Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO2 Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman

Golkar, Foroogh; Al-Wardy, Malik; Saffari, Seyedeh Fatemeh; Al-Aufi, Kathiya; Al-Rawas, Ghazi

doi:10.3390/w12010101

Cited by 24 publications

(11 citation statements)

References 35 publications

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“…The occurrence of the summer monsoons is a major source of precipitation and thus an important factor controlling the vegetation in these regions. Generally, March to May is treated as pre-monsoon, June to September as monsoon and October to November as post-monsoon in most of the Asian regions [63]. The CO 2 concentration is found to be maximum in the pre-monsoon and minimum in monsoon (Figure 6c,d).…”

Section: Monthly Averaged Time-series Comparisonmentioning

confidence: 96%

“…The low wind speed during the pre-monsoon causes slight mixing in the boundary layer which also contributes in the CO2 concentration [66]. Moreover, a large Asia experiences a monsoon climate, which plays a significant role in the CO 2 concentrations [63]. Asian monsoon may be further classified into the Southwest Monsoon and the East Asian Monsoon [64].…”

Section: Monthly Averaged Time-series Comparisonmentioning

confidence: 99%

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Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia

Mustafa

Wang

et al. 2020

Remote Sensing

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Accurate knowledge of the carbon budget on global and regional scales is critically important to design mitigation strategies aimed at stabilizing the atmospheric carbon dioxide (CO2) emissions. For a better understanding of CO2 variation trends over Asia, in this study, the column-averaged CO2 dry air mole fraction (XCO2) derived from the National Oceanic and Atmospheric Administration (NOAA) CarbonTracker (CT) was compared with that of Greenhouse Gases Observing Satellite (GOSAT) from September 2009 to August 2019 and with Orbiting Carbon Observatory 2 (OCO-2) from September 2014 until August 2019. Moreover, monthly averaged time-series and seasonal climatology comparisons were also performed separately over the five regions of Asia; i.e., Central Asia, East Asia, South Asia, Southeast Asia, and Western Asia. The results show that XCO2 from GOSAT is higher than the XCO2 simulated by CT by an amount of 0.61 ppm, whereas, OCO-2 XCO2 is lower than CT by 0.31 ppm on average, over Asia. The mean spatial correlations of 0.93 and 0.89 and average Root Mean Square Deviations (RMSDs) of 2.61 and 2.16 ppm were found between the CT and GOSAT, and CT and OCO-2, respectively, implying the existence of a good agreement between the CT and the other two satellites datasets. The spatial distribution of the datasets shows that the larger uncertainties exist over the southwest part of China. Over Asia, NOAA CT shows a good agreement with GOSAT and OCO-2 in terms of spatial distribution, monthly averaged time series, and seasonal climatology with small biases. These results suggest that CO2 can be used from either of the datasets to understand its role in the carbon budget, climate change, and air quality at regional to global scales.

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Section: Monthly Averaged Time-series Comparisonmentioning

confidence: 96%

Section: Monthly Averaged Time-series Comparisonmentioning

confidence: 99%

Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia

Mustafa

Wang

et al. 2020

Remote Sensing

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“…To minimize the differences between the spatial and temporal resolutions of the different orbital sensors used in this study, the process described in Figure 5 was employed, which establishes a standard in the acquisition of data from the coordinates obtained in the OCO-2 platform. We emphasize that several studies have been conducted using different time and spatial scales [6,7,27].…”

Section: Study Regionmentioning

confidence: 99%

“…Several studies con rm that photosynthesis is the main regulator of atmospheric carbon sinks [10,24,25,26]. However, photosynthesis is a process sensitive to climatic variations such as relative humidity [27], precipitation [28], evapotranspiration [29], and incident solar irradiance [30].…”

Section: Introductionmentioning

confidence: 99%

Daily Model of Xco2 Based on Orbital Observations for the State of São Paulo – Brazil

Costa

Santos

Panosso

et al. 2022

Preprint

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Background: Understanding the behavior of carbon dioxide (CO2) it is crucial to create strategies to deal with climate change. Several space missions were designed to monitor this and other greenhouse gases and studies at global and large regional scales were developed using these remote sensing tools. However, there is still a time gap in orbital data, since the revisit time of a satellite at the same location for this type of observation is not daily. Daily measurements of CO2 can be made using Eddy Covariance technique, although this type of study is at a very local scale. In this study, we aimed to build a daily model to estimate the natural CO2 in the atmosphere.Results: The data was retrieved from the Orbiting Carbon Observatory-2 (OCO-2), NASA-POWER and, Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) and consist in a time-series from 2015 to 2019. To summarize the most relevant factor we used the Variance Inflation Factor (VIF) after we performed the Pearson’s Correlation and apply descriptive statistics in the significant variables (p < 0.05). The model was construed using the stepwise regression method and the selected model was defined by the lowest RMSE in training (~0.6 ppm). The most related variables with Xco2 were Global Radiation (Qg), Sun Induced Chlorophyll Fluorescence (SIF) and, Relative Humidity (RH), all these factors were negatively correlated with the CO2 concentration. The model has the best perform with the Qg and RH (RMSE = 0.47 ppm, R2 = 0.44, p < 0.01).Conclusion: In summary, the cycle of atmospheric CO2 in the state of São Paulo has higher average values during April to October, and the lowest averages of Xco2 were usually observed between December to March and the inverse behavior was observed for SIF 757, Global Radiation (Qg) and Relative Humidity (RH). Concerning the daily model, despite the differences between the spatial observations, the model derived here was capable to estimate the cycle of atmospheric CO2 using only meteorological data.

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“…51 On the other hand, a study in India and Oman showed that CO 2 concentration decreased during rainy seasons as it also allowed vegetation growth that took up CO 2. 52,53 This may be the reason for the negative correlation of CO 2 , malaria incidence, and NTD in the Philippines as malaria, dengue fever, and leptospirosis are common diseases during the rainy season. 29 In addition, schistosomiasis was also found to be influenced by rainfalls and flooding by which the latter can "facilitate human-parasite contact".…”

Section: Correlation Of Global Co 2 Levels With Health Factorsmentioning

confidence: 99%

Correlation of Climate Change Indicators with Health and Environmental Data in the Philippines

2022

Acta Med Philipp

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Introduction. The Lancet Countdown used Global Burden of Disease (GBD) data to track mortality from diseases influenced by climate change. The Philippines is one of the most vulnerable nations to climate change. Objective. This study aimed to provide summative data on climate change and health-environmental factors based on several large databases. It looked into the correlation of climate change to selected health variables and correlated environmental factors to health chosen variables in the Philippines. Methods. The database was assembled through a compilation of different secondary data. Climate change variables were acquired from the Global Burden of Disease (GBD 2017) Study on Health-related Sustainable Development Goals Indicators from 1990 to 2030. The data for the Philippines were obtained. These indicators include air pollution mortality, disaster mortality, household air pollution, malaria incidence, mean PM2.5, non-communicable disease mortality, neglected tropical diseases mortality, unimproved sanitation, and unsafe water. The resulting database was analyzed using exploratory data analysis techniques with descriptive statistics and line graphs to analyze trends over the years. Then Pearson correlation analysis was done to explore the linear relationship between health indicators, climate indicators, and environmental indicators. Results. The study results showed that the trend in the Philippines for air pollution mortality, household air pollution, malaria incidence, and neglected tropical diseases mortality is in a downward direction. However, non-communicable disease mortality was constantly increasing from 41.99 in 1990 to 55.00 in 2016. Meanwhile, the mean temperature is significantly negatively correlated to household air pollution, malaria incidence, and neglected tropical diseases and significantly correlated with non-communicable diseases. Also, NOAA adjusted sea level is significantly positively correlated with air pollution mortality, malaria incidence, disaster mortality, and non-communicable diseases. It is negatively correlated with malaria incidence and neglected tropical diseases prevalence. Global mean CO2 is significantly negatively correlated with household air pollution, malaria incidence, and neglected tropical diseases prevalence. On the other hand, it was significantly and positively correlated with air pollution mortality and non-communicable diseases mortality. Household air pollution health risk was significantly positively correlated to mean PM2.5 levels in the Philippines. Unimproved sanitation was positively correlated with household air pollution, malaria incidence, and neglected tropical disease prevalence. Conclusion. As recordings of heat index increased, there was a correlation with NCD, Malaria, Disaster, and NTD infection mortality. With the evidence of the correlation of increasing temperature and pollution to health, the urgency to focus on addressing these problems was present in this study. Further research may help in policymaking to target drivers of pollution which affect extreme climate changes.

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Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO2 Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman

Cited by 24 publications

References 35 publications

Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia

Multi-Year Comparison of CO2 Concentration from NOAA Carbon Tracker Reanalysis Model with Data from GOSAT and OCO-2 over Asia

Daily Model of Xco2 Based on Orbital Observations for the State of São Paulo – Brazil

Correlation of Climate Change Indicators with Health and Environmental Data in the Philippines

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