Global Sea Surface Temperature

2 GLOBAL SEA SURFACE TEMPERATURE Christopher J. Merchant*, Peter J. Minnett†, Helen Beggs‡, Gary K. Corlett§, Chelle Gentemann¶, Andrew R. Harrisk, Jacob Hoyer#, Eileen Maturi** *University of Reading and National Centre for Earth Observation, Reading, United Kingdom. †University of Miami, Coral Gables, FL, United States. ‡Bureau of Meteorology, Melbourne, VIC, Australia. §EUMETSAT, Darmstadt, Germany. ¶ Earth and Space Research, Seattle, WA, United States. kUniversity of Maryland, College Park, MD, United States. #Danish Meteorological Institute, Copenhagen, Denmark. **NOAA Center for Weather and Climate Prediction, College Park, MD, United States

CHAPTER OUTLINE 2.1 Introduction 6 2.1.1 Importance of Global Sea Surface Temperature 6 2.1.2 Definitions of Sea Surface Temperature 7 2.1.3 Global and Seasonal Distributions of SST 11 2.1.4 Large-Scale SST-Atmosphere Interactions 13 2.1.5 Sea Surface Temperature and Climate 13 2.2 Retrieval and Measurement Methodology 14 2.2.1 Relationship of SST to Top-of-Atmosphere Radiances 14 2.2.2 Satellite Infrared Retrievals of SST 15 2.2.3 Satellite Microwave Retrievals of SST 20 2.3 Validation 21 2.3.1 Types of In Situ Measurements of Sea Surface Temperature 21 2.3.2 Factors Causing Alternative Sea Surface Temperature Measurements to Differ 23 2.3.3 Practical Validation Approaches 24 2.3.4 Sea Surface Temperature Validation and Uncertainty Budgets 30 2.4 Satellite Data Availability 31 2.4.1 Selected Missions Past and Present 32 2.4.2 International Collaboration on Data Sharing 35 2.4.3 Future Developments in Satellite SST 37 2.5 Science Applications 39 2.5.1 Operational Forecasting 39 2.5.2 Climate Monitoring and Research 41 Taking the Temperature of the Earth. https://doi.org/10.1016/B978-0-12-814458-9.00002-2 # 2019 Elsevier Inc. All rights reserved.

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2.5.3 Marine Biology 42 2.5.4 Concluding Remarks 46 References 46 Further Reading 55

2.1 2.1.1

Introduction Importance of Global Sea Surface Temperature

Two-thirds of Earth’s surface is liquid water, the upper boundary of the global oceans. This vast surface is in constant interaction with the atmosphere. In these interactions, the sea surface temperature (SST) plays a central role. Knowing the distribution of SSTis essential for numerical weather prediction (i.e., modern weather forecasting) and operational oceanography (such as forecasts for shipping and resource exploitation). Estimates of the large-scale changes in SST over the last 150 years are a sufficient constraint to drive simulations of a large proportion of the climatic variability and change seen during the period, because SST has a pivotal role in the climate system. As well as being a contributing factor to air-sea interactions and the large-scale climate, SST is a signature of many processes of scientific and practical interest, including impacts on highly prized ecosystems such as fisheries and coral reefs. Global sea surface temperatures are therefore important to measure. For the measurements to be relevant to a wide range of processes and applications (see Table 2.1), SST needs to be quantified on a wide range of scales of space and time. The only practicable means of obtaining data across the global oceans with sufficient frequency is from satellite-based Earth observation, also known as remote sensing. In situ measurements are crucial to the success of the overall SST observing system, even though their coverage is inevitably “spotty.” In situ measurements are needed to link satellite SSTs to the vertical variations of temperature below the surface and to correct errors, reduce uncertainties, and validate satellite SST observations. (In situ measurements of SST are also generally made together with other important variables.) Sea surface temperature has been an important application of remote sensing from space for nearly four decades, during which time user requirements have become more demanding, even as the resolution and uncertainty of SST products have improved. Improvements have been driven by technological developments in satellite sensors, by increased understanding of the physics relating SST to top-of-atmosphere radiance measurements, and by applying that physics to the inverse problem of inferring SST from

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Table 2.1 Selected Phenomena With a Signature in Sea Surface Temperature, and Their Space and Time Scales Phenomenon

Typical Temperature Impact

Length Scale

Coastal-zone responses to wind and orography Diurnal variation in response to solar cycle Coral bleaching events

0.2–2 K 0.1–5 K 0.3–3 K

1–100 km 5–1000 km 20–2000 km

Meanders and eddies in strong ocean currents

1–8 K

5–2000 km

Tropical instability waves (north-south traveling oscillations along equator) El Nin˜o and interannual variability

0.5–5 K

200–2000 km

0.5–5 K

500–5000 km

Climatology of ocean basins

35 K

104 km

Ocean trend response to anthropogenic climate forcing

0.1 K per decade

104 km

Extracted and updated from Robinson (2004), which provides further examples.

radiance using more sophisticated mathematical approaches. Using satellite and a variety of in situ data together has been essential to the progress made. The remainder of this introduction will elaborate on the general importance and context of SST observation, addressing: • the definitions of sea surface temperature • the spatial distribution and variability of SST across the globe and ocean basins • examples of the large-scale interactions of the atmosphere and ocean • the roles SST plays in air-sea heat and momentum fluxes at local scales • the relevance of SST to the climate system

2.1.2

Definitions of Sea Surface Temperature

2.1.2.1

Foundation Temperature

The concept of sea surface temperature sounds rather obvious: it is the temperature of sea water close to the ocean surface. In the context of thinking about different processes, spatio-temporal

Time Scale Hours Hours Days to weeks Weeks to months Months to years Months to years Seasons to decades Decades

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scales, and measurement technologies, it is helpful to define SST more precisely. Vertical variations in temperature are often present within the “surface” waters of the ocean (let’s say, within the upper 10 m), and so the key to a refined definition is to distinguish the sea surface temperatures at different depths. An internationally agreed set of definitions based on SST depths has been developed (Donlon et al., 2007) and will be described below. But before doing so, it is worth reflecting on the limitations of any depth-based classification of SSTs in the face of real-world variability. Depth-based definitions may be clear enough for relatively calm conditions, but what is the meaning of “1 m depth” during a violent storm, where wave heights may be an order of magnitude >1 m and the air-sea interface is characterized by prevalent foam and spray? Depth in the near-surface zone is clearly problematic in such conditions, although in this scenario mechanical mixing efficiently equalizes the water temperature near the surface; depth-based distinctions are then less relevant, and we can concentrate on definitions that work for less extreme situations. Nonetheless, we bear in mind that these definitions are ideas imposed on nature and not fully descriptive of nature’s possibilities. At 10 m depth, between 50% and 90% of sunlight impinging on the surface has typically been absorbed above that depth level. The light that does penetrate further is essentially blue-green. The vertical rate of change of the flux of light at 10 m on a clearsky day around noon at the equator gives an estimate of the maximum rate at which the sea water temperature is directly heated by sunshine at this depth and turns out to be of order 0.02 K h1. A globally representative daily-averaged rate would be 3 mK h1 which is <0.1 K/day. The temperature at 10 m is therefore relatively stable with respect to the direct heating by the daily cycle of solar heating. For this reason, the SSTaround this depth is conceptualized as a foundation temperature above which larger subdaily variability is superimposed, particularly under conditions of low to moderate wind speeds and high insolation (Soloviev and Lukas, 1997; Ward, 2006). This means that the depth of the foundation temperature at a given location changes from day to day, but not within the day. The “diurnal variability” will be described further below. The 10 m SST is measured by in situ profilers, with such measurements being made globally and routinely since about 2005 within the context of the Argo program (Roemmich et al., 2009). Prior to Argo, and continuing now, vertical profiles of temperature have been measured from ships, such as the current GO-SHIP program (Talley et al., 2016), and by single use

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

eXpendable BathyThermographs (XBTs, Abraham et al., 2013). The 10 m SST is not directly measurable from remote sensing, although a satellite-derived SST observed around local dawn can be used to estimate the foundation SST. Near-surface temperature stratification that develops during daytime is usually eroded by dawn by surface heat loss, causing density-driven mixing and wind-driven turbulent mixing (e.g., Sutherland et al., 2014). Satellite SSTs observed under well-mixed moderate (e.g., 7–12 m/s) wind conditions are also considered to be reasonable estimates of foundation temperature.

2.1.2.2

Near-Surface Depth Temperatures

Between the foundation temperature and the ocean skin (see below), the solar heating cycle in interaction with low wind-driven mixing may drive near-surface stratification, which may be quantified by the difference in temperature just below the skin (at a depth of order 1 cm) to that at a foundation depth. (“Stratification” refers to the concept that strata of water at different depths are distinct in their density because of differences in temperature; in reality, the temperature profile is continuously varying.) Since vertical stratification may form and dissipate on subdaily timescales driven by the solar cycle, the effect is referred to as “diurnal warming.” Over the majority of the ocean at a given time, the diurnal warming signal is zero or small (<0.1 K difference), but in areas of adequate insolation and persistent slow wind speeds, diurnal warming of order 1 K is readily observed (Fig. 2.1A–C). In satellite data, amplitudes of 5 K or more have been demonstrated (Gentemann et al., 2008; Merchant et al., 2008a,b). Fig. 2.1D illustrates a calculation of the maximum diurnal warming during a 24-h period using a 1-d ocean model. It illustrates the patchy and streaky nature of significant diurnal warming events, which are also seen in analyses of SST from geostationary satellites that resolve this cycle with regular observations (at intervals of 10 min to 1 h). The patterns reflect areas with low winds during the period of high sunshine. Large diurnal warming in terms of 1 cm to 10 m difference corresponds to strong stratification over a relatively shallow layer (<1 m), whereas events with lesser amplitude at the surface have the warmer temperatures over a deeper range (several meters). This reflects that roughly the same net-energy gain is involved, but it is differently distributed by wind-driven mixing. This is illustrated in Fig. 2.1A–C.

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0

0

–2

–2 Depth (m)

Depth (m)

10

–4

–6 –8

0

–1

–4

–6 –8

6

0

ms –10 31 28 29 30 Temperature (deg C)

(A)

–1

6

ms

Time: 06 h

(B)

–10 31 28 29 30 Temperature (deg C)

Time: 12 h

0

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40

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0 ms

(C)

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–10 28 29 30 31 Temperature (deg C)

–1

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Time: 12 h

–60

(D)

–1 –150

–100

–50

0

50

100

150

Fig. 2.1 (A–C) Near-surface profile of temperature from mooring data in the Indian Ocean. Each panel shows a depth profile of temperature: a “sun” whose area is relative to the daily maximum at noon and a scale showing wind speed. (A) Typical profile shortly after dawn, temperature is close to isothermal. (B) Mid-day profile under low wind (<3 m/s) conditions, showing a warm shallow layer. (C) Mid-day profile from the following day with moderate (>4 m/s) conditions, showing heat is mixed down more evenly over the water column by the greater wind mixing energy. (D) Model calculations of the daily maximum diurnal warming on May 24, 2014. The significant events are mainly in the summer hemisphere and tropics, and reflect areas of predominantly low wind conditions between dawn and mid-afternoon. (A–C) Data courtesy of Bob Weller, Woods Hole Oceanographic Institute.

2.1.2.3 The Thermal Skin of the Ocean The processes that influence the sea temperature in the uppermost few millimeters and less of the ocean are complex and very difficult to measure, both in the field and the laboratory. They are also difficult to address theoretically. Yet, the sea-water properties on these small vertical scales have profound influences on the way the ocean and atmosphere are coupled, how we can remotely sense SST, and how gases, including greenhouse gases, are exchanged. Our focus here is on the physical properties of the near-surface, but there is an ecosystem on the aqueous side of the interface, collectively called neuston (Zaitsev, 1997), which is influenced by chemical effects at the surface (Hardy, 1997). These, in turn, through the formation of biochemical surface film, influence the physics of the air-sea exchanges (Engel et al., 2017). For a fuller discourse on the near-surface properties, the reader is referred to Soloviev and Lukas (2014).

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Despite the complexity, the difference in temperature across the “thermal skin” is understood and modeled based on current understanding of the physics of the suppression of turbulence near the air-sea interface and thermal conduction. In general, the ocean loses heat to the atmosphere (see below). The subskin temperature is typically warmer than the temperature at the airsea interface to enable conduction of heat from below to the interface from which heat is lost. The “skin effect” quantifies this difference and is typically of order 0.2 K. An important point is that the thermal radiation to which infrared satellite sensors are sensitive is emitted from a layer near the interface and within the skin layer. There is therefore a difference, often on average stated to be 0.17 K (Donlon et al., 2002; Minnett et al., 2011), between the radiometric temperature of the ocean derived from infrared radiometer measurements and the subskin water temperature below the skin layer.

2.1.3

Global and Seasonal Distributions of SST

The dominant spatial variation of SST is of course latitudinal with colder and ultimately freezing temperatures occurring with increasing distance from the equator. This variation reflects the latitudinal distribution of annual heating of the surface by the Sun. The mean latitudinal variation is modulated zonally by the oceanic circulation, which is the 3-D response of the oceans to the supply of momentum to the ocean surface from winds, constrained by bathymetry and the geography of the ocean basins. The SST distributions for January and July in Fig. 2.3 illustrate the mean signature in SST of surface heating and circulation effects at two points in the annual cycle. There are several features of note in Fig. 2.2. The western boundaries of many ocean basins show areas of high SST gradient and intrusions of relatively warm water to higher latitudes. Very clear examples are the “Gulf Stream” along the eastern seaboard of the United States and the “Kuroshio Current” that intrudes into the north western Pacific around southern Japan. These temperature patterns reflect the slow circulation of the main ocean gyres, which are basin-scale circulations that transport significant quantities of heat from tropical to Polar Regions. Less obvious is the more distributed, equator-ward return flow in the eastern sectors of ocean basins. The seasonal cycle in SST in mid-latitudes is evident in the difference in SST distributions between the two plots.

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5

10

15

20

20

30

0

SST (C)

5

10

15

20

20

30

SST (C)

Fig. 2.2 Global distribution of sea surface temperature in (left) January and (right) July 2017. Data from Maturi, E., Harris, A., Mittaz, J., Sapper, J., Wick, G., Zhu, Z., Dash, P., Koner, P., 2017. A new high-resolution sea surface temperature blended analysis. Bull. Am. Met. Soc. 98, 1015–1026. https://doi.org/10.1175/BAMS-D-15-00002.1.

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

−2

−1

0

1

2

SST annual anomaly (k)

Fig. 2.3 Annual-mean sea surface temperature anomalies for 19 calendar years. Reproduced under CC-BY 4.0 from Merchant, C.J., Embury, O., 2014. Annual Sea Surface Temperature Anomalies: 1992-2010 (Version 1). figshare. https://doi.org/10.6084/ m9.figshare.1183470.v1.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

2.1.4

Large-Scale SST-Atmosphere Interactions

Water evaporates from the surface, representing a flux of both moisture (kg m2 s1) and energy (Wm2) that powers processes of convection, cloud formation, precipitation, and atmospheric circulation. The ocean surface is in motion in response in part to the stress imposed by winds, and these currents also transport energy around the planet. Analysis of remotely sensed wind-stress and temperature fields has demonstrated coupling across scales between the SST and the atmosphere—an important component of air-sea interaction (e.g., Chelton, 2013). The effects include local enhancement of precipitation by modification of low-level wind convergence, but larger, more persistent effects have been proposed. For example, it is proposed that the western boundary current in the North Atlantic, the Gulf Stream, influences the location of the prevalent storm tracks headed toward northern Europe by anchoring the atmospheric Gulf Stream Convergence Zone (e.g., Minobe et al., 2008). Further analysis suggests the interactions are both stronger and more episodic than previously recognized (O’Neill et al., 2017), which argues against a time-mean interpretation. Irrespective of the complexities of particular examples, accumulating evidence demonstrates the importance of SST-atmosphere interaction across scales and will be further elucidated by a mixture of observations, modeling and re-analysis exploiting high-resolution SST fields (Masunaga et al., 2018).

2.1.5

Sea Surface Temperature and Climate

SST is an essential climate variable for understanding the climate system and quantifying ongoing climatic change. Global mean SST has risen from decade-to-decade since the 1970s, with consequences for global weather patterns and oceanic ecosystems. Most striking is the increasing frequency of mass bleaching events of coral reefs (Hughes et al., 2017). Long-term climate data records of SST have been developed from satellite data (for example, Fig. 2.3) that confirm the quantification of climate change seen in in situ measurements. Fig. 2.3 shows SST annual anomaly over a period of years. An anomaly value means the difference for a particular place and time period from the average value (evaluated on a particular baseline period). As well as illustrating the warming tendency of ocean temperatures between 1992 and 2010, Fig. 2.3 also illustrates the interannual variability in the distribution of annualmean SST anomaly. For example, the contrast in pattern between 1997 and 1999 is marked, with the anomaly in most locations

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˜ o phechanging sign. This reflects the different phase of the El Nin nomenon in these years, which is the dominant mechanism and pattern of interannual variability in the climate system. The remainder of this chapter addresses how SSTs are remotely sensed, the validation of satellite products, the current state of availability of SST data via networks of international collaboration, and a selection of specific case studies of applications of SST information.

2.2

Retrieval and Measurement Methodology

Satellite radiometers can operate for a decade or more, collecting accurate radiance measurements from which brightness temperature at the top of the atmosphere can be derived, with low noise levels (give examples and refs). These brightness temperatures are related to but not the same as the SST because of the absorption, emission and scattering of radiation from the intervening atmosphere, and because the sea surface does not act as an ideal emitter (its emissivity is not 100%). The dominant effect (near nadir) in the infrared is the atmospheric influence, and in the microwave, it is the variability of surface emissivity around relatively low values over the ocean. For more detail, refer to the review by Merchant and Embury (2014). Prior to retrieval, the validity of radiance measurements for retrieval SST needs to be considered. In the infrared, the prime task is to identify and reject measurements that include significant radiance originating from clouds. Microwave measurements must be screened for radio frequency interference and areas of precipitation. Imperfections in these screening processes are a source of significant errors. The screening step is therefore of great importance in deriving SST products; for further information, see references.

2.2.1

Relationship of SST to Top-of-Atmosphere Radiances

The first physical effect that requires consideration is the link between the thermodynamic temperature (T ) of the water surface and the radiation that is emitted into the atmosphere. For an ideal surface (perfect blackbody), the spectral emittance, Bλ(T ), is given by the Planck equation (Planck, 1901):
1 (2.1) Bλ ðT Þ ¼ 2hc2 λ5 e hc=ðλkT Þ  1

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

where h is Planck’s constant, c is the speed of light in a vacuum, k is Boltzmann’s constant, λ is the wavelength, and T is the temperature above absolute zero. Bλ(T ) is the radiant energy flux per unit wavelength. From natural surfaces, a fraction of Bλ(T ) is emitted, and that wavelength-dependent fraction is called the emissivity of the emitting surface, denoted by ελ. Emissivity is also dependent on the angle of emission, generally becoming smaller as the emission angle increases. In the infrared, the ocean surface emissivity is very high (0.99 at λ ¼ 11 μm near nadir; Masuda et al., 1988; Hanafin and Minnett, 2005; Nalli et al., 2008a,b). The polarization and emission angle effects become pronounced at large emission angles (Masuda et al., 1988; Hanafin and Minnett, 2005; Nalli et al., 2008a,b; Branch et al., 2016), close to or beyond the edges of the swaths of most imaging radiometers. The emission from a natural surface is ελBλ(T ). The emitted radiation from the surface then interacts with the atmosphere, a wavelength-dependent fraction fλ reaching the top of atmosphere. 0 < fλ < 1, because the surface-emitted radiation is at least partly absorbed and/or scattered. Additionally, the atmosphere emits radiation at long wavelengths by Planck emission, both upwards, and also downwards. The downwards radiation is partly reflected at the surface. If the total atmosphere-related radiation is Lλ then the total at the satellite is Lλ + fλελBλ (T). When considering infra-red observations, radiance is often expressed as brightness temperature. The brightness temperature is the temperature required in Planck’s function assuming perfect emissivity to return the observed radiance. Thus, the brightness temperature, the temperature derived from the radiance measurement though Planck’s function using the spectral response function of the instrument channel depends on the SST but is not identical to it. The top-ofatmosphere radiance can be calculated in detail using a forward model, taking as input the distribution of temperature and composition of the atmosphere from surface to high altitudes. To derive SST, we are interested in the inverse process of deducing from the radiance the surface temperature. This inverse process is often referred to as retrieval.

2.2.2

Satellite Infrared Retrievals of SST

As the infrared radiation emitted from the sea surface propagates through the atmosphere, some is absorbed or scattered out of the path to the radiometer, attenuating the signal. When clouds are present, very little, if any, of the surface emission

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reaches the radiometer. Even in cloud-free conditions, a significant fraction of the sea surface emission may be absorbed by atmospheric molecules, which also emit energy at the same frequencies but at the molecules’ temperature, and in all directions. So, the brightness temperatures measured through the clear atmosphere at satellite height are cooler than the SST. This atmospheric effect (along with surface emissivity effects) must be compensated for to obtain accurate SST. The infrared spectral intervals where the atmosphere is relatively transparent, so-called atmospheric windows, are situated between wavelengths of 3.5–4.1 and 8–12 μm (Fig. 2.4). The peak of the Planck function for typical SSTs is close to the longer wavelength window. However, the main gas that contributes to the signal attenuation is water vapor, which is very variable both in space and time. Other molecules that contribute to the atmospheric effect are quite well mixed, and so cause a relatively constant effect that is simple to correct. The water vapor variability necessitates that an atmospheric correction algorithm be based on the information contained in

Atmospheric transmission

1.00 0.75

Polar Mid-latitude Tropical

0.50 0.25

Spectral radiance (W m-2 sr-1 μm-1)

0.00 30

T=30 °C

MODIS FM-1 20 (Aqua)

20

22 23 31

10

32

T=0 °C 0 0

2

4

6 8 Wavelength (μm)

10

12

14

Fig. 2.4 Spectra of atmospheric transmission in the infrared calculated for three typical marine atmospheres with integrated water vapor content of 7 (polar), 29 (mid-latitude), and 54 kg m2 (tropical). Spectral intervals where the transmission is high are suited to satellite remote sensing of SST. The lower panel shows Planck’s Function for four SSTs (0, 10, 20, and 30°C) with the relative spectral response functions for channels 20, 22, 23, 31, and 32 of the MODIS on the NASA Aqua satellite. The so-called “split-window” channels, 31 and 32, are situated where the sea surface emission is high and where the atmosphere is comparatively transparent but still exhibits a strong dependence on atmospheric water vapor content. Reprinted with permission from Minnett, P.J. (2019). Satellite Remote Sensing of Sea Surface Temperatures. In J.K. Cochran, H.J. Bokuniewicz, & P.L. Yager (Eds.), Encyclopedia of Ocean Sciences (Third Edition) (pp. 415-428). Oxford: Academic Press. ISBN 978-0-12-813082-7.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

the measurements themselves, pixel-by-pixel, or for a small group of pixels. Taking measurements at different spectral intervals in the windows where the water vapor attenuation is different means the measurements contain information about both the surface and the atmospheric state. The spectral intervals used are chosen to be useful and are defined by the designed characteristics of the radiometer, including the relative spectral response functions of the of the band-pass filters. They are usually referred to as bands or channels. McMillin and Crosby (1984) proposed that the atmospheric correction algorithm can be formulated thus: SSTij  Ti ¼ f ðTi  TiÞ

(2.2)

where SSTij is the derived SST and Ti, Tj are the brightness temperatures in two channels i, j. Further, by assuming adequate linearity, a simple SST algorithm takes the form: SSTij ¼ ao + aiTi + ajTj

(2.3)

where ao, ai, and aj are coefficients that have to be determined. This determination may be done by regression analysis of coincident satellite and in situ measurements (e.g., Kilpatrick et al., 2001), regression analysis of off-line simulations using radiative transfer modeling (e.g., Llewellyn-Jones et al., 1984), or “on-thefly” using numerical weather prediction fields and fast radiative transfer to give a dynamic linearization point closer to the observed state (e.g., Merchant et al., 2008a,b). The simple algorithm was applied to measurements of the Advanced Very High Resolution Radiometer (AVHRR) to retrieve the multichannel SST (MCSST), where i is channel 4 and j is channel 5. The simple algorithm has been modified to correct some of the limitations of the linearizations. One such modification is: SSTij ¼ bo + b1Ti + b2ðTi  TjÞSSTr + b3ðTi  TjÞ ð sec ðθÞ  1Þ (2.4) where SSTr is a reference SST (or first-guess temperature), and θ is the zenith angle to the satellite radiometer measured at the sea surface. This algorithm is the nonlinear SST (NLSST; Walton et al., 1998). Further improvements have been made (e.g., Kilpatrick et al., 2001, 2015; Marsouin et al., 2015; Petrenko et al., 2014), but all rely on the association between differences in measurements in different channels and the effect of the atmosphere in one of them. All multichannel atmospheric correction algorithms are limited by the small number of available wavelength channels, which are inadequate in providing the information needed to fully

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compensate for atmospheric variability, especially of water vapor. The retrieved SSTs are less accurate when atmospheric conditions deviate from the average. Alternative physically-based approaches utilize local atmospheric and surface conditions extracted from numerical weather prediction models as input to a radiative transfer model, providing both a “first guess” of the top-of-atmosphere brightness temperatures and a linearization point. The difference between these calculated brightness temperatures and the ones observed by the satellite instrument are used to calculate an adjustment to the “first guess.” If the first guess is reasonably accurate, the difference in brightness temperatures may be obtained by a linear expansion, i.e.: Δy ¼ KΔx

(2.5)

where Δy ¼ (yobserved  yguess), Δx ¼(xtrue  xguess), and K is the matrix of partial derivatives (the Jacobian) of the components of y w.r.t. the components of x. The Jacobian is also calculated by the radiative transfer model for the first-guess conditions. Although the radiative transfer calculation uses the full-profile information, the number of channels available for the retrieval is inadequate to adjust the full atmospheric state, thus only a few (the most influential) parameters are contained in x, e.g., x ¼ [SST, TCWV]T. Eq. (2.5) is still only an expression of the forward problem, since we already know Δy and wish to obtain Δx. While a simple inversion of Δx ¼ K1Δy is always possible, this solution leads to the prospect of substantial noise amplification, particularly when the absorption by water vapor is high and the Jacobian component of SST is concomitantly small. The normal equation (Gauss, 1828) for the least squares solution to Eq. (2.5) gives:  1 (2.6) Δx ¼ KT K KT Δy The term that multiplies Δy (in this case, (KTK)1KT ) is sometimes referred to as the gain matrix (K1 from the simple inversion is another example); thus, Eq. (2.6) may be simply expressed as Δx ¼ GΔy. However, even the least squares solution will be subject to some noise amplification, and two distinct approaches are used to further mitigate the impact of this. The approach known as optimal estimation uses prior estimates of uncertainty in the initial guess and observed brightness temperatures to weight the components of the gain matrix. As an example of the stochastic approach, an optimal estimation scheme has been developed (Merchant et al., 2008a,b, 2009). An alternative approach uses a dynamic case-by-case estimation of the noise in the inversion

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

(Koner et al., 2015; Koner and Harris, 2016a,b). The net effect of both methods is to “damp” the gain matrix, resulting in a final retrieval that is part-way between the first guess and the solution that would be obtained from Eq. (2.6), meaning that a solution that reduces errors from amplified noise is delivered as the result. Irrespective of the mathematical complexities, these schemes essentially use dynamic coefficients to multiply the observedmodeled brightness temperature differences. The key is that the coefficients themselves are calculated on a case-by-case basis using localized information on atmospheric conditions, thereby minimizing nonlinearity effects, with concomitant reduction of regional biases. The atmospheric correction algorithms are applicable to cloud-free conditions. Clouds in the field of view of the infrared radiometer must be identified and screened from the SST retrieval process. Even a small cloud fraction, say 3%–5% dependent on cloud type and height, can produce intolerable errors in the retrieved SST. Thin, semi-transparent clouds, such as cirrus, can have similar effects. Thus, the identification of clouds is of paramount importance in deriving accurate SSTs (e.g., Kilpatrick et al., 2001; Merchant et al., 2005). The presence of clouds is the greatest disadvantage to retrieving SSTs by satellite infrared radiometry. Clouds cover large ocean areas so compositing the cloud-free parts of many images is necessary to produce gap-free SST fields, but this produces substantial sampling errors in spatially and temporally averaged fields (Liu and Minnett, 2016; Liu et al., 2017). Atmospheric aerosols can also introduce errors in SST retrievals. Volcanic eruptions may inject aerosols into the cold upper troposphere and stratosphere that can bias the SST too cold by 1–2 K or more (Reynolds, 1993; Blackmore et al., 2012); SSTs from dual-view radiometers are much more robust to aerosol contamination (Brown et al., 1997; Merchant and Harris, 1999). A more difficult issue results from aerosols in the lower, warmer levels of the atmosphere that cause errors in the derived SSTs, smaller but still significant amplitude (Arbelo et al., 2003; Vazquez-Cuervo et al., 2004; Martı´nez Avellaneda et al., 2010), since the aerosol layers may be optically thick enough to cause significant multiple scattering and the relationship between brightness temperatures in the two views is less well-defined than in the volcanic case. However, with sufficient channels (e.g., MODIS) and adequate first guess information on aerosols (e.g., NGAC, Wang et al., 2018), SST retrieval may be improved in the presence of aerosols (Koner and Harris, 2016a,b).

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2.2.3

Satellite Microwave Retrievals of SST

The first passive microwave SST algorithm was developed for the Scanning Multichannel Microwave Radiometer (SMMR) on NIMBUS-7 (Wilheit and Chang, 1980; Lipes et al., 1979). The algorithm was a two-stage linear regression using a linear combination of brightness temperatures and the earth incidence angle. It was a two-stage algorithm, first calculating wind speed then selecting the SST regression coefficients based on whether the winds were greater than or <7 m s1. SMMR had significant calibration problems that resulted in large errors in the retrieved SST, limiting its usefulness (Milman and Wilheit, 1985). Since December 1997, a series of satellites have carried wellcalibrated passive microwave radiometers capable of accurately retrieving SST. The first accurate PMW SST data was from the high-inclination orbit Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). This was quickly followed in 2002 by the first global PMW SST data from AQUA’s Advanced Microwave Scanning Radiometer, Earth Observing System (AMSR-E). In 2003, the Coriolis satellite carried the WindSAT instrument, capable of calculating SST (Wentz et al., 2005). In 2012, a second AMSR was launched on JAXA’s Global Change Observation Mission—Water satellite (Hihara et al., 2015; Gentemann and Hilburn, 2015). These data demonstrated how through-cloud SST measurements offer unique opportunities for research into air-sea interactions, improved coverage in persistently cloudy regions, and could improve the existing operational SST products (Wentz et al., 2000; Xie, 2004; Chelton and Wentz, 2005). Two groups have routinely produced official JAXA/NASA SSTs from AMSR-E and AMSR2, each using different retrieval algorithms developed and refined over years of experience with PMW data. The NASA group has also produced SSTs from WindSAT. These algorithms have a long heritage with both institutions. The JAXA algorithm produces SST using the 6 V channel (Shibata, 2005, 2006). The NASA algorithm determines SST and wind speed at the same time using a radiative transfer-based two-step algorithm (Wentz and Meissner, 2007). For both products, data that are affected by rain, high wind speeds, land contamination, sea ice contamination, sun glitter, and RFI are excluded, but each group has developed their own exclusion criteria. Hosoda et al. (2006) compared the JAXA AMSR-E SSTs to collocated infrared satellite SSTs, for 7 months of data in 2003, and found a 0.0 K bias and standard deviation of 0.71/0.60 K for day/night. Gentemann (2014) compared the NASA AMSR-E SSTs from June 2002 through October

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

2011 to global drifter SST data, and results showed a bias of 0.05 K and standard deviation of 0.48 K. Both products are widely used in the operational ocean and atmospheric modeling communities as well as for scientific research and applications (REFs). A more recent approach to retrieval of PMW SSTs using optimal interpolation has demonstrated a bias of 0.02 K and accuracies of 0.47 K (Nielsen-Englyst et al., 2018). This algorithm uses a forward model, which includes a priori information about the atmospheric and ocean state to calculate simulated brightness temperatures. Optimal interpolation provides estimates of retrieval uncertainty and sensitivity, which can be used to exclude erroneous retrievals and for error estimation.

2.3

Validation

Validation of satellite-derived SSTs is an essential step to provide confidence in both the derived SST and its associated uncertainty. The traditional approach to SST validation is to compare the satellite SST to in situ surface-based SST measurements by generating a set of coincidences, referred to as a match-up database (MDB), within defined spatial and temporal limits. Statistical methods are then applied to the MDB, from which the mean difference (the in situ data are often erroneously treated as if they are truth, i.e., they are free of error) and standard deviation are usually calculated, the latter considered to be the uncertainty of the satellite SST measurement (Minnett, 2010). Recent developments have highlighted limitations to this approach where the availability of suitable in situ data limits the validation and the distribution of available matchups do not properly describe the real errors in the satellite SST retrieval. In this chapter, we look at advances in SST validation methods since the review by Minnett (2010) reconsidering its limitations and assumptions. We begin by reviewing available in situ measurements of SST.

2.3.1

Types of In Situ Measurements of Sea Surface Temperature

In situ measurements of SST have been routinely made since the early 1850s. Over this time, methods have changed considerably from early measurements from Voluntary Observing Ships (VOS) using mercury-in-glass thermometers taking the temperature of surface seawater in a bucket to the current calibrated thermistors on drifting buoys. A review of the impacts of these changes over time on in situ SSTs is provided by Kent et al.

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Monthly measurement count

Measurement count

1.25 × 106

Fig. 2.5 Temporal variation in total number of available monthly reference measurements for SST CDR assessment from 1978 to 2018.

Key: Drifter VOS GTMBA Argo Radiometer

9.37 × 105

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0 1980

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(2010) and how their uncertainties are estimated is provided by Kennedy (2014). For satellite validation, we are only really interested in in situ data from about 1978 onwards (with the launch of TIROS-N). Ideally, we are interested in in situ data that provides global or near global coverage.1 Consequently, we have five main in situ measurement types available for satellite SST validation, and the total numbers are shown in Fig. 2.5. Drifting buoy networks are the most prevalent in situ measurement type (Lumpkin et al., 2016), which provides good coverage from the early 2000s across most regions. Near-global coverage can be obtained using measurements from Argo floats (Roemmich et al., 2009), but its temporal sampling is much smaller. The Global Tropical Moored Buoys Array (GTMBA) (McPhaden et al., 2010), has the advantage of being in open ocean locations and having consistent long-term calibrated records at many locations. Measurements from engine room intakes or hull-mounted sensors from VOS, although not global in nature, are an important source of data for satellite SST validation owing to their availability back to the start of the satellite SST era. Finally, we have ship-borne radiometers (Donlon et al., 2014), which are the only in situ data source to provide a temperature at the same depth (SSTskin) as the satellites (for IR satellite sensors at least). 1

Although VOS, GTMBA, and ship-borne radiometers do not provide global coverage, they are essential for satellite SST validation due to their unique data qualities (see text for further details).

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45

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Fig. 2.6 Spatial variation of in situ measurement types for satellite SST validation for January 2017 from iQuam http:// www.star.nesdis.noaa.gov/sod/sst/iquam/ (Xu and Ignatov, 2014).

Recent advances in collating available in situ data for satellite SST validation include the In Situ SST Quality Monitor (iQuam) developed by Xu and Ignatov (2014), which provides quality controlled in situ data in a common data format through an online portal (http://www.star.nesdis.noaa.gov/sod/sst/iquam/). An example of the available data from iQuam for 1 month is shown in Fig. 2.6. Most of the available in situ SST measurements come from platforms measuring the temperature at the surface. However, the use of nearest to surface measurements from Argo for validation (Martin et al., 2012) as well as more recent validation using unpumped Argo data that provides measurements closer to the surface (Castro et al., 2014; Zenghong et al., 2017) have highlighted the suitability of subsurface temperature measurement for satellite SST validation. The UK Met Office now offers an Integrated database of temperatures and salinity, HadIOD (Atkinson et al., 2014), which combines long-term surface and subsurface temperatures taken in situ, making it an excellent source of data for validation of long-term satellite SST records.

2.3.2

Factors Causing Alternative Sea Surface Temperature Measurements to Differ

A number of factors can cause SST measurements to differ, including the measurement depth, the wind speed, and the degree of solar insolation (as discussed in Section 2.1.2). The difference

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between SSTskin and SSTsubskin reduces to a mean bias of 0.17 K when the surface wind speed is >6 m s1 (Donlon et al., 2002). However, at low wind speeds and high solar insolation, the difference can vary up to several kelvin (Donlon et al., 2002; Minnett et al., 2011). We also have differences arising from the defined spatial and temporal limits used in generating the MDB, and these can be significant as determined by Minnett (1991), who recommended limits of 10 km and 2 h, which would introduce a geophysical match-up error of up to 0.2 K. All of these sources of error will have an associated uncertainty that needs to be considered in the process of satellite SST validation. The traditional approach in estimating uncertainties has been to calculate the mean difference and standard deviation from the MDB and assume these define the uncertainty of the satellite SST. An alternative method was shown by O’Carroll et al. (2008), which deduced the uncertainty for each dataset in a 3-way analysis between AATSR, AMSRE, and drifting buoys. This approach was also adopted by Gentemann (2014) in a comparison of MODIS, AMSRE, and drifters as well as by Xu and Ignatov (2016) to identify uncertainties between different datasets in iQuam from comparisons against AVHRR and AATSR data. A more robust approach is to consider the geophysical effects as well as the uncertainties of the satellite and in situ measurements. We will revisit this in Section 2.3.4.

2.3.3

Practical Validation Approaches

Practical methods to ensure the quality of satellite SST products by validation against in situ data are numerous, including using data from ships, aircraft, and many floating platforms. In all cases, the satellite data should be those flagged as highest quality (that generally means “confidently cloud free”). In reality, as there are no contiguous global in situ datasets, all available in situ data should be used based on its own unique contribution. The first reported validation of satellite SSTs was in 1967 (Allison and Kennedy, 1967), and many additional papers summarizing the validation of satellite SSTs retrieved from individual satellite sensors have been published since. The intention here is to focus on the complementarity of the different methods used and to highlight ongoing challenges. The first part of any validation approach is to compute several quantitative metrics. Initially, these will simply be the mean (or median) difference and standard deviation of all the matchups in the MDB, and these can be calculated for each in situ type individually. The next step is to look at the geographical variation

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

of these differences. Again, this can be done for any in situ type, but the near global nature of drifters and Argo make these ideal for this application. Although not global in nature, ship-borne radiometers, GTMBA, and VOS matchups provide complementary results to those from the global comparisons to drifters and Argo. Ship-borne radiometers provide a measure of SSTskin, which is the same SST depth as IR satellite instruments provide (although SSTsubskin is often retrieved to compensate for issues with sensor calibration). The importance of SSTskin measurements from ship-borne radiometers cannot be overemphasized as, if done correctly, any comparisons to drifters or Argo will have used adjustments from models to account for differences in time and depth between the satellite and in situ data. For example, Embury et al. (2012) used an implementation of a combined skin (Fairall et al., 1996) and diurnal variability, DV (Kantha and Clayson, 1994), model when comparing the ATSR Reprocessing for Climate (ARC) dataset to drifters. An example of comparisons between AATSR with drifters, GTMBA, and Argo is shown in Fig. 2.7, where the methods of Embury et al. (2012) have been used to adjust the in situ SST measurement to be equivalent to the satellite SSTskin measurement. Once we understand the geographical variation of the validation results, we then need to consider the main features that would likely affect the SST retrieved from the satellite sensor. For an IR sensor, this may include retrieval effects (e.g., dependence on total column water vapor [TCWV]) or match-up effects (e.g., dependence on wind speed, spatial and temporal separation of in situ, and satellite matchups). In the end, it is up to each data provider to show there is no dependence in the validation results on any of these effects, and each satellite instrument will have its own set of effects. An example comparison of the dependence of data from the Sea and Land Surface Temperature Radiometer (SLSTR) with both drifters as a function of several retrieval and geophysical effects is shown in Fig. 2.8. The methods of Embury et al. (2012) have again been used to adjust the drifter SST measurement to be equivalent to the satellite SSTskin measurement. An ongoing challenge for satellite SST validation is in understanding the temperature distribution in the upper few meters of the ocean where more profile measurements (e.g., Minnett, 2010) are needed to improve our knowledge in this area (e.g., Scanlon et al., 2013). However, by having both ship-borne radiometer and in situ measurements at depth we can directly compare the validation results for each type and, of course, they should agree (within uncertainties), providing not only a validation

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ESA SST_CCI CDR 2.0 ATSR L2P SSTskin 1-pix QL = 5 versus drifters/GTMBA/Argo (skin-skin) Drifters

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Fig. 2.7 (Upper) Latitude/longitude variation of the median discrepancy for the ESA SST_CCI CDR2.0 AATSR dataset compared to drifters, GTMBA, and Argo for day and night matchups, and (lower) time/latitude variation of the same statistical measure. The in situ SST measurements have been adjusted to the same depth and time as the satellite measurement, as described in the text.

of the satellite SST but the skin and DV models as well. This approach is also recommended for demonstrating traceability to SI for long-term satellite-derived SST datasets (Minnett and Corlett, 2012; Corlett et al., 2014). Traceability to SI standards

SLSTR WCT SSTskin versus drifter SSTskin

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Fig. 2.8 Dependence of the median and robust standard deviation between SLSTR SSTskin and drifter SSTs as a function of (upper row) latitude, satellite zenith angle, solar zenith angle, and (lower row) total column water vapor (TCWV), time difference and wind speed. Daytime results are shown in red and night time results are shown in blue (for 2-channel retrievals) and in green (for 3-channel retrievals). The drifter SST measurements have been adjusted to the same depth and time as the satellite measurement as described in the text.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

0.5

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Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

matters, as it justifies the combination of measurements from different sensors used to validate the satellite SST retrievals, within the calibration uncertainties. SI-traceability also permits the generation of long time series of validation data that may include improvements in in situ sensors, and permit merging of satellite data from successive instruments that, for whatever reason, do not have a period of overlap. Measurements from ship radiometers are a good example of establishing SI-traceable calibration, as in addition to having internal blackbody calibration targets (Minnett et al., 2001; Donlon et al., 2008), the accuracy of which is established by laboratory measurements before and after deployment at sea, there have been a series of international workshops involving National Metrology Laboratories, NIST (National Institute of Standards and Technology) in the United States, and NPL (National Physical Laboratory) in the United Kingdom, to establish an unbroken chain of calibration from the measurements at sea to national SI standards (Rice et al., 2004; Barton et al., 2004). The latest, fourth, workshop in this series was held at the NPL in summer of 2016 (Theocharous et al., 2018, 2019). Validation of long-term satellite SST datasets has increased in recent years as the need for stable long-term satellite SST records has increased. Such records are mainly based on satellite data from the AVHRR series (Ignatov et al., 2016; Pisano et al., 2016) and ATSR series (Tsamalis and Saunders, 2018). The number of long-term satellite SST records will only increase over time with new sensors such as VIIRS capable of extending data records from the MODIS series (Kilpatrick et al., 2015). Validation of long-term satellite SST records requires long-term stable in situ records. Of the available in situ measurement types, the GTMBA moorings provide consistent SSTs in a defined geographical region across the whole time period of long-term satellite-based SST datasets, with the number of observations available increasing over time due to (a) changes in reporting frequency (e.g., every hour to every minute) and (b) further deployments. In Merchant et al. (2012) the stability of the long-term ARC record was assessed relative to components of the GTMBA over a 20-year period. Merchant et al. (2012) concluded that over the period 1994–2010 that regionally collocated ARC and GTMBA SSTs are stable, with better than 95% confidence, to within 0.005 Kyr1. Subsequently, Berry et al. (2018), applied the same step detection methods, using a Penalized Maximal t-Test applied to drifters, GTMBA and Argo matchups. An example from Berry et al. (2018), for GTMBA matchups is given in Fig. 2.9, showing (A) the monthly mean differences (satellite in situ) for the individual ensemble members are shown in red, and (B) histogram of the data of the change points detected across all ensemble members.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

29

0.3

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Fig. 2.9 Application of a Penalized Maximal t-Test to matchups between ATSR and the GTMBA highlighting step changes (instabilities) between the two records. (A) Monthly mean differences for individual ensemble members. (B) Histogram of the change points detected across all ensemble members. Reproduced under CC-BY 4.0 from Berry, D.I., et al., 2018. Stability assessment of the (A)ATSR Sea Surface Temperature climate dataset from the European Space Agency Climate Change Initiative. Remote Sens. 10, 126. doi:10.3390/rs10010126.

As well as a need for long-term stable in situ records for satellite SST validation there are other challenges to be addressed, especially in coastal and other data sparse regions. Coastal areas are especially challenging due to often shallow bathymetry and tidal mixing influencing diurnal warming in these regions (Zhu et al., 2014). The next generation of geostationary sensors with more frequent (10 min) temporal sampling will provide a unique insight in coastal and other shallow water areas (Ditri et al., 2018). Ongoing efforts to find new ways to validate satellite SSTs using benthic temperature loggers (Brewin et al., 2018) and surfers Brewin et al. (2017) are welcome. Other data sparse regions include the Tropical Warm Pool (Zhang et al., 2016) where significant atmospheric water vapor loading can limit SST retrievals, and the Arctic (Castro et al., 2016), an area where anomalous, dry atmospheric conditions are often found and also ice aerosols that are difficult to detect in the satellite measurements (Vincent et al., 2008a,b).

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2.3.4

Sea Surface Temperature Validation and Uncertainty Budgets

A robust approach to satellite SST validation requires consideration of the differences between the satellite and in situ SSTs as discussed in Section 2.3.2. Corlett et al. (2014) proposed a validation uncertainty budget with five main contributing terms. Here, we expand the satellite SST validation uncertainty budget to include an additional term to account for cloud contamination in the satellite data, so that our uncertainty budget is now given in Eq. (2.7): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi (2.7) σ Total ¼ σ 21 + σ 22 + σ 23 + σ 24 + σ 25 + σ 26 The first five components σ 1 (satellite), σ 2 (in situ), σ 3 (geophysical—spatial), σ 4 (geophysical—depth), and σ 5 (geophysical—temporal) retain their same definition as in Corlett et al. (2014), and the additional term σ 6 is included to allow for cloud contamination. However, owing to asymmetric and intermittent nature of cloud contamination errors, this term is not likely to be Gaussian and requires new methods to determine its magnitude and shape. It is important to note that σ 1—the satellite uncertainty—is provided with the data and is not an output of the validation (which would be circular). Methods to define and refine models for the uncertainty budget of the satellite SSTs are emerging (e.g., Bulgin et al., 2016). A key advantage of this approach to satellite SST validation is that it allows us to examine the distribution of satellite-reference SST differences as a function of uncertainty and to subsequently validate the product uncertainties themselves. An example of uncertainty validation is shown in Fig. 2.6 from comparing ESA SST_CCI V1.1 AVHRR product uncertainties against drifting buoys. The green lines in Fig. 2.10 indicate the theoretical dispersion of uncertainties and the blue lines indicate the calculated dispersion; the red lines indicate the standard error. In an ideal case the dispersion of the blue lines tracks the shape of the green lines, which we see is the case for night time. However, in daytime the calculated dispersion diverges within the theoretical bounds for product uncertainties >0.4 K indicating the uncertainties are overestimated. Further details on the concept and methods of uncertainty validation are given in Corlett et al. (2014) and these methods have been successfully applied to ATSR ARC data (Lean and Saunders, 2013), ESA SST_CCI ATSR data (Bulgin et al., 2016) and ESA SST_CCI AMSR2 data (Nielsen-Englyst et al., 2018).

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

AVHRR18_G SSTskin

versus drifter: SST_CCI v1(dt3)

Day time

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Fig. 2.10 Uncertainty validation of ESA SST_CCI V1.1 AVHRR data against drifting buoys.

One of the main assumptions in current methods for uncertainty validation that requires careful consideration is the treatment of σ 3 (geophysical—spatial), which represents the error introduced by comparing an in situ point measurement to a satellite footprint. Castro et al. (2017) measured the subpixel variability within 1-km MODIS SSTs using an IR radiometer (BESST; Emery et al., 2014) flying on a UAV. Although significant variability was observed, their results showed a mean value of O(0.1 K), a result very similar to the original work of Minnett (1991). Further such studies to examine the spatial variability within a satellite SST pixel would be welcome (Castro et al., 2018).

2.4

Satellite Data Availability

Satellites capable of observing the SST accurately are typically onboard satellites with two types of orbit around the earth: sun synchronous polar orbiting and geostationary. The specific satellite orbit has consequences for the spatial and temporal sampling (see e.g., Montenbruck and Gill, 2012). The polar-orbiting satellites typically provide a complete global SST coverage within a day or two, based upon the 100 min revolutions around the earth (see e.g., Goldberg et al., 2013). Conversely, the geostationary satellites are placed over the same position at equator and sample the same disk of the earth with a subdaily temporal resolution, from 3 hourly to 10 min (Schmetz et al., 2002; Menzel and Purdom, 1994).

0.8

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2.4.1

Selected Missions Past and Present

The number of satellite mission series with the capability of observing SST that is currently active is estimated by the Committee on Earth Observation Satellites (CEOS) to be at least 20. Including past and planned satellites’ series, this number will more than double. It is therefore not possible within the scope of this book to provide a full overview of all the satellite series. Instead, focus will be on describing a few of the most used selected satellite mission time series.

2.4.1.1 AVHRRs The longest record of satellite observed SSTs is from the Advanced Very High Resolution Radiometer (AVHRR; Cracknell, 1997) series on board the NOAA polar-orbiting satellites. The series represents an unbroken record of SST observations since 1981 with the same type of instrument that was first seen on the NOAA-7 satellite. The AVHRR instrument measures the top of atmosphere radiation in five spectral bands as listed in the table below. The satellite is in a sun-synchronous orbit with local equator crossing times in the morning, early night, or afternoon/night and have a swath width of 3000 km, giving a very good overview. The full spatial resolution of the AVHRR observations is 1.1 km, but due to onboard data storage and transmission limitations prior to the launch of Metop in 2006, the full resolution observations (HRPT) are only available if they have been transmitted directly to a receiving station, or recorded and stored for small sections (up to 10 min) of an orbit. The global data are derived from an onboard subsampling and averaging of the full resolution AVHRR data. Four out of every five samples along the scan line are used to compute one average value and the data from only every third scan line are processed, yielding 1.1 km by 4 km resolution at the subpoint for the Global Area Coverage (GAC). The first AVHRR was a four-channel radiometer, first carried on TIROS-N (launched October 1978). This was subsequently improved to a five-channel instrument (AVHRR/2) onboard the NOAA-7 satellite (launched June 1981). The AVHRR/3 with six channels was first flown on NOAA-15, launched in May 1998. For SST applications, most of the observational records start with the AVHRR/2 instrument, in 1981, even though efforts are currently being made within the ESA CCI project to extend the SST record back to 1978. The primary channels for the AVHRR instruments to observe the SSTare the spectral channels at 11 and 12 μm (McClain et al., 1985; Walton et al., 1998). These channels are

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

centered in atmospheric window and can be used for traditional split-window retrievals as described in Section 2.2.2. For nighttime SST retrievals, the spectral channel at 3.7 μm can also be used. The observations from the visible and SWIR channels are primarily used for cloud masking (Saunders and Kriebel, 1988; Dybbroe et al., 2005; Kilpatrick et al., 2001). A form of the split-window algorithm was applied to generate the most often used long time series AVHRR SST product from the Pathfinder project (Kilpatrick et al., 2001; Casey et al., 2010). AVHRR SST is a global 4 km daily. Since the late 1980s, SST retrievals in cloud-free areas can be combined into weekly, monthly, and yearly SST fields. The Pathfinder data set has been used for a large number of applications. In recent years, an alternative global AVHRR climate data record has been generated and validated within the ESA Climate Change Initiative (Merchant et al., 2014).

2.4.1.2

(A)ATSRs

Another long term SST record is the satellite series of (Advanced) Along Track Scanning Radiometers (A)ATSRs, which are multichannel imaging radiometers with the principal objective of providing data concerning global Sea Surface Temperature (SST) to the high levels of accuracy and stability required for monitoring and carrying out research into the behavior of the Earth’s climate (e.g., Llewellyn-Jones and Remedios, 2012). These radiometers have been designed to achieve very high precision and stability of calibration (Smith et al., 2012), and it uses a unique dual-view of the Earth’s surface. This means that the Earth is viewed sequentially through two different atmospheric pathlengths, which is useful for performing an improved atmospheric correction. The major advantage of the dual-view is that the change in optical path is defined by geometry rather than spectroscopy, thus precise knowledge of the spectral properties of the intervening absorber(s) is less critical. It is this capability that allows SSTs from the (A)ATSR series to be largely robust to the effects of volcanic aerosol when the algorithms are designed correctly (e.g., Merchant and Harris, 1999). The spectral channels are similar to the AVHRR instruments, with the main channels for deriving the SSTs being the 3.7, 11, 12 μm and with an extra channel at 1.6 μm, which is used for cloud screening. The spatial resolution of the satellite observations is 1 km at nadir, with a swath width of 500 km. The first ATSR was launched on the ERS-1 satellite, launched in July 1991, followed by the ATSR-2 on the ERS satellite (launched in April 1995) and

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the AATSR on ENVISAT (launched, March 2002). The full record of (A)ATSR observations thus covers from August 1991 to April 2012. The additional information from the two different views and the good stability of the instruments means that the SST observations are very good, and the AATSRs have often been used as a climate reference data set that other satellite data sets can be referenced against (O’Carroll et al., 2012; Høyer and Karagali, 2016). Recently, the satellite series with dual view radiometers has been extended with the launch of the Sentinel 3 satellites carrying the SLSTR sensor.

2.4.1.3 MODIS The Moderate Resolution Imaging Spectroradiometers (MODIS) were launched on the polar-orbiting Terra and Aqua satellites in December 1999 and May 2002, respectively, and are still operating (as of September 2018). They included many instrumental developments and were designed to take measurements for studying many components of the earth system, land, atmosphere, and cryosphere, as well as the ocean (Kilpatrick et al., 2015). The MODIS observations have a spatial resolution of 1 km at nadir with a swath width of 2330 km; it has 36 spectral bands, including three bands at 4 μm and the split-window bands—11 and 12 μm—for SST retrievals (Esaias et al., 1998; Hosoda et al., 2007; Kilpatrick et al., 2015). The additional bands of the MODISs are particularly useful for cloud masking (Ackerman et al., 2008).

2.4.1.4 VIIRS Exploiting the experience gained with the two MODISs, the first VIIRS (Visible Infrared Imaging Radiometer Suite) was launched on the Suomi-National Polar-orbiting Partnership satellite (S-NPP) on October 28, 2011. VIIRS, developed to replace the AVHRRs on the NOAA satellites, is a scanning radiometer with 22 channels in the visible and infrared parts of the electromagnetic spectrum; the channels are a subset of those of MODIS. The spatial resolution at the sea-surface of the bands used for SST is 0.75 km at nadir, but VIIRS has an innovative approach to reduce the growth of pixel size across the swath away from nadir (Schueler et al., 2013; Gladkova et al., 2016). The infrared bands used for SST measurements are the familiar two in the 10–12 μm wavelength interval, and two in the 3.7–4.1 μm atmospheric transmission window for use at night. NOAA-20, the first of four planned satellites with a VIIRS, was launched on November 18, 2017. The VIIRS series is intended to provide SSTs into the late 2030s.

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2.4.1.5

SEVIRI

The main payload of the current Meteosat geostationary satellites, the Spinning Enhanced Visible and Infrared Imager (SEVIRI; Aminou, 2002), has 12 spectral channels including eight in the infrared with a surface resolution of 3 km at the subsatellite point. The primary location of Meteosat is at 0°E above the Equator, and SEVIRI provides full-disk images in about 12 min. Some of the infrared channels are used for SST retrieval.

2.4.1.6

GOES Imager

There are two Geostationary Operational Environmental Satellites (GOES) in operation at the same time, one at 75°W and the other at 135°W. The GOES Imager on GOES 12–15 satellites from 1994 to the present is still operating and has five spectral channels. The spatial resolution of the infrared channels is 4 km at the subsatellite point (Maturi et al., 2008).

2.4.1.7

Advanced Himawari Imager (AHI) and the Advanced Baseline Imager (ABI)

The first of a new generation of geostationary meteorology satellites of the Japan Meteorological Agency (JMA), Himawari-8, began operation on July 7, 2015. At 140.7°E above the equator, it carries the first of a new series of visible and infrared imagers: the Advanced Himawari Imager (AHI; Bessho et al., 2016). The AHI has 16 spectral bands with five in the infrared and can be used for SST retrievals (Kurihara et al., 2016). These IR bands have a spatial resolution of 2 km at nadir. The AHI provides full-disk images of most of the Pacific Ocean as rapidly as every 10 min, with smaller areas being sampled more frequently as needed. The first of a new series of NOAA geostationary satellites, GOES 16, became operational on December 16, 2017. It is above 75.2°W. This series of geostationary satellite, planned to operate into the late 2020s, carries the Advanced Baseline Imager (ABI; Schmit et al., 2005; Schmit et al., 2017; Griffith et al., 2017), which has very similar capabilities to those of the AHI, including infrared bands suitable for the derivation of SST.

2.4.2

International Collaboration on Data Sharing

As described earlier, satellite SST observations are obtained from numerous missions from space agencies all over the world. To aid the users and facilitate the use of the different SST products, international coordination and collaboration of the SST products are crucial. Within the SST community, there is a long tradition

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for international collaboration and data sharing. The main forum for the collaboration is the Group for High Resolution Sea Surface Temperature (GHRSST), which was established about 20 years ago (Donlon et al., 2009). The GHRSST, an open international science group with an elected science team of experts worldwide, was established 20 years ago and coordinates the data production and related research. GHRSST has developed the concept of a Regional/Global Task Sharing Framework (R/GTS), which established an international set of Regional Data Assembly Centres (RDACs), each of which delivers data in a timely fashion to the GHRSST Global Data Assembly Centre (GDAC) online at https://podaac.jpl.nasa.gov/GHRSST and the regional user communities. All data products within the GHRSST data assembly centers comply with the file naming and formats that have been developed within GHRSST and described in the GHRSST Data Processing Specification 2.0 revision 5 (GDS2.0, https://www. ghrsst.org/wp-content/uploads/2016/10/GDS20r5.pdf ). Currently, there are more than a 100 different SST datasets on the GHRSST GDAC that services a wide variety of applications, from operational to climate applications. International collaboration and coordination on SST is organized within the Group for High Resolution Sea Surface Temperature (www.ghrsst.org). Marine satellite-data distribution services include the GHRSST Global Data Access Centre, the US National Centres for Environmental Information, Copernicus Marine Environment Services (CMEMS), and the Ocean Sea ice satellite Application Facility (OSI-SAF). Satellite SST data sets have large numbers of users and data product downloads. The challenges and the way forward for future international data sharing and collaboration within the GHRSST community is to broaden the involvement and commitment across participating agencies. The many different satellite SST products and the large and increasing data volumes are very demanding to the distribution centers. Data files of satellite-derived SST are available from other data centers not directly associated with GHRSST. For example, SSTs from Terra and Aqua MODISs and from S-NPP VIIRS are available from the Ocean Biology DAAC (OB.DAAC) at NASA Goddard Space Flight Center (https://oceancolor.gsfc.nasa.gov/data/overview/), and a range of SST fields are available from the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF; http://www. osi-saf.org/?q¼content/sst-products). Data from the Advanced Himawari Imager on Himawari-8 are available from the Japan Aerospace Exploration Agency (JAXA) at their Earth Observation Center (http://www.eorc.jaxa.jp/ptree/userguide.html).

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Image display and manipulation packages are available for uses, such as NASA’s SeaDAS (https://seadas.gsfc.nasa.gov/) and ESA’s SentiNel Application Platform (SNAP); http://step.esa.int/ main/toolboxes/snap/), and a number of web-based image display systems, such as NASA’s Worldview (https://worldview. earthdata.nasa.gov/) and JAXA’s Himawari Monitor (http:// www.eorc.jaxa.jp/ptree/).

2.4.3

Future Developments in Satellite SST

2.4.3.1

Higher Spatial-Resolution Infra-Red Radiometers

Typical spatial resolution of infra-red radiometers for quantifying SST to date have spanned of order 300 m to 10 km. Such resolutions are not optimized for regions of high spatial variability, particularly the coastal zones of the ocean, where the subdaily dynamics of tides in interaction with shorelines, reefs, etc., are associated with temperature variations on scales down to meters. Other coastal-zone features in SST are associated with river plumes and thermal discharges from major coastal installations using seawater as cooling. These temperature variations can be of relevance to coastal ecosystems and their management, as well as providing validation and understanding of coastal-zone dynamics. The LandSat series of imagers have provided imagery at infrared wavelengths with true resolution of order 100 m. LandSat 8 has two thermal channels at AVHRR-like wavelengths. The uncertainty (from noise and calibration) is larger than necessary for quantifying SST with comparable accuracy to “traditional” SST sensors, and the revisit time (16 days if not limited by cloud) makes the use of such imagery in the coastal zone most relevant to qualitative case studies (looking at thermal patterns). Technological advances make it feasible to obtain wellcalibrated, multichannel thermal imagery at spatial resolution of order 30 m. A “Land Surface Temperature” mission is under consideration for the European Union space program, Copernicus, with a requirement to provide such imagery with a daily or near-daily repeat cycle. Such a mission will represent a significant advance in the ability to quantify coastal dynamics, with applications to coastal-zone ecosystems, leisure, and resource management.

2.4.3.2

More Capable Microwave Radiometers

As discussed, the benefit of microwave radiometers within the SST observing constellation is their near all-weather capability, whereas in terms of spatial resolution, inability to observe the coastal and SST uncertainty, microwave radiometers have been

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less capable than infrared sensors. For example, the AMSR-E and AMSR-2 systems delivered real aperture resolution of order 60 km, required a margin of 1.5  resolution (therefore,  100 km) around land and ice to avoid undue errors in SSTs from side-lobe signals, and using empirical regression algorithms were able to achieve standard deviations of difference with respect to drifting buoys of order 0.4 K, after careful quality control. The benefits of through-cloud observation and the comparative limitations of resolution and uncertainty derive from fairly fundamental physical considerations. At the most useful SST frequencies, the wavelength greatly exceeds the scale of cloud droplets, minimizing the scattering interactions with the radiation. Real aperture resolution is driven by fundamental relationships of wavelength and the radiometer’s antenna dimensions. Higher SST uncertainty comes from radiometric noise amplified by the “gain” of the SST retrieval, and some irreducible ambiguity in the relationships of SST to microwave brightness temperatures linked to the greater variability of sea surface emissivity at microwave frequencies compared to the infrared. Technological developments mean it is now feasible to deploy antennas with a diameter of order 6 m, after the pioneering NASA SMAP (Soil Moisture Active Passive) mission, which uses a mesh antenna with a 6 m diameter antenna, but does not have an SST capability (Entekhabi et al., 2010; Piepmeier et al., 2017). Being about four times the diameter of the antenna for AMSR-E, the corresponding resolution on the ground would be 15 km in the SSTrelevant channels. Simultaneously reducing SST uncertainty at this higher resolution requires optimal channel selection (Prigent et al., 2013), lower-bias, and lower-noise radiometry. Additionally, SST uncertainty could be reduced further by employing a (nearly) full-circle Earth-view conical scan (rather than a semicircle), enabling noise reduction by averaging the fore SST (obtained before the satellite overpass) and aft SST (obtained after). These technological developments are under investigation by space agencies. For example, the Copernicus Imaging Microwave Radiometer (CIMR), is (as of 2018) under consideration for a future expansion of the European Union’s operational space program of
rez, 2012), aiming for 15 km Sentinels (Aschbacher and Milagro-Pe resolution SST with uncertainty more commensurate with infrared sensors. The main driver application for CIMR is observation of the Arctic in response to policy interests, which drives the mission toward both high-spatial resolution (for improved operational seaice monitoring as the Arctic warms and becomes more navigable) and improved radiometric specifications (to achieve required fidelity of SST observation in highly cloudy high-latitude environments).

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An alternative approach to increase the spatial resolution is to use 2-D aperture synthesis as used in the ESA Soil Moisture and Ocean Salinity (SMOS) mission (Drinkwater et al., 2009; Mecklenburg et al., 2012; Piepmeier et al., 2017), which has 69 microwave radiometer deployed on a Y-shaped antenna that gives a spatial resolution of 35–50 km at a microwave frequency of 1.4 GHz; a fourfold improvement could be expected at 6 GHz for SST measurements for the same geometrical configuration.

2.5

Science Applications

Science applications that benefit from satellite SST cover the full range of temporal and spatial scales. Time and space scales associated with ocean processes range from submesoscale processes that include high spatial (<1 km) and temporal (<1 min) variability to diurnal, multiday, intraseasonal, seasonal, annual, interannual, decadal, and longer-term trends occurring over 1000 km to global spatial scales. While satellite SSTs only give a snapshot of the ocean-surface variability, the surface correlation to subsurface dynamics and the response to atmospheric forcing and ocean mixing allow for investigation of a broad range of science questions that impact prediction of weather and climate.

2.5.1

Operational Forecasting

The knowledge of the three-dimensional structure of the oceans requires the combined use of satellite observations, in situ observations, and ocean numerical models through assimilation techniques. Due to the limitations in the coverage of in situ measurements and the systematic errors in Numerical Weather Prediction (NWP) models, observations are required for SST, radiative shortwave, and longwave fluxes. Together with near-surface wind vectors and ice-cover observations, these parameters can be used for the modeling of heat and momentum exchange. This coherent set of information can then be used for characterizing the ocean surface and the energy fluxes through it.

2.5.1.1

Numerical Weather Prediction

Numerical weather prediction uses current weather conditions as input into mathematical models of the atmosphere to predict the weather. SST and sea-ice affect the behavior of the overlying atmosphere and vice versa. Consequently, NWP systems need to be regularly updated with the latest SST and

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sea-ice observations to ensure an accurate forecast. Daily analyses of both SST and sea-ice extent and concentration are required by many operational NWP systems. SST affects the formation and subsequent evolution of tropical cyclones, convection and thunderstorms, cyclogenesis, sea fog, and sea breezes. It can also help upper air forecasters at the World Aviation Forecast Centre to monitor areas more likely to develop Cumulonimbus activity, which can produce significant threats to aircraft. Historically, operational NWP systems have used either a blended day and night, or night-only, SST analysis, equivalent to ocean temperatures at around 1 m depth, as a lower boundary condition over the ocean (Puri et al., 2013). This takes no account of the temperature gradient between the air-sea interface and the foundation SST and may therefore introduce errors into the weather forecasts. Recently, air-sea coupled models have been developed, such as the Met Office Unified Model Global Coupled Model 2.0 (Williams et al., 2015), which apply cool-skin and warm-layer models on top of the standard configuration to predict the actual ocean skin temperature. In 2018, the European Centre for Medium-Range Weather Forecasts (ECMWF) implemented coupling to all ECMWF forecasts from 1 day (NWP) to 1 year by including the three-dimensional ocean and sea-ice model in the single highresolution forecast (HRES) (https://www.ecmwf.int).

2.5.1.2 Ocean Forecasting Marine High Seas Forecasting is important for commerce and transportation. Several national forecast centers and navies use SSTas input into their marine high seas forecast models. Operational ocean general circulation models providing forecasts of currents, temperature, and salinity fields are used for a variety of operational applications, including coral reef management, naval applications, tide predictions, diving operations, shipping, and search and rescue operations. The ocean models range from regional high-resolution systems, which include tides and may be updated as frequently as hourly, to global eddy-resolving systems that provide estimates of the ocean state, updated (with daily to monthly frequencies) and providing forecasts from a few days to 1 month in advance (Dombrowsky et al., 2009; https://www.godae-oceanview.org). SSTstrongly covaries with the ocean temperature over the mixed layer depth of the order 50–100 m and complements altimetry data in multivariate ocean analyses (Brassington, 2009). Short-range ocean forecast systems assimilate level 2 SST data, obtained from passive microwave and infrared radiometers aboard satellites.

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Knowledge of the heat content of the ocean is critical to hurricane-intensity forecasting for coastal areas. More than 90% of the warming on earth over the past 50 years occurred in the ocean. The heat content of the ocean is the amount of heat energy (in kilojoules per square centimeter over a predefined area) in the ocean and is defined as the measure of the integrated vertical temperature from the sea surface to the depth of the 26 degrees isotherm. It can be computed from the altimeter-derived isotherm depths in the upper ocean relative to 20 degrees.

2.5.1.3

Seasonal and Interannual Forecasting

Several operational centers routinely issue seasonal forecasts of earth’s climate using coupled ocean–atmosphere models, which require near-real-time knowledge of the state of the global ocean (Balmaseda et al., 2009). Most seasonal forecasting systems are based on coupled ocean-atmosphere general circulation models that predict SSTs and their impact on atmospheric circulation. The aim of seasonal forecasts is to predict climate anomalies (e.g., temperature, rainfall, frequency of tropical cyclones) for the forthcoming seasons (Balmaseda et al., 2009). The strongest relationship between SST patterns and seasonal weather trends are found in tropical regions. Most operational seasonal prediction models have horizontal spatial resolutions over the ocean of the order of 100–200 km; recently, higher resolution coupled models have forecast the ocean state at weekly temporal resolution and 25 km spatial resolution (e.g., MacLachlan et al., 2014). An example application for seasonal forecasting models is given in Section 2.5.3.1. Operational seasonal forecast models are commonly initialized using global SST analysis products, such as the global daily SST analysis of in situ and microwave and infra-red satellite SST data, GAMSSA (Zhong and Beggs, 2008).

2.5.2

Climate Monitoring and Research

A good source for climate studies in the ocean basins is the heat content of the ocean. Ocean Heat Content is an important climate change indicator and provides a high quality data record. It provides valuable insight to key climate questions: (1) the extent of the warming (or cooling) in the warm pools of the Atlantic and the Pacific ocean basins; (2) thermodynamic processes in the equatorial wave guides associated with the eastward propagating

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Kelvin Waves (associated with ENSO and the Madden-Julian Oscillation) across the tropics.

2.5.2.1 Teleconnection Studies Teleconnections are large-scale atmospheric variability patterns, occurring over months to years, resulting from changes in surface fluxes (SST, precipitation, and momentum anomalies). These patterns are related to stationary planetary waves as well as zonal nonlinear interactions (for a recent review, Feldstein and Franzke, 2017). Recently, unprecedented oceanic warming in the Northeast Pacific revealed linked teleconnections between the North Pacific and the weak 2014/2015 El Nin˜o to the atmospheric forcing patterns (Bond et al., 2015; Gentemann et al., 2017; Lorenzo and Mantua, 2016). The oceanic warming resulted in major ecological consequences throughout the eastern Pacific (Cavole et al., 2016).

2.5.2.2 Monitoring Long-Term Trends in SST The global ocean is the main sink of heat accumulation in the climate system from anthropogenically elevated greenhouse gases in the atmosphere (e.g., Cheng et al., 2017). SST partially reflects this accumulation through a positive SST trend on decadal scales. This trend in turn drives changes in the weather and climate experienced by human societies. Given the importance of long-term trends in SST, several centers internationally analyze data to quantify the decadal variability. As noted in Hartmann et al. (2013), it builds confidence in our quantification of longterm changes for independent in situ and satellite SST datasets to be constructed, as well as blended analyses. Independent satellite SST time-series must be constructed using radiativetransfer-based techniques and understanding of satellite calibration, which is a demanding approach. However, significant progress has been made (Merchant et al., 2012).

2.5.3

Marine Biology

2.5.3.1 Coral Reefs Coral bleaching results from the loss of symbiotic algae, known as zooxantheallae, from coral tissues during times of stress. This is often due to temperatures higher than the coral colony’s tolerance level (Glynn, 1993). NOAA’s Coral Reef Watch Program’s satellite SST data set (CoralTemp; https://coralreefwatch.noaa.gov/ satellite/index.php) provides a global, daily 5 km resolution gap-free

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foundation SST from 1985 to present, produced using three optimally-interpolated analyses: the Met Office OSTIA SST reanalysis (Roberts-Jones et al., 2012), the NOAA Geo-Polar Blended SSTreanalysis, and the real-time NOAA Geo-Polar Blended SST analysis (Maturi et al., 2017). CoralTemp, along with an SST climatology derived from the same data sets, is used to provide current reef environmental conditions to quickly identify areas at risk for coral bleaching. Continuous monitoring of SST at global scales provides researchers and stakeholders with tools to understand and better manage the complex interactions leading to coral bleaching. When bleaching conditions occur, these tools can be used to trigger bleaching response plans and support appropriate management decisions. An example is shown in Fig. 2.11 of the daily SST anomaly obtained from the CoralTemp data set over the Great Barrier Reef. Infrared satellite SST observations are valuable for monitoring environmental conditions around coral reefs due to their higher spatial resolution and accuracy, compared with passive microwave satellite SST observations. Composites formed from direct broadcast (1 km at nadir) SST observations from the NOAA Polar NOAA CRW daily 5 km SST anomalies (Version 3.1) 29 mar 2016 140 145 150 155 160

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Fig. 2.11 Daily 5 km SST anomaly for March 29, 2016, obtained from the NOAA Geo-Polar Blend SST analysis minus the CoralTemp 1985–2012 climatology. Sourced from the NOAA/NESDIS Center for Satellite Applications and Research website: https://coralreefwatch.noaa.gov/product/5km/index.php (Accessed on September 5, 2018).

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Orbiting Environmental Satellites have been used since the mid2000s to monitor the Great Barrier Reef (Garde et al., 2014, http:// www.bom.gov.au/environment/activities/reeftemp/reeftemp. shtml). These 2 km resolution multisensor composites provide information at a higher spatial scale, and closer to coasts and reefs, than SST analyses that interpolate between observations using a weighted combination of previous analyses and climatology, such as NOAA’s Geo Polar Blend level 4 SST analyses. An example of the ReefTemp NextGen daily SST anomaly is shown in Fig. 2.12. While some shallow corals may be very close to the surface or even exposed to the air at extremely low tides, the majority of corals grow well below the surface, and this begs the question whether the surface SST derived from satellite measurements accurately represents the temperatures experienced by the corals. By analyzing temperature time series at the depths of corals in the Caribbean Sea and Great Barrier Reef off Australia, Zhu et al. (2014) studied the growth and decay of the diurnal heating at the corals. Generally, at night the convection driven by surface heat loss brings the coral temperatures close to those near the surface, but during the day when winds are low and insolation is high, diurnal heating can introduce large temperature differences between the surface and the corals. The amplitudes of the diurnal heating at the coral depths are smaller than those at the surface but by an amount that is very variable, depending not only on the coral depths, but also on tidal and wind-driven currents over and around the corals. For the Great Barrier Reef, 20% of days had diurnal amplitudes >1 K, with a maximum of 4 K. For four stations in the Caribbean Sea, the average diurnal heating amplitude was 0.4–0.7 K, mainly dependent on depth; the maximum amplitude was 2.1 K. Thus, ignoring diurnal effects, by using night-time data, will underestimate the thermal conditions at the coral depth, whereas using averages of satellite-derived SST that include diurnal signals will give an overestimate. Seasonal forecasts from coupled ocean-atmosphere models can be used to predict anomalous SST several months in advance (Spillman and Alves, 2009; http://www.bom.gov.au/ oceanography/oceantemp/sst-outlook-access.shtml). For example, the Australian Bureau of Meteorology produces operational seasonal forecasts over the Coral Sea to aid in the management of the Great Barrier Reef Marine Park using the coupled ocean-atmosphere model, “ACCESS-S1” (Hudson et al., 2017), with real-time predictions of weekly SST anomalies at 25 km horizontal resolution out to 6–12 months (http://www.bom. gov.au/oceanography/oceantemp/sst-outlook-map.shtml). This

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IMOS 1-day: SST anomaly 29 March 2016 GBR region

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Fig. 2.12 Daily 2 km SST anomaly for March 29, 2016, obtained from the Australian Bureau of Meteorology’s daily nighttime HRPT AVHRR 2 km composite SST minus the ReefTemp NextGen 2002–2011 climatology. Sourced from http://www. bom.gov.au/environment/activities/reeftemp/reeftemp.shtml (Accessed on 5 September 2018). Reproduced by permission of Bureau of Meteorology, # 2018 Commonwealth of Australia.

Australian service is complemented by NOAA Coral Reef Watch’s global forecasts of weekly coral bleaching outlooks at 50 km resolution out to 4 months (https://coralreefwatch.noaa.gov/satellite/ bleachingoutlook_cfs/outlook_cfs.php).

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2.5.3.2 Fisheries Different species of fish are sometimes known to be found at certain temperature ranges. Near real-time SST maps derived from satellite data can help inform where good fishing locations might be (e.g., http://www.fishtrack.com and https://www.satfish.com). For example, SST is one of the most important environmental parameters used by longline fishers to locate good fishing areas (Beverly and Choi, 2011). Pelagic fish such as albacore tuna (Thunnus alulunga), bigeye tuna (Thunnus obesus), skipjack tuna (Katsuwonus pelamis), striped marlin (Tetrapturus audax), swordfish (Xiphias gladius), and yellowfin tuna (Thunnus albacores) have preferences for waters with certain temperature ranges. This is true for both horizontal and vertical temperature ranges, but longline fishers are more interested in horizontal, or surface, temperatures when searching for fish (Beverly and Choi, 2011). Frontal zones are also interesting to fishers because baitfish and predator fish often accumulate near them, often on the warm side of the frontal zone. In this case, the location of the front, rather than absolute temperature, is of greater interest to fishers.

2.5.4

Concluding Remarks

This section has illustrated several applications of SSTobservations. Further coastal zone, ecological, navigational, military, and industrial applications could have been added to these examples. From a climatological, societal, and ecological viewpoint, quantifying the thermal state of the global oceans is a crucial element in “taking the temperature of the Earth.”

References Abraham, J.P., Baringer, M., Bindoff, N.L., Boyer, T., Cheng, L.J., et al., 2013. A review of global ocean temperature observations: implications for ocean heat content estimates and climate change. Rev. Geophys. 51, 450–483. Ackerman, S.A., Holz, R.E., Frey, R., Eloranta, E.W., Maddux, B.C., McGill, M., 2008. Cloud detection with MODIS. Part II: validation. J. Atmos. Oceanic Technol. 25 (7), 1073–1086. Allison, L.J., Kennedy, J., 1967. An Evaluation of Sea Surface Temperature as Measured by the Nimbus I High Resolution Infrared Radiometer. (NASA Technical Note No. D-4078) Washington, DC, National Aeronautics and Space Administration. Aminou, D.M.A., 2002. MSG’s SEVIRI instrument. ESA Bull. 11, 15–17. Arbelo, M., Podesta´, G.P., Herna´ndez-Leal, P.A., Diaz, J.P., 2003. Use of TOMS data to correct the saharan dust effects on SST retrievals from satellite. Adv. Space Res. 32, 2175–2180. Aschbacher, J., Milagro-P
erez, M.P., 2012. The European Earth monitoring (GMES) programme: status and perspectives. Remote Sens. Environ. 120, 3–8.

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Atkinson, C.J., et al., 2014. An integrated database of ocean temperature and salinity observations. J. Geophys. Res. Oceans 119, 7139–7163. https://doi.org/ 10.1002/2014JC010053. Balmaseda, M.A., Alves, O.J., Arribas, A., Awaji, T., Behringer, D.W., et al., 2009. Ocean initialization for seasonal forecasts. Oceanography 22, 154–159. Barton, I.J., et al., 2004. The Miami 2001 infrared radiometer calibration and intercomparison: 2. Ship comparisons. J. Atmos. Oceanic Technol. 21, 268–283. Berry, D.I., et al., 2018. Stability assessment of the (A)ATSR Sea Surface Temperature climate dataset from the European Space Agency Climate Change Initiative. Remote Sens. 10, 126. https://doi.org/10.3390/rs10010126. Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., et al., 2016. An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteorol. Soc. Jpn. Ser. II 94, 151–183. Beverly, S., Choi, Y., 2011. A beginner’s Guide to Using Remote Sensing for Offshore Tuna Fishing. Secretariat of the Pacific Community, Noumea, New Caledonia, p. 16. Blackmore, T., O’Carroll, A., Fennig, K., Saunders, R., 2012. Correction of AVHRR Pathfinder SST data for volcanic aerosol effects using ATSR SSTs and TOMS aerosol optical depth. Remote Sens. Environ. 116, 107–117. Bond, N.A., et al., 2015. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42 (9), 3414–3420. Branch, R., Chickadel, C.C., Jessup, A.T., 2016. Infrared emissivity of seawater and foam at large incidence angles in the 3–14 μm wavelength range. Remote Sens. Environ. 184, 15–24. Brassington, G.B., 2009. Ocean prediction issues related to weather and climate prediction. In: Pre-CAS Public Web Consultation: Vision Paper (Agenda Item 8.5), WMO CAS XV, Seoul, Korea, 18-25 Novemberp. 11. Brewin, R.J.W., et al., 2017. Evaluating operational AVHRR Sea surface temperature data at the coastline using surfers. Estuar. Coast. Shelf Sci. 196, 276–289. https:// doi.org/10.1016/j.ecss.2017.07.011. Brewin, R.J.W., et al., 2018. Evaluating operational AVHRR sea surface temperature data at the coastline using benthic temperature loggers. Remote Sens. 10, 925. https://doi.org/10.3390/rs10060925. Brown, S.J., Harris, A.R., Mason, I.M., Za´vody, A.M., 1997. New aerosol robust sea surface temperature algorithms for the along-track scanning radiometer. J. Geophys. Res. 102, 27973–927989. Bulgin, C.E., et al., 2016. Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the along-track scanning radiometer instruments. Remote Sens. Environ. 178, 213–222. https://doi.org/10.1016/j. rse.2016.02.022. Casey, K.S., Brandon, T.B., Cornillon, P., Evans, R., 2010. The past, present, and future of the AVHRR Pathfinder SST program. In: Oceanography From Space. Springer, Dordrecht, pp. 273–287. Castro, S.L., Wick, G.A., Buck, J.J.H., 2014. Comparison of diurnal warming estimates from unpumped Argo data and SEVIRI satellite observations. Remote Sens. Environ. 140, 789–799. https://doi.org/10.1016/j.rse.2013.08.042. Castro, S.L., Wick, G.A., Steele, M., 2016. Validation of satellite sea surface temperature analyses in the Beaufort Sea using UpTempO buoys. Remote Sens. Environ. 187, 458–475. https://doi.org/10.1016/j.rse.2016.10.035. Castro, S.L., et al., 2017. Submesoscale sea surface temperature variability from UAV and satellite measurements. Remote Sens. 9, 1089. https://doi.org/ 10.3390/rs9111089. Castro, S.L., et al., 2018. Subpixel variability and quality assessment of satellite sea surface temperature data using a novel high resolution multistage spectral

47

48

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

interpolation (HRMSI) technique. Remote Sens. Environ. 217, 292–308. https:// doi.org/10.1016/j.rse.2018.08.019. Cavole, L.M., et al., 2016. Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: winners, losers, and the future. Oceanography 29 (2), 273–285. Chelton, D., 2013. Ocean-atmosphere coupling: mesoscale eddy effects. Nat. Geosci. 6, 594–595. https://doi.org/10.1038/ngeo1906. Chelton, D.B., Wentz, F.J., 2005. Global microwave satellite observations of sea surface temperature for numerical weather prediction and climate research. Bull. Am. Meteorol. Soc. 86 (8), 1097–1115. Cheng, L., Trenberth, K.E., Fasullo, J., Boyer, T., Abraham, J., Zhu, J., 2017. Improved estimates of ocean heat content from 1960 to 2015. Sci. Adv. 3, E1601545. Corlett, G.K., et al., 2014. Assessment of long-term satellite derived sea surface temperature records. In: Zibordi, G., Donlon, C.J., Parr, A.C. (Eds.), Experimental Methods in the Physical Sciences. In: vol. 47. Academic Press, pp. 639–677. https://doi.org/10.1016/B978-0-12-417011-7.00021-0 (Chapter 6.2). Cracknell, A.P., 1997. The Advanced Very High Resolution Radiometer. CRC Press/ Taylor and Francis, London, UK. Ditri, A.L., et al., 2018. The accuracies of Himawari-8 and MTSAT-2 sea-surface temperatures in the Tropical Western Pacific Ocean. Remote Sens. 10, 212. https://doi.org/10.3390/rs10020212. Dombrowsky, E., Bertino, L., Brassington, G.B., Chassignet, E.P., Davidson, F., et al., 2009. GODAE systems in operation. Oceanography 22 (3), 80–95. Donlon, C.J., Minnett, P.J., Gentemann, C., Nightingale, T.J., Barton, I.J., et al., 2002. Toward improved validation of satellite sea surface skin temperature measurements for climate research. J. Clim. 15, 353–369. Donlon, C.J., Robinson, I., Casey, K.S., Vazquez-Cuervo, J., Armstrong, E., et al., 2007. The global ocean data assimilation experiment high-resolution sea surface temperature pilot project. Bull. Am. Meteorol. Soc. 88, 1197–1213. Donlon, C., et al., 2008. An infrared sea surface temperature autonomous radiometer (ISAR) for deployment aboard Volunteer Observing Ships (VOS). J. Atmos. Oceanic Technol. 25, 93–113. Donlon, C.J., Casey, K.S., Robinson, I.S., Gentemann, C.L., Reynolds, R.W., et al., 2009. The GODAE High-Resolution Sea Surface Temperature Pilot Project. Oceanography 22, 34–45. Donlon, C.J., et al., 2014. Ship-borne thermal infrared radiometer systems. In: Zibordi, G., Donlon, C.J., Parr, A.C. (Eds.), Experimental Methods in the Physical Sciences. In: vol. 47. Academic Press, pp. 305–404. https://doi.org/10.1016/ B978-0-12-417011-7.00011-8 (Chapter 3.2). Drinkwater, M.R., Kerr, Y., Font, J., Berger, M., 2009. The soil moisture and ocean salinity (SMOS) mission. Bull. Eur. Space Agency 137, 7–15. Dybbroe, A., Karlsson, K.G., Thoss, A., 2005. NWCSAF AVHRR cloud detection and analysis using dynamic thresholds and radiative transfer modeling. Part I: algorithm description. J. Appl. Meteorol. 44 (1), 39–54. Embury, O., Merchant, C.J., Corlett, G.K., 2012. A reprocessing for climate of sea surface temperature from the along-track scanning radiometers: initial validation, accounting for skin and diurnal variability effects. Remote Sens. Environ. 116, 62–78. Emery, W.J., et al., 2014. A microbolometer airborne calibrated infrared radiometer: the ball experimental sea surface temperature (BESST) radiometer. IEEE Trans. Geosci. Remote Sens. 52, 7775–7781. https://doi.org/10.1109/ TGRS.2014.2318683. Engel, A., Bange, H.W., Cunliffe, M., Burrows, S.M., Friedrichs, G., et al., 2017. The ocean’s vital skin: toward an integrated understanding of the sea surface microlayer. Front. Mar. Sci. 4, 14.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Entekhabi, D., et al., 2010. The Soil Moisture Active Passive (SMAP) mission. Proc. IEEE 98 (5), 704–716. https://doi.org/10.1109/jproc.2010.2043918. Esaias, W.E., Abbott, M.R., Barton, I., Brown, O.B., Campbell, J.W., et al., 1998. An overview of MODIS capabilities for ocean science observations. IEEE Trans. Geosci. Remote Sens. 36, 1250–1265. Fairall, C.E., et al., 1996. Cool-skin and warm-layer effects on sea surface temperature. J. Geophys. Res. 101 (C1), 1295–1308. Feldstein, S.B., Franzke, C.L.E., 2017. Atmospheric teleconnection patterns. In: Nonlinear and Stochastic Climate Dynamics. Cambridge University Press, pp. 54–104. https://doi.org/10.1017/9781316339251.004. Garde, L.A., Spillman, C.M., Heron, S., Beeden, R., 2014. ReefTemp next generation: a new operational system for monitoring reef thermal stress. J. Oper. Oceanogr. 7, 21–33. Gauss, C.F., 1828. Theoria residuorum biquadraticorum—Commentatio Prima. Pub. Dieterichianis. Gentemann, C.L., 2014. Three way validation of MODIS and AMSR-E sea surface temperatures. J. Geophys. Res. 119, 2583–2598. https://doi.org/ 10.1002/2013JC009716. Gentemann, C.L., Hilburn, K., 2015. In situ validation of sea surface temperatures from the GCOM-W1 AMSR2 RSS calibrated brightness temperatures. J. Geophys. Res. Oceans 120 (5), 3567–3585. https://doi.org/ 10.1002/2014JC010574. Gentemann, C.L., Minnett, P.J., LeBorgne, P., Merchant, C.J., 2008. Multi-satellite measurements of large diurnal warming events. Geophys. Res. Lett. 35. Gentemann, C.L., Fewings, M.R., Garcı´a-Reyes, M., 2017. Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophys. Res. Lett. 44 (1), 312–319. Gladkova, I., Ignatov, A., Shahriar, F., Kihai, Y., Hillger, D., Petrenko, B., 2016. Improved VIIRS and MODIS SST imagery. Remote Sens. 8, 79. Glynn, P.W., 1993. Coral reef bleaching: ecological perspectives. Coral Reefs 12, 1–7. Goldberg, M.D., Kilcoyne, H., Cikanek, H., Mehta, A., 2013. Joint Polar Satellite System: the United States next generation civilian polar-orbiting environmental satellite system. J. Geophys. Res.: Atmos. 118 (24), 13–463. Griffith, P., Naarden, J.V., Roop, L., Gall, D., 2017. GOES-R ABI on-orbit performance. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX. IEEE, pp. 275–278. Hanafin, J.A., Minnett, P.J., 2005. Infrared-emissivity measurements of a windroughened sea surface. Appl. Opt. 44, 398–411. Hardy, J.T., 1997. Biological effects of chemicals in the sea-surface microlayer. In: Liss, P.S., Duce, R.A. (Eds.), The Sea Surface and Global Change. Cambridge University Press, Cambridge, pp. 339–370. Hartmann, D.L., Klein Tank, A.M.G., Rusticucci, M., Alexander, L.V., € nnimann, S., et al., 2013. Observations: atmosphere and surface. Bro In: Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K. et al., (Eds.), Climate Change 2013: The Physical Science Basis. Contribution of Working Group 1 to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, pp. 159–254. https://doi.org/10.1017/ CBO9781107415324.008. Hihara, T., Kubota, M., Okuro, A., 2015. Evaluation of sea surface temperature and wind speed observed by GCOM-W1/AMSR2 using in situ data and global products. Remote Sens. Environ. 164, 170–178. https://doi.org/10.1016/j.rse.2015.04.005. Hosoda, K., Murakami, H., Shibata, A., Sakaida, F., Kawamura, H., 2006. Difference characteristics of sea surface temperature observed by GLI and AMSR aboard ADEOS-II. J. Oceanogr. 62 (3), 339–350. https://doi.org/10.1007/s10872-0060059-1.

49

50

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Hosoda, K., Murakami, H., Sakaida, F., Kawamura, H., 2007. Algorithm and validation of sea surface temperature observation using MODIS sensors aboard Terra and Aqua in the western North Pacific. J. Oceanogr. 63 (2), 267–280. Høyer, J.L., Karagali, I., 2016. Sea surface temperature climate data record for the North Sea and Baltic Sea. J. Clim. 29 (7), 2529–2541. Hudson, D., Alves, O., Hendon, H.H., Lim, E., Liu, G., et al., 2017. ACCESS-S1: the new Bureau of Meteorology multi-week to seasonal prediction system. J. South. Hemisphere Earth Syst. Sci. 67 (3), 132–159. https://doi.org/10.22499/3.6703.001. ´ lvarez-Noriega, M., A ´ lvarez-Romero, J.G., Anderson, K.D., Hughes, T.P., Kerry, J.T., A et al., 2017. Global warming and recurrent mass bleaching of corals. Nature 2017, 543. https://doi.org/10.1038/nature21707. Ignatov, A., et al., 2016. AVHRR GAC SST Reanalysis Version 1 (RAN1). Remote Sens. 8, 315. https://doi.org/10.3390/rs8040315. Kantha, L.H., Clayson, C.A., 1994. An improved mixed layer model for geophysical applications. J. Geophys. Res. 99 (C12), 25235–25266. Kennedy, J.J., 2014. A review of uncertainty in in situ measurements and data sets of sea surface temperature. Rev. Geophys. 52, 1–32. https://doi.org/ 10.1002/2013RG000434. Kent, E.C., et al., 2010. Effects of instrumentation changes on sea surface temperature measured in situ. WIREs Clim. Change 1 (5), 718–728. https://doi.org/ 10.1002/wcc.55. Kilpatrick, K.A., Podesta´, G.P., Evans, R.H., 2001. Overview of the NOAA/NASA Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res. 106, 9179–9198. Kilpatrick, K.A., Podesta´, G., Walsh, S., Williams, E., Halliwell, V., et al., 2015. A decade of sea surface temperature from MODIS. Remote Sens. Environ. 165, 27–41. https://doi.org/10.1016/j.rse.2015.04.023. Koner, P.K., Harris, A., 2016a. MODIS sea surface temperature retrievals using different channel combinations and hybrid cloud detection. pp. 988204–988211. https://doi.org/10.1117/12.2223465. Koner, P.K., Harris, A., 2016b. Sea surface temperature retrieval from MODIS radiances using truncated total least squares with multiple channels and parameters. Remote Sens. 8 (9), 725. https://doi.org/10.3390/rs8090725. Koner, P.K., Harris, A., Maturi, E., 2015. A physical deterministic inverse method for operational satellite remote sensing: an application for sea surface temperature retrievals. IEEE Trans. Geosci. Remote Sens. 53, 5872–5888. Kurihara, Y., Murakami, H., Kachi, M., 2016. Sea surface temperature from the new Japanese geostationary meteorological Himawari-8 satellite. Geophys. Res. Lett. 43, 1234–1240. Lean, K., Saunders, R.W., 2013. Validation of the ATSR reprocessing for climate (ARC) dataset using data from drifting buoys and a three-way error analysis. J. Clim. 26, 13. Lipes, R.G., Bernstein, R.L., Cardone, V.J., Katsaros, K.B., Njoku, E.G., et al., 1979. Seasat scanning multichannel microwave radiometer: results of the Gulf of Alaska workshop. Science 204, 1415–1417. Liu, Y., Minnett, P.J., 2016. Sampling errors in satellite-derived infrared sea-surface temperatures. Part I: global and regional MODIS fields. Remote Sens. Environ. 177, 48–64. Liu, Y., Chin, T.M., Minnett, P.J., 2017. Sampling errors in satellite-derived infrared sea-surface temperatures. Part II: sensitivity and parameterization. Remote Sens. Environ. 198, 297–309. Llewellyn-Jones, D., Remedios, J., 2012. The Advanced Along Track Scanning Radiometer (AATSR) and its predecessors ATSR-1 and ATSR-2: an introduction to the special issue. Remote Sens. Environ. 116. https://doi.org/10.1016/j.rse.2011.06.002.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Llewellyn-Jones, D.T., Minnett, P.J., Saunders, R.W., Zavody, A.M., 1984. Satellite multichannel infrared measurements of sea surface temperature of the N.E. Atlantic Ocean using AVHRR/2. Q. J. R. Meteorol. Soc. 110, 613–631. Lorenzo, E., Mantua, N., 2016. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Chang. 6 (11), 1042–1047. Lumpkin, R., Centurioni, L., Perez, R., 2016. Fulfilling observing system implementation requirements with the global drifter array. J. Atmos. Oceanic Technol. 33, 685–695. MacLachlan, C., Arribas, A., Peterson, K.A., Maidens, A., Fereday, D., et al., 2014. Global seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Q. J. R. Meteorol. Soc. https://doi.org/10.1002/qj.2396.
re
, S., Roquet, H., 2015. Six years of OSIMarsouin, A., Le Borgne, P., Legendre, G., Pe SAF METOP-A AVHRR sea surface temperature. Remote Sens. Environ. 159, 288–306. Martin, M.J., et al., 2012. Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: a GHRSST multi-product ensemble (GMPE). Deep-Sea Res. II Top. Stud. Oceanogr. 77–80, 21–30. https://doi.org/10.1016/j.dsr2.2012.04.013. Martı´nez Avellaneda, N., Serra, N., Minnett, P., Stammer, D., 2010. Response of the eastern subtropical Atlantic SST to Saharan dust: a modeling and observational study. J. Geophys. Res. Oceans 1978–2012, 115. Masuda, K., Takashima, T., Takayama, Y., 1988. Emissivity of pure and sea waters for the model sea surface in the infrared window regions. Remote Sens. Environ. 24, 313–329. Masunaga, R., Nakamura, H., Kamahori, H., Onogi, K., Okajima, S., 2018. JRA55CHS: an atmospheric re-analysis produced with high-resolution SST. SOLA 14, 6–13. https://doi.org/10.2151/sola.2018-002. Maturi, E., Harris, A., Mittaz, J., Sapper, J., Wick, G., Zhu, Z., Dash, P., Koner, P., 2017. A new high-resolution sea surface temperature blended analysis. Bull. Am. Met. Soc. 98, 1015–1026. https://doi.org/10.1175/BAMS-D-15-00002.1. Maturi, E., Harris, A., Merchant, C., Mittaz, J., Potash, B., et al., 2008. NOAA’s sea surface temperature products from operational geostationary satellites. Bull. Am. Meteorol. Soc. 89, 1877–1888. McClain, E.P., Pichel, W.G., Walton, C.C., 1985. Comparative performance of AVHRR-based multichannel sea surface temperatures. J. Geophys. Res. 90, 11587–11601. McMillin, L., Crosby, D.S., 1984. Theory and validation of the multiple window sea surface temperature technique. J. Geophys. Res. 89, 3655–3661. McPhaden, M.J., et al., 2010. The global tropical moored buoy array. In: Proceedings of OceanObs 9. Mecklenburg, S., Drusch, M., Kerr, Y.H., Font, J., Martin-Neira, M., et al., 2012. ESA’s soil moisture and ocean salinity mission: mission performance and operations. IEEE Trans. Geosci. Remote Sens. 50, 1354–1366. Menzel, W.P., Purdom, J.F., 1994. Introducing GOES-I: the first of a new generation of geostationary operational environmental satellites. Bull. Am. Meteorol. Soc. 75 (5), 757–782. Merchant, C.J., Embury, O., 2014. Simulation and inversion of satellite thermal measurements. In: Zibordi, G., Donlon, C.J., Parr, A.C. (Eds.), Optical Radiometry for Ocean Climate Measurements. Experimental Methods in the Physical Sciences. In: vol. 47(47). Academic Press. ISBN 9780124170117, pp. 489–526. https://doi.org/10.1016/B978-0-12-417011-7.00015-5. Merchant, C.J., Harris, A.R., 1999. Toward the elimination of bias in satellite retrievals of skin sea surface temperature. 2: comparison with in situ measurements. J. Geophys. Res. 104, 23579–23590.

51

52

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Merchant, C.J., Harris, A.R., Maturi, E., MacCallum, S., 2005. Probabilistic physically based cloud screening of satellite infrared imagery for operational sea surface temperature retrieval. Q. J. R. Meteorol. Soc. 131, 2735–2755. Merchant, C.J., Filipiak, M.J., Le Borgne, P., Roquet, H., Autret, E., et al., 2008a. Diurnal warm-layer events in the western Mediterranean and European shelf seas. Geophys. Res. Lett. 35 (4), L04601. ISSN 0094-8276 https://doi.org/10. 1029/2007GL033071. Merchant, C.J., Le Borgne, P., Marsouin, A., Roquet, H., 2008b. Optimal estimation of sea surface temperature from split-window observations. Remote Sens. Environ. 112 (5), 2469–2484. ISSN 0034-4257 https://doi.org/10.1016/j.rse.2007.11.011. Merchant, C.J., Le Borgne, P., Roquet, H., Marsouin, A., 2009. Sea surface temperature from a geostationary satellite by optimal estimation. Remote Sens. Environ. 113, 445–457. Merchant, C.J., et al., 2012. A 20 year independent record of sea surface temperature for climate from along-track scanning radiometers. J. Geophys. Res. 117. Merchant, C.J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C.E., et al., 2014. Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI). Geosci. Data J. 1 (2), 179–191. Milman, A.S., Wilheit, T.T., 1985. Sea surface temperatures from the scanning multichannel microwave radiometer on Nimbus-7. Geophys. Res. Lett. 90, 11587–11601. Minnett, P.J., 1991. Consequences of sea surface temperature variability on the validation and applications of satellite measurements. J. Geophys. Res. 96, 18475–18489. Minnett, P.J., 2010. The validation of sea surface temperature retrievals from Spaceborne infrared radiometers. In: Barale, V., Gower, J.F.R., Alberotanza, L. (Eds.), Oceanography From Space, Revisited. Springer Science + Business Media B.V., pp. 273–295 Minnett, P.J., Corlett, G.K., 2012. A pathway to generating climate data records of sea-surface temperature from satellite measurements. Deep-Sea Res. II Top. Stud. Oceanogr. 77-80, 44–51. Minnett, P.J., et al., 2001. The Marine-Atmospheric Emitted Radiance Interferometer (M-AERI), a high-accuracy, sea-going infrared spectroradiometer. J. Atmos. Oceanic Technol. 18, 994–1013. Minnett, P.J., Smith, M., Ward, B., 2011. Measurements of the oceanic thermal skin effect. Deep-Sea Res. II Top. Stud. Oceanogr. 58, 861–868. Minobe, S., Kuwano-Yoshida, A., Komori, N., Xie, S.-P., Small, R.J., 2008. Influence of the Gulf Stream on the troposphere. Nature 452, 206–209. https://doi.org/ 10.1038/nature06690. Montenbruck, O., Gill, E., 2012. Satellite Orbits: Models, Methods and Applications. Springer Science & Business Media. Nalli, N.R., Minnett, P.J., van Delst, P., 2008a. Emissivity and reflection model for calculating unpolarized isotropic water surface-leaving radiance in the infrared. I: theoretical development and calculations. Appl. Opt. 47, 3701–3721. Nalli, N.R., Minnett, P.J., Maddy, E., McMillan, W.W., Goldberg, M.D., 2008b. Emissivity and reflection model for calculating unpolarized isotropic water surfaceleaving radiance in the infrared. 2: validation using Fourier transform spectrometers. Appl. Opt. 47, 4649–4671. Nielsen-Englyst, P., Høyer, J.L., Toudal Pedersen, L., Gentemann, C.L., Alerskans, E., et al., 2018. Optimal estimation of sea surface temperature from AMSR-E. Remote Sens. 10 (2), 229. https://doi.org/10.3390/rs10020229.

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

O’Carroll, A.G., Eyre, J.R., Saunders, R.W., 2008. Three-way error analysis between AATSR, AMSR-E, and in situ sea surface temperature observations. J. Atmos. Oceanic Technol. 25, 1197–1207. https://doi.org/10.1175/2007JTECHO542.1. O’Neill, L.W., Haack, T., Chelton, D.B., Skyllingstad, E., 2017. The Gulf Stream convergence zone in the time-mean winds. J. Atmos. Sci. 74. https://doi.org/ 10.1175/JAS-D-16-0213.1. O’Carroll, A.G., Blackmore, T., Fennig, K., Saunders, R.W., Millington, S., 2012. Towards a bias correction of the AVHRR Pathfinder SST data from 1985 to 1998 using ATSR. Remote Sens. Environ. 116, 118–125. Petrenko, B., Ignatov, A., Kihai, Y., Stroup, J., Dash, P., 2014. Evaluation and selection of SST regression algorithms for JPSS VIIRS. J. Geophys. Res.: Atmos. 119. https://doi.org/10.1002/2013JD020637. Piepmeier, J.R., Focardi, P., Horgan, K.A., Knuble, J., Ehsan, N., et al., 2017. SMAP L-band microwave radiometer: instrument design and first year on orbit. IEEE Trans. Geosci. Remote Sens. 55 (4), 1954–1966. Pisano, A., et al., 2016. The new Mediterranean optimally interpolated Pathfinder AVHRR SST Dataset (1982–2012). Remote Sens. Environ. 176, 107–116. https:// doi.org/10.1016/j.rse.2016.01.019. Planck, M., 1901. On the law of distribution of energy in the normal spectrum. Ann. Phys. 4, 553. Prigent, F., Aires, F., Bernardo, J.-C., Orlhac, J.-M., Goutoule, H., et al., 2013. Analysis of the potential and limitations of microwave radiometry for the retrieval of sea surface temperature: definition of MICROWAT, a new mission concept. J. Geophys. Res.: Oceans 118, 3074–3086. https://doi.org/10.1002/jgrc.20222. Puri, K., Dietachmayer, G., Steinle, P., Dix, M., Rikus, L., Logan, L., et al., 2013. Implementation of the initial ACCESS numerical weather prediction system. Aust. Meteorol. Oceanogr. J. 63, 265–284. Reynolds, R.W., 1993. Impact of Mount Pinatubo aerosols on satellite-derived sea surface temperature. J. Clim. 6, 768–774. Rice, J.P., et al., 2004. The Miami 2001 infrared radiometer calibration and intercomparison: 1. Laboratory characterization of blackbody targets. J. Atmos. Oceanic Technol. 21, 258–267. Roberts-Jones, J., Fiedler, E.K., Martin, M.J., 2012. Daily, global, high-resolution SST and sea ice reanalysis for 1985-2007 using the OSTIA system. J. Clim. https:// doi.org/10.1175/JCLI-D-11-00648.1. Robinson, I.S., 2004. Measuring the Oceans From Space: The Principles and Methods of Satellite Oceanography. Springer Science & Business Media. ISBN 978-3-540-42647-9. Roemmich, D., Johnson, G., Riser, S., Davis, R., Gilson, J., et al., 2009. The Argo program: observing the global ocean with profiling floats. Oceanography 22, 34–43. Saunders, R.W., Kriebel, K.T., 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens. 9, 123–150 (Correction, 1988, Int. J. Remote Sens., 1989, 1393–1394). Scanlon, B., Wick, G.A., Ward, B., 2013. Near-surface diurnal warming simulations: validation with high resolution profile measurements. Ocean Sci. 9, 977–986. https://doi.org/10.5194/os-9-977-2013. Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., Ratier, A., 2002. An introduction to Meteosat Second Generation (MSG). Bull. Am. Meteorol. Soc. 83 (7), 977–992. Schmit, T.J., Gunshor, M.M., Menzel, W.P., Gurka, J.J., Li, J., Bachmeier, A.S., 2005. Introducing the next-generation advanced baseline imager on GOES-R. Bull. Am. Meteorol. Soc. 86, 1079–1096.

53

54

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

Schmit, T.J., Griffith, P., Gunshor, M.M., Daniels, J.M., Goodman, S.J., Lebair, W.J., 2017. A closer look at the ABI on the GOES-R series. Bull. Am. Meteorol. Soc. 98, 681–698. Schueler, C.F., Lee, T.F., Miller, S.D., 2013. VIIRS constant spatial-resolution advantages. Int. J. Remote Sens. 34, 5761–5777. Shibata, A., 2005. Calibration of AMSR-E SST toward a monitoring of global warming. In: Paper Presented at IEEE 2005 International Geoscience and Remote Sensing Symposium Proceedings, Seoul, Korea. Shibata, A., 2006. Features of ocean microwave emission changed by wind at 6 GHz. J. Oceanogr. 62 (3), 321–330. https://doi.org/10.1007/s10872-006-0057-3. Smith, D., Mutlow, C., Delderfield, J., Watkins, B., Mason, G., 2012. ATSR infrared radiometric calibration and in-orbit performance. Remote Sens. Environ. 116, 4–16. Soloviev, A., Lukas, R., 1997. Observation of large diurnal warming events in the near-surface layer of the western equatorial Pacific warm pool. Deep-Sea Res. I Oceanogr. Res. Pap. 44, 1055–1076. Soloviev, A.V., Lukas, R., 2014. The Near-Surface Layer of the Ocean: Structure, Dynamics, and Applications, second ed. Springer, New York. Spillman, C.M., Alves, O., 2009. Dynamical seasonal prediction of summer sea surface temperatures in the Great Barrier Reef. Coral Reefs 28, 197–206.
, L., Ward, B., 2014. Mixed and mixing layer Sutherland, G., Reverdin, G., Marie depths in the ocean surface boundary layer under conditions of diurnal stratification. Geophys. Res. Lett. 41, 8469–8476. Talley, L.D., Feely, R.A., Sloyan, B.M., Wanninkhof, R., Baringer, M.O., et al., 2016. Changes in ocean heat, carbon content, and ventilation: a review of the first decade of GO-SHIP global repeat hydrography. Annu. Rev. Mar. Sci. 8, 185–215. Theocharous, E., et al., 2018. The 2016 CEOS infrared radiometer comparison: part 1: laboratory comparison of blackbodies. J. Atmos. Oceanic Technol. (in review). Theocharous, E., Fox, N.P., Barker-Snook, I., Niclo`s, R., Garcia Santos, V., € ttsche, F.M., Poutier, L., Morgan, N., Nightingale, T., Minnett, P.J., Go Wimmer, W., Høyer, J., Zhang, K., Yang, M., Guan, L., Arbelo, M., Donlon, C.J., 2019. The 2016 CEOS infrared radiometer comparison. Part 2: laboratory comparison of radiation thermometers. J. Atmos. Oceanic Technol. https://doi.org/10.1175/JTECH-D-18-0032.1. Tsamalis, C., Saunders, R., 2018. Quality assessment of sea surface temperature from ATSRs of the climate change initiative (phase 1). Remote Sens. 10, 497. https://doi.org/10.3390/rs10040497. Vazquez-Cuervo, J., Armstrong, E.M., Harris, A., 2004. The effect of aerosols and clouds on the retrieval of infrared sea surface temperatures. J. Clim. 17, 3921–3933. Vincent, R.F., et al., 2008a. Arctic waters and marginal ice zones: a composite Arctic sea surface temperature algorithm using satellite thermal data. J. Geophys. Res. 113. https://doi.org/10.1029/2007JC004353. Vincent, R.F., et al., 2008b. Arctic waters and marginal ice zones: part 2—an investigation of Arctic atmospheric infrared absorption for AVHRR sea surface temperature estimates. J. Geophys. Res. 113. https://doi.org/ 10.1029/2007JC004354. Walton, C.C., Pichel, W.G., Sapper, J.F., May, D.A., 1998. The development and operational application of nonlinear algorithms for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. J. Geophys. Res. 103, 27999–28012. Wang, J., Bhattacharjee, P.S., Tallapragada, V., Lu, C.-H., Kondragunta, S., et al., 2018. The implementation of NEMS GFS Aerosol Component (NGAC) version

Chapter 2 GLOBAL SEA SURFACE TEMPERATURE

2.0 for global multispecies forecasting at NOAA/NCEP: part I model descriptions. Geosci. Model Dev. 11, 2315–2332. Ward, B., 2006. Near-surface ocean temperature. J. Geophys. Res. 111. Wentz, F.J., Meissner, T., 2007. AMSR-E Ocean Algorithms. (Supplement 1 Report) Remote Sensing Systems, Santa Rosa, CA 6 pp. Wentz, F.J., Gentemann, C.L., Smith, D.K., Chelton, D.B., 2000. Satellite measurements of sea surface temperature through clouds. Science 288 (5467), 847–850. Wentz, F.J., Meissner, T., Smith, D.K., 2005. Assessment of the WindSat retrievals produced by NRL. (Technical Report Rep., 30) Remote Sensing Systems, Santa Rosa, CA. Wilheit, T.T., Chang, A.T.C., 1980. An algorithm for retrieval of ocean surface and atmospheric parameters from the observations of the scanning multichannel microwave radiometer. Radio Sci. 15 (3), 525–544. https://doi.org/10.1029/ RS015i003p00525. Williams, K., Harris, C., Bodas-Salcedo, A., Camp, J., Comer, R., Copsey, D., et al., 2015. The met office global coupled model 2.0 (GC2) configuration. Geosci. Model Dev. 8 (5), 1509–1524. https://doi.org/10.5194/gmd-8-1509-2015. Xie, S.-P., 2004. Satellite observations of cool ocean-atmosphere interaction. Bull. Am. Meteorol. Soc. 85 (2), 195–208. Xu, F., Ignatov, A., 2014. In situ SST quality monitor (iQuam). J. Atmos. Oceanic Technol. 31, 164–180. https://doi.org/10.1175/JTECH-D-13-00121.1. Xu, F., Ignatov, A., 2016. Error characterization in iQuam SSTs using triple collocations with satellite measurements. Geophys. Res. Lett. 43 (20), 10826–10834. https://doi.org/10.1002/2016GL070287. Zaitsev, Y., 1997. Neuston of seas and oceans. In: Liss, P.S., Duce, R.A. (Eds.), The Sea Surface and Global Change. Cambridge University Press, Cambridge, UK, pp. 371–382. Zenghong, L., et al., 2017. A comparison of Argo nominal surface and near-surface temperature for validation of AMSR-E SST. Chinese J. Oceanol. Limnol. 35, 712. https://doi.org/10.1007/s00343-017-5330-3. Zhang, H., et al., 2016. Investigating sea surface temperature diurnal variation over the Tropical Warm Pool using MTSAT-1R data. Remote Sens. Environ. 183, 1–12. https://doi.org/10.1016/j.rse.2016.05.002. Zhong, A., Beggs, H., 2008. Analysis and Prediction Operations Bulletin No. 77—Operational Implementation of Global Australian Multi-Sensor Sea Surface Temperature Analysis. (2 October). Zhu, X., Minnett, P.J., Berkelmans, R., Hendee, J., Manfrino, C., 2014. Diurnal warming in shallow coastal seas: observations from the Caribbean and Great Barrier Reef regions. Cont. Shelf Res. 82, 85–98. https://doi.org/ 10.1016/j.csr.2014.03.002.

Further Reading Minnett, P.J. (2019). Satellite Remote Sensing of Sea Surface Temperatures. In J.K. Cochran, H.J. Bokuniewicz, & P.L. Yager (Eds.), Encyclopedia of Ocean Sciences (Third Edition) (pp. 415-428). Oxford: Academic Press. ISBN 978-0-12-8130827. https://doi.org/10.1016/B978-0-12-409548-9.04340-2.

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