Sea Surface Salinity from
Space:
A Canadian Perspective
Sea Surface Salinity from Space:
A Canadian Perspective
A Report Prepared
by
B. J. Topliss1, J.F.R. Gower2,
J.A. Helbig3, A. W. Isenor1, and I. Rubinstein4
for
The Canadian Space Agency
Earth and Environmental Applications Program
March 2002
1
Bedford Institute of Oceanography, Fisheries and Oceans Canada
Institute of Ocean Sciences, Fisheries and Oceans Canada
3
Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada
4
York University/CRESTech
2
ii
_______________________
Four reports were commissioned in 2001 on various aspects of sensing sea surface
salinity from space (Borstad and Horniak, 2001; Rubinstein 2001; Seibert, 2001; and
Simms, 2001). A contract (DFO number F6093-1-JAH01) was issued to E.L. Simms and
A. Simms to combine these reports into a draft of the present one.
iii
Executive Summary
Under its Earth and Environment Applications Program, the Canadian Space Agency has
funded the authors of this report to study developments in space-based remote sensing of
sea surface salinity (SSS). The broad project goals are to define Canadian interests
relative to various proposals for salinity satellites, to identify potential Canadian benefits,
and to promote Canadian involvement in all aspects of SSS monitoring. Particular
objectives are to provide the background necessary for Canadian scientists and industry
to anticipate and thus plan participation in the remote sensing of salinity and to identify
partnering and Canadian industry opportunities for the Canadian Space Program. The
purpose of this first report is to review recent developments in the remote sensing of
salinity and to place these developments in a Canadian context. In doing so, a number of
issues are addressed, including:
• What significance does the routine remote sensing of SSS hold for Canadian science
and resource management?
• What special issues must be addressed for SSS remote sensing in Canadian waters,
which are generally cold, are fed by low salinity estuarine runoff, and are sometimes
ice covered?
• What can Canada contribute to the remote sensing/physical-biological oceanographic
community?
Ocean temperature and salinity have been routinely measured since the earliest
oceanographic expeditions, and they comprise the two most basic physical variables that
define the state of the ocean. For the past 30 years, sea surface temperature (SST) has
been monitored operationally from space, but the concomitant capability to measure sea
surface salinity has not existed. This is a critical shortcoming, as there are no salinity
data for about 40% of the 1° latitude by 1° longitude areas that make up the earth's ocean
surface. The capability to measure SSS from space now exists. In fact, proposals for a
passive microwave satellite are currently before both European Space Agency (ESA) and
National Aeronautics and Space Administration (NASA) with the goal of monitoring SSS
and/or its terrestrial counterpart, soil moisture (SM). These proposals all aim for a 2006
launch date.
From an oceanographic viewpoint, salinity is important because:
• Together with temperature, it determines water density and thus is intimately linked
to the global ocean circulation.
• It determines the depth to which water, cooled at the surface in winter, can sink. That
is, it is an important component of the thermohaline circulation and is thus directly
linked to the dynamics of the earth’s climate.
• Through its effect on density, it partially controls the stability of the upper mixed
layer of the ocean. This in turn has important physical and ecological consequences.
• It is one of the prime determinants of the environment in which fish and other marine
life live.
• It modulates air-sea interaction including gas and heat exchange.
iv
In terms of applications, sea surface salinity has significance to a large number of areas
including:
• climate dynamics and prediction,
• global scale ocean-atmosphere modelling,
• regional scale ocean-atmosphere modelling,
• fisheries management, and
• environmental monitoring.
Large-area, long-term surface salinity anomalies, having significant effects on ocean
circulation, climate and fisheries, have been observed in the past in the northern Atlantic.
Tracking of the 1965 to 1980 "Great Salinity Anomaly (GSA)" relied extensively on
observations from ocean weather ships, which have since been discontinued. Similar
patterns may well be undetected today, both in the North Atlantic, and in other ocean
basins, which have always been less well monitored. The proposed satellite missions
provide a good match to the time and space scales of the GSA.
Spaceborne SSS mapping should provide significant benefits to Canada. From a scientific
vantage point, it should provide important information for climate studies, both in terms
of data for assimilation into numerical models as well providing observations in regions
where historical data coverage is extremely poor. Similar considerations also apply to
regional modelling activities. As well, benefits will accrue in the areas of fisheries
management and environmental monitoring as workers gain access to data on a longterm, year-round basis never before possible. This will permit, for example, better
estimates of mixed layer depths that are important in ecological models. Similarly,
workers will be able to better quantify the fresh water distribution due to estuarine
discharge.
Sea surface salinity signals in Canadian waters are large, both spatially in terms of
horizontal gradients and temporally on seasonal and interannual time scales.
Consequently, Canada should be able to usefully exploit remotely sensed SSS. The
accuracy of the proposed satellite sensors are about 0.4 practical salinity units (psu) for a
single pixel, single pass observation and about 0.1 psu for data averaged over space and
time into standard products (200 km and 10 days). Salinity varies by at least 1 psu across
Canada’s Pacific and Atlantic continental shelves, a value at least twice as large as the
largest expected errors. Similarly, the amplitude of the seasonal cycle exceeds 1 psu in
many areas. Year-to-year variations are generally smaller, but still often exceed 0.5 psu.
The remote detection of salinity exploits the relatively weak dependence of the emissivity
of seawater on salinity at microwave (L-band) frequencies. At these frequencies the
atmosphere is essentially transparent, so that atmospheric contamination of the signal is
minimal. However, significant error can be introduced by reflected galactic and solar
radiation as well as by changes in apparent emissivity due to sea surface roughness and
foam. The sea surface temperature must also be known, because the emissivity is
temperature dependent.
v
There exist two problems in remote sensing of SSS that are of special significance to
Canada, namely cold water and ice. The emissivity varies less with salinity in cold water
as compared to warm water, thus reducing sensor sensitivity. Similarly, since ice has a
different microwave signature from seawater, partially ice covered pixels will present a
data processing challenge. On the other hand, the Canadian science community has
considerable experience with these issues and is especially well suited to address them.
vi
Table of Contents
EXECUTIVE SUMMARY ........................................................................................................................ IV
LIST OF FIGURES.................................................................................................................................... IX
LIST OF TABLES.....................................................................................................................................XII
1.0
INTRODUCTION............................................................................................................................... 1
1.1
2.0
WHY SEA SURFACE SALINITY?......................................................................................................... 2
REMOTE SENSING OF SEA SURFACE SALINITY ................................................................... 5
2.1 MICROWAVE EMISSION FROM THE OCEAN........................................................................................ 5
2.2 PASSIVE MICROWAVE RADIOMETRY ................................................................................................ 8
2.2.1
Environmental Factors Affecting TB ..................................................................................... 10
2.2.1.1
2.2.1.2
2.2.1.3
Contributions from Space and Sun.................................................................................................. 10
Atmosphere Interactions.................................................................................................................. 11
Effect of Sea State: Waves, Breaking Waves and Foam ................................................................. 13
2.3 REVIEW OF SPACEBORNE AND AIRBORNE EXPERIMENTS................................................................ 15
2.3.1
Remote Sensing of Sea Surface Salinity in Canada .............................................................. 15
2.3.2
Spaceborne Experiments ...................................................................................................... 16
2.3.3
Airborne Experiments........................................................................................................... 17
2.4 PROPOSED SEA SURFACE SALINITY SYSTEMS ................................................................................. 19
3.0
SALINITY DATABASES ................................................................................................................ 22
3.1 GLOBAL DATABASES ...................................................................................................................... 22
3.1.1
USA National Oceanographic Data Center ......................................................................... 22
3.1.1.1
3.1.1.2
3.1.1.3
3.1.1.4
3.1.1.5
Online Oceanographic Profile Database.......................................................................................... 23
World Ocean Database 1998 Version 2 (WOD98).......................................................................... 23
World Ocean Atlas 1998 (WOA98) ............................................................................................... 23
World Ocean Atlas 1998 (WOA98F) ............................................................................................. 25
World Ocean Atlas 1994 (LEVITUS94) ......................................................................................... 27
3.1.2
Marine Environmental Data Service (MEDS) ...................................................................... 31
3.2 CANADIAN REGIONAL DATABASES ................................................................................................ 32
3.2.1
Atlantic Region ..................................................................................................................... 32
3.2.2
Pacific Region ...................................................................................................................... 35
4.0
SEA SURFACE SALINITY IN CANADIAN WATERS............................................................... 37
4.1 SPATIAL SEA SURFACE SALINITY DISTRIBUTIONS .......................................................................... 37
4.1.1
Northwest Atlantic ................................................................................................................ 37
4.1.2
Northeast Pacific .................................................................................................................. 41
4.2 TEMPORAL DISTRIBUTION OF SEA SURFACE SALINITY ................................................................... 43
4.2.1
Northwest Atlantic ................................................................................................................ 43
4.2.2
Northeast Pacific .................................................................................................................. 49
4.2.2.1
4.2.2.2
Lighthouse Stations ......................................................................................................................... 49
Station Papa..................................................................................................................................... 52
5.0
SCIENCE REQUIREMENTS ......................................................................................................... 56
6.0
ONGOING APPLICATIONS OF SEA SURFACE SALINITY................................................... 57
6.1 FISHERIES RESOURCES IN CANADIAN WATERS ............................................................................... 57
6.1.1
West Coast ............................................................................................................................ 57
6.2 OPTICAL WATER PROPERTIES ......................................................................................................... 57
7.0
EFFECTS OF SEA ICE ON THE REMOTE SENSING OF SSS................................................ 61
vii
7.1 L-BAND RADIOMETRIC CHARACTERISTICS OF SEA ICE ................................................................ 62
7.1.1
Brine ..................................................................................................................................... 62
7.1.2
First Year and Multiyear Ice ................................................................................................ 62
7.1.3
Melt Ponds on Consolidated Ice........................................................................................... 65
7.2 EFFECTS OF ICE IN THE FIELD OF VIEW ........................................................................................... 65
8.0
CONCLUDING REMARKS ........................................................................................................... 67
9.0
REFERENCES.................................................................................................................................. 69
9.1
9.2
9.3
CITED REFERENCES ........................................................................................................................ 69
RELEVANT WEBSITES ..................................................................................................................... 72
CORRESPONDENCE .......................................................................................................................... 74
viii
List of Figures
Figure 1. The terms ∂ρ/∂T and ∂ρ/∂S as functions of T and S. The solid lines are lines of
constant density. The dashed lines are contour lines of ∂ρ/∂T or ∂ρ/∂S. ................... 4
Figure 2. Horizontal profiles of surface temperature, surface salinity, surface density, and
the changes in density due to changes in T and S between stations along the
Bonavista transect in November 2001. The variable σt ≈(ρ - 1015)/1000. Figure 1
indicates that density is almost independent of temperature when the temperature is
low............................................................................................................................... 4
Figure 3. Dielectric loss factor or sea water (S=32.54 psu) and pure water as a function
of the frequency, for T=0°C and 20°C (Ulaby et al., 1986). ...................................... 7
Figure 4. Spectral variation of the salinity sensitivity at T=293K or 20°C (Ulaby et al.,
1986). .......................................................................................................................... 7
Figure 5. The variation of brightness temperature with salinity for a number of
temperatures computed with the Klein and Swift (1977) model. ............................... 8
Figure 6. Attenuation coefficients of water clouds as a function of temperature (Ulaby et
al., 1986). .................................................................................................................. 12
Figure 7. Attenuation coefficient of ice clouds as a function of temperature (Ulaby et al.,
1986). ........................................................................................................................ 12
Figure 8. Brightness temperature as a function of surface wind speed, for surface salinity
of 36 psu and surface temperature of 5°C. x indicates HH polarisation, while o
indicates VV. Parameters from Hollinger and Lo (1981). ..................................... 14
Figure 9. Top of atmosphere brightness temperature as a function of surface salinity for
wind velocity of 10m s-1 and surface temperature of 5°C, at 1.4 GHz. x indicates
HH polarisation, while o indicates VV. Goodberlet-Swift model . ......................... 14
Figure 10. Vertical salinity profile for a station off the Queen Charlotte Islands on July 1,
1997........................................................................................................................... 24
Figure 11. Global annual mean surface salinity (PSS), from the WOA98F database ..... 24
Figure 12. Winter mean surface salinity (PSS), from WOA98F. .................................... 26
Figure 13. Annual mean surface salinity (PSS) in the Pacific Ocean, from WOA98F. .. 26
Figure 14. Global long-term mean surface salinity (psu) from LEVITUS94.................. 29
Figure 15. January to March global mean surface salinity (psu), from LEVITUS94. .... 29
Figure 16. Annual mean surface salinity (psu) in the Pacific Ocean, from LEVITUS94.
................................................................................................................................... 30
Figure 17. Annual mean surface salinity (psu) in the Gulf of Alaska from LEVITUS94.
................................................................................................................................... 30
Figure 18. Location of temperature-salinity profiles acquired by MEDS over the global
ocean, for 1998. The colour scale indicates the number of observations. ............... 32
Figure 19. Samples distribution and count of surface salinity data archived in the
Computer Atlas of the NW Atlantic. ........................................................................ 33
Figure 20. Distribution and count of samples collected in May of all years between 1900
and 1997.................................................................................................................... 34
Figure 21. Map of the British Columbia coast showing the location of the lighthouse
stations. ..................................................................................................................... 35
Figure 22. General geographic location of the stations along the P-Line........................ 36
ix
Figure 23. Annual cycle of surface salinity from 1956 to the present ............................. 36
Figure 24. Average salinity based on all years from the Computer Atlas of the NW
Atlantic...................................................................................................................... 38
Figure 25. The horizontal distribution of surface salinity over the Grand Banks of
Newfoundland and the Northeast Newfoundland Shelf during the period of AugustNovember 2000. (Figure courtesy of E.B. Colbourne, Northwest Atlantic Fisheries
Centre, DFO)............................................................................................................. 39
Figure 26. The horizontal distribution of surface salinity anomalies over the Grand Banks
of Newfoundland and the Northeast Newfoundland Shelf during the period of
August-November 2000. (Figure courtesy of E.B. Colbourne, Northwest Atlantic
Fisheries Centre, DFO). ............................................................................................ 40
Figure 27. Strait of Georgia and Gulf Islands; Salinity and chlorophyll data collected
from a ferry on March 23, 2001................................................................................ 42
Figure 28. Salinity horizontal profile from Research Vessel ‘Tully’, February 2001..... 42
Figure 29. Horizontal profiles on the North West coast of Vancouver Island, in 1979. . 43
Figure 30. Stations where the seasonal cycle in SSS was computed............................... 45
Figure 31. Upper panel: The seasonal cycle in surface salinity at Station 27 (see Figure
25 for location). The vertical bars denote the variability around the long term mean.
Lower panel: August anomalies in surface salinity at Station 27. The anomalies are
computed relative to the 1971 to 2000 mean. ........................................................... 46
Figure 32. Surface salinity at Ocean Weather Station Bravo in the North Atlantic. ....... 48
Figure 33. The path of the Great Salinity Anomaly (from Belkin, et al., 1998).............. 48
Figure 34. Strait of Georgia; Annual cycles of monthly mean salinity. .......................... 50
Figure 35. Amphitrite Point; Annual cycle of monthly mean salinity............................. 50
Figure 36. Langara Island; Annual cycle of monthly mean salinity................................. 50
Figure 37. Amphitrite Point; Monthly average surface salinity anomalies. .................... 51
Figure 38. Langara Island; Monthly average surface salinity anomalies. ....................... 51
Figure 39. Surface salinities in the vicinity of Station Papa from 1956 to 1996 ............. 53
Figure 40. Annual cycle of surface salinity at Station Papa from data in Figure 39. ...... 53
Figure 41. Residual anomalies in surface salinity data at Station Papa, from 1956 to
1996........................................................................................................................... 54
Figure 42. Monthly surface salinity anomalies at Station Papa, from 1980 to 2000....... 54
Figure 43. The Southern Oscillation Index plotted for 1979-1998.................................. 55
Figure 44. Detrended monthly surface salinity anomaly at Papa station plotted with the
Southern Oscillation Index. ...................................................................................... 55
Figure 45. Relationship between dissolved organic material concentration (OD350) and
surface salinity in Barkley Sound, April 1987.......................................................... 59
Figure 46. Correlation between Gelbstoff (OD400) and sea surface salinity in
Atchafalaya River, March 2001................................................................................ 59
Figure 47. Sea surface salinity distribution maps estimated from SeaWiFS water colour
acquired on February 24, 1999. ................................................................................ 60
Figure 48. Brine salinity in sea ice as a function of negative temperature (Ulaby et al.,
1986) ......................................................................................................................... 63
Figure 49. Dielectric constant of liquid brine as a function of frequency (Ulaby et al.,
1986). ........................................................................................................................ 63
x
Figure 50. Calculated penetration depth in pure ice and in first year and multi-year sea
ice (Ulaby et al., 1986). ............................................................................................ 64
Figure 51. Brightness Temperature evolution from open water to 15 cm thick young ice.
Nadir angle=50°. Just before the 20th hour the high brightness values are reached at a
time when the ice became optically thick; with the L-band the ice was then 4 to 5 cm
thick (Grenfell et al., 1998)....................................................................................... 65
Figure 52. Brightness temperatures at L-band as a function of ice thickness (Ulaby et al.,
1986, sea ice parameters used in simulation; CRESTech radiometer simulation
model). ...................................................................................................................... 66
Figure 53. Simulation of L-band brightness temperature for an ice-covered ocean. The
assumption is made that the ocean fraction could have variable salinity. ................ 66
xi
List of Tables
Table 1. Typical brightness temperature contribution from space and atmosphere, for
nadir observations (Blume et al., 1978).................................................................... 11
Table 2. Airborne microwave radiometers and some of their characteristics.................. 17
Table 3. Airborne deployments that used passive L- and S-band sensors....................... 18
Table 4. Summary of activities and publications related to space borne passive
microwave remote sensing missions for sea surface salinity mapping..................... 20
Table 5. Technical and performance specifications of passive radiometry satellite
missions currently in progress. Sources: HYDROS - HYDROS web site;
AQUARIUS - Koblinsky et al. (2001); SMOS - Kerr (1998). ................................. 21
Table 6. Search results for oceanographic profiles in the NE Pacific, 1980 to 2000. ..... 27
Table 7. Example of annual mean salinity data (psu) from LEVITUS94 ....................... 28
Table 8. Sea Surface Salinity Data Statistics ................................................................... 43
Table 9. Time series statistics for selected stations of the Canadian eastern seaboard. .. 44
Table 10. Time Series Statistics for three British Columbia lighthouse stations and
Station Papa. ............................................................................................................. 49
Table 11. Summary of scientific requirement for the study of surface circulation patterns.
................................................................................................................................... 56
xii
1.0
Introduction
Ocean temperature and salinity have been routinely measured since the earliest
oceanographic expeditions of the 19th century, and they comprise the two most basic
physical variables that define the state of the ocean. For the past 30 years, sea surface
temperature (SST) has been monitored operationally from space, but the concomitant
capability to measure sea surface salinity (SSS) has not existed. This is a critical
shortcoming. Indeed, SSS (as well as subsurface salinity) has never been observed for
42% of the world ocean1 and has been observed fewer than four times over the past 125
years for 88% (Levitus et al.,1998).
On the other hand, the potential for measuring SSS with passive L-band microwave
radiometers is well known and was proved in the 1970s and 80s in airborne and
spaceborne (Skylab) experiments. Now, proposals for a passive microwave satellite are
before both ESA and NASA with the goal of monitoring SSS and/or its terrestrial
counterpart, soil moisture (SM). These proposals all aim for a 2006 launch date.
The Canadian Space Agency Earth and Environment Applications Program has funded
the authors of this report to study developments in space-based remote sensing of sea
surface salinity. The broad project goals are to define Canadian interests relative to
various proposals for salinity satellites and to identify and promote the widest range of
Canadian benefits and involvement in measuring ocean salinity from space. Particular
objectives are to provide the necessary scientific background to enable Canadians to
participate in the planning of salinity sensor systems and to identify partnering and
Canadian industry opportunities for the Canadian Space Program. The purpose of this
first report is to review recent developments in the remote sensing of salinity and to place
these developments in a Canadian context. In doing so, a number of issues are addressed,
including:
• What significance does the routine remote sensing of SSS hold for Canadian
science and resource management?
• What special issues must be addressed for SSS remote sensing in Canadian
waters, which are generally cold, are fed by low salinity estuarine runoff, and are
sometimes ice covered?
• What can Canada contribute to the remote sensing/physical-biological
oceanographic community?
For the purposes of this document, it is useful to partition the ocean into two zones, the
coastal and the offshore. In the Canadian Atlantic zone, the coastal zone is conveniently
defined to extend from the coast to the continental slope, a distance ranging from about
100-400 km. In the Pacific, where the continental shelf is narrow, we define the coastal
zone to be that region within which the effects of large rivers (e.g. the Fraser and
Columbia) runoff are significant, i.e. about 200 km from the coast. Almost all of the
1
Based on surface area and a 1° latitude by 1° longitude observational grid.
1
documentation supporting the salinity satellite missions have focused on the offshore
regions and deep-sea oceanographic processes including climate.
1.1
Why Sea Surface Salinity?
From an oceanographic viewpoint, salinity is important because:
• Together with temperature, it determines water density and thus is intimately
linked to the horizontal circulation of the ocean.
• It determines the depth to which water, cooled at the surface in winter, can sink.
That is, it is an important component of the thermohaline circulation and is thus
directly linked to the dynamics of the earth’s climate.
• Through its effect on density, it partially controls the stability of the upper mixed
layer of the ocean. This in turn has important physical and ecological
consequences.
• It is one of the prime determinants of the environment in which fish and other
marine life live.
• It modulates air-sea interaction including gas and heat exchange.
We now consider these in more detail.
The density of seawater is important for at least three reasons. First, horizontal
differences in density generate ocean currents. Second, the vertical distribution of density
determines the vertical stability of the ocean and governs the degree to which forcing at
the sea surface by winds and heating and cooling is felt by subsurface waters. Third, the
vertical density distribution also determines the depth over which convection due to
cooling can take place.
The density of seawater depends on its temperature and salinity through the equation of
state. Within the ocean’s offshore temperate zone (approximately equatorward of 50°),
temperature is by far the most important variable. However, in coastal waters that
experience large fresh water discharge or in waters at low temperatures, salinity is
significant and can, in fact, dominate temperature effects. Such conditions are typical of
Canadian waters and have received relatively little discussion in the documents
supporting SSS satellite missions.
To illustrate the influence of salinity on density, consider Figure 1 which displays the
dependence of density (ρ) on temperature (T) and salinity (S) as a function of T and S.
At low temperatures, ρ is almost T-independent; by contrast ∂ρ/∂S shows little
temperature dependence. Figure 2 further illustrates the importance of salinity with an
example from the Canadian Atlantic zone. The three upper panels show horizontal
profiles of surface temperature, salinity, and density [through the variable σt ≈ (ρ 1015)/1000] measured in November 2001 along the Bonavista transect (see Figure 25 for
location). The lowest panel shows the change in density between stations due to the
respective changes in temperature and salinity. It is apparent that salinity plays an
important role in determining the horizontal density gradient and thus in forcing the
horizontal circulation over the shelf.
2
Through its effect on density, surface salinity also indirectly determines the depth of
penetrative convection in high latitudes. In the autumn and winter when surface waters
are cooled to near-freezing temperatures, the salinity governs the ultimate density and
thus determines the depth to which it will sink. As such, SSS plays a critical role in the
formation of the intermediate and deep ocean water masses that feed the thermohaline
conveyer belt; SSS is thus directly linked to the climatic cycle. In the sub-polar North
Atlantic, interannual variations in SSS modulate the thermohaline circulation, and
intrusions of low salinity water can prevent the formation of deep water (see the
discussion of the Great Salinity Anomaly in Section 4.2.1).
Salinity is also one of the prime determinants of the ecosystem within which fish and
other marine life live, and it is recognized as a significant factor in fisheries management
and stock prediction. In addition, year-to-year and annual variations in sea salinity are
important variables in ocean productivity forecast models, especially as they affect the
depth and stability of the surface mixed layer. The annual cycle of primary productivity
in most Canadian waters is linked directly to the pumping of nutrients from deeper waters
into the generally nutrient-poor mixed layer. If salinity is relatively low, the mixed layer
will be more stable, and the nutrient pump may be partially inhibited, possibly leading to
reduced productivity or a delay in the onset of spring and autumn phytoplankton blooms.
Finally, salinity plays a potentially important role in the air-sea exchange of gases. In the
tropical ocean, heavy precipitation can create pools of relatively fresh water that locally
increase the stability of the upper layer and lead to significantly reduced rates of gas
transfer across the pycnocline (i.e. the base of the mixed layer). Similarly, current
atmospheric circulation models used in global and regional climate studies ignore the
influence of salinity on the saturation vapour pressure of seawater. However, SSS may
have a measurable influence on the surface specific humidity gradient, thus affecting
computed evaporation rates.
3
T (d e g C )
Figure 1. The terms ∂ρ/∂T and ∂ρ/∂S as functions of T and S. The solid lines are lines of
constant density. The dashed lines are contour lines of ∂ρ/∂T or ∂ρ/∂S.
8
6
4
2
0
50
100
150
200
250
300
350
400
450
200
250
300
350
400
450
S (p s u )
36
34
32
30
σ
t
26
25
0 .4
| (∂ ρ / ∂ S ) δ S |
| (∂ ρ / ∂ T) δ T |
0 .2
0
0
50
100
150
d is ta n c e o ff s h o r e ( k m )
Figure 2. Horizontal profiles of surface temperature, surface salinity, surface density, and
the changes in density due to changes in T and S between stations along the Bonavista
transect in November 2001. The variable σt ≈(ρ - 1015)/1000. Figure 1 indicates that
density is almost independent of temperature when the temperature is low.
4
2.0
Remote Sensing of Sea Surface Salinity
This section presents the principles of remote sensing of sea surface salinity. In particular
we include the space and airborne experiments in sea surface salinity retrieval.
Information currently made available on designs and status of proposed sea surface
salinity missions is also presented.
2.1
Microwave Emission from the Ocean
All matter emits electromagnetic radiation as a result of the thermally induced random
motion of atoms. An object that is a perfect absorber and emitter of electromagnetic
radiation at all frequencies is defined as a blackbody. According to Planck's radiation law,
the spectral brightness Bνbb (Wm-2sr-1Hz-1) of a radiating blackbody is:
Bνbb =2hν3c-2[1/(ehν/kT-1)]
where:
h is Planck's constant (6.63 x 10 -34 J s ),
ν is the frequency (Hz),
k is Boltzmann's constant (1.38 x 10-23 J K-1),
T is the absolute temperature (K), and
c is the speed of light (3 x 10 8 m s-1).
(1)
For most purposes, the microwave region is defined as the interval from about 1 to 300
GHz (corresponding to wavelengths of 20 - 0.1 cm). For the ocean at microwave
frequencies we have T > 270 K and so hν << kT. In this regime, Planck's law can be
approximated by the Rayleigh-Jeans formula (Kraus, 1966):
Bνbb =2ν2kc-2T
(2)
This approximation is useful because it allows one to express the signal observed by a
radiometer in terms of the physical temperature of the observed scene.
However, most natural objects are not perfect blackbodies. The ratio of the spectral
brightness of an object to that of a blackbody at the same temperature defines the
emissivity ε of that body:
ε = Bν/ Bνbb
(3)
The emissivity is dependent on the surface dielectric properties, composition, object's
shape and surface roughness. For "grey bodies", we may define a brightness temperature
TB through
Bν= ε Bνbb = 2ν2kc-2TB
(4)
5
Note that TB is always smaller or equal to the object's physical temperature.
Detection of the variations in brightness temperatures of bodies at the same physical
temperature enables one to differentiate between types of material, such as fresh and salt
water, or land and ocean surfaces.
When considering seawater as the target, we note that the emissivity of seawater varies
with salinity, temperature, and the surface texture (i.e., roughness and foam coverage).
For a smooth surface, the ocean can be represented by a flat, infinite, half-space. In this
approximation the emissivity is given by:
εH,V = 1 – RH,V
(5)
Here the subscripts H and V refer to horizontally or vertically polarised radiation while
RH,V are the Fresnel reflection coefficients:
RH = {[cosθ i-(ε-sin2θi)1/2 ] / [cosθ i +(ε-sin2θ i ) 1/2 ]}2, and
(6)
RH = {[εcosθ i-(ε-sin2θ i)1/2 ] / [εcosθ i +(ε-sin2θ i)1/2 ]}2 ,
(7)
where:
ε is the complex dielectric constant (in this case of seawater), and
θi is the angle of incidence.
ε is given to sufficient accuracy by the Debye (1929) equation,
ε = ε ∞+(εs ε∞)/(1+iωτ)-iσ/ωε0
= [ε ∞+(εs ε∞)/(1+ω2τ2)] –i [σ/ωε0 + (εs ε∞ ωτ)/(1+ω2τ2)]
where:
(8)
ω= 2πν is the radian frequency,
ε∞ is the dielectric constant at infinite frequency,
εs is the static dielectric constant,
τ is the relaxation time,
σ is the ionic conductivity, and
ε0 is the permitivity of free space.
It is seen that the imaginary part of the complex dielectric constant contains a term
directly proportional to the conductivity and hence is strongly dependent on the salinity.
However, the relationship is actually more complex as εs, σ, and τ are all functions of the
temperature and salinity. For given values of salinity and temperature, the emissivity of
seawater can be calculated using the model of Klein and Swift (1977). Figure 3
demonstrates that the dielectric loss of seawater and pure water are nearly identical at
frequencies above 5 GHz for 0°C surface temperature, and above 11 GHz for 40°C
surface temperature. Figure 4 shows that the brightness temperature for vertically
6
polarised radiation is significantly more sensitive to salinity variations than for
horizontally polarisation. Figure 5 displays the dependence of brightness temperature on
salinity for a number of different temperatures and for two frequencies (L band at 1.43
GHz and S band at 2.65 GHz). The dependence is essentially linear and is clearly much
greater at the lower frequency. From these considerations, Ulaby et al. (1986) concluded
that salinity remote sensing is best done with vertically polarised radiation with
frequencies less than 5 GHz.
Figure 3. Dielectric loss factor or sea water (S=32.54 psu) and pure water as a function
of the frequency, for T=0°C and 20°C (Ulaby et al., 1986).
Figure 4. Spectral variation of the salinity sensitivity at T=293K or 20°C (Ulaby et al.,
1986).
7
Figure 5. The variation of brightness temperature with salinity for a number of
temperatures computed with the Klein and Swift (1977) model.
2.2
Passive Microwave Radiometry
Instruments designed to measure thermally induced electromagnetic energy are called
radiometers. A typical microwave radiometer consists of three units:
1) an antenna subsystem for receiving radiation,
2) an electronic subsystem for detecting and amplifying the received signal within
a specific frequency band, and
8
3) a control and data processing subsystem to process and record radiometric data
(Ulaby et al., 1986; Skou, 1989).
The performance of microwave radiometers is measured in terms of their sensitivity at
different wavelengths, gain stability, absolute calibration accuracy, angular resolution,
beam efficiency, and stability of the instrument spin rate. Properties of a passive
microwave radiometer include the angular resolution (9) and the surface resolution (10).
Θr ≈λ/L
(9)
r = zΘr = λz/L
where:
(10)
Θr is the angular resolution,
λ is the wavelength,
L is the antenna length,
r is the surface resolution, and
z is the altitude above sea level.
A spaceborne radiometer pointing towards the ocean measures a radiation field that is
made up of microwave energy emitted by the ocean, energy emitted by the atmosphere,
and external energy reflected from the sea surface. To good approximation for a smooth
sea, the radiative transfer equation describing the radiation field is given by
TB(θ) = ε t(θ) Ts + sec(θ) ∫0h TA(z) α(z) t(z,h,θ) dz
+ (1- ε) t(θ) sec(θ) ∫0h TA (z) α(z) t(θ,0,z) dz + (1 - ε) t2(θ) Text
where
(11)
θ is the viewing angle relative to the vertical;
t(a,b,θ) = exp{- sec(θ) ∫ab α(s) ds} is the atmospheric transmittance from
height a to height b;
t(θ) = t(0,h,θ) is the total atmospheric transmittance from the sea surface
to the satellite;
α(z) is the atmospheric attenuation due to absorption and scattering;
(1 - ε) is the reflectance from the sea surface;
TB is the brightness temperature observed at the radiometer,
TA(z) is the height dependent temperature of the atmosphere;
Ts is the sea surface temperature; and
Text is the external radiation from space.
The respective terms in (11) represent radiation emitted from the ocean and attenuated by
atmospheric absorption and scattering, radiation upwelled from the atmosphere,
atmospheric radiation reflected from the sea surface, and external radiation reflected from
the sea surface and attenuated on its downward and upward paths.
9
The external brightness temperature contains contributions from the isotropic cosmic
background, galactic radiation, and the sun,
Text = Tcos + Tgal + Tsun
(12)
In order to extract the sea surface brightness temperature, and hence to estimate the sea
surface salinity, it is necessary to account for all the other contributions to TB. This is
clearly a complex task and requires models of the various components. The validation of
these simulation models is crucial because spaceborne sensors provide observations for
areas and weather conditions that cannot be ground-truthed by airborne surveys. Existing
spaceborne data should be used to extract information on variables such as precipitation
intensity and wind velocity, then to account for their effect in sea surface salinity
estimations.
2.2.1 Environmental Factors Affecting TB
2.2.1.1
Contributions from Space and Sun
The Cosmic radiation term in (12) is isotropic and is represented by a constant brightness
temperature contribution of 2.7 K (Table 1). Since it is constant, it does not affect
measurement accuracy.
The Galactic radiation term in (12) originates in our galaxy and is strongest in the
direction of the Milky Way. Its magnitude varies from 0.8 - 16°K between the galactic
pole and centre. An average value of 0.98°K is generally used to represent the galactic
noise for budget estimations in the atmosphere brightness temperature calculation.
Galactic noise contributions may be minimised by selecting an appropriate satellite orbit
or by mapping galactic noise contributions and correcting radiometer measurements.
The Sun glint term in (12) can dominate the radiation upwelling from the ocean surface
and therefore must be avoided through appropriate orbit selection (e.g. morning sunsynchronous). However, when the sea surface is rough, sunlight will be scattered in all
directions. This indicates the requirement for a near-dawn or dusk orbit.
10
2.2.1.2
Atmosphere Interactions
Microwave radiation is attenuated by absorption and scattering as it propagates through
the atmosphere, and its plane of polarisation is rotated as it interacts with the ionosphere.
Absorption and scattering occur mainly in the lower atmosphere (troposphere) and are
primarily due to oxygen, water vapour, and water droplets. The oxygen concentration is
approximately constant in the atmosphere and the oxygen absorption coefficient is
known, so it is relatively straightforward to correct for it. On the other hand, water
vapour content is highly variable. Fortunately, absorption by water vapour at L-band
frequencies is very small and is usually neglected.
Correcting for scattering by water droplets is more difficult. Limited data is available for
L-band observations, largely because airborne surveys are usually carried out under fair
weather conditions. For L-band frequencies, Rayleigh theory can be used to compute the
scattering by clouds and hydrometeors. Figure 6 and Figure 7 show attenuation
coefficients for several values of cloud temperatures associated with water and ice clouds,
respectively.
Table 1. Typical brightness temperature contribution from space and atmosphere, for
nadir observations (Blume et al., 1978).
Source of contribution to the brightness temperature
T2.65GHz (K)
T1.43GHz (K)
Cosmic Background, TCOS
2.7
2.7
Galactic Radiation, TGAL
0.2
1.0
Downward atmospheric temperature, TDN
2.2
2.1
Beam efficiency corrections
0.4
0.14
Wind speed corrections (surface roughness)
1.1
0
0.0091
0.008
5.2
4.2
-
-
Total atmospheric opacity
Typical total correction
Sun (not yet determined)
The plane of polarization of a signal emitted from a target within a radiometer’s field of
view will undergo Faraday rotation before reaching the radiometer. The degree of
rotation depends on the locations of the emitting object and the radiometer, as well as on
the orientation of the Earth's magnetic field. The magnitude of the rotation is estimated
to be 8.7° at a frequency of 1.4 GHz (Hollinger and Lo, 1981; Skou, 2001).
11
Figure 6. Attenuation coefficients of water clouds as a function of temperature (Ulaby et
al., 1986).
Figure 7. Attenuation coefficient of ice clouds as a function of temperature (Ulaby et al.,
1986).
12
2.2.1.3
Effect of Sea State: Waves, Breaking Waves and Foam
The emissivity of a smooth ocean surface can be computed using (5) – (8) together with
additional equations for εs, τ, and σ. For a surface roughened by the wind, one must use
empirically derived relationships. In addition, for wind speeds higher than 7 m s-1, the
emissivity of surface foam becomes significant.
Figure 8 presents the modelled top of atmosphere brightness temperature at L-band, as a
function of the ocean surface wind speed, based on parameters from Hollinger and Lo
(1981).
Figure 9 presents the Goodberlet-Swift simulation of the top of atmosphere brightness
temperature as a function of surface salinity, for wind velocity of 10 m s-1 and surface
temperature of 5°C, at 1.4 GHz. Simultaneous observations with an optical sensor,
sensitive to white caps, foam patches, and streaks are required to correct for wind effects.
An alternative solution is to use a dual frequency radiometer system that allows for the
uncoupling of the wind effects. Current spaceborne sensors such as SSM/I,
RADARSAT, and ERS-SAR can also be used to correct for wind effects.
Stogryn and Cardone (in Ulaby et al., 1986) modelled the emissivity of the sea surface
foam. Aircraft observations were used to derive equations that relate the fraction
coverage of the sea surface with foam and ocean surface wind speed. However, these
equations were not validated.
The Virtual Centre for Ocean Satellite Salinity (VCOSS; whose executive members are
the authors of this report) are developing a simulation model for L-band brightness
temperature based on the radiative transfer codes developed by Hollinger (1971) and
modified by Rubinstein (1996). The effects of weather conditions such as wind induced
sea surface roughness and foam, as well as precipitation will be accounted for by some of
the modules in the simulation model. The simulation model will be used to establish the
uncertainty in sea surface salinity induced by the errors in the sea surface temperature
input. In addition, sun-glint and galactic contribution to the TOA radiance will be
calculated using sea surface reflectance properties. The simulation model will contain the
following four modules:
•
•
•
In the scenario initialisation the user may select meteorological conditions and
surface state. Variables and their range of variability are specified. For example,
the user will have a choice of selecting the air temperature, the surface
temperature, the wind speed, and the wind direction. Using wind speed
information, the foam fraction is calculated.
The specular reflectance subroutine will compute the dielectric constant for a
given salinity and temperature, based on the Klein and Swift (1977) model. The
specular reflectance module also calculates the Fresnel vertical and horizontal
reflectivity coefficients.
The surface roughness and foam module will estimate the roughness effects using
a two-scale model, and the contribution of emission from sea surface foam.
13
•
The TSKY module will account for the space, sun, and atmospheric effects. It
includes the estimation of galactic contributions, sun-glint effects, atmospheric
attenuation, and atmospheric brightness temperature.
Figure 8. Brightness temperature as a function of surface wind speed, for surface salinity
of 36 psu and surface temperature of 5°C. x indicates HH polarisation, while o indicates
VV. Parameters from Hollinger and Lo (1981).
Figure 9. Top of atmosphere brightness temperature as a function of surface salinity for
wind velocity of 10m s-1 and surface temperature of 5°C, at 1.4 GHz. x indicates HH
polarisation, while o indicates VV. Goodberlet-Swift model .
14
2.3
Review of Spaceborne and Airborne Experiments
Microwave remote sensing was first applied in the 1930s by radio astronomers to observe
and measure extraterrestrial sources of electromagnetic energy. In the late 1950s,
radiometers recording in the 4.3 cm wavelength band (6.98 GHz) were designed to
measure solar temperature and atmospheric attenuation. These measurements were also
used for the observation of terrestrial targets such as grass, asphalt, and water (Straiton et
al., 1958).
2.3.1 Remote Sensing of Sea Surface Salinity in Canada
Universities, government agencies, and private companies conduct Canadian research and
development activities relevant to remote sensing of sea surface salinity. Currently, three
Canadian universities host research groups on passive microwave remote sensing. Sea
ice and snow properties are being studied at the University of Manitoba and the
University of Sherbrooke. Applications to soil moisture mapping are being developed at
the University of Guelph and the University of Sherbrooke.
The University of Guelph formed, with the Centre for Research and Space Technology
and the Department of Agriculture, a research group to support the development of a
RADARSAT soil moisture algorithm. Data collected for these projects included in situ
measurements at agricultural sites in Ontario using a track mounted L-band radiometer
and L- and C-band radar systems. An airborne survey was also flown in order to test
coarser scale capabilities of the microwave sensors. This involved the deployment of the
step frequency radiometer, SLFMR, over agricultural fields in Ontario and Quebec. The
Compact Airborne Scanning Imager, recording in the visible and near infrared range, was
also mounted on the aircraft. This allowed the research group to test the synergy between
optical and microwave sensors and to evaluate the effects of different land surface types
on passive microwave observations. An analysis of the data indicated that soil moisture
retrievals can be made by using L-band radiometry. Xu et al. (1998) published the results
of these surveys. In addition, dry-down experiments were conducted at the University of
Guelph during the 1999 summer season. Unfortunately, due to the funding limitations,
research on L-band radiometry was discontinued at University of Guelph.
Environment Canada owns and operates several very-low and high frequency radiometers
for in situ and airborne campaigns. The 1.4 GHz and 5 GHz radiometers were acquired
recently and are still undergoing calibration testing. Other sensors record in the 18 GHz
to 90 GHz band and are used to study clouds, sea ice, and snow. Most of these sensors
were acquired 6 to 10 years ago. However, because of calibration or other technical
problems, very little information was published on the analysis of the observations.
An evaluation of publications related to the status of radiometry applications for the
monitoring of ocean salinity indicates that only prototype retrieval algorithms exist. As
well, these prototypes are specific to their respective studies. A consistent method for
15
accurate derivations of the ocean surface salinity must be developed to test and evaluate
L-band sensor capabilities. This is a research subject of particular interest for Canadian
university networks such as the Geomatics for Informed Decisions Network (GEOIDE),
as well as individual research centres.
Numerous industrial groups are working on sensor development and microwave antenna
design. For example, EMS is building a L-band synthetic aperture antenna and ComDev
is developing up- and down-link spaceborne communication sensors. In addition, several
Canadian government and private agencies are focusing on building and setting up
receiving stations (e.g. MDA, CCRS), developing signal processing methods (e.g. MDA,
AUG Signals, RSI) and planning efficient data delivery to the user (e.g. CCRS, RSI,
MEDS).
2.3.2 Spaceborne Experiments
In 1962, Mariner 2 carried the first spaceborne passive microwave instrument. During
that mission, data were recorded with a two-channel radiometer sensitive to the
wavelengths of 1.35 cm (2.22 GHz) and 1.9 cm (1.58 GHz). In 1968 and 1970, passive
microwave observations of the Earth were made with four radiometers on the Russian
Cosmos satellites. In 1973 and 1974, a 1.4 GHz radiometer was mounted on the US
Skylab-4 (Lerner and Hollinger, 1977). The Cosmos data acquisition lasted only a few
days and was not sufficient for evaluation of the radiometer sensitivity to sea-surface
temperature and salinity variability. The Skylab radiometer measurements provided data
that confirmed the theoretical dependence of brightness temperature on salinity and sea
surface temperature at the 1.4 GHz frequency.
In 1978, a team of US scientists prepared a report describing the potential applications of
spaceborne passive microwave systems. This team recommended the development of an
imaging multi-frequency passive radiometer sensitive to the 1.4 to 185 GHz range, with a
spatial resolution of about 5 km, a full orbital cycle of 6 to 12 hours, and a 3 to 4 m
antenna.
At present, none of the spaceborne passive microwave radiometers has an antenna larger
than 2 m. This size antenna limits applications to about 100 km resolution (21 cm
wavelength). The sensors working in the 19 to 150 GHz frequency range permit the
monitoring of oceanic and atmospheric variables such as ocean surface wind speed, sea
surface temperature, ice cover type and concentration, atmospheric water vapour and
droplets density, and atmospheric temperature profiles. One of the terrestrial applications
gaining the acceptance of user groups is the monitoring of snow accumulation and
depletion. The most recent research and development activities in the application of
microwave radiometry involve the measurement of precipitation rates, ocean surface
salinity, and soil moisture.
16
2.3.3 Airborne Experiments
Airborne observations are the main data source used in evaluating passive microwave
radiometers for estimating sea surface salinity. Experiments started in the early 1970’s
(Thomann 1973, 1976; Armand et al., 1979). More recently, experiments are being
conducted at research facilities located in the University of Massachusetts at Amherst, the
NASA’S JPL and GSFC laboratories, as well as at the Naval Research Laboratories
(Table 2).
Currently, only a few airborne salinity sensors are being used or developed for salinity
measurements. A majority of the applications have been made in the last 7 years (see
Table 3), starting with Scanning Low Frequency Microwave Radiometer (SLFMR)
commissioned over the Chesapeake Bay and Delaware Bay areas. The 1996 mission,
part of the Chesapeake Outflow Plume Experiment sponsored by NOAA and NRL
(Goodberlet et al., 1997), produced the first sea surface salinity map (Lagerloef, 1998;
Miller et al., 1998).
The SLFMR was also flown over Charleston Harbor, South Carolina and the nearby
coastal zone. Miller (2000) produced a 2 km resolution salinity map of Charleston
Harbor and salinity transects for the coastal waters North and South of Charleston. The
salinity retrievals have yet to be verified with in situ data.
Table 2. Airborne microwave radiometers and some of their characteristics.
Instrument
ESTAR
SLFMR
PALS
Developer
GSFC-NASA
Univ. of Massachusetts
Quadrant Engineering
JPL-NASA
System
Configuration
Electronically
Scanning Thinned
Array Radiometer
Pushbroom
-
Coastal, Gulf Stream
Various sites
3 V-polarised beam
3 H-polarised beam
Step-frequency radiometer
used by NOAA and NRL
(Miller et al., 1998)
Passive and
active
L- and S-band
Area of Interest Gulf Stream
Technical
Specifications
of Interest
Imaging with
synthetic aperture
techniques;
2-channel correlation
radiometer connected
to two mobile
antennas mounted on
a positioner and
operating as a two
element
interferometer
17
Table 3. Airborne deployments that used passive L- and S-band sensors.
Sensor
Over Flight Location
ESTAR
Gulf Stream
PALS
1999 Southern Great Plains Experiment (SGP99)
SLFMR
Date
1999
July 1999
2001 Soil Moisture Experiments
In Progress
Chesapeake Bay and Delaware Bay.
April 1994
Charleston Harbor
November 1996
STAR-Light
Planned for Arctic land-surface hydrology investigation.
2003-2004
L- and Cband
radiometers
Arctic Ocean from Thule, Greenland
July 2000.
Salinity data and simultaneous in situ data were obtained with the ESTAR airborne
radiometer in 1999. Howden and LeVine report the preliminary results of these surveys
in Lagerloef (2000).
In 1999, the Passive/Active L/S-band Airborne Sensor (PALS) was used for sea surface
salinity test flights. The PALS and ship track data were recorded on the same day
(although recorded 10 km apart). Results of this experiment are presented in Lagerloef
(1998).
Publications reporting airborne data experiments for sea surface salinity mapping do not
include the retrieval algorithms. Given the information provided, one can assume that a
look-up table approach or sets of regression equations are used. Sea surface temperatures
are always measured in those surveys and theoretical values were used to correct for the
atmospheric contributions and wind speed effects. Therefore, the sea surface salinity can
be derived as a function of the brightness temperature and the sea surface temperature.
However, a dual frequency radiometer recording in the 1.4 GHz and 2.65 GHz
frequencies will eliminate the need for simultaneous sea surface temperature data,
whereby theoretical or empirical equations can be used to derive sea surface salinity
values (Blume et al., 1978).
Experiments in an open ocean environment have been limited to the Gulf Stream, where
NASA deployed the ESTAR and the SLFMR in 1999. The Gulf Stream represents an
area of choice for the testing of remote sensing of sea surface salinity. This is because
over a distance of about 300 km, from near shore bays to the Sargasso Sea, the salinity
variation may exceed 20 psu. For the 1999 experiment, a NASA P3 aircraft was flown
over the continental shelf waters, the continental slope waters, and the Gulf Stream,
measuring the brightness temperature at 1.4 GHz with ESTAR and SLFMR. In addition,
the NASA C130 carried prototype L- and S-band radiometers built by JPL. The aircraft
18
measurements were validated using in situ measurements from the R/V Cape Henlopen,
the M/V Oleander, and three surface drifters. The aircraft also carried a pyrometer to
measure sea surface temperature and a scatterometer for wind speed. The results of these
experiments have not yet been made public.
2.4
Proposed Sea Surface Salinity Systems
Results from the most recent airborne experiments with ESTAR and SLFMR are
encouraging for potential spaceborne measurements. Indeed, the airborne experiments
indicate that space measurements could approach a SSS accuracy of 0.1 psu averaged
over a 300 km resolution cell for a period of one month. This level of accuracy would be
useful for climate studies.
The encouraging airborne results have prompted both American and European plans for
spaceborne salinity measurement. Presently, the spaceborne concept is being promoted
in the USA through the HydroSphere and Aquarius programmes and in Europe with the
Soil Moisture and Ocean Salinity Mission (SMOS) mission. SMOS is at the most
advanced stage of development with a mission planned for a satellite launch in 2006.
Instrumentation related to these spaceborne programmes is also under development. In
the United States, two instrument designs have received special NASA funding. The first
system, OSIRIS, was developed by the JPL. OSIRIS is a large mesh antenna system
aimed at attaining the highest possible SSS measurement accuracy. The system operates
with a conical scanner, uses L- and S-band radiometers, and reads H- and V-polarised
signals. In addition, a L-band radar system is designed to concurrently record energy
backscattered from the surface. This last component is meant to collect data for wind and
sea state correction. Pre-phase A studies are proceeding with the NASA Instrument
Incubator Program.
The second instrument design plan (so far unnamed) will be implemented by the GSFC.
This plan calls for a two-dimensional aperture system to measure soil moisture and sea
surface salinity.
A vast amount of information on the most recent advances in spaceborne sea surface
salinity remote sensing is regularly assembled in workshop reports of the Salinity Sea Ice
Working Group (SSIWG). The mandate of this research group is to evaluate both the
scientific merit and technical feasibility of measuring sea surface salinity using a
spaceborne remote sensing platform. In 1998, the first SSIWG workshop established
guidelines for the development of research avenues (Lagerloef, 1998).
The second SSIWG workshop focused on defining the scientific requirements for sea
surface salinity mapping. As well, the workshop outlined the technical support available
for the collection of in situ salinity data (Lagerloef, 1999). The third workshop report
(Lagerloef, 2000) provides an insight on the various in situ salinity data collection
experiments conducted in 1999 and concludes with a series of scientific requirements.
19
Currently, research activities are being directed to improving antenna design and
scanning mechanisms. As well, planning for satellite missions continue in research
centres in the U.S. and in Europe (see Table 4). Aquarius is the most recent NASA
remote sensing program for sea surface salinity.
Table 4. Summary of activities and publications related to space borne passive
microwave remote sensing missions for sea surface salinity mapping.
2005
Sponsor (s) or
Author
ESA
2001
ESA Pilot project
2001, Feb. 28
NASA
Oceanography
Scientific
Conference.
2000, Dec.
JPL/NASA
2000, Nov.
Njoku et al., 2000
2000, March
SSIWG
1999, July
USDA ARS
Hydrology Lab
1999, June
1999, June
SSIWG
ESA and CNES
1999, May 15
ESA
1998, Dec.
1998, Nov.
SSIWG
ESA and CNES
U. of Michigan,
NASA-GSFC, LRC, and others.
Year
1998
Activity
Planned launch of SMOS mission.
CASA plans to have a full demonstrator of
MIRAS finished.
The Aquarius Mission – Sea Surface Salinity
from Space. Talk presented by C. Koblinsky,
NASA-GSFC.
HydroSphere proposal development in progress
for submission to the NASA Earth System
Science Pathfinder (ESSP) program.
Published paper: A large-Antenna Microwave
Radiometer-Scatterometer Concept for Ocean
Salinity and Soil Moisture Sensing.
Report of the 3rd Workshop; Lagerloef, 2000.
Report by T.J. Jackson (1999): NASA Post-2002
Land Surface Hydrology Mission: Soil Moisture
Research Mission (EX-4)
Report of the 2nd Workshop; Lagerloef, 1999.
MIRAS-SMOS Phase A study approved by ESA
Report by NERSC: Study of Critical
Requirements for Ocean Salinity Retrieval using
a Low Frequency Microwave Radiometer. (ESA
report 98-S30)
Report of the 1st Workshop; Lagerloef, 1998.
SMOS space borne mission proposed to ESA
HYDROSTAR space borne mission rejected by
NASA ESSP program
20
HYDROS
Application
Radiometer Frequency
Soil Moisture
University of Michigan
and Goddard Space
Flight Centre
2006
3 years
Sun-synchronous at 670
km; ascending node at
0600 local time; 3 days
revisit
L-band radiometer; Lband radar
1.4 GHz
Radiometer Polarization
H, V
Agency
Planned Launch
Planned lifetime
Orbit
Instruments
Antenna Type
Incidence angle
Swath Width (km)
Spatial Resolution (km)
Single observation
error
(psu)
Conically scanning
parabolic
constant 40°
900-1000 km
40 km
AQUARIUS
SSS
SMOS
2006
2 years
SSS and Soil Moisture
European Space Agency and
Centre National d’Études
Spatiales
2006
3 years
Sun-synchronous, ascending
node at 0600 local time; 8 day
revist
Sun-synchronous at 755 km;
ascending node at 0600 local
time; 3 day revisit
L-band radiometer; L-band
scatterometer
1.4 GHz
L-band 2D interferometer
23.3°, 33.7°, 41.7°
250
70-90 km
1.4 GHz
H-H and V-V (cross-polarization
mode optional)
Thinned array,
Two-dimensional synthesis
15 - 50°
620 to 1050
35 to 50 km
0.43 at mid-latitude
1.2 in warm seas
H-H and V-V
parabolic
Table 5. Technical and performance specifications of passive radiometry satellite missions currently in progress. Sources: HYDROS
- HYDROS web site; AQUARIUS - Koblinsky et al. (2001); SMOS - Kerr (1998).
21
3.0
Salinity Databases
The importance of salinity in the oceanographic framework has been established in
previous sections. However, available salinity data must also be considered to place in
context the ocean salinity signals of importance to Canadian issues. As well, the
potential contribution of satellite based salinity measurements to the global data archives
should be established to indicate the importance of such measurements.
3.1
Global Databases
Global databases containing information on sea surface salinity are maintained through
the Intergovernmental Oceanographic Commission (IOC). The IOC, a component of
UNESCO, co-ordinates a network of data centres operated through the Intergovernmental
Oceanographic Data and Information Exchange system (IODE). The IODE, made up of
co-operating national data centres from United Nations member countries, distributes
regional or data type responsibility to participating national data centres. Also, IODE
maintains the network of World Data Centres, designated deep archives for global ocean
data. Within the IODE system and particularly important for salinity data, are the USA
National Oceanographic Data Center and the Canadian Marine Environmental Data
Service (MEDS)
National data centres of the member countries collect national oceanographic datasets.
These centres then transfer the dataset, through international agreements, to the IODE
responsible data centres to become part of the IODE system.
3.1.1 USA National Oceanographic Data Center
The USA National Oceanographic Data Center (NODC) is one of three NOAA
environmental data centres. Co-located with World Data Centre-A, NODC serves as a
national repository and dissemination facility for global ocean data. The Ocean Climate
Laboratory, a division of NODC, is mandated by the NOAA Climate and Global Change
program to maintain quality controlled oceanographic databases. Data include historical
in situ measurements of variables including temperature, salinity, oxygen, phosphate,
nitrate, silicate, and chlorophyll.
Data within the IODE system is publicly available through various avenues. The NODC
makes data available online and on CD at different levels of processing. In terms of
salinity data, relevant NODC online products include the Oceanographic Profile
Database. Relevant CD products include the World Ocean Database 1998, the World
Ocean Atlas 1998, the World Ocean Atlas Figures 1998, and the World Ocean Atlas 1994
- LEVITUS94.
22
3.1.1.1
Online Oceanographic Profile Database
The Oceanographic Profile Database is an interface for locating and retrieving
oceanographic profile data from NODC. Queries to this database are made in two steps:
• Searching and generating an inventory of stations, and
• Plotting and retrieving the data from the inventory.
The system allows the user to search for oceanographic stations, to plot station location,
and to plot 2D graphs of a selection of 26 variables, geographic location, and time. All
variables can be plotted as horizontal or vertical profiles. The search may also be filtered
by sampling method. For example, filtering based on bottle samples or ConductivityTemperature-Depth (CTD) profiles. The collated datasets can be downloaded via FTP.
A standard data query session in the Oceanographic Profile Database consists of:
1) accessing the database,
2) making lists of the number of data files containing salinity,
3) plotting a map of the station location, and
4) generating sample vertical profiles.
Figure 10 illustrates a vertical plot extracted from the NODC online Global
Oceanographic Profile Database for the Queen Charlotte Islands area.
3.1.1.2
World Ocean Database 1998 Version 2 (WOD98)
The World Ocean Database 1998 Version 2 (WOD98) is the NODC database containing
raw data. Available data files are queried using filter options based on data type, time
period, or year of interest.
3.1.1.3
World Ocean Atlas 1998 (WOA98)
The World Ocean Atlas 1998 (WOA98) is an analysed fields dataset. Data files matching
selected criteria are provided as “gz compressed” files for downloading. This
comprehensive site allows salinity data to be selected by:
• time period: annual, seasonal, or monthly,
• grid size: 1 or 5 degree, and
• type of analysis: data distribution, objectively analysed climatological mean, observed
mean minus the analysed mean, standard deviation of the observed mean, standard
error of the observed mean, or analysed mean minus the annual mean.
23
Figure 10. Vertical salinity profile for a station off the Queen Charlotte Islands on July 1,
1997.
Figure 11. Global annual mean surface salinity (PSS), from the WOA98F database
24
3.1.1.4
World Ocean Atlas 1998 (WOA98F)
The World Ocean Atlas 1998 Figures (WOA98F) is a searchable database of images,
where queries are based on the:
• time period: annual, seasonal, or monthly,
• area of interest: global or regional (Atlantic, Indian, or Pacific Ocean), and
• type of analysis: data distribution, objectively analysed climatological mean, observed
mean minus the analysed mean, standard deviation of the observed mean, standard
error of the observed mean, or analysed mean minus the annual mean.
Datasets are displayed in a variety of filled colour or contour formats. Contour intervals
are automatically set as a function of the full salinity range. The WOA98F database
generates global map images with a 0.2 psu resolution over a salinity range of 33.0 - 37.4
psu.
Figure 11 and Figure 12 present examples of global images from this database. Figure 11
displays the objectively analysed climatological mean surface salinity for all annual data
available, while Figure 12 shows the winter season (January – March) mean surface
salinity.
Regional annual surface salinity image maps can be obtained from the WOA98F for
three predefined geographic areas: the Pacific, Atlantic, and Indian Oceans. Figure 13
presents an example of the 1998 annual average surface salinity in the Pacific Ocean.
The NODC maintains a global oceanographic profile database that is queried through the
“Station Search Form”, whereby user-defined latitude and longitude boundaries are used
to define the geographical extent for data extraction and representation. Time filtered
data are available for monthly or annual periods in MM/DD/YYYY format. A search of the
area 140° to 124°W, 55° to 46°N for the continuous period of 01/01/1980 to 01/01/2000
returned the summary in Table 6.
25
Figure 12. Winter mean surface salinity (PSS), from WOA98F.
Figure 13. Annual mean surface salinity (PSS) in the Pacific Ocean, from WOA98F.
26
Table 6. Search results for oceanographic profiles in the NE Pacific, 1980 to 2000.
Variable
Profiles
TEMP (Temperature)
17600
SAL (Salinity)
17688
DOXY (Dissolved Oxygen)
1293
PHOS (Inorganic Phosphate)
101
SLCA (Silicate)
95
NTRA (Nitrate)
44
CHPL (Chlorophyll)
1722
NTRI (Nitrite)
44
TPHS (Total Phosphorus)
2
AMON (Ammonium)
5
LGT$ (Light transmissivity)
1671
NTRZ (Nitrite + Nitrate Content)
BEAC ()
1672
LBSC ()
1780
File Alias
C100 (OCEAN STATION DATA
(NANSEN))
F022 (CTD/STD, HIGH
RESOLUTION)
L303 (CTD)
Total
3.1.1.5
57
Profiles
Stations
5011
1729
24854
12427
13909
3532
43774
17688
World Ocean Atlas 1994 (LEVITUS94)
The World Ocean Atlas 1994 (LEVITUS94) is an atlas of objectively analysed major
ocean variables and is an excellent resource that appears to provide the most detailed
salinity data. It comprises multi-source archival data from 1900 to 1992. Global or
regional scale data are presented in tabular or graphic form.
Table 7 gives an example of annual mean salinity. Graphic options include colour,
contour, and line drawings. These forms are useful for illustrative purposes, but they
show a very crude contour resolution. Time filtered summaries can be obtained by
accessing the website from three separate points:
ANNUAL - LEVITUS94 ANNUAL
27
MONTHLY - LEVITUS94 MONTHLY
SEASONAL - LEVITUS94 SEASONAL
Three unique display features permit the user to customise the data plot:
• geographic windows, which may be used as filters thus allowing one to zoom into
an area of interest,
• selectable salinity ranges, which allow one to display only the data from within
the area of interest, and
• line drawing option, which is unique and very useful for the display of a salinity
profile along broad latitude or longitude lines.
Figure 14 and Figure 15 are examples of annual and seasonal salinity maps. The colour
plots are informative in that regions with high salinity variability are easily highlighted.
The salinity contour resolution depends on the salinity range selected, as only 10 contour
values are displayed. For example, if the user selects a salinity range of 32 - 34 psu, then
each contour line will represent a 0.2 psu step. Regional annual SSS map reports from
LEVITUS94 are available for variable geographic regions, whose extents are defined by
user-defined minimum and maximum latitudes and longitudes. Examples for the Pacific
Ocean and the Gulf of Alaska are presented in Figure 16 and Figure 17.
Table 7. Example of annual mean salinity data (psu) from LEVITUS94
28
Figure 14. Global long-term mean surface salinity (psu) from LEVITUS94.
Figure 15. January to March global mean surface salinity (psu), from LEVITUS94.
29
Figure 16. Annual mean surface salinity (psu) in the Pacific Ocean, from LEVITUS94.
Figure 17. Annual mean surface salinity (psu) in the Gulf of Alaska from LEVITUS94.
30
3.1.2 Marine Environmental Data Service (MEDS)
MEDS is the Canadian national oceanographic data centre and is a branch of the
Canadian Department of Fisheries and Oceans (DFO). Its mandate is:
1) to archive ocean data collected by DFO as well as data acquired through
national and international programmes conducted in ocean areas adjacent to
Canada, and
2) to disseminate data, data products, and services in accordance with DFO
policies.
Within the IODE system, MEDS is the responsible data centre for upper ocean thermal
data. In this capacity, MEDS collects and manages global upper ocean temperature and
salinity data under the international co-operative Global Temperature and Salinity Profile
Program (GTSPP) (see www.nodc.noaa.gov/GTSPP/gtspp-home.html). The main
purpose of this project is to collect all relevant upper ocean data and maintain the
necessary databases to support this process.
MEDS acquires the data via two distinct data streams. The first, termed the real-time
data stream, uses the Global Telecommunication System (GTS) to deliver the reduced (or
lower resolution) data profiles to numerous clients including MEDS. MEDS quality
controls the real-time data and archives these data within their end-to-end management
framework. On an annual basis, MEDS acquires approximately 60,000 oceanographic
profiles over the world’s oceans via the GTS. As contributor to the IODE system, MEDS
forwards the acquired profile data to World Data Centre-A three times per week.
The real-time database (located at www.nodc.noaa.goc/GTSPP/gtspp-rt.html) contains
global ocean temperature and salinity measurements that are regularly made by shipboard
observers or automated instruments such as buoys. The database allows the operator to
select a month and to
1) view the data file summary,
2) view the location plot (typically ship routes), and
3) download the dataset in MEDS ASCII file format.
The GTSPP real-time datasets provide monthly data from January 2000 to present.
All real-time data are acquired by MEDS in the form of reduced profiles or “messages”
(e.g. inflection points of the profile). The real-time message may relate to only a
temperature profile, or to a temperature-salinity (TS) profile. Figure 18 shows the
number of TS profiles collected in real-time by MEDS for 1998, over the global ocean.
The general lack of data over the global ocean is evident.
MEDS also receives the full-resolution data profiles at a 1 to 4 year time frame after the
real-time stream. This constitutes the delayed mode (higher resolution) data stream.
Upon receipt, MEDS quality controls the dataset and updates the end-to-end database
with the most current and complete dataset.
31
Within the GTSPP system, the updated dataset becomes part of the “Best Copy”
database. This database is assembled to provide the most complete dataset without
duplication (see www.nodc.noaa.gov/GTSPP/gtspp-bc.html). Real-time profiles are
contained in the file if the higher resolution delayed-mode profiles have not been replaced
by any of the contributing data centres. Available files are arranged in quarterly
segments. GTSPP has incorporated the World Ocean Circulation Experiment (WOCE)
Upper Ocean Thermal (UOT) science centre flags, and thus continues the co-operation
between science programs and the data management community.
Data are available for download, but the only graphical output available is that of the ship
track routes. The records are typically provided by the “Ships of Opportunity” and thus
cover the major routes of the Pacific, Atlantic, and Indian Ocean. The Best Copy database
is complete and is not filterable on-line. Thus, the client must extract specific variables
such as the surface salinity. Also, data files are available only at quarterly periods.
Figure 18. Location of temperature-salinity profiles acquired by MEDS over the global
ocean, for 1998. The colour scale indicates the number of observations.
3.2
Canadian Regional Databases
3.2.1 Atlantic Region
The Bedford Institute of Oceanography (BIO) Climate Database consists of assembled
historical bottle and CTD data sets that were collected using standard quality control
procedures (Petrie et al., 1996). Data compiled for winter and summer seasons are used
32
to calculate mean temperature, salinity, and density fields. These data are interpolated
onto a grid in a format useful for numerical modelling and process studies.
BIO also maintains the Computer Atlas of the Northwest Atlantic (Yashayaev 1999a and
b). This atlas contains all available temperature-salinity data from approximately 1900 to
1997, and includes data from the MEDS archive, the Russian Sections Program, BIO
archives including non-released WOCE cruises, as well as data contributed by
collaborators, and contained in WOA98F. The majority (90%) of the data used in the
Atlas originates from bottle sampling, whereas the remaining data was collected in recent
years with modern equipment such as CTD sensors and current meters.
The existing version of the Atlas has undergone a preliminary data quality control. Some
erroneous profiles and noisy data exist, but these only affect the statistics in areas where
there is sparse sampling. Simple queries allow the user to visualise the data distribution,
display salinity contour maps, and produce time series for a point or for a region.
Figure 19. Samples distribution and count of surface salinity data archived in the
Computer Atlas of the NW Atlantic.
33
Figure 20. Distribution and count of samples collected in May of all years between 1900
and 1997.
The Atlas covers the Northwest Atlantic including the Gulf of St. Lawrence, the Grand
Banks, the Scotian Shelf, and the Bay of Fundy. The data distribution images have a
resolution of 0.5° latitude by 0.5° longitude.
Figure 19 illustrates the spatial distribution and number of samples for the entire data
collection period. The database comprises over 750,000 bottle samples and 82,000 CTD
records. The highest data counts are found in the Bay of Fundy with more than 600
observations per unit area. The Scotian Shelf has counts greater than 500 per unit area,
while the Grand Banks show counts of 300 to 400 observations per unit area in a few
places.
Figure 20 shows the data distribution at the surface for May and demonstrates that much
of the Atlantic zone is only sparsely sampled. In fact, a large percentage of the area
shown has no surface observations in May throughout the 97 year period.
34
3.2.2 Pacific Region
Three databases maintained by the Institute of Ocean Sciences (IOS) for the Pacific
region are important for salinity related studies: the British Columbia (BC) Shore Station
Database, the Line P Database, and the Station Papa Database.
The BC Shore Station Database contains sea surface salinity data from BC lighthouse
locations. The data, some available since the 1910’s, have a reported precision of 0.1
psu. Figure 21 shows the location of the lighthouse stations along the British Columbia
coastline. Unfortunately, the very nature of the dataset places it close to land and of
limited use for offshore studies at scales comparable to proposed satellite datasets.
Line P is a series of oceanographic stations managed by IOS. It extends about 1500 km
from the mouth of Juan de Fuca Strait, south of Vancouver Island, to Ocean Station Papa
at 50°N 145°W in the Pacific Ocean. Figure 22 shows the original 13 stations. In 1981
the number of stations was increased to 26. The location and history of Line-P are also
available on-line. IOS has archived the data recorded at Station Papa since 1956. These
data include CTD profiles of the water column.
Figure 23 displays the annual cycle of surface salinity averaged over all data gathered
from 1956 to the present time along Line P. The maximum variation in salinity occurs
within 100 km of the coast and gradients are stronger during the rainy winter months.
Figure 21. Map of the British Columbia coast showing the location of the lighthouse
stations.
35
Figure 22. General geographic location of the stations along the P-Line
Figure 23. Annual cycle of surface salinity from 1956 to the present
36
4.0
Sea Surface Salinity in Canadian Waters
4.1
Spatial Sea Surface Salinity Distributions
4.1.1 Northwest Atlantic
Figure 24 was generated using data obtained from the Computer Atlas of the NW
Atlantic and displays the mean salinity in the North Atlantic averaged over all years. The
average SSS is 32.48 psu with a standard deviation of 1.78 psu.
Figure 25 presents a more local view and was generated using data available from
MEDS. It shows the horizontal distribution of surface salinity on the Grand Banks of
Newfoundland. Several features are evident including the general increase in salinity
away from the land towards the offshore. Over the continental shelf, salinity spans a
range of 31-33 psu. If the plot extended into the deeper waters of the Labrador Sea, we
would see salinity increase by another 2 units to about 35 psu. However, the figure does
not extend further because sufficient data do not exist for the area off the shelf. The data
used for this figure were collected by DFO during a series of annual fishery assessment
and directed oceanographic cruises spanning the fall season (August to November) and
such cruises do not extend beyond the 2000 m isobath.
The oceanography of this region is dominated by the Labrador Current, which is fed by
arctic water draining through Baffin Bay and Hudson Strait as well as by the West
Greenland Current. The main branch of the Labrador Current is trapped along the
continental slope (600 to 2000 m) and separates shelf waters from the warmer, more
saline waters of the Labrador Sea. A weaker inner branch of the Labrador Current is
trapped along the coastline. The surface salinity jump across the roughly 80 km wide
outer branch is about 0.5 psu, similar to that across the approximately 50 km wide inner
branch.
Figure 26 shows the horizontal distribution of surface salinity anomalies in fall 2000.
This field was computed by subtracting the spatially varying 1971-2000 mean from the
field displayed in Figure 25. While there are features that appear suspicious and that are
probably related to the fact that the data were collected over a four-month period, a
number of spatially coherent features with amplitudes as large as 0.75 psu are apparent.
37
Figure 24. Average salinity based on all years from the Computer Atlas of the NW
Atlantic.
38
Figure 25. The horizontal distribution of surface salinity over the Grand Banks of
Newfoundland and the Northeast Newfoundland Shelf during the period of AugustNovember 2000. (Figure courtesy of E.B. Colbourne, Northwest Atlantic Fisheries
Centre, DFO).
39
3LNO : Fall 2000
-
Surface Salinity Anomalies
48
49
3L
0.50
46
0.25
0.00
45
-0.25
-0.50
44
-0.75
43
LATITUDE
47
0.75
3N
3O
54
53
52
51
50
49
48
47
LONGITUDE
Figure 26. The horizontal distribution of surface salinity anomalies over the Grand Banks
of Newfoundland and the Northeast Newfoundland Shelf during the period of AugustNovember 2000. (Figure courtesy of E.B. Colbourne, Northwest Atlantic Fisheries
Centre, DFO).
40
4.1.2 Northeast Pacific
DFO occasionally utilises the British Columbia ferries to collect salinity and in vivo
chlorophyll fluorescence measurements. Figure 27 presents the salinity (heavy line, left
axis) and chlorophyll fluorescence (light line, right axis) measurements recorded by the
“Spirit of Vancouver Island” ferry, on its return trip between Swartz Bay and
Tsawwassen on March 23, 2001. The total distance of the profile represents the return
trip, whereby the beginning and end correspond to Swartz Bay and the middle part
matches the vicinity of Tsawwassen. The two salinity dips, visible on the plot,
correspond to the passage through the brackish plume of the Fraser River, where high
levels of chlorophyll were also recorded. A small patch of high chlorophyll water was
measured just after leaving Swartz Bay, but was not encountered on the return trip.
Figure 28 presents the salinity values recorded by the Research Vessel Tully, in February
2001, as it travelled between Victoria and the start of Line P. The record begins South of
Victoria at 123.3° W, continues along Juan de Fuca Strait to its mouth at 124.8° W, and
crosses the continental shelf to deep water at 126° W. The seawater input on the Tully is
at a depth of about 3 m. Fresher surface water at the mouth of Juan de Fuca Strait shows
a salinity reduction of about 1 psu. Fresher water at the edge of the continental shelf
causes a reduction of about 0.7 psu.
Another, much older, example of continuous surface salinity horizontal profiles comes
from a “Ship of Opportunity” operated in 1979 by Seakem Oceanography (Borstad et al.,
1980). Figure 29 shows the horizontal profiles of salinity, temperature, in vivo
fluorescence and zooplankton, measured from an intake at 1 m depth. The platform for
the data collection was a commercial tug-boat travelling between Howe Sound, North of
Vancouver, and Nootka Sound on the North West coast of Vancouver Island. From
Howe Sound to about East Point, the records indicate relatively fresh water, which
correspond to the southern Strait of Georgia. Between East Point past Sheringham Point,
the vessel was traversing the cold well mixed, high salinity waters of Juan de Fuca Strait.
Along the southwest coast of Vancouver Island, passing Carmanah Point , the vessel
crossed a region of only slightly lowered salinity. Salinity increased to the North as it
passed Cape Beale, Amphitrite Island, and Lennard Island. The salinity is slightly lower
and stable beyond Estevan Point, until a sharp decrease occurred as the vessel entered the
Nootka Sound, at the very end of the record.
The salinity values tended to change concurrently with the measurements of the three
other biophysical variables - temperature, fluorescence, and zooplankton - as the vessel
transited different water masses with varying characteristics. Notably, the fresh waters of
the southern Strait of Georgia are warmer, have higher phytoplankton fluorescence, and
greater abundance of zooplankton, than Juan de Fuca waters.
41
Vancouver/Victoria Ferry
Salinity (ppt
31
12
30
29
28
8
27
26
4
25
-3
Chlorophyll (mg.m
)
16
32
24
0
23.78 23.8 23.82 23.84 23.86 23.88 23.9 23.92 23.94 23.96
Day of March 2001
Figure 27. Strait of Georgia and Gulf Islands; Salinity and chlorophyll data collected
from a ferry on March 23, 2001.
Figure 28. Salinity horizontal profile from Research Vessel ‘Tully’, February 2001.
42
Figure 29. Horizontal profiles on the North West coast of Vancouver Island, in 1979.
4.2
Temporal Distribution of Sea Surface Salinity
Time series for the Canadian eastern seaboard were extracted from the Computer Atlas of
the NW Atlantic dataset and are presented in this section, along with time series for the
Pacific region (British Columbia lighthouse stations and Station Papa).
4.2.1 Northwest Atlantic
Seibert (2001) computed a number of SSS statistics using data in the Computer Atlas of
the NW Atlantic. Monthly averages were calculated based on all the profiles available in
the geographic area from 42°-70°N and 40°- 68°W. The monthly averaged sea surface
salinities, standard deviations, and number of stations are presented in Table 8.
Table 8. Sea Surface Salinity Data Statistics
Jan
Mean (psu)
Std. Dev.
Sample
Count
Feb
Mar
Apr
May
June
July
Aug
Sept Oct
Nov
Dec
Year
33.1
33.4
33.0
33.3
32.3
32.4
32.1
32.0
31.6
32.1
32.6
33.4
32.6
1.5
1.8
1.5
1.3
1.7
1.8
1.7
2.0
2.1
1.7
1.5
1.6
1.9
2972 3836 6962 9666
11319
12227
13720
11361 7422 7313 7699
2231
96728
43
The data were analysed to estimate the seasonal SSS cycle (annual harmonic) at seven
different locations on the Canadian eastern seaboard (Figure 30). Table 9 presents the
results of these analyses. Phase indicates the number of days after January 1 on which
SSS reaches its minimum. Total root-mean-square (rms) indicates the overall standard
deviation at each station while the residual rms refers to the variance unexplained by the
annual harmonic and includes both high frequency and interannual variability.
The table indicates that the high frequency and interannual variability can be between 58
and 95% of the total signal. However, a more detailed investigation to separate water
masses (including the removal of eddies) using cluster analysis (Yashayeav, 2000)
indicates that the seasonal - interannual split of variance is about 50% for near-surface
salinity.
Figure 31 displays similar information for Station 27, which is just outside St. John’s in
Newfoundland (see Figure 25 for location). This station has been routinely sampled
since 1946. The mean annual signal has an amplitude of almost 1.3 psu (upper panel),
although the variations in a given year can be much greater. Interannual variations are
considerably smaller (lower panel) with a standard deviation of 0.42 psu.
Table 9. Time series statistics for selected stations of the Canadian eastern seaboard.
Location
Scotian
Shelf
Western
Gulf
St. Lawrence
Grand
Banks
Labrador
Shelf
Labrador
Sea
Southern
Baffin Bay
Northern
Baffin Bay
Latitude (°N)
44
47
47
57
56
67
70
Longitude (°W)
62
62
49
59
51
57
58
4843
1477
3107
521
2304
1120
276
Phase (days)
265
237
237
-
258
304
254
Amplitude (psu)
0.30
1.33
0.49
0.42
0.20
0.74
0.88
Total rms (psu)
0.64
1.05
0.46
0.86
0.24
0.82
0.63
Residual (psu)
0.59
0.85
0.35
0.84
0.20
0.66
0.61
A-to-R ratio
0.5
1.6
1.4
0.5
1.0
1.2
1.4
Variance
unexplained by
Annual
Harmonic (%)
85
66
58
95
69
65
94
Sample Count
44
Greenland
30
00
M
Labrador
100
2000M
Figure 30. Stations where the seasonal cycle in SSS was computed.
45
Station 27 Surface Salinity Seasonal Cycle
33
32.5
S (psu)
32
31.5
31
30.5
30
Jan
Mar
May
Jul
S ep
Nov
Station 27 August Surface Salinity Anomalies
1
S
anom
(psu)
0.5
0
-0.5
-1
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
Figure 31. Upper panel: The seasonal cycle in surface salinity at Station 27 (see Figure
25 for location). The vertical bars denote the variability around the long term mean.
Lower panel: August anomalies in surface salinity at Station 27. The anomalies are
computed relative to the 1971 to 2000 mean.
Probably the most striking example of interannual variability is afforded by the so-called
Great Salinity Anomaly (GSA; Dickson et al., 1988) that was detectable in the North
Atlantic over the period from of about 1969 until 1982. It is useful to discuss the GSA
from the viewpoint of available data. Starting in 1964, an international effort was made to
collect improved weather data from ocean stations. Weather station Bravo in the
Labrador Sea was occupied from 1964 to 1974, and Figure 32 shows surface salinity
collected there for the period of 1964-74. The years 1964 to 1967 display a regular annual
cycle of about 0.3 psu peak to peak (freshest water in late summer due to ice melt).
Between 1969 and 1972 a significant drop of about 0.5 psu occurred, after which the
annual cycle increased to about 0.5 psu. The drop corresponds to the passage of the GSA,
and represents a volume of surface water fresher than normal by up to 1 psu, about 500800 m thick, and covering an area of about 100,000 square kilometres. The GSA was also
46
detected over the Canadian continental shelf; its effect at Station 27 (Figure 31) is
apparent in the early 1970s.
This anomaly could be traced around the Atlantic subpolar gyre for over 14 years from its
origin north of Iceland in the mid-to-late 1960s until its return to the Greenland Sea in
1981-82. Figure 33 shows the trajectory of the GSA based on data from Belkin et al.
(1988). It is anticipated that the Great Salinity Anomaly would have had a profound
impact on deep-water formation and associated climate variability, but our ability to
detect such anomalies has decreased since the demise of the ocean weather stations.
47
Figure 32. Surface salinity at Ocean Weather Station Bravo in the North Atlantic.
Figure 33. The path of the Great Salinity Anomaly (from Belkin, et al., 1998).
48
4.2.2 Northeast Pacific
4.2.2.1
Lighthouse Stations
Data from six stations located in the Strait of Georgia is displayed in Figure 34. The
summer minimum at all stations is due to melt water from the summer freshet of the
Fraser and other Rivers. A lower minimum in winter is due to local rainfall.
The annual cycle of salinity at the Amphitrite Point light station (Figure 35), on the west
coast of Vancouver Island, includes the standard deviations of individual monthly means
about this cycle. Again, surface water near the coast is freshened by terrestrial run-off.
The summer salinity maximum is attributed to coastal upwelling of deeper saline water.
The Langara Island station is located at the northern tip of the Queen Charlotte Islands
and is the most distant from fresh water effluents. The annual cycle of salinity at the
station is shown with the standard deviations of individual monthly means (Figure 36).
The time series of Departure Bay (not shown), Amphrite Point (Figure 37), and Langara
Island (Figure 38) were analysed to extract the monthly average surface salinity
anomalies, which are expressed as the rms variation that remains after the annual cycle is
removed. Table 10 summarises the results of these analyses. The figures also show the
anomaly values for the period of observation. The location of the three stations relative
to freshwater runoff and currents is reflected in the anomalies. The anomalies are the
highest at Departure Bay station, which is in the Strait of Georgia, followed by
Amphitrite Point station, and Langara station.
Table 10. Time Series Statistics for three British Columbia lighthouse stations and
Station Papa.
Station
Latitude (°N)
Departure
Amphitrite
Langara
Papa
49
49
54
50
124
125
133
145
23 to 27
28.5 to 31
31.9 to 32.1
32.2 to 33.0
Period (years)
100
60
60
40
Anomalies (psu)
1.8
1.0
0.2
0.1
Trend (psu per year)
0
-0.007
-0.01
-0.005
Total variation
(psu/period)
0
0.4
0.6
0.2
Longitude (° W)
Salinity Range (psu)
49
Figure 34. Strait of Georgia; Annual cycles of monthly mean salinity.
Figure 35. Amphitrite Point; Annual cycle of monthly mean salinity.
Figure 36. Langara Island; Annual cycle of monthly mean salinity.
50
Y=-0.0066X+13.03; R2=0.021
Figure 37. Amphitrite Point; Monthly average surface salinity anomalies.
2
Y==-0.0099X+19.553; R =0.3177
Figure 38. Langara Island; Monthly average surface salinity anomalies.
51
4.2.2.2
Station Papa
Salinity is slightly higher (0.2 psu) at Station Papa than at Langara station, but the annual
cycle is similar. There is an annual cycle and a downward trend of about 0.005 psu per
year, amounting to 0.2 psu over the period (Figure 39). The annual cycle was calculated
as a combination of two harmonics (annual and semi-annual) giving a total peak-to-peak
variation of 0.15 psu (Figure 40). The data in Figure 39 were re-plotted in Figure 41 with
the annual cycle removed to show residual anomalies. The anomalies at Station Papa are
lower that those observed at the three coastal stations and the individual year salinity
values are about 0.1 psu above or below the long-term mean. Short term variations
occurring in the time series may be correlated with the passage of mesoscale eddies. The
surface salinity reaches higher levels after winter storms have transported deep, more
saline water to the surface. Lower salinity values may be associated with rain. A more
probable cause for decreased salinity at Station Papa in the summer is the advection of
less saline water from the coast of North America. The data appear to show a statistically
significant freshening trend which in this case has been linked to an increase in stability
of surface waters with implications for large scale primary productivity of the region
(Freeland et al., 1997).
The Station Papa time series was analysed to evaluate the incidence of El Niño events in
1982-1983 and 1997-1998. Figure 42 presents the 1980 to 2000 monthly surface salinity
anomalies as well as the long-term trend line based on the 1956 to 1999 measurements.
This representation of salinity anomalies does not reveal the last two El Niño events.
However, it is noted that the salinity measurements recorded at Station Papa have been
0.1 to 0.2 psu above the long-term trend line.
A comparison of the time series from Station Papa (Figure 42) with the Southern
Oscillation Index variation for the same period (Figure 43) indicates a possible
correlation. There are three negative anomalies in both time series, but the first negative
salinity anomaly at Papa was about a year prior to the 1982 SOI minimum, while those of
1987 and 1992 more or less coincided with the Southern Oscillation Index minima. For
the 1983 to 1996 period, the Papa salinity anomaly and the Southern Oscillation Index
agree very well, but in 1997, the Papa salinity anomaly rose sharply showing the greatest
positive anomaly for the period, while the Southern Oscillation Index declined strongly.
In Figure 44, the Station Papa surface salinity anomaly with trend removed is compared
with the Southern Oscillation Index. The two time series show similar time scales of
variation, leading to occasional periods of close correlation, but no correlation over
longer intervals.
52
All surface salinities (ppt
Station Papa
33.4
33.2
33
32.8
32.6
32.4
32.2
1950
y = -0.0048x + 42.11
R2 = 0.0945
1960
1970
1980
1990
2000
Year
Figure 39. Surface salinities in the vicinity of Station Papa from 1956 to 1996
Figure 40. Annual cycle of surface salinity at Station Papa from data in Figure 39.
53
Station Papa
All surface salinity
anomalies (ppt)
0.8
0.6
0.4
0.2
0
-0.2
-0.4
1950
y = -0.0051x + 10.11
R2 = 0.1359
1960
1970
1980
1990
2000
Year
Figure 41. Residual anomalies in surface salinity data at Station Papa, from 1956 to
1996.
Y=-0.0027X+5.3932;
Figure 42. Monthly surface salinity anomalies at Station Papa, from 1980 to 2000.
54
Figure 43. The Southern Oscillation Index plotted for 1979-1998.
Figure 44. Detrended monthly surface salinity anomaly at Papa station plotted with the
Southern Oscillation Index.
55
5.0
Science Requirements
Table 11 lists a number of science requirements for sea surface salinity that have been
derived by a number of groups (Lagerloef, 2000; Schmitt, 1995; CESBIO) as well as
some proposed by the authors of this report. These requirements are based either on
resolving some ocean process (e.g. deep-water formation) or monitoring a particular
ocean area. For most purposes, a product accuracy of 0.1 psu is indicated. The required
spatial and temporal scales differ significantly, however, and would seem to prohibit use
of any proposed satellite sensor for coastal ocean purposes. This issue is further discussed
in Section 8.0. On the other hand, higher resolution sensors are to be expected as
technology improves.
Table 11. Summary of scientific requirement for the study of surface circulation patterns.
Process/Objective
Accuracy
(psu)
Spatial
Resolution
(km)
Temporal
Resolution
(days)
North Atlantic
thermohaline circulation
0.1
100
30
Halosteric Adjustments
0.2
200
7
Tropical Pacific heat flux barrier
layer effects
0.2
100
30
Surface freshwater flux balance
0.1
300
30
Global scale climate modelling
(GODAE)
0.1
200
10
Fisheries Management
0.1
≤ 50
45
Environmental Monitoring
(Coastal)
0.1
10
5
56
6.0
Ongoing Applications of Sea Surface Salinity
6.1
Fisheries Resources in Canadian Waters
6.1.1 West Coast
Surface salinity data is used in fishery forecasts (Dr. Blackbourn, pers. comm.). It has
been demonstrated that salinity levels are related to the survival of juvenile salmon in
very nearshore waters. It has been speculated that surface salinity away from the coast
may be related to migration, but this kind of data has never been available. At present,
the only available time series are those taken at the coastal lighthouses.
DFO stock assessment scientists state that surface salinity is the most common physical
variable used in fishery forecasts off the coast of British Columbia. Usually, a simple
regression model is used to predict returning salmonids, particularly in the spring. These
studies tend to use monthly mean salinity at lighthouses (very close to the coast). Such
studies may be enhanced through the use of offshore surface salinity data.
Studies indicate that there is a strong correlation between salinity and Barkley Sound
Sockeye salmon (Oncorhynchus nerka) juvenile survival rate. The salinity signal is very
small, and probably indicates either the onset of the spring transition, increased amount of
rain, or an adjustment of the coastal ecosystem. Low salinity may also be the result of a
mild winter with lots of rain, or an ENSO event, while higher coastal salinity might be
caused by cooler drier winters.
Hyatt et al, (1990) and DFO (1999) report that shifts in ocean climate result in changes in
the community structure and productivity of both the coastal and offshore ecosystems
where the West Coast Vancouver Island sockeye salmon spend 1 to 3 years of their life.
Marine conditions are especially unfavourable for juvenile survival during years when
coastal ocean temperatures are high and salinities are low, because migratory predators,
including Pacific hake (Merluccius productus) and mackerel (Scomberomorus sierra),
arrive earlier and in greater abundance.
6.2
Optical Water Properties
Measurements of water quality variables such as chlorophyll a, coloured dissolved
organic matter, or sea surface salinity from satellite sensors would be of great interest to
resource managers monitoring coastal regions. The contribution to blue light absorption
by coloured dissolved organic matter is recognised, but the standard ocean colour
algorithms to estimate such variables generally fare poorly in these waters due to the
complex nature of the light field. Independent salinity measurements via passive
57
microwave might help improve chlorophyll a retrievals in coastal Case 2 waters.
Freshwater runoff, on many parts of the British Columbia coast, exhibits a strong
absorption at short wavelengths by dissolved organic materials (tannins, lignin and other
products of plant litter breakdown). This is particularly evident when the waters of local
creeks and rivers are often visibly brown.
Figure 45 illustrates the inverse relationship between the surface salinity and absorption
and concentration of dissolved organics as measured by the optical density at 350 nm of
filtered surface (OD350) water samples (Borstad, 1987). These were collected in April
1987 from Barkley Sound on the West coast of Vancouver Island. This suggests that
fresh water from local rivers, and possibly effluent from the pulp mill on the Alberni
Canal at the head of Trevor Channel, are important sources of coloured dissolved organic
matter. The geographic variations in OD350 should be apparent in blue to green ratio
images. Higher OD350 values recorded at stations 6, 8, 9 and 21 have not been
satisfactorily explained, and they may be related to a lab measurement error.
An inverse relationship between coloured dissolved organic matter and salinity is also
documented for the coast of Florida (D’Sa, et al., In prep.) and in the Atchafalaya delta in
the Mississippi River system (Figure 46). Several sets of field measurements collected
for Florida coastal waters indicate a conservative mixing or dilution behaviour for the
optical properties of the dissolved material (Carder et al., 1993; Blough et al., 1993; D’Sa
et al., 2000a,b; Hu et al., 2000).
The intercept for OD350=0 occurs at a salinity of about 34 psu for the British Columbia
coast and for OD400=0 at 37 psu for the Atchafalaya delta. This possibly represents the
characteristic of the salinity in Northeast Pacific and North Atlantic Oceans, respectively.
The slopes of the mean trend lines, 0.0066 and 0.119 (m-1 psu-1), describe the absorption
coefficient of light where the latter value indicating the much higher concentration of
dissolved organic matter in the fresh waters of the Mississippi.
Similarly, a high correlation (r2=-0.85, n>7500) between coloured dissolved organic
matter and sea surface salinity in the continental margin of northeastern Gulf of Mexico
suggests that sea surface salinity distribution maps, as presented in Figure 47, may be
derived from satellite watercolour data (Hu et al., 2000).
Algorithms based on SeaWiFS data are being developed with the objective to retrieve
realistic and accurate estimates of chlorophyll a and coloured dissolved organic matter
distributions in Florida Bay and Shelf waters. One of the algorithms is for atmospheric
correction while the other is for bio-optical calibration. The often-conservative mixing or
dilution behaviour in coastal waters permits the calculation of salinity from coloured
dissolved organic matter absorption.
58
Figure 45. Relationship between dissolved organic material concentration (OD350) and
surface salinity in Barkley Sound, April 1987.
Figure 46. Correlation between Gelbstoff (OD400) and sea surface salinity in
Atchafalaya River, March 2001.
59
Chlorophyll-a from NASA atmospheric
correction and OC2 algorithms
CDOM from MODIS
Chlorophyll-a from turbid water correction
and MODIS semi-analytical algorithms
Salinity estimated from DO400
Figure 47. Sea surface salinity distribution maps estimated from SeaWiFS water colour
acquired on February 24, 1999.
60
7.0
Effects of Sea ice on the Remote Sensing of SSS
The presence of sea ice in various forms and concentrations is one of the difficulties
associated with SSS remote sensing in the polar regions. More generally than the effects
of sea ice, the remote sensing of SSS in high latitudes raises four main concerns:
1) the relatively low brightness temperature/SSS ratio at low water temperatures,
2) the effects of ice on the retrieval of SSS,
3) salinity anomalies due to ice melt, and
4) natural sea surface salinity variability in space and time.
This latter point is of great importance to the understanding and the monitoring of largescale events such as:
• Break-up of Antarctic ice shelves;
• Interannual sea surface salinity variations in the Nordic Seas for long time scale
climate predictions and modelling;
• Monitoring of events such as the Great Salinity Anomaly in the Northern North
Atlantic (Dickson et al., 1988);
• North Atlantic Oscillation (Hurrel, 1995; AWI website);
• Observation of the North Atlantic thermohaline circulation (Schmitt, 1998).
The interest in SSS measurements in areas with sea ice coverage is twofold. First, low
frequency radiometry is attractive, as it would contribute to the knowledge of the ice
characteristics. The SSIWG (Lagerloef, 1998) suggested ice mapping with L-band data
would:
1) differentiate melt ponds on consolidated sea ice from open water in
combination with C-band radiometric data and
2) allow estimates of multiyear sea ice concentrations, which is not currently
feasible with the high frequency microwave data.
Second, sea ice information is needed to evaluate its effect on the
1) accuracy of sea surface salinity retrievals and
2) surface salinity anomalies resulting from ice melt.
Various organisations have studied the characteristics of sea ice related salinity anomalies
from buoy data. However, the only literature sources on data from L-band passive
remote sensing of sea ice are the Arctic Ice Dynamics Joint Experiment (AIDJEX)
reports. Theoretical L-band models have been compiled by Ulaby et al. (1986).
The AIDJEX study dealt with the 0.1 to 40 GHz microwave radiometric characteristics of
snow and ice (Vant et al., 1978; England, pers. com.). AIDJEX project reports were
complied by Vant et al. (1978), Campbell (1972), Edgerton (1972), Ramseier (1972), and
Weller (1972). Vant et al. (1978) published a comprehensive report on the modelled as
well as the measured dielectric permitivity and dielectric loss values. The data were, in
part, collected using the 1.4 GHz band over three sites of first year ice with different
salinities (i.e. 5.1, 5.5 and 10.5 psu).
61
A second data source involves current research from data collected in the Greenland area.
As reported at the second SSWIG workshop, the plan called for a fly-over of the Arctic
Ocean, in July 2000, from Thule, Greenland using the Wallops P-3. Gloersen (pers.
com.) reported that flights were carried out in July 2000 over Baffin Bay. During this
mission, C- and L- band radiometric data were recorded over thin and water-covered sea
ice. The principal investigator for this project, Dr. D. Cavalieri of NASA Goddard Space
Flight Center, has just begun analysis of these data.
7.1
L-BAND Radiometric Characteristics of Sea Ice
7.1.1 Brine
In the –10 to 0°C temperature range, the salinity of liquid brine (Sb) is less than 100 psu
but, may reach 250 psu at temperatures lower than –10°C (Figure 48). At a frequency of
1.4 GHz, the dielectric loss of brine of salinity 85.6 psu (Figure 49) is three times higher
than that of seawater at 32.5 psu (Figure 3) at the same temperature (–5°C for brine and
0° for seawater) (Strogryn and Desargeant, 1985). Brine is therefore expected to be a
radiometrically significant constituent of sea ice.
7.1.2 First Year and Multiyear Ice
The brightness temperature of sea ice depends on its thickness and growth state. The
salinity of first year sea ice may vary from 5 to 15 psu, depending on the depth, its age
and temperature.
Thin first year ice is transmissive to L-band energy upwelling from the ice-water
interface. Thicker first year ice has a penetration depth of 20 to 80 cm, and multi-year ice
of 80 cm to 2 meters (Figure 50). The penetration depth of newly formed ice (20 to 100
hours old) is less, in the order of 5 cm (Figure 51), reflecting the higher dielectric losses
(i.e. for higher ice salinities) of newly formed ice. During the growth process, from open
water to “opaque” ice, the measured brightness temperature gradually increases until the
ice becomes "optically thick" to L-band energy (Figure 51).
62
Figure 48. Brine salinity in sea ice as a function of negative temperature (Ulaby et al.,
1986)
Figure 49. Dielectric constant of liquid brine as a function of frequency (Ulaby et al.,
1986).
63
Figure 50. Calculated penetration depth in pure ice and in first year and multi-year sea
ice (Ulaby et al., 1986).
In order to determine the dielectric properties, one must consider the heterogeneous
structure of ice. Debye-like dielectric behaviour pertains for salinities (of water or ice)
below 40 psu. However, given the brine inclusion in sea ice, a combination of three
dielectric constants, Εi of ice, Εsi sea ice, and Εb of brine, must be used. The shape and
orientation of the brine inclusions in sea ice may vary, but are usually columnar and
vertical (Tucker et al., 1992). The orientation of brine inclusions is usually random in
newly formed ice and gradually takes a vertical orientation as the age of the ice increase.
However, after a period of time the brine drains to the lower layers and the ice becomes
more homogeneous and thicker.
The complex dielectric constant of ice is difficult to predict because it depends on the
ionic conductivity of the ice. This parameter is, in turn, determined by many factors
including the ice temperature, the brine volume, and the shape, orientation, size and
length of the brine pockets (Hallikainen and Winenbrenner, 1992; Vant et al. 1978).
Mixing models such as the Polder-Van Santen described by Ulaby et al. (1986), have
64
been used by Hallikainen (1977) to model the dielectric loss of sea ice (for sea ice salinity
of 8 psu) at frequencies below 10 GHz. Figure 51 shows how the brightness temperature
of newly formed ice varies with time as a result of these combined effects.
7.1.3 Melt Ponds on Consolidated Ice
The salinity of melt pond water is less than 1 psu and if refrozen it may display a crystal
structure similar to frozen fresh water ponds (Tucker et al., 1992). The spectral
characteristics of low salinity seawater indicate that the TB of melt pond water would, if
detectable, be higher than the TB of the slightly more saline multiyear ice, where the
salinity is about 1 psu (Ulaby et al., 1986).
7.2
Effects of Ice in the Field of View
Numerical simulations (Figure 52 and Figure 53) indicate that the uncertainty in SSS
retrievals will greatly increase if sea ice is present within the sensors field of view. Using
the slope of about 0.2 K/psu at SST=0 (shown in Figure 5) implies that a change of 0.5
psu in SSS corresponds to about 0.1K in Tb. Figure 51 show that first year ice gives a Tb
value about 125 K higher than sea water. An equivalent number for multi-year ice is
about 110 K. A radiometer will therefore measure an increase of 0.1K in Tb when the
sea water in its field of view contains 0.08% of first year ice, or 0.09% of multiyear ice.
This corresponds to extremely low concentrations of ice, though for a typical field of
view of order 50 km across (Table 5), a single floe would need to cover about two km2 to
cause this error. If uncorrected, this increase in Tb corresponds to an SSS measurement
0.5 psu too low (Figure 5). High resolution ice charts generated using Radarsat or ERS
SAR sensors or even coarser resolution sea ice cover information derived from SSM/I
might be used to separate sea ice and ice-free surface signal contributions at L-band.
Figure 51. Brightness Temperature evolution from open water to 15 cm thick young ice.
Nadir angle=50°. Just before the 20th hour the high brightness values are reached at a time
when the ice became optically thick; with the L-band the ice was then 4 to 5 cm thick
(Grenfell et al., 1998).
65
Figure 52. Brightness temperatures at L-band as a function of ice thickness (Ulaby et al.,
1986, sea ice parameters used in simulation; CRESTech radiometer simulation model).
Figure 53. Simulation of L-band brightness temperature for an ice-covered ocean. The
assumption is made that the ocean fraction could have variable salinity.
66
8.0
Concluding Remarks
Sea surface salinity is an important oceanic variable with significance for a large number
of ocean and atmosphere applications including:
•
climate dynamics and prediction;
•
global scale ocean-atmosphere modelling;
•
regional scale ocean-atmosphere modelling;
•
fisheries management; and
•
environmental monitoring.
The capability to measure SSS remotely from space now exists, and at least one satellite
carrying an SSS should be launched by 2006. Sea surface salinity signals in Canadian
waters are large, both in terms of spatial gradients and temporally on seasonal and
interannual time scales. Canada thus would stand to gain significantly from remotely
sensed SSS.
Proponents of SSS satellites have justified their missions primarily in terms of offshore
applications, in particular those related to climate change. These include thermohaline
forcing in the North Atlantic and equatorial dynamics as they relate to El Niño in the
Pacific basin. In fact, the SMOS team (CESBIO) claims that application to the coastal
zone, especially for operational modelling where data is required on a time scale of 10
days, is probably beyond the technological capabilities currently proposed. This would
seem to limit application of remotely sensed SSS to the offshore region, as important as
they are to Canada. However, it is far from clear if this pessimistic view is justified for
the many coastal applications identified earlier in this report (e.g., fisheries management).
Nor does it consider possible synergies with existing in situ observational programs like
DFO’s Atlantic Zonal Monitoring Program or Project Argo.
Given the large single pass, single pixel expected error, achievement of a specified SSS
accuracy will require the averaging of images over space and/or time. For example, to
achieve an accuracy of 0.1 psu, SMOS will average images over 10 day periods and 200
km by 200 km squares (Table 5). The reduction in uncertainty gained by averaging
depends on the spatial and temporal structure of the individual errors that contaminate an
SSS estimate. If all the sources of noise are white (i.e. if they are uncorrelated between
pixels and between successive passes), the error will decrease as the inverse square root
of the number of pixels averaged. However, it appears that significant additional effort
will be required to determine the error structures. Until then, it will be difficult to predict
the success of applying remotely sensed SSS.
There exist two special problems in remote sensing of SSS that are of special significance
to Canada, namely cold water and ice. The emissivity of cold water is smaller than that of
warmer waters, and partially ice filled pixels may prevent useful measurements from
being made during winter and spring in the Atlantic zone. On the other hand, the
Canadian science community is especially well suited to address such issues.
67
In terms of potential for Canadian benefit, sea surface salinity mapping from spaceborne
platforms will provide important information for climate studies. In particular, progress
will be significant if it translates to observing salinities in northern regions where
historical data coverage is extremely poor. Possibly with the involvement of the
Canadian Ice Service, a Canadian contribution to the application of L-band radiometry to
salinity monitoring could realistically include a study on the effects of sea ice. As well,
benefits will accrue in the areas of fisheries management and environmental monitoring
as workers gain access to data on a year-round basis never before possible. This will
permit, for example, better estimates of mixed layer depths that are important in
ecological models. Similarly, workers will be able to better quantify the fresh water
distribution due to estuarine discharge.
Canadian expertise in high-latitude ocean studies is recognised in the global science
community. As well, Canadian contributions to existing space-based experiments are
well known. Canadian businesses, universities and government can make a significant
contribution to and benefit from involvement in the satellite based salinity measurements
over the global ocean.
68
9.0
References
This section includes references cited in the report, a set of relevant websites, and
correspondence on the status of various research activities.
9.1
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70
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the 3rd SSIWG Workshop held in San Antonio, Texas, 22-23 January, URL:
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Levitus, S., T.P. Boyer, M.E. Conkright, T. O’Brien, J. Antonov, C. Stephens, L.
Statoplos, D. Johnson, and R. Gelfeld, 1998. NAA/NESDIS 18, World Ocean
Database, 1, 346pp.
Lerner, R.M. and J.P. Hollinger, 1977. Analysis of 1.4 GHz Radiometric Measurements
from Skylab, Remote Sensing of the Environment, 6: 251-269.
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Miller, J. L., M. A. Goodberlet, and J. B. Zaitzeff. 1998. Airborne Salinity Mapper Makes
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173, 176-177.
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http://www.bom.gov.au/bmrc/ocean/GODAE/gosamor.htm
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71
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9.2
Relevant Websites
Alfred Wegener Institute for Polar and Marine Research (AWI):
http://www.awi-bremernaven.de/index.html
Databases
http://www.nodc.noaa.gov/General/salinity.html
http://www.nodc.noaa.gov/OC5/inv_all.html
http://www.nodc.noaa.gov/OC5/data_woa.html
http://www.nodc.noaa.gov/OC5/WOA98F/woaf_cd/search.html
http://www.nodc.noaa.gov/cgi-bin/JOPI/jopi
http://ingrid.ldeo.columbia.edu/SOURCES/.LEVITUS94
http://www.meds-sdmm.dfo-mpo.gc.ca/
http://www.nodc.noaa.gov/GTSPP/gtspp-home.html
http://www.ios.bc.ca/ios/osap/data/
ESTAR
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http://www.esr.org/ssiwg3/SSIWG_3.html
GODAE
http://www.bom.gov.au/bmrc/ocean/GODAE/
HYDROS
http://hydros.gsfc.nasa.gov/
Kuiper Airborne Observatory Publications:
http://sofia.arc.nasa.gov/project/library/kao/pub72.html
Microwave Geophysics Group, University of Michigan:
http://www.eecs.umich.edu/grs/intro.html
MIRAS
http://www.estec.esa.nl/ap2000/abstracts/a00565.html
http://www.esa.int/est/prod/prod0157.htm
http://www.esa.int/est/prod/prod0662.htm
http://esapub.esrin.esa.it/bulletin/bullet92/b92marti.htm
PALS (Passive/Active L/S-band Airborne Sensor)
http://eis.jpl.nasa.gov/msh/mission+exp/pals.html
Project ARGO
http://argo.jcommops.org/index.html
Salinity Sea Ice Working Group:
http://www.esr.org/
SLFMR (Scanning Low Frequency Microwave Radiometer)
http://www.csc.noaa.gov/crs/AECS/SLFMR/
SMOS and MIRAS
http://www.esa.int/export/esaLP/smos.html
http://www.cesbio.ups-tlse.fr/indexsmos.html
www.esa.int/export/esaLP/ESAQGA2VMOC_smos_0.html
SMOS and the US missions AQUARIUS and HYDROS
www.knmi.nl/~meulenvd/esa/DOSTAG/ESA_PB-EO_2002_2.pdf
STAR-Light (Scanning Thinned Array Radiometer-Light)
http://www.eecs.umich.edu/grs/projects/starlight/index.html
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9.3
Correspondence
Researchers involved in sea ice and sea surface salinity mapping were contacted through
electronic mail. The information obtained is primarily about the status of recent airborne
deployments, and the availability of very low frequency radiometric data on sea ice.
From Dr. E.G. Njoku, JPL/NASA
Dear Ms. Simms:
I don't know of much work that has been carried out at 1.4 GHz in looking at sea
ice. There have not been many (if any) L-band airborne radiometer flights over ice. Seaice researchers who may be able to provide more information are Don Cavalieri or Joey
Comiso, both at Goddard Space Flight Center, or Frank Carsey at JPL.
Regards,
Eni Njoku
Eni G. Njoku
M/S 300-233, Jet Propulsion Laboratory
4800 Oak Grove Drive, Pasadena, CA 91109
Tel:(818) 354-3693; Fax:(818) 354-9476
E-mail: eni.g.njoku@jpl.nasa.gov
From T. England, University of Michigan
Dear Dr. Simms,
STAR-Light is an airborne radiometer we are currently building for use in the
Arctic. It won't be operational for 2-3 years. Our work in the arctic so far has been from
a tower over tundra and at the SSM/I frequencies of 19, 37, and 85 GHz. I wish I could
help with L-band data but I can't yet. Please ask again in a couple of years.
The cold regions folks at Goddard might have some L-band ice data. Also, I
vaguely remember that Edgerton at Aerojet looked at sea ice at L-band back in the late
'60's or early 70's. Their reports were all in the gray literature but well done. I would
look in my files for you but we recently moved them and they are not in a configuration
that I can reasonably access.
Sincerely,
Tony England
Microwave Geophysics Group
c/o Prof. A.W. England
University of Michigan
EECS Building Room 3120
1301 Beal Ave
74
Ann Arbor, MI 48109-2122
From Dr. Per Gloersen, NASA/GSFC
Dear Dr. Simms,
I have researched our compatriots at Wallops Flight Center on your behalf and
have uncovered the following: 1) The WFC P-3 was out of commission, but a Navy P-3
was obtained as a substitute, and flew some missions out of Thule in July 2000, carrying,
alternately, C- and L-band radiometers. 2) Dr. Don Cavalieri (cc'd on this message) was
the mission scientist. 3) Flights dedicated to studying thin and water-covered sea ice were
carried out in Baffin Bay only. There were no flights over the Arctic Ocean, to the best
of my knowledge. 4) Dr. Cavalieri has just received copies of the raw data, and is a long
way from making them available for distribution.
I hope this helps.
Sincerely,
Dr. Per Gloersen/Code 971
NASA/Goddard Space Flight Center
Greenbelt, MD 20771-0001
per.gloersen@gsfc.nasa.gov
301-614-5710; FAX: 301-614-5644
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