Diurnal warming in Eastern Arctic ocean and ...

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Aug 31, 2015 - Diurnal warming in Eastern Arctic ocean and chlorophyll-a special role. Author: Anastasiia Tarasenko tad.ocean@gmail.com. Scientific advisor:.
Universit´ e de Bretagne Occidentale Master 2 ”Physical oceanography and Climate”

Diurnal warming in Eastern Arctic ocean and chlorophyll-a special role

Author: Anastasiia Tarasenko [email protected]

Scientific advisor: Dr. St´ephane Saux-Picart M´et´eo-France

August, 31 2015

Contents 1 Introduction 1.1 Diurnal warming concept . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Arctic ocean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Methods 2.1 GOTM - general ocean turbulence model 2.1.1 Temperature equation . . . . . . 2.1.2 Air-sea interaction . . . . . . . . 2.1.3 Input files and forcing . . . . . . 2.2 GOTM sensitivity tests . . . . . . . . . . 2.2.1 Chlorophyll-a and wind speed . . 2.2.2 Salinity test . . . . . . . . . . . . 2.2.3 Jerlov water types . . . . . . . .

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3 Application 3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Temperature . . . . . . . . . . . . . . . . . 3.1.2 Chlorophyll-a . . . . . . . . . . . . . . . . 3.1.3 Meteorological forcing . . . . . . . . . . . 3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Meteorological and climatological situation 3.2.2 Detailed description, WASPARC data . . 3.2.3 Simulation with GOTM . . . . . . . . . .

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4 Conclusion and discussion

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List of abbreviations CDOM - coloured dissolved organic matter DW - diurnal warming ECMWF - European Centre for Medium-Range Weather Forecasts SST - sea surface temperature TOA - top of the atmosphere WASPARC - ”Warm spot data set for the Arctic”, database of DW events

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Chapter 1 Introduction Temperature is one of the most important parameters for climate system, and sea surface temperature plays a special role there also driving air-sea interaction, so it needs a particular attention. Scientific community has been monitoring the temperature of the ocean more than a century, and by now several high quality datasets of sea surface temperature (SST) were created using in situ and satellite data. However, the temporal and spatial resolution of most datasets is still not sufficient to represent neither daily temperature variations, nor meso-scale eddies - those smaller scale features that influence bigger seasonal and annual variations (of SST and other parameters) and such large events like El-Nino and ENSO (El-Nino -Southern Oscillation)in general. The availability of the accurate SST measurements is finally one of the critical questions for weather predictions and climate modelling as well as for biogeochemical oceanography and fishery (Danabasoglu et al. (2006), Kawai and Wada (2007)). Diurnal variation of surface temperature exists due to the solar radiation and the Earth rotation. The day-night SST difference can reach > 6K, though in general is of the order of several 0.1K - fig.1.1 (Kawai and Wada (2007), Merchant et al. (2008)). Diurnal warming (DW) events were first observed in low and mid-latitudes in low wind speed and high insolation conditions (Sverdrup et al. (1942), Roll (1965), Cornillon and Stramma (1985); Gentemann and Minnett (2008)), but can be also found in Arctic (Eastwood et al. (2011)). In Arctic, diurnal warming events can have the same amplitudes as in tropics, but may become more significant for intrasesonal temperature variations because of the heat accumulation in the mixed layer (one can imagine that in some extreme cases these warm sea waters still can dive under lighter (non salty) river waters and thus, be conserved in the Arctic ocean interior for a longer time). The principle question of a DW study is usually how the heat is transported from the surface to deeper layers. Thus, the importance of properly understanding a diurnal warming process lies in possible ameliorating our estimations for at least a seasonal mixed layer depth temperature. In other words, using rapidly changing but accessible surface data we can try to guess the state of deeper and slower responding waters, that is less covered with measurements. The main goal of this work was a study of the influence of sea water optical properties on diurnal cycle and its modelling. In a context of water optical properties, a particular attention was paid to the role of chlorophyll-a concentration. A chlorophyll is a green pigment contained in phytoplankton cells allowing photosynthesis, the concentration of the former is considered to be representative for the amount of the latter. Though apart chlorophyll-a there are other pigments contained in phytoplankton known as accessory pigments (such as phycobiliproteins, xanthophylls, etc), their concentrations are much lower, so for non-biological studies they are neglected. The phytoplankton itself is at the base of the marine food web (being an agent of primary production) and also is a key player in ”ocean carbon pump” - the biochemical cycle of atmospheric carbon fixation in ocean waters, which is, in other words, the energy fixation in carbon compounds. 3

Phytoplankton amount depends mostly on abiotic factors: temperature, light availability and nutrients, but the rate of zooplankton graze is another biotic controlling factor (Thurman and Burton (1997)). It should be noticed that phytoplankton is distributed non uniformly in the water column: depending on conditions the chorophyll maxima is found near the surface, near the pycnocline or even below pycnocline in close association with nitracline (Morozova et al. (2013), Martin et al. (2010)). This fact complicates estimations of phytoplankton amount using remote sensing. Chlorophyll-a pigment reflects and absorbs sunlight in the water, so changes its optical properties and finally temperature. About 0.5% of the energy of visible light is used for photosynthesis, the rest is conserved to heat. It should be mentioned that in sea water there are other coloured matters (e.g. CDOM - coloured dissolved organic matter) that have another origin, but can also significantly change the optical properties; and sometimes it can be difficult to distinguish the effect of phytoplankton presence and other processes (Roy et al. (2011), p.521). The basic hypothesis for this study was rather simple - higher is the chlorophyll concentration (more there are small algae particles) more intense is the diurnal warming. For example, Kahru et al. (1993) has shown that in southern Baltic sea the DW can reach 1.5K due to the locally high concentration of cyanobacteria, so strong phytoplankton blooms can provoke rather strong DW events. Nevertheless, the straight comparison in terms of correlation of relatively long timeseries of temperature and chlorophyll-a concentration can be confusing: in the World Ocean there are zones with positives and negative correlations (fig.1.2). In general, the main reason for negative correlations on a long scale, can be the fact that the role of dynamical forcing (ocean currents and horizontal advection, upwelling and vertical advection, and also wind induced waves and turbulent mixing) is more efficient that the role of chlorophyll concentration for ocean water warming. A good example is the Pacific ocean equatorial upwelling and a negative temperaturechlorophyll correlation in this region. On the other hand, there is usually a positive correlation between the diurnal cycle of temperature and chlorophyll-a concentration. This work is dedicated more to the question if and when the role of chlorophyll concentration is not negligible for diurnal temperature variations. The question of the incoming radiation attenuation in a water column has been investigated before by Sathyendranath and Platt (1988), Kahru et al. (1993) and Merchant et al. (2008), but has not been applied for the Arctic region. The basic modelling tools for our study were 1D turbulence model GOTM (Chapter 2.1) and Radiative transfer model of Sathyendranath and Platt (1988). GOTM system of equations permits to study temperature variations on the surface and in water column with different forcing. The objectives of the study were: (i) sensitivity tests for different chlorophyll concentration and wind conditions; (ii) the modelling of the real case diurnal warming event in Arctic described in Eastwood et al. (2011). For the real case study we used a regional database of DW events - WASPARC (WArm SPot dataset for the ARCtic) created from satellite data in the Centre of Spatial Meteorology of M´et´eo France.

1.1

Diurnal warming concept

In general, the process can be described as following (from Eastwood et al. (2011)). The incoming solar energy is stored in the upper - warm layer of the ocean, beneath which there is a diurnal thermocline and associated with it ”foundation temperature” (see fig.1.3). The foundation temperature is a minimum for this day temperature close to the midnight time (+/4 hours) and corresponds to the minimum insolation at the top of the atmosphere (TOA). Then DW amplitude is a difference between maximum SST and foundation temperature. The notion of ”surface temperature” also needs some comment. What is called SST in different studies is not actually the same temperature on the same depth and depends on the measuring 4

Figure 1.1: Seasonal mean day-night difference of the AMRS-E ver.5 SST produced by Remote Sensing Systems during June 2002 - May 2006. Seasonal mean calculated in 1deg grids. Nominal observation time is approximately 0130/1330 LST (from Kawai and Wada (2007)).

Figure 1.2: Correlation between temperature and chlorophyll-a concentration for June-July 2008, using MODIS-Aqua 9km data (produced with the Giovanni online data system, developed and maintained by the NASA GES DISC (Acker and Leptoukh (2007)). 5

Figure 1.3: Idealized upper ocean temperature profile (a) typical night time profile or well-mixed day time, (b) typical day time stratified profile (from Gentemann and Minnett (2008)). instrument. In fact, the closest to the real air-sea interface temperature are the measurements of infrared radiometer penetrating approximately to 10-20 µm depth, and these measurements are called skin SST (definitions proposed by Donlon et al. (2007)). The skin SST is associated with the conductive diffusion-dominated layer. There is also the subskin SST measured with microwave radiometers and sea temperature at depth, measured with traditional in situ sensor (thermistor, CTD, XBT, etc). The latter needs a mention of depth, and is often represented in different atlases and climatologies as ”surface temperature”. The microwave sensor has some advantage for diurnal cycle studies, because it observes both temperature and wind through the clouds, but the infrared sensors on geostationary orbit have higher temporal and spatial resolution. In this work we used both the infra-red and microwave radiometer satellite data, who are close to each other in comparison to ”SST at depth”, so for the simplicity hereafter the skin and subskin SST will be called just SST. Maximum temperature variations develop in the skin layer, the amplitudes can reach 1-5 Kelvin per day. At depth 0.5 m the most of DW events are in the range of 0.5-1K, rarely > 1K (Kawai and Wada (2007)). Noticeable DW events take place usually with a wind speed < 5m/s and when a wind speed has been lower that 5m/s more than 6 hours (Soloviev and Lukas (1997), Eastwood et al. (2011)). The occurrence of observed DW in June 2008 estimated by Eastwood et al. (2011) in Arctic is < 10% (3-4 days per month) (fig.1.8) and Cornillon and Stramma (1985) have found 25% for Sargasso sea in summer. Though there are some first results of studying DW in Arctic, the process is not yet fully understood. The difficulties of conducting such research in this region lie within limited SST data accessibility due to the long time of sea ice presence and also the frequent cyclone formation - such that SST from satellite IR measurements become not available though the large clouds area. With microwave sensor’s low spatial resolution a diurnal warming event is not always detected. Other particularities of Arctic region are introduced in the section 1.2.

1.2

Arctic ocean

The Arctic ocean is influenced by several phenomena that affect the diurnal warming drastically. In this section some of them are described. The Arctic ocean has a particular insolation regime known as polar nights and polar days - a phenomenon when the sun is very close to the horizon (night) or above the horizon (day) during more than 24 hours. The closer to the Pole, the longer is polar night/day. At 70o N in 6

2014 year, e.g. the polar day period was 69 days (21/05/2014-29/07/2014), and polar night 53 days (25/11/2013-17/01/2014). For the reference, at the polar circle (66o 340 N ) polar days and nights occur only once per year, at the June and December solstices, and other time the polar night can be described more like ”polar twilight”. It is interesting that due to the longer presence of sun during the summertime the integrated daily insolation on the top of atmosphere can be higher for Arctic than for tropics: Eastwood et al. (2011) estimated for July, 1, 2008 at 75o N - 517W/m2 , and at 15o N - 447W/m2 . This fact indicates that there is sufficient heat input for a diurnal event formation at high latitudes (see fig.1.8). The temperature pattern in Eastern Arctic ocean on a large scale is controlled by the warm North Atlantic current (Barents sea) and warm river waters of Ob’ and Yenisey (Kara sea); the cold water mass arrives from central Arctic - fig.1.4, fig.1.5. During summer months (July-September) in eastern and central part of Barents sea and estuary area of Kara sea the surface temperature can reach 8°C and above, though in the northern areas it stays between +2 and -1 ◦ C, (Crane and Galasso (1999)). The mixed layer depth depends on bathymetry, but in general is rather close to the surface (e.g. 20-40 m for central Kara sea, fig. 1.6). Eastwood et al. (2011) have shown is their study, that diurnal warming events in Arctic happen more frequently in shallower areas with low wind conditions. Thus, on the one hand the regime of circulation in Arctic ocean should be taken into account for the studies of temperature, but on the other hand, as far as the in situ data for currents in Arctic is not usually available with necessary for diurnal warming events spatial resolution (most of them are measured during cruises or with sparse autonomous bottom stations), in this work currents and river inflows were almost not considered. This assumption seems not to be very crucial for the event of short time period (several days) and rather small spatial scale (a few hundreds of km maximum). As an illustration, the DW event described in Eastwood et al. (2011) and chosen for this work lasted about 3 days and had the horizontal size 350 km along meridian. The vertical structure of salinity is another particularity of Arctic ocean. Arctic water salinity is strongly modified by a huge freshwater input from Russian and Canadian grand rivers, like Ob’, Yenisey, Lena, etc. Secondly, salinity is changed with the ice formation and melting. Both processes results in a strong warm halocline formation during summer months in the upper layer of Arctic ocean - where diurnal warming happens (see fig.1.6). With a decreasing air temperature the winter convection starts and can create a ”cold halocline layer”, that prevent the upward heat flux from warmer intermediate waters (mostly from North Atlantic current in Eastern Arctic, (Kikuchi et al. (2004))). As it will be shown later in section 2.2, the presence of strong halocline prevents the deepening of the diurnal warmer layer during night-time, so can not be ignored during DW modelling. The amount of phytoplankton and thus chlorophyll-a concentration in Arctic is regulated by all listed factors. Apart from insolation regime, among one of important there is a river water input, carrying nutrients in estuary and coastal areas (fig.1.7). During summer months these areas have mostly positive correlation between SST and surface chlorophyll-a concentration even on the long term scale (look at the Kara sea on fig.1.2). These are the regions potentially interesting for DW studies that take into account chlorophyll concentrations. As an example, in 2008 summer months DW events frequency was noticeable there (fig.1.8). A deep chlorophyll-a maximum can be found due to the particular vertical nutrient stratification formed with the freshwater input. These deep maxima can be brought up to the surface with turbulent eddy mixing, breaking of internal waves or Langmuir circulation (Denman and Gargett (1983)). Ocean currents, convergence and divergence zones help to redistribute the phytoplankton patches spatially. The presence of deep chlorophyll maximum is one of the limitations of the use of ocean color remote sensing products, because, e.g. the surface concentrations of chlorophyll-a can not always be extrapolated to some depth. 7

To sum up, diurnal warming in Arctic is possible even with its peculiar insolation regime, and there are some areas potentially interesting for a study of optical properties influence on diurnal cycle. At the same time, there are some difficulties related to regional features in Arctic: possible lack of observations due to meteorological conditions (sea ice, cyclones), particular vertical structure of salinity and a non-uniform vertical distribution of phytoplankton that can both influence the depth of diurnal thermocline. In a following chapter the impact of some of these features will be illustrated with the help of modelling with GOTM.

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Figure 1.4: Arctic ocean surface temperature [◦ C] summer (Jul-Sep) climatology from NOAA regional Arctic atlas

Figure 1.5: Arctic ocean surface currents and the sea ice summer extent (http://library.arcticportal.org/1375/)

Figure 1.6: August monthly climatology for temperature and salinity profile at 77N, 77E (Kara sea) from NOAA regional Arctic database

Figure 1.7: Chlorophyll-a concentration mean value for June-July 2008 using SeaWiFS and MODIS data, produced with Giovanni tool (Acker and Leptoukh (2007))

Figure 1.8: Occurence of significant daily DW events (amplitude larger than 1K) in Arctic in June 2008 (left panel) and July 2008 (right panel). Bathymetry is given in grey (from Eastwood et al, 2011) 9

Chapter 2 Methods A diurnal cycle study, as many others, needs modelling and real data. A modelling with sensitivity tests can help to understand and estimate the role of every factor in diurnal warming event formation, real data is necessary to evaluate and validate results of modelling. In this chapter a description of a chosen model and its sensitivity tests are given. GOTM model was employed as a main tool with some modifications. For sensitivity tests most of the forcing (chlorophyll-a concentration, wind speed and directions, and also some additional meteo-forcing like air pressure, air temperature, humidity, etc) was prepared manually, and on this stage the only used input data were temperature and salinity vertical profiles from the NOAA Arctic regional climatology atlas (Sei (2015)). The main interest of sensitivity tests was the study of chlorophyll potential influence on diurnal warming process along with the general test of GOTM capability to perform a reasonable diurnal warming cycle.

2.1

GOTM - general ocean turbulence model

GOTM is a one-dimensional water column model for hydrodynamic and thermodynamic processes related to vertical mixing in natural waters (from www.gotm.net). GOTM was chosen to compute the vertical temperature profile because it permits to take into account the absorption of the downward solar irradiance spectra I. The parametrization used in GOTM distinguishes the attenuation of IR and visible part of the spectra, and can be developed furthermore to distinguish also the red part and the rest part of visible light.

2.1.1

Temperature equation

The main formula for calculation water temperature Θ and solar insolation I in GOTM is: ˙ = DΘ − 1 (Θ − Θobs ) + 1 dI Θ τRΘ Cp ρ0 dz

(2.1)

˙ denotes the material derivative of the mean potential temperature Θ, and DΘ is where Θ the sum of the turbulent and viscous transport terms modelled according to (2.2). Relaxation to the observed (or prescribed) profile of temperature Θobs on the time scale τRΘ is possible, but was not used. d dΘ ((νtΘ + ν Θ ) − Γ˜Θ ) (2.2) dz dz In this equation, νtΘ and ν Θ are the turbulent and molecular diffusivity of heat, respectively, and Γ˜Θ denoted the non-local flux of heat. The sum of latent, sensible, and long wave radiation is treated as a boundary condition. Solar radiation is treated as an inner source, I(z). In DΘ =

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fact, I should be understood as a radiance flux density (energy per unit area per unit mass) on a horizontal surface. It is computed according the exponential law of downward irradiance distribution, and hence approximately the absorption ((Paulson and Simpson, 1977): z

z

I(z) = I0 (Aeη1 + (1 − A)e η2 )β(z)

(2.3)

where I0 is the incident irradiance just below the surface, z is the vertical coordinate positive upward with the origin at mean sea level, A is a constant, η - absorption coefficients representing the attenuation (or absorption) length (distance into a material when the probability has dropped to that a particle has not been absorbed). The damping term due to bioturbidity, β(z) is calculated in the biogeochemical routines, but for our study is assumed constant and equal 1. The constant A and absorption coefficients η1 and η2 in eq. 2.3 are empirical and depend on the so called ”water type” and have to be prescribed either by means of choosing a Jerlov (1968) class, or by reading in a file through the namelist extinct in obs.nml. Jerlov water types are based on their spectral optical attenuation depth zk = k1d . Since ocean color is proportional to 1/a ∼ 1/kd this spectra is linked to the color observed. Jerlov discretized his observations into a set of typical oceanic and coastal spectra. The most clear oceanic water with low productivity is type I, a clear coastal water is type II, and fairly turbid is type III. Additional types lie in between of the formers. For a closer look at diurnal cycle, the equation 2.3 was modified to split the visible into the red and the rest parts of incoming radiation with attenuation coefficients η1 , k1 , k2 respectively: z

z

z

I(z) = I0 (Ae η1 + (1 − A)(Bek1 + (1 − B)e k2 ))

(2.4)

where B is another constant partitioning the visible light in the water column. The A coefficient were chosen as constant for any chlorophyll-a concentration and equal A = 0.62 (corresponds to Jerlov type IA - rather clear ocean water). Other coefficients, η1 , k1 , k2 , are calculated using a model of Sathyendranath and Platt (1988), that describes a profile of visible light in water column I(z). Extinction coefficients are, in fact, a result of a double exponential fitting of irradiance profile calculated with this ”spectral irradiance model”. The model takes into account mainly the amount of solar radiation on the surface and the amount of the chlorophyll. The amount of chlorophyll is treated as constant for the whole water column, so can be easily prescribed for sensitivity tests or given from a satellite data for the simulation run. The amount of surface solar radiation depends on solar zenithal angle and the latter - on the latitude and the day of the year. The complete classical formula can be found in (Platt et al. (1990), formula 14, 15). The simple schema of consequence of such dependence of light extinction on particular day and latitude can be described in a following way. The more northern is the zone, the bigger is solar zenithal angle, the less solar radiation will reach the surface. Further from summer solticies - less is the day duration, less solar radiation is accumulated in one day. More close to the local zenith time, more energy can be gained.

2.1.2

Air-sea interaction

The role of diurnal warming in air-sea heat fluxes estimation is discussed in Kawai and Wada (2007). The modelling tools help to assess it quantitatively. Fairall et al. (1996) and Ward (2006) provide values of 50-60 W m− 2 increase in net heat flux from the ocean in clear and calm conditions. In the GOTM there is a special module calculating heat, momentum and freshwater fluxes between the ocean and the atmosphere as well as the incoming solar radiation. Fluxes and solar radiation may be prescribed or calculated by means of bulk formulae from observed or modelled meteorological parameters and the solar radiation may be calculated from longitude, 11

latitude, time and cloudiness (the second option was chosen). The basic formula for total surface heat flux Qtot is a sum of latent heat flux Qe , sensible heat flux Qh and long wave back radiation Qb , eq. 2.5. This sum is a boundary condition for temperature equation, so the accurate calculation of heat fluxes is important. Qtot = Qe + Qh + Qb

(2.5)

The Kondo (1975) model was used for calculation of heat fluxes using meteorological forcing data. Based on the model sea surface temperature, the wind vector at 10 m height, the air pressure at 2 m, the dry air temperature and the air pressure at 2 m, and the relative humidity (either directly given or recalculated from the wet bulb or the dew point temperature), the GOTM routine first computes the transfer coefficients for the surface momentum flux vector, (τxs , τys ) (cdd ), the latent heat flux, Qe , (ced ) and the sensible heat flux, Qh , (chd ) heat flux according to the Kondo (1975) bulk formulae. Afterwards, these fluxes are calculated according to the following formulae: τxs = cdd ρa Wx W τys = cdd ρa Wy W Qe = ced Lρa W (qs − qa )

(2.6)

Qh = chd Cpa ρa W (Tw − Ta ) with the air density ρa , the wind speed at 10 m, W , the x- and the y-component of the wind velocity vector, Wx and Wy , respectively, the specific evaporation heat of sea water, L, the specific saturation humidity, qs , the actual specific humidity qa , the specific heat capacity of air at constant pressure, Cpa , the sea surface temperature, Tw and the dry air temperature, Ta . A diurnal warming enters into the equation for sensible heat flux (eq.2.6), such that larger is the difference between air and sea surface temperature, larger is sensible heat flux.

2.1.3

Input files and forcing

To launch GOTM several *.txt files should be provided: temperature and salinity vertical profiles (containing profiles for at least the initial moment of time), file containing meteo forcing: wind speed and direction Uwind , Vwind , air pressure, air temperature, humidity (or temperature of dew point), cloudiness. Additionally, a file containing extinction coefficients (in the form of A, B, η, k1 , k2 , see equation 2.4) can be provided. In other case Jerlov water type should be chosen. The forcing can be given with any frequency, because in GOTM the temporal interpolation is performed according to the chosen time step. Temperature and salinity monthly climatologies for initial conditions for the Arctic region were taken from the NOAA Arctic regional database (Sei (2015)). A chosen spatial resolution is 1/4°. The database has 57 standard layers of depth, with the first measurement at ’0 meters’ and the second - at ’5 meters’ depth. Salinity vertical profiles were not changed anyhow and were considered the same for the whole GOTM evaluation time period (3 days) as far as no internal currents were introduced to the model.

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Figure 2.1: SST with various wind speed and chlorophyll-a concentration (color represent chl-a concentration, form of curve - wind speed)

2.2

GOTM sensitivity tests

To test the modified model, several sensitivity tests were performed with different values of wind speed at 10 m altitude ([0.1, 1, 5, 10] m/s) and chlorophyll concentration ([0.01, 0.1, 1, 10] mg/m3 ). The wind speed was constant for the whole period of evaluation, the chlorophyll-a concentration was constant for the whole water column. Clear sky condition was used. The model was run for 10 days with a timestep of 1 minute. Coordinates of a chosen point are 75.5 N, 76.5 E - a central part of Kara sea, the first day for initialization - 1/08/2012.

2.2.1

Chlorophyll-a and wind speed

A general sensitivity test is important is to understand the influence of chlorophyll concentration and the influence of wind speed. Figure 2.1 shows the ensemble of SST obtained for every combination of chlorophyll concentration and wind speed. The minimum SST is observed at midnight, 00-1:00 of local time, the maximum - at noon, 12-13:00. The role of wind speed for SST is clearly seen: in the calm conditions (wind 0.1-1 m/s, solid and dashed line respectively) the diurnal warming is more effective since the first day of the evaluation and after 10 days of evaluation a water mass can have a gain of about 7°C above the initial temperature value (fig.2.2a). With a strong wind (5 or 10 m/s) the diurnal warming is much more moderate, and stronger is the wind weaker is diurnal warming and warming in general: +1, +3°C after 10 days (fig.2.2b). In fact, a strong wind destroy the initial profile of temperature in the first day, mixing warm surface water with colder deep water, resulting in more cool state of the whole water column in general. The depth of diurnal warming layer is also remarkable (fig.2.2a, 2.2b, table 2.1). DW layer depth was assumed as a depth where a temperature difference between neighbour vertical layers greater than 25% of total surface-bottom temperature difference on the last day of evaluation. For highly stratified water column (like with temperature and salinity from Arctic climatologies, see fig.1.6), the diurnal layer depth varies almost linearly for wind speeds 1 - 10 m/s (column 2 in the table 2.1). For the weak wind conditions (0.1 - 1 m/s) the depth of diurnal layer does not change much - diurnal warming due to the low mixing rest in the first 2 meters. With winds speeds greater than 5 m/s the turbulent mixing deepen the DW layer to 6-10 m, but still do not arrive to the seasonal thermocline depth (which is, e.g. 25 m for 75N, 76E from Arctic 13

climatologies). That means that even with strong winds the diurnal warming is on the upper part of stabilized water column in terms of buoyancy. These results correspond well to known facts about wind speed influence on the diurnal layer formation process (Gentemann et al. (2003), Gentemann et al. (2009)), so this GOTM configuration can be used for further investigation. The role of chlorophyll-a concentration is more important for low and medium wind speed - higher is concentration, stronger is warming (fig.2.1). Comparing two cases in calm wind speed conditions (0.1 m/s) with very high concentration of chlorophyll 10 mg/m3 (values corresponding to strong phytoplankton bloom) and with low concentration 0.1 mg/m3 (clear sea water), one can see that the surface temperature difference reaches 1.4°C after 9 days of model evaluation (fig.2.3a). This is the first quantitative estimation of the effect of light absorption due to the phytoplankton presence in Arctic conditions. Comparing diurnal cycle in two water masses [chl]=10 mg/m3 and [chl]= 0.1 mg/m3 , the interesting detail is observed after 5-6 day in calm wind conditions (fig.2.3a): the difference of temperature is higher in the interior of the diurnal layer, between 1 and 2 meters depth and not at the surface. In other words, the chl=10 mg/m3 water mass is accumulating heat in the interior more effectively than at surface for a timescale of several days. One of the possible explanation can be a night evaporation that affect the upper 1st m of chl=10 mg/m3 water mass strongly, but the layer between 1 and 2 meters is less influenced than the surface. The argument for this theory is the time lag between maximum of temperature for chl=10 mg/m3 water mass (at 14:00) and the maximum temperature difference between chl=10 mg/m3 and chl=0.1 mg/m3 (at midnight). This effect is seen only when the wind speed is low.

2.2.2

Salinity test

Salinity test was performed to study the role of freshwater layer. As Soloviev and Lukas (1997) have shown, the freshwater lens effectively traps the upper heat, that stabilize the water column even more. This is the case for the Arctic ocean, as its climatology shows (fig.1.6). To compare the effect of strong halocline with its absence we set up a second GOTM configuration with a vertical temperature profile from climatologies and quasi-constant salinity S ≈ 35‰ The halocline depth is controlled by wind speed and initial stratification, because in the chosen GOTM configuration there is no advection, so no source of salt. The time-depth evaluation of halocline is clearly seen for the climatology salinity profile because initially the difference between surface salinity and salinity at some depth was very strong (up to 13‰). Salinity can not control temperature directly, but influences the vertical mixing through buoyancy equation. Finally it results in the depth of mixed layer: weaker is salinity stratification, larger is the mixed layer (table 2.1). With the same solar radiation input (and same chlorophyll-a concentration), the SST values are not equal for general climatology and constant salinity cases. As far as the depth of mixed layer depends mostly on wind speed, stronger is the wind, larger is the difference between climatology and constant salinity case. On the figure (fig.2.4)one can see that for wind speed higher than 5 m/s, the difference in DW is more than 0.3 °C. For the wind = 10 m/s the diurnal warming doesn’t seem to exist for the weak stratification: the diurnal Wind speed [m/s] 0.1 1.0 5.0 10.0

depth [m] (TS climatology) -1.55 -1.60 -4.44 -9.42

depth [m] (S=const) -1.55 -1.66 -10.76 -41.46

Table 2.1: Diurnal warming layer depth on the 10th day of evaluation, [chl]=10 mg/m3 14

temperature variation is negative on average, though it has a high temporal variability (there is a light gain of SST during the daytime, but it is lost during night heat exchange). To sum up, salinity stratification is an important parameter for DW modelling in very windy conditions and in extremely unstratified case can cause even negative diurnal temperature variation.

2.2.3

Jerlov water types

In marine optics there exist several technics that help to classify ocean water depending on its color and clarity. Classification can be based on human eye, analysis of apparent optical property (AOP) spectra or analysis of inherent optical properties (Boss (2014)). As it was mentioned before, Jerlov (1968) proposed a classification of water body types using their spectral optical attenuation depth (from type I the clearest water, to type III - the most turbid). Jerlov water types are implemented into GOTM using the approximation coefficients A, η1 and η2 described in (Paulson and Simpson, 1977), see section 2.3. Results of a test case with Jerlov coefficients are shown on the fig.2.5. In fact the comparison of Jerlov coefficient with different concentration of chlorophyll-a is not reasonable. The main issue is the fact that Jerlov coefficients A, and z/ηi (eq.2.3), being empirical approximation of light absorption, include the effect of presence both chlorophyll-a (algae) and other dissolved organic and non-organic matter (like sand or humin substances). This has a straightforward consequence for the modified equation of downward irradiance distribution (eq.2.4), because the A coefficient can not be changed depending only on chlorophyll there, so A was assumed to be constant for any chlorophyll-a concentration (and corresponding to the most general type IA - rather clear ocean water). If we regard the initial equation for downward irradiance (eq.2.3) more closely, one can realize z that the I(z) profile is the sum of two exponents. In some sense the first exponent I0 (Ae η1 ) helps to represent the rapid absorption of IR radiation close to the surface (in first mm), so the A coefficient is the most important very close to the surface. From the other hand, the second z η1 I0 (1 − A)e term controls the amount of penetrating visible light. For Jerlov types the more turbid is a water mass, higher is A=[0.58 - 0.70], but at the same time the z/η1 terms become smaller [z/0.35 - z/1.40]. All in all this leads to an unintuitive result at the first glance: for the weak wind speed conditions (low turbulent mixing) the type I water mass has the highest values of SST (fig.2.5) and also the thinnest diurnal layer, where the daily gain of heat is stored (less than 2 meters, not shown). The more turbid is water mass (type II and III), the thicker is its diurnal layer, so the heat is distributed further downwards. The hypothesis explaining this phenomena of strong diurnal cycle of clear water (type I) can be proposed in terms of surface layer heat exchange. For the clear water most of the heat is stored in the very thin surface layer. This layer is very quickly warmed up during the day-time, but during the nigh-time the most of the heat goes back to the atmosphere in a form of sensible heat flux due to the big difference of air and water temperature. Additionally, the low wind speed of 0.1 m/s can be sufficient to mix the whole thin diurnal layer favouring the heat exchange, so a little of heat is stored in the long-term perspective. In case of turbid water with its’ thick layer, the night-time turbulent mixing affects only the upper part of diurnal layer, and much of heat is kept underneath. This stored heat slowly rise up the mean temperature in the diurnal layer. This helps to understand also that by the end of evaluation the SST of water type I and type III have close values (about 7.5 ◦ C). For more agitated sea (wind 5-10 m/s) the schema of SST evaluation is closer to the first general test cases results: the more turbid is the water, the higher is the SST. In general the SST for Jerlov coefficients is very slightly lower than SST computed with chlorophyll. Due to the stated problems of interpretation the studied case with chlorophyll-a concentration and case of Jerlov coefficient were not compared furthermore. 15

(a)

(b)

(c)

(d)

Figure 2.2: General sensitivity test: (a)-(b) temperature variation for chl-a=10 mg/m3 and wind speed=0.1 m/s (a) or 10 m/s (b), (c)-(d) salinity variation for same conditions (a)

(b)

Figure 2.3: The difference of temperature for chl-a=[10 mg/m3 - 0.1 mg/m3 ], wind speed=0.1 m/s (a), and wind speed=10.0 m/s (b) 16

(a)

(b)

Figure 2.4: Mean surface diurnal warming for 9 days GOTM evaluation: case with TS from climatologies (in red) and constant salinity (in blue). The standard deviation is shown as whiskeys plot. Chlorophyll-a concentration is 0.10 mg/m3 (a) and 10 mg/m3 (b)

Figure 2.5: Sensitivity test with Jerlov water types

17

Chapter 3 Application Following the main goal of this work - a modelling of diurnal warming process with chlorophyll-a taken into account, GOTM was adjusted to reproduce a real DW event happened in Barents sea on 27/07/2008 and described in Eastwood et al. (2011). North-Eastern part of the Barents sea (to the South of Frants-Jozef Land) where DW event was found, is not very deep, but only the first 100 m were considered for calculation with the zoom on surface. Evaluation was performed for 3 days from 26/07/2008 00:00:00 to 29/07/2008 00:00:00 UTC, with a time step 1 minute. A model configuration was the same as for sensitivity tests reported in Chapter 2.2. GOTM was run for every point in the rectangular zone 100x140 grid points, chosen in WASPARC coordinates on polar stereographic grid, which is equal to 350 km along meridian in reality. The appropriate forcing was given for every point hereby. Additionally, a simulation with anterior version of GOTM (without chlorophyll module) and the same forcing was performed over some smaller zones to assess the effect of modifications.

3.1

Data

Main necessary input files for GOTM evaluation are described 2.1.3. The following data were used for initialization and evaluation GOTM: SST, wind speed and chlorophyll-a concentration from WASPARC database created in M´et´eoFrance for studying diurnal warming in Arctic in 2008, temperature and salinity climatologies from the NOAA regional atlas, meteorological forcing (air temperature, air pressure, humidity) from the atmospheric reanalysis database from European Centre for Medium-Range Weather Forecasts (ECMWF). A clear sky condition was assumed.

3.1.1

Temperature

Climatological data gives only an averaged image of a real vertical distribution of temperature and salinity, but stays a possible solution in conditions with a severe lack of in situ measurements for the precise date and area, which is the case for Arctic. Therefore, the NOAA Arctic regional atlas data were used (see section 2.1.3 for details). Obviously, provided climatological temperature at ”0 m depth” weakly corresponds to the SST measurements for the precise date and area, so for our study the upper layer from climatologies was adjusted to the real SST to obtain a smooth and more realistic vertical profile of temperature. The linear interpolation was used, and the ”upper mixed layer” or, in other words, diurnal thermocline depth was defined as a part of vertical profile with temperature difference (between neighbour layers) greater than 25% of total surface-bottom temperature difference.

18

Sea surface temperature The WASPARC database was created as experimental to discover diurnal events in Arctic in 2008 and more easily collocate the SST images with winds and chlorophyll concentration (so all the data was reprojected to the same spatial polar stereographic grid). Satellite infrared images with 1 km space resolution received from METOP AVHRR measurements together with microwave from AMSRE 25 km resolution were used as the principle source of SST. Retrieving SST from infra red satellite data at high latitudes is a challenging task, due to frequent cloud cover, presence of ice, frequent twilight illumination conditions and extreme atmospheric conditions (dry and cold air), so finally the amount of satellite data is quite limited compared to lower latitudes. In total, there were 419 different images of SST over the chosen zone, but overall (except several points in the north) every point in the zone had about 15−25% of temporal coverage - so about 80 measurements during 3 days, which is a good resolution.

3.1.2

Chlorophyll-a

Chlorophyll concentration can be obtained from in situ measurements or from satellite data, but in Arctic the spatial resolution of in situ data is not enough for the DW studies, so the chlorophyll-a concentration was taken from spectraradiometers on MODIS. MODIS spectraradiometer, carried on Terra and Aqua satellites, is used to provide daily (or 8-days, or monthly) gridded product, with original spatial resolution of 9.2 km at the equator (Level-3). As it is mentioned on the official MODIS website http://modis.gsfc.nasa.gov: ”chlorophyll-a concentration can be estimated based on an empirical algorithm, or derived from a semi-analytic algorithm that involve the inversion of a radiance model to determine the absorption coefficient due to phytoplankton at 675 nm, and the absorption coefficient of CDOM (also called ”yellow substance”) at 400 nm”. Working in visible bands, MODIS can not penetrate clouds, so part of the zone of interest is often masked. To solve the problem of missing data, we assumed that the chlorophyll-a concentration does not vary drastically during 3 days of our simulation, and produced an average value for every point (fig.3.6).

3.1.3

Meteorological forcing

Wind speed and directions come from ASCAT and QuikSCAT. The temporal resolution of wind data is much lower than for SST - 45 images for 3 days, and for every point there are from 20 to 30 measurements. Since wind is considered one of the main forcing for DW, the lack of wind data strongly affects the GOTM evaluation, which results can become unrealistic for particular points. Other meteorological parameters: air temperature at 2 m, surface pressure and temperature of a dew point were taken from the Numerical weather prediction model from European Centre for Medium-Range Weather Forecasts (ECMWF), (Hersbach et al. (2015)). The meteorological forcing has a three hours standard time resolution a initial low space grid of 125 km length for the cell at 60N, that was reprojected to the WASPARC spatial grid. ECMWF also provides the wind speed and direction data, that were used for additional analysis.

3.2 3.2.1

Results Meteorological and climatological situation

Following the ECMWF reanalysis charts, on the 27/07/2008 the western, central and northeastern part of Barents sea received favourable conditions for DW event formation: an anticyclone with a surface pressure up to 1030 hPa occupied the region (fig.3.1). Over the zone of 19

Table 3.1: Surface pressure from ECMWF

Table 3.2: General circulation in Barents sea from Stiansen et al. (2009)

Table 3.3: Wind speed in m/s from ECMWF

Table 3.4: Sea ice chart for 30/08/2008 from Arctic and Antarctic Research Institute

Table 3.5: Example of SST at 27/07/2008 11:09UTC, Barents sea 20

Table 3.6: Mean chlorophyll-a concentration for 26-29/07/2008 with indicated zones (”Chlorophyll”, ”Center”, ”East”) for detailed analysis

diurnal warming event (77-79N, 40-50E) the wind speed ranged from 0 to 8 m/s, in general decreasing from the 26/07 to the 27/07 (fig.3.3), and then increasing back from the evening of 27/07 to the end of 28/07 (will be discussed later). The chlorophyll-a concentration was not very high over the zone, but noticeable for open sea water (from 0.1 to 0.5 mg/m3 , fig.3.6). Used as an initial condition, climatological haline stratification is weak: salinity from 32.6 to 34.6 ‰ in upper 100 m), so hypothetically the diurnal warming process is not blocked by the halocline, situated between 15 and 30 m depth. At the same time, for 23-30/07/2008 AARI (Arctic and Antarctic Research Institute, Russia) ice charts show rather close presence of ice on 80N between the islands of Frants-Jozef Archipelago (fig.3.4), so climatological values of temperature and salinity can differ from the real values of water mass potentially influenced by ice melting.

3.2.2

Detailed description, WASPARC data

For the detailed analysis three small zones (10x10 points) were chosen (fig.3.6): one with the highest chlorophyll-a concentration - up to 0.5 mg/m3 (79N, 45E, called ”Chl”, mean [Chla]=0.38 mg/m3 ), the second in the central part, where the maximum heating is observed (78N, 45E, called ”Center”, mean [Chl-a]=0.23 mg/m3 ), and the third one in the North-East (78N, 51E, called ”East”, mean [Chl-a]=0.23 mg/m3 ) as a reference zone. For the comparison, mean SST in every zone were calculated for every time step. The wind speed and direction were taken in the middle point of every zone. During three days of observation, the clearest diurnal cycle appears on 27/07 (fig.3.1a). The maximum of SST happens around 10-12:00 UTC which corresponds to 14:00 of local time and is a usual time for peak of DW (see section 2.2.1). Zone ”Center” , fig.3.1a. The highest values of SST are found in the zone ”Center” with an amplitude of 4.5 °C (from 2.1 to 5.6°C). SST in central zone in general dominates over zone ”Chl” or ”East” on 26/07 and 27/07, and have close to other zones values of 2.5°C on 28/07. During the first day, 26/07, when the wind speed decreases from 8 m/s to 0.5-1 m/s, the noon diurnal warming peak is not forming, though the morning warming and evening cooling is clearly seen. The highest SST of the day correlates very well with the minimum wind speed, even though it happens about 22:00 UTC. The second day, 27/07, from 2:00 UTC a diurnal warming process develops almost linearly until the 10:00 UTC. After the noon, the diurnal process stops abruptly, and the SST decreases. Unfortunately, between 2:00 and 16:00 UTC on 27/07, there is almost no information about the wind nor from QuikSCAT, neither from ASCAT over the most part of DW event area. Meanwhile, from the low resolution ECMWF wind speed data one can see that the maximum SST time (10:00 UTC) almost corresponds to the minimum wind speed (0m/s) at 12:00 UTC, and then the wind speed starts increasing again (fig.3.1b). In other words, we suggest that the lowest wind speed permitted to develop the highest SST. This explanation seems to be realistic, though in general there is a difference between wind speed and directions from ASCAT and QuikSCAT and winds from ECMWF reanalysis. Zone ”Chl” and ”East”, fig.3.1a. A diurnal cycle for these areas is well observed but in zone ”Chl” it does not reach its definite sinusoidal form during the observation period, and in zone ”East” a clear SST peak is detected only on 27/07. Its clearly seen that the warming process is disturbed from 10:00 UCT on 26/07. The values of SST are close to each other, except the period from 00:00 to 10:00 UTC on 26/07, when the SST in zone ”Chl” is higher that in ”East” zone; and then on 27/07 there is a peak of SST in ”East” region, but in ”Chl” zone it is again destroyed. As it was mentioned, the chlorophyll concentrations do not differ very much 21

between three zones (especially their mean values), so obviously, the amount of phytoplankton was not the primary factor for this diurnal event formation. This difference in SST can be explained more by the difference in wind speed (though the data is quite sparse again): from the WASPARC data about 1:00 and 7:00 UTC the wind speed over ”Chl” zone was very low 0.5 - 1.5 m/s, so the warming was in more favourable conditions, whereas over the zone ”East” the wind speed was 7-8 m/s. Lower amplitudes of SSTs over these zones for 27/07 day time can not be explained due to the absence of wind data again. It can be suggested only that the increase of a wind speed happened over northern areas earlier than over more southern zone ”Center”, so diurnal warming could not develop after 10:00 (according to ECMWF data).

3.2.3

Simulation with GOTM

For the analysis of GOTM simulation, it is convenient to take timeseries of SST over the zones in a same way as it was done in previous section. To facilitate the comparison, only ”Center” and ”Chl” zones will be discussed. For the additional simulation with initial GOTM version (without chlorophyll) a Jerlov type II was chosen, based on sensitivity tests results: knowing that the order of magnitude of chlorophyll concentration is about 0.1 mg/m3 we suppose this water as ”rather clear oceanic water” (see section 2.2.3). Simulated SST curves are very smoothed and do not represent all the variations of real SST (fig.3.2). Simulated SST fit the real SST better in their minima between midnight and 2:00 UTC. The maximum values are difficult to compare taking into account the irregular form of SST curves and the absence of main diurnal peak for real SST, but for the 27/07 the simulated maxima are observed on 12:00 - 14:00 UTC - which is slightly shifted from the real SST maximum at 10-12:00 UTC. The amplitudes are not resolved properly due to the lack of wind forcing information presumably - in case of no data (like for 27/07) GOTM interpolates the nearest available values linearly. For the day time on 27/07 the wind speed used by GOTM over these zones was about 1-2 m/s, which seems to be underestimated looking on the data from ECMWF, and should be considered as the main reason for low values of simulated SST. Nevertheless, it should be mentioned, that ECMWF data can not be understood as a ”real forcing”, because it is a product of reanalysis and not real measurements. Regarding the effect of adding the chlorophyll concentrations in GOTM, one can see that in the zone ”Chl” a modelled diurnal process advances more effectively. There is a difference of 0.3°C between maximum SST at 12:00 UTC reproduced with initial and modified versions of GOTM (”modified” values are higher). For the zone ”Center” the effect is weaker - only 0.1°C for the SSTs at noon on 27/07. Though a question of reasonableness of comparison between Jerlov coefficients and chlorophyll concentrations was opened in section 2.2.3), the effect of adding the chlorophyll with other parameters being equal is bringing the simulated SST to some extent closer to real values. There are several additional hypothesis why GOTM was not able to reproduce a real DW event in details. Among them the following can be mentioned: a clear sky condition, the absence of other particles (e.g. CDOM, SPM - suspended particulate matter) as input parameters, and the non representation of UV part of incoming radiation. Clouds diffuse and refract light, so incoming radiation is different from clear sky conditions - a part of SST values could have been obtained under cloudy conditions with microwave measurements. CDOM and other dissolved matter absorb light supplementary to chlorophyll - and, e.g. there was about CDOM index = 2 according to SeaWIFS data regarded with Giovanni tool (Acker and Leptoukh (2007)). With including UV part of spectra the water column is also warmed additionally. Speculating about other possible reasons of poor modelled representation of real DW cycle, one can return also to the hydrometeorological instant situation, that can be only guessed for water mass state in the absence of measurements. 22

(a) Sea surface temperature from WASPARC

(b) Wind speed

Figure 3.1: Temperature and wind for three zones from WASPARC and ECMWF in three zones : ’Chl’- 79N 45E, ’Center’- 78N 45E, ’East’- 78N 51E (see the text for explanations)

Figure 3.2: Comparison between the SST from WASPARC, and from modified and initial 23 version of GOTM)

Figure 3.3: Temperature variations for the zone ”Center” (upper) and ”Chl” (lower), GOTM simulation One of the possible reasons of such a high diurnal variation in zone ”Center” in reality can be the heat accumulated previously in this particular water mass, and the lack of heat in more northern zones. In this case a thinner ”Chl” water mass looses all the heat more rapidly and then SST is not fed by interior; whereas the ”Central” water mass provides additional energy for SST. The colder surface waters in the zone ”Chl” comes usually from Arctic, to be precise from Frants-Josef Archipelago where we can see the sea ice in addition to usual icebergs (fig.3.2, fig.3.4). The zone ”Center” is situated in the area of rather warm intermediate Atlantic water mass advection (3.2, Stiansen et al. (2009)), which itself can not guarantee higher SST values, but being less influenced by Arctic surface waters can keep average values overall higher due to mixing processes. Vertical temperature profiles - climatic and then generated by GOTM, somehow confirm this hypothesis (fig.3.3). Initial climatic temperature profile in zone ”Center” is much warmer (1.7 - 3°C in upper 10 m), than in the northern zone ”Chl”(1.6 - 2°C in upper 5 m). According to GOTM results (even underestimated), the depth of DW layer varies from 2-5 m in the zone ”Chl”, to 10 in the ”Center”, which corresponds to the proposed situation. On 27/07 over both zones there is a strong DW in the very upper 2 m depth - the maximum possible depth of mixing with low wind speed, but the mean temperature for a thicker DW layer in ”Center” zone is higher, and DW is stronger also. Further investigation of the diurnal cycle in water column with GOTM results seems to be unreliable and thus was not performed. The question of possible use of GOTM for detailed 3D DW study is open until an event with full dataset for the meteorological forcing is found. Nevertheless, some improvement for ”modified” GOTM can be proposed, e.g.: including UV radiation and CDOM concentrations.

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Chapter 4 Conclusion and discussion A regional study of diurnal warming cycle was done for the Eastern Arctic ocean and polar day insolation conditions. The role of the chlorophyll was assessed with the use of GOTM together with parametrization of model of radiative transfer in water created by Sathyendranath and Platt (1988). Several sensitivity tests have shown that high chlorophyll concentrations can increase the DW amplitudes, and the lower is the wind speed the more important is the role of chlorophyll-a concentration. A sanity test has shown that strong halocline can block the DW event on the surface, and thus, firstly, the heat is not transferred into interior but more active exchange with atmosphere happens, and secondly, the DW event can be destroyed more easily with the sudden increase of wind speed. This result is in particular important for Arctic as far as strong salinity stratification is a regional feature of Arctic ocean. Jerlov optical water types within interior version of GOTM were also regarded as an alternative for modelling, though we suggest that comparison between integral Jerlov water mass classification with our parametrization is not appropriate, because of the presence of other particles in water that are considered only in Jerlov types. A real diurnal warming event in Barents sea was studied with a use of satellite data mainly and modified GOTM calculations. The lack of wind speed forcing affected the modelling process, so real amplitudes were not reproduced, though the foundation temperatures (minimum of SST during one day) were achieved. Several other hypothesis were provided to explain the difference in modelled and real diurnal cycle (clear sky condition used, non representation of other particles and/or UV part of incoming radiation, non accurate initial profiles of temperature and salinity, advection). Nevertheless, modified GOTM results for the region with rather high chlorophyll-a concentrations (0.4 mg/m3 in average) were slightly better than result of initial GOTM configuration and Jerlov water type. As possible improvements of GOTM modified version with prescribed optical properties, there is a suggestion to take into account some other components of sea water like CDOM and UV part of incoming solar radiation. All in all, a study of the influence of optical parameters on diurnal temperature variations in polar regions should be continued with other examples of real DW events. The development can be proposed for GOTM in the context of using other water components available from satellite data for modelling parametrization. The approach used for light extinct parametrization can be also used in 2D or 3D models, taking into account horizontal velocity fields.

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