Seasonal, intraseasonal, and interannual variability of global land fires and their effects on atmospheric aerosol distribution

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D23, 4697, doi: /2002jd002331, 2002 Seasonal, intraseasonal, and interannual variability of global land fires and their effects on atmospheric aerosol distribution Yimin Ji School of Computational Sciences, George Mason University, Fairfax, Virginia, USA Erich Stocker NASA Goddard Space Flight Center, Greenbelt, Maryland, USA Received 18 March 2002; revised 12 July 2002; accepted 17 July 2002; published 7 December [1] In order to understand the variability of global land fires and their effects on the distribution of atmospheric aerosols, statistical methods were applied to the Tropical Rainfall Measuring Mission (TRMM) fire products as well as the Total Ozone Mapping Spectrometer (TOMS) aerosol index products for a period of 4 years from January 1998 to December The fire data in this period manifested a strong annual cycle of land fires in Southeast Asia with a peak in March and in Africa and North and South America with a peak in August. The data also indicated interannual variations in Indonesia and Central America associated with the El Niño-Southern Oscillation cycle. The variability of global atmospheric aerosol is consistent with the fire variations over these regions. However, in southwestern Australia, intense fires were recorded in TRMM fire data, but no smoke was observed in the TOMS aerosol product. Excluding the Australian region, the correlation between fire count and TOMS aerosol index is about 0.55 for fire pixels. Empirical orthogonal function analysis (EOF) and singular spectrum analysis methods were used to analyze the TRMM Science Data and Information System pentad fire composite data and TOMS pentad aerosol index data for this 4 year period. The EOF analyses showed contrast between the Northern and Southern Hemispheres and also intercontinental transitions in Africa and America. These analyses also identified day intraseasonal oscillations that were superimposed on the annual cycles of both fire and aerosol data. The intraseasonal variability of fires showed a similarity of Madden-Julian oscillation mode. INDEX TERMS: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 6062 Planetology: Comets and Small Bodies: Satellites; 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 1610 Global Change: Atmosphere (0315, 0325); KEYWORDS: fire, aerosol, remote sensing Citation: Ji, Y., and E. Stocker, Seasonal, intraseasonal, and interannual variability of global land fires and their effects on atmospheric aerosol distribution, J. Geophys. Res., 107(D23), 4697, doi: /2002jd002331, Introduction [2] Wild land fires are frequent menaces to human lives and property. They also release large amount of greenhouse gases and aerosols into the atmosphere. It is estimated that the fire may contribute about 30% to the total amount of tropospheric ozone, global carbon monoxide and carbon dioxide [Levine, 1991]. The release of aerosol during fires may lead to significant changes of cloud microphysics and radiative properties. A recent study of TRMM data in Indonesia showed evidence that smoke from sustained fire may suppress regional rainfall completely for certain rain types and therefore create even more favorable environment for fire to occur [Rosenfeld, 1999]. In order to understand the seasonal, intraseasonal and interannual variability of the fires and the associated Copyright 2002 by the American Geophysical Union /02/2002JD aerosols, this study uses statistical empirical orthogonal function (EOF) method and singular spectrum analysis (SSA) to analyze the global fire and aerosol products derived from satellite measurements. [3] Some of the most frequently used satellite fire products are the Advanced Very High Resolution Radiometer (AVHRR) fire products [Matson and Dozier, 1981; Kaufman et al., 1990]. The AVHRR has a full resolution about 1.1 km 1.1 km at nadir and 2.4 km 6.9 km at the edge of scan (EOS). Unfortunately, the full resolution AVHRR data, which includes HRPT (High Resolution Picture Transmission) and LAC (Local Area Coverage), is not recorded globally. There have been efforts [Pinnock and Gregoire, 1999] to integrate the AVHRR high-resolution data into a global 10-day composite. However, the 10-day composite only covers a small portion of the Earth s surface. Further, since the coverage of LAC varies significantly from one region to another, the composite count does not have absolute quantitative representation. ACH 10-1

2 ACH 10-2 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES Figure 1. Global distribution of fire count (resolution: ). (a) Annual mean from 1998 to 2001 (unit: count/year). (b) FMAM mean from 1998 to 2001 (unit: count/4-month). (c) JJAS mean from 1998 to 2001 (unit: count/4-month). See color version of this figure at back of this issue. The available daily AVHRR data is the reduced resolution GAC (Global Area Coverage) for which every third scan of the full resolution orbit data is processed and four out of every five pixels along the third scan are averaged. Therefore, the GAC has a resolution of 1.1 km 4.4 km at nadir with a 3 km gap between pixels across the scan. While the long history and global coverage of GAC data are very attractive for the study of interannual fire variability, the GAC resampling scheme would result in unreliable characterization as well as bias in the detection of fire. Since there is no long-term global GAC fire product readily available, substantial efforts may be needed to further analyze the quality of GAC fire. [4] The fire data used in this study is the TRMM fire products [Ji and Stocker, 2002]. The TRMM fire products are derived from the measurements of the Visible and Infrared Scanner (VIRS) onboard the TRMM satellite. With two visible channels and three IR channels, the VIRS sensor is very similar to AVHRR. The VIRS data are well calibrated and recorded at VIRS full resolution

3 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES ACH 10-3 Figure 2. Global distribution of aerosol index (resolution ). (a) Annual mean from 1998 to (b) FMAM mean from 1998 to (c) JJAS mean from 1998 to See color version of this figure at back of this issue. (2.1 km at nadir, 3.1 km at edge of scan) globally. The daily global fire data from VIRS have been readily available since December The VIRS fire algorithm and products are summarized in the next section. It is expected that better global fire products may emerge in the near future from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imager Radiometer Suite (VIIRS) of National Polar-Orbiting Operational Environmental Satellite System (NPOESS) and the NPOESS Preparatory Project (NPP). Fire is one of the major parameters in both EOS and NPOESS missions. The TRMM/VIRS will also continue to operate until the end of the mission. [5] The aerosol data used in this study is TOMS aerosol index. Although smoke plumes from fires have been observed from visible/infrared satellite instruments such as AVHRR and VIRS [Ignatov and Stowe, 2000], these observations are only reliable over ocean due to the requirement of low nonvariable surface reflectance. The principle advantage of TOMS UV-absorbing aerosol detec-

4 ACH 10-4 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES tion over visible/infrared retrieval is that the UV reflectivity of the land surface is typically very small. The major sources of UV-absorbing aerosols are desert dust and smoke. The other sources include air pollution and sea salt. The TOMS detectability of these aerosols has been demonstrated by a number of studies [e.g., Hsu et al., 1996; Herman et al., 1997]. The TOMS has been aboard a number of satellites such as the Nimbus-7 and Earth Probe. The TOMS mission will continue in NPOESS OMPS (Ozone Mapping Profiler Suite). The next section will give a brief summary of the TOMS aerosol algorithm and products. 2. Data Description [6] The TRMM daily fire product is an image that displays all hot spot pixels determined by the TRMM fire algorithm. The TRMM fire algorithm uses day/night threshold methods to detect fire pixels. At nighttime, a pixel is defined as a fire pixel if the 3.75 mm brightness temperature (T b ) is greater than 315 K and the difference of 3.75 mm T b and 11 mm T b is greater than 15 K. In daytime, the threshold of 3.75 mm T b is 320 K and the difference is 20 K. In daytime, a 1km resolution global land type data along with the two visible channels of VIRS are used to reduce false fire pixels. The details of the TRMM algorithm are given by Ji and Stocker [2002]. The TRMM monthly product is a composite of fire count at resolution. [7] Both TOMS daily and monthly products provide mean aerosol index at resolution. The TOMS aerosol algorithm is a modified spectral contrast method, or a residue method. It utilizes the contrast between TOMS 340 nm reflectance and 380 nm reflectance. One of the unique strengths of this technique is that the presence of sub-pixel clouds does not affect the aerosol detection since cloud produces near-zero residue. For UVabsorbing aerosol, the residues are positive. However, since the residue has a strong altitude dependence due to the effects of aerosol absorption on molecular scattering, the TOMS aerosol index has a decreasing sensitivity at low altitudes. The UV-absorbing aerosol within the boundary layer near the ground is hardly detectable by TOMS. However, for most sustained fires, the related aerosol transports occur in midtroposphere and can be readily detected by TOMS. For weak fires and in cold season, the smoke is shallow and less detectable by TOMS. The details of the TOMS aerosol algorithm are given by Herman et al. [1997]. [8] The TRMM yearly mean fire count of the 4 years (Figure 1a) showed intensive fires in South America, Africa, Australia, Southeast Asia, and Indonesia. Moderate fires occurred in China, North and Central America. In February, March, April, and May (FMAM) season, most fires occurred in the Northern Hemisphere (Figure 1b), especially in Southeast Asia, sub-saharan Africa and Central America. In June, July, August, and September (JJAS), most fires were observed in the Southern Hemisphere and North America (Figure 1c). [9] The dominant pattern of aerosol index is the Sahara dust (Figure 2a). However, the contribution of intensive fires is evident. Since fires occur only in certain season while the Sahara dust is almost a static feature, the magnitude of mean aerosol index in burning areas is significantly smaller than that of the Sahara dust. In FMAM season, the magnitude of aerosol index over fire centers of Southeast Asia and Indonesia is comparable to that over Saharan desert area (Figure 2b). Almost no aerosols were observed over the Southern Hemisphere in this season. In JJAS season (Figure 2c), the strength of aerosol index in southern Africa and southern America is quite strong but still considerably weaker than that of Sahara dust. There were moderate aerosols in northern America in this season. The aerosol index maps are generally in agreement with the fire maps except the desert area. Since the smoke can be transported far beyond its fire origin, the unconditional correlation between fire count and aerosol index is only about For conditional (only compare pixels where fire exists) cases, the correlation between aerosol index and fire count is as high as 0.55 if the Australia is excluded. In Australia, there were virtually no aerosols observed while substantial fires were observed. This inconsistency may reflect false fire in TRMM fire algorithm for vegetation/desert mixed pixels. The false fire pixels have also been noticed in sub-saharan and other areas. However, the decreasing sensitivity of the TOMS algorithm toward lower altitudes may also contribute to this difference. The depth of smoke in the southwestern Australia fire during the winter season may be quite small. [10] Interannual variations were significant in Indonesia and the Central America. For example, in FMAM 1998, an El Niño season, intensive fires and aerosols in the two regions were observed by TRMM and TOMS, respectively (Figure 3a and Figure 4a). The magnitude of aerosol index in Indonesia and Central America in 1998 FMAM season is comparable to that of Saharan dust. However, in FMAM 1999, a normal season, fires were moderate and no aerosols were observed (Figure 3b and Figure 4b). Since sustained fires are associated with the climate variations though they often occur randomly, the spectrum of the fire and aerosol data in the 4 years would show similarities to the short-term climate variability. In the next two sections, the EOF and SSA methods are used to capture and analyze the time-space variability of global fires and aerosols. 3. Empirical Orthogonal Function Analyses [11] EOF is one of the most commonly used techniques to extract qualitative information from two-dimensional (temporal and spatial) data in atmospheric science [e.g., Lorenz, 1956; Barnett, 1977]. The EOF method involves the solution of the eigenvector equation: ðr miþe m ¼ 0 ð1þ where R is the data covariance matrix, I is a unit matrix, m is eigenvalue and e m is the resulting eigenvector (EV), and m = 1, 2,..., M, where M is the dimension of the spatial field. Each of the eigenvalues explains a fraction of total variance and leads to an eigenvector solution that describes one mode of the spatial variability. The temporal behavior of data associated with the EV mode is represented by the principal components (PC) that are

5 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES ACH 10-5 Figure 3. FMAM mean fire count (unit: count/4-month, resolution ). (a) (b) See color version of this figure at back of this issue. Figure 4. FMAM mean aerosol index (resolution ). (a) (b) See color version of this figure at back of this issue.

6 ACH 10-6 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES Figure 5. fire data. (a) First, (b) second, and (c) third eigenvectors of EOF analyses derived from TRMM global coefficients in reconstructing the data in eigenvector space. The details of the notion of EOF are given by Broomhead and King [1986]. In normal cases, the dimension of the temporal vector (N) should be larger than the dimension of the spatial vector (M) to generate a normal covariance matrix R(M, M). This often limits the resolution of the spatial data. This section uses EOF to identify spatial variations and the associated temporal spectrum. In the next section, the SSA method is used to study the detail of interesting spectrums. [12] The data used in EOF and SSA analysis are 5-day fire composites and 5-day mean aerosol index derived from the TRMM daily product and TOMS daily product, respectively. The time series has only 288 pentads for this

7 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES ACH 10-7 Figure 6. Principal components of (a) first, (b) second, and (c) third eigenvectors of TRMM fire data EOF analyses. 4-year period. The temporal resolution may not go higher than the pentad because the data would be too noisy. The temporal resolution is also limited by the spatial coverage of these sensors. Both VIRS and TOMS take daily global coverage. However, there are gaps between orbits in daily coverage. The TRMM covers an area of 180 W to 180 E in longitude and 40 o Sto40 N in latitude. For a resolution, the spatial dimension is about 288. However, at this latitude range, area from 160 E to 130 W is pure ocean area (except islands which should not affect the global fire pattern). Therefore, the EOF analyses actually excluded this area, so that the dimension of spatial vector is reduced to about 220. In this case, there are about 220 eigenvectors. However, only a few leading EVs are used in analyses to describe dominant patterns of the variability. The rest of the EVs, which explain much less of the total variance, are usually neglected. [13] If the annual cycle is not removed from the data, it was anticipated that the spatial patterns of the first EV of the EOF analysis would show seasonal mode. The first fire EV (Figure 5a) indeed showed contrast between Northern and Southern Hemispheres. The corresponding PC (Figure 6a) indicated seasonal variations with contrast between spring and summer and also day oscillations that were superimposed on the seasonal cycle. This EV explained 24% of the total variance. The second EV (Figure 5b), which explained 11% of the total variance, showed intercontinental correlation. There are contrasts between sub- Saharan and southern Africa, and also between Central America and South America. The PC of EV showed weak

8 ACH 10-8 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES Figure 7. Second (a), third (b), and fourth (c) eigenvectors of EOF analyses derived from TOMS global aerosol data. annual cycles with January as positive peak and July as negative peak. This contrast indicates a transition of fires from the sub-saharan region to the south in Africa during a course from early spring to the summer. Similar patterns can be found in South America. The third EV (Figure 5c) does not indicate any contrast. The pattern is similar to the mean fire distribution of the 4 years. However, the PC (Figure 6c) indicates intraseasonal oscillations with a period about days. This eigenvector explains 7% of the total variance. [14] Since the dominant aerosol variation is the Saharan dust, the first EV (not shown), that explained 34% of the total variance of the TOMS aerosol EOF, showed static patterns of the desert dust. However, the second EV of

9 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES ACH 10-9 Rasmusson et al., 1990]. Although the SSA involves the solution of the same eigenvector equation, it addresses the spectrum aspect of the chaotic data instead of the spatial patterns in EOF. The data used in SSA is a one-dimensional time series. In SSA, the R in equation (1) is an autocorrelation matrix of the time series. The size of the autocorrelation matrix of the times series is determined by the number of lags (M). Normally, the number of lags must be at least an order of magnitude smaller than the size of the time series. This requires that the time period of Figure 8. Principal components of (a) second, (b) third, and (c) fourth eigenvectors of aerosol EOF analyses. TOMS aerosol EOF (Figure 7a), that explained 12% of the total variance, showed similar patterns to the first fire EV except in the Australia area, where no aerosol was observed. The PC of this EV (Figure 8a) showed strong annual cycle. Similar to the first fire PC, the second aerosol PC showed a contrast between spring and summer. However, the high frequency signal was weaker compared to the fire results. This may reflect a weak intraseasonal variation of aerosol as compared to the fire. The third aerosol EV (Figure 7b) showed intercontinental transition. The contrast exists between sub-saharan and southern Africa, and also between Central America and South America. The PC of this EV also indicated seasonal cycle (Figure 8b). This aerosol mode bears similarity of the second fire EOF mode. The fourth EV and PC showed intraseasonal variation of aerosol with a cycle about days. The ENSO signal did not appear in both fire and aerosol EVs. The effect of El Niño on fires was only evident in Indonesia and Central America, and it ended in May Singular Spectrum Analysis [15] The SSA has been developed and used in atmospheric science quite recently [Vautard and Ghil, 1989; Figure 9. (a) First, (b) second, (c) third, (d) fourth, and (e) fifth eigenvectors of SSA analyses from TRMM fire data in Southeast Asia.

10 ACH JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES [16] In this paper we chose Southeast Asia and the East- Kalimantan region of Indonesia as examples for SSA analyses. Fire and smoke in these regions have gained considerable attention in recent years [e.g., Giri and Sherestha, 2000; Rosenfeld, 1999]. In Southeast Asia, the first three EVs of SSA (Figures 9a 9c) showed a 25-day oscillation. The total variance explained by the three EVs is 42%. The PCs of these spectrums (Figures 10a 10c) show annual cycles and also interannual variations. The amplitude of these oscillations is significant during fire season, usually in late spring to early summer. The fourth EV, which explained 6% of the total variance (Figure 9d), showed a 60-day oscillation. The fifth EV (Figure 9e) indicated a fast 15-day oscillation. The 15-day oscillation indicated that some of these fires lasted only a few days. The fifth EV also explained 6% of the total variance. [17] In the East-Kalimantan region of Indonesia, the first two EVs (Figures 11a and 11b), which explained a total of 58% of the variance, showed mean and trend characteristics. The PCs (Figures 12a and 12b) of the two leading EVs indicated a strong ENSO signal. They indicate that the variability in this region was only significant in early The third EV (Figure 11c) showed 25-day oscillation pattern. However, this EV only explained 6% of the total variance. 5. Summary [18] The dominant modes of EOF showed contrasting of fire and aerosols between Northern and Southern Hemi- Figure 10. Principal components of the (a) first, (b) second, (c) third, (d) fourth, and (e) fifth eigenvectors of fire SSA in Southeast Asia. data be much longer than the spectrum of interest. In this study, the SSA is used to capture the intraseasonal spectrum, the number of lags is about 20 (100 days), which is an order smaller than the length of the time series (4 years). The eigenvectors of SSA capture the spectrum of the time series, while the principal components show the temporal behavior of the spectrum. Therefore, the EOF method was applied to the time series of global data, while the SSA was applied to time series in regions of interest. Figure 11. (a) First, (b) second, and (c) third eigenvectors of fire SSA in the East-Kalimantan region of Indonesia.

11 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES ACH the fire data showed a similarity of the Madden-Julian [Madden, 1986] day oscillation mode of meteorological parameters in the tropics such as the wind and precipitation. Figure 12. Principal components of the (a) first, (b) second, and (c) third eigenvectors of fire SSA in the East- Kalimantan region of Indonesia. spheres. The PCs of these modes indicating the peaks of fires and aerosol index in the Northern Hemisphere including Southeast Asia, sub-sahara and Central America were during late spring to early summer. In southern Africa and southern America, the peaks appeared in late summer to early autumn. The intraseasonal variability of fire and aerosols were also captured in the leading EOF modes. SSA analyses confirmed the intraseasonal and interannual variability of fires in Southeast Asia and Indonesia. The dominant spectrum of intraseasonal variability is about days. The intraseasonal spectrum derived from References Barnett, T. P., The principal time and space scales of the Pacific trade wind fields, J. Atmos. Sci., 34, , Broomhead, D. S., and G. P. King, Extracting qualitative information from experimental data, Physica D, 20, , Giri, C., and S. Shrestha, Forest fire mapping in Huay Kha Khaeng wildlife sanctuary, Int. J. Remote Sens., 21, , Herman, J. R., P. K. Bhartia, O. Torres, C. Hsu, C. Seftor, and E. Celarier, Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data, J. Geophys. Res., 102, 16,911 16,922, Hsu, N. C., J. R. Herman, P. K. Bhartia, C. J. Seftor, O. Torres, A. M. Thompson, T. F. Eck, and B. N. Holben, Detection of biomass burning smoke from TOMS measurements, Geophys. Res. Lett., 23, , Ignatov, A., and L. Stowe, Physical basis, premises, and self-consistency checks of aerosol retrievals from TRMM VIRS, J. Appl. Meteorol., 39, , Ji, Y., and E. Stocker, An overview of the TRMM/TSDIS fire algorithm and products, Int. J. Remote Sens., 23, , Kaufman, Y. J., C. J. Tucker, and I. Fung, Remote sensing of biomass burning in the tropics, J. Geophys. Res., 95, , Levine, J. S., Global biomass burning: atmospheric, climate, and biospheric implications, in Global Biomass Burning, edited by J. S. Levine, pp. 1 2, MIT Press, Cambridge, Mass., Lorenz, E. N., Empirical orthogonal functions and statistical weather prediction, Sci. Rep. 1, p. 49, Dep. of Meteorol., Mass. Inst. of Technol., Cambridge, Mass., Madden, R. A., Seasonal variations of the day oscillation in the tropics, J. Atmos. Sci., 43, , Matson, M., and J. Dozier, Identification of subresolution high temperature sources using a thermal IR sensor, Photogramm. Eng. Remote Sens., 47, , Pinnock, S., and J. M. Gregoire, World Fire Web: A global fire observation system, paper presented at IUFRO Remote Sensing and Forest Monitoring Conference, Int. Union of For. Res. Organ., Rogon, Poland, 1 3 June Rasmusson, E. M., X. Wang, and C. F. Ropelewski, The biennial component of ENSO variability, J. Mar. Syst., 1, 71 96, Rosenfeld, D., TRMM observed first direct evidence of smoke from forest fire inhibiting rainfall, Geophys. Res. Lett.., 26, , Vautard, R., and M. Ghil, Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series, Physica D, 35, , Y. Ji, School of Computational Sciences, George Mason University, Fairfax, VA , USA. (yji@tsdis.gsfc.nasa.gov) E. Stocker, TRMM/TSDIS, NASA/GSFC, Code 902, Greenbelt, MD 20770, USA. (stocker@tsdis.gsfc.nasa.gov)

12 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES Figure 1. Global distribution of fire count (resolution: ). (a) Annual mean from 1998 to 2001 (unit: count/year). (b) FMAM mean from 1998 to 2001 (unit: count/4-month). (c) JJAS mean from 1998 to 2001 (unit: count/4-month). ACH 10-2

13 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES Figure 2. Global distribution of aerosol index (resolution ). (a) Annual mean from 1998 to (b) FMAM mean from 1998 to (c) JJAS mean from 1998 to ACH 10-3

14 JI AND STOCKER: VARIABILITY OF GLOBAL LAND FIRES Figure 3. FMAM mean fire count (unit: count/4-month, resolution ). (a) (b) Figure 4. FMAM mean aerosol index (resolution ). (a) (b) ACH 10-5