A Case Study for Data Exchange in the Wider Caribbean LME

1. Frank Muller-Karger, Gerardo Toro-Farmer, Digna Rueda
Institute for Marine Remote Sensing
University of South Florida
Satellite Observations in Support of
LME Governance:
A Case Study for Data Exchange in the Wider
Caribbean LME
Exchange of Experiences on LME- related data and information
issues
Buenos Aires, Argentina, June, 2013

2. Requirement for Dynamic
LME Governance
2
 Governance requires ‘knowledge’
(understanding of what is happening). Knowledge
has to be:
 Co-derived (joint natural + social science effort)
 Inexpensive to local governments
 Timely
 LMEs are ‘Large’: They require a synoptic
framework of observations
 LME’s change continuously: They require time
series of observations

3. Synoptic ocean time series
3
 Regional-global context to understand
processes, stocks, and diversity within
different parts of a dynamic LME
 Means to quantitative measure change in
LME’s
 Place point observations in regional
context
 Initialize and validate simulations /
ecological forecasting

4. Today’s Tools
4
 Atlas – today we can show dynamic aspects of
an ecosystem
 Climatologies (monthly, annual) as ‘baseline’ to
measure
 short-term change
 Long-term trends
 Occurrence and impacts of extreme events
 Time series (observations and anomalies)
 Other dynamic information: individual historic and
current observations, forecasts

5. Prototype datasets for the
Caribbean
5
 Regional-scale and local satellite data
products
 Printed Atlas: Wider Caribbean LME
 Digital Atlas examples:
 Caribbean Marine Atlas (IODE)
http://www.caribbeanmarineatlas.net/
 NOAA Gulf of Mexico Data Atlas
http://gulfatlas.noaa.gov

6. Satellite derived synoptic
data
6
 Sea surface temperature (SST) 1 km
 Ocean color (turbidity, CHL, CDOM)250 m – 1
km
 Wind 25 km
 Sea Surface Height/currents ~100-
300 km
 Sea Surface Salinity ~300
km
 Geomorphological and Habitat: ~2 m
> 30 m

7. Examples of satellite derived
data for the Wider Caribbean
LME
7

8. 8
Longitude
Latitude
SST (C) bimonthly climatogy: Mar-Apr
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Mar-Apr
Longitude
Latitude
SST (C) bimonthly climatogy: May-Jun
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
May-Jun
Latitude
Longitude
Latitude
SST (C) bimonthly climatogy: Sep-Oct
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Sep-Oct
-85 -80 -75 -70 -65 -60 -55 -50 -45
Longitude
Latitude
SST (C) bimonthly climatogy: Nov-Dec
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Nov-Dec
-85 -80 -75 -70 -65 -60 -55 -50 -45
Longitude
Longitude
Latitude
SST (C) bimonthly climatogy: Jul-Aug
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Jul-Aug0
5
10
15
20
25
-85 -80 -75 -70 -65 -60 -55 -50 -45
Longitude
Latitude
SST (C) bimonthly climatogy: Jan-Feb
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
20
22
24
26
28
30
Jan-Feb0
5
10
15
20
25
SST long term bimonthly means (1985-2009)
Longitude
SST(C)bimonthlyclimatogy:Sep-Oct
-85-80-75-70-65-60-55-50-45
20
22
24
26
28
30
30 28 26 24 22 20 (°C)
Application 1: Satellite Climatologies
Example: Sea Surface Temperature (SST) from AVHRR

9. 9
Longitude (°W)
Time(month)
SST (°C)
9°
10°
11°
12°
9°
10°
11°
12°
-76° -74° -72° -70° -68° -66° -64° -62°
Latitude(°N)
Application 1: Satellite Climatologies
Example: Southern Caribbean upwelling system (coastal SSTs)

10. 10
Application 2: Satellite Time Series
Southern Caribbean upwelling system
20
21
22
23
24
25
26
27
28
29
30
20
21
22
23
24
25
26
27
28
29
30
-4
-3
-2
-1
0
1
2
3
4
Climatology
Time Series
Anomaly
Weekly time series (March 12-18, 2005)
20
21
22
23
24
25
26
27
28
29
30
°C
°C
Anomaly = Time series - Climatology

11. Application 2: Satellite Time Series
Southern Caribbean upwelling system (coastal SST anomalies time
series)
Longitude (°W)
9°
10°
11°
12°
9°
10°
11°
12°
-76° -74° -72° -70° -68° -66° -64° -62°
Latitude(°N) °CTime(year)
11

12. 12
Application 2: Satellite Time Series
Southern Caribbean upwelling system (coastal SST anomalies time
series)
°CTime(year)
Spanish sardine capture
crashed after two
consecutive years of weak
upwelling

13. SSTBimonthlytrend(C10years-1
)months:Mar-Apr
Longitude
Latitude
-85-80-75-70-65-60-55-50-45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
(°C/decade)
1 0.5 0 -0.5 -1
ns
Application 4: Satellite Tendencies
Bimonthly SST linear trends (1985-2009)
SST Bimonthly trend (C 10 years-1
) months: Jan-Feb
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
Jan-Feb
SST Bimonthly trend (C 10 years-1
) months: May-Jun
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
May-Jun
SST Bimonthly trend (C 10 years-1
) months: Sep-Oct
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
Sep-Oct
SST Bimonthly trend (C 10 years-1
) months: Mar-Apr
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
Mar-Apr
SST Bimonthly trend (C 10 years-1
) months: Jul-Aug
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
Jul-Aug
SST Bimonthly trend (C 10 years-1
) months: Nov-Dec
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
-1
-0.5
0
0.5
1
mask
Nov-Dec
Latitude
Longitude
13

14. 14
What is the relation between Climate
Change and Coral (benthic) health?
Eakin et al.
(2010)
Application 5: Thermal Stress and Coral Bleaching
A) Maximum NOAA Coral Reef Watch
Degree Heating Week (DHW) during
2005.
(B) means of coral bleached as either
percent live coral colonies (circles) or cover
(diamonds).

15. 15
Can we identify benthic composition?
Is Coral (benthic) coverage changing over
time?
• Benthic coverage is affected by natural /
anthropogenic events
• Need to monitor / understand interannual variations
and ecological shifts
Application 6: Mapping Benthic Coverage
Classified dataset based on Landsat for Looe Key Reef
(red: coral, brown: covered hardbottom, yellow: bare
hardbottom, green: sand. Palandro et al. (2008)

16. Decision Support Tools for an Ecosystem
Based Management
Developing a flexible framework for integrated,
distributed,
and interlinked regional coastal and marine data atlases
based on the NOAA Gulf of Mexico data atlas
16
Goals
Integrate scientific and socio-economic
information
through an online data atlas
to help visualize and analyze historical
datasets,
understand connectivity, trends, and
variability

17. Objectives:
• Identify and integrate additional specific data sets
• Implement a framework for embedding regional
data atlases
• Enhance the user interface of existing web-based
data atlas(es) for displaying, querying and
analyzing information, providing meaningful
statistics for decision-making
• Develop a prototype for a mobile platform
Decision Support Tools for Ecosystem-Based
Management
17

18. Decision Support Tools for Ecosystem-Based
Management
18
Gulf of Mexico Data Atlas
(NOAA)
http://gulfatlas.noaa.gov/

19. 19
Decision Support Tools for Ecosystem-Based
Management
Currents
(m s-1)
CHL long term annual climatology (mg m-3
)
Longitude
Latitude
-85 -80 -75 -70 -65 -60 -55 -50 -45
0
5
10
15
20
25
0.02
0.05
0.1
0.2
0.5
1
2
5
10
mask
Chlorophyll
(mg m-3)
Wind (m s-1)
Gulf of Mexico Data Atlas
(NOAA)
http://gulfatlas.noaa.gov/

20. 20
 Use existing datasets developed for the
Caribbean LME atlas as initial layers for the IODE
Caribbean Marine Atlas:
(http://www.caribbeanmarineatlas.net/)
 Link the Gulf of Mexico and Caribbean Atlases
 Develop a framework for an integrated global
atlas that:
 Uses existing (easily available) ocean and land
satellite data
 Provides the framework and technology tools to
incorporate new regions around the world
 Develop an inter-operable data platform
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