Uncertainties, risks and challenges relating to CO&lt;SUB align="right"&gt;2 emissions and its possible impact on climate change in the United Arab Emirates

Arman, Hasan

doi:10.1504/ijgw.2015.071575

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…The zonally-averaged XCO 2 over India increased at a rate of around 2.8 ppmv/year from July 2014 to December 2018, even though the 2015-16 El Nino that increased XCO 2 concentrations through hotter and drier conditions modulated the 5-year trend. While there are several studies on the CO 2 's spatial and temporal variability in the midlatitudes (e.g., Helfter et al, 2016;Cheng et al, 2022) and even in the polar regions (e.g., Cristofanelli et al, 2011;Wickland et al, 2020), very little research has been done in the Middle East, which is one of the world's emission hotspots (e.g., Arman, 2015;He et al, 2020;Mustafa et al, 2021). One of the reasons for this is the dearth of ground-based observations of CO 2 .…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…Average monthly rainfall pattern varies widely throughout the * corresponding author; e-mail: abasarir@uaeu.ac.ae year and over 80% of the annual rainfall occurs during the winter season. The monsoon winds blowing across the UAE are stronger in the spring and late summer months, and are of two types: the northern dry wind, which helps mitigate the temperature, as far as it is not laden with dust and the eastern wind, which is usually very humid [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Trend Detection in Climate Change Indicators Using Non-Parametric Statistics: A Case Study of Abu Dhabi, United Arab Emirates

et al. 2017

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This study focuses on detecting trends for climate change indicators, such as average monthly temperature, rainfall, humidity, and wind speed for fourteen stations located between Al Ain and Abu Dhabi cities of the United Arab Emirates. Mann-Kendall non-parametric test was run on monthly time series data for the period 2003-2015 with consideration of seasonality. Significant increasing/decreasing trends in rainfall (for four stations), humidity (for six stations), and wind speed (for nine stations) were detected while no significant trend was observed in temperature for all stations.

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Trend Detection in Annual Temperature and Precipitation Using Mann–Kendall Test—A Case Study to Assess Climate Change in Abu Dhabi, United Arab Emirates

Başarir

Arman

Hussein

et al. 2018

Lecture Notes in Civil Engineering

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Uncertainties, risks and challenges relating to CO<SUB align="right">2 emissions and its possible impact on climate change in the United Arab Emirates

Cited by 5 publications

References 16 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

Trend Detection in Climate Change Indicators Using Non-Parametric Statistics: A Case Study of Abu Dhabi, United Arab Emirates

Trend Detection in Annual Temperature and Precipitation Using Mann–Kendall Test—A Case Study to Assess Climate Change in Abu Dhabi, United Arab Emirates

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