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The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hindered obtaining complete spatiotemporal information. Traditional methods for deriving Chl-a are based on the modeling of measured sensor data and in situ measurements. Spatiotemporal imputation of Chl-a is difficult due to the inaccessibility of the measured Chl-a. In this study, we introduce an innovative approach that incorporates an ocean dynamics dataset and utilizes the random forest algorithm for predicting the Chl-a concentration in the SCS. The method combines the spatiotemporal feature pattern of Chl-a and the main influencing factors, and it introduces ocean dynamics data, which has a high correlation with the spatiotemporal distribution of Chl-a, as the input data through feature engineering. Also, we compared Random Forest (RF) with other Machine Learning (ML) methods. The results show that (1) ocean dynamics datasets can provide important data support for Chl-a imputation by capturing the impact of dynamical processes on ecological roles in the South China Sea. (2) The RF method is the superior imputation method for the reconstruction of Chl-a in the South China Sea, with better model performance and smaller errors. This study provides valuable insight for researchers and practitioners in choosing suitable machine learning methods for the imputation of the Chl-a concentration in the SCS, facilitating a better understanding of the region’s marine ecosystems and supporting effective environmental management.

Accurate knowledge of the location and magnitude of ocean heat content (OHC) variability and change is essential for understanding the processes that govern decadal variations in surface temperature, quantifying changes in the planetary energy budget, and developing constraints on the transient climate response to external forcings. We present an overview of the temporal and spatial characteristics of OHC variability and change as represented by an ensemble of dynamical and statistical ocean reanalyses (ORAs). Spatial maps of the 0–300 m layer show large regions of the Pacific and Indian Oceans where the interannual variability of the ensemble mean exceeds ensemble spread, indicating that OHC variations are well-constrained by the available observations over the period 1993–2009. At deeper levels, the ORAs are less well-constrained by observations with the largest differences across the ensemble mostly associated with areas of high eddy kinetic energy, such as the Southern Ocean and boundary current regions. Spatial patterns of OHC change for the period 1997–2009 show good agreement in the upper 300 m and are characterized by a strong dipole pattern in the Pacific Ocean. There is less agreement in the patterns of change at deeper levels, potentially linked to differences in the representation of ocean dynamics, such as water mass formation processes. However, the Atlantic and Southern Oceans are regions in which many ORAs show widespread warming below 700 m over the period 1997–2009. Annual time series of global and hemispheric OHC change for 0–700 m show the largest spread for the data sparse Southern Hemisphere and a number of ORAs seem to be subject to large initialization ‘shock’ over the first few years. In agreement with previous studies, a number of ORAs exhibit enhanced ocean heat uptake below 300 and 700 m during the mid-1990s or early 2000s. The ORA ensemble mean (±1 standard deviation) of rolling 5-year trends in full-depth OHC shows a relatively steady heat uptake of approximately 0.9 ± 0.8 W m −2 (expressed relative to Earth’s surface area) between 1995 and 2002, which reduces to about 0.2 ± 0.6 W m −2 between 2004 and 2006, in qualitative agreement with recent analysis of Earth’s energy imbalance. There is a marked reduction in the ensemble spread of OHC trends below 300 m as the Argo profiling float observations become available in the early 2000s. In general, we suggest that ORAs should be treated with caution when employed to understand past ocean warming trends—especially when considering the deeper ocean where there is little in the way of observational constraints. The current work emphasizes the need to better observe the deep ocean, both for providing observational constraints for future ocean state estimation efforts and also to develop improved models and data assimilation methods.

This paper describes version 3 of the Simple Ocean Data Assimilation (SODA3) ocean reanalysis with enhancements to model resolution, observation, and forcing datasets, and the addition of active sea ice. SODA3 relies on the ocean component of the NOAA/Geophysical Fluid Dynamics Laboratory CM2.5 coupled model with nominal ¼° resolution. A scheme has also been implemented to reduce bias in the surface fluxes. A 37-yr-long ocean reanalysis, SODA3.4.2, created using this new SODA3 system is compared to the previous generation of SODA (SODA2.2.4) as well as to the Hadley Centre EN4.1.1 no-model statistical objective analysis. The comparison is carried out in the tropics, the midlatitudes, and the Arctic and includes examinations of the meridional overturning circulation in the Atlantic. The comparison shows that SODA3.4.2 has reduced systematic errors to a level comparable to those of the no-model statistical objective analysis in the upper ocean. The accuracy of variability has been improved particularly poleward of the tropics, with the greatest improvements seen in the Arctic, accompanying a substantial reduction in surface net heat and freshwater flux bias. These improvements justify increasing use of ocean reanalysis for climate studies including the higher latitudes.

The Pacific Decadal Oscillation (PDO), the leading mode of Pacific decadal sea surface temperature variability, arises mainly from combinations of regional air-sea interaction within the North Pacific Ocean and remote forcing, such as from the tropical Pacific and the Atlantic. Because of such a combination of mechanisms, a question remains as to how much PDO variability originates from these regions. To better understand PDO variability, the equatorial Pacific and the Atlantic impacts on the PDO are examined using several 3-dimensional partial ocean data assimilation experiments conducted with two global climate models: the CESM1.0 and MIROC3.2m. In these partial assimilation experiments, the climate models are constrained by observed temperature and salinity anomalies, one solely in the Atlantic basin and the other solely in the equatorial Pacific basin, but are allowed to evolve freely in other regions. These experiments demonstrate that, in addition to the tropical Pacific’s role in driving PDO variability, the Atlantic can affect PDO variability by modulating the tropical Pacific climate through two proposed processes. One is the equatorial pathway, in which tropical Atlantic sea surface temperature (SST) variability causes an El Niño-like SST response in the equatorial Pacific through the reorganization of the global Walker circulation. The other is the north tropical pathway, where low-frequency SST variability associated with the Atlantic Multidecadal Oscillation induces a Matsuno-Gill type atmospheric response in the tropical Atlantic-Pacific sectors north of the equator. These results provide a quantitative assessment suggesting that 12–29% of PDO variance originates from the Atlantic Ocean and 40–44% from the tropical Pacific. The remaining 27–48% of the variance is inferred to arise from other processes such as regional ocean-atmosphere interactions in the North Pacific and possibly teleconnections from the Indian Ocean.

During the past 25 years, altimetric observations of the ocean surface from space have been mapped to provide two dimensional sea surface height (SSH) fields that are crucial for scientific research and operational applications. The SSH fields can be reconstructed from conventional altimetric data using temporal and spatial interpolation. For instance, the standard Developing Use of Altimetry for Climate Studies (DUACS) products are created with an optimal interpolation method that is effective for both low temporal and low spatial resolution. However, the upcoming next-generation SWOT mission will provide very high spatial resolution but with low temporal resolution. The present paper makes the case that this temporal–spatial discrepancy induces the need for new advanced mapping techniques involving information on the ocean dynamics. An algorithm is introduced, dubbed the BFN-QG, that uses a simple data assimilation method, the back-and-forth nudging (BNF), to interpolate altimetric data while respecting quasigeostrophic (QG) dynamics. The BFN-QG is tested in an observing system simulation experiments and compared to the DUACS products. The experiments consider as reference the high-resolution numerical model simulation NATL60 from which are produced realistic data: four conventional altimetric nadirs and SWOT data. In a combined nadirs and SWOT scenario, the BFN-QG substantially improves the mapping by reducing the root-mean-square errors and increasing the spectral effective resolution by 40 km. Also, the BFN-QG method can be adapted to combine large-scale corrections from nadir data and small-scale corrections from SWOT data so as to reduce the impact of SWOT correlated noises and still provide accurate SSH maps.

The relationship between the Indian Ocean dipole (IOD) and the South Asian summer monsoon (SASM), which remains a subject of controversy, was investigated using data analyses and numerical experiments. We categorized IOD events according to their sea surface temperature anomaly (SSTA) pattern: type W and type E are associated with stronger SSTA amplitudes in the western and eastern poles of the IOD, respectively, while type C has comparable SSTA amplitudes in both poles during boreal autumn. TypeWis associated with a weak SASM from May to summer, which contributes to substantial warming of the western pole in autumn; the east–west SST gradient linked to the warming of the western pole causes weak southeasterly wind anomalies off Sumatra and feeble and cold SSTAs in the eastern pole during the mature phase. Type E is associated with a strong SASM and feeble warming of the western pole; interaction between the strong SASM and cold SSTAs in the eastern pole in summer results in strong southeasterly wind anomalies off Sumatra and substantial cooling of the eastern pole during the mature phase. For type C, warming of the western pole and cooling of the eastern pole develop synchronously without apparent SASM anomalies and reach comparable intensities during the mature phase. Observations and numerical simulation results both indicate the role of disparate SASM anomalies in modulating SSTA patterns during the development of positive IODs. Warming of the tropical Indian Ocean becomes established in the winter and spring following typeWand type C IODs but not following type E events.

Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out.

The Indian Ocean basin (IOB) mode is the dominant mode of the interannual sea surface temperature (SST) variability in the Indian Ocean, with the Indian Ocean dipole (IOD) as the second mode. An IOB event normally occurs after an El Niño or a concurrent IOD–El Niño event, the dynamics of which are traditionally believed as forced by ENSO through the Walker circulation anomalies over the tropical Indian Ocean. A strong IOB in 2020 took place after the strongest 2019 IOD on record but independent of El Niño, which challenges the traditional atmospheric bridge dynamics of the IOB event. In this study, the dynamics of the 2020 IOB event are investigated using the numerical seasonal climate prediction system of the National Marine Environmental Forecasting Center of China. It is found that the initialization of the Indian Ocean subsurface temperature during the 2019 IOD event has led to the outburst of the 2020 IOB event successfully, the dynamics of which are the propagation and the western boundary reflection of the equatorial and off-equatorial Rossby waves, inducing heat content recharge over the tropical Indian Ocean upper thermocline. In comparison, experiments of SST initialization over the tropical Indian Ocean, with the subsurface temperature in a climatological state, were unable to reproduce the onset of the 2020 IOB event, suggesting that the local air–sea interaction within the Indian Ocean basin is of secondary importance. The numerical experiments suggest that the thermocline ocean wave dynamics play an important role in forcing the IOB event. The revealed thermocline dynamics are potentially useful in climate prediction associated with IOB events.

The mean temperature of the catch, an index designed to characterize the effect of climate change on global fisheries catch, increased at a rate of 0.19 degrees Celsius per decade between 1970 and 2006, showing that ocean warming has already affected global fisheries. Response of fish populations to warming In a warming climate, we would expect the rise of warm-water marine species at the expense of those adapted to cooler waters. That characteristic pattern has now been detected in a study of catch composition in 52 large marine ecosystems between 1970 and 2006, a sample that includes most of the world's major fisheries. The authors develop an index, the MTC (mean temperature of the catch), calculated from the average inferred temperature preference of exploited species weighted by their annual catch. Over these years, global temperature preference increased at a rate of about 0.2 °C every decade, and the effects were even more pronounced in non-tropical areas. Taken together, these findings highlight the need to develop adaptation plans to minimize the impacts of climate change on the economy and food security of coastal communities. Marine fishes and invertebrates respond to ocean warming through distribution shifts, generally to higher latitudes and deeper waters. Consequently, fisheries should be affected by ‘tropicalization’ of catch 1 , 2 , 3 , 4 (increasing dominance of warm-water species). However, a signature of such climate-change effects on global fisheries catch has so far not been detected. Here we report such an index, the mean temperature of the catch (MTC), that is calculated from the average inferred temperature preference of exploited species weighted by their annual catch. Our results show that, after accounting for the effects of fishing and large-scale oceanographic variability, global MTC increased at a rate of 0.19 degrees Celsius per decade between 1970 and 2006, and non-tropical MTC increased at a rate of 0.23 degrees Celsius per decade. In tropical areas, MTC increased initially because of the reduction in the proportion of subtropical species catches, but subsequently stabilized as scope for further tropicalization of communities became limited. Changes in MTC in 52 large marine ecosystems, covering the majority of the world’s coastal and shelf areas, are significantly and positively related to regional changes in sea surface temperature 5 . This study shows that ocean warming has already affected global fisheries in the past four decades, highlighting the immediate need to develop adaptation plans to minimize the effect of such warming on the economy and food security of coastal communities, particularly in tropical regions 6 , 7 .

This paper presents a software tool that enables the identification and automated tracking of oceanic eddies observed with satellite altimetry in user-specified regions throughout the global ocean. As input, the code requires sequential maps of sea level anomalies such as those provided by Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) data. Outputs take the form of (i) data files containing eddy properties, including position, radius, amplitude, and azimuthal (geostrophic) speed; and (ii) sequential image maps showing sea surface height maps with active eddy centers and tracks overlaid. The results given are from a demonstration in the Canary Basin region of the northeast Atlantic and are comparable with a published global eddy track database. Some discrepancies between the two datasets include eddy radius magnitude, and the distributions of eddy births and deaths. The discrepancies may be related to differences in the eddy identification methods, and also possibly to differences in the smoothing of the sea surface height maps. The code is written in Python and is made freely available under a GNU license ( http://www.imedea.uib.es/users/emason/py-eddy-tracker/ ).