DESC:DESCRIPTION:
Field of the invention:
[0001] The present disclosure generally relates to the technical field of marine chemistry, in specific, relates to a system and method for predicting carbon dioxide (CO2) air-sea fluxes by integrating physical and biogeochemical processes to accurately determine CO2 exchange in coastal regions.
Background of the invention:
[0002] Carbon dioxide (CO2) fluxes at the air-sea interface play a critical role in regulating the Earth’s climate system. Coastal regions are particularly important in this context due to their dynamic nature, where physical and biogeochemical processes interact to influence CO2 exchange between the ocean and the atmosphere. Despite their importance, a comprehensive understanding of the drivers and mechanisms controlling these fluxes remains a challenge for scientists, especially in complex coastal zones.

[0003] Physical processes, such as sea surface temperature, salinity, wind patterns, ocean currents, and precipitation, are known to impact the distribution and behavior of CO2 in oceanic waters. Simultaneously, biogeochemical processes, such as primary production, respiration, nutrient cycling, and the carbon cycle, significantly affect the absorption and release of the CO2. The interplay between these two sets of processes is highly complex, leading to variability in CO2 fluxes over time and across different spatial scales.

[0004] In the coastal regions, the interactions between these physical and biogeochemical processes are further complicated by seasonal monsoons, riverine inputs, and high biological productivity. These factors create a unique environment where CO2 fluxes can fluctuate significantly, contributing to both carbon sequestration and release. However, the lack of integrated data and models that combine physical and biogeochemical factors has resulted in significant knowledge gaps in understanding these dynamics.

[0005] Current research efforts have often focused on either physical or biogeochemical processes in isolation, leading to fragmented insights into the overall system. Additionally, the scarcity of high-resolution datasets and the complexity of oceanographic phenomena in coastal regions like the Coastal regions have limited the ability to predict CO2 fluxes accurately. These limitations hinder the development of effective strategies for managing the environmental and climatic impacts of CO2 in coastal zones.

[0006] Given the importance of CO2 fluxes for global climate regulation and the challenges posed by the unique environment of the coastal regions, there is a need for an integrated approach to analyze and predict these fluxes. This approach should combine physical and biogeochemical data, utilize advanced modeling techniques, and provide accurate insights into the factors influencing CO2 dynamics at the air-sea interface.

[0007] By addressing all the above-mentioned problems, there is a need for a system and method for predicting carbon dioxide (CO2) air-sea fluxes by integrating physical and biogeochemical processes to accurately determine CO2 exchange in coastal regions. There is also a need for a system that collects physical data, such as sea surface temperature, salinity, wind speed, ocean currents, and biogeochemical data, such as dissolved inorganic carbon, pH, and nutrient levels, using satellite remote sensing and in-situ sensors. There is also a need for a system that integrates physical processes, such as mixing and advection, with biogeochemical cycles like carbon uptake by marine organisms, to simulate CO2 fluxes over time. There is also a need for a system that incorporates parameterization schemes for processes like gas exchange, primary production, and respiration, thereby ensuring that the model accurately reflects the physical and biological interactions in the environment.

[0008] Additionally, there is also a need for a system that provides predictive simulations of CO2 fluxes over different spatial and temporal scales, thereby allowing for detailed analysis of CO2 exchange patterns in coastal regions. There is also a need for a system that compares model outputs with independent datasets and observational data, thereby ensuring the reliability and accuracy of the predictions. There is also a need for a system that identifies key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions. Further, there is also a need for a system that presents the spatial and temporal patterns of CO2 fluxes and generates comprehensive reports on the methodology, results, and implications for marine science and environmental policy.
Objectives of the invention:
[0009] The primary objective of the present invention is to provide a system and method for predicting carbon dioxide (CO2) air-sea fluxes by integrating physical and biogeochemical processes to accurately determine CO2 exchange in coastal regions.

[0010] Another objective of the present invention is to provide a system that collects physical data, such as sea surface temperature, salinity, wind speed, ocean currents, and biogeochemical data, such as dissolved inorganic carbon, pH, and nutrient levels, using satellite remote sensing and in-situ sensors.

[0011] Another objective of the present invention is to provide a system that integrates physical processes, such as mixing and advection, with biogeochemical cycles like carbon uptake by marine organisms, to simulate CO2 fluxes over time.

[0012] Another objective of the present invention is to provide a system that incorporates parameterization schemes for processes like gas exchange, primary production, and respiration, thereby ensuring that the model accurately reflects the physical and biological interactions in the environment.

[0013] Another objective of the present invention is to provide a system that provides predictive simulations of CO2 fluxes over different spatial and temporal scales, thereby allowing for detailed analysis of CO2 exchange patterns in coastal regions.

[0014] Another objective of the present invention is to provide a system that compares model outputs with independent datasets and observational data, thereby ensuring the reliability and accuracy of the predictions.

[0015] Another objective of the present invention is to provide a system that identifies key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions.

[0016] Another objective of the present invention is to provide a system that assesses the influence of various parameters and processes on CO2 flux predictions, thereby enabling researchers to fine-tune the model for improved accuracy.

[0017] Yet another objective of the present invention is to provide a system that combines physical and biogeochemical datasets using advanced data assimilation techniques to ensure consistency and improve prediction accuracy.

[0018] Further objective of the present invention is to provide a system that presents the spatial and temporal patterns of CO2 fluxes and generates comprehensive reports on the methodology, results, and implications for marine science and environmental policy.
Summary of the invention:
[0019] The present disclosure proposes a system and method for predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes. The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview. It is not intended to identify key/critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

[0020] In order to overcome the above deficiencies of the prior art, the present disclosure is to solve the technical problem to provide a system and method for predicting carbon dioxide (CO2) air-sea fluxes by integrating physical and biogeochemical processes to accurately determine CO2 exchange in coastal regions.

[0021] According to one aspect, the invention provides a system and method for predicting carbon dioxide (CO2) air-sea fluxes. In one embodiment herein, the system comprises a computing device having a processor and a memory for storing one or more instructions executable by the processor. The computing device is in communication with a server via a network. The processor is configured to execute plurality of modules.

[0022] In one embodiment herein, the plurality of modules comprises an input module 110, a data assimilation module, an interaction module, a parameterization module, a simulation module, a validation module, an analysis module, and a reporting module.

[0023] In one embodiment herein, the input module is configured to receive input parameters, which include physical data and biogeochemical data. In one embodiment herein, the physical data comprises sea surface temperature, salinity, wind speed, ocean currents, and precipitation. In one embodiment herein, the biogeochemical data comprises dissolved inorganic carbon, pH, alkalinity, nutrient concentrations, and chlorophyll-a levels.

[0024] In one embodiment herein, the data assimilation module is configured to integrate the physical data and the biogeochemical data using advanced data assimilation techniques to ensure consistency and accuracy. The data assimilation module employs advanced data assimilation techniques to ensure the accuracy of integrated datasets.

[0025] In one embodiment herein, the interaction module is configured to simulate the interactions between physical processes and biogeochemical cycles to predict CO2 fluxes based on the physical data and biogeochemical data. The interaction module simulates interactions such as mixing, advection, and carbon uptake by phytoplankton.

[0026] In one embodiment herein, the parameterization module is configured to incorporate parameterization schemes for gas exchange, primary production, and respiration, thereby reflecting accurate physical and biological interactions in the environment. The parameterization module includes parameterization schemes for gas exchange processes and primary production dynamics.

[0027] In one embodiment herein, the simulation module is configured to run predictive simulations of CO2 fluxes over different temporal and spatial scales, thereby enabling effective analysis of CO2 exchange patterns in coastal regions. The simulation module incorporates multi-scale modeling techniques to analyze local and regional CO2 exchange patterns.

[0028] In one embodiment herein, the validation module is configured to validate model outputs using independent datasets and observational data, thereby ensuring reliability and accuracy of the predictions.

[0029] In one embodiment herein, the analysis module is configured to identify key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions. The analysis module utilizes statistical methods and machine learning techniques to identify significant patterns in CO2 flux variability.

[0030] In one embodiment herein, the reporting module is configured for determining the spatial and temporal patterns of CO2 fluxes to generate comprehensive reports on the methodology, results, and implications of the predicted CO2 fluxes. The reporting module comprises one or more visualization tools for a graphical representation of CO2 fluxes, thereby facilitating better understanding for stakeholders.

[0031] According to another aspect, the invention provides a method for predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes using the system. At one step, the input module receives one or more input parameters that include physical data and biogeochemical data. At another step, the data assimilation module integrates the physical data and the biogeochemical data using advanced data assimilation techniques, thereby obtaining integrated data to ensure consistency and accuracy.

[0032] At another step, the interaction module simulates interactions between physical processes and biogeochemical cycles to predict CO2 fluxes based on the integrated data. At another step, the parameterization module incorporates parameterization schemes for gas exchange, primary production, and respiration to reflect accurate physical and biological interactions.

[0033] At another step, the simulation module executes predictive simulations of CO2 fluxes over different temporal and spatial scales. At another step, the validation module validates the model outputs using independent datasets and observational data to ensure the reliability and accuracy of the predictions. At another step, the analysis module identifies key drivers of CO2 flux variability to determine the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions. Further, at other step, the reporting module determines the spatial and temporal patterns of CO2 fluxes to generate comprehensive reports on the methodology, results, and implications of the predicted CO2 fluxes.

[0034] Further, objects and advantages of the present invention will be apparent from a study of the following portion of the specification, the claims, and the attached drawings.
Detailed description of drawings:
[0035] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate an embodiment of the invention, and, together with the description, explain the principles of the invention.

[0036] FIG. 1 illustrates a block diagram representing a system predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes, in accordance to an exemplary embodiment of the invention.

[0037] FIGs. 2A-2B illustrate heat maps depicting the variability in concentration of temperature and salinity during the predicting period of carbon dioxide air-sea fluxes in at least one coastal region, in accordance to an exemplary embodiment of the invention.

[0038] FIGs. 3A-3C illustrate heat maps depicting the variability in concentration of nitrate, phosphate, and silicate during the predicting period of carbon dioxide air-sea fluxes in at least one coastal region, in accordance to an exemplary embodiment of the invention.

[0039] FIGs. 4A-4D illustrate heat maps depicting the variability in the concentration of pH, total alkalinity (TA), dissolved inorganic carbon (DIC), and partial pressure of carbon dioxide (pCO2) during the predicting period of carbon dioxide air-sea fluxes in at least one coastal region, in accordance to an exemplary embodiment of the invention.

[0040] FIGs. 5A-5B illustrate scatter plots depicting the relation between pCO2, total alkalinity (TA) with sea surface salinity (SSS) during the predicted period of carbon dioxide air-sea fluxes, in accordance to an exemplary embodiment of the invention.

[0041] FIG. 6 illustrates a flow chart of a method for predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes using the system, in accordance to an exemplary embodiment of the invention.
Detailed invention disclosure:
[0042] Various embodiments of the present invention will be described in reference to the accompanying drawings. Wherever possible, same or similar reference numerals are used in the drawings and the description to refer to the same or like parts or steps.

[0043] The present disclosure has been made with a view towards solving the problem with the prior art described above, and it is an object of the present invention to provide a system and method for predicting carbon dioxide (CO2) air-sea fluxes by integrating physical and biogeochemical processes to accurately determine CO2 exchange in coastal regions.

[0044] According to one exemplary embodiment of the invention, FIG. 1 refers to a block diagram representing a system 100 predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes. The system 100 integrates physical processes, such as mixing and advection, with biogeochemical cycles like carbon uptake by marine organisms, to simulate CO2 fluxes over time. The system 100 incorporates parameterization schemes for processes like gas exchange, primary production, and respiration, thereby ensuring that the model accurately reflects the physical and biological interactions in the environment.

[0045] The system 100 provides predictive simulations of CO2 fluxes over different spatial and temporal scales, thereby allowing for detailed analysis of CO2 exchange patterns in coastal regions. The system 100 compares model outputs with independent datasets and observational data, thereby ensuring the reliability and accuracy of the predictions. The system 100 identifies key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions.

[0046] The system 100 can be accessed through multiple methods, one of which involves integration with a user device using various approaches, such as REST APIs. This integration method ensures seamless operation and generally employs HTTPS for secure communication. The user device is connected to a server 126, which includes a processor 104 and memory 106 for storing and executing instructions. The server 126 communicates with client devices via a network 124, thereby enabling access to the system 100 through web-based interfaces using secured credentials. Data is collected from various sources and then stored in a database 128.

[0047] In one embodiment, the term user device generally refers to any device configured to interact with the web-based system. This includes personal computers, cellular telephones, smartphones, personal data assistants (PDAs), laptop computers, tablet computers, smart books, palm-top computers, wireless email receivers, multimedia internet-enabled cellular telephones, and similar personal electronic devices. These devices can connect to the network using wireless technologies such as Wi-Fi. In one embodiment, the network 124 could include, but is not limited to, Wi-Fi, Bluetooth, a wireless local area network (WLAN), an internet connection, and radio communication. The user device can be touchscreen or non-touchscreen and may operate on various operating systems, such as iOS, Windows, Android, Unix, Linux, and others.

[0048] In one embodiment, the system 100 is hosted on the server 126, which could be a computer or server designed for general or specialized purposes. This server could operate independently or as part of a broader configuration, which could include hardware servers, workstations, desktop PCs, laptops, tablets, mobile phones, mainframes, supercomputers, or server farms. Importantly, the server 126 is interconnected with the processor 104 and the memory 106, which is connected to the database 128 for efficient data management.

[0049] In one embodiment herein, the system 100 comprises a computing device 102 having the processor 104 and the memory 106 for storing one or more instructions executable by the processor 104. The computing device 102 is in communication with the server 126 via the network 124. The processor 104 is configured to execute plurality of modules 108.

[0050] In one embodiment herein, the plurality of modules 108 comprises an input module 110, a data assimilation module 112, an interaction module 113, a parameterization module 114, a simulation module 116, a validation module 118, an analysis module 120, and a reporting module 122.

[0051] In one embodiment herein, the input module 110 is configured to receive input parameters, which include physical data and biogeochemical data. In one embodiment herein, the physical data comprises sea surface temperature, salinity, wind speed, ocean currents, and precipitation. In one embodiment herein, the biogeochemical data comprises dissolved inorganic carbon, pH, alkalinity, nutrient concentrations, and chlorophyll-a levels.

[0052] In one embodiment herein, the data assimilation module 112 is configured to integrate the physical data and the biogeochemical data using advanced data assimilation techniques to ensure consistency and accuracy. The data assimilation module 112 employs advanced data assimilation techniques to ensure the accuracy of integrated datasets.

[0053] In one embodiment herein, the interaction module 113 is configured to simulate the interactions between physical processes and biogeochemical cycles to predict CO2 fluxes based on the physical data and biogeochemical data. The interaction module 113 simulates interactions such as mixing, advection, and carbon uptake by phytoplankton.

[0054] In one embodiment herein, the parameterization module 114 is configured to incorporate parameterization schemes for gas exchange, primary production, and respiration, thereby reflecting accurate physical and biological interactions in the environment. The parameterization module 114 includes parameterization schemes for gas exchange processes and primary production dynamics.

[0055] In one embodiment herein, the simulation module 116 is configured to run predictive simulations of CO2 fluxes over different temporal and spatial scales, thereby enabling effective analysis of CO2 exchange patterns in coastal regions. The simulation module 116 incorporates multi-scale modeling techniques to analyze local and regional CO2 exchange patterns. In one embodiment herein, the validation module 118 is configured to validate model outputs using independent datasets and observational data, thereby ensuring reliability and accuracy of the predictions.

[0056] In one embodiment herein, the analysis module 120 is configured to identify key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions. The analysis module 120 utilizes statistical methods and machine learning techniques to identify significant patterns in CO2 flux variability.

[0057] In one embodiment herein, the reporting module 122 is configured for determining the spatial and temporal patterns of CO2 fluxes to generate comprehensive reports on the methodology, results, and implications of the predicted CO2 fluxes. The reporting module 122 comprises one or more visualization tools for a graphical representation of CO2 fluxes, thereby facilitating better understanding for stakeholders.

[0058] According to another exemplary embodiment of the invention, FIGs. 2A-2B refer to heat maps (200, 202) depicting the variability in concentration of temperature and salinity during the predicting period of carbon dioxide air-sea fluxes in at least one coastal region. In one embodiment herein, the heat maps (200, 202) depict the variability in temperature and salinity concentrations in the at least one coastal region (for example: Coastal Bay of Bengal) during the predicting period. The surface water temperature fluctuated significantly, ranging from 18.20°C to 33.60°C, with the lowest values being recorded during the year 2019, particularly 30.35±1.92°C, compared to 30.94±0.76°C in 2018.

[0059] The notable difference in temperature trends of both years is also captured, showing cooler temperatures in 2019, in contrast to 2018. The heat maps (200, 202) also demonstrate that higher temperatures are coupled with lower salinity, which suggests significant freshwater input from rainwater and land sources during this period. Salinity varied between 5.32 and 35.90 psu, with mesohaline waters being observed towards the end of the year, likely due to monsoon rains. This variability highlights the impact of both seasonal and inter-annual changes in environmental conditions. In one embodiment herein, the concentration ranges of physico-chemical parameters and their mean (SD) during the predicting periods of 2018 and 2019 years are shown in Table 1.

[0060] Table 1:
Parameter 2018 2019
Min Max Mean±SD Min Max Mean±SD
Temperature (oC) 27.40 32.00 30.94±0.76 18.2 31.6 30.35±1.92
Salinity (psu) 5.90 35.90 28.05±5.57 5.32 34.91 26.2±6.26
DIN (µmol/L) 0.01 11.87 2.92±2.97 0.19 14.39 3.17±2.92
DIP(µmol/L) 0.12 6.07 3.91±1.56 0.27 6.7506 2.73±0.85
DSi(µmol/L) 0.17 127.21 8.40±23.51 2 134.71 36.9±41.66
N:P 0.001 8.86 0.94±1.09 0.07 11.11 1.3±1.37
Si:N 0.06 338.5 8.01±27.3 0.001 645.1 26.9±65.3
pH 7.39 8.77 8.15±0.15 7.151 8.59 8.13±0.20
Alkalinity (µmol/L) 399.0 2984.3 2233.1±488.8 281.6 2995 2201.7±440.10
DIC (µmol/L) 403.0 2712.8 1933.6±428.5 1234 2673 1964.7±315.01
pCO2 (µatm) 32.50 1613.1 377.4±210.1 43.05 2330.5 382.8±306.29

[0061] According to another exemplary embodiment of the invention, FIGs. 3A-3C refer to heat maps (300, 302, 304) depicting the variability in the concentration of nitrate (N), phosphate (P), and silicate (Si) during the predicting period of carbon dioxide air-sea fluxes in at least one coastal region. In one embodiment herein, the heat maps (300, 302, 304) provide a visual representation of the intra and inter-annual variability of nutrient concentrations in the at least one coastal region during the predicting period. The heat maps (300, 302, 304) depict how the concentrations of nitrate, phosphate, and silicate fluctuated between the years 2018 and 2019.

[0062] During dry periods, the elevated levels of nitrate and phosphate are likely due to nutrient inputs from groundwater seepage and sediment-water exchanges. FIGs. 3A-3C also capture the contrasting seasonal trends, where silicate concentrations are generally lower in 2018. Interestingly, the periods of higher nitrate and phosphate concentrations align with lower silicate levels, suggesting a complex interaction between nutrient inputs and water mixing processes. The variations in the N and Si ratios also indicate significant imbalances, with the N ratio being notably lower than the Redfield ratio, thereby highlighting nitrogen depletion in relation to phosphate. The N:P and N:Si ratios ranged from 0.001 to 8.86 and 0.002 to 16.98 with mean values of (as shown in Table 1) 1.06±1.11 and 0.94±1.70 respectively. The mean value of N:P ratio during the entire predicting period was 1.06±1.11 which is much lower than that of the Redfield ratio (N:P =16) which suggests that the process of faster removal of nitrogen from the coastal region or addition of phosphate through the land or domestic sewage.

[0063] According to another exemplary embodiment of the invention, FIGs. 4A-4D refer to heat maps (400, 402, 404, 406) depicting the variability in the concentration of pH, total alkalinity (TA), dissolved inorganic carbon (DIC), and partial pressure of carbon dioxide (pCO2) during the predicting period of carbon dioxide air-sea fluxes in at least one coastal region. In one embodiment herein, the heat maps (400, 402, 404, 406) depict the variability in pH, total alkalinity (TA), dissolved inorganic carbon (DIC), and the partial pressure of carbon dioxide (pCO2) during the predicting period. The heat maps (400, 402, 404, 406) illustrate significant seasonal changes, with higher DIC and TA values being associated with lower pH during the dry season, thereby indicating a strong connection between carbon components and salinity gradients. The concentrations of pH, dissolved inorganic carbon (DIC) and total alkalinity (TA), were varied from 7.15 to 8.77 (8.15±0.17), 403.0 to 2712.8 (1947.29±382.61) µmol L-1, 281.6 to 2995.0 (2219.27±467.69) µmol L-1, during the entire predicting period. The pCO2 showed not much variation from 2018 year to 2019 year. It varied between 32.50 to 2330.52 (379.78±256.58) with higher values noticed in 2019 (43.05 to 2330 µatm) with mean value of 382.8±306.3 when compared to in 2018 2019 (32.5 to 1613.1 µatm) with a mean value of 377.4±210.1.

[0064] The relationship between pCO2 and TA with sea surface salinity is clearly visible in these heat maps (400, 402, 404, 406), exhibiting that lower salinity periods, coinciding with increased freshwater inputs, result in higher carbon levels in the water column. Additionally, the figure highlights contrasting patterns in pCO2 between 2018 and 2019, with generally higher values recorded in 2019. Lower pCO2 levels are observed in both years, thereby pointing to a likely reduction in carbon levels during the rainy season. The significant fluctuations in inorganic carbon components emphasize the role of seasonal and environmental factors in modulating carbon cycling in the at least one coastal region.

[0065] According to another exemplary embodiment of the invention, FIGs. 5A-5B refer to scatter plots (500, 502) depicting the relation between pCO2, total alkalinity (TA) and sea surface salinity (SSS) during the predicted period of carbon dioxide air-sea fluxes. In one embodiment herein, the scatter plots (500, 502) depict the relationship between partial pressure of carbon dioxide (pCO2) and total alkalinity (TA) with sea surface salinity (SSS) during the predicting period. The scatter plots (500, 502) capture a strong positive correlation between pCO2 and TA with salinity, thereby suggesting that higher salinity values are associated with greater concentrations of inorganic carbon. This correlation indicates that higher land runoff and subsequent addition of freshwater through rainfall led to a dilution of salinity, which in turn influenced the levels of pCO2 and TA.

[0066] The scatter plots (500, 502) also show the negative correlation between sea surface temperature (SST) and pCO2, demonstrating that cooler water temperatures, particularly during the rainy season, are linked to elevated pCO2 levels. This relationship suggests the occurrence of upwelling events, where cooler, carbon-rich waters are brought to the surface, further enhancing the carbon dioxide content in the water. The scatter plots (500, 502) help to elucidate the interplay between salinity, temperature, and carbon components in this coastal region.

[0067] According to another exemplary embodiment of the invention, FIG. 6 refers to a flow chart 600 of a method for predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes using the system 100. At step 602, the input module 110 receives one or more input parameters that include physical data and biogeochemical data. At step 604, the data assimilation module 112 integrates the physical data and the biogeochemical data using advanced data assimilation techniques, thereby obtaining integrated data to ensure consistency and accuracy.

[0068] At step 606, the interaction module 113 simulates interactions between physical processes and biogeochemical cycles to predict CO2 fluxes based on the integrated data. At step 608, the parameterization module 114 incorporates parameterization schemes for gas exchange, primary production, and respiration to reflect accurate physical and biological interactions.

[0069] At step 610, the simulation module 116 executes predictive simulations of CO2 fluxes over different temporal and spatial scales. At step 612, the validation module 118 validates the model outputs using independent datasets and observational data to ensure the reliability and accuracy of the predictions. At step 614, the analysis module 120 identifies key drivers of CO2 flux variability to determine the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions. Further, at step 616, the reporting module 122 determines the spatial and temporal patterns of CO2 fluxes to generate comprehensive reports on the methodology, results, and implications of the predicted CO2 fluxes.

[0070] Numerous advantages of the present disclosure may be apparent from the discussion above. In accordance with the present disclosure, a system and method for predicting carbon dioxide (CO2) air-sea fluxes is disclosed. The proposed invention provides the system 100 and method for predicting carbon dioxide (CO2) air-sea fluxes by integrating physical and biogeochemical processes to accurately determine CO2 exchange in coastal regions.

[0071] The proposed system 100 collects physical data, such as sea surface temperature, salinity, wind speed, ocean currents, and biogeochemical data, such as dissolved inorganic carbon, pH, and nutrient levels, using satellite remote sensing and in-situ sensors. The system 100 integrates physical processes, such as mixing and advection, with biogeochemical cycles like carbon uptake by marine organisms, to simulate CO2 fluxes over time. The system 100 incorporates parameterization schemes for processes like gas exchange, primary production, and respiration, thereby ensuring that the model accurately reflects the physical and biological interactions in the environment.

[0072] The system 100 provides predictive simulations of CO2 fluxes over different spatial and temporal scales, thereby allowing for detailed analysis of CO2 exchange patterns in coastal regions. The system 100 compares model outputs with independent datasets and observational data, thereby ensuring the reliability and accuracy of the predictions. The system 100 identifies key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions.

[0073] The system 100 assesses the influence of various parameters and processes on CO2 flux predictions, thereby enabling researchers to fine-tune the model for improved accuracy. The system 100 combines physical and biogeochemical datasets using advanced data assimilation techniques to ensure consistency and improve prediction accuracy. The system 100 presents the spatial and temporal patterns of CO2 fluxes and generates comprehensive reports on the methodology, results, and implications for marine science and environmental policy.

[0074] It will readily be apparent that numerous modifications and alterations can be made to the processes described in the foregoing examples without departing from the principles underlying the invention, and all such modifications and alterations are intended to be embraced by this application.
,CLAIMS:CLAIMS:
I/We Claim:
1. A system (100) for predicting carbon dioxide (CO2) air-sea fluxes based on physical and biogeochemical processes, comprising:
a computing device (102) having a processor (104) and a memory (106) for storing one or more instructions executable by the processor (104), wherein the computing device (102) is in communication with a server (126) via a network (124),
wherein the processor (104) is configured to execute plurality of modules (108), wherein the plurality of modules (108) comprises:
an input module (110) configured to receive input parameters, which include physical data and biogeochemical data;
a data assimilation module (112) configured to integrate the physical data and the biogeochemical data using advanced data assimilation techniques to ensure consistency and accuracy;
an interaction module (113) configured to simulate interactions between physical processes and biogeochemical cycles to predict CO2 fluxes based on the physical data and biogeochemical data;
a parameterization module (114) configured to incorporate parameterization schemes for gas exchange, primary production, and respiration, thereby reflecting accurate physical and biological interactions in the environment;
a simulation module (116) configured to run predictive simulations of CO2 fluxes over different temporal and spatial scales, thereby enabling effective analysis of CO2 exchange patterns in coastal regions;
a validation module (118) configured to validate model outputs using independent datasets and observational data, thereby ensuring reliability and accuracy of the predictions;
an analysis module (120) configured to identify key drivers of CO2 flux variability, thereby assisting in determining the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions; and
a reporting module (122) configured for presenting the spatial and temporal patterns of CO2 fluxes to generate comprehensive reports on the methodology, results, and implications of the predicted CO2 fluxes.
2. The system (100) as claimed in claim 1, wherein the physical data comprises sea surface temperature, salinity, wind speed, ocean currents, and precipitation.
3. The system (100) as claimed in claim 1, wherein the biogeochemical data comprises dissolved inorganic carbon, pH, alkalinity, nutrient concentrations, and chlorophyll-a levels.
4. The system (100) as claimed in claim 1, wherein the data assimilation module (112) employs advanced data assimilation techniques to ensure the accuracy of integrated datasets.
5. The system (100) as claimed in claim 1, wherein the interaction module (113) simulates interactions such as mixing, advection, and carbon uptake by phytoplankton.
6. The system (100) as claimed in claim 1, wherein the parameterization module (114) includes parameterization schemes for gas exchange processes and primary production dynamics.
7. The system (100) as claimed in claim 1, wherein the simulation module (116) incorporates multi-scale modeling techniques to analyze local and regional CO2 exchange patterns.
8. The system (100) as claimed in claim 1, wherein the analysis module (120) utilizes statistical methods and machine learning techniques to identify significant patterns in CO2 flux variability.
9. The system (100) as claimed in claim 1, wherein the reporting module (122) comprises one or more visualization tools for a graphical representation of CO2 fluxes, thereby facilitating better understanding for stakeholders.
10. A method for predicting for predicting carbon dioxide air-sea fluxes based on physical and biogeochemical processes using a system (100), comprising:
receiving, by an input module (110), one or more input parameters that include physical data and biogeochemical data;
integrating, by a data assimilation module (112), the physical data and the biogeochemical data using advanced data assimilation techniques, thereby obtaining integrated data to ensure consistency and accuracy;
simulating, by an interaction module (113), interactions between physical processes and biogeochemical cycles to predict CO2 fluxes based on the integrated data;
incorporating, by a parameterization module (114), parameterization schemes for gas exchange, primary production, and respiration to reflect accurate physical and biological interactions;
executing, by a simulation module (116), predictive simulations of CO2 fluxes over different temporal and spatial scales;
validating, by a validation module (118), the model outputs using independent datasets and observational data to ensure reliability and accuracy of the predictions;
identifying, by an analysis module (120), key drivers of CO2 flux variability to determine the primary physical and biogeochemical factors influencing CO2 exchange in coastal regions; and
determining, by a reporting module (122), the spatial and temporal patterns of CO2 fluxes to generate comprehensive reports on the methodology, results, and implications of the predicted CO2 fluxes.