Interactive comment on “ An EC-Earth coupled atmosphere-ocean single-column model ( AOSCM ) for studying coupled marine and polar processes

This manuscript describes a new coupled single-column model (SCM) based on onedimensional (1D) configurations of NEMO ocean and sea-ice model, OpenIFS atmospheric model and OASIS3-MCT coupler. The technical implementation of the coupling between the models is well described and can be used as guidelines for further coupled models developments. It must be noted that SCM are extensively employed to develop and compare ocean and atmospherics models and vertical parameterizations, but independently most of the time. The originality of this work relies on the possibility to couple each component (ocean, sea-ice and atmosphere) in the same 1D framework,

We would like to thank the anonymous reviewer for the useful feedback.We have restructured the section describing the model setups and data to better motivate the choice of experiments, their similarities and differences.A table now summarises all main experiments used in this study and also gives a brief overview of sensitivity experiments.We have furthermore corrected typos and adapted suggestions for textual changes.The changes may easily be followed in the attached document containing track changes.For suggestions requiring more explanations, we provide answers below.

* General comments:
This manuscript describes a new coupled single-column model (SCM) based on one-dimensional (1D) configurations of NEMO ocean and sea-ice model, OpenIFS atmospheric model and OASIS3-MCT coupler.The technical implementation of the coupling between the models is well described and can be used as guidelines for further coupled models developments.It must be noted that SCM are extensively employed to develop and compare ocean and atmospherics models and vertical parameterizations, but independently most of the time.The originality of this work relies on the possibility to couple each component (ocean, sea-ice and atmosphere) in the same 1D framework, and consequently to revisit and to extend the classical SCM approach.The limitations of this approach (horizontal terms are not represented in the SCM equations) are also carefully discussed and different existing solutions (prescribing lateral/vertical advection, geostrophic wind and nudging) are proposed and tested.The authors also give some useful recommendations to carefully design numerical experiments and to check the validity of the SCM results.The originality of the manuscript also relies on the multiple applications created with the coupled SCM at three different latitudes: the tropics (Pirata mooring), midlatitudes (Papa station) and polar regions (ACSE campaign).The sensitivity of the simulations to the different relaxation methods, strength and frequency is also discussed.Hence, the manuscript gives a complete description of what can be expected from a coupled SCM in realistic conditions.However, despite all these positive aspects, I found the manuscript quality uneven.This is especially true regarding the "Results" part which is poorly constructed and quite difficult to follow.I think this is mainly due to the authors intent to show a too much comprehensive study.The experimental setups and diagnostics are very different at the three locations, so no clear conclusion regarding the SCM behaviour and the relaxation methods can be easily drawn.Consequently, some work is needed to improve this disappointing part in order to get a more globally coherent and qualitative manuscript.
The main objective of our manuscript is to demonstrate the validity and the performance of the newly developed AOSCM.To this end, we choose to run several sensitivity studies at the three locations presented.At each site, the default simulation is intended to showcase the usability of the model.This is achieved by checking that the model is not exhibiting drifts, by comparison to ocean mooring data and atmospheric reanalysis profiles.All of the experiments are also intended to analyse potential sensitivity to forcing and model settings as well as to showcase the versatility of the tool.The latter is possible in a more natural way by presenting a broad range of experiments, even though their relationship is not immediately obvious.However, we tried to clarify similarities and differences of the simulations at the three locations.
In addition, we aim to demonstrate the usability of the model by applying it to investigate current scientific questions.This is for example done at the PIRATA location to study why global climate models produce a warm bias in the tropical cold tongue region of the Atlantic, and at an Arctic location to study the lifecycle of mixed-phase clouds associated with the intrusion of warm and moist air masses.Even though our results can point to interesting scientific analysis it is not our goal here to comprehensively present scientific results but to highlight the application possibilities and potential.
Regarding the general form of the manuscripts, I suggest to merge the setup subsections 2.3.1 to 2.3.3 with the corresponding results subsections (3.1 to 3.3) to avoid any confusion between the three test cases.
By separating setup and results for the three locations, we can compare the motivation between the cases, highlight similarities and point to differences in the setups.The introduction to Section 2.3 is updated (now Section 4.1) to clarify similarities and differences, including the table suggested in one of the specific comments.We now combine all experiment sections together to highlight their relation.
Most of the figures legends must be completed to get a better description.I also recommend a better usage of punctuation (especially commas) and a proofreading by a native English speaker to improve the manuscript readability.
We asked a native English speaker to check for punctuation, grammar and spelling.
* Specific comments: The section 2.3 structure should be improved by merging specific experimental setup sections (2.3.x) with the corresponding results section (3.x).Some experiments are named (AOSCM-3h, : : :), others are not.An additional table summarising all the experiments can improve the readability of the results section.Please also address the comments and questions in the following section (especially from p.9 to p.16).
We prefer to provide the setup and results separately as this allows us to focus more on similarities and differences in the experiments.In addition, we can motivate all three locations and the experiments performed before going into the results.We also added Table 1 to Section 2.3 (now Section 4.1) to provide a clearer overview of the experiments.This table also gives the basic model settings used in these simulations.
* Specific and technical corrections: p.3 l.5: "GCSS" acronym is not defined Yes, "GCSS" stands for GEWEX cloud system study and we now include the definition.In addition, we have added the definition for GEWEX, which was previously missing.
p.3 l.13: "SCM studies" -> please give some references We have removed the ambiguous reference and restructured the text to clarify all references.
p.3 l.14: please explain why a stably stratified ABL should not be forced by surface fluxes The paper of Basu et al (2008) describes the problem with using a prescribed sensible heat flux as a boundary conditions for a stably stratified boundary layer.Such a flux is extracting heat from the atmosphere and it can not be ensured that the atmospheric turbulence is able to keep up with the surface flux, which would lead to unrealistic development of the boundary layer.The details can be found in the referenced paper, it would be deviating too much from the subject to discuss it in detail in our manuscript.p3.l.22: near-surface observations and reanalysis cannot be considered as idealized forcing, please clarify This should be "using prescribed forcing".p.3 l.28-31: can you give more details about the main results achieved by these studies please?
We find it outside the scope of this model development paper to discuss results of these studies, they are referenced mainly to acknowledge previous work along these lines.
p.4 l.10: give reference and link for OASIS3-MCT please We have included a reference to the official website of OASIS.
p.5 l.23: surface emissivity only concerns the longwave radiation emitted by the surface and not the net surface Lwflux.Please correct the equation.
We have corrected this mistake in the revised version.
p.5 l.26: is there a skin layer conductivity parameterization for the ocean ?If not it could be better to talk about diffusivity for the ocean instead of conductivity.
There is a skin layer parameterization for the ocean, details are provided in Beljaars (1997) and Zeng and Beljaars (2005).Thus, we judge it to be detailed enough in this description to give the surface energy budget equation in a general form, for details we refer to the IFS documentation.
p.5 l.27-28:I don't understand this sentence.Does it mean that the albedo is prognostic ?
The albedo is not a prognostic variable in ASCM, there it is provided as a boundary condition.In AOSCM, it is updated prognostic when sea-ice is present.It should indeed be upward coupling, so we changed the phrasing.p.6. l.8-9: please add a reference about equation of state formulation We have added the references Fofonoff and Millard (1983) for EOS-80 and IOC et al. (2010) for : what is the interest to change the equation of state for the 1D ?numerical cost ?
To run the model based on TEOS-10, potential temperature and practical salinity need to be converted to conservative temperature and absolute salinity (which are conservative state variables).This approach does not notably increase the runtime.However, with EOS-80 less preparation of data is required and enhanced accuracy is not as important for the 1D model.p.6 l.14: please give different time scale variable names for the different components (ocean, atmosphere, ice) Thank you for this suggestion.We have renamed the timescales tau_a and tau_o.
The Reffray et al. (2015) reference is concerned with the PAPA mooring and other references exist in general about mixing parametrizations.We have included a more explicit reference to Reffray et al. (2015) at the beginning of Section 4.1.1.
p.8 l.34: can you give a practical example or reference about this statement please ?
We have added a reference for this statement.
p. 9 l.14: can you give more details or practical recommendations/exemples about the relationship between the horizontal resolution of the host model and the SCM please ?
We added a sentence explaining "The resolution of the forcing is the main scale information applied in the model, apart from potential time-scale settings dependent on the horizontal grid settings".p.9 l.24: the computation of the forcing data is not the same depending on the considered experiment and should consequently be moved in the corresponding experiment section.
The method applied to calculate the forcing information is the same for all experiments.Only the forcing frequency, vertical resolution and vertical extent (e.g.cut off above certain height for Arctic case) are specific to the three locations.We have tried to highlight differences and similarities between simulations more clearly, also by introducing Table 1.p.9 l.25: can you give explanations/practical reasons about the T511 resolution choice please?And the convective time step The horizontal resolution is set to T511 to reduce instabilities, occurring due to a too long convective adjustment time scale, which can be found for example in the 2m temperature.The convective adjustment time scale is constant for grids finer than T511 and the choice of the horizontal resolution does not influence other parameters.We have updated the sentence to read "to T511, mainly reducing the convective adjustment timescale and thereby alleviating instabilities."p.9 l.25: "ASCM" acronym is not defined Now we have defined both "ASCM" and "OSCM" in the general introduction (Sec 4.1).
p.9 l.31: specify that ORAS4 is a reanalysis We have added this information.
p.10 l.4: satellite chlorophyll climatology is used for Papa only or also in the 2 other locations ?If Papa only, this sentence can be moved in the 2.3.1 section.Satellite chlorophyll data is used for all three locations.For increased clarity we have added in text " […] climatologies are used.For the PAPA location the data is the same as presented in Reffray et al. (2015)."p.10 l.21: please give the start date of the long simulation The long simulations are started on the first of the respective month at 18 UTC.This was added to the manuscript.p.10 l.24-26: what about relative humidity?
Observations of relative humidity have not been used in our study.
p.11 l.5: if all simulations are done with 60 vertical levels, this information can be moved in the general setup section.
Simulations are done on 60 levels for the PAPA and PIRATA locations but in the Arctic the number of levels is increased to 137.This information is now included in Table 1, which is describing the performed experiments.
We have removed the phrase "loosely", rewritten the sentence and also added the reference in which the complete setup is described at this point.
p.11 l.27: what is the LES boundary layer height ? is it constant ?
No, it is not constant.The vertical distribution of the forcing, and thus of the boundary layer, is shown in Figure 3 of Sotiropoulou et al. (2018).
p.11 l.28: why vertical advection from ERAI generates unrealistic results ?which kind of results ?please give more details about this point.
The vertical velocity is a difficult parameter to estimate from observations or models, as discussed in Section 2.2.In this particular case, we have observations that indicate that the cloud top height is decreasing in the beginning of the period and then remain constant at about 250 m.The vertical velocity from ERA-Interim is upward for some periods and results in a deeper cloud that is rising to about 1.5 km.We think that this information does not fit the technical description paper of our model and can further be explained in a later paper.
p.12 l.6: what about the wind conditions associated with the cold advection event ?
Figure 7 shows that the period of weak advection is also associated with relatively weak winds.During the first four days of the considered period the winds at 10 m do not exceed 6 m/s and mostly stay slower than 4 m/s.p.12 l.15: what can you conclude from the fact that results are similar between AOSCM3h and 6h? Please add a few comments.
We have added the following sentence: "During a period of weak atmospheric advection the frequency with which forcing information is updated is thus not influencing the evolution of the coupled column."p.12 l.18-19: can you add in the text the local inertial period at Papa station to compare it with the simulation oscillations please ?
Yes, this is a good idea.We have extended the text by: "At the location of the PAPA mooring the frequency of inertial oscillations is about 16 h."p.12 l.27: can you add the reanalysis in Figure 7 please to facilitate the comparison ?
See comment on Figure 7.
p.12 l.30-31: you should add a figure showing this result.
In our study the focus is not on analysing physical phenomena but on demonstrating the model.Therefore, we choose to not present more detail on this result here, so we have added "(not shown)" at the end of this description.
p.12 l.31-32: the fact that nudging improves cloud and LW but deteriorate temperature and LH suggest there is errors compensation in your simulation.This should be stated in the text.
Not necessarily, a mismatch of observations and reanalysis (when the model is strongly nudged) can also indicate that local processes make it difficult to compare gridded and point information.We now mention both potential error sources in the text.

p.13 l.1: what about the skin SST parameterization in ERAI ?
ERA-Interim, as IFS in general, does include both a cool skin and a warm layer parametrizations.The same parametrisations are acting in OIFS cycle 40r1.
The results shown in Tables 2 and 3 (new numbers) are mean and standard deviation of the RMSE from 16 29-day experiments which are done for 5 different setups (see new Table 1).The captions for tables 2-3 now include this information more clearly.p.13 l.14:I think you have inverted "warm" and "cold" in this sentence.
Thank you for pointing out this mismatch.The reference to the months has been switched.Warm periods have a shallow mixed-layer and occur during June-September.
p.13 l.15: "daily-mean" -> "observed daily-mean" SST data used for ERA-Interim is not just based on observations but also including some post-processing, even data assimilation for some periods (Dee et al., 2011).These SST fields are also used here.
p.14 l.18-19:You cannot conclude that just by looking at the surface total heat flux in Fig. 8.A surface heat budget is needed for that.
We calculated the total surface energy budget (see equation ( 5) in the manuscript), which consists of the turbulent and radiative fluxes.We added the reference to the equation in the revision.
p.14 l.22-23: a timeseries with observed and simulated precipitations would be more convincing than Fig. 8b.
Accumulated precipitation reduces the noise compared to resolving the time series temporally.In addition, we are not interested if the timing and strength of each precipitation event is correctly modelled but just if the overall mass flux to the ocean is comparable.
p.15 l.34 -p.16 l.1: can you explain why the cloud formation is different from the LES ?Is it because the subsidence is not represented in the model ?If yes, why not force the model with a negative vertical velocity ?
The subsidence in the atmospheric component of the AOSCM is implemented in the same way as it is applied in the LES.The grid is coarser in the SCM, which gives a slightly different representation of the vertical distribution of heat and moisture, which gives a cloud with slightly more water in the AOSCM.Another difference is that whenever a cloud is formed in the LES, the region becomes turbulent due to radiative cooling.That process is not represented the same way in the IFS and may lead to subtle differences which then gives a different evolution.These detailed discussions are outside the scope of the present manuscript.They will be discussed in subsequent papers when the tool is used to answer questions on how the parameterisations interact and are able -or not -to capture observed evolutions.
p.16 l.10: the liquid water path is integrated over the entire atmosphere height ?
Yes, the integrated LWP is calculated over the depth of the atmosphere, however, during the period only low-level clouds are contributing.
p.16 l.22-23: is there any observations for the surface albedo ?if yes, can you validate your model albedo ?or directly use the observed albedo in your simulations ?
During ACSE, no albedo observations were made since that is not possible to do from a ship.Other sources of albedo for the location and time are discussed in Sotriopoulou et al. (2018).A longer discussion on this topic will be part of a later paper.
p.17 l.12: "infrequent": not sure if this the correct word to describe low-frequency forcing or the problems related to inertial frequency forcing We changed the phrasing to be "temporally coarser data".
p.18 (should be page 17) l.26: you recommend pressure gradient forcing without testing it directly.
It would be better to present it as a promising possibility that need to be tested.
We motivate pressure gradient forcing from a physical point of view on page 8 ll 15 (location in previous manuscript).However, it is correct that we do not present results from sensitivity experiments without pressure gradient forcing.We extend this sentence as "Based on the fluid dynamical theory and our results [...]".

* Figures comments:
Figure 1: please detail the acronyms such as "GWD", : : : Yes, this is a good suggestion.We have removed all acronyms in figures 1-3.
Figure 4: This schematic is confusing because LIM3 is a part of NEMO.Perhaps A big "NEMO" box with inside 2 small boxes such as "OPA" and "LIM3" would be easier to understand.
We have updated the figure as suggested.
Table 2: a "Table 3" with oceanic RMSE would be interesting Oceanic RMSE are very similar and not differing more than one standard deviation (calculated from 16 cases) between setups (5 different experiments).Therefore, we do not show this table.However, we now include a brief comment on this in Section 4.2.2.

Figure 6:
-Should be moved before Tables 1-2 Yes, we moved two of the PAPA result figures ahead of the tables.Note that at this stage figures might not be in the correct order because latex is optimising page space.
-A third panel showing data from ERAi and Papa mooring would greatly improve this figure.
Information from ERA-Interim is included in the difference maps on the top row.The bottom row includes ocean profiling information in the difference.Surface information from the PAPA mooring does not fit into this figure and is instead shown in Figure 7.

-Color bars are missing
This figure presents the values on the contour lines instead of through a colour bar, which would add more components to this complex figure .-The initial mooring data appears to be missing in the figure, how do you initialize the ocean model if so ?-Why are mooring oceanic observations missing between the surface and -10m ?
We cannot explain why ocean data is missing for some periods or height levels.However, some information is not missing but just appears to do (like initial profiles or surface values of salinity and temperature) because the contour plot does not resolve data if it is only available for one time step or one height level.
-Why did you chose this period if there are a lot of missing oceanic data ?
We choose this period because it is characterised by weak atmospheric advection.We do not want to investigate the origin of ocean biases further and therefore decided it is okay if data are not complete.
-BLH is computed from the AOSCM or ERAI ?Please clarify it.
-MDL is computed from observations or the AOSCM ?Please clarify it.
The BLH and MDL are computed in the AOSCM.We have added a clarification in the caption.

-(c) and (d) panels description are missing
Panel descriptions for (c) and (d) are included at the top of (a) and (b).We have added a note on this in the caption.This is a good idea in principal but most surface parameters are only available in the forecast fields and not in the 6-hourly ERA-Interim fields, from which the forcing was derived.The turbulent heat fluxes and radiation are model products and thus not suitable for evaluation of the AOSCM.
Figure 8: -please separate panels descriptions for (a) and (b) -legend is missing for grey squarespanel (b): wrong x axis: "fluxes" -> rain I would remove panel (b) and replace it by precipitation timeseries in Figure 6.
We have added separate labels for precipitation and now call it "Precipitation measured" and "Precipitation modelled".What seems to be grey squares are violet squares but with a brighter boundary instead of a dark blue boundary to indicate warm and cold points.Keeping the accumulated precipitation here simplifies the analysis compared to a noisy time series.The figure caption was incorrect and we do not mention the red dots anymore.

Introduction
Single-column models (SCM) have already been used for several decades to advance our understanding of physical processes and their parametrizations in numerical models.SCMs originated from bulk (mixed-layer) models (Kraus and Turner, 1967;Niiler and Kraus, 1977).The first vertically resolved SCMs were developed in the late 1980s.For example, Betts and Miller (1986) demonstrated added value of an atmospheric SCM framework for the development and evaluation of a convective adjustment scheme in atmospheric models, : and Price et al. ( 1986) used an ocean SCM to study the diurnal cycle of the mixed layer in the subtropical Pacific.Research with SCMs is a valuable addition to studies with three-dimensional numerical weather prediction (NWP) models and global climate models (GCM).By zooming into a single grid column of a host model, either in the atmosphere, the ocean, or the sea-ice, one achieves a separation between resolved large-scale processes and processes parametrized in the vertical column.This means that physical processes, and the ability of their associated parametrization schemes to produce the correct physical tendencies, can be studied in a controlled framework (Randall et al., 1996).Similar to the setup of a three-dimensional model, initial conditions are provided, typically from a sounding, mooring or a reanalysis profile.Though :::::::: Although : the column is decoupled from the large-scale flow, forcing mimicking the influence of the largescale circulation on the column of interest can be applied.In practice, this is done by applying pressure gradient forcing via the geostrophic wind, horizontal advection and vertical velocity forcing to the atmospheric component of the SCM.Relaxation (nudging) is an alternative way to include forcing by the large-scale environment.All forcing types can be used individually and complementary ::::::: Forcing :::: types ::: can :::: also :: be ::::::: applied :: in ::::::::::: combination, :::::::: depending ::: on ::: the :::: type :: of ::::: model :::::::::: experiment :::: being ::::::::: performed.
In the controlled environment of an SCM, the evolution of idealized or realistic initial profiles exposed to forcing of varying complexity can be studied in an Eulerian or Lagrangian setting.The choice of experimental setup will affect the possibility to study the performance of ::::::::: determines :::: how ::: and :: to ::::: what :::::: extent, : different physical parametrizations ::::: within ::: the :::::: model ::: can ::: be :::::: studied.Thus, an experiment needs to be designed carefully, : depending on the underlying scientific question.Furthermore, by :: By : only evolving a single grid column, : the computational cost is reduced considerably compared to experiments with a threedimensional model.This allows for comprehensive parameter testing as more sensitivity experiments can be carried outat much reduced time cost.In summary, an SCM can be a powerful tool if its limitations are handled with care.
For these reasons, : SCMs have regularly been employed to investigate physical processes :::::::: modelling :: of ::::::: physical ::::::::: processes :: in :: the :::::: ocean, :::::: sea-ice :::: and :::::::::: atmosphere.In the ocean, single-column models, sometimes just called column models, started off as bulk mixed-layer models (Kraus and Turner, 1967;Price et al., 1986).From the start they were used to study the impact of air-sea exchange and vertical mixing on the temporal evolution of the oceanic mixed-layer.In Gaspar et al. (1990) and Large et al. (1994), these bulk models are extended to 1D turbulence models which can be applied in the whole column and are thus suitable for GCMs.More recent examples of oceanic SCM models being used for model development are Ling et al. (2015) and Reffray et al. (2015).
In the last two decades a few coupled single-column models have been developed.Clayson and Chen (2002) coupled an atmosphere and an ocean SCM to study tropical atmosphere-ocean feedbacks and Goyette and Perroud (2012) combined a 1D lake model with an atmospheric column model.More recently, West et al. (2016) coupled a one-dimensional sea-ice and an atmospheric column model to investigate the optimal interface at which to calculate the surface energy budget.
Following this line of work, we present a coupled atmosphere-ocean sea-ice SCM (AOSCM) following the global climate host model EC-Earth (Hazeleger et al., 2010(Hazeleger et al., , 2012)).The AOSCM provides a platform to study coupling processes, both physical and numerical , at the marine and polar ::::::: coupling :::::::: processes :: at ::: the surface interface.First, we present and discuss ways to set up and force the model.This encompasses idealized and realistic initial conditions and forcing, Eulerian and Lagrangian setups, short-term case based or long-term statistical analysis.Application of the AOSCM is demonstrated at three locations, namely mid-latitudes, tropics and the Arctic.Varying experimental designs display the versatility of the tool.The atmospheric part of the AOSCM solves the one-dimensional version of the primitive equations: − η ∂q ∂η + F q + P q + q r − q τ q r − q τ a ::::: A :: As :: in ::: the :::: full ::::: model ::::::: system, : a : two-time level semi-Lagrangian scheme is used (as in the full model system, an Eulerian scheme is optional :::: also ::::::: available) to integrate the momentum with horizontal wind components u and v (Eq.( 1) and ( 2)), thermodynamics T (Eq.( 3)), moisture q (Eq.( 4)) as well as the continuity equation.The vertical coordinate is based on η levels, : which merge orography following σ coordinates near the surface with pressure coordinates in the free atmosphere.
Here, η and ω are vertical velocities, in η and pressure coordinates, respectively.F i is the horizontal advection, P i summarizes physical parametrizations and u r , v r , T r , q r denote the reference profiles used for nudging with a time scale τ :: τ a .Furthermore, f is the Coriolis parameter, u g and v g the horizontal components of the geostrophic wind, R the moist air gas constant, c p the heat capacity of moist air at constant pressure and p the pressure.In addition to the atmospheric state variables (Eq.( 1) -( 4)), the model prognostically calculates cloud liquid, ice, rain, snow and cloud cover.
The total tendency (right-hand sides of Eq. (1) -Eq.( 4)) to each prognostic variable is calculated as the sum of dynamical (first three terms on the left-hand side) and physical parametrization tendencies P i (fourth term), possibly updated by relaxation (i.e.nudging, fifth term).The order of the left-hand side of the equation is, in a simplified way, equivalent to the sequence in which the tendencies are calculated in the model (Fig. 1).In the time-stepping loop, the dynamical tendencies are determined, mainly aggregating available prescribed forcing.The pressure gradient forcing is represented by the geostrophic wind.The third term of the heat equation captures adiabatic heating through vertical motion.Calculations of tendencies from physical parametrizations are done in the same was as in the three-dimensional OIFS.Detailed discussion of the parametrizations used for these processes, namely, the radiation, turbulence, cloud and convection parametrization schemes as well as the nonorographic gravity wave drag, orographic gravity wave drag and surface drag, can be found in the IFS documentation for cycle 40r1 (https://www.ecmwf.int/sites/default/files/IFS_CY40R1_Part4.pdf).Relaxation tendencies are calculated weighing the difference between the new state, as determined by physical and dynamical tendencies, and a reference state, with the relaxation timescale τ .All forcing is :: τ a .:::::::::: References ::::: states :::: can, ::: for ::::::: example, ::: be ::::::: observed ::: or :::::::: modelled :::::: profiles :: of ::::::::::: atmospheric :::::::: variables.::: All :::::: forcing ::::: fields ::: are read in at forcing time steps and linearly interpolated at intermediate model steps.
Here, : ν t and K t are the vertical turbulent viscosity and diffusivity, respectively.I(F sol , z) denotes the penetrative part of the solar surface heat flux, : and E and P are the evaporation and precipitation fluxes.P i summarize physical parametrizations and u r , v r , T r , S r again describe reference profiles to which the modelled profiles can be relaxed with a time scale τ :: τ o .The terms on the left hand sides of the equation system capture the column forcing.
The AOSCM setting includes physical parametrizations P i , for example describing the turbulence closure.In the standard setting : , the vertical mixing scheme is based on a TKE dependent eddy coefficient and a 1.5 turbulent closure for convection but other turbulence schemes are implemented in the code and can easily be selected.A Langmuir circulation parametrization is also turned on and the effect of chlorophyll for :: on : heating due to solar penetration is taken into account.Advection of tracers is not possible in the one-dimensional framework but can, in a way similar :::::: similar :::: way to that applied in the atmospheric model, be approximated by relaxing profiles of both tracer and momentum fields towards reference profiles.However, this procedure is not utilized in the examples presented here.
Again communications :::::::::::::: Communications with other components during the work-flow are highlighted in red (Fig. 2).Coupling actions are performed at the beginning of the time stepping, namely receiving fields as part of the boundary condition routines, and at the end of the time stepping, when the updated SST and ice parameters are send :::: sent to the atmospheric part of the AOSCM.The boundary conditions at the surface ::::: surface ::::::::: boundary ::::::::: conditions for the momentum and tracer variables are given in Eq. ( 10) -( 13).There, τ u,v are the surface wind stress components, ρ 0 is the in situ density : , and S t the rate of change of the sea-ice thickness budget.Only the non-penetrative part of the net surface heat flux (see Eq. ( 5)) influences the temperature boundary condition.and ::::::::: potentially :::::: several ::: ice :::::: layers.A brief description of the model sub-components is given in Fig. 3.

OASIS3-MCT
The OASIS3-MCT coupler (Valcke, 2006)  Variable transfer between NEMO and OIFS is implemented in both directions (Fig. 4).NEMO receives from OIFS surface stress, solar radiation, longwave radiation, sensible and latent heat fluxes, the temperature sensitivity of the non-solar heat fluxes (long-wave radiation, sensible and latent heat flux), precipitation, : and evaporation.In the reverse direction, only the sea surface temperature is passed in ice-free conditions.In presence of sea-ice, sea-ice albedo, thickness, fraction, temperature, and snow thickness are also transferred :::: from :::: LIM :: to ::::: OIFS.Sea-ice parameters are available for the different sea-ice thickness categories but the aggregated mean is transferred to the atmosphere.If sea-ice is present, some ice parameters are also coupled to the ocean model.The ocean receives, in addition to the atmospheric parameters, sea-ice fraction ::::: (areal :::::::: coverage), thickness, temperature, and albedo.The rate of change in ice thickness is added to the mass flux received from the atmosphere, evaporation : , and precipitation.OASIS3-MCT allows to pass either instantaneous values of the coupling fields at the time of coupling : , or transform the field by means of calculating an average, maximum, minimum or sum over the period since the last coupling.

How to design an (AO)SCM experiment
3 :::: How :: to :::::: design ::: an ::::::::: (AO)SCM :::::::::: experiment As mentioned in Sec 1, the freedom in setting up the model initial conditions and forcing is both an advantage and a challenge when using the AOSCM.One needs to find a balance of forcing settings, : based on the research question to be studied.Here, we briefly present some possibilities of using the (AO)SCM.
Figure 5 shows the main options to consider when designing an SCM experiment.Firstly, the questions ::::::: question is if the model should be used in an idealized setting or following measurements, reanalysis, : or model data.In idealized simulations : , the vertical structure of initial conditions and forcing, : as well as the vertical extent of the forcing can be simplified.If no forcing is prescribed, the model column evolves in a Lagrangian way.In an SCM it would usually be assumed that the whole column is migrating simultaneously, although this is likely not ::: this :: is ::::::: unlikely :: to :: be true in reality.The Lagrangian approach of following an air parcel needs to be adapted in an AOSCM : , as disregarding relative horizontal velocities of the components is unrealistic, especially for longer simulations.
More complex experiments can be designed in a variety of ways, as for example described in Randall and Cripe (1999).
They are presented here in order of increasing control on the model evolution and complexity of the setup.It is often advisable to combine several of these forcing options.
Pressure-gradient forcing is one of the most basic large-scale forcings.It ensures that energy is supplied from the nonresolved large-scale pressure field to counteract energy loss through frictional dissipation near the surface.As the wind is forced to be close to the geostrophic wind, modulated by the timscale prescribed by the Coriolis parameter, it can be understood as a physically motivated relaxation.Unless nudging of the wind is applied, this forcing is necessary and it is in general advisable for longer simulations.Forcing with geostrophic winds is known to introduce inertial-type oscillations into the column (e.g.Egger and Schmid, 1988).Advective tendencies of prognostic variables and vertical velocity also emulate the influence of neighbouring columns on the column of interest.As the vertical structure in the AOSCM might differ from the host model column or from measurements, one needs to ensure that the tendencies are physically reasonable and, if possible, prevent the model from driftingaway.Thus, it might be necessary to apply advective tendencies only , or not, over a specific height interval or to add relaxation forcing.It should be noted that the vertical velocity is often corrected from large-scale forcing (Sigg and Svensson, 2004) : , since it is a parameter not easily diagnosed in large-scale models.For example, in ERA-Interim (Dee et al., 2011) the vertical velocity is a combination of the diagnosed vertical velocity and residuals from the calculation of physical parameterizations ::::::::::::: parametrizations : (Nils Wedi(ECMWF), :::::::: ECMWF, personal communication).Finally, the model column can be forced by relaxation (also called nudging).This is the forcing option which is the most dependent on the actual model state at the time the forcing is applied : , and the only one which is not mimicking a process resolved in a three-dimensional model.
After designing initial and forcing data, the number and length of simulations needs to be decided.Measurement campaigns are usually limited in time and thus motivate shorter simulation lengths.Even if relaxation of the profile is used to prevent model drift, the impact of initial condition and forcing sensitivity might limit the model run length to which parametrizations can be evaluated.

North Polar region
To explore the AOSCM in an experimental setting with idealized forcing, and to show the additional interaction with sea ice, we choose an Arctic summer case.For this location (76 o N, 160 o E), we have observations from the ACSE (Arctic Clouds in Summer Experiment) campaign during a warm-air advection episode in early August 2014 causing rapid ice melt (Tjernström et al., 2015).Sotiropoulou et al. (2018) apply idealized forcing to a LES case ::: use :: an :::: LES : to study the importance of advection for cloud evolution during this period.Here, we present results from the LES (Savre et al., 2014) : , in comparison with results from the ASCM, : using the same experimental setup as in Sotiropoulou et al. (2018).Furthermore, we explore the importance of coupling to the ocean/sea ice : , as well as the sensitivity to atmospheric model time step and coupling frequency, : in ASCM and AOSCM experiments.With the aim to separate the influence of local and remote processes, as in Sotiropoulou et al. (2018), we turn off large-scale advection of heat and moisture.
AOSCM-6h and ASCM-6h exhibit similar monthly mean biases in the considered parameters.Daily-mean SSTs used to force ASCM-6h simulations are one-sided statistically significantly superior to SSTs modelled by the AOSCM-6h.Reduced variability is due to a coarser temporal resolution of the forcing.The signal is largest in summer months and can be explained by SST cold biases in AOSCM runs, in some cases also present during winter.This SST bias in :: the : AOSCM is part of a temperature bias dipole in the ocean column which intensifies with runtime.Reffray et al. (2015) discuss a sensitivity of the mixing depth to a TKE length parameter, : describing the deepening of the mixed layer by near-inertial waves and ocean swell or waves.In the standard TKE setup used in EC-Earth v3, the parameter is either a function of latitude and set as :: to : 30 m at the PAPA station (standalone ocean model) or set to 0 m so that no additional mixing is supplied (coupled model).Setting the parameter to 0 m, thus not considering additional mixing by waves, produces very similar results to the ones presented here (Tables 2 and 3), but cold biases during summer months are now replaced by warm biases of roughly equal strength and too shallow mixed layers (not shown).Reducing the value of the parameter to 10 m, as suggested by Reffray et al. (2015), and thus limiting an increase of mixing depth by internal mixing, alleviates the observed summer cold biases (not shown).
In general, the AOSCM can successfully reproduce atmosphere-only results.The added benefit of a coupled simulation is that the interactions between the marine and atmospheric boundary layer are resolved and can be studied directly.AOSCM-3h, forced with atmospheric data of higher temporal frequency, is better able to represent measurements and model reference data than AOSCM-6h, with largest impact on momentum.Again the annual mean signal originates mainly from one subperiod, in this case the cold months, when AOSCM-3h performance exceeds that of AOSCM-6h in several aspects.Firstly, wind biases are statistically significantly reduced in the whole atmospheric column.Secondly, the mean column state bias is reduced, although not statistically significantly :: to :: an ::::: extent :::: that : is :::::::::: statistically ::::::::: significant.In addition to improvements in the mean state, an increase in the depth of the mixed layer is found in both atmosphere and ocean (not shown), related to reduced coupling biases, though :::: again ::: the :::::: change :: is : not statistically significant.
Higher frequency forcing is : , in many cases, : linked to pronounced improvements in wind representation through reduction of oscillations in wind speed.One way of emulating this effect is to relax horizontal wind profiles in the model towards those provided by reanalyses.Results from simulations applying wind relaxation over the whole column with a timescale of 1 h :::: with ::::::::::::::::: AOSCM-Nuv0km1h ::::::: settings are summarized in the 4th columns of the tables :::::: column ::: of ::::: Tables : 2 and 3. Atmospheric column and surface wind biases can be reduced by nudging the wind, as well as SST biases : .:::: SST :::::: biases ::: are ::: also : alleviated during cold months (not shown) .Atmospheric ::: but :::::::::: atmospheric : temperature and humidity biases are not sensitive to wind nudging.However, the ::: The ocean is affected through momentum transport during cold months.The ocean responds similarly as in AOSCM-3h simulations, though only one-sided statistically significant.The ocean mixed layer is deepening :::::: deeper whereas the annual mean atmospheric boundary layer is shallower than in all other configurations.Thus, nudging of the wind components can be used to reduce model biases.However, it has to be noted that wind nudging perturbs the momentum balance.Especially when studying boundary layer turbulence parametrization, nudging interferes with the performance of the parametrization.
In some simulations, : the free troposphere drifts away from the reanalysis state.A weak atmospheric nudging of the four main prognostic variables temperature, moisture and horizontal wind above 3 km (i.e.well above the boundary layer, ::::::::::::::: AOSCM-N3km6h) reduces biases in the troposphere even below 3 km (Table 3).At the same time, the ocean state is only weakly influenced by deepening the ocean mixed layer.This way of nudging can be used even when the momentum balance at the surface is required to be unperturbed in the boundary layer.(F01 :::::::::::: AOSCM-Jun1 in Fig. 9).Within ten days, two main biases develop, one atmospheric and one oceanic.Firstly, :::::::::: atmospheric temperatures between 0.5 km and 1.5 km are overestimated, while moisture is underestimated over the same height interval (not shown).The patterns of these atmospheric biases are closely correlated and peak between June 14 and 17.Both biases are flow-dependent, i.e. they are not connected to a model drift but reduce again after the June 17.The integrated bias (RMSE ) :::::: RMSE in the lower 1.5 km develops similarly for temperature (Fig. 9a) and moisture (not shown).Secondly, although the cooling of the ocean surface layer is partly captured, its amplitude is underestimated, leading to a warm bias of around 2 • C at the end of the simulation (Fig. 9b).It is worth noting that the ocean column follows the observations well until five days into the simulation, when the observed ocean cooling can no longer be matched by the model.The SST bias grows, and after a short period of recovery around day 7 to 10, it increases during the course of two days and does not reduce significantly afterwards.
Emergence of a model warm bias during the build up of SST cooling is a common model bias in the tropical Atlantic (Breugem et al., 2008;Toniazzo and Woolnough, 2014;Voldoire et al., 2018).
Finally, the SST bias can be studied by decoupling the ocean from the atmosphere.This can either be done by nudging the atmospheric column strongly (e.g.τ = 0.25 :::::::: τ a = 0.25 : h) down to the surface (not shown) or by performing an ocean-only simulation (OSCM, Fig. 9b).Both simulations produce very similar evolutions of the SST bias (not shown).The similarities point to an oceanic origin of the SST bias, while differences to F01 :: the ::::::::: simulation :::::::: indicated :: 1 :::: June : indicate the impact of additional feedbacks on the bias development.Observations of the ocean current vector (available at 10 m depth during this period) indicate two maxima of about 50 cm s −1 at June 5 and June :::: June ::: and : 10 ::: June : (not shown), coinciding with periods of maximum SST bias in all simulations initialized at June 1.: 1 ::::: June.The ocean model currently does not capture horizontal temperature advection.Temperature changes related to advection hence cannot be reproduced by the SCM :::::: OSCM.Heat budget analyses shows these terms to be small in the region of the experiment (Giordani et al., 2013;Deppenmeier et al., 2018).
However, short time scale events are likely to be missed and can impact the budget on shorter times.Another possible oceanic origin of the bias is insufficient ocean vertical mixing of near-surface warming into the ocean.The importance of and sensitivity to vertical ocean mixing has been observed and demonstrated by Hazeleger and Haarsma (2005) and Hummels et al. (2013), among others.Too little mixing of cold water masses into the well-mixed layer as well as too little heat transport from the upper layer into the :::: deep : ocean leads to artificially warm SSTs, similar to those observed towards the end of the simulation.
In the current setup, upper ocean vertical mixing only penetrates the first upper meters of the ocean column, and then stops albedo is at 58 %.The LES value is 65 % and constant in time.Some of these differences can be explained by how the cloud affects the diffuse radiation and thereby the amount of reflected light at the surface.The albedo decreases over the 48 hours in all simulations, the most (≥ 15 %) in the simulation where the cloud disappears.This illustrates the complexity of the coupling and all these :::: how ::::: these ::::::: different processes influence the net energy received by the surface.
In Fig. 13, the net mean energy at the surface, with and without the sensible and latent heat flux contribution, is shown.The deviation from the dashed 1-to-1 line gives the magnitude of the turbulent fluxes.In all simulations, the turbulent fluxes present a net source of energy for the surface i.e. stably stratified conditions dominate.However, the observational estimate (black dot) shows a small net upward flux and the overall available energy at the surface is about 40 W m −2 less.This analysis points to differences in the vertical structure of the atmosphere.
5 Summary and outlook 4.1 ::::::::: Evaluation :: of ::::::::::: experiments We demonstrate a coupled atmosphere-ocean single-column model (AOSCM) following the setup of the next version climate model EC-Earth (Hazeleger et al., 2010).The AOSCM is designed for studying the physical interaction of oceanic and atmospheric boundary layer processes as well as technical aspects of the coupling.Here, we demonstrate the functionalities of the model by applying it at three locations and present analysis showing the versatility of the tool.Furthermore, we highlight avenues of how to design process studies using the AOSCM.
The AOSCM reproduces the evolution of the Earth system column and can simulate it reliably during short and month-long experiments.We demonstrate the model at three different locations, with varying degree of forcing complexity, in a framework with coupled and individual model component simulations.Based on results from the PAPA station and considering atmosphereonly setups as a benchmark, the AOSCM is performing well and is in some cases even superior to the ASCM(atmosphere single-column model). .: Extending an ASCM to an AOSCM allows us to resolve coupled processes.A sensitivity to the forcing frequency is apparent, which is largely related to deteriorated winds in simulations forced with infrequent ::::::::: temporally :::::: coarser data.Both the horizontal advection and the vertical wind forcing are captured more realistically with increased forcing frequency.It should be noted that a linear interpolation will result in deteriorated results even for perfect forcing data.A linear functionality is likely not a good assumption for the temporal evolution of the forcing fields.Wind components can be nudged to alleviate oscillations in wind speed, while this process does not influence temperature and moisture evolution.Nudging wind down to the surface ensures that atmospheric momentum biases do not deteriorate ocean performance : , but the nudging interferes with parametrizations connected to momentum, e.g.turbulence.Nudging all fields above the boundary layer with weak nudging time scale remedies biases in the free troposphere while allowing to focus on the freely evolving surface interactions.
At the PIRATA buoy : , nudging above 3 km also reduces time-dependent atmospheric biases considerably.Biases are almost completely removed when reducing the lowest nudged height to 1 km.At the sea surface, a temperature bias remains even in an ocean-only setting or with a strongly nudged atmosphere.Both biases are sensitive to initialization time of the simulation.
The sensitivity tests performed for the Arctic case, compared with both observations and an idealized LES simulation, show In the marine environment simulations similar to ASTEX (Bretherton et al., 1999;de Roode et al., 2016), describing stratocumulus to cumulus transition, can be performed with coupled models.Independent of the location where it is placed, a coupled atmosphere-ocean SCM allows to study the concept of stochastic air-sea fluxes decoupled from large-scale motions (Williams, 2012).An AOSCM, including sea-ice, presents the opportunity to study physical processes in the polar regions.
The atmosphere-ice-ocean system is strongly coupled and sensitive to even small energy imbalances at the interfaces : , and thus to the correct representation of sea-ice fluxes (Bourassa et al., 2013;Spengler et al., 2016).Understanding of the processes relevant for sea-ice melting and freeze-up in the changing polar regions is crucial.Work can be done along the lines of previous studies, like ?::::::::::::::: Pithan et al. (2016), investigating Arctic air mass transformations, and the local interactions with the snow surface (Sterk et al., 2013;Lecomte et al., 2015).
The AOSCM is a tool for investigating :::: where the coupling at the interface plays a considerable :: an ::::::::: important : role.With its low computational costit can , :: it :::: can :::::::::: furthermore help understand how choices of coupling parameters and numerical setup influence the evolution of the whole column.

Figure 7 :
Figure 7: -please add Q2m and precipitation timeseries -please add ERAI to check how ERAI compare with observations and the model (it will also clarify your discussion).This is a good idea in principal but most surface parameters are only available in the forecast fields and not in the 6-hourly ERA-Interim fields, from which the forcing was derived.The turbulent heat fluxes and radiation are model products and thus not suitable for evaluation of the AOSCM.

Figure 10
Figure 10 l.5: the red dots (cloud base) are not visible.The figure caption was incorrect and we do not mention the red dots anymore.

2
Model ::::::::: description, model setups ::::: setup and data2.1 Model componentsIn this study, the AOSCM is realized by combining :::: build ::::: from : the atmospheric model OpenIFS (Open Integrated Forecasting System, https://software.ecmwf.int/wiki/display/OIFS/Single+column+model+40r1+release+notes),including the land model H-Tessel(Balsamo et al., 2009), and the ocean model NEMO (Nucleus for European Modelling of the Ocean, https: //www.nemo-ocean.eu/)including :::: with the sea-ice model LIM (Louvain-la-Neuve Sea Ice Model, http://www.elic.ucl.ac.be/ repomodx/lim/).All coupling actions between the column versions of the sub-components NEMO and OpenIFS are performed by the coupling software OASIS3-MCT : (https://portal.enes.org/oasis:).For model development purposes, : the column model should follow the specifications of a GCM host model.In an iterative process, findings from the SCM, and specifically their impact on the large-scale circulation, can then be directly tested and evaluated in the GCM.In this way the computational cost for coupled model development is reduced.Here, the AOSCM is set up to closely match the development version of the EC-Earth model.Presently, this means that the default setup is a column version of EC-Earth v3, except that instead of using IFS cycle 36r4 the AOSCM uses OpenIFS cycle 40r1.Future versions of EC-Earth will be based on OpenIFS.The other components, namely NEMO3.6,LIM3 and OASIS3-MCT, are used with the same version in both EC-Earth v3 and the AOSCM.
marine test location we choose the PAPA mooring in the east Pacific.Results are presented from one short summer case study and from 16 month-long simulations distributed throughout the year.4.1.1Case study 4.1.1::::: PAPA :::::::: mooring : -::::: Case ::::: studyDuring 11-15 July 2014, the PAPA mooring briefly experienced an atmospheric cold advection event, followed by a period of weak advection, which was finally ended by warm advection (not shown).A cloud, which initially caps the boundary layer, rises and dissipates after about two days.Only during the last day does a cloud form again, associated with the warm advection.

Figure 4 .Figure 5 .5
Figure 4. Schematic of coupling variables exchanged between the model components.In the polar environment all red lines represent the coupling (dashed and full) and without sea-ice coupling reduces to the dashed line.From the atmosphere the horizontal wind stress τu,v, the solar flux Qs, the non-solar fluxes Qns and precipitation minus evaporation P − E are passed to the ocean.In the presence of ice, the temperature sensitivity of the non-solar fluxes dQnsdT is coupled as well.The ocean model sends the sea-surface temperature SST and in the presence of sea-ice the aggregated sea-ice concentration SIC, sea-ice thickness SIT, surface temperature Ts, surface albedo α and the snow thickness hs.In a coupled simulation with sea-ice the ocean also receives the ice parameters SIC, SIT, Ts and α and in addition the rate of change of the sea-ice thickness St.

Figure 10 .
Figure 10.Time-height evolution of the simulated cloud liquid water content (g kg −1 ) in the Arctic setup for hours 12 to 48 with a color scale that maximize at about 0.8 (g kg −1 ) for a) LES results from Sotiropoulou et al. (2018), b) ASCM simulation with a time step of 450 s and 132 ::: 137 layers, c) AOSCM with time step 450 s in all components and coupling, d) AOSCM with conditions similar to EC-Earth i.e. 2700 s for all time steps and coupling, e) as in d) but with 900 s time step for the atmospheric component, and f) as in e) but with no temperature advection.Observational estimates of cloud base (red dots) and top (black dots) from ACSE are also included in a).

Figure 11 .
Figure 11.Liquid-water path in (g m −2 ) for all Arctic simulations presented in Fig. 10, LES -red line, ASCM blue dashed line, AOSCM with various time steps -blue (all 450 s), magenta (all 2700 s) and cyan (IFS 900 s, other 2700 s).Also included are the results from simulations without advection of temperature (dashed cyan line) and without humidity (dash-dotted cyan thin line).Observations are shown as running averages over approximately 10 min (black dots).

Figure 12 .
Figure 12.Mean albedo (%) change over the simulated 40 hours plotted against the mean albedo for the first simulated hour for the experiments in Fig. 10, same colors as in Fig. 11, ASCM open blue symbol, AOSCM simulations with no advection of temperature (cyan star) and no humidity advection (cyan diamond) are also included.

Figure 13 .
Figure 13.Average radiative energy as function of average energy received at the surface for hour 12 to 48 for the simulations (same symbols as in Fig. 12) and observations (black dot).The thin dotted lines around the 1-to-1 line represents ±10 and 20 W m −2 .
(Day et al., 2017) OIFS) is developed by the European Centre for Medium-Range Weather Forceasts (ECMWF) as a version of IFS intended for research and education(Day et al., 2017).The main difference of OIFS40r1 to IFS 40r1 is the exclusion of the data assimilation component of IFS.Extensive documentation is available for IFS at: https://www.ecmwf.int/en/forecasts/documentation-and-support/changes-ecmwf-model/cycle-40r1/cycle-40r1.