patrickbdevaney/mia9 · Datasets at Hugging Face

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correlation was also detected at the Bahamas area, which was characterized as a “hot
spot” for the formation of MHWs (Figure 7). Relatively low correlation coefficients were
computed over the Straits of Florida, and especially west of 83◦ W and east of 81◦ W, with
very weak correlation along the northern Straits and the EFS (Rp < 0.80). The lower values
mainly occurred over the area where the FC flows, controlling the distribution of physical
properties (see Section 4.2). The lowest correlation coefficients (Rp < 0.75) were computed at
the coastal area north of Miami Beach (26◦ N). The lag (in days) between the two parameters
is around 5 days (Rp = 0.90; Figure 10c). Once the seasonal cycle is removed, the interaction
between them is more direct, showing smaller correlation (Rp = 0.57) with a 3-day lag
(Figure 10e). The agreement between the two parameters confirms that the effect of the
atmospheric temperature on the ocean sea surface temporal and spatial evolutions also
holds for the nearshore and coastal study areas. This effect varies among different marine
Water 2022, 14, 3840 17 of 28
regions because of other environmental factors, both atmospheric (e.g., wind speed) and
oceanic (e.g., river plumes, lateral fluxes, vertical mixing, general circulation).
Water 2022, 14, x FOR PEER REVIEW 19 of 31
Figure 10. Horizontal distribution of Pearson correlations coefficients (Rp) derived between (a) the
daily Sea Surface Temperature (SST; satellite observations) and air temperature (ERA 5), and (b) the
SST and wind speed (ERA5) data over the entire study area and period (1982–2021). Cross correlation between SST and air temperature, and SST and wind speed with (c,d), and without (e,f) the
seasonal cycle, respectively. The y- and x-axes represent the Pearson correlation coefficients and lag
(days) between each pair of timeseries, respectively.
The wind speed also contributes to the variability of the SST, showing negative Pearson coefficients (counter-correlation) over all areas (Figure 10b). High SST anomalies usually coincide with weak wind anomalies throughout the study period. The strongest impact of the wind on SST was detected over the broader Miami area (25–26° N) with correlation coefficients close to −0.40. The majority of the Straits of Florida showed correlation
coefficients around −0.35, while the smallest values were computed for the Bahamas,
where the air temperature effect is stronger (Figure 10a). Relatively weak correlation (Rp
= −0.29) was also detected along the western coast of Florida and north of the Florida Keys.
Generally, the impact of the wind on SST is weaker and more spatially homogenous compared to the air temperature effect. The general correlation coefficient, derived from all
daily values is approximately Rp = −0.43 and reveals the same small lag (~3 days) for
Figure 10. Horizontal distribution of Pearson correlations coefficients (Rp) derived between (a) the
daily Sea Surface Temperature (SST; satellite observations) and air temperature (ERA 5), and (b) the
SST and wind speed (ERA5) data over the entire study area and period (1982–2021). Cross correlation
between SST and air temperature, and SST and wind speed with (c,d), and without (e,f) the seasonal
cycle, respectively. The y- and x-axes represent the Pearson correlation coefficients and lag (days)
between each pair of timeseries, respectively.
The wind speed also contributes to the variability of the SST, showing negative Pearson
coefficients (counter-correlation) over all areas (Figure 10b). High SST anomalies usually
coincide with weak wind anomalies throughout the study period. The strongest impact
of the wind on SST was detected over the broader Miami area (25–26◦ N) with correlation
coefficients close to −0.40. The majority of the Straits of Florida showed correlation coefficients around −0.35, while the smallest values were computed for the Bahamas, where the
air temperature effect is stronger (Figure 10a). Relatively weak correlation (Rp = −0.29) was
Water 2022, 14, 3840 18 of 28
also detected along the western coast of Florida and north of the Florida Keys. Generally,
the impact of the wind on SST is weaker and more spatially homogenous compared to the
air temperature effect. The general correlation coefficient, derived from all daily values is
approximately Rp = −0.43 and reveals the same small lag (~3 days) for seasonal (Figure 10d)
and non-seasonal (Figure 10f) timeseries. The correlation coefficient of the non-seasonal
correlations, which is derived from variations related to extreme events is smaller and
equal to −0.25.
The mean monthly evolution of the SST (seasonal cycle removed) at specific coastal
areas (insert maps in Figure 8) shows statistically significant increasing trends for all
regions (pvalues < 0.01; Figure 11). The respective air temperature anomalies (seasonal
cycle removed) reveal a similar variability to the SST anomalies, leading to high positive
correlation coefficients (Rp > 0.60).
Water 2022, 14, x FOR PEER REVIEW 20 of 31
seasonal (Figure 10d) and non-seasonal (Figure 10f) timeseries. The correlation coefficient
of the non-seasonal correlations, which is derived from variations related to extreme
events is smaller and equal to −0.25.
The mean monthly evolution of the SST (seasonal cycle removed) at specific coastal
areas (insert maps in Figure 8) shows statistically significant increasing trends for all regions (pvalues < 0.01; Figure 11). The respective air temperature anomalies (seasonal cycle
removed) reveal a similar variability to the SST anomalies, leading to high positive correlation coefficients (Rp > 0.60).
Figure 11. Monthly evolutions, without the seasonal cycle, of the Sea Surface Temperatures (SST
anomaly, black line, °C), air temperature (red line in upper panels; °C) and wind speed (red line in
lower panels; m/s), averaged over: (a,g) All Regions (entire study domain); (b,h) Miami Beach; (c,i)
Biscayne Bay; (d,j) North Key West; (e,k) South Key West; (f,l) Tampa Bay, over the 1982–2021 period (areas marked in Figure 8). The linear trends and the Pearson correlation coefficients (Rp) for
each case are shown. The asterisks (*) indicate statistically significant correlations of 99%.
The stronger impact of air temperature was observed at areas where the effects of the
Gulf’s mesoscale ocean dynamics are weaker, such as Tampa Bay in the west Florida coast
(Figure 11f), and Key West (Figure 11d,e). The general correlation is around 0.84 (Figure
11a) but the Miami Beach and Biscayne Bay areas are characterized by significantly
weaker correlations (Rp < 0.65; Figure 11b,c). The latter showed slightly stronger correlation between the measured atmospheric temperature at Buoy FWYF1 and SST (Figure
12a). The respective correlation between the SST and the air temperature is weaker in Miami Beach (Rp = 0.81; Figure 12b). The correlation without the seasonal cycle in Biscayne
Bay (Rp = 0.40) is double than the respective correlation in Miami Beach (Rp = 0.20). The
linear regression between the two variables is closer to the x = y identity line for the enclosed basin of Biscayne Bay. This suggests that factors other than atmospheric conditions
also control the processes impacting SST and MHW there (see Section 4.2). It is noted that
these Pearson coefficients are derived from monthly means and, therefore, the general
value (Figure 11a) is different from the one derived from daily values to estimate the lag
between the two time series (Figure 10e; Rp = 0.57). The wind speed interannual trends are
negative at all areas with smaller correlation coefficients between the wind speed and the
SST anomalies compared to the ones correlating air temperature with SST. These coefficients are similar between the coastal areas, in agreement with the spatial distribution
presented in Figure 10b. The highest values at central EFS were derived for Miami Beach,
where the air temperature conditions showed the weakest impact on SST among all study
coastal areas. Moreover, very strong decreasing trends of wind speed were computed for
both Miami Beach and Biscayne Bay, contributing to the increasing interannual trends of
SST.
Figure 11. Monthly evolutions, without the seasonal cycle, of the Sea Surface Temperatures (SST
anomaly, black line, ◦C), air temperature (red line in upper panels; ◦C) and wind speed (red line in
lower panels; m/s), averaged over: (a,g) All Regions (entire study domain); (b,h) Miami Beach; (c,i)
Biscayne Bay; (d,j) North Key West; (e,k) South Key West; (f,l) Tampa Bay, over the 1982–2021 period
(areas marked in Figure 8). The linear trends and the Pearson correlation coefficients (Rp) for each
case are shown. The asterisks (*) indicate statistically significant correlations of 99%.
The stronger impact of air temperature was observed at areas where the effects of
the Gulf’s mesoscale ocean dynamics are weaker, such as Tampa Bay in the west Florida
coast (Figure 11f), and Key West (Figure 11d,e). The general correlation is around 0.84
(Figure 11a) but the Miami Beach and Biscayne Bay areas are characterized by significantly
weaker correlations (Rp < 0.65; Figure 11b,c). The latter showed slightly stronger correlation
between the measured atmospheric temperature at Buoy FWYF1 and SST (Figure 12a).
The respective correlation between the SST and the air temperature is weaker in Miami
Beach (Rp = 0.81; Figure 12b). The correlation without the seasonal cycle in Biscayne Bay
(Rp = 0.40) is double than the respective correlation in Miami Beach (Rp = 0.20). The linear
regression between the two variables is closer to the x = y identity line for the enclosed
basin of Biscayne Bay. This suggests that factors other than atmospheric conditions also

correlation was also detected at the Bahamas area, which was characterized as a “hot

spot” for the formation of MHWs (Figure 7). Relatively low correlation coefficients were

computed over the Straits of Florida, and especially west of 83◦ W and east of 81◦ W, with

very weak correlation along the northern Straits and the EFS (Rp < 0.80). The lower values

mainly occurred over the area where the FC flows, controlling the distribution of physical

properties (see Section 4.2). The lowest correlation coefficients (Rp < 0.75) were computed at

the coastal area north of Miami Beach (26◦ N). The lag (in days) between the two parameters

is around 5 days (Rp = 0.90; Figure 10c). Once the seasonal cycle is removed, the interaction

between them is more direct, showing smaller correlation (Rp = 0.57) with a 3-day lag

(Figure 10e). The agreement between the two parameters confirms that the effect of the

atmospheric temperature on the ocean sea surface temporal and spatial evolutions also

holds for the nearshore and coastal study areas. This effect varies among different marine

Water 2022, 14, 3840 17 of 28

regions because of other environmental factors, both atmospheric (e.g., wind speed) and

oceanic (e.g., river plumes, lateral fluxes, vertical mixing, general circulation).

Water 2022, 14, x FOR PEER REVIEW 19 of 31

Figure 10. Horizontal distribution of Pearson correlations coefficients (Rp) derived between (a) the

daily Sea Surface Temperature (SST; satellite observations) and air temperature (ERA 5), and (b) the

SST and wind speed (ERA5) data over the entire study area and period (1982–2021). Cross correlation between SST and air temperature, and SST and wind speed with (c,d), and without (e,f) the

seasonal cycle, respectively. The y- and x-axes represent the Pearson correlation coefficients and lag

(days) between each pair of timeseries, respectively.

The wind speed also contributes to the variability of the SST, showing negative Pearson coefficients (counter-correlation) over all areas (Figure 10b). High SST anomalies usually coincide with weak wind anomalies throughout the study period. The strongest impact of the wind on SST was detected over the broader Miami area (25–26° N) with correlation coefficients close to −0.40. The majority of the Straits of Florida showed correlation

coefficients around −0.35, while the smallest values were computed for the Bahamas,

where the air temperature effect is stronger (Figure 10a). Relatively weak correlation (Rp

= −0.29) was also detected along the western coast of Florida and north of the Florida Keys.

Generally, the impact of the wind on SST is weaker and more spatially homogenous compared to the air temperature effect. The general correlation coefficient, derived from all

daily values is approximately Rp = −0.43 and reveals the same small lag (~3 days) for

Figure 10. Horizontal distribution of Pearson correlations coefficients (Rp) derived between (a) the

daily Sea Surface Temperature (SST; satellite observations) and air temperature (ERA 5), and (b) the

SST and wind speed (ERA5) data over the entire study area and period (1982–2021). Cross correlation

between SST and air temperature, and SST and wind speed with (c,d), and without (e,f) the seasonal

cycle, respectively. The y- and x-axes represent the Pearson correlation coefficients and lag (days)

between each pair of timeseries, respectively.

The wind speed also contributes to the variability of the SST, showing negative Pearson

coefficients (counter-correlation) over all areas (Figure 10b). High SST anomalies usually

coincide with weak wind anomalies throughout the study period. The strongest impact

of the wind on SST was detected over the broader Miami area (25–26◦ N) with correlation

coefficients close to −0.40. The majority of the Straits of Florida showed correlation coefficients around −0.35, while the smallest values were computed for the Bahamas, where the

air temperature effect is stronger (Figure 10a). Relatively weak correlation (Rp = −0.29) was

Water 2022, 14, 3840 18 of 28

also detected along the western coast of Florida and north of the Florida Keys. Generally,

the impact of the wind on SST is weaker and more spatially homogenous compared to the

air temperature effect. The general correlation coefficient, derived from all daily values is

approximately Rp = −0.43 and reveals the same small lag (~3 days) for seasonal (Figure 10d)

and non-seasonal (Figure 10f) timeseries. The correlation coefficient of the non-seasonal

correlations, which is derived from variations related to extreme events is smaller and

equal to −0.25.

The mean monthly evolution of the SST (seasonal cycle removed) at specific coastal

areas (insert maps in Figure 8) shows statistically significant increasing trends for all

regions (pvalues < 0.01; Figure 11). The respective air temperature anomalies (seasonal

cycle removed) reveal a similar variability to the SST anomalies, leading to high positive

correlation coefficients (Rp > 0.60).

Water 2022, 14, x FOR PEER REVIEW 20 of 31

seasonal (Figure 10d) and non-seasonal (Figure 10f) timeseries. The correlation coefficient

of the non-seasonal correlations, which is derived from variations related to extreme

events is smaller and equal to −0.25.

The mean monthly evolution of the SST (seasonal cycle removed) at specific coastal

areas (insert maps in Figure 8) shows statistically significant increasing trends for all regions (pvalues < 0.01; Figure 11). The respective air temperature anomalies (seasonal cycle

removed) reveal a similar variability to the SST anomalies, leading to high positive correlation coefficients (Rp > 0.60).

Figure 11. Monthly evolutions, without the seasonal cycle, of the Sea Surface Temperatures (SST

anomaly, black line, °C), air temperature (red line in upper panels; °C) and wind speed (red line in

lower panels; m/s), averaged over: (a,g) All Regions (entire study domain); (b,h) Miami Beach; (c,i)

Biscayne Bay; (d,j) North Key West; (e,k) South Key West; (f,l) Tampa Bay, over the 1982–2021 period (areas marked in Figure 8). The linear trends and the Pearson correlation coefficients (Rp) for

each case are shown. The asterisks (*) indicate statistically significant correlations of 99%.

The stronger impact of air temperature was observed at areas where the effects of the

Gulf’s mesoscale ocean dynamics are weaker, such as Tampa Bay in the west Florida coast

(Figure 11f), and Key West (Figure 11d,e). The general correlation is around 0.84 (Figure

11a) but the Miami Beach and Biscayne Bay areas are characterized by significantly

weaker correlations (Rp < 0.65; Figure 11b,c). The latter showed slightly stronger correlation between the measured atmospheric temperature at Buoy FWYF1 and SST (Figure

12a). The respective correlation between the SST and the air temperature is weaker in Miami Beach (Rp = 0.81; Figure 12b). The correlation without the seasonal cycle in Biscayne

Bay (Rp = 0.40) is double than the respective correlation in Miami Beach (Rp = 0.20). The

linear regression between the two variables is closer to the x = y identity line for the enclosed basin of Biscayne Bay. This suggests that factors other than atmospheric conditions

also control the processes impacting SST and MHW there (see Section 4.2). It is noted that

these Pearson coefficients are derived from monthly means and, therefore, the general

value (Figure 11a) is different from the one derived from daily values to estimate the lag

between the two time series (Figure 10e; Rp = 0.57). The wind speed interannual trends are

negative at all areas with smaller correlation coefficients between the wind speed and the

SST anomalies compared to the ones correlating air temperature with SST. These coefficients are similar between the coastal areas, in agreement with the spatial distribution

presented in Figure 10b. The highest values at central EFS were derived for Miami Beach,

where the air temperature conditions showed the weakest impact on SST among all study

coastal areas. Moreover, very strong decreasing trends of wind speed were computed for

both Miami Beach and Biscayne Bay, contributing to the increasing interannual trends of

SST.

Figure 11. Monthly evolutions, without the seasonal cycle, of the Sea Surface Temperatures (SST

anomaly, black line, ◦C), air temperature (red line in upper panels; ◦C) and wind speed (red line in

lower panels; m/s), averaged over: (a,g) All Regions (entire study domain); (b,h) Miami Beach; (c,i)

Biscayne Bay; (d,j) North Key West; (e,k) South Key West; (f,l) Tampa Bay, over the 1982–2021 period

(areas marked in Figure 8). The linear trends and the Pearson correlation coefficients (Rp) for each

case are shown. The asterisks (*) indicate statistically significant correlations of 99%.

The stronger impact of air temperature was observed at areas where the effects of

the Gulf’s mesoscale ocean dynamics are weaker, such as Tampa Bay in the west Florida

coast (Figure 11f), and Key West (Figure 11d,e). The general correlation is around 0.84

(Figure 11a) but the Miami Beach and Biscayne Bay areas are characterized by significantly

weaker correlations (Rp < 0.65; Figure 11b,c). The latter showed slightly stronger correlation

between the measured atmospheric temperature at Buoy FWYF1 and SST (Figure 12a).

The respective correlation between the SST and the air temperature is weaker in Miami

Beach (Rp = 0.81; Figure 12b). The correlation without the seasonal cycle in Biscayne Bay

(Rp = 0.40) is double than the respective correlation in Miami Beach (Rp = 0.20). The linear

regression between the two variables is closer to the x = y identity line for the enclosed

basin of Biscayne Bay. This suggests that factors other than atmospheric conditions also