Multi-temporal variability forecast of particulate organic carbon in the Indonesian seas

The current global condition characterized by high levels of CO₂ is altering the carbon cycle and elemental biogeochemistry, resulting in subsequent global warming, climate change, ocean acidification, and the indirect response of deoxygenation. The features of Indonesia's coastal ecosystems and continental shelf waters also contribute to spatio-temporal ocean carbon variability. For instance, the level of particulate organic carbon (POC) will change annually, and thus, over a decadal period, ocean dynamics may affect the temporal variability of POC. Motivated by such conditions, future forecasting is needed to envision the productivity of Indonesian seas by predicting vital parameters such as POC. This research aimed to forecast the temporal variability of POC in Indonesian waters. The Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model was used by considering the lowest value of the Akaike information criterion (AIC) and the mean absolute percentage error/MAPE (threshold < 10%). Using the highest correlation coefficient (threshold: 0.75), we obtained the best fit for forecasting POC temporal variability. Hindcast POC data (2002-2020/2021) was used to train the forecasting model. The result shows that forecasting of POC temporal variability can be conducted up to 2030. The validity of prediction is ensured for less than 5 years forward after 2020 with correlation coefficients of 0.65 and 0.83 for seasonal and monthly POC, respectively. The hindcast and forecast estimates of POC in the Indonesian seas show a decreasing trend. The present study emphasizes the different forecasting results obtained using the different approaches of annual versus inter-annual variability. A sustained research effort is still required to assess POC forecasting for its potential benefits in marine system monitoring and assessment.

Variability of particulate organic carbon and assessment of satellite retrieval algorithms over the eastern Arabian Sea

Particulate organic carbon (POC) and its variability were studied to assess the accuracy of ocean colour retrieval algorithms over the eastern Arabian Sea (EAS) as it controls the carbon sequestration, oxygen minimum zone and biogeochemical (C, N and P) cycles. The seasonality in the physical and biological processes strongly influenced the distribution of POC along the EAS. Higher POC and chlorophyll a (chl a) during the spring inter monsoon (SIM) in the north EAS were due to detrainment bloom. The lower POC:chl a ratios during the winter monsoon (WM) (299.8 ± 190.8) than the SIM (482.1 ± 438.3) were due to the influence of freshly derived organic matter with high nutrient levels. The moderate coefficient of regression values of POC with chl a (R² = 0.49, N = 59) suggests the importance of dead organic materials in controlling the POC distribution in the EAS. Validation between satellite and in situ POC using the four ocean colour retrieval algorithms showed that the algorithm based on the ratio of remote sensing reflectance (Rrs) performed better (R² = 0.6, N = 17). It also showed a linear trend of POC with absorption coefficients suggesting it as an optical proxy for the POC retrieval.

Seasonal Variability in Chlorophyll and Air-Sea CO2 Flux in the Sri Lanka Dome: Hydrodynamic Implications

Multiple upwelling systems develop in the Indian Ocean during the summer monsoon. The Sri Lanka dome (SLD), which occurs in the open ocean off the east coast of Sri Lanka from June to September, is distinct from those near the coast. The SLD is characterized by uplifted thermocline and increased chlorophyll concentration. Mechanisms of the upwelling and its biogeochemical response are not well understood. Here, we explored the dynamics of the chlorophyll and sea-to-air CO2 flux in the SLD using ocean color and altimetry remote sensing data, together with other reanalysis products. We found that the occurrence of high chlorophyll concentration and sea-to-air CO2 flux happens along the pathway of the southwest monsoon current (SMC). The annual cycle of chlorophyll in the SLD has a one-month lag relative to that in the southern coast of Sri Lanka. The positive wind stress curl that forms in the SLD during the summer does not fully explain the seasonal chlorophyll maximum. Transport of the SMC, eddy activity, and associated frontal processes also play an important role in regulating the variability in chlorophyll. In the SLD, upwelled subsurface water has excess dissolved inorganic carbon (DIC) relative to the conventional Redfield ratio between DIC and nutrients; thus, upwelling and sub-mesoscale processes determine this region to be a net carbon source to the atmosphere.

Spatiotemporal Variability of Remote Sensing Ocean Net Primary Production and Major Forcing Factors in the Tropical Eastern Indian and Western Pacific Ocean

Based on widely used remote sensing ocean net primary production (NPP) datasets, the spatiotemporal variability of NPP is first analyzed over the tropical eastern Indian and western Pacific Ocean for the period 1998–2016 using the conventional empirical orthogonal function (EOF), the lead–lag correlation and the ensemble empirical mode decomposition (EEMD) technique. Barnett and Preisendorfer’s improved Canonical Correlation Analysis (BPCCA) is also applied to derive covariability patterns of NPP with major forcing factors of the chlorophyll a concentration (Chla), sea surface temperature (SST), sea level anomaly (SLA), ocean rainfall (Rain), sea surface wind (Wind), and current (CUR) under climate changes of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). We find that: (1) The first two seasonal EOF modes capture significant temporal and meridional NPP variability differences, as NPP reaches peaks approximately three months later in the western Pacific Ocean than that of in the eastern Indian Ocean. (2) The second and third interannual EOF modes are closely related with ENSO with a two-month lag and synchronous with IOD, respectively, characterized by southwesterly positive anomaly centers during positive IOD years. (3) NPP presents different varying tendencies and similar multiscale oscillation patterns with interannual and interdecadal cycles of 2~3 years, 5~8 years, and 9~19 years in subregions of the Bay of Bengal, the South China Sea, the southeastern Indian Ocean, and the northwestern Pacific Ocean. (4) The NPP variability is strongly coupled with negative SST, SLA, and Rain anomalies, as well as positive Chla, Wind and CUR anomalies in general during El Niño/positive IOD years. The results reveal the diversity and complexity of large-scale biophysical interactions in the key Indo-Pacific Warm Pool region, which improves our understanding of ocean productivity, ecosystems, and carbon budgets.