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Tropical cyclone-induced bloom in NIO
We have analysed the tropical cyclones that occurred over the bay from 1997 to 2019 in accordance with the availability of satellite Chl-a measurements. Out of 51 storm events, 30 are identified as the phytoplankton bloom events (i.e. the Chl-a values greater than of 0.2 mg/m3) in BoB and 18 in AS across all seasons35,36,37. In the case of BoB, we have divided our analyses for pre-monsoon and post-monsoon, as the cyclone occurrences are rare in other seasons (e.g. winter and monsoon). Over AS, some cyclones also occur in the beginning of monsoon season. The spatio-temporal variability of cyclones is closely connected to the seasonal changes in the monsoon trough35,38. In pre-monsoon season, the trough passes over the northern BoB, but it passes through the central bay with an east-west orientation in the post-monsoon, and facilitates the formation of more number of TCs during the period39. The big seasonal change in wind shear and relative vorticity are the reasons for the lower number of cyclones in the pre-monsoon season. In general, the upwelling driven nutrient influx to the surface together with sunlight leads to the enhancement of Chl-a or phytoplankton bloom after the passage of cyclones in the open ocean40.
The Bay of Bengal cyclones
During pre-monsoon, the bay is least productive, but the western boundary current helps more production in the coastal regions41. The higher wind speed associated with TCs deepens (about 30 m) Mixed Layer Depth (MLD), and rupture the pycnocline and pumps nutrients to the surface24. In general, TCs occurring during pre-monsoon move northwards and pass north-eastern coast of India or Bangladesh42 (Fig. 1). To estimate the cyclone-induced phytoplankton bloom, we performed spatial analyses for each cyclone during the period 1997–2019 and selected cases are shown in Supplementary Fig. 1 for BOB01 in 2003, BOB01 in 2004 and Mala in 2006. The maps of Chl-a concentrations superimposed with Sea Level Anomaly (SLA) for the same period are shown in Supplementary Fig. 1. The cyclone BOB01 was a category 1 storm with a maximum sustained wind (MSW) of 39 m/s, which occurred during 10–19 May 2003. The Chl-a remained well below 0.2 mg/m3 prior to occurrence of the cyclone, which enhanced to 0.5 mg/m3 with a small area of about 1 mg/m3 at 9°–10° N, 86°–87° E, just after passage of the cyclone. This is consistent with its high Ekman Pumping Velocity (EPV) and small Translational Speed (TS) in that period. In addition, the Chl-a enhancement was higher for the Category 1 cyclone BOB01 that occurred during 14–19 May 2004 and was about 0.5 mg/m3 after passage of the cyclone. The bloom sustained for the next 5 days as also shown in Supplementary Fig. 1. On the other hand, Mala was the strongest cyclone occurred during the pre-monsoon period (24–28 April 2006) in the last two decades over BoB with a MSW speed of 61 m/s. The Chl-a increased from 0.1 to 1.0 mg/m3 after the cyclone passage in five days, with a small area of Chl-a about 1.0 mg/m3 on the immediate left of its track. The Chl-a concentration remained close to 0.5 mg/m3 in the next 5 days. Both EPV and TS were favourable for sustained upwelling and Chl-a bloom in the case of cyclone Mala. In all three cases, the closed contours of Sea surface Height Anomaly (SSHA) is negative, which indicate the presence of cold-core (cyclonic) eddies that triggered turbulent mixing and sustained Chl-a bloom. We have not used any specific eddy detection method but used the composite of SSHA to identify the presence of eddies, as done by Girishkumar et al.43.
Fig. 1: The cyclone tracks and pre-cyclone background Chl-a value.

a The study region North Indian Ocean (NIO) and the tracks for the pre- (green) and post-monsoon (yellow) cyclones for BoB and AS (red), as analysed for all cyclones occurred during the study period (1997–2019).

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We applied the same method to identify the phytoplankton bloom that occurred for each cyclone event in BoB after 1996 and estimated the corresponding EPV and TS for diagnosing the physical mechanisms that made different scales of blooms. The results are presented in Table 1 and Fig. 2. The analyses show that the bloom was comparatively larger for the cyclone BOB01 in 2004, about 3.28 mg/m3. The increase in Chl-a is negatively correlated with TS and is in agreement with the intensity of cyclone with a statistically significant correlation value of −0.30 (at the 95% confidence interval as per the P-test44). The TS is lower and bloom is larger for BOB01 in 2003, as the faster moving storms tend to intensify rapidly when compared to slower moving storms (i.e. wind speed 14 m/s). In contrast, the slow-moving storms expend more time over the ocean and thereby, increases the magnitude of upwelling to enhance the Chl-a over the region45. The estimated EPV is about 1.8 × 10−4 m/s for BOB01 in 2003 and is consistent with the observed Chl-a concentrations, whereas the EPV is about 1 × 10−4 m/s and Chl-a concentration is about 1.87 mg/m3 for the cyclone Mala. It suggests that TS has a prominent role in cyclone-induced upwelling and associated phytoplankton bloom.
Table 1 The cyclone-induced Chl-a blooms in the pre-monsoon seasons since 1997 in BoB.
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Fig. 2: The cyclone-induced Chl-a in BoB.

The observed enhancement in Chl-a (mg/m3) following the cyclone passage (5-day average) in the post and pre-monsoon seasons in BoB. The translational speed of the closest track points where the bloom occurred in (m/s), the ratio of wind speed to translational speed (WS/TS), and EPV (Ekman Pumping Velocity) as the cyclones reach their maximum intensity (m/s) are also shown in the lower panels.

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Post-monsoon is the active storm season over BoB, and about 25 cyclones with significant enhancement in Chl-a concentration are identified during the 1997–2019 period. As the haline stratification is stronger in BoB due to the monsoon rain and river water influx, the presence of BL increases SST, which fuel the storms over the bay46. Presence of BL weakens the impact of cooling in the mixed layer driven by cyclones and favours the intensification of post-monsoon cyclones2. We have also analysed the variability in BL, MLD, isothermal layer depth (ILD) and Chl-a for selected storms passed over the Argo Floats (see next section). The cyclones either form or develop further over the southeast BoB, but some move west northwest and cross the peninsular coast. Some cyclones recurve towards the west central bay and pass the central and northeast coast of India, but some hit Bangladesh and upper Burma coast47, as illustrated in Fig. 1.
Figure 3 presents a closer look at the bloom and its spatial distribution for selected cyclones during the post-monsoon season; e.g. the cyclones Sidr, Madi and Vardah overlaid with SSHA contours. Sidr, a category 4 cyclone occurred during 11–16 November 2007 with a MSW of about 44 m/s. The Chl-a is about 0.5 mg/m3 during the cyclone period at the right side of the track, but the bloom has spread to a wider area with values close to 0.5 mg/m3 just after the passage of cyclone. The analyses of SSHA further provide evidence for the eddy-mediated phytoplankton bloom. The phytoplankton bloom also sustained for another 5 days. This is also in agreement with that reported in other analyses, although bloom values were estimated for 19 November in the other studies48,49. The cyclone Madi, occurred during 6–13 December 2013, showed an enhancement of about 0.5 mg/m3 during the cyclone period with a region of 1 mg/m3 in the left side of the track. Some regions with 2–3 mg/m3 are also observed at the right and left sides of cyclone track, and the bloom sustained for the next 5 days with values of about 1 mg/m3 in the adjacent areas. The closed contours of negative SSHA suggest the presence of cyclonic eddies there. The phytoplankton bloom during this particular period is also contributed by the cyclone Lehar that occurred a week before, in 23–28 November; demonstrating the impact of occurrences of consecutive storms over the same oceanic region. Nevertheless, the cyclone Vardah showed an enhancement of about 1.92 mg/m3, which is higher than that of Sidr due to the higher EPV of the former. As for Lehar and Madi, there was another cyclone Nada that appeared during the period 29 November–2 December 2016, just before the appearance of Vardah, and that storm might have also contributed to the Chl-a bloom during the period of Vardah.
Fig. 3: The spatial extension of Chl-a bloom for selected post-monsoon cyclones in BoB.

The Chl-a averaged, overlaid with SSHA contours (solid—positive and dashed—negative), for 5 days before cyclone, during the entire cyclone period, five days just after the passage of cyclone and next 5 days for a Sidr (2007), b Madi (2013), and c Vardah (2016). The tracks of the respective cyclones are also shown.

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Figure 4 shows the time evolution of physical and biological observations during the period of TCs Phailin, Hudhud and Vardah. The biogeochemical Argo float WMO ID 2902086 was closer to the track of TC Phailin, and the float WMO ID 2902114 was near the tracks of Hudhud and Vardah. Supplementary Table 1 shows the name of cyclones, Argo IDs, and distance between the float and nearest track point of respective cyclones. Figure 4 (right) represents the subsurface temperature up to 200 m with MLD, ILD, BLT and D23 (23° isotherm) for selected cyclones occurred over BoB. Figure 4 (left) represents the subsurface Chl-a concentration up to 200 m driven by the same cyclones. Prior to the passage of cyclones, the profiles represent typical hydrographic state of the oceans with warm waters near the surface and cold waters in the subsurface. The MLD was shallower about 20 m and the ocean was warm from September to December in 2013 during the passage of cyclone Phailin. The other cyclones occurred in 2013 were Helen, Lehar and Madi. Similar situation was observed in September–December of 2014 for Hudhud, but a colder and deeper MLD is observed in September–December of 2016 for Vardah. These are consistent with the climatological oceanic characteristics observed in BoB during the post-monsoon seasons. The time-depth cross-section of Chl-a reveals that the Chl-a concentration remains small in the surface, but about 0.8–1.0 mg/m3 at 40–60 m for the cyclone Phailin. The Chl-a concentration is about 3 mg/m3 for the cyclone Hudhud and about 1.5 mg/m3 for Vardah.
Fig. 4: Bio-Argo measurements.

Temporal evolution (right panel) of depth-time section up to 200 m of temperature of some selected cyclones in the BoB. The MLD (cyan), ILD (blue), BLT (green), and D23 (black) are also indicated in the figure. The vertical black lines indicate the cyclone period. The subsurface Chl-a concentration (left panel) up to 200 m for some selected cyclones in BoB. The vertical black lines indicate the cyclone period.

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To identify the differential oceanic response of the cyclones at the Argo float locations, we further examined the presence of eddies that play a major role in regulating the physical and biogeochemical processes. The analysis of 7-day SSHA composite before and during the cyclone period at the location of Argo float shows the presence of cold-core (negative SSHA) eddies before the passage of cyclone Phailin, Madi and Hudhud, whereas a warm-core (positive SSHA) eddy prior to the passage of Vardah (Fig. 5). The lower TS and a cold-core eddy during the cyclone Hudhud, and higher TS and a warm-core eddy during the cyclone Vardah produce contrasting oceanic response43. The temperature measurements during the periods of Hudhud and Vardah exhibit comparable response to cold and warm-core eddies, respectively. Another feature of cold-core eddies is trapping the near inertial oscillations in the mixed layer50, which accelerates the entrainment at the bottom of mixed layer and vertical shear as observed during the period of Hudhud. Conversely, the proximity of warm-core eddies triggers rapid vertical dispersion of near inertial energy, which suppresses the mixing and shear as for Vardah50.
Fig. 5: Eddies and primary productivity.

The 7-day composite of sea level anomaly (m) before and during the cyclones in BoB. The black solid lines represent the cyclone tracks, the stars represent the genesis location of the cyclone and the box represents the Argo float location.

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Vardah was a category 1 cyclone that occurred during 6–13 December 2016. The Chl-a amount before the passage of cyclone was about 0.23 mg/m3, but it escalated to 1.92 mg/m3 in 5 days after the passage of cyclone. The bloom continued to exist for the next 5 days as shown in Fig. 3. Unlike the pre-monsoon cases, for which the Chl-a is restored back to open ocean values in 10 days after the passage of cyclones, the bloom continued to persist even longer periods for the post-monsoon cases. The changes in Chl-a concentrations before and after the passage of cyclones in all three cases are greater than 0.2 mg/m3 and are higher for the lower category tropical storms. These analyses are consistent with the frequent occurrence of cyclones over the south east BoB during this season, as shown in Supplementary Table 2. It is also compelling to note that higher intensity cyclones occur over the north as compared to south BoB, which may be due to the presence of BL in the northern BoB as BL does not exist or insignificantly shallow in the south BoB in any season. The barrier layer in turn favours intensification of tropical cyclones whereas the absence of BL favours the storm-induced upwelling that eventually makes the Chl-a blooms45. Note that the stratification is very strong in northern BoB due to the river water input there51.
Supplementary Table 2 and Fig. 2 also show the results of Chl–bloom events in the post-monsoon seasons in 1997–2019. For instance, the Chl-a increased from 0.53 to 1.13 mg/m3 in the northern and southwestern BoB after the super cyclone of 1999 (25 October–3 November), as also shown by Madhu et al.52. Similar enhancements in Chl-a are estimated for BOB08 (1997), BOB05 (2000) and Madi (2013), about 0.6–3 mg/m3, depending on the cyclones. Although the cyclones BOB06 (1999), Sidr (2007), Giri (2010) and Phailin (2013) were category 4 or 5 cyclones, the high TS and lower EPV did not magnify the Chl-a concentrations to the level of bloom initiated by other cyclones. The enhancement of Chl-a estimated in our study during the cyclone Phailin is in agreement with the reported value of 0.9 mg/m3 for the period 16–24 October 2013 by Vidya et al.21. They also computed the bloom associated with Thane, about 0.7 mg/m3 in the post-cyclone period (1–8 January 2012) at 10°–13° N and 82°–86° E. Nevertheless, we have estimated about 1.8 mg/m3 for the post-cyclone period for Thane. The cyclone Hudhud produced a Chl-a bloom of up to 2.8 mg/m3 in 8–15 October 2014 along the track, as analysed by Chacko27 using the MODIS data, which is very close to our estimate of 2.8 mg/m3. We find similar enhancements in Chl-a that reported by Rao et al.26 for BOB05 in 16–23 November 2000, about 1.2 mg/m3 as deduced from the MODIS data. The other cyclones show moderate bloom values, below 1 mg/m3 as listed in Supplementary Table 2. Nevertheless, the low intensity cyclones such as BOB08 (1997) and Thane (2011) exhibit notable increment in Chl-a following the passage of cyclone, about 1.25–1.8 mg/m3, which is in agreement with their comparatively lower TS and higher EPV during the cyclone period. It also attests the impact and significance of TS in deciding the amplitude of phytoplankton bloom; suggesting sustained low intensity winds trigger strong upwelling to cause intense bloom events.
The Arabian Sea cyclones
In Arabian Sea, about 18 out of 33 cyclones are identified as phytoplankton bloom events (55%) during the study period 1997–2019, in which one occurred in pre-monsoon, three in monsoon and nine in post-monsoon seasons. Since the frequency of occurrences is very small, we have not separated the analyses into seasons or regions of landfall, but a selected case is presented in Supplementary Fig 2. For a better understanding of the behaviour of cyclones, we have selected three cyclones, ARB01 (2001), Mukda (2006) and Megh (2015), one in each season for this discussion. Supplementary Fig 3 illustrates the spatial distribution of Chl-a superimposed with SSHA for the selected cyclones. The ARB01 (2001) was a category 3 cyclone that occurred during 21–28 May 2001 with a MSW of about 60 m/s. The surface Chl-a concentration before the cyclone appearance was below 0.2 mg/m3 due to the profound heating in May53,54. It was one of the strongest cyclones appeared over AS, but measurements were sparse during the cyclone period, and thus, only a small area of about 1 mg/m3 is observed at 16°–17° N, 69°–72° E after passage of the cyclone. Analysis of IRS–P4 measurements by Subramanyam et al.29 found a very large bloom of about 5–8 mg/m3 at 17° N, from 67° E to 71° E. However, our analyses show the bloom of about 2.07 mg/m3 in the region 67°–68° E, 16°–17° N. The difference in bloom values could be due to the difference in datasets, region and period of analyses. As found in the case of BoB, there are closed contours of negative SSHA, which strengthens the observed cyclone-induced and eddy-mediated phytoplankton bloom.
Mukda was a tropical storm that occurred during 21–24 September 2006 with a MSW of 28 m/s. The Chl-a along the right side of the track was above 0.5 mg/m3 even before the cyclone period. The cyclone Megh was considered as the worst to hit Yemen and it occurred just after the passage of another cyclone Chapala over the same region. Megh was a category 3 cyclone with a MSW of 57 m/s. The Chl-a was about 0.7 mg/m3 in the post-cyclone stage, but a small region of about 1.0 mg/m3 was also observed at the right end of the track. Table 2 and Fig. 6 show the analysis of phytoplankton bloom occurrences in AS during the study period (1997–2019). It shows higher Chl-a concentrations in connection with the cyclones ARB01 and ARB02 in 2001, Mukda in 2006 and Megh in 2015, and are consistent with their lower TS. The situation in 2015 was also similar, in which the Category 4 cyclone Chapala showed higher bloom than that of the category 3 cyclone Megh. Similarly, the Category 4 cyclone Kyaar triggered higher bloom than that of the Category 3 cyclone Maha in 2019.
Table 2 The cyclone-induced Chl-a blooms in the post-monsoon seasons since 1997 in AS.
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Fig. 6: The Chl-a blooms associated with cyclones in Arabian Sea.

The observed enhancement in Chl-a (mg/m3) following the cyclone passage (5-day average) over AS. The translational speed of the closest track points where the bloom occurred in (m/s), the ratio of wind speed to translational speed (WS/TS) and EPV when cyclone reached its maximum intensity (m/s) are also shown.

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Supplementary Fig. 3 shows the time evolution of biological and physical observations from the Argo float (WMO ID 2902120) in the period of TCs Nilofar, Chapala and Megh. The distance between float location and nearest track point is provided in Supplementary Table 1. The temperature profiles show warm waters near the surface and cold waters in the subsurface before the cyclone passage, as for a typical oceanic state (Supplementary Fig. 4). The MLD is shallower about 20 m and the ocean is warm throughout September–December 2014 and October–December 2015 during the passage of cyclones Nilofar, Megh and Chapala. The time-depth cross-section of chlorophyll shows about 0.8–1 mg/m3 at 40–60 m. However, the Chl-a values remain about 1 mg/m3 for cyclones Megh and Chapala close to the surface; supporting the satellite measurements. The analysis of 7-day SSHA composite shows a cold-core eddy before the passage of cyclone Nilofar whereas warm-core eddies before the passage of Chapala and Megh at the buoy location (Supplementary Fig. 5). The warm-core eddies before the passage of Chapala and Megh could also be the reason for their rapid intensification.
Tropical storms and category 1 cyclones
Although a number of cyclones occurred over NIO during the 1997–2019 period, the phytoplankton bloom happened mostly for the storms and lower category cyclones. For instance, there were five cyclones that appeared over BoB in the pre-monsoon seasons that triggered phytoplankton bloom, four (80%) of them were either tropical storms or category 1 cyclones (Table 1). Similarly, out of 25 cyclones that made Chl-a blooms in BoB during the post-monsoon seasons, 20 of them were (80%) either tropical storms or category 1 cyclones. An analogues occurrence of cyclone-induced phytoplankton bloom is observed for the lower category cyclones in AS, where eight cyclones out of 18 (44.4%) were either tropical storms or category 1 cyclones. These analyses suggest that the slow-moving storms stay more time over the oceans and impart high momentum to upwell the subsurface nutrient-rich water, leading to the phytoplankton blooms in the open oceans with a time lag of 4–12 days, as illustrated in Fig. 7 (blue coloured bar chart). The bloom is as higher as about 20–500% with respect to the pre-cyclone Chl-a levels, and is even up to 1385% as for the case of BoB01 in 2003 and 3758% for the cyclone Gonu in AS (Supplementary Fig. 6); demonstrating the impact and scale of cyclone-induced primary productivity in the open oceans. This slow-moving cyclone-induced primary productivity is very important in the context of climate change, as there is a global slowdown in the translational speed of tropical cyclones.
Fig. 7: The change in cyclone-induced bloom and time lag.

Left: with respect to pre-cyclone Chl-a values (blue), 0.5 mg/m3 (dark blue) and 0.2 mg/m3 (magenta) for the post-monsoon cyclones in Bay of Bengal (BoB). Right: The time lag in days in cyclone-induced Chl-a bloom with respect to the pre-cyclone Chl-a (blue histograms, left) for the post-monsoon cyclones of BoB.

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To test robustness of the estimates of cyclone-induced change in Chl-a (i.e. Fig. 7), we also considered two other background Chl-a values (i.e. 0.2 and 0.5 mg/m3), which were also taken as the background Chl-a of the ocean basins and the Chl-a threshold for bloom detection. Since the value extracted from the 1° × 1° latitude-longitude region at the track (i.e. blue diagrams) is different from the basin average and bloom threshold values, there are significant differences in the amplitude of blooms, as displayed in Fig. 7. It shows that the pre-cyclone Chl-a values are between 0.5 and 0.2 mg/m3. Therefore, the change in Chl-a is higher with the estimates based on 0.2 mg/m3 (magenta histogram) and about 10 cyclones show a change in Chl-a of about 400%. The highest bloom of about 800–850% is found for Madi (2013), Hudhud (2014) and BOB05 (1999). On the other hand, the change in Chl-a with respect to 0.5 mg/m3 (dark blue histogram) is lower than that with the pre-cyclone estimates, and the change is mostly within 250%, although few cyclones show around 400%. The highest bloom is observed for the cyclone Madi (2013), about 300%. In AS, the Chl-a bloom is mostly between 300 and 1000%, except for Gonu in 2007. The change in Chl-a is about 6000% with respect to the basin average of 0.2 mg/m3, and about 3500% based on the bloom threshold for the cyclone Gonu. The assessment confirm that the cyclone-induced bloom (change in percent) in AS is about five times higher than that of BoB.
The impact of ENSO and IOD on Chl-a blooms
Several studies have examined the relationship between El Niño and Southern Oscillation (ENSO) and cyclone activity across different oceanic basins12,55,56. The influence of ENSO on tropical cyclone activity in BoB during the period 1997–2010 is also investigated by Girishkumar et al.19. We have chosen the dates after the cyclone passage, and considered the Niño and IOD indices to classify the cyclones occurred in the El Niño, La Niña, normal, positive IOD (PIOD) and negative IOD (NIOD) years, as listed in Table 1, Table 2 and Supplementary Table 2. In addition, we have prepared the composites of Chl-a and SSHA for 10 days before and after the passage of each cyclone to assess the inter-annual variability in Chl-a and SSHA with respect to the El Niño, La Niña, normal, PIOD and NIOD years (Fig. 8, for BoB). Out of the 25 cyclones, three of them occurred in El Niño, fourteen in La Niña, nine in normal, four in PIOD and five in NIOD years. The cyclones those occurred in PIOD or NIOD years also happened to be in the El Niño/La Niña years and therefore included in both analyses, and are shown in the figure. The number of cyclones are more in the La Niña years, which were mostly followed by the normal, PIOD and NIOD years. The magnitude of phytoplankton bloom is higher in the PIOD years than that in the NIOD years. In the El Niño years, the magnitude of bloom is comparatively smaller and the bloom in normal years is around 0.5 mg/m3.
Fig. 8: The differences in Chl-a during El Niño, La Niña, normal, PIOD and NIOD years.

The SSHA a 10-day before and b during the passage of cyclone) and Chl-a composite maps with respect to El Niño, La Niña, normal, PIOD and NIOD years in the BoB. The respective cyclone tracks are also shown.

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Supplementary Fig. 9 shows the composite of SSHA and Chl-a with respect to El Niño, La Niña, normal, PIOD and NIOD years in AS. Here, more number of cyclones occurred in the El Niño years as compared to that in the La Niña and normal years. In contrast, there are more number of cyclones in the PIOD years than the NIOD years in AS, but the amplitude of bloom is higher for the NIOD years. These are also the reasons for the differences in phytoplankton bloom in AS and BoB, as the impact of ENSO and IOD events is different in both basins. The response of cyclones in IOD years are similar to those in the La Niña years. Although the spatial extent of bloom is larger in the El Niño years owing to the higher number of cyclone occurrences, the magnitude of bloom is higher for the cyclones occurred in La Niña and NIOD years. The normal years exhibit bloom similar to that of the El Niño years. In BoB, the analysis of SSHA composite for the El Niño, La Niña, IOD and normal years is dominated by negative SSHA (suggesting the presence of cold-core eddies), but the normal years are more influenced by warm-core eddies (e.g. Fig. 8). In AS, on the other hand, the normal, La Niña and IOD years are dominated by cold-core eddies, whereas the El Niño years are overwhelmed by warm-core eddies (e.g. Supplementary Fig. 9). The influence of IOD is higher than that of ENSO, which is one of the reasons for the inter-annual variability of phytoplankton blooms. The characteristics of phytoplankton blooms in AS and BoB are in contrast with the differences in SST in IOD years in both basins, and this feature is also found with the cyclone-induced blooms. There are noticeable difference in Chl-a concentrations among the normal and El Niño, La Niña, PIOD or NIOD years, and are exhibited in Supplementary Figs. 7 and 8. More

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Overview of methods
First, we modeled and mapped investible forest carbon, and its climate mitigation potential across the tropics at 1-km resolution. Second, we compared our estimates of investible forest carbon with actual volumes of VCUs reported by 25 real-world VCS forest protection projects. Third, we modeled the relative profitability of investible forest carbon sites to produce a global forest carbon return-on-investment map based on their NPV.
All calculations were based on data dated between 2012 and 2017 and at a resolution of 0.00833 degrees (~1 km). To ensure data standardization, we resampled (bilinear) finer-scaled data where necessary, for example, for data sourced from the European Space Agency – Climate Change Initiative -Land Cover27. We only considered tropical forests between ~23.44°N and 23.44°S, and excluded all land cover types that would preclude forests, for example, savannas, bare ground, water, agriculture and urban areas27.
Investible forest carbon
We first estimated the total volume of CO2 associated with three carbon pools in tropical forests: aboveground carbon, belowground carbon, and soil organic carbon. Next, we applied key VCS criteria, including additionality, to model and map investible forest carbon across the tropics.
Mapping total volume of CO2 associated with tropical forests
Aboveground carbon
We applied a stoichiometric factor of 0.475 to recent spatial data on aboveground carbon biomass12 (i.e., for period 2012–2016), to convert it from biomass to carbon stock values, based on established carbon accounting methodology3,28,29. We performed an uncertainty analysis to account for potential variability in this stoichiometric factor (see ‘Uncertainty analysis’ section below). We applied a conversion factor of 3.67 to derive the volume of CO2 associated with this carbon pool3.
Belowground carbon
We derived belowground carbon biomass by applying two different allometric equations relating root to shoot biomass30 to the most recent spatial dataset on aboveground carbon biomass12, following established carbon accounting methodology3,28,29. The two equations are: belowground biomass = 0.489 × aboveground biomass^0.89; and belowground biomass = 0.26 × aboveground biomass. We then applied a stoichiometric factor of 0.475 to the estimated belowground carbon biomass to convert it from biomass to carbon stock values. Next, we calculated the mean, minimum and maximum values for belowground carbon based on an uncertainty analysis (see ‘Uncertainty analysis’ section below). We applied a conversion factor of 3.67 to derive the volume of CO2 associated with this carbon pool3.
Soil organic carbon
We also considered soil carbon due to its potentially significant contributions to carbon storage31 and despite potential uncertainties and variability surrounding its measurements32. Specifically, we utilized the organic carbon density of the topsoil layer (0–30 cm) obtained from the European Soil Data Centre33 as it represented the best data available of soil organic carbon. We applied a conversion factor of 3.67 to derive the volume of CO2 associated with this carbon pool3.
Applying VCS criteria to map investible forest carbon
The criterion of additionality is a pre-condition for certifying all carbon credits under the VCS. This implies that only the volume of forest carbon that are under imminent threat of decline or loss if left unprotected by a conservation intervention can be certified under the VCS. We derived the volume of forest carbon under threat of loss based on best available proxy data on projected future deforestation rates across the tropics13 (through to the year 2029), and annualized over the prediction period (15 years). We applied this estimated annual deforestation rate to the total volume of CO2 associated with tropical forests as estimated above, to derive the volume of CO2 that would be certifiable and therefore investible under the VCS.
We also assumed a conservative 10-year decay estimate for the belowground carbon pool9.
Additionally, we excluded lands that will likely not be certifiable for other reasons9, including recently deforested areas34 (i.e., for the period 2010–2017), as well as human settlements located within these forests35.
Lastly, we accounted for the VCS requirement to set aside buffer credits of 20% to account for the risk of non-permanence associated with Agriculture, Forestry and Other Land Use projects (AFOLU)9.
Comparing estimates of investible forest carbon to verified carbon units
We compared our estimates of investible forest carbon with actual volumes of VCUs reported by real-world VCS forest protection projects (https://verra.org/).
We identified a set of 25 VCS forest protection projects from across 16 countries that met the following criteria: ii) includes spatial data on project boundary in their project documentation; ii) the project extent is located entirely within the tropics; and 3) has been verified (i.e., either “verified, under verification” or “verification approve”) (Table S2).
We extracted the shapefiles (i.e., geometric polygons) of these VCS projects, and overlay them on our map of investible forest carbon to extract the volume of investible forest carbon (CO2) from our analysis that corresponds to each of the 25 VCS forest project.
We then compared our estimates of investible forest carbon to the volume of VCUs issued between 2005 and 2018 for each VCS project. The number of data points reported per year for each project ranged from 1–10, and generated a total of 111 data points for comparison. We then assessed the degree of correlation (i.e., Pearson’s correlation), relative accuracy (i.e., Root Mean Square Error; RMSE), and statistical difference (i.e., paired t-test) between the two datasets.
Estimating return-on-investment
Based on our map of investible forest carbon, we modeled the relative profitability of investible forest carbon sites to produce a global forest carbon return-on-investment map based on their NPV. We calculated NPV of these returns based on several simplifying assumptions following established values from previous studies19.
First, we estimated the cost of project establishment at $25 ha−1. This was based on a wide range of costs that are key to the development of a project, including but not limited to project design, governance and planning, enforcement, zonation, land tenure and acquisition, surveying and research19,36,37.
Second, we estimated an annual maintenance cost of $10 ha−1, which included aspects such as education and communication, monitoring, sustainable livelihoods, marketing, finance and administration19,36,37.
Third, we assumed a constant carbon price of $5.8 t−1CO2 for the first five years. This price was based on an average price of carbon for avoided deforestation projects recently reported by Forest Trends’ Ecosystem Marketplace6 (i.e., for the period 2006–2018). After the first five years, we assumed a 5% price appreciation for subsequent years over a project timeframe of 30 years19.
Based on these criteria, we calculated NPV of annual and accumulated profits over the 30 years, based on a 10% risk-adjusted discount rate.
Separately, we repeated the analysis using a range of starting carbon prices, including $1, $5, $10, $15, $25, $50, $100 t−1CO2, based on cost effectiveness thresholds from previous studies1. In these analyses, other assumptions remain unchanged, including the project establishment and annual maintenance cost, price appreciation, discount rates and timeframe. Based on these criteria and excluding sites that would be unable to breakeven (i.e., yielding net negative NPVs), we calculated the potential profitable forest areas, as a percentage of the total investible forest areas, associated with these different starting carbon prices.
All values of investible carbon and return-on-investment (based on NPV) were summarized to global, regional, and country level estimates (see Table 1). For countries that extend beyond tropical latitudes, we only analyze and present data for their tropical extents. These values were rounded to the nearest 1000 values.
Uncertainty analyses
Stoichiometric factor
Previous studies utilized a range of stoichiometric factors, typically ranging between 0.45 and 0.503,28,29. We account for this variability by first using a stoichiometric factor of 0.475, which was based on the median value across these reference studies3,28,29. We then repeated the analyses with stoichiometric factors of 0.45 and 0.50 to calculate the respective minimum and maximum values of above and belowground carbon per cell.
Root to shoot biomass allometric equations
Many site-specific factors can influence the ratio of root to shoot biomass, resulting in variability of the best-fit allometric equations30. Here, we account for this variability by utilizing the two allometric equations that best matches global data30. This produced two sets of spatially explicit estimates of belowground biomass, from which we calculated the average, minimum and maximum values per cell.
Aboveground biomass
We incorporated uncertainties, reported at standard deviations, which were inherent to the aboveground biomass dataset12.
Leakage effects
We considered three scenarios of leakage, where the protection of an area of forest results in deforestation beyond its borders to the amounts of 10%, 20%, and 30% of the areas’ carbon volume. This reduces the total investible carbon within each cell, thereby causing a decrease in return-on-investment and the climate mitigation potential within profitable areas to 81.9 ± 51.1, 64.6 ± 40.4 and 48.3 ± 30.3%, or 909.1 ± 567.4, 716.7 ± 448.4, 535.8 ± 336.3 MtCO2 yr−1, respectively (Table S1).
Establishment and maintenance costs
We also considered two scenarios of establishment and maintenance cost, where the overall direct cost of protecting areas from deforestation increases by 50% and 100%. We find that this reduces the climate mitigation potential in profitable areas to 80.4 ± 50.4 and 65.7 ± 41.6% or 892.1 ± 559.2 and 728.8 ± 462.2 MtCO2 yr−1 respectively (Table S1).
Opportunity costs
We also considered the potential for alternative land-use such as agriculture or timber extraction to outcompete the value of protecting forests through carbon financing means. Utilizing agricultural rents (based on 18 crops) and timber value as a proxy for opportunity cost38, we excluded areas where opportunity cost exceeds projected net present values. This results in a large decrease in overall climate mitigation potential, almost comparable to the 30% leakage scenario, to 52.3 ± 33.2% or 580.6 ± 368.6 MtCO2 yr−1 within remaining areas.
All analyses were performed in R version 3.6.039, utilizing the package “raster” for processing and calculations of raster layers40. Map visualizations were formed in QGIS41.
Reporting summary
Further information on research design is available in the Nature Research Reporting Summary linked to this article. More

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Ethics statement
All procedures used in these experiments were in accordance with protocol SW1401 of the SWFSC Animal Care and Use Committee. YOY California yellowtail were collected under California Department of Fish and Wildlife Scientific Collection Permit #SC-12372. Experimental protocols were approved by the National Oceanic & Atmospheric Administration Southwest Fisheries Science Center (NOAA SWFSC) Animal Care & Use Committee. Reporting of methods and results were in compliance with ARRIVE guidelines for animal research53.
Collection and captive husbandry of yellowtail
YOY California yellowtail (14–19 cm) were collected from offshore floating kelp mats near San Diego, CA, USA on September 12, 2012. Fish were caught using unbaited sabiki bait rigs, then immediately transferred to an onboard flow-through holding tank. On land, fish were transported in the holding tank on the trailered vessel to NOAA’s SWFSC Experimental Aquaria Facility in La Jolla, CA where they were transferred via dipnet to holding tanks. Fish were first held in 300 × 150 × 90 cm oval tanks (~ 3200 L) with flow-through, filtered local seawater at local ambient seawater temperature (~ 18 °C) and reared on a diet of Bio-Oregon BioTrout feed pellets. Pellets were presented immediately to newly transferred fish and fish began feeding 0–7 days after capture. Yellowtail were fed pellets 6 days/week to apparent satiation. After 525 days, the now larger yellowtail (42–50 cm) were transferred to a larger circular tank (diameter 3.7 m, capacity 9600 L) with the same filtered seawater at the same ambient temperatures (~ 18 °C) and tagged in the dorsal musculature with uniquely numbered and colored Floy plastic spaghetti tags. Yellowtail were switched to a diet of Pacific mackerel (Scomber japonicus) and market squid (Doryteuthis opalescens), both sourced off the coast of southern California (McRoberts Sales Co.). Yellowtail were fed mackerel and squid (by mass: 62% mackerel, 38% squid) 6 days/week to apparent satiation.
Yellowtail muscle was sampled at t = 0 (the day of diet switch), followed by sampling intervals ranging from 27 to 119 days (mean interval 60 ± 27 days) depending on perceived condition of yellowtail and conditions for sampling. Samples were collected by lowering tank water levels, capturing yellowtail in a rubber knotless net, then transferring fish to vinyl cradles. Biopsy punches (Cook Quick-Core G07821) were used to remove 0.1–0.2 g of white muscle from the dorsal musculature. When possible, fork length (FL; cm) was measured at the time of sampling. Muscle tissue was also collected from the diet (dorsal musculature from mackerel and mantle tissue, with the outer membrane removed, from squid) throughout the study for SIA. Sampling continued until yellowtail were removed due to poor condition, suffered natural mortality, or became too large to remain in the holding tank (t = 753 days after diet switch, the endpoint of the study).
YOY yellowtail (13.7–18.8 cm, 15.7 cm ± 1.7; 0.02–0.08 kg; 0.04 kg ± 0.02) fed well on pellet diet, increasing ~ threefold in length and ~ 40-fold in mass over 525 days. After 525 days, 21 similar-sized (42–50 cm FL, 46.0 cm ± 2.5; 1.0–2.2 kg, 1.7 kg ± 0.3) individual yellowtail were selected for the diet switch experiment to fish/squid (Fig. 1). During the course of the diet switch experiment, 11 individual fish were removed from captive conditions before reaching apparent isotopic steady-state due to poor physical condition, lack of feeding, or natural mortality. A total of 10 yellowtail fed consistently in captivity for a long enough period to reach apparent steady-state with new diet (595–753 days) allowing calculation of individual turnover rates in these fish. This allowed for estimates of Δ15Nlow and Δ13Clow and population-wide isotopic turnover estimates from 21 fish subjected to the diet switch, and Δ15Nhigh and Δ13Chigh and individual yellowtail turnover estimates from 10 individuals in captive conditions for 595–753 days that reached steady-state with new diet.
SIA of yellowtail and diet
Yellowtail dorsal muscle tissue and prey muscle tissue samples were immediately stored in cryovials at  − 20 °C. Feed pellets were analyzed whole. All samples were then frozen at  − 80 °C and subsequently lyophilized and ground to a homogenous powder for isotope analysis. The δ13C and δ15N values of all samples were determined at the University of Hawaii using an on-line C–N analyzer coupled with a Delta XP isotope ratio mass spectrometer. Replicate reference materials of atmospheric nitrogen and V-PDB were analyzed every 10 samples, and analytical precision was  More

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