PDF cloud parameterizations where if far fromsaturation, a larger variance (stronger inversion) leads tolarger cloud fraction and, if close to saturation, a largervariance leads to a smaller cloud fraction.

3. Coupled simulations

In this experiment, the coupled system is integrated for3 months starting on 1 June 2002. The model is initializedwith atmospheric fields from the NOGAPS analysissystem (Goerss and Phoebus, 1992) and ocean fields fromthe Navy Coupled Ocean Data Assimilation (NCODA)system (Cummings, in press). Both assimilation schemesare based onmultivariate optimal interpolation and use allavailable in situ and remotely sensed data to make dailyestimates of model initial conditions.

Fig. 1A shows the Low2 Cloud Cover— LCC (in %)mean global distribution according to ISCCP (Interna-tional Satellite Cloud Climatology Project) satelliteobservations (Rossow and Schiffer, 1991) for August2002. This figure is based on ISCCP visible and infrared(VIS–IR) observations. The white areas correspond toregions where not enough data were available. Thestratocumulus regions off the west coasts of North andSouth America, and Africa, together with low stratusand fog areas off the mid-latitude east coasts of theNorthern Hemisphere (NH) and the Southern Ocean, areamong the areas of the globe where low clouds are moreprevalent during August and the NH summer in general.In the stratocumulus regions LCC reaches values of upto about 60% in large regions of the subtropics.

Fig. 1B shows the LCC3 produced by the coupledsystem with the old cloud parameterization, duringAugust 2002, where it is quite obvious that NOGAPS

2 For ISCCP, low means from the surface to 680 hPa.3 For NOGAPS, low means from the surface to 700 hPa.

with the old cloud scheme severely underestimates lowclouds, particularly stratocumulus. Although there issome cloudiness in these regions, its value is too lowand its spatial distribution does not agree well with theobservations. The problems are actually worse thanwhen NOGAPS is utilized in an uncoupled mode forcedby observed SSTs. This issue will be discussed in somemore detail later on in this paper.

In contrast, the corresponding LCC obtained with thenew cloud parameterization, shown in Fig. 1C, looksmuch more realistic when compared to the ISCCPobservations. The values in the regions where clouds aremore prevalent are relatively close to the observedvalues, and the cloud amount in the stratocumulusdominated regions is well-reproduced. Although there isa substantial improvement with the new scheme, thereare still some problems over the equatorial EasternPacific, where the low clouds in the new model seem toextend too far to the west. This feature has a relevantimpact on the SST bias, as will be seen in the followingresults.

Another problem detected with this particular modeland cloud parameterization (Teixeira and Hogan, 2002),and also with the ECMWF model (Teixeira, 1999),associated with the peak values of cloud cover in thestratocumulus regions being too far off the coast, is notso clear in the present simulations. This seems to be atleast partially associated with the horizontal resolutionof the atmospheric model since operational forecastswith NOGAPS running at T239L30 resolution do notshow the problem to such an extent either. It should benoted that the stratocumulus part of the scheme is themain responsible for the substantial increase in theamount of stratus and stratocumulus off the east andwest coast of continents, with the cumulus part of thescheme contributing for the increase of cloud in thetropics and subtropics.

Fig. 2A shows the global distribution of SSTdifference (bias) between the coupled model results forAugust 2002 and theNCODAocean analysis for the sameperiod (Cummings, in press) for the simulation with theoldNOGAPS cloud parameterization. Large positive SSTbiases are apparent on regions associated with thepresence of low clouds as seen in the ISCCP LCCAugust2002 distribution. In the North Pacific, where fog and lowstratus clouds are prevalent, SST biases are larger than5 °C over extensive areas of the ocean. Off the west coastsof continents where stratocumulus are prevalent, positiveSST biases are also quite common, though they are not aslarge and extensive. Values larger than 2 °C are evenfound in subtropical and tropical areas off continentalcoasts where stratocumulus are not common. From the

Fig. 2. Monthly mean surface temperature, Sea Surface Temperature (SST) over the ocean, differences (K) for August 2002 between (A) the controlversion and the analysis, (B) the new version and the analysis and (C) the new and the control versions of the coupled simulations.

34 J. Teixeira et al. / Journal of Marine Systems 69 (2008) 29–36

analysis of the LCC results it is clear that a substantial partof these positive SST biases is associated with a lack oflow clouds in the old model that leads to values of short-

wave radiation at the sea surface that are too large, andconsequently to large positive SST biases in simulationslike the present ones.

35J. Teixeira et al. / Journal of Marine Systems 69 (2008) 29–36

The corresponding SST results for the simulationwith the new NOGAPS cloud parameterization areshown in Fig. 2B. It is clear that the SST biases aregreatly reduced due to a much more realistic distributionof low clouds in the new model. The large regions ofpositive SST bias previously seen off the north-eastcoast of Asia show a substantial bias reduction. Inregions off the west coast of North and Central Americathe positive biases are virtually gone, and the positivebiases off the west coasts of Europe and North Africaalmost disappear as well. Even in the warm pool region,where a large area of positive SST bias is present (inprinciple not directly related to low cloud cover), there isa substantial reduction of the bias. Although the resultsare generally positive in terms of the quality of the SSTforecasts by the NOGAPS/POP coupled system with thenew cloud parameterization, the increase of LCC by thenew version also contributes to the enhancement of coldbiases in some regions. One example of increased coldbias occurs around 30 N in the central North Pacificwhere a negative SST bias of up to −2 °C in the oldmodel becomes about 2° colder in the new. Another areaof cold bias occurs in the equatorial East Pacific, where(see Fig. 1C) the new version is apparently producing anexcessive amount of low clouds as compared to theISCCP observations. Note that these patterns and biasesare already quite noticeable during July 2002.

In Fig. 2C, where the surface temperature (SST overthe ocean) difference between the two experiments forAugust 2002 is shown, it is possible to see the highdegree of correlation (in the summer hemisphere)between the presence of low clouds and the SSTdecrease in the new scheme in several regions, includingthe East and West North Pacific where extensive sheetsof stratus and stratocumulus are commonly presentduring NH summer.

4. Summary

We have described the sensitivity of Sea SurfaceTemperature (SST) forecasts by a global ocean–atmosphere coupled system to an improved representa-tion of boundary layer clouds. This new cloudparameterization (Teixeira and Hogan, 2002) wasshown to produce more realistic distributions of lowstratus and stratocumulus cloud cover in atmosphericstand-alone simulations (forced with analyzed SST). Incoupled simulations the new cloud parameterizationsubstantially improves the representation of boundarylayer clouds in general, with a significant increase ofstratus and stratocumulus cloud cover off the east andwest coast of continents. The negative bias in low cloud

from the old model coupled simulations is much worsethan for the control stand-alone atmospheric simulationsshown in Teixeira and Hogan (2002). This is due to astrong positive feedback that exists between low cloudsand the SST underneath that has been hypothesized andpartly demonstrated by many authors during the last fewyears (e.g. Philander et al., 1996). With this feedback aninitial negative low cloud bias in a coupled system isseverely amplified since it leads to higher SST valuesthan in reality, which in turn lead to less low clouds.

Due to a severe underestimation of low clouds by theold atmospheric model version (relatively typical amongatmospheric models) the SST forecasts of the corre-sponding coupled system have significant positive biasesof up to about 5 °C in some extensive regions in 2- to 3-month forecasts. These biases are substantially reduced inthe ocean–atmosphere coupled system that contains thenew cloud parameterization, clearly improving SSTforecasts in many ocean regions. These improvementsare crucial for ocean–atmosphere coupled forecast sys-tems that are utilized in a real-time operational context.

Acknowledgements

The authors acknowledge R. Schmidt, two anony-mous reviewers and the Office of Naval Research thatsupported this work under ONR Program Element0602435N.

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