Dominant timescales of variability in global satellite chlorophyll and SST revealed with a MOving Standard deviation Saturation (MOSS) approach

Jönsson, Bror; Salisbury, Joseph E.; Atwood, Elizabeth C.; Sathyendranath, Shubha; Mahadevan, Amala

doi:10.1016/j.rse.2022.113404

Cited by 5 publications

(5 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Upon examination of specific oceanic basins, our results align with evidence indicating lower productivity in the central Pacific Ocean compared to other oceanic regions [55][56][57]. Furthermore, our findings in the Equatorial Pacific region are in agreement with previous observations, showing Chl-a concentrations ranging from 0.08 to 0.6 mg•m −3 with low temporal variability [58][59][60]. In contrast, the Indian Ocean and West Pacific exhibit the highest Chl-a concentrations and variability (Chl-a CV).…”

Section: Discussionsupporting

confidence: 91%

Global Chlorophyll Concentration Distribution and Effects on Bottom Reflectance of Coral Reefs

Bonelli,

Martin,

Noel

et al. 2024

Oceans

View full text Add to dashboard Cite

Despite the limited coverage of coral reefs in the world’s oceans, they play a crucial role in global marine biodiversity and providing essential ecosystem services. This study explores the influence of chlorophyll-a (Chl-a) concentration in the water column on the estimation of bottom reflectance (rb) in coral reefs monitored by the Allen Coral Atlas coral reef monitoring system, using satellite imagery from a Sentinel-2 MSI sensor. We conducted a comprehensive analysis, considering Chl-a global distribution and variability, and its combined effect with water column depth over rb calculation. Our results demonstrated that the impact of Chl-a on rb estimation becomes significant when the water column depth exceeds 3 m. While suggesting the optionality of using regional Chl-a values, our study highlights potential overestimations of Chl-a in optically complex environments, such as along the Brazilian coast. This research contributes to refining coral reef monitoring systems and underscores the importance of accurate Chl-a assessments for robust environmental evaluations.

show abstract

Section: Discussionsupporting

confidence: 91%

Global Chlorophyll Concentration Distribution and Effects on Bottom Reflectance of Coral Reefs

Bonelli,

Martin,

Noel

et al. 2024

Oceans

View full text Add to dashboard Cite

show abstract

“…This is to be expected, since these areas are characterized by complex interactions between physical and biological processes over short spatial scales. Other studies have also found that the dominating timescales of variability in these regions are very short, which most global biogeochemical models are not developed to resolve (Jönsson et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll

Jönsson,

Follett,

Bien

et al. 2023

Geosci. Model Dev.

Self Cite

View full text Add to dashboard Cite

Abstract. Global biogeochemical ocean models are invaluable tools to examine how physical, chemical, and biological processes interact in the ocean. Satellite-derived ocean color properties, on the other hand, provide observations of the surface ocean, with unprecedented coverage and resolution. Advances in our understanding of marine ecosystems and biogeochemistry are strengthened by the combined use of these resources, together with sparse in situ data. Recent modeling advances allow the simulation of the spectral properties of phytoplankton and remote sensing reflectances, bringing model outputs closer to the kind of data that ocean color satellites can provide. However, comparisons between model outputs and analogous satellite products (e.g., chlorophyll a) remain problematic. Most evaluations are based on point-by-point comparisons in space and time, where spuriously large errors can occur from small spatial and temporal mismatches, whereas global statistics provide no information on how well a model resolves processes at regional scales. Here, we employ a unique suite of methodologies, the Probability Density Functions to Evaluate Models (PDFEM), which generate a robust comparison of these resources. The probability density functions of physical and biological properties of Longhurst's provinces are compared to evaluate how well a model resolves related processes. Differences in the distributions of chlorophyll a concentration (mg m−3) provide information on matches and mismatches between models and observations. In particular, mismatches help isolate regional sources of discrepancy, which can lead to improving both simulations and satellite algorithms. Furthermore, the use of radiative transfer in the model to mimic remotely sensed products facilitates model–observation comparisons of optical properties of the ocean.

show abstract

“…In some places, the spatio-temporal resolution (i.e., monthly on a 1°grid) used in the present study may be too coarse to capture the overall Chl variability. In particular, this would mainly explain the lack of correlation observed in the tropical southeastern and northwestern Pacific where the dominating timescales of Chl variability have been very recently reported to be below 30 days (Jönsson et al, 2023; see their Figure 7B). This may also explain part of the Chl underestimation observed in highly energetic areas with mesoscale and sub-mesoscale eddies (<100 km scales) that may impact phytoplankton along dynamical fronts (Levy et al, 2018).…”

Section: Chl Reconstruction Improvement From Mono-mode Cnn 1 To Multi...mentioning

confidence: 91%

A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers

et al. 2023

View full text Add to dashboard Cite

Time series of satellite-derived chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass), continuously generated since 1997, are still too short to investigate the low-frequency variability of phytoplankton biomass (e.g. decadal variability). Machine learning models such as Support Vector Regression (SVR) or Multi-Layer Perceptron (MLP) have recently proven to be an alternative approach to mechanistic ones to reconstruct Chl synoptic past time-series before the satellite era from physical predictors. Nevertheless, the relationships between phytoplankton and its physical surrounding environment were implicitly considered homogeneous in space, and training such models on a global scale does not allow one to consider known regional mechanisms. Indeed, the global ocean is commonly partitioned into biogeochemical provinces (BGCPs) into which phytoplankton growth is supposed to be governed by regionally-”homogeneous” processes. The time-evolving nature of those provinces prevents imposing a priori spatially-fixed boundary constraints to restrict the learning phase. Here, we propose to use a multi-mode Convolutional Neural Network (CNN), which can spatially learn and combine different modes, to globally account for interregional variabilities. Each mode is associated with a CNN submodel, standing for a mode-specific response of phytoplankton biomass to the physical forcing. Beyond improving performance reconstruction, we show that the different modes appear regionally consistent with the ocean dynamics and that they may help to get new insights into physical-biogeochemical processes controlling phytoplankton spatio-temporal variability at global scale.

show abstract

Dominant timescales of variability in global satellite chlorophyll and SST revealed with a MOving Standard deviation Saturation (MOSS) approach

Cited by 5 publications

References 57 publications

Global Chlorophyll Concentration Distribution and Effects on Bottom Reflectance of Coral Reefs

Global Chlorophyll Concentration Distribution and Effects on Bottom Reflectance of Coral Reefs

Using Probability Density Functions to Evaluate Models (PDFEM, v1.0) to compare a biogeochemical model with satellite-derived chlorophyll

A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers

Contact Info

Product

Resources

About