Spatial and temporal distribution and seasonal prediction of satellite measurement of CO<sub>2</sub> concentration over Iran

Golkar, Foroogh; Shirvani, Amin

doi:10.1080/01431161.2020.1788743

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…However, even steeper trends have been measured such as an OCO-2 trend of 2.8 ppmv/year over India from July 2014 to December 2018 (Kunchala et al, 2022), and a GOSAT-derived trend of 2.6 ± 0.3 ppmv/year over the Arctic Ocean for July-September 2009-2016 (Payan et al, 2017). The trend reported here for the UAE is steeper than the 1.92 to 2.16 ppmv/year estimated over Iran for May 2009 -June 2016 using GOSAT data (Golkar and Shirvani, 2020), and the 2.1 ppmv/year for the same region using GOSAT and Environmental Satellite (ENVISAT) data for the period 2003-2015 (Safaeian et al, 2023). The fact that the period over which the trend is computed in this study (namely, 2015-2022) includes more recent years when the rise in the CO 2 concentration has become steeper (Tans and Keeling, 2023), and the biases in the satellite-derived estimates which, as noted by Kuttippurath et al (2022), can be substantial, may explain the higher trend magnitude compared to that reported elsewhere.…”

Section: Trends In Co 2 Concentrationcontrasting

confidence: 48%

“…A comparison with an anthropogenic CO 2 emission inventory indicated anthropogenic sources play an important role in the spatial distribution of the XCO 2 concentration, in particular in arid and semi-arid regions largely devoid of vegetation. The trend in the XCO 2 values over the UAE for 2015-2022 is estimated to be 2.50 ± 0.4 ppmv/year, slightly higher than that reported at other sites in the Middle East (e.g., Golkar and Shirvani, 2020) but slightly lower than that estimated over India in the recent years (Kunchala et al, 2022). This has been attributed to biases in the satellite-derived estimates (Kuttippurath et al, 2022) and to the fact that more recent years have been considered in the analysis, when the CO 2 concentration has increased at a faster rate.…”

Section: Discussionmentioning

confidence: 53%

“…In general, the XCO 2 concentration peaks in spring and reaches its annual minimum in the autumn. This is also the case in Iran using GOSAT data (Golkar and Shirvani, 2020), and over West Asia using OCO-2 data over 2015-2019 (Mustafa et al, 2021). CO 2 is a well-mixed gas and its annual variability is largely governed by the seasonal growth in land vegetation (Keeling et al, 1996).…”

Section: Co 2 Anthropogenic Emissionsmentioning

confidence: 75%

“…One of the goals of this work is to investigate the trends in XCO 2 data. This is typically achieved through linear regression (e.g., Golkar and Shirvani, 2020;Jain et al, 2021) but alternative methods are available. One of such methods is based on the principal component analysis, in which trend empirical orthogonal functions (TEOFs) and trend principal components (TPCs) are extracted.…”

Section: Trend Empirical Orthogonal Functions (Trend-eofs)mentioning

confidence: 99%

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Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

Fonseca,

Francis

2024

Front. Environ. Sci.

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The Middle East has major sources of anthropogenic carbon dioxide (CO2) emissions, but a dearth of ground-based measurements precludes an investigation of its regional and temporal variability. This is achieved in this work with satellite-derived estimates from the Orbiting Carbon Observatory-2 (OCO-2) and OCO-3 missions from September 2014 to February 2023. The annual maximum and minimum column (XCO2) concentrations are generally reached in spring and autumn, respectively, with a typical seasonal cycle amplitude of 3–8 ± 0.5 ppmv in the Arabian Peninsula rising to 8–10 ± 1 ppmv in the mid-latitudes. A comparison of the seasonal-mean XCO2 values with the CO2 emissions estimated using the divergence method stresses the role played by the sources and transport of CO2 in the spatial distribution of XCO2, with anthropogenic emissions prevailing in arid and semi-arid regions that lack persistent vegetation. In the 8-year period 2015–2022, the XCO2 concentration in the United Arab Emirates (UAE) increased at a rate of about 2.50 ± 0.04 ppmv/year, with the trend empirical orthogonal function technique revealing a hotspot over northeastern UAE and southern Iran in the summer where anthropogenic emissions peak and accumulate aided by low-level wind convergence. A comparison of the satellite-derived CO2 concentration with that used to drive climate change models for different emission scenarios in the 8-year period revealed that the concentrations used in the latter is overestimated, with maximum differences exceeding 10 ppmv by 2022. This excess in the amount of CO2 can lead to an over-prediction of the projected increase in temperature in the region, an aspect that needs to be investigated further. This work stresses the need for a ground-based observational network of greenhouse gas concentrations in the Middle East to better understand its spatial and temporal variability and for the evaluation of remote sensing observations as well as climate models.

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Section: Trends In Co 2 Concentrationcontrasting

confidence: 48%

Section: Discussionmentioning

confidence: 53%

Section: Co 2 Anthropogenic Emissionsmentioning

confidence: 75%

Section: Trend Empirical Orthogonal Functions (Trend-eofs)mentioning

confidence: 99%

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Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

Fonseca,

Francis

2024

Front. Environ. Sci.

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“…Carbon uptake through the photosynthesis process by a terrestrial ecosystem termed gross primary productivity (GPP) [1,2] is the way CO 2 enters into the biosphere from the atmosphere [3,4]. GPP not only drives the ecosystem functioning but also takes part in terrestrial carbon sequestration through phenological and physiological processes [5].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

et al. 2022

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The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.

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Variation patterns and driving factors of regional atmospheric CO2 anomalies in China

Sun

Luo

et al. 2021

Environ Sci Pollut Res

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Spatial and temporal distribution and seasonal prediction of satellite measurement of CO₂ concentration over Iran

Cited by 9 publications

References 20 publications

Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

Variation patterns and driving factors of regional atmospheric CO2 anomalies in China

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Spatial and temporal distribution and seasonal prediction of satellite measurement of CO2 concentration over Iran

Cited by 9 publications

References 20 publications

Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

Satellite derived trends and variability of CO2 concentrations in the Middle East during 2014–2023

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

Variation patterns and driving factors of regional atmospheric CO2 anomalies in China

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Spatial and temporal distribution and seasonal prediction of satellite measurement of CO₂ concentration over Iran