ReefTemp Nex t Generation: A new operational …23 ReefTemp Nex t Generation: A new operational...

Reef Temp Nex t Generation: A new operational system for monitoring reef thermal stress

21Volume 7 No 1 February 2014 Journal of Operational Oceanography

Reef Temp Nex t Generation: A new operational system for monitoring

reef thermal stress

INTRODUCTION

Coral bleaching has been observed sporadically

since 1982 on the Great Barrier Reef (GBR), with

mass bleaching events occurring in 1997/1998

and 2002 and a more localised but severe event in

2006 affecting the southern GBR.1,2,3 Mass coral bleaching

events are caused by anomalously warm ocean tempera-

tures1,4,5, which can result in mortality when thermal stress is

prolonged and/or severe. These major bleaching events tend

to occur during the summer and early-autumn months when

ocean temperatures are warmest.1 Coral bleaching is expected

to increase in frequency and severity as ocean temperatures

increase under climate change1,6,7,8, which poses many chal-

lenges for reef management worldwide.9,10

Corals have a symbiotic relationship with dinoflagellates

known as zooxanthellae. Zooxanthellae provide corals with

photosynthetically derived carbohydrates and, in return, corals

provide zooxanthellae with protection and inorganic nutrients

from metabolic and respiratory processes. If ocean tempera-

tures increase beyond the thermal tolerance of the coral, the

expulsion of zooxanthellae from the coral host can occur. The

loss of zooxanthellae, and associated photosynthetic pigments,

causes the corals to appear pastel or white; hence the term

coral bleaching.11 Anomalously high ocean temperatures have

also been linked to coral disease outbreaks.1,12,13 Corals can

recover and be recolonised by zooxanthellae once stress levels

decline and more favourable conditions return.3 However,

the length of the recovery period depends on the severity of

bleaching. Extreme or prolonged stress levels can result in the

death of coral colonies. Reefs with widespread mortality can

take one to two decades to fully recover.14,15

The original ReefTemp system16 (hereafter RT1) was

developed as an experimental system in 2007 through a

collaboration between Australian agencies; Commonwealth

LA Garde BAppSci, BSc (Hons), Climate Prediction,

National Climate Centre, Bureau of Meteorology, Melbourne, Australia

CM Spillman PhD, BSc, BE (Hons), Centre for Australian Weather and

Climate Research (CAWCR), Bureau of Meteorology, Melbourne Australia

SF Heron PhD, BSc (Hons) Coral Reef Watch, National Oceanic and Atmospheric

Administration (NOAA) and Marine Geophysical Laboratory, Physics Department, School of

Engineering and Physical Sciences, James Cook University, Townsville, Australia

RJ Beeden MAppSci, BSc (Hons), Climate Change Group,

Great Barrier Reef Marine Park Authority (GBRMPA), Townsville, Australia

The expected increase in the frequency of mass coral bleaching under climate change

underlines the importance of thermal stress monitoring systems for coral reef manage-

ment. ReefTemp Next Generation (RTNG) is a sophisticated remote sensing application

designed to operationally monitor the ocean temperatures that can lead to coral bleach-

ing across the Great Barrier Reef. Products are derived from state-of-the-art satellite

data; and newly calculated climatologies and management thresholds for bleaching are

presented. RTNG is a key component of the Great Barrier Reef Marine Park Authority’s

Early Warning System, which informs management action and response strategies.

22


Journal of Operational Oceanography Volume 7 No 1 February 2014

Scientific and Industrial Research Organisation (CSIRO),

the Bureau of Meteorology (the Bureau) and the Great

Barrier Reef Marine Park Authority (GBRMPA). Because

of its fine spatial resolution (0.018°; see Tables 1 and 2 for

system details and products), RT1 was designed to provide

GBRMPA managers with a reef-scale assessment of tem-

perature conditions across the vast 344,000km2 expanse of

the GBR Marine Park. Used on a daily-to-weekly basis, par-

ticularly during summer, RT1 was designed to monitor envi-

ronmental conditions such as sea surface temperature (SST)

and accumulated thermal stress across the GBR. Near real

time remote sensing at this scale improves the efficiency of

resource deployment and improves capacity to detect health

impacts. Maynard et al.17 suggest that providing early warn-

ing of conditions that may lead to coral bleaching events

will both open communications early with stakeholders and

the public, and assist planning and implementation of effi-

cient processes to assess and monitor ecological impacts. In

addition, RT1 has been employed to assess patterns of ther-

mal stress exposure and has also informed research on the

vulnerability and resilience of reefs under a range of climate

change scenarios.

For the past 5 years, RT1 has been employed by the

GBRMPA as part of its strategic framework to assess the

threat of coral bleaching.17 As part of this strategy, RT1 is

used in conjunction with SST outlooks18,19 generated from

the Bureau’s seasonal forecast model POAMA (Predictive

Ocean Atmosphere Model for Australia), and tools provided

by the NOAA Coral Reef Watch monitoring seasonal outlook

systems20, to form the GBRMPA’s Early Warning System.

Together these tools support management decisions and

response plans, ultimately leading to accurate and timely

information prior to coral bleaching events.17,21

However, RT1 is an experimental system, run in

research mode at CSIRO with no operational support. In

addition, products created by the RT1 system are based

on the Bureau’s legacy 14-day Advanced Very High

Resolution Radiometer (AVHRR) mosaic composite SST

product16,22, which is soon to be replaced by new daily

satellite SST products developed under the Integrated

Marine Observing System23 (IMOS). With funding from

the National Plan for Environmental Information (NPEI)

eReefs program, a new operational (ie, 24/7 supported)

version of RT1, known as ReefTemp Next Generation24

(RTNG), was designed, developed and launched at the

Bureau during late 2012. RTNG will replace RT1, aug-

menting the existing suite of products with a new set of

products for reef management.

RTNG produces SST and thermal stress products that

take advantage of the latest scientific advances and new

state-of-the-art, multi-sensor composite AVHRR satel-

lite SST products.23 RTNG uses IMOS real-time daily

night-only High Resolution Picture Transmission (HRPT)

AVHRR composite SST products for the Australian region23

at a resolution of 0.02° × 0.02°. Table 1 and Table 2 sum-

marise the main features of RT1 and RTNG coral bleach-

ing systems and their product suites respectively. A full

technical description of the RTNG system is also provided

by Garde et al.24 In developing RTNG, both the design and

delivery of thermal stress products were redesigned in close

consultation with GBRMPA, incorporating feedback to

ensure that the needs of reef management were addressed.

RTNG product suites are uploaded daily online, provid-

ing reef managers with the latest available data on reef

conditions (http://www.bom.gov.au/environment/activities/

reeftemp/reeftemp.shtml).

Table 1: Summaries of ReefTemp coral bleaching thermal stress monitoring systems: Legacy (RT1) and Next Generation (RTNG).

Please note: Degree Heating Days (DHD) are a measure of accumulated thermal stress and Mean Positive Summer Anomalies

(MPSA) are a measure of average stress severity.

LegacyÊReefTemp(2007)16

ReefTemp:ÊNextÊGeneration(2012)24

SST Input Product Bureau Legacy 14-day mosaic SST IMOS 1-day L3S SST

Grid Resolution 0.018° × 0.018° 0.02° × 0.02°

SST Dataset 14-day (day + night) composite 1-day night-time

Climatology Dataset31 23

31

Climatology Baseline 1993–2003 2002–2011

Climatology Calculation 65th percentile, day + night Monthly means, night only

Climatology Resolution 0.042° × 0.036° 0.02° × 0.02°

DHD/MPSA analysis period 1 December to 28/29 February 1 December to 31 March

Product Deployment Experimental at CSIROOperational at the Bureau

(24/7 support)

23


Volume 7 No 1 February 2014 Journal of Operational Oceanography

LegacyÊReefTemp16

(2007)ReefTempÊNextÊGeneration24

(2012)

1-day IMOS SST

(IMOS climatology)

1-day IMOS SST

(CSIRO climatology)

14-day mosaic IMOS SST

(IMOS Climatology)

14-day mosaic IMOS SST

(CSIRO climatology)

REEF TEMP NEXT GENERATION (RTNG)

RTNG 1-day SST product: IMOS Satellite DataRTNG system details and products are summarised in Tables

1 and 2. Within IMOS, the Bureau produces high-resolution

satellite SST products for the Australian region. Raw data

measured by AVHRR sensors on-board National Oceanic

and Atmospheric Administration (NOAA) polar-orbiting sat-

ellites (NOAA-11, 12, 14, 15, 16, 17, 18, and 19) have been

broadcast via HRPT to several ground receiving stations

across Australia and Antarctica. A combination of available

data on any given day are then used to produce real-time

daily AVHRR skin SST data files beginning 2002.23,25 The

IMOS SST data were calibrated with in-situ measurements

to the ocean skin temperature (around 10-20 micron depth)

by first regressing the AVHRR brightness temperatures

against drifting buoy SST observations over the Australian

region, followed by conversion from buoy depths to the

cool skin by subtraction of 0.17°C.23 From the raw data,

each Level 2 pre-processed (L2P) geolocated single swath

dataset is re-mapped to a standard Level 3 un-collated grid-

ded product (L3U). A new Level 3 collated product (L3C) is

created from a combination of multiple swaths from a single

sensor. Finally a super-collated, multi-sensor, multi-swath

product (L3S) is produced. An overview of the data format

is provided by the Group for High-Resolution Sea Surface

Temperature (GHRSST) and can be viewed online at http://

imos.org.au. In addition, Casey et al.25 provides detailed

information regarding the content and format of these prod-

ucts. IMOS L3S grids do not contain any spatial or temporal

smoothing, nor use interpolation to fill in missing data within

the dataset. RTNG is based on the L3S 1-day night-only SST

data (Table 1). The night-only SST restriction was chosen

to avoid observations that are affected by diurnal warming

Table 2: Summary of ReefTemp coral bleaching thermal stress products: Legacy (RT1) and Next Generation (RTNG)

24



of the thin surface layer caused by solar heating, solar glare

effects26 and to therein represent the temperature variability

observed at the depths of reef organisms, particularly during

thermal stress events.27,28

An important stage in the creation of the L3S SST grids

is the identification of clouds. Clouds prevent an accurate

measure of the underlying ocean surface temperature, and

tend to lower the observed temperature and result in an

overall negative temperature bias at the affected grid cell.23

However, it is also crucial to ensure that pixels are not unnec-

essarily masked out during this process, as spatial coverage is

also important.23 The IMOS processing system uses a set of

brightness temperature, uniformity and two-channel thresh-

olds to identify cloud.23 At each IMOS SST grid location,

quality level (QL) data are also provided which is assigned

based on proximity to cloud (in terms of distance), satellite

zenith angle and day or night pass23. IMOS L3S metadata

states that QL values 0 through 5 represent no and bad data

(QL 0 and QL 1) then worst, low, acceptable and best quality

(QL 2 to QL 5), respectively.23 This enables users to select

the desired quality level dependent upon the needs of each

specific application.

Comparing brightness temperatures observed from the

Multifunctional Transport Satellites (MTSAT-2) platform

with the daily IMOS L3S SST indicates that the cloud

identification process is successful. Fig 1 shows MTSAT-2

brightness temperatures and IMOS L3S 1-day SST and QL

data over the GBR domain for two example dates. On 1

February 2011, extensive and persistent cloud cover was

observed (brightness temperature range 190-260K), associ-

ated with Severe Tropical Cyclone Yasi29 (Fig 1a), which

caused extensive damage to approximately 90,000km2

(~26%) of the GBR Marine Park.30 MTSAT-2 observed

brightness temperatures for 8 January 2012 shows that the

cloud cover was minimal across the domain (brightness

temperature range 280-290K; Fig 1d).

Fig 1: (a) Infrared brightness temperatures acquired by MTSAT-2 platform at 16:32 UTC, 1 February 2011, showing tropical

are illustrated as solid white lines in (a) and (d), and black lines in (b), (c), (e) and (f).

25



In Fig 1b and 1e, white shading represents regions where

the IMOS SST could not be measured due to cloud cover. As

expected, due to cloud cover extent, data coverage is lower

for 1 February 2011 (Fig 1b), as compared with 8 January

2012 (Fig 1e). However, despite greater coverage, a large

number of SST grid cells have been flagged as low quality

(QL 3) for 8 January 2012, with best quality (QL 5) pixels

confined to the north-western and south-eastern regions of the

Coral Sea (Fig 1f). For this specific date, the likely explana-

tion for the swath of low quality data is high satellite sensor

viewing angle (see distinct swath edges in Fig 1f). If it had

been decided that only the most robust data points (QL 5

threshold only) be considered by the analysis, minimising the

potential for cloud contamination, it is clear that data cover-

age would be significantly sacrificed. For this reason and

after substantial examination of the SST and QL data, it was

decided that RTNG would use SST data with QL 3, 4 and 5.

Development of RTNG 14-day SST Mosaic As the daily IMOS L3S dataset does not incorporate any fill-

ing of spatio-temporal gaps, a potential issue was raised dur-

ing development that poor coverage could lead to an under-

estimate of the total accumulated thermal stress. This is of

major concern to GBRMPA regarding the timely monitoring

of thermal stress episodes. One potential solution to the issue

of inadequate spatial coverage was to generate a new SST

mosaic and provide derived gap filled products in addition

to the 1 day products (Table 2). This was based on the IMOS

L3S 1-day night-only SST data and followed the methodol-

ogy of the RT1 SST mosaic16,22; ie, filling each missing data

grid cell in the current day using the most recent daily SST

for that grid cell from up to 13 days prior.

Comparing the new RTNG 14-day SST mosaic product

(Fig 2a and c) with the IMOS SST L3S 1-day product (Fig

1b and e) for the same dates, it is evident that the mosaic

product provides markedly improved spatial coverage over

the GBR region. However, it is important to note that the

new SST field can be spatially inhomogeneous as a result

of the varying dates when the grid data were recorded. As a

result, during periods of warming and cooling, it is possible

that pixels within the mosaic product may contain values

that do not represent current conditions. This may result in

the appearance of unrealistic SST gradients at small scales.

Furthermore, persisting temperatures which trigger thermal

stress thresholds has the potential to over predict accumulated

thermal stress. However, from the perspective of reef man-

agement, it is preferable to over predict (and respond) than

to under predict (and not be aware of a bleaching event12,17).

To quantify the inhomogeneity of the 14-day SST mosaic,

a new Pixel Age product was developed to report the age of

Fig 2: RTNG 14-day (a) SST mosaic

and (b) mosaic Pixel Age grids for 1

February 2011. (c) and (d) as in (a)

and (b) for 8 January 2012.

26



filled data points. This product provides a means to assess

the temporal relevance of each SST value within the mosaic

product. The RTNG 14-day SST mosaic product created

for 1 February 2011 (Fig 2b) contains SST grids from up to

13 days prior, across most of the Coral Sea. In contrast, the

mosaic generated for 8 January 2012 (Fig 2d) contains more

contemporary data and more likely provides a better repre-

sentation of the SST conditions on the reported date.

To quantify the sparse spatial coverage of the IMOS L3S

1-day night-time only dataset, the persistence of missing data

was examined by Garde et al24 at each grid cell. Measured

in days, the longest consecutive period with no SST data

was found over the period 2002 to 2012, for each grid cell.

The study showed that isolated regions of the GBR have, at

times, lacked IMOS L3S SST data for more than 14 days.24

For some locations near Papua New Guinea and within the

inshore reef regions, the longest consecutive period with no

SST data was in excess of 28 days.24 The lack of SST data is

most likely due to persistent cloud cover24, with greater gap

counts measured in the northern Coral Sea. However, there

is a possibility that the absence of satellite retrievals or the

unavailability of data of sufficient quality during this period

could also be the cause. However, regardless of the cause,

a lack of SST data will affect the daily cumulative thermal

stress values.24 Grid cells where SST data are missing for

more than 14 consecutive days will also result in missing data

within the 14-day SST mosaic grid.

SST ClimatologyThe long-term average monthly SST conditions (the SST

climatology) are used to calculate SST anomalies and ther-

mal stress levels across the GBR. However, the values within

the SST climatology are sensitive to the period it is calcu-

lated, which can have an effect on reported stress levels. Two

options were considered to represent the long-term monthly

SST conditions for the GBR: (1) the existing CSIRO clima-

tology employed in RT1; and (2) a new climatology devel-

oped from the IMOS L3S 1-day SST data used in RTNG.

Reef managers requested that two separate suites of products

be generated using the two climatologies. This permits daily

comparison of stress metrics derived from two different base-

lines, and thus allowing management to test and update alert

levels for action given observed conditions.

The RT1 monthly climatology was derived by CSIRO

from composite 3-day day-and-night-time HRPT AVHRR

SST data from NOAA polar-orbiting satellites for the period

1993-2003.16,31 The SST data were calibrated to observa-

tions at depths around 20-30cm from global drifting buoys.

Exclusion of anomalous values caused by cloud and diurnal

surface warming was considered by taking the SST value at

the 65th percentile of the cumulative frequency distribution

within each group of monthly data.16

The new RTNG monthly climatology was derived from

night-time IMOS L3S 1-day SST data, by calculating the

average temperature for each month during the 10 year period

of January 2002 to December 2011 (the period for which data

were available). This climatology is calculated by averaging

the available data at an individual pixel for a particular month

across all years, regardless of the amount of missing data at

that location during the period. The data coverage per pixel

over the climatological period is presented in Garde et al.24

To ensure consistency with the real-time SST data, only SST

data of QL 3, 4 and 5 were included.

Comparing the CSIRO (1993-2003) and IMOS (2002-

2011) climatologies for December, January, February and

March over the GBR region (Fig 3a to d) shows that the

IMOS climatology is slightly warmer than the CSIRO clima-

tology across most of the GBR region during these months,

with the exception of the far northern and inshore regions of

the GBR. This finding is in spite of the 0.17°C cold offset

between the IMOS skin SST and CSIRO sub-skin SST and

the use of night-time only data for the IMOS climatology.

March shows the greatest difference with SSTs located in

the central Coral Sea in excess of 1.5°C warmer in the IMOS

climatology than the CSIRO climatology.

There are two explanations for the IMOS climatology

being warmer on average than the CSIRO climatology. First,

the climatologies are calculated for two different decades.

SSTs in the Australian region were the warmest on record

during the recent decade (2001 to 2010) as compared to the

1961–1990 average using NOAA Extended Reconstruction

SSTs.32 Research has shown that SSTs averaged along

Australia’s NE tropical coasts had a warming trend of 0.12°C/

decade over the 1950-2007 period.33 Secondly, the cloud

quality control methods differ between the IMOS and CSIRO

products. Griffin et al31 states that approximately 40% of thin

cloud at night is not detected by the Saunders and Kribel

cloud detection algorithm34, which was used in the CSIRO

dataset. This would result in a cooler SST for the CSIRO SST

climatology although the effect could be, at least partially,

offset by using the 65th percentile median SST. For the IMOS

SST and climatology, a new threshold test based on bright-

ness temperatures measured from thermal infrared channels

was found to be very effective at identifying cloud.23 Here it

is postulated that the observed climatology differences could

be due to these combined factors. However, it is worth high-

lighting that the two climatologies showed very little overall

difference (within ± 0.5°C) across the GBR Marine Park,

which is of importance to primary management users of the

RTNG product.

Given the difference between the two climatologies, both

the CSIRO (1993-2003; used in RT1) and IMOS (2002-2011;

new state-of-the-art SST dataset) climatologies are utilised to

provide two parallel suites of products in the RTNG operational

system (Table 2). There is ongoing scientific research as to

which climatological baseline is best used to indicate thermal

stress and thus predict coral bleaching14, particularly in the con-

text of global warming and coral adaptation. In future, products

based on the legacy CSIRO climatology may be phased out,

should the IMOS climatology be deemed as a more appropri-

ate baseline for coral stress by reef managers and stakeholders.

Thermal stress productsEach day during the extended summer period, 1 December

to 31 March, RTNG calculates the following thermal stress

metrics across the GBR region: Sea Surface Temperature

Anomaly (SSTA), Degree-Heating Days (DHD) and Mean

Positive Summer Anomaly (MPSA).16 As stated above, to

satisfy requests of reef managers and allow operational evalu-

ation of the new RTNG system, thermal stress products are

27



calculated using each SST input (1-day, 14-day mosaic prod-

uct) and each climatology (IMOS, CSIRO) to create four par-

allel suites of products (1-day IMOS; 1-day CSIRO; 14-day

mosaic IMOS; 14-day mosaic CSIRO; Table 2).

SSTA fields are calculated by subtracting the correspond-

ing monthly climatology from the SST values, to indicate

the deviation of current SST conditions from the long-term

monthly mean.

SSTA SSTx y x y, ,= − climatology thismonth

Degree-Heating Days (DHD) is a measure of the accu-

mulated thermal stress, calculated progressively from 1

December through to 31 March each year (inclusive). A DHD

of 1°C-days is equivalent to an SSTA value of 1°C for one

day; ie, SST exceeds the corresponding monthly climatol-

ogy by 1°C for one day at a particular grid point (x, y). Only

positive SSTA values are accumulated during the period, viz.

DHD SSTA SSTAx y x yt Dec

t today

xst, , ,==

=

∑0

1

1where ,,y > 0°C

If no SST value is available at a particular pixel, there is

no accumulation to the relevant DHD value; ie, DHD value

remains the same as the previous day. RTNG also includes a

complementary product called DHD counts, which indicates the

number of days where the SSTA was greater than 0°C at each

grid cell; ie, days on which DHD was incremented (Table 2).

DHD values can represent a broad range of thermal stress;

for example, 3 days at 1°C above the local long-term aver-

age results in the same DHD value as 1 day at 3°C, with the

latter representing more intense stress to corals.16 For this

reason RTNG produces the Mean Positive Summer Anomaly

(MPSA35; formerly called Heating Rate in RT116). This is the

number of DHDs divided by the DHD count and it provides a

measure of the average severity of thermal stress:

MPSADHD

DHDx y

x y

count x y

,

,

,

=

The way the MPSA is calculated means that differences

between the 1-day and 14-day mosaic-based MPSA values

are unlikely to be as significant as in the DHD products.

Interpretation of thermal stressIn RT1, DHD (MPSA) values above 60 °C-days (1.7 °C)

and 100 °C-days (2.4 °C) were statistically linked to moder-

ate and severe bleaching observations on the Great Barrier

Reef, respectively.16 In RTNG, as the 14-day mosaic products

Fig 3: Difference between monthly

climatology values (IMOS–CSIRO)

in the RTNG GBR domain for (a)

December, (b) January, (c) February

and (d) March.

28



derived from 1-day IMOS SST data were created to closely

mimic the legacy SST mosaic product used in RT1, we

assume that the RT1 bleaching thresholds are thus applica-

ble to RTNG 14-day DHD and MPSA products. However,

the RTNG 1-day and the 14-day mosaic DHD products will

potentially show different accumulated totals, primarily aris-

ing from differences in spatial coverage, ie in locations where

data are missing in the 1-day IMOS SST product but are gap

filled in the 14-day mosaic. For example, if the SST at a loca-

tion is above the monthly climatology and is persisted for the

following four cloudy days in the 14-day mosaic, then the

accumulation of the 14-day DHD will be five times that of the

1-day DHD during that five day period. The DHD products

may differ even at the beginning of summer (1 December)

Fig 4: 1-day and 14-day mosaic product comparison for the 2002-2012 period covering the GBR domain. Degree-Heating Days

(DHD) using the (a) IMOS and (b) CSIRO climatology baseline. Mean Positive Summer Anomaly (MPSA) using the (c) IMOS

and (d) CSIRO climatology baseline. Dashed black line is the line of best fit (equation shown on each plot). Orange and red

dashed lines represent moderate and severe bleaching thresholds as calculated by Maynard et al16, respectively. See Table 3 for

new thresholds. Note the different 1-day DHD scale used in (a) and (b).

29



if there is no corresponding 1-day SST field and the 14-day

mosaic product SST contains a persisted value from late

November.

This discrepancy was first noted by Garde et al24 who

showed that during the 2011-2012 Australian summer, 1-day

DHD values were significantly lower than the corresponding

14-day DHD values. Further to this, over the extended period

of 2002-2012, 1-day DHD values very rarely exceeded 50

°C-days anywhere in the GBR region, compared to 14-day

DHD values which were up to 200 °C-days, regardless of

climatology used (Fig 4a and b). Therefore the original

RT1 moderate (60 °C-days) and severe (100 °C-days) coral

bleaching thresholds16 are not appropriate when evaluating

thermal stress using the new IMOS 1-day SST based products

and thus must be revised.

In order to determine a new set of coral bleaching

thresholds appropriate for the use with the 1-day products,

the relationship between 1-day and corresponding 14-day

product (DHD, MPSA) values was determined (Fig 4).

DHD and MPSA values at the end of each summer

(31 March) for each year of 2002 to 2012 for all grid cells

were binned into increments of 1 °C-days and 0.1 °C for the

14-day (horizontal) DHD and MPSA products, respectively

(n = 11 years × number of wet cells). The average of the

associated 1-day value pair (vertical) for each bin was then

determined.

Bin pairs exhibited a linear relationship in the high-den-

sity regions of the distribution (ie, all but the highest values

of the DHD/MPSA). Linear regression of these bin pairs

was undertaken using only bins that contained >1% of the

population maximum value (defined as the bin with greatest

number of pairs). This ensured any outliers did not influence

the regression, resulting in very high correlations (r>0.99;

Table 3). Regression parameters indicate that the 1-day DHD

bleaching thresholds are approximately 20% of the 14-day

DHD thresholds (60 and 100 °C-days16), regardless of cli-

matology used (Fig 4a to b). New 1-day DHD thresholds

are 11.6 (12.2) and 18.9 (20.2) °C-days for moderate and

severe bleaching for IMOS (CSIRO) climatologies (Table

3). This suggests that on average, positive SSTA values were

persisted for five days in the 14-day mosaic product for every

day of data in the 1-day product. For the MPSA products,

the 1-day MPSA thresholds are roughly 80% of the 14-day

MPSA thresholds (1.7 and 2.4 °C for moderate and severe

bleaching, respectively16), again regardless of climatology

used (Fig 4c to d). New 1-day MPSA thresholds are 1.4 (1.5)

and 2.0 (2.1) °C for moderate and severe bleaching for IMOS

(CSIRO) climatologies (Table 3). The reduction in MPSA

values indicate that on average, higher SST values were

persisted for longer in the 14-day mosaic. This is reasonable

given that increases in SST will predominantly result from

closely spaced cloud-free days.36

Fig 5 and Fig 6 represent DHD and MPSA values as of the

end of the 2012 extended summer respectively. It can be seen

that when the colour schemes are appropriately scaled (as in

Fig 5b and e for DHD; and Fig 6b and e for MPSA) there is

improved thermal stress spatial pattern agreement between

the 1-day and 14-day products. However, it is evident that the

DHD products differ depending on the use of the IMOS (Fig

5a to c) or CSIRO climatology (Fig 5d to f). As discussed

previously, the IMOS climatology is generally warmer than

the CSIRO climatology for most of the GBR (Fig 3). It is

therefore not surprising that the DHD values were higher

when using the CSIRO climatology. The MPSA maps (Fig 6)

highlight small isolated regions of high MPSA values (orange

to red colours) within the GBR Marine Park off the Cape

York Peninsula, as well as south of Papua New Guinea. In

this example (extended 2012 summer), these values are likely

erroneous. Importantly, it is apparent that all four MPSA

products show similar spatial structure, identifying regions

of acute stress.

FUTURE WORKIn its current form, RTNG provides the foundation for

the development of additional coral products. GBRMPA

management have expressed interest in updating RTNG

products to assess the risk of coral diseases such as White

Syndrome and Black Band disease outbreaks, which can

be related in part to temperature stress.13 Another area of

Table 3: Newly derived RTNG thermal stress thresholds for 1-day products. RT1 thresholds defined as 60 and 100 °C-days

(DHD), and 1.7 and 2.4 °C (MPSA), for moderate and severe bleaching, respectively16. The new RTNG thresholds divided by the

original corresponding RT1 thresholds are denoted as percentages in brackets.

Degree-HeatingÊDays(DHD;Ê¡C-days)

MeanÊPositiveÊSummerÊAnomaly(MPSA;Ê¡C)

Climatology IMOS CSIRO IMOS CSIRO

Regression y = 0.183x + 0.572 y = 0.199x + 0.247 y = 0.799x + 0.086 y = 0.838x + 0.075

Correlation 0.993 0.998 0.996 0.999

Moderate Threshold

(RT1: 60 °C-days; 1.7 °C-days)

11.6

(19%)

12.2

(20%)1.4

(82%)

1.5

(88%)

Severe Threshold

(RT1: 100 °C-days; 2.4 °C-days)

18.9

(19%)

20.2

(20%)

2.0

(83%)

2.1

(88%)

30



future development would be to expand the RTNG system

to include additional geographic regions. Regions of inter-

est include northern and western coastal Australian waters,

including Ningaloo Reef, in addition to the western Pacific

Ocean.

Given the potential grid spatial inhomogeneity relating

to the use of a mosaic SST product, it is advised that further

research be targeted at developing a more accurate back-filled

daily SST product. Accuracy could be improved using spatio-

temporal interpolation techniques; eg, the SSTA from the

previous day could be damped (reducing over-prediction of

the thermal stress metrics). However, sensitivity and perform-

ance evaluations would be needed to measure the accuracy of

such techniques.

Finally, a verification study is needed to assess the skill

of the RTNG system in its identification of coral bleach-

ing events using coral health surveys and field data. This

would also assess the performance of the new 1-day DHD

and MPSA thresholds. It should be noted that the use of two

different climatologies will also impact DHD and MPSA,

though to a lesser extent, in that products utilising the warmer

IMOS climatology will accumulate more slowly than those

based on the cooler legacy CSIRO climatology. Quantifying

these impacts and implications for bleaching thresholds are

the subject of future work. These improvements are strongly

recommended as they will significantly increase both the

system functionality and the number of end user applications,

leading to further product validation and refinement.

Fig 5: GBR DHD calculated for the 2012 extended summer (1 December to 31 March inclusive) using IMOS climatology with

(a) IMOS L3S 1-day SST (b) IMOS L3S 1-day SST with rescaled colour scheme and (c) 14-day IMOS SST mosaic. (d), (e) and

(f) as in (a), (b) and (c) but using CSIRO climatology.

31



CONCLUSIONRTNG is a sophisticated real-time, reef-scale remote sensing

system that monitors ocean temperatures and coral thermal

stress across the GBR. Now employing the latest in high

resolution gridded daily L3S multi-sensor composite satellite

SST data, RTNG presents and communicates daily thermal

stress across the GBR at a very high resolution. The RTNG

development during 2012 has resulted in an operationally

supported Bureau system with multiple enhancements and

new product suites arising from extensive consultation with

GBRMPA and RT1 users.

The use of new cutting edge data inputs and shifting

climatological periods in RTNG has required revised guide-

lines to interpretation of thermal stress products. New 1-day

DHD thresholds are approximately 20% of legacy RT1

DHD thresholds and have been defined as 11.6 (12.2) and

18.9 (20.2) °C-days for moderate and severe bleaching for

IMOS (CSIRO) climatologies. Corresponding new MPSA

thresholds are 1.4 (1.5) and 2.0 (2.1) °C for moderate and

severe bleaching for IMOS (CSIRO) climatologies. These

revised coral bleaching thresholds for RTNG 1-day DHD and

MPSA products provide reef management with the means of

better monitoring and communicating thermal stress events.

The RTNG aids reef managers in generating and actioning

appropriate strategic plans for minimising potential impacts

of coral bleaching on reef health. The development and

operational support for these types of systems is extremely

important, as the frequency and severity of coral bleaching is

expected to increase due to climate change.

ACKNOWLEDGMENTSFunding was provided by eReefs/NPEI. Data were

sourced from IMOS. IMOS is supported by the Australian

Fig 6: GBR MPSA calculated for 31 March 2012 using IMOS climatology with (a) IMOS L3S 1-day SST (b) IMOS L3S 1-day SST

with rescaled colour scheme and (c) 14-day IMOS SST mosaic. (d), (e) and (f) as in (a), (b) and (c) but using CSIRO climatology.

32



Government through the National Collaborative Research

Infrastructure Strategy and the Super Science initiative. Raw

HRPT AVHRR SST data was collected by the Australian

Bureau of Meteorology in collaboration with Australian

Institute of Marine Science (AIMS) and CSIRO Marine and

Atmospheric Research.

The authors would like to acknowledge Dr Oscar Alves,

Dr Debbie Hudson, Dr Helen Beggs, Aurel Griesser, Elaine

Miles, and Griff Young (Centre for Australian Weather

and Climate Research, Australian Bureau of Meteorology)

for their guidance and helpful discussion as well as Leon

Majewski, Christopher Griffin and George Kruger (Bureau

of Meteorology) for the preparation of IMOS SST data.

Thanks are also extended to GBRMPA for their support and

feedback during the RTNG development process. Thanks also

to the reviewers of this manuscript. The manuscript contents

are solely the opinions of the authors and do not constitute a

statement of policy, decision, or position on behalf of NOAA

or the U.S. Government.

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