Severe Wildfires Near Moscow, Russia in 2010: Modeling of Carbon Monoxide Pollution and Comparisons with Observations

Safronov, A. N.; Fokeeva, E. V.; Rakitin, V. Yu.; Grechko, E. I.; Shumsky, R. A.

doi:10.3390/rs70100395

Cited by 20 publications

(17 citation statements)

References 80 publications

(110 reference statements)

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“…In our research of the Baikal, the IGBP vegetation type classification (the International Geosphere-Biosphere Program), including 16 species of vegetation, was used; for convenience, the IGBP types are presented in Table S1 (Supplementary Materials). As already specified earlier in References [24,25], the global land-cover satellite maps are not well adapted to the local natural-climatic zones, and using MCD12Q1/MCD12C1 land-cover maps makes no exception.…”

Section: The Mcd12c1 Land-cover Mapmentioning

confidence: 99%

Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin

Safronov

2020

Climate

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The vegetation changes in the area of the Russian part of the Lake Baikal water basin for the period 2010–2018 were investigated using MCD12C1 land cover. The decline in swamp systems area began in 2012 and continued until 2015, after which it partially recovered during the heavy rain season in 2018. During the period of 2010–2018, the area covered by forests did not exceed 20.3% of the Baikal basin of the total portion of the Baikal basin under study. Deforestation began in 2013 and continued until 2017. Over 2013–2018, the forest level decreased by 12.1% compared to the forest state in 2013. The analysis of summer rainfalls and aridity indexes was performed by using CRU TS and GPCC climatic datasets. It is shown that the interannual variations of precipitation and aridity changes are determined by the variability of the global circulation of moist air masses. The MCD64A1 (burned area) and MCD14ML (active fires) MODIS products were used for investigation of the influence of wildfires on vegetation changes. The spatial hotspot distributions and burned areas in general correspond to aridity zones, but they cannot explain the 20-fold increase in the number of wildfires. Most of the hotspot locations are away from settlements, roads, and loggings, in difficult-to-access mountainous areas, as well as in the low-inhabited areas of Siberia. We assume that the nature of such ignitions includes dry thunderstorms, pyrocumulus lightning, or remote impact.

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Section: The Mcd12c1 Land-cover Mapmentioning

confidence: 99%

Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin

Safronov

2020

Climate

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“…With respect to air quality, this is a well-known and widely studied episode. Many of these works analyzed the physicochemical characteristics of the smoke from wildfires and the effects on air quality of the transport (both particles and trace gases) to surrounding areas (Zvyagintsev et al, 2011;Witte et al, 2011;van Donkelaar et al, 2011;Gorchakov et al, 2014;Safronov et al, 2015); medium-range transport (e.g. Finland) (Portin et al, 2012;Mielonen et al, 2013) or long-range transport, even reaching Greece (Diapouli et al, 2014).…”

Section: The 2010 Russian Wildfires and Heatwave Episodementioning

confidence: 99%

Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave

Palacios-Peña¹,

Stier²,

Lorente‐Plazas³

et al. 2020

Preprint

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Abstract. The impact of aerosol-radiation and aerosol-clouds interactions on the radiative forcing is subject to large uncertainties. This is caused by the limited understanding of aerosol optical properties and the role of aerosols as cloud condensation/ice nuclei (CCN/IN). On the other hand, aerosol optical properties and vertical distribution are highly related and their uncertainties come from different processes. This work attempts to quantify the sensitivity of aerosol optical properties (i.e. aerosol optical depth; AOD) and their vertical distribution (using the extinction coefficient, backscatter coefficient, and concentrations species profiles) to key processes. In order to achieve this objective sensitivity tests have been carried out, using the WRF-Chem regional fully coupled model by modifying the dry deposition, sub-grid convective transport, relative humidity and wet scavenging. The 2010 Russian heatwave/wildfire episode has been selected as case study. Results indicate that AOD is sensitive to these key processes in the following order of importance: 1) modification of relative humidity, causing AOD differences up to 0.6; 2) modification of vertical convection transport with AOD differences around 0.4; and 3) the dry deposition with AOD differences up to −0.35 and 0.3. Moreover, these AOD changes exhibit a non-linear response. Both, an increase and a decrease in the RH result in higher AOD values. On the other hand, both, the increase and offset of the sub-grid convective transport lead to a reduction in the AOD over the fire area. In addition, a similar non-linear response is found when reducing the dry deposition velocity; in particular, for the accumulation mode where the concentration of several species increases (while a decrease might be expected). These non-linear responses are highly dependent on the equilibrium of the thermodynamics system sulphate-nitrate-SOA (secondary organic aerosol). In this sense, small changes in the concentration of one species can strongly affect others, finally affecting aerosol optical properties. Changes in this equilibrium could come from modifications in relative humidity, dry deposition or vertical convective transport. By itself, dry deposition also presents a high uncertainty influencing the AOD representation.

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“…Ñïåêòðîìåòðû ñòàíöèé ÈÔÀ (ÇÍÑ, ZOTTO è Ïåêèí) ïðîøëè ïðåäâàðèòåëüíóþ èíòåðêàëèáðîâêó ìåaeäó ñîáîé è ñ Ôóðüå-ñïåêòðîìåòðàìè ñòàíöèé NDACC [25]. Êðîìå òîãî, äàííûå óïîìÿíóòûõ ñïåêòðîìåòðîâ NDACC è ñïåêòðîìåòðà ÇÍÑ ÈÔÀ íåîäíîêðàòíî èñïîëüçîâàëèñü äëÿ âàëèäàöèè ñïóòíèêîâûõ èçìåðåíèé è ìîäåëüíûõ ðàñ÷åòîâ [1,2,25,26]. Êîîðäèíàòû è îñîáåííîñòè ðàñïîëîaeåíèÿ íàçåìíûõ ñòàíöèé, à òàêaeå êîëè÷åñòâî äíåé èçìåðåíèé, èñïîëüçîâàííûõ äëÿ ñîïîñòàâëåíèÿ, ïðèâåäåíû â òàáë.…”

Section: îðáèòàëüíûå ñïåêòðîìåòðûunclassified

Results of comparison of satellite and ground-based spectroscopic CO, CH4, and CO2 columns measurements

2015

AOO

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Ïîñòóïèëà â ðåäàêöèþ 8.06.2015 ã. Ñîáðàí, ñèñòåìàòèçèðîâàí è ïðîàíàëèçèðîâàí çíà÷èòåëüíûé îáúåì ñïóòíèêîâûõ è íàçåìíûõ äàííûõ îá îáùåì ñîäåðaeàíèè â àòìîñôåðå CO, CO 2 , CH 4 â 2010-2013 ãã. Ïîëó÷åíû ïåðåõîäíûå ñîîòíîøåíèÿ ìåaeäó ñïóòíèêîâûìè è íàçåìíûìè äàííûìè îá îáùåì ñîäåðaeàíèè èññëåäóåìûõ ïðèìåñåé â ðàçíûõ èçìåðèòåëüíûõ ïóíêòàõ (ñòàíöèè NDACC/GAW, à òàêaeå ñòàíöèè ÈÔÀ ÐÀÍ) ñ ðàçíûì ïðîñòðàíñòâåííûì è âðåìåííûì ðàçðåøåíèåì. Äëÿ ôîíîâûõ óñëîâèé óñòàíîâëåíà âûñîêàÿ êîððåëÿöèÿ ñðåäíåäíåâíûõ ñïóòíèêîâûõ ñîäåðaeàíèé CO, ïðîäóêòû AIRS v6 (R 2 = 0,48-0,96), IASI MetOp-A (R 2 = 0,25-0,86) è MOPITT v6 Joint (R 2 = 0,30-0,83), óñðåäíåíèå 1  1, ñ íàçåìíûìè äàííûìè ñîëíå÷íûõ ñïåêòðîìåòðîâ. Â ñëó÷àå âûñîêîãî çàãðÿçíåíèÿ ñëîÿ ïåðåìåøèâàíèÿ îòìå÷åíà çíà÷èòåëüíàÿ íåäîîöåíêà îðáèòàëüíûìè ñåíñîðàìè ñîäåðaeàíèÿ CO (îò 1,7 äî 4,7 ðàçà â çàâèñèìîñòè îò ñåíñîðà, ïðîñòðàíñòâåííîãî ðàçðåøåíèÿ è ïóíêòà íàáëþäåíèé). Ðåïðåçåíòàòèâíûå ïåðåõîäíûå ñîîòíîøåíèÿ è êîýôôèöèåíòû êîððåëÿöèè (R 2 ≥ 0,5) ìåaeäó ñðåäíåäíåâíûìè äàííûìè î CH 4 è íàçåìíûìè äàííûìè äèôðàêöèîííûõ ñïåêòðîìåòðîâ ÈÔÀ ÐÀÍ è Ôóðüå-ñïåêòðîìåòðîâ ñòàíöèé GAW ïîëó÷åíû òîëüêî äëÿ ñåíñîðà AIRS. Íàèëó÷øàÿ êîððåëÿöèÿ ñ íàçåìíûìè äàííûìè ïî CO 2 (R 2 = 0,25 äëÿ ñðåäíåäíåâíûõ çíà÷åíèé, óñðåäíåíèå 1  1) ïîëó÷åíà äëÿ ñåíñîðà IASI. Ñðåäíåäíåâíûå çíà÷åíèÿ îáùåãî ñîäåðaeàíèÿ CH 4 ñåíñîðà IASI MetOp-A ñëàáî êîððåëèðóþò ñ íàçåìíûìè äàííûìè, à òàêaeå ñ äàííûìè AIRS. Êëþ÷åâûå ñëîâà: îêèñü óãëåðîäà, ìåòàí, óãëåêèñëûé ãàç, àòìîñôåðíàÿ ñïåêòðîñêîïèÿ, äèñòàíöèîííîå çîíäèðîâàíèå, ñïóòíèêîâûå ìåòîäû, ôîíîâûå è çàãðÿçíåííûå ðàéîíû, ïîãðàíè÷íûé ñëîé àòìîñôåðû; carbon monoxide, methane, carbon dioxide, atmospheric spectroscopy, remote sensing, satellite sensing, background and polluted regions, atmospheric boundary layer.

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Severe Wildfires Near Moscow, Russia in 2010: Modeling of Carbon Monoxide Pollution and Comparisons with Observations

Cited by 20 publications

References 80 publications

Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin

Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin

Quantifying the sensitivity of aerosol optical properties to the parameterizations of physico-chemical processes during the 2010 Russian wildfires and heatwave

Results of comparison of satellite and ground-based spectroscopic CO, CH4, and CO2 columns measurements

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