Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha'apai

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Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha'apai

https://doi.org/10.21203/rs.3.rs-3335677/v1

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We report on observed trend anomalies in climate-relevant global ocean biogeochemical properties, as derived from satellite ocean color measurements, that show a substantial decline in phytoplankton carbon concentrations following eruptions of the submarine volcano Hunga Tonga-Hunga Ha'apai in January 2022. The anomalies are seen in remotely-sensed ocean color data sets from multiple satellite missions, but not in situ observations, thus suggesting that the observed anomalies are a result of ocean color retrieval errors rather than indicators of a major shift in phytoplankton carbon concentrations. The enhanced concentration of aerosols in the stratosphere following the eruptions results in a violation of some fundamental assumptions in the processing algorithms used to obtain marine biogeochemical properties from satellite radiometric observations, and it is demonstrated through radiative transfer simulations that this is the likely cause of the anomalous trends. We note that any future stratospheric aerosol disturbances, either natural or geoengineered, may lead to similar artifacts in satellite ocean color and other remote-sensing measurements of the marine environment, thus confounding our ability to track the impact of such events on ocean ecosystems.

Earth and environmental sciences/Biogeochemistry/Carbon cycle

Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Environmental health

Earth and environmental sciences/Ocean sciences/Marine biology

Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry

Earth and environmental sciences/Ocean sciences/Marine chemistry

A recent analysis of the global satellite ocean color (OC) trends from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua revealed peculiar patterns in derived concentrations of the phytoplankton pigment chlorophyll-a and phytoplankton carbon for the year 2022, with annual trends in phytoplankton carbon concentrations indicating behavior well outside of the 25-yr climatological record¹. The anomalies in global phytoplankton metrics were driven by deviations in the equatorial region (23.5° N to 23.5° S) and the southern hemisphere. This analysis did not detect visible impact to the OC products from the northern hemisphere, and speculated that the unusual trends were likely due to recent volcanic eruptions near Tonga¹.

On the 13th and 15th of January 2022, two major eruptions of the submarine volcano Hunga Tonga-Hunga Ha'apai (located at 20.55°S, 175.4°W) injected an unprecedented amount of water vapor (estimated 146 ± 5 Tg) and a large amount of SO₂ (estimated ∼0.4 Tg) into the stratosphere^2,3,4. Carr et al. reported visible plumes (via stereo imaging) to a record height of 50–55 km [5]; Legras et al. (2022) observed oxidation of this injected SO₂ to sulfate aerosols over several weeks, and the formation of aerosol layers from 24–26 km [6]. While gradually declining in concentration and altitude, elevated stratospheric aerosol loading persisted throughout 2022 and beyond⁴, and the H₂O enhancement is expected to persist for some time as well².

While the atmospheric and radiative impacts of the eruptions have been discussed⁷, here we focus on reporting the impact of the eruption on ocean color products retrieved from a host of spaceborne spectral radiometers measuring in the visible to near-infrared spectral range. These satellite sensors are designed to measure variations in the bio-optical and biogeochemical properties of the global oceans, including distributions of the phytoplankton pigment chlorophyll-a (Chla; mg m^− 3) or concentration of phytoplankton carbon (Cphy; mg m^− 3). These properties are inferred from measurements of ocean color, as characterized through spectral variations in remote sensing reflectance (Rrs(l); sr^− 1): the radiometric signal upwelling from beneath the ocean surface and measured just above the surface at multiple sensor wavelengths, l, in the visible spectral range. The retrieval of Rrs(l) from the satellite sensor radiometry requires a correction for the effects of the atmosphere on the signal received at the sensor. The atmospheric correction (AC) process accounts for the effects of scattering and absorption by atmospheric aerosols, as well as Rayleigh scattering by air molecules and transmittance losses due to absorbing atmospheric gases^8,9,10.

In this study, we provide a detailed assessment of the impact of the Tonga eruptions on satellite OC retrievals through analysis of the OC time series from multiple satellite missions processed by both NASA and ESA/EUMETSAT and contrast that analysis with time series of in situ measurements from bio-optical floats. We conclude that the primary impact of the eruptions is to introduce systematic error in the AC process, leading to erroneous spatiotemporal anomalies that could be misinterpreted as indicators of biological response. Furthermore, through simulation studies we investigate how the addition of stratospheric aerosols propagates through the NASA standard AC algorithm to produce the observed anomalies in the OC time series and thereby reveal a potential path to mitigation.

Satellite ocean color data from six currently active missions were obtained from NASA’s Ocean Biology Distributed Active Archive Center (OB.DAAC). These six missions represent three distinct sensor designs, including MODIS on both the Aqua and Terra platforms, Visible and Infrared Imaging Radiometer Suite (VIIRS) on the SNPP and NOAA-20 platforms, and Ocean and Land Color Instrument (OLCI) on ESA’s Sentinel-3A and Sentinel-3B platforms. The NASA-provided multi-mission OC data correspond to reprocessing version R2022.0 (https://oceancolor.gsfc.nasa.gov/data/reprocessing/), which utilized a common AC algorithm⁹. The acquired data products included Rrs(l) and two products that are inferred from Rrs(l): Chla^11,12 and particulate backscattering coefficient at 443 nm (b_bp(443); m^− 1)¹³. The b_bp(443) is a standard NASA product that is linearly related to Cphy¹⁴. We used the global monthly (Level-3 binned) data products with a resolution of approximately 4.6 x 4.6 km², expressed on a sinusoidal grid, which were then spatially averaged using arithmetic means to produce regional time-series analyses. The ocean regions used in our analysis include the subpolar northern hemisphere (NH) spanning 0° to 50° N latitude, the subpolar southern hemisphere (SH) spanning 0° to 50° S latitude, and the South Pacific Subtropical Gyre (SPSG)¹⁵.

To evaluate any unique dependence on the NASA AC algorithm, OLCI OC time series data for the SPSG region, as processed using an independent atmospheric correction algorithm¹⁰, were acquired from EUMETSAT (https://metis.eumetsat.int/data/oc/).

When comparing OC trends across missions, results are presented using standardized anomalies from the pre-2022 annual mean value. For each sensor, the pre-2022 mean and standard deviation of monthly means are used to create a time series of anomalies quantified as standard deviations from the mean, such that a value of +/-1 indicates a month that is one standard deviation above or below, respectively, the pre-2022 annual mean value for that sensor. Reporting standardized anomalies removes the influence of small differences in magnitude and seasonality between the raw Rrs datasets that may arise due to differences in sampling or mean time of day, or minor calibration or algorithm errors.

To track changes in aerosol distribution, we used NASA’s Ozone Mapping and Profiler Suite (OMPS) limb profiler monthly stratospheric aerosol data set (available starting March 2012-present)¹⁶. This provides monthly mean multi-wavelength stratospheric aerosol optical thickness (AOT) on a 5-degree latitude by 15-degree longitude grid. We use the AOT at 675 nm as representative, as retrieved AOT at shorter wavelengths is less quantitatively reliable¹⁷. For regional analyses, we calculated area-weighted monthly geometric means from the gridded data. We used geometric means because AOT data distributions tend to be skewed, and are thus better approximated with lognormal rather than normal distributions¹⁸.

To evaluate in situ changes in ocean optical properties, we used measurements of b_bp at 700 nm within the SPSG from BGC-Argo floats (https://usgodae.org/argo/argo.html), which were downloaded using the OneArgo-MAT toolbox¹⁹. The BGC-Argo b_bp(700) profiles were de-spiked using a three-point moving median filter, and then aligned using the average b_bp(700) in 700–750 m intervals. Surface data (5- to 20-m depth) from all floats (n = 33) were then grouped into 7-day intervals, and values outside of the 5th and 95th percentile were removed. The resulting 58901 data points from 2016 to 2022 were used in our regional analysis.

To assess the expected impact on ocean color retrievals due to the unusual aerosol distributions associated with the eruptions, we utilized a vector radiative transfer model that fully couples light scattering and absorption in the atmosphere-ocean system^20,21. We simulated an atmosphere with two aerosol layers, an underlying ocean surface with 5 m/s wind speed, and a clear water body with a Chla concentration of 0.3 mg m^− 3. We account for absorbing water vapor and ozone gases in the atmosphere with concentrations of 1.25 cm and 320 Dobson units, respectively, both with the U.S. standard atmosphere 1976. The aerosol vertical distribution is modeled as a Gaussian function with a center height defined as the layer height of each aerosol layer and a layer width of 2 km [22]. A lower boundary aerosol layer (tropospheric) is assumed at 2 km height with an optical depth of 0.1, and a stratospheric sulfate type aerosol layer is assumed at 23 km with an optical depth of 0.05, both at the reference wavelengths of 869 nm for MODIS Aqua. The boundary layer aerosols are assumed to be bimodal and coarse mode dominant with a fine mode fraction of 0.01 and relative humidity of 70% following the aerosol modeling of Ahmad et al.²³ The stratospheric aerosol, modeled following Taha et al.⁴, is a monomodal lognormal distribution with a refractive index of 1.45-0i (purely scattering), a number median radius of 0.2 microns, and a distribution width sigma of 1.6.

Satellite remote sensing of ocean color, as characterized by Rrs(l), provides critical information to support global and regional-scale research and applications on biological and biogeochemical constituents of the world’s oceans, including assessment of large-scale changes in the distribution of marine phytoplankton due to natural or anthropogenically-induced variations in the Earth’s climate^24,25. The capability to track such climate-driven impacts is paramount, as ocean phytoplankton are responsible for roughly 50% of global net primary production, and rapid variations in phytoplankton populations can dramatically alter ocean ecosystems and the services those ecosystems provide, including impacts to food security and global biogoechemical cycles^26,27. In open oceans, temporal variations in the ocean color signal over a fixed region typically demonstrate a clear seasonal cycle due to natural variations in light and nutrient availability that drive phytoplankton productivity, with deviations from that climatological cycle attributable to regional or global disturbances to the environment that impact phytoplankton growth.

Analysis of the MODIS Aqua OC data record for two key wavelengths in in the blue and green spectral region (Rrs at 443nm and 547nm, respectively), and two OC-derived phytoplankton biomass metrics, b_bp(443) (directly related to phytoplankton carbon biomass) and Chla, for the NH and SH regions, demonstrates the distinct seasonal cycles in phytoplankton growth that follow the variation in solar illumination for the period of 2002–2021 (Fig. 1). In 2022, however, the seasonal cycle in Rrs(l) for the SH (red lines in Fig. 1) indicates a strong negative deviation from that 20-year record. A similar discrepancy is visible in the b_bp(443) seasonal cycle in SH, while b_bp(443) and Rrs(l) in the NH are consistent with the historical norm. In contrast, the Chla seasonal cycle for 2022 is in-family with the climatological record for the SH region and is slightly elevated over much of the year for the NH, consistent with expectations due to the persistent El Nino conditions prevailing in 2022.¹

The same anomalous behavior in ocean color measurements is visible across several other satellite sensors operational in 2022, as shown here for Rrs in the green spectral range, for the SPSG region of the SH (Fig. 2). This cross-sensor comparison demonstrates that the year 2022 was consistently anomalous relative to the climatological record, with deviations typically around 3 standard deviations below the long-term mean for each sensor. We note that, while there is some temporal cross-calibration applied between MODIS-Terra and MODIS-Aqua²⁸, all other sensors are fully independent with respect to calibration, thus ruling out the possibility that observed anomalies in the SH (Fig. 1) are due to systematic error in the MODIS-Aqua calibration related to uncorrected radiometric degradation. We note also that similar deviations are visible in datasets independent of the NASA atmospheric correction process. Seasonal cycles in the EUMETSAT-derived OLCI Rrs, while flatter relative to the NASA-derived OLCI Rrs of Fig. 2(e,f), clearly demonstrate the year 2022 to be out of family and with a negative deviation relative to their historical record (Fig. 2g,h),.

The observed rapid change in SH ocean color parameters in 2022 coincided with the eruptions of Hunga Tonga-Hunga Ha'apai, which had an unprecedented impact on the concentration and distribution of atmospheric aerosols. The annual progression of stratospheric aerosol, as measured by OMPS over 2022, differs substantially from the previous 10 years of measurements (Fig. 3). Near the equator and extending well into the SH, the stratospheric AOT reported at 675 nm increased dramatically, reaching a peak in June-July of 2022 that was 6–7 times the historical average. This positive anomaly in the SH stratospheric aerosol follows closely with the progression of negative Rrs(λ) anomalies observed in NASA and ESA/EUMETSAT OC data (Figs. 1 and 2).

The similarity in ocean color (Figs. 1 and 2) and aerosol trends (Fig. 3) over the year 2022 suggests that there is likely a causal link between the unusual distribution of aerosols from the Tonga eruptions and the OC SH anomalies, drawing two possible hypotheses. The first hypothesis is that we are observing a biological response of the phytoplankton community to the eruption, as has been observed in previous volcanic events and linked to transport of volcanic ash, which typically contains nutrients such as iron needed for photosynthesis, to nutrient deplete regions of the oceans^29,30,31. What we observe in the ocean color trends for 2022 in the SH is a marked decrease in backscatter paired with no change in chlorophyll concentration, which could be interpreted as a physiological response of the phytoplankton community that resulted in a decrease in phytoplankton biomass (Cphy). The second hypothesis is that the anomalies observed in the satellite ocean color record are the result of an error in the atmospheric correction process due to the unusual aerosol conditions following the eruption. To evaluate these hypotheses, an independent dataset of phytoplankton biomass collected in situ by the BGC-Argo fleet was assessed. The BGC-Argo measurements of b_bp from within the SPSG indicate no change in 2022 relative to previous years (2016–2022). This supports the assertion in Franz et al. that the anomalies observed in the satellite ocean color record are likely the result of error in the atmospheric correction process rather than an indication of a marked decrease in phytoplankton biomass in 2022¹.

The NASA AC algorithm assumes that aerosols are primarily scattering, with only weak absorption, and that those aerosols are located in the troposphere⁹. When first injected, the additional aerosols from the Tonga eruptions are believed to have been moderately absorbing³², but after aging and transport the sustained anomalous aerosol population in the stratosphere is found to be weakly absorbing⁷, consistent with the NASA AC algorithm assumptions. The AC algorithm also assumes that the aerosol (and most of the atmosphere) is below the stratospheric ozone layer, and therefore a correction for ozone absorption can be applied before attempting to separate the atmosphere and ocean contributions from the total signal reaching the satellite sensor⁹. The additional aerosols from the Tonga eruptions, however, were injected into the stratosphere and thus co-mingled with the ozone, breaking an inherent assumption in the NASA AC algorithm.

To evaluate the second hypothesis, we assess the impact of this unusual aerosol-ozone mixing on the AC process, and consequently on the OC products, through a sensitivity analysis that was conducted using a fully-coupled ocean-atmosphere vector radiative transfer simulation (Fig. 5). With atmospheric conditions modeled to be representative of the eruption, including stratospheric aerosol distributions following Taha et al.⁴, the total reflectance at the satellite sensor was simulated with and without the presence of ozone. These two simulation sets enable identification of the influence of stratospheric aerosol scattering effects on ozone absorption, highlighting the significance of the radiative interaction between these two components. If we apply the NASA standard correction for ozone absorption⁹, which assumes the aerosols are well below the ozone layer, to a signal that includes aerosol scattering contributions from within the ozone layer, the effect is to underestimate the correction, especially in the green-red region of the spectrum where ozone absorption is strongest. Once the remainder of the atmospheric contributions to the observed signal are subtracted, the result is an underestimation of the water-leaving signal with a spectral bias that roughly follows the ozone absorption spectrum. This spectral bias is further propagated into remote sensing reflectance and biogeochemical products, but due to the spectral characteristics of the bias and nature of the algorithms it is not affecting all products in the same way. While both b_bp(443) and Chla are derived from Rrs(l), the Chla algorithm, as applied over relatively clear open-ocean waters, is based on spectral band differences and is thus less sensitive to changes in Rrs(l) that affect all bands in the same direction, as would be expected from (for example) an error in the AC algorithm due to over or underestimated aerosol contributions¹¹. In contrast, b_bp at 443 (and at all other wavelengths in the visible spectral range), is derived from the spectral fitting of a 3-parameter model to the Rrs(l) distribution, with fixed assumptions about pure seawater contributions, and is thus more sensitive to absolute values in Rrs(l)¹³.

Our findings also suggest a path forward to mitigate the impact of the Tonga eruptions and, potentially, other events that have contributed to unusual enhancements in stratospheric aerosol loads. Specifically, a series of sensitivity analyses could be performed to characterize the effect of statospheric aerosols as a function of concentration, altitude, and microphysical properties to produce a model or look-up table. Ancillary data from OMPS or similar sensors could then be used to compute a correction using that model, as a precursor to application of the standard AC algorithm; however, such a correction will take some time to develop and validate, and it will be limited by the availability, quality, and resolution of the ancillary aerosol information.

The presence of an anomalously high stratospheric aerosol loading biases satellite OC data products. Such an anomalous stratospheric AOT following the Hunga Tonga-Hunga Ha'apai eruptions in January 2022 caused regionally and temporally covarying out-of-family behavior in OC retrieval products. These OC anomalies are seen in observations from multiple satellites, and they are not seen in in-situ metrics of phytoplankton biomass, from which we conclude that the anomalies must be due to error in the OC retrieval process. Radiative transfer simulations demonstrate that the effect of enhanced stratospheric aerosol loading is to induce biases of the type observed in the retrieved OC products, as it invalidates a key assumption in the AC algorithm on the separation of aerosol scattering and ozone absorption.

This analysis presents a caution to the OC data user community until such time as algorithms can be improved to properly compensate for enhanced stratospheric aerosols from this and other (historical and future) eruptions. Implementing such an improvement is beyond the scope of the current study and is a longer-term effort that will require accurate knowledge of the spatiotemporal variation not only of stratospheric AOT but also the aerosol microphysical properties (e.g., size, refractive index).

Looking forward, various proposals for geoengineering to counter global warming involve the deliberate injection of aerosol precursors such as SO₂ into the stratosphere^32,33; indeed, major eruptions have been considered as ‘natural experiments’ for this type of geoengineering^35,36. Such proposals also raise concern due to potentially severe effects on other aspects of the Earth system^35,36,37,38. Deliberate aerosol injection would likely introduce similar biases to satellite OC and other satellite data products (e.g., sea surface temperature³⁹) for the same physical reasons as observed here, which would complicate efforts to assess the true response of Earth’s biosphere to anthropogenic geoengineering.

Acknowledgments

This work was supported through NASA’s Terra and Aqua Senior Review Proposals for MODIS Algorithm Maintenance, NASA’s Research Opportunities for Space and Earth Sciences Program #NNH20ZDA001N-SNPPSP, and the PACE Project. IC’s contributions additionally supported NSF 222980. We also acknowledge Dr. Pengwang Zhai of University of Maryland Baltimore County for supporting the Radiative Transfer code used in this analysis.

Author Contributions

IC provided analysis of the BGC-Argo data and expertise on phytoplankton ecology. IA provided the sensitivity analysis to show the impact of aerosols within the ozone layer. AS provided analysis of the OMPS data to characterize the aerosol distribution and generated all time-series figures in the manuscript. BF provided the analysis of the satellite ocean color data. All authors contributed substantially to the development of the manuscript text.

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Anomalous trends in global ocean carbon concentrations following the 2022 eruptions of Hunga Tonga-Hunga Ha'apai

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Journal Publication

Version 1