PL

with p and L being the water density and the latent heat of evaporation, as defined before. The equation above is used to calculate the bulk aerodynamic evaporation rate Ea from Qea and the heat-balance evaporation rate Er from

Qer.

8.9.3 Sea data for computing heat storage

For the calculation of the heat storage, AH we need the seawater temperature, salinity and pressure in a three-dimensional grid. We can use such data from two sources. The first is the Mediterranean Data Archaeology and Rescue (MEDAR) MEDATLAS 2002 database, a comprehensive data product of multidisciplinary in-situ data and information on the Mediterranean and Black Seas, through a wide co-operation of the Mediterranean countries. The MEDAR data are climatological values at a monthly 0.2x0.2 degree resolution, down to a depth of 4000 m. The second is the World Ocean Atlas 2001 from the National Oceanographic Data Center (NODC) of the National Oceanic and Atmospheric Administration (NOAA), which gives climatological temperature and salinity data down to a 1500 m depth, at a monthly 1x1 degree resolution.

The temperature structure at the surface from MEDAR is shown in Fig. 8.34 for March. Similarly, we present the temperature structure at a depth of 75 m in Fig. 8.35. The warmest water is in the southeast basin, while the coldest is generally in the north. There are large seasonal differences in the surface water temperature, which oscillates between 10-25 °C. It is apparent that the temperature seasonality becomes attenuated already at 75 m.

We include also an image of the July salinity at the surface (Fig. 8.36). The salinity is not characterized by a seasonality as strong as that of the water temperature. An area with little salinity is to the east of Gibraltar, showing the less salty Atlantic water flowing into the Mediterranean. Over a smaller spatial extent, the North Aegean and North Adriatic also have low salinity. In the first case we see less salty water flowing from the Black Sea and in the second, freshwater from the river Po diluting the shallow seawater.

Fig. 8.34. MEDAR water temperature (°C) at 0 m depth for March. (Mat-soukas et al. 2005)

Fig. 8.34. MEDAR water temperature (°C) at 0 m depth for March. (Mat-soukas et al. 2005)

12 13 14 15 16 17

Fig. 8.35. MEDAR water temperature (°C) at 75 m depth for March. (Mat-soukas et al. 2005)

12 13 14 15 16 17

Fig. 8.35. MEDAR water temperature (°C) at 75 m depth for March. (Mat-soukas et al. 2005)

8.9.4 Data for computing turbulent fluxes

The heat-balance latent heat Qer can be estimated from data already mentioned above. For the bulk aerodynamic latent heat Qea we can use eqn (8.13), with two data sources for the necessary climatic quantities. The first is the 40-year European Centre for Medium-range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) and the second is the International Comprehensive Ocean-Atmosphere Data Set (ICOADS), which is a collection of global marine surface observations and their monthly summary statistics. ICOADS observations are taken primarily

Fig. 8.37. Model Qs (W m-2) for July 1990. (Matsoukas et al. 2005)

from ships (merchant, ocean research, fishing, navy, etc.) and from moored and drifting buoys. Data from an earlier version of ICOADS in the period 1980-1993 were used to generate a global air-sea heat flux climatology by the Southampton Oceanography Centre (SOC). The aerodynamic latent heat flux Qea is also available directly from ERA-40, SOC, and NESDIS (NOAA Environmental Satellite Data Information Service) databases. The sensible heat flux Qh is available directly from ERA-40.

CS I

-100

-200

-100

Fig. 8.38. Monthly climatology of the heat-budget components. Here, positive values correspond to available energy for evaporation, while negative values to energy losses to the atmosphere or heat storage. (Matsoukas et al. 2005)

Jan Mar May

Fig. 8.38. Monthly climatology of the heat-budget components. Here, positive values correspond to available energy for evaporation, while negative values to energy losses to the atmosphere or heat storage. (Matsoukas et al. 2005)

8.9.5 Radiation fluxes

As an example we show a 2.5x2.5 model net solar flux Qs, for July 1990 in Fig. 8.37. The values shown are averaged over 24 h. As expected, Qs generally decreases from south to north and the summer values are much larger than those in winter. The Qs values over sea are larger than over land in July, because the sea albedo is much smaller than the land albedo. Thus, the largest part of the downwelling component is absorbed by the sea. On the other hand, in January the solar zenith angle is low and the sea reflectivity increases due to the Fresnel effect. Also, the downwelling component is not very large and therefore Qs differences between sea and land are small. Model monthly components of the heat budget using ISCCP-D2 monthly 2.5x2.5 degree and daily 1x1 degree NASA-Langley climatological data are shown in Fig. 8.38. The seasonality of Qs is quite pronounced, with winter values around 70 W m~2 and summer values just under 300 W m~2. The coarser data span the period 1984-2000, while the finer-resolution data are for 1984-1995. Nevertheless, the two climatologies are very close and differ by only a few W m~2 for any month.

The net longwave flux Qi for July 1990 is shown in Fig. 8.39. In this figure and in Fig. 8.38 one can see that the seasonality of Ql is considerably less pronounced compared to Qs.

8.9.6 Heat storage

We can derive the monthly climatological heat-storage change using the MEDAR Atlas temperature and salinity climatological data and eqn (8.11). Positive AH values denote increase in the heat content, i.e. water temperature increase, and correspond to spring and summer, when the solar radiation is at its maximum. Negative values are seen during winter and autumn, when the solar radiation decreases. We plot the change in the negative of heat storage, - AH in Fig. 8.38 to allow a visual check that the sum of all terms of eqn (8.10) are close to zero. The heat content itself H is at its maximum in October and at its minimum in March. The H maximum and minimum, coincide with the points of intersection between AH and the zero line in Fig. 8.38. The World Ocean Atlas 2001 (WOA1) dataset produces similar monthly AH values to MEDAR, although the amplitude of the WOA01 seasonal cycle is stronger by 20 W m~2.

During spring and summer the Mediterranean stores solar energy at a rate of 100150 W m~2, a value of the same order of magnitude as Qs. On the other hand, during autumn and winter, it gives out this energy back to the environment. On an annual basis, AH has a value of zero. We note here that the gridded MEDATLAS 2000 data are given as long-term monthly climatologies. Therefore, we can only estimate the AH maps as 1 x 1 degree monthly climatologies and not as monthly time series for the study period 1984-2000.

8.9.7 Turbulent fluxes

8.9.7.1 Sensible heat flux The sensible heat flux Qh is taken directly from ERA-40 and its monthly climatological behaviour can be observed in Fig. 8.38 (dot-dash line). It has its maximum (absolute) values during winter and its min-

50 60 70 80 90 100

Fig. 8.39. Model Ql (W m~2) for July 1990. (Matsoukas et al. 2005)

50 60 70 80 90 100

Fig. 8.39. Model Ql (W m~2) for July 1990. (Matsoukas et al. 2005)

1 1 Taken from da Silva

Fig. 8.40. Monthly estimates of Ea. (Matsoukas et al. 2005) imum during summer, while its magnitude is the smallest of all components.

1 1 Taken from da Silva

Fig. 8.40. Monthly estimates of Ea. (Matsoukas et al. 2005) imum during summer, while its magnitude is the smallest of all components.

8.9.7.2 Latent heat flux We can use two methodologies to estimate the latent heat flux Qe and evaporation E. One is the bulk aerodynamic and the other the heat-balance method. With Qea and Ea we denote the bulk aerodynamic latent heat and evaporation estimates, while with Qer and Er, we denote the heat-balance values. The latent-heat flux is presented in Figs. 8.40 and 8.41. In these plots the latent-heat estimates are translated into evaporation rates, using eqn (8.17). Figure 8.40 shows the evaporation estimates that are given directly by data sources, namely the monthly climatological evaporation rates from NOAA/NESDIS, SOC, and ERA-40. All three are estimates of bulk aerodynamic evaporation Ea, derived using variations of eqn (8.13). Also, the Ea calculated by the model using standard climatological quantities and eqn (8.13) are shown in the same figure. We present the aerodynamic evaporation Ea calculated from ERA-40 and from the ICOADS data. The ICOADS derived Ea is larger than the ERA-40 due to its higher wind speeds. Note that the evaporation that we derive from ERA-40 data using eqn (8.13) and assuming neutral atmospheric conditions (thin solid line) is very close to the ERA-40 evaporation (thick solid line) although ERA-40 does not make the neutral atmosphere assumption. Thus, Brutsaert (1984) seems justified in saying that for monthly evaporation the neutral atmospheric stability assumption is valid. A further check on this assumption was performed, including atmospheric instability in our derivation of Ea. The derived values (not shown) were similar to the ones calculated with the neutral stability assumption, although larger by about 5% during winter.

Fig. 8.41. Monthly estimates of Ea and Er. The Ea are calculated from ICOADS and ERA-40 data. (Matsoukas et al. 2005)

8.9.8 Seasonal evaporation rate

We now examine the evaporation derived from the heat-balance method Er, using eqn (8.16) and eqn (8.17). In Fig. 8.41 we show Er and for comparison purposes, we also include calculations of Ea estimates using ERA-40 and ICOADS data. It is interesting to note that the minimum evaporation in May given by the bulk aerodynamic formula, is also reproduced by the heat-balance method. Interpreting this behaviour from the energy standpoint, we see in Fig. 8.38 that most of the solar energy that month is used to increase the sea heat content. After subtracting the longwave sea-emitted radiation, the energy available to drive the evaporation process is very small. This results in the May evaporation minimum. On the other hand, the maximum evaporation in winter can be linked to the very large heat content release from the sea during the same time. The released thermal energy is larger even than the absorbed solar energy then, and is the primary energy source for the strong evaporation process.

8.9.9 Annual evaporation rate

Estimates of the mean annual evaporation are shown in Table 8.17. In the first row Er is the evaporation determined by the heat balance method. Rows 2, 3, and 4 correspond to evaporation estimates from the NOAA/NESDIS, SOC and ERA-40 databases. Finally, rows 5 and 6 give the Ea estimated from ERA-40 and ICOADS climatological data.

The differences between Er and Ea are rather large, and can rise to more than 350 mm yearr-1. There are also qualitative differences as seen in Fig. 8.41, where

Table 8.17 Annual Mediterranean evaporation rates. (Matsoukas et al. 2005)

Index Source Value (mm year

1 Er 1500

2 Ea from NOAA/NESDIS 1130

3 Ea from SOC 1090

4 Ea from ERA-40 1060

6 Ea from eqn (8.13) and ICOADS 1280

Ea has one maximum (during winter), while Er has two. The largest maximum is in winter, when the surface winds achieve their largest speeds and also there is a peak in energy released from the heat content of the sea. In this case both Er and Ea take their largest values. The second Er maximum is in July and August, when more energy is provided to the sea by the Sun than is stored as heat content. This leads to a secondary maximum in the available energy for evaporation Qer.

8.9.10 Comparison with Red and Black Seas

We compare the annual heat budget components of the Mediterranean, Red and Black Seas in Table 8.18. The Red Sea is located in the warmest region of the three seas and so its heat budget components are generally the highest, followed by the Mediterranean, and Black Seas. In Table 8.19 we compare the annual evaporation from each sea. The energy balance method gives higher values than the bulk aerodynamic method, and again the evaporation rate is highest for the Red Sea which has the highest solar heating of the three seas.

Table 8.18 Annual heat budget components for the Red, Black and Mediterranean Seas, in W m-2 (Matsoukas et al. 2005, 2007).

Component Red Medi. Black

Table 8.18 Annual heat budget components for the Red, Black and Mediterranean Seas, in W m-2 (Matsoukas et al. 2005, 2007).

Component Red Medi. Black

Qs

238

186

139

Qi

70

63

50

Qh

12

11

14

Qea

130

90

60

Qer

164

117

75

Table 8.19 Annual evaporation rates (mm year-1) for the Red, Black and Mediterranean Seas. Comparison of energy balance estimates (Er) from Matsoukas et al. (2005, 2007) and bulk aerodynamic estimates (Ea) from other databases.

Source

Red

Medi.

Black

Matsoukas et al.

1960

1500

830

da Silva et al.

1680

1130

-

SOC

1750

1090

-

ERA-40

1280

1060

8.10.1 Notes

For the early work on the Earth's radiation budget see Abbot and Fowle; Dines; Houghton; London; and Budyko's work.

For WCRP GEWEX projects see Rossow and Schiffer, Rossow et al. (ISCCP), and Stackhouse et al. (SRB). For ISCCP cloud optical properties see Rossow et al., and for cloud parameterizations see Slingo; Ebert and Curry; Del Genio et al.

For correlations between TOA and surface radiative fluxes to derive surface fluxes directly from the satellite measurements of TOA fluxes see for example; Ra-manathan; Schmetz; Weare; Cess et al.; Darnell et al.; Pinker and Laszlo; and Li and Leighton.

Early work using models and climatological data from satellites, following the early work of London, include the studies of Manabe and Strickler; Vonder Haar and Suomi; Sasamori et al.; Raschke et al.; Ellis and Vonder Haar; Jacobowitz et al.; Gautier et al.; Stephens et al.

For a review of early estimates of planetary albedo see Hunt et al. For cloud physical thickness see early work of Peng et al.

Studies related to ERBE data include those of Barkstrom et al.; for ScaRaB see Kandel et al.; for CERES see Wielicki et al.; for MSG see Sandford et al.

References for the reanalyses projects include; Kistler et al. for NCEP/NCAR; Uppala et al. for ECMWF ERA-40; Schubert et al. for GEOS-1.

For the GADS aerosol climatology database see Koepke et al. For model results using GADS and actual global relative humidity data see Hatzianastassiou et al. For changes to atmospheric general circulation patterns by aerosols based on GCM studies see Kristjansson et al. and Lau et al.

See Vitale et al. for the ACE-2 aerosol field experiment; for the Global Fire Atlas see Arino and Melinotte. For other field studies on aerosols see Kaufmann et al.; Ramanathan et al.; Lelieveld et al. Also, see the Indian Ocean Experiment, INDOEX.

For the GEBA database see Gilgen and Ohmura, and for the BSRN database see Ohmura et al.

For trends in radiation fluxes see: Gilgen et al.; Stanhill and Cohen; Liepert; Allan and Slingo; Wild et al.; Pinker et al., Hatzianastassiou et al.; Wielicki et al.; Fotiadi et al.

For cloud overlap schemes see Chen et al.; and Zhou and Cess.

Climatic consequences of the greenhouse effect on Mediterranean outflow and NAO are discussed in Johnson.

For the estimation of turbulent-exchange coefficients required for the aerodynamic approach for the computation of sensible and latent heat flux see Brut-saert; Miller et al.; Gilman and Garrett; Angelucci et al. For the estimation of the sea heat content see Krahmann et al.

For oceanic databases see Levitus. For the ICOADS database see Woodruff et al. and for the SOC air-sea flux climatology see Josey. Aerodynamic latent heat flux data are available from ERA-40, SOC and NOAA/NESDIS (da Silva et al.) databases. Sensible heat flux data are available from ERA-40.

For the estimation of lake evaporation from standard meteorological measurements using the Penman method see Vardavas and Fountoulakis.

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