patrickbdevaney/mia9 · Datasets at Hugging Face

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Mean Annual
Duration
Mean Annual
Number MHW
Mean Annual
SST
Sen’s
Slope pvalue
Sen’s
Slope pvalue
Sen’s
Slope pvalue
West Palm Beach 5.6 0.0005 0.7 0.0002 0.14 0.001
Miami Beach 10 <0.0001 1.1 0.0001 0.15 0.0005
Biscayne Bay 7.2 0.0002 0.9 <0.0001 0.1 0.021
Key Largo 7.9 0.0002 0.9 <0.0001 0.16 0.0002
Marathon 9.3 0.0007 1.0 0.0007 0.18 0.0002
North Key West 4.6 0.0486 0.6 0.022 0.14 0.036
South Key West 7.5 0.0007 0.8 0.0008 0.18 <0.0001
Dry Tortugas 7.6 0.0011 0.8 0.0011 0.18 <0.0001
Fort Myers 4.6 0.0088 0.5 0.0053 0.14 0.045
Tampa 4.9 0.0006 0.5 0.0003 0.12 0.021
Different trends were computed between the north and south coastal areas of the
Florida Keys. Although the northern coasts of the Florida Keys (southern WFS) summed
more events during the entire study period (Figure 7c), showing larger annual numbers
of MHWs especially before 2008, the general Sen’s Slopes are weaker in the North Key
West (Figure 8f) than in the South Key West area (Figure 8g) for all variables (number
of events, duration and mean SST; Table 2). The broader area of Dry Tortugas showed
significantly high interannual slopes (Figure 8h) with more than 11 events lasting approximately 110 days in 2015; very high numbers were also computed for 2019 and 2020. The
southern coastal area of Marathon (Figure 8e) was also characterized by high Sen’s Slope of
MHW events (1.0 event/decade) and the strongest interannual trend of the event durations
among all areas (9.3 days/decade). Very strong trends were also computed for Key Largo
(0.9 events/decade, 7.9 days/decade and 0.16 ◦C/decade). The coastal areas of the Dry
Tortugas and Florida Keys, and especially along the Straits of Florida (southern coasts),
are characterized by very strong increasing trends with high frequencies of MHW events,
especially during the last seven years (2015–2021; Figure 8). The highest number of events
over the Florida Keys before 2015 were computed for 1997 and 1998 (Figure 8) during the
El Niño event, causing extensive coral bleaching ([55]; see Section 4.3). The increasing
numbers of MHWs along the southern coastline of the Florida Keys during the last decade
agree with the stronger and statistically significant trends of SST computed over the same
areas (Figure 5).
Water 2022, 14, 3840 16 of 28
4. Discussion
4.1. Effects of Atmospheric Conditions on SST Variability
The air temperature variability averaged annually and over the entire study domain
is presented in Figure 3a, showing a similar but slightly sharper interannual trend than
the SST increase during the 40-year period. The air temperature and the the respective
net heat flux variability are both well correlated with the formation of MHWs showing all
significant increasing trends (Figure 9). The highest mean annual air temperatures and
the strong positive (downward) heat fluxes generally coincide with the MHW peaks. The
Sen’s Slope of all trends are statistically significant (pvalue < 0.0001), with lower significance
for the heat fluxes (pvalue = 0.0142), which also show smaller correlation with the MHW
frequency (RP = 0.44) in comparison to the air temperature (RP = 0.84).
coasts), are characterized by very strong increasing trends with high frequencies of MHW
events, especially during the last seven years (2015–2021; Figure 8). The highest number
of events over the Florida Keys before 2015 were computed for 1997 and 1998 (Figure 8)
during the El Niño event, causing extensive coral bleaching ([55]; see Section 4.3). The
increasing numbers of MHWs along the southern coastline of the Florida Keys during the
last decade agree with the stronger and statistically significant trends of SST computed
over the same areas (Figure 5).
4. Discussion
4.1. Effects of Atmospheric Conditions on SST Variability
The air temperature variability averaged annually and over the entire study domain
is presented in Figure 3a, showing a similar but slightly sharper interannual trend than
the SST increase during the 40-year period. The air temperature and the the respective net
heat flux variability are both well correlated with the formation of MHWs showing all
significant increasing trends (Figure 9). The highest mean annual air temperatures and the
strong positive (downward) heat fluxes generally coincide with the MHW peaks. The
Sen’s Slope of all trends are statistically significant (pvalue < 0.0001), with lower significance
for the heat fluxes (pvalue = 0.0142), which also show smaller correlation with the MHW
frequency (RP = 0.44) in comparison to the air temperature (RP = 0.84).
Figure 9. Annual variability (continuous lines) and trends (dashed lines) of the mean annual number
of all Marine Heat Waves (MHWs; red line), the air temperature (°C; black line), and the surface net
heat flux (J/m2
; blue line). The Sen’s Slopes and the Pearson correlation coefficients between both
atmospheric variables (air temperature and heat flux) and the number of MHWs are presented. The
pvalues of the MK trend and correlation tests are also shown.
The correlation coefficients between the SST and air temperature timeseries show a
strong spatial variability over the South Florida region (Figure 10a). High correlations
were computed over the inner WFS with very large correlation coefficients (>0.90) at the
broader Tampa and Fort Myers bays, confirming the determining role of air temperature
on the SST variability over the western Florida coastal zone; the inner parts of these two
bays revealed the weakest impact of air temperature on SST (smaller correlation
Figure 9. Annual variability (continuous lines) and trends (dashed lines) of the mean annual number
of all Marine Heat Waves (MHWs; red line), the air temperature (◦C; black line), and the surface net
heat flux (J/m2
; blue line). The Sen’s Slopes and the Pearson correlation coefficients between both
atmospheric variables (air temperature and heat flux) and the number of MHWs are presented. The
pvalues of the MK trend and correlation tests are also shown.
The correlation coefficients between the SST and air temperature timeseries show a
strong spatial variability over the South Florida region (Figure 10a). High correlations were
computed over the inner WFS with very large correlation coefficients (>0.90) at the broader
Tampa and Fort Myers bays, confirming the determining role of air temperature on the SST
variability over the western Florida coastal zone; the inner parts of these two bays revealed
the weakest impact of air temperature on SST (smaller correlation coefficients). The impact
of air temperature on SST gradually reduces towards the WFS shelf slope (0.85–0.75), an
area where the Gulf of Mexico mesoscale ocean circulation patterns usually prevail [34].
Loop Current interactions with the WFS shelf control the physical characteristics over
the slope contributing to the upwelling of colder waters toward the surface layers [30] or
supply warmer Loop Current waters over the shelf through advection [56]. Very strong

Mean Annual

Duration

Mean Annual

Number MHW

Mean Annual

SST

Sen’s

Slope pvalue

Sen’s

Slope pvalue

Sen’s

Slope pvalue

West Palm Beach 5.6 0.0005 0.7 0.0002 0.14 0.001

Miami Beach 10 <0.0001 1.1 0.0001 0.15 0.0005

Biscayne Bay 7.2 0.0002 0.9 <0.0001 0.1 0.021

Key Largo 7.9 0.0002 0.9 <0.0001 0.16 0.0002

Marathon 9.3 0.0007 1.0 0.0007 0.18 0.0002

North Key West 4.6 0.0486 0.6 0.022 0.14 0.036

South Key West 7.5 0.0007 0.8 0.0008 0.18 <0.0001

Dry Tortugas 7.6 0.0011 0.8 0.0011 0.18 <0.0001

Fort Myers 4.6 0.0088 0.5 0.0053 0.14 0.045

Tampa 4.9 0.0006 0.5 0.0003 0.12 0.021

Different trends were computed between the north and south coastal areas of the

Florida Keys. Although the northern coasts of the Florida Keys (southern WFS) summed

more events during the entire study period (Figure 7c), showing larger annual numbers

of MHWs especially before 2008, the general Sen’s Slopes are weaker in the North Key

West (Figure 8f) than in the South Key West area (Figure 8g) for all variables (number

of events, duration and mean SST; Table 2). The broader area of Dry Tortugas showed

significantly high interannual slopes (Figure 8h) with more than 11 events lasting approximately 110 days in 2015; very high numbers were also computed for 2019 and 2020. The

southern coastal area of Marathon (Figure 8e) was also characterized by high Sen’s Slope of

MHW events (1.0 event/decade) and the strongest interannual trend of the event durations

among all areas (9.3 days/decade). Very strong trends were also computed for Key Largo

(0.9 events/decade, 7.9 days/decade and 0.16 ◦C/decade). The coastal areas of the Dry

Tortugas and Florida Keys, and especially along the Straits of Florida (southern coasts),

are characterized by very strong increasing trends with high frequencies of MHW events,

especially during the last seven years (2015–2021; Figure 8). The highest number of events

over the Florida Keys before 2015 were computed for 1997 and 1998 (Figure 8) during the

El Niño event, causing extensive coral bleaching ([55]; see Section 4.3). The increasing

numbers of MHWs along the southern coastline of the Florida Keys during the last decade

agree with the stronger and statistically significant trends of SST computed over the same

areas (Figure 5).

Water 2022, 14, 3840 16 of 28

4. Discussion

4.1. Effects of Atmospheric Conditions on SST Variability

The air temperature variability averaged annually and over the entire study domain

is presented in Figure 3a, showing a similar but slightly sharper interannual trend than

the SST increase during the 40-year period. The air temperature and the the respective

net heat flux variability are both well correlated with the formation of MHWs showing all

significant increasing trends (Figure 9). The highest mean annual air temperatures and

the strong positive (downward) heat fluxes generally coincide with the MHW peaks. The

Sen’s Slope of all trends are statistically significant (pvalue < 0.0001), with lower significance

for the heat fluxes (pvalue = 0.0142), which also show smaller correlation with the MHW

frequency (RP = 0.44) in comparison to the air temperature (RP = 0.84).

coasts), are characterized by very strong increasing trends with high frequencies of MHW

events, especially during the last seven years (2015–2021; Figure 8). The highest number

of events over the Florida Keys before 2015 were computed for 1997 and 1998 (Figure 8)

during the El Niño event, causing extensive coral bleaching ([55]; see Section 4.3). The

increasing numbers of MHWs along the southern coastline of the Florida Keys during the

last decade agree with the stronger and statistically significant trends of SST computed

over the same areas (Figure 5).

4. Discussion

4.1. Effects of Atmospheric Conditions on SST Variability

The air temperature variability averaged annually and over the entire study domain

is presented in Figure 3a, showing a similar but slightly sharper interannual trend than

the SST increase during the 40-year period. The air temperature and the the respective net

heat flux variability are both well correlated with the formation of MHWs showing all

significant increasing trends (Figure 9). The highest mean annual air temperatures and the

strong positive (downward) heat fluxes generally coincide with the MHW peaks. The

Sen’s Slope of all trends are statistically significant (pvalue < 0.0001), with lower significance

for the heat fluxes (pvalue = 0.0142), which also show smaller correlation with the MHW

frequency (RP = 0.44) in comparison to the air temperature (RP = 0.84).

Figure 9. Annual variability (continuous lines) and trends (dashed lines) of the mean annual number

of all Marine Heat Waves (MHWs; red line), the air temperature (°C; black line), and the surface net

heat flux (J/m2

; blue line). The Sen’s Slopes and the Pearson correlation coefficients between both

atmospheric variables (air temperature and heat flux) and the number of MHWs are presented. The

pvalues of the MK trend and correlation tests are also shown.

The correlation coefficients between the SST and air temperature timeseries show a

strong spatial variability over the South Florida region (Figure 10a). High correlations

were computed over the inner WFS with very large correlation coefficients (>0.90) at the

broader Tampa and Fort Myers bays, confirming the determining role of air temperature

on the SST variability over the western Florida coastal zone; the inner parts of these two

bays revealed the weakest impact of air temperature on SST (smaller correlation

Figure 9. Annual variability (continuous lines) and trends (dashed lines) of the mean annual number

of all Marine Heat Waves (MHWs; red line), the air temperature (◦C; black line), and the surface net

heat flux (J/m2

; blue line). The Sen’s Slopes and the Pearson correlation coefficients between both

atmospheric variables (air temperature and heat flux) and the number of MHWs are presented. The

pvalues of the MK trend and correlation tests are also shown.

The correlation coefficients between the SST and air temperature timeseries show a

strong spatial variability over the South Florida region (Figure 10a). High correlations were

computed over the inner WFS with very large correlation coefficients (>0.90) at the broader

Tampa and Fort Myers bays, confirming the determining role of air temperature on the SST

variability over the western Florida coastal zone; the inner parts of these two bays revealed

the weakest impact of air temperature on SST (smaller correlation coefficients). The impact

of air temperature on SST gradually reduces towards the WFS shelf slope (0.85–0.75), an

area where the Gulf of Mexico mesoscale ocean circulation patterns usually prevail [34].

Loop Current interactions with the WFS shelf control the physical characteristics over

the slope contributing to the upwelling of colder waters toward the surface layers [30] or

supply warmer Loop Current waters over the shelf through advection [56]. Very strong