A quantitative link between CO 2 emissions from tropical vegetation fires and the daily tropospheric excess (DTE) of CO 2 seen by NOAA-10 (1987-1991)

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tropical vegetation fires and the daily tropospheric

excess (DTE) of CO 2 seen by NOAA-10 (1987-1991)

A. Chedin, N. Scott, R. Armante, C. Pierangelo, C. Crevoisier, O. Fossé, P.

Ciais

To cite this version:

A. Chedin, N. Scott, R. Armante, C. Pierangelo, C. Crevoisier, et al.. A quantitative link between CO 2 emissions from tropical vegetation fires and the daily tropospheric excess (DTE) of CO 2 seen by NOAA-10 (1987-1991). Journal of Geophysical Research: Atmospheres, American Geophysical Union, 2008, 113 (D5), pp.n/a-n/a. �10.1029/2007JD008576�. �hal-02926785�

A quantitative link between CO

emissions from tropical vegetation

fires and the daily tropospheric excess (DTE) of CO

seen by

NOAA-10 (1987–1991)

A. Che´din,1 N. A. Scott,1R. Armante,1C. Pierangelo,2 C. Crevoisier,1O. Fosse´,1 and P. Ciais3

Received 23 February 2007; revised 3 July 2007; accepted 7 December 2007; published 4 March 2008.

[1] Monthly mean mid-tropospheric CO₂ columns over the tropics are retrieved from evening and morning observations of NOAA-10 (1987 –1991). We find that the difference between these two columns (‘‘Daily Tropospheric Excess’’, DTE) increases up to 3 ppm over regions affected by fires. At regional scale over Africa, America, and Australia, the variations of the DTE are very similar to those of independently derived biomass burning CO2 emissions. A strong correlation (R2
0.8) is found between regional mean DTE and fire CO2emissions values from the Global Fire Emissions Database (GFEDv2) even though the two products span over periods ten years apart from each other. The DTE distribution over Africa indicates that the southern hemisphere experiences 20% more fire activity during El Nin˜o conditions than during La Nin˜a conditions and the reverse for the northern hemisphere. Such an African dipole of ENSO-related fire variability is comparable to changes analyzed from GFEDv2 CO2 emission maps. However, the estimated one sigma uncertainty on the DTE remains close to this DTE ENSO signal. The physical mechanism linking DTE with emissions is not fully elucidated. Hot convective fire plumes injecting CO2 into the troposphere during the afternoon peak of fire activity, seen by the satellite at 1930 LT, and then being diluted by large scale atmospheric transport, before the next satellite pass at 0730 LT, could explain the tight observed relationship between DTE and CO2emissions. We conclude that DTE data can be very useful to quantitatively reconstruct fire emission patterns before the ATSR and MODIS era when better quality fire count and burned area data became available.

Citation: Che´din, A., N. A. Scott, R. Armante, C. Pierangelo, C. Crevoisier, O. Fosse´, and P. Ciais (2008), A quantitative link between CO2emissions from tropical vegetation fires and the daily tropospheric excess (DTE) of CO2seen by NOAA-10 (1987 –

1991), J. Geophys. Res., 113, D05302, doi:10.1029/2007JD008576.

1. Introduction

[2] Biomass burning is a large source of atmospheric

CO2, aerosols and chemically important gases. CO2 alone

represents about 90% of the emissions. Biomass burning is one of the major sources of reactive compounds which control the atmospheric oxidizing capacity [Crutzen and Andreae, 1990]. Average annual biomass burning emissions were of 2.5 Pg C a1over the 1997 – 2004 period [van der Werf et al., 2006]. The interannual variability of emissions is large, up to 1 Pg C a1, and exhibits a strong ENSO forcing [van der Werf et al., 2006]. Following Seiler and

Crutzen [1980], the yearly total amount of CO2emitted in a

given area encompassing N different ecosystems may be written as:

M COð 2Þ ¼

X

i

AiBieifi

where Aiis the area burnt, Biis the biomass load, eiis the

burning efficiency, and fi is the CO2 emission factor

(amount of CO2 released per dry matter unit of burned

biomass) for the ith ecosystem (i = 1, N). Each of these variables must thus be estimated separately to infer CO2

emissions. Overall, the level of uncertainty on emissions remains high, particularly at the global scale [Boschetti et al., 2004]. Palacios-Orueta et al. [2005] evaluated diverse methods to quantify emissions and assessed their uncertain-ties (see their Table 1). Their analysis suggests that burned area Aiis the most uncertain parameter affecting emission

estimates [van der Werf et al., 2006].

[3] Within the tropics, savanna fires alone contribute

roughly 20% of the emissions [Andreae, 1996]. The large 1

Laboratoire de Me´te´orologie Dynamique, IPSL, Ecole Polytechnique, Palaiseau, France.

2

Centre National d’Etudes Spatiales, Toulouse, France.

3_{Laboratoire des Sciences du Climat et de l’Environnement, IPSL,}

CEA-Orme des Merisiers, Gif sur Yvette, France. Copyright 2008 by the American Geophysical Union. 0148-0227/08/2007JD008576

source of CO2associated to savanna fires is highly seasonal

and it is offset during the wet season by a CO2 sink

associated with plant growth. The extent to which savanna ecosystems are today carbon neutral with respect to fires is largely unknown, although there is a clear evidence that savanna fires have increased during the 20th century in response to rising population [Mouillot and Field, 2005; Crutzen and Zimmermann, 1991; van Aardenne et al., 2001]. In contrast to savannas burning, the combustion of tropical forests, in particular fires affecting moist forests on peat soils [Siegert et al., 2001; Page et al., 2002], is a net source of CO2 durably added to the atmosphere.

Simula-tions of the impact of biomass burning on the atmospheric CO2 distribution suggest a moderate effect of fires on the

CO2seasonal cycle [Wittenberg et al., 1998; van der Werf et

al., 2004] but a large impact on the interannual growth rates variability [Langenfelds et al., 2002; van der Werf et al., 2004, Patra et al., 2005]. Biomass burning CO2emissions

are generally not explicitly prescribed as prior information in atmospheric inversions of CO2 sources and sinks,

al-though recent studies suggested that it is the largest con-tributor to the CO2 interannual variability [Schimel and

Baker, 2002; Langenfelds et al., 2002; Rodenbeck et al., 2003].

[4] The Global Fire Emission Database, version 2

(GFEDv2) [van der Werf et al., 2006; Randerson et al., 2006] provides global monthly emission maps for CO2and

a number of gases at a spatial resolution of 1° 1° over the period 1997 to 2005. In this data set, the regions which are the largest contributors to biomass burning are Africa (49%), South America (13%), and equatorial Asia (11%) as reported by van der Werf et al. [2006].

[5] Combustion products are sometimes uplifted at

alti-tudes of several kilometers [Andreae et al., 2004; Edwards et al., 2003; Kar et al., 2004; Freitas et al., 2005; Pradier et al., 2006] and then transported over long distances [Hauglustaine et al., 1998; Andreae et al., 2001; Stohl et al., 2002; Chatfield et al., 2002; Freitas et al., 2005]. A better understanding of the role of biomass burning emis-sions on regional atmospheric chemistry has been obtained from various campaigns such as the Southern Africa Fire-Atmosphere Research Initiative SAFARI [Lindesay et al., 1996], the Transport and Atmospheric Chemistry near the Equator-Atlantic TRACE-A [Fishman et al., 1996], or the

Experiment for Regional Sources and Sinks of Oxidants EXPRESSO [Delmas et al., 1999].

[6] Progress in the remote sensing of trace gas global

distributions nicely complements in situ ground-based observations by adding extensive spatial and temporal coverage. This is the case, for example, of instruments like Measurement of Air Pollution from Satellites (MAPS) launched in 1981 [Reichle et al., 1986], like the Interfero-metric Monitor for Greenhouse Gases (IMG) launched in 1996 [Clerbaux et al., 2003], or the Measurement Of Pollution In The Troposphere (MOPITT) operating since 1999, or, more recently, the Advanced Infrared Sounder (AIRS) launched in 2002 [Crevoisier et al., 2004]. Che´din et al. [2003, 2005] showed that upper air CO2concentration

can be retrieved from observations made on board the NOAA polar satellites by the Television and InfraRed Operational Satellite-Next generation (TIROS-N) Opera-tional Vertical Sounder (TOVS). Note that this series of sensors was not at all designed to measure CO2, but rather

to retrieve atmospheric temperature and moisture at global scale.

[7] In the following, we first describe improvements

made in the retrieval of CO2from NOAA/TOVS

observa-tions. We then analyze maps of the difference of CO2

concentration retrieved from the two daily passes of the NOAA satellite. The spatial and temporal distribution of this diurnal CO2signal retrieved from TOVS is compared to

independent fire emissions data (active fire counts, burned area). The quantitative relationship found between emission data and the CO2diurnal signal is then discussed in detail.

2. Observed Daily Tropospheric Excess (DTE) of CO2 Concentration

2.1. Methodology

[8] Flying aboard the National Oceanic and Atmospheric

Administration (NOAA) polar meteorological satellites since 1978 [Smith et al., 1979], the TOVS instrument consists of the High resolution Infrared Radiation Sounder (HIRS-2), the Microwave Sounding Unit (MSU) and the Stratospheric Sounding Unit (SSU). In the 15mm and 4.3 mm spectral bands, HIRS-2 radiances mostly depend on the temperature of the atmosphere but also, although weakly [Che´din et al., 2002], on the CO2concentration. The MSU

observations are also sensitive to temperature, but are insen-sitive to CO2. Combining HIRS-2 and MSU allows

separat-ing the two signals. The approach developed by Che´din et al. [2003, 2005] to retrieve CO2 is based upon a non-linear

regression inverse radiative transfer model based on the Multilayer Perceptron [Rumelhart et al., 1986] and was applied to NOAA-10 observations.

[9] The retrieved CO₂columns are weighted to the

mid-to-high tropospheric region. This is illustrated by Figure 1 which shows, for a sample of 820 atmospheric situations representative of the tropical belt, the mean and the standard deviation of the averaging kernels of the inverse model (right) and of the temperature profiles (left) in function of pressure (40 levels, from the surface to 0.05 hPa). It is clearly seen that while the temperature profiles show large variations throughout the entire atmospheric column, the mean averaging kernel shows no sensitivity to the surface, a low sensitivity below about 600 hPa as well as at low

Table 1. Limits in Latitude and Longitude of the 12 Continental Regions Used in This Studya

Code Lat Long Description AfNW 0N – 15N 20W – 20E Africa NH-15N West AfNE 0N – 15N 30E – 45E Africa NH-15N East AfNEC 0N – 15N 20E – 45E Africa NH-15N East-Central AfN 0N – 15N 20W – 45E Africa NH-15N

AfNt 0N – 25N 20W – 45E Africa NH-25N AfSW 0S – 20S 10E – 25E Africa SH-20S West AfSE 0S – 20S 25E – 40E Africa SH-20S East AfSt 0S – 25S 10E – 40E Africa SH-25S Aft 25N – 25S 20W – 43E Africa 25N-25S AmSE 0S – 25S 60W – 35W America SH East (Brazil) AmC 0N – 25N 110W – 62W America NH-25N Central Aus 12S – 25S 110E – 160E Australia-25S

a_{Af, Am, Aus, respectively stand for Africa, America, Australia. N, S, E,}

W, respectively stand for North, South, East, West, and t stands for total. NH: northern hemisphere; SH: southern hemisphere.

pressures, the highest sensitivity being around 200 hPa. Mid-tropospheric CO2data were retrieved between January

1987 and August 1991 in the tropical zone (30N – 30S) where most of the biomass burning emissions occur. This period is marked by the end of the 1986 – 1987 El Nino, followed by a strong La Nina episode in 1988 – 1989, and by the beginning of a weak El Nino at the end of 1990.

[10] Being sun-synchronous, NOAA-10 observes the

same point of the Earth surface in the tropics at 0730 LT (daytime) and 1930 LT (nighttime). As most ‘‘morning’’ platforms, it did not experience a significant orbit drift. The difference between collocated nighttime and daytime data forms a signal hereafter referred to as the ‘‘Daily Tropo-spheric Excess’’ (DTE) of CO2concentration. Details on the

approach followed are given by Che´din et al. [2005] (note the change from the acronym ‘‘Night minus Day Difference (NDD)’’ to DTE). The most striking feature of the DTE signal is the existence of regional maxima (1 – 3 ppm) over areas affected by intense fires. Both the seasonal and the interannual variability of DTE were shown to be in quali-tative agreement with burned area data from the European Space Agency’s monthly Global Burnt Scar (GLOBSCAR; Simon et al. [2004]) or from the Global Burned Area (GBA; Gre´goire and Tansey [2003]) data sets. Che´din et al. [2005] proposed that the DTE can be explained by a vigorous fire-enhanced convective uplift of biomass burning plumes to the mid troposphere [Andreae et al., 2004; Edwards et al., 2003; Kar et al., 2004; Freitas et al., 2005, 2006a, 2006b; Pradier et al., 2006] occurring after the peak of the diurnal cycle of fire activity [Justice et al., 2002] and detected by the satellite at 1930 LT, followed by a dilution of the

burning emission-laden air in altitude during the night, before the next satellite pass in the morning.

[11] In this work, mid-tropospheric CO₂ column values

are retrieved from the NOAA-10 observations at 0730 LT and 1930 LT made between January 1987 and May 1991. Individual cloud free daily CO2retrievals are produced at a

spatial resolution of 1° 1°. Night and day CO2retrievals

are then collocated: to be retained a 1° 1° daytime daily CO2retrieval must be followed by at least one nighttime

(same day) CO2retrieval within a 5° 5° box centered on

the daytime CO2retrieval. If this ‘‘collocation’’ criterion is

not met, the daytime CO2item is rejected. Remaining CO2

retrievals, nighttime and daytime separately, are then aver-aged spatially over grid-boxes of 5°  5° and temporally over a month. At least 40 individual CO2retrievals must be

found, both for nighttime and daytime, in each 5° grid-box each month for making the average). If this ‘‘averaging’’ criterion is not met, data from the whole grid-box are rejected. The DTE is finally computed as the difference between nighttime and daytime monthly averaged CO2 at

the resolution of 1° 1°. Areas with no DTE data reflect an insufficient number of measurements each month as caused, for instance, by persistent cloudiness.

[12] Following the algorithm developed by Che´din et al.

[2005], significant technical improvements have been brought to the CO2retrieval procedure. The code has been

entirely revisited, leading to a better handling of the evening-morning collocations, to the introduction of an aerosol filtering procedure (see section 2.2.2.), and to the correction of a few minor errors. The much higher spatial resolution obtained in this paper (5° instead of 15° formerly) Figure 1. Mean and standard deviation (±1s) of the averaging kernels of the inverse model (in ppm of

perturbation of the retrieved CO2column in response to a 20 ppm perturbation in each atmospheric layer,

right) and of the temperature profiles (in Kelvin, left) as a function of pressure (40 levels, from the surface to 0.05 hPa) for a sample of 820 atmospheric situations representative of the tropical belt.

results from these improvements and allows more accurate comparisons of DTE with other independent fire emission-related data sets. The DTE accuracy may be assessed by analyzing the standard deviation of the DTE monthly retrievals as a function of the mean DTE value. Figure 2 displays the ratio of the DTE standard deviation to its mean over 5°  5° boxes (at most, 25 DTE retrievals at

1° resolution are thus included in each box), expressed in percent, versus the corresponding monthly mean DTE binned into intervals of 0.2 ppm. This figure shows that the level of significance of the DTE signal rapidly decreases for mean values below about 0.8 ppm. Further, the DTE standard deviation remains relatively constant at a value of about 0.4 – 0.5 ppm if the mean DTE exceeds 1 ppm. Thus 0.4 to 0.5 ppm is probably a good estimate of the uncertainty associated with the retrieval method. The DTE standard deviation should not be significantly perturbed by the natural variability of the fires within one month since we deal with small (5°  5°) and thus rather homogeneous regions. Because the error bar (±1s) asso-ciated with monthly DTE rapidly decreases with increas-ing DTE values, larger signals are retrieved more precisely than smaller ones.

[13] Four year averaged seasonal maps of DTE are shown

in Figure 3. During the dry season in each hemisphere, one can see regional maxima of 1 to 3 ppm over Africa, South America, and Australia (southern Asia being obscured by persistent cloudiness). More detailed but similar signals can be seen on a monthly basis (not shown).

2.2. Potential Contamination of the DTE Signal 2.2.1. Smoke Aerosols and Ozone

[14] The sources of potential contamination of the CO₂

retrievals by emission products other than CO2were

exam-ined in detail by Che´din et al. [2005]. Fire plumes present in the atmosphere obviously contain CO2but also carry smoke

aerosols [Andreae et al., 2004; Koren et al., 2004] and ozone formed by precursors, as often accompanying wide-Figure 2. Ratio of the DTE standard deviation to its mean

over 5° 5° boxes (in percent) versus the DTE value for the period January 1987 – December 1990 and all con-tinental surfaces within the latitude band 30N – 30S. Crosses: mean; vertical bars: ±1s.

Figure 3. Seasonal mean DTE (difference between 1930 LT and 0730 LT, of mean mid tropospheric mixing ratio of CO2over the tropics) averaged over the period January 1987 – December 1990. Spatial

spread fires [Schultz et al., 1999; Thompson et al., 2001]. Such additional absorbers impact the HIRS 15mm channels. Ignoring their presence in the retrieval may result in over-estimating the DTE values because the atmospheric load in non-CO2fire emitted absorbers is certainly larger at 1930 LT

than it is at 0730 LT. From the detailed sensitivity analysis of Che´din et al. [2005], it was concluded that only high altitude aerosols (above 4 – 5 km) with high optical depths (higher than 0.7 in the visible or, equivalently, 0.05 at 14 mm) or ozone concentration at least twice higher than normal could contribute significantly to the enhancement of the DTE signal, each roughly by an effect of 1 ppm on the retrieved CO2column. Results of the present study show

that, despite these uncertainties, a strong correlation exists between DTE and fire emissions (see section 5).

2.2.2. Dust Aerosols

[15] Dust aerosols main characteristics (optical depth,

mean altitude, and even effective radius) were retrieved by Pierangelo [2005], over both land (except the radius) and ocean, from the HIRS or Atmospheric Infrared Sounder (AIRS) observations. They can constitute another source of contamination for the CO2retrievals. To represent a

signif-icant contamination of the DTE signal, dust events must have a diurnal cycle roughly in phase with that of fires. N’Tchayi Mbourou et al. [1997], using surface visibility as an indicator of dust, have shown that, in the central Sahel, diurnal changes in the surface thermal inversion and in surface winds produce a strong diurnal cycle in dust frequency with a maximum around mid-day. Elsewhere there is only a weak diurnal cycle. A diurnal cycle of the dust atmospheric load over Sahel is likely to be the explana-tion of the spurious summer peaks seen in the DTE time series (not shown). These summer DTE peaks correspond to the peak activity of the dust season which is opposite in phase with the fire season. However, even if over northern Africa dust events predominate in summer, the early fire season starting in May can still be contaminated by dust activity. This is in particular the case for Chad and Sudan (central Sahel). On the other hand, southern Africa, central and southern America are not or almost not contaminated by dust. Australia is characterized by a short, but relatively intense, dust season peaking in January – February, again in opposite phase with the fire season. These undesirable signatures of dust aerosol events are eliminated to a large extent by rejecting the DTE retrievals corresponding to an aerosol optical depth [Pierangelo, 2005] larger than 0.15 at 10mm (the sensitivity to dust aerosol of the HIRS channels around 14mm is about 7 – 8 times smaller than at 10 mm). Unfortunately, when the fire and dust seasons partially overlap, as in central Sahel, rejection of months with dust optical depths larger than 0.15 simultaneously leads to reject significant DTE contributions and, con-sequently produces too low annual mean DTE values (see section 5.2).

3. Seasonal and Diurnal Variations of Fire Emissions in the Tropics

[16] Over the past decades, major improvements to detect

and map fires using a number of different satellite platforms have been achieved. Several studies used different remotely sensed data (AVHRR, ATSR, SPOT, GOES, or TRMM, for

example) and techniques to either detect active fires [Cahoon et al., 1992; Prins and Menzel, 1992; Cooke et al., 1996; Giglio et al., 2003, 2006a, 2006b] or burned areas [Barbosa et al., 1999; Gre´goire and Tansey, 2003; Simon et al., 2004; Tansey et al., 2004; Carmona-Moreno et al., 2005]. Various maps of the distribution of fire emissions have been produced [Barbosa et al., 1999; Schultz, 2002; Duncan et al., 2003; Ito and Penner, 2004; Kasischke and Penner, 2004; Hoelzemann et al., 2004; van der Werf et al., 2003, 2006] though often incomplete or of unequal quality on the global scale. Yet, quantifying large-scale fire emis-sions still suffers from large uncertainties, mainly due to uncertainties in burned area, fuel loads, and burning effi-ciency (see section 1 and references therein).

[17] Fire activity is seasonal, generally lasting 3 –

4 months. The burning season is longer in the southern hemisphere, lasting up to six months according to the dry season length [Duncan et al., 2003; Cooke et al., 1996]. Hoelzemann et al. [2004] compared the seasonality of monthly burned areas from the GLObal Burn SCARs (GLOBSCAR) data set [Simon et al., 2004] with the fire counts from the World Fire Atlas (WFA) based on ATSR observations [Arino and Rosaz, 1999; Arino and Plummer, 2001] for the year 2000. The GLOBSCAR burnt area maxima occur in June and December, while the fire count maxima occur in August and December. Boschetti et al. [2004] compared GBA and GLOBSCAR burnt area and WFA fire counts. The seasonality of both burned area data sets is similar, but differs systematically from the one of fire counts. In particular the WFA fire count maximum in the southern hemisphere lags behind the GBA one. Possibly, the maximum of the ATSR active fire counts in August is caused by a large number of small fires causing small burnt scars, while the maximum in burnt area in June is caused by a smaller number of larger fires [Hoelzemann et al., 2004]. In southern Africa, the fire season extends from April to October and shows a peak in June; in northern Africa, the fire season extends from November to April, and shows a maximum in December. These results are in agreement with the seasonality described by Cahoon et al. [1992] and Barbosa et al. [1999]. In South America, the fire seasonality is weaker than in Africa and shows a peak in August for southern South America and in January for northern South America [Hoelzemann et al., 2004].

[18] Fire activity generally exhibits a strong diurnal cycle.

Using Geostationary Operational Environmental Satellite (GOES-8) observations, Prins et al. [1998] report a clear diurnal signature over South America, with peak in burning occurring in the early to mid afternoon LT. The number of fire pixels observed at this time of the day is 2 – 3 times greater than observed 3 h earlier or later and nearly 7 times greater than 6 h earlier. Similar results are reported by Hsu et al. [1996]. The existence of a strong diurnal cycle of African savanna fires is also reported by Langaas [1993], using field observations and thermal satellite images. Justice et al. [2002], using the highly inclined orbit of TRMM, confirmed this diurnal cycle with a peak at 1500 LT over southern Africa. Recently, Giglio [2007], using TRMM and MODIS data, has shown that the time period of the central 50% of total daily fire activity varied from a minimum of 1.3 h in north central Africa to a maximum of 5.5 h in eastern

Australia and that, in general, shorter periods of burning were associated with greater tree cover.

4. Seasonal and Interannual Variability of the DTE Signal

[19] The seasonality of the DTE maxima in Figure 3

follows that of fire activity, indicating that above burning areas there is a significant excess of CO2 in the

mid-troposphere at 1930 LT, a few hours after the daily peak burning, compared to 0730 LT. Seasonal changes of the DTE signal compare well with the monthly temporal evolution of the vegetation fires over Africa as described by Cahoon et al. [1992], using Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) data, by Cooke et al. [1996], using AVHRR data, or, more recently at global scale, by Carmona-Moreno et al. [2005] using AVHRR data and by Giglio et al. [2006a] using MODIS data.

[20] The DTE seasonal variations are analyzed below for

several regions representative of the various fire seasons [Cahoon et al., 1992; Cooke et al., 1996]. Our choice of regions (see Figure 4 and Table 1) takes into consideration results of the recent global scale study by Giglio et al. [2006a] (hereafter referred to as G2006a) who identified regions characterized either by a high or by a low degree of annual reproducibility measured by the 12-month lagged temporal autocorrelation (12-LTA). South Asia, being ob-scured by persistent cloudiness, has too few DTE results and has not been selected. It must however be kept in mind that the periods analyzed here and in the above references are different and that the DTE is a mid-tropospheric signal whereas TRMM, AVHRR or MODIS fire counts describe a surface product.

4.1. Africa: Continental Scale

[21] Cooke et al. [1996] describe a burning season

spreading in a counterclockwise direction starting southwest

of the central African rain forests (peak around June) and finishing north of the rain forest in western Africa (peak around February). This specific spatial and seasonal evolu-tion is seen on Figure 5 which displays the latitude (5° bands, 1° moving average) versus time (month) evolution of the DTE signal over Africa (Ho¨vmoller diagram).

[22] In southern Africa, a displacement of vegetation fires

from west to east between April and November follows the spread of drier conditions, starting from Namibia-Angola to the east. In May, burning activity is widespread in the west and over the interior of southern Africa. In June, burning is at its peak in the southern part of the Democratic Republic of Congo. Between July and October, the fires spread to the east and wanes in western and interior regions. Fire activity then continues along the east coast of Africa (Kenya, Tanzania) up until November but ceases in December. The evolution of the DTE high values shown in Figure 5 well illustrates this pattern. In southern Africa, the phase of the DTE is in good agreement with the one of the GLOBS-CAR burnt areas, and in advance by about one month compared to the emissions of van der Werf et al. [2003]. In northern Africa, DTE starts to increase due to fires in the sub Saharan region (Sudan, Chad, and Ethiopia) by October and spread to the west and south by December (see Figures 3 and 5), in agreement with burnt area data from GBA or GLOBSCAR (see also Carmona-Moreno et al. [2005]). 4.2. Africa: Regional Scale

[23] Figure 6 shows the DTE time series for two regions

in southern Africa. Region AfSW (Democratic Republic of Congo, Angola, part of Zambia; Figure 6a) is characterized by a strongly periodic seasonal cycle in agreement with the high reproducibility index given by G2006a. Inter-annual variations show a decrease from 1987 to 1988 and then a regular increase from 1988 to 1990, significantly faster after 1989. As for most of the regions shown in the following, year 1990 appears as the most intense of the period analyzed. This feature is in agreement with Barbosa et al. Figure 4. Map of the regions used in this study. Abbreviations are explained in Table 1. Only

[1999], who studied the amount of biomass burned over the whole Africa (keeping in mind that their year starts in November and stops in October) and with Liousse et al. [2004] who mapped black carbon emissions. Region AfSE (Tanzania, Mozambique, part of Kenya and of Zambia) shows a fire season occurring slightly later and lasting longer than AfSW (compare Figure 6a and Figure 6b). The DTE time series for region AfSE is more ‘‘bimodal’’ than for AfSW, in agreement with Cooke et al. [1996]. The DTE time series averaged over the Democratic Republic of Congo (not shown) displays the largest DTE values of our records, followed by Angola. This is in agreement with the G2006a analysis of the fire radiative power and of the reproducibility index. The inter-annual variability of DTE in AfSE is approximately similar to that of AfSW.

[24] Figure 7 shows the DTE time series over Africa,

north of the equator. Region AfNW (Figure 7a) covers Mali and Nigeria, the largest contributors, and also Senegal, Guinea, Burkina, Ivory Coast, Ghana, or Cameroon. If the mean DTE signal recorded in this region is relatively small compared to southern Africa, the area covered is much larger. The DTE seasonality, peak months and length of the season, is in agreement with most studies on burned surfaces or biomass burning emissions. The year 1987 is characterized by a less intense DTE signal than during other years of the period, in agreement with Barbosa et al. [1999]. Similar conclusions apply to the whole northern region African region AfNt (Figure 7b).

[25] There is an opposite behavior of the interannual

variability between the southern hemisphere (Figures 6a –

6b), and the northern hemisphere (region AfNt, 0N-25N), shown on Figure 7b. This result is in agreement with van der Werf et al. [2004] who report, for the period 1997 – 2001, less fire emissions (15%) during El Nino conditions for northern Africa and more emissions (+25%) for southern Africa. This opposite response of northern and southern African fire emissions to El Nino conditions can be explained by changes in precipitation patterns during the transition from El Nino to La Nina and their consequences on biomass production in arid and semiarid African eco-systems [Anyamba et al., 2002; see also: http://www.giss. nasa.gov/research/projects/cafe/component4.html]. We infer a roughly similar transition from the 1986 – 1987 El Nino to the 1988/1989 La Nina in the DTE data set. Regions AfNEC and (to a lesser extent) AfNE, contaminated by the presence of dust aerosols, have too large a number of data points rejected which precludes obtaining reliable monthly statistics. Even the annual mean DTE of region AfNEC (but not that of region AfNE) remains strongly affected by aerosols as shown in section 5.2.

4.3. South America

[26] Figure 8 shows the DTE variations over the regions

AmSE and AmC (see Figure 4 and Table 1). Region AmSE (Figure 8a) covers a part of Brazil, by far the largest emitting country of South America. DTE in the AmSE region shows a weakly periodic seasonal cycle and a large interannual variability. This is in agreement with G2006a who find low seasonal reproducibility of emissions over this region. The DTE starts increasing in May and peaks in June – July. Emission maps from van der Werf et al. [2003] show scattered fires over AmSE with low-intensity emissions in May, and a peak in burning activity in August-September (see also Duncan et al. [2003]). The fire maximum of G2006a occurs 1 – 2 months later than the DTE maximum in Figure 8a. As in G2006a, the fire season in southern America is long (six months or more) and areas with DTE peaks are still detected in December-January (November – December in G2006a). Region AmC (Figure 8b) corre-sponds to central America, from southern Venezuela and Columbia to southern Mexico. Except for the year 1987 and the beginning of 1988, characterized by small DTE values, the DTE seasonality shows a peak in December – January (even February in 1988), in agreement with Hoelzemann et al. [2004] but earlier than van der Werf et al. [2003] and Duncan et al. [2003]. Interannually, the AmSE region behaves approximately as southern Africa, when AmC behaves approximately as northern Africa.

Figure 5. Time-latitude (5°, 1° moving average) monthly Hovmoller diagram of the DTE signal (ppm). White parts correspond to rejections by the algorithm.

Figure 6. Time series (solid) and annual mean (dashed) of the DTE signal (in ppm) for southern hemisphere Africa; (a) region AfSW; (b) region AfSE.

4.4. Australia

[27] Figure 9 shows the DTE time series for Australia

(Aus, 12S – 25S). Australia experiences dramatic wild bush fires due to extreme weather conditions and vegetation [Kershaw et al., 2001]. The nature of the Australian environment, with long periods of dry, hot weather and highly flammable natural vegetation, makes many parts of the country particularly vulnerable to fire. In GFEDv2, the peak month of emissions shows an important variability and is mostly observed in September-October with a start of the season in April – May (the G2006a reproducibility index essentially displays low values, less than 0.3, throughout the continent). The DTE record also shows large variability of the peak month which ranges from June to September (most frequently in July) and a fire season starting in April-June. Interannually, Australia behaves approximately as southern Africa.

5. Comparison Between CO2 Emissions and DTE: A Quantitative Relationship

[28] As seen in the preceding section, the variability

(seasonal and interannual) of the DTE signal closely matches that of fire activity as reported by numerous authors, even if some differences are seen regarding the month of the peak of fires. For example, as already noted (and at least partly explained) by several authors, active fire products, in particular for the southern hemisphere, gener-ally lag behind burned area product (with which the DTE agrees better). Starting from these similarities, we have tried to establish a quantitative relationship between DTE expressed in ppm of CO2and the CO2emissions from the

GFEDv2 database expressed in gCO2m2.

5.1. Africa: Impact of ENSO Events

[29] Table 2 gives the ratios of regional annual mean DTE

between El Nino and La Nina conditions for Africa as a whole (Aft, 25N – 25S), northern Africa (AfNt, 25N-0), and southern Africa (AfSt, 0-25S). For the years the most affected by El Nino [1987] or by La Nina (1988 or 1989), the ratio (El Nino/La Nina) is less than one (
0.8) for northern Africa, and larger than one (
1.1) for southern Africa. These ratios are in good agreement with those of the GFEDv2 data set when the 1998 El Nino emissions are divided by either the 1999 or the 2000 La Nina emissions (Table 2). We use the ENSO multivariate index [see Wolter and Timlin, 1993, 1998] to define El Nino and La Nina periods during 1987 – 1990 and 1998 – 2001 (year 1997, characterized by an exceptionally strong El Nino event, was not retained). The similarities in fire activity changes from El Nino to La Nina between DTE and GFEDv2 shown in Table 2 must however be taken with care at least for two reasons. First, variations in fine-scale climate conditions (in particular, precipitations) preclude any easy generalization over a large region of the fuel load production. For example, He´ly et al. [2003], studying areas in southern Africa presenting contrasting fuel load distributions for the two periods 1991 – 1992 and 1999 – 2000 observed that arid areas produced more fuel in the first period, when the more humid areas produced more fuel loads in the second period (see also end of section 4.2.). Second, the DTE ratios in Table 2 only differ by 0.4 from AfNt to AfSt, a value close to the expected accuracy of the method. Moreover, the time periods analyzed here, although similar, are not the same.

[30] The impact of ENSO events may also be quantified

by comparing southern to northern African contributions. Table 3 shows the ratio AfSt (0S – 25S) to AfNt (0N – 25N) Figure 7. Time series (solid) and annual mean (dashed) of the DTE signal (in ppm) for northern

hemisphere Africa; (a) region AfNW; (b) region AfNt.

Figure 8. Time series (solid) and annual mean (dashed) of the DTE signal (in ppm) for south and central America; (a) region AmSE; (b) region AmC.

for the DTE signal and the four years analyzed here (columns 1 and 2), and for CO2 emissions and six years

GFEDv2 data (columns 3 and 4).

[31] However, for the above reasons, if the comparisons

appear suggestive, they cannot be considered as sufficient.

5.2. Comparison Between DTE and CO2Mean Annual

Emissions at Regional Scale

[32] Figure 10 compares the DTE regional annual mean

values (in ppm) for the four years of NOAA-10 observa-tions (1987 – 1990, upper curves), to the annual mean CO2

fire emissions (in gCO2m-2) of the GFEDv2 database

(1997 – 2004, lower curves). The DTE values are first multiplied by a constant factor aiming at ‘‘reconciling’’ the units and found to be close to 16.6. Then, a constant arbitrary offset of 50 is added to the resulting DTE values in order to separate the two sets of curves in the figure. At first sight, the DTE and emissions curves display the same region-to-region spatial differences. A noticeable exception is region AfNEC and, may be, AmSE. Figure 10 also shows that there are similarities in the interannual variability between DTE and GFEDv2. The smaller amplitude of the DTE variations is expected from its sensitivity to the mid-tropospheric CO2 concentration, contrary to the GFEDv2

estimates of CO2emissions at the surface.

[33] Figure 11 compares the regional annual mean values

of DTE and GFEDv2 averaged over the periods 1987 – 1990 (DTE) and 1997 – 2004 (GFEDv2), respectively. A very tight linear relationship can be seen between the two variables over a relatively large range of variation (from 5

to 25). Such a tight 1:1 relationship supports the interpre-tation of the DTE signal proposed by Che´din et al. [2005] as a measure of fire emissions. The DTE likely results from the combined effect of vigorous fire-enhanced convective uplift of biomass burning plumes of CO2to the mid troposphere

occurring during the peak of the diurnal cycle of fire activity, and detected by the satellite at 1930 LT, followed by advective dilution of the burning emission-laden air in altitude during the night, before the next satellite pass in the morning. Two regions stand apart: AfNEC with a strong deficit of the DTE value compared to the emission value, and AmSE, with an excess of the DTE value. We see two main explanations for these discrepancies.

[34] Underestimated DTE values over AfNEC are due to

the presence of dust aerosols. In section 2.2.2., we have seen that this source of contamination of the DTE signal is important over northern Africa and essentially over central Sahel [N’Tchayi Mbourou et al., 1997]. This is illustrated by comparing AfNEC and AfNE which only differ by a narrow longitude strip of 10° width (Figure 4). If the AfNEC shows too low DTE values, the AfNE region is in agreement with GFEDv2. Transport could also play a role, although cer-tainly less important, through the winter (DJF) anticyclonic circulation existing at 300 – 400 hPa, centered around 10 – 15N and 20E, as shown by Jenkins and Ryu [2004] which may lead to some northward migration of emissions. The fact that the region AfNt including the band 15N – 25N, not significantly affected by the fires, agrees well with GFEDv2 when AfN, bounded by 15N, has too low DTE values favors this explanation. Moreover, similar results are obtained for the latitude band 15N – 25N when the aerosol optical depth rejection threshold is divided by 3 (0.05 instead of 0.15) which excludes a contamination of the DTE by remaining aerosols. However, more work remains to be done before coming to a really convincing and complete explanation.

[35] In the southern America region AmSE,

overesti-mated DTE values as compared to GFEDv2 reflect higher cloud cover than over Africa or Australia (see, for example, cloudiness data at isccp.giss.nasa.gov). In southern Amer-ica, too many months, principally outside the fire season with low DTE values, but also within the fire season with higher DTE values, are affected by clouds and cannot be correctly sampled and represented in the DTE time series. This leads to a positive bias of the DTE over the eastern part of this region (missing months outside the fire season) and to a negative bias over the central part (missing months

Table 2. Ratio El Nino/La Nina Years for Three Large Regions: Africa (25N-25S), Northern Africa (25N-0N), and Southern Africa (0S-25S)a Region DTE 87/88 DTE 87/89 GFEDv2 98/00 GFEDv2 98/99 Aft 1.1 1.0 1.0 1.1 AfNt 0.8 0.7 0.8 0.9 AfSt 1.2 1.1 1.3 1.4 a

The second and third columns are for DTE (in ppm) and the ratios 1987/ 1989 and 1987/1988; the fourth and fifth columns are for the CO2

emissions (in gCO2m2) of van der Werf et al. [2006] and the ratios 1998/

2000 and 1998/1999.

Figure 9. Time series (solid) and annual mean (dashed) of the DTE signal (in ppm) for Australia (Aus, 12S – 25S).

Table 3. Ratio AfSt (25N – 0N)/AfNt (0S – 25S) of the DTE Signal (in ppm) for the Four Years Analyzed (Columns 1 and 2) Compared to the Ratio of GFEDv2 CO2Emissions (in gCO2m2)

of van der Werf et al. [2006] for Six Years Roughly Similar on the Basis of the ENSO Multivariate Index

Year DTE S/N Year GFEDv2 S/N

1997 1.3 1987 2.2 1998 2.6 1988 1.5 1999 1.8 1989 1.4 2000 1.7 1990 1.9 2001 1.7 2002 1.9

Figure 11. Relationship, region by region, between GFEDv2 CO2 annual mean emissions (in

gCO2m2) averaged over the period 1997 – 2004 [van der Werf et al., 2006] and annual mean DTE (in

ppm 16.6; see text section 5.2) averaged over the period 1987 – 1990.

Figure 10. Annual mean DTE (in ppm 16.6, offset of +50 to separate the two sets of curves; see text, section 5.2.) for the regions of Table 1 (numbered from 1 to 12 in abscissa) and for the four years of NOAA-10 (upper curves); CO2 annual mean emissions (in gCO2m2) for the same regions and eight

outside and within the fire season), the overall bias of the annual DTE for this region being positive. Meteorological regimes prevailing during the dry season of this region, with frequent cold fronts coming from the south [Freitas et al., 2005], explain this higher cloud cover. The greater com-plexity of the fire diurnal cycle over this region [Giglio, 2007] could also be a potential source of discrepancy between DTE and GFEDv2 emissions. In spite of their variable distribution year after year (as is also the fire season in this region, see Figure 8a and Giglio et al. [2006a]), dust aerosols are not likely expected to play a significant role due to their relatively low mean optical depth.

5.3. Discussion

[36] From the results shown in Figures 10 and 11, it

comes out that there is a tight linear relationship (R2of 0.8 after removing the two regions AfNEC and AmSE where DTE is biased) between regional mean vegetation fire emissions of CO2 and the Daily Tropospheric Excess

(DTE) of CO2 seen by TOVS in the mid tropical

tropo-sphere. This relationship is robust as resulting from the comparison of emissions and DTE over different time periods. This relationship supports the mechanism of fire-enhanced convection of plumes of CO2(about 85-90% of fire

emissions) being rapidly uplifted into the mid troposphere during daytime, and diluted at night by large scale atmo-spheric transport as explained, for example by Krishnamurti et al. [1996] [see also Che´din et al., 2005, page 9]. However, no theoretical support of this mechanism can be given at the moment. Freitas et al. [2006a, 2006b] have described a parameterization to include the vertical transport of hot gases emitted from biomass burning into low resolution atmospheric-chemistry-transport models. Their method consists of embedding a 1-D cloud-resolving model in each column of the larger-scale host model. They show the effect of their ‘‘plume rise mechanism’’ on the vertical distribution of CO. Without a plume rise, the entire CO would remain confined to the boundary layer (BL). With it, the BL appears polluted by emissions from the smoldering phase and the troposphere, where a large excess of CO is delivered at about 7500 m altitude [see Freitas et al., 2006b, Figure 7], polluted by emissions from the flaming phase. This parameterization appears to be a very promising way to validate (or invalidate) our interpretation of the DTE. Recently, the excess of CO2 in the troposphere due to

biomass burning has been modeled with the ECMWF Integrated Forecasting System and GFEDv2 emissions [Kaiser et al., 2006; Kaiser, 2006]. The results (although still to be validated) suggest the presence of plumes of CO2in altitude (even above 200 hPa) detected by AIRS data

assimilation with concentration excess of 1 to 2 ppm, in good agreement with our DTE signal (see their Figure 12).

6. Conclusion

[37] A new processing of the TOVS infra-red and

micro-wave observations has reduced uncertainty on the retrieval of the evening minus morning difference in column CO2

(DTE). The new DTE fields show variations that are well correlated with those of fires at regional scales. Monthly variations of the DTE match the seasonality of burned areas over Africa, South America and Australia, but, as already

pointed out by several authors, they may lag the seasonality of active fire counts by one to two months. The interannual dipole shown by the DTE variations over large regions of Africa between El Nin˜o and La Nin˜a conditions is very comparable with the one observed in biomass burning emissions from van der Werf et al. [2006] (GFEDv2 data set). A high correlation (R2
0.8) was found between the annual mean DTE and fire CO2emissions over most regions

of the tropics. Exceptions are central Sahel (contamination by dust aerosol) and southern America (insufficient sam-pling due to cloudiness). The physical mechanism which links DTE with actual emissions is not fully elucidated. Hot convective fire plumes injecting CO2into the troposphere

during the afternoon peak of fire activity, seen by the satellite at 1930 LT, and then being diluted by large scale atmospheric transport, before the next satellite pass at 0730 LT, could explain the tight observed relationship between DTE and CO2emissions. Modeling of a case study plume in

Brazil and southern Africa by Freitas et al. [2006b] has demonstrated that such a mechanism is realistic [see also Kaiser, 2006]. However, we still do not know how it could be generalized over each region, given variable atmospheric stability conditions and variable amounts of heat contained by fire plumes subject to updraft. Next generation plat-forms, equipped with very high spectral resolution infrared sounders, as AIRS on Aqua or IASI on Metop, present contrasted capabilities for observing such a fire-induced diurnal signal: IASI, crossing the equator at 09h30 appears better adapted to that kind of research than is AIRS, crossing the equator at 1h30. Both, however, should per-form much more accurately than TOVS. Thus the new data from AIRS and IASI may help to shed light on the mechanisms responsible for injecting fire products in alti-tude. We also conclude that DTE data can be very useful to quantitatively reconstruct fire emission patterns before the ATSR and MODIS era when better quality fire count and burned area data became available.

[38] Acknowledgments. This work has been supported in part by the European Community under the contract EVG1-CT-2001-00056 (‘‘COCO’’) and under the contract FP6-516099 (‘‘GEMS’’). Warm thanks are also due to the two anonymous referees for their constructive and helpful comments and criticism.

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R. Armante, A. Che´din, C. Crevoisier, O. Fosse´, and N. A. Scott, Laboratoire de Me´te´orologie Dynamique, IPSL, Ecole Polytechnique, Palaiseau, France. (chedin@lmd.polytechnique.fr)

P. Ciais, Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CEA-Orme des Merisiers, Gif sur Yvette, France.