Figure 2.1
Tropical, temperate, and boreal forest geographical distribution and some of the main issues related to their climate impacts include above-ground carbon storage, evaporative cooling, and absorption of solar radiation (Prăvălie et al., 2018).
Figure 2.2
The annual rate of Brazilian Amazonian deforestation in km2 per year from 1977 to 2021. The data from 77/88 are estimates derived from ground measurements. The remaining data are from the INPE/PRODES system that calculates deforestation areas based on extensive analysis of Landsat images. From: http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodes.
Figure 3.1.1
Time series of PM1 monthly averaged aerosol composition in Central Amazonia (ATTO tower), from January 2014 to October 2017. Measurements were performed with an Aerodyne Aerosol Chemical Speciation Monitor (ACSM).
Figure 3.1.2
Average composition of PM1 aerosol in the wet (left) and dry (right) seasons of central Amazonia at the ATTO tower (the same color coding as in figure 3.1.1). Measurements of PM1 composition with an ACSM at the ATTO tower from January 2014 to December 2016. Measurements of eBC were obtained with a MAAP instrument.
Figure 3.1.3
PM1 composition for Amazonian wet season aerosol under background conditions, with episodic long-range transport removed from the time series. Measurements with an Aerodyne ACSM at the ATTO tower from January 2014 to December 2016. Measurements of eBC were obtained with a MAAP instrument.
Figure 3.1.4
(a) PM1 Monthly mean (± sdev) mass concentrations of organics (Org), sulfate (SO4), nitrate (NO3), ammonium (NH4), and equivalent black carbon (eBC) recoded within the boreal forest canopy with an aerosol chemical speciation monitor (ACSM) and an aethalometer. (b) the mean (± sdev) concentrations of Org, monoterpenes (MT), carbon monoxide (CO), and eBC were recorded under different ambient temperatures (5°C bins). The measurement period at SMEAR II was 2012–2018. The data presented here are adapted from Heikkinen et al., 2019.
Figure 3.1.5
Seasonal and interannual variations in the PM10 aerosol mass and total carbonaceous material (TCM) concentrations at the ZOTTO tower from April 2010 to April 2014 (a) and of daily averaged meteorological parameters (b): temperature – red line; precipitation – grey line and symbols.
Figure 3.1.6
Average PM2.5 aerosol composition at the ZOTTO tower for summer and wintertime. Details on the data can be found in Mikhailov et al., 2017.
Figure 3.1.7
Increase in SOA formation from background to polluted conditions for the wet season in Central Amazonia, as measured at the GoAmazon2014/15 experiment. Urban air pollution from Manaus interacts with natural biogenic emissions producing significant amounts of SOA. Four meteorological conditions were separated using fuzzy cluster analysis in 4 groups, from background to polluted conditions. The increase in SOA mass due to pollution impact is very significant, with PM1 values around 1.2 µg/m3 going up to 3.3 µg/m3 under impact of Manaus pollution. From de Sá et al., 2018.
Figure 3.1.8
Dry season increase in SOA in Central Amazonia, for each species (Upper part) and each PMF organic aerosol factor (lower) when urban air pollution from Manaus interacts with biomass burning aerosols. Baseline represents conditions with no Manaus plume impact, and Event and Urban represent when the Manaus plume hits the site. It is possible to see an increase in PM1 from 8.5 µg/m3. to 12 µg/m3 (from de Sá et al., 2019).
Figure 3.2.1
Amazonian aerosol number size distributions for the coarse and accumulation modes, dN/dlogD: (a) seasonal averages adapted from Moran-Zuloaga et al., 2018; (b) total (NT) and fluorescent (NF) particle distributions. In (a), “Wet” means wet season, “LRT” means African long-range transport periods, when Sahara dust is observed, “Transition” is the wet-to-dry transition season, and “Dry” are measurements from the dry season. Data from Moran-Zuloaga et al., 2018; Huffman et al., 2012 and Andreae et al., 2015.
Figure 3.2.2
Seasonality in total (NT,c) and fluorescent (NF,c) coarse mode particle concentrations in tropical (Amazon) vs. boreal forest (Finland). The tropical forest data shown here has been adapted from Huffman et al. (2012), Whitehead et al. (2016), and Moran-Zuloaga et al. (2018). The boreal forest data has been adapted from Schumacher et al. (2013).
Figure 3.2.3
Characteristic diurnal cycles of temperature, number of PBAP and PBAP diameter in tropical (wet season) and boreal forests (summer) – both measured with the UV-APS and thus being comparable. Also shown is the percentage of the number of fluorescent particles (NF,C) to the total number of coarse mode particles (NTC). The lower part of both plots shows the diurnal variability of PBAP size distribution from 1 to 10 micrometers. The data has been adapted from Huffman et al. (2012) and Schumacher et al. (2013).
Figure 3.2.4
Characteristic size distributions, dN/dlogD, of fluorescent particles in tropical and boreal forests – both measured with the UV-APS and thus being comparable. The data has been adapted from Huffman et al. (2012) and Schumacher et al. (2013).
Table 3.3.1
Mean values for aerosol mass concentration observed at different sites in the Amazon Basin: PM10 (inhalable particle matter, Dp < 10 μm), FPM (fine particle mode, Dp < 2.0 μm), CPM (coarse particle mode (2.0 < Dp < 10 μm). Amazonian sub-regions: AD (Arc of Deforestation) and CA (Central Amazonia). At all forest sites, measurements reported here were taken above the canopy.
| YEAR | SEASON | LANDSCAPE | SUB REGION | AIR MASSES | PM10 (µg/m3) | FPM (µg/m3) | CPM (µg/m3) | REFERENCE |
|---|---|---|---|---|---|---|---|---|
| 1992–1995 | Wet | Forest | AD | mostly clean | 21.9 | 5.5 | 16.4 | Echalar et al., 1998 |
| 1996–1998 | Wet | Forest | AD | mostly clean | 25.0 | 9.9 | 15.1 | Maenhaut et al., 2002 |
| 1998–2002 | Wet | Forest | CA | mostly clean | 8.8 | 2.2 | 6.6 | Pauliquevis et al., 2012 |
| 1999 | Wet | Forest | AD | mostly clean | 6.0 | 2.2 | 3.8 | Guyon et al., 2003 |
| 2008–2012 | Wet | Forest | CA | mostly clean | 9.5 | 1.9 | 7.6 | Arana et al., 2014 |
| 2009–2012 | Wet | Forest | AD | mostly clean | 8.7 | 1.8 | 6.9 | Arana et al., 2014 |
| 1999 | Wet | Pasture | AD | mostly clean | 8.6 | 2.9 | 5.7 | Artaxo et al., 2002 |
| 2014–2017 | Wet | Forest | CA | mostly clean, dust episodes excluded | – | – | 4.0 | Moran-Zuloaga et al., 2018 |
| 2014–2017 | Wet | Forest | CA | Dust episodes | – | – | 11–39 | Moran-Zuloaga et al., 2018 |
| 1992–1995 | Dry | Forest | AD | aged and fresh biomass burning | 81.0 | 47.0 | 34.0 | Echalar et al., 1998 |
| 1996–1998 | Dry | Forest | AD | aged and fresh biomass burning | 100.0 | 63.0 | 37.0 | Maenhaut et al., 2002 |
| 1996–1998 | Dry | Forest | CA | aged biomass burning | 13.4 | 6.2 | 7.2 | Pauliquevis et al., 2012 |
| 1999 | Dry | Forest | AD | aged biomass burning | 40.1 | 33.5 | 6.6 | Guyon et al., 2003 |
| 2004 | Dry | Forest | CA | aged biomass burning | 13.3 | 8.1 | 5.2 | Rizzo et al., 2010 |
| 2008–2012 | Dry | Forest | CA | aged biomass burning | 7.8 | 3.4 | 4.4 | Arana et al., 2014 |
| 1999 | Dry | Pasture | AD | aged and fresh biomass burning | 83.0 | 66.0 | 17.8 | Artaxo et al., 2002 |
| 2009–2012 | Dry | Pasture | AD | aged biomass burning | 43.2 | 33.0 | 10.2 | Arana et al., 2014 |
| 2014–2017 | Dry | Forest | CA | aged and fresh biomass burning | – | – | 6.5 | Moran-Zuloaga et al., 2018 |
Figure 3.3.1
Median particle number size distributions at a forest site in Central Amazonia between 2008 and 2014 during the (a) wet and (b) dry season. Please note the different values in the Y-axis. Measurements were taken for dry aerosols under low RH conditions (30–40%) and 10 m above the canopy. Shadows represent the 25–75th percentile range. Data and analysis from Rizzo et al., 2018.
Table 3.3.2
Ranges of reported mean values for submicrometer particle size distribution parameters in Amazonia for the aerosol modes: accumulation, Aitken, and nucleation (N: modal particle number concentration; Dpg: modal mean geometric diameter; σpg: modal geometric standard deviation). All measurements reported here were taken within the boundary layer and above the forest canopy.
| SEASON | LANDSCAPE | AIR MASSES | MODES | OCCURRENCE (%) | N (cm–3) | Dpg(nm) | σpg | REFERENCES |
|---|---|---|---|---|---|---|---|---|
| Wet | Forest | mostly clean | Accumulation | 100 | 159–240 | 145–172 | 1.34–1.41 | Pöhlker et al., 2016; Rissler et al., 2004; Rizzo et al., 2018; Zhou et al., 2002 |
| Aitken | 92–100 | 167–475 | 67–70 | 1.34–1.50 | ||||
| Nucleation | 18–79 | 40–191 | 17–25 | 1.31–1.50 | ||||
| Wet-Dry transition | Forest | aged biomass burning | Accumulation | 100 | 736 | 139 | 1.45 | Rissler et al., 2004 |
| Aitken | 100 | 304 | 68 | 1.32 | ||||
| Nucleation | 55 | 276 | 15 | 1.29 | ||||
| Dry | Forest | aged biomass burning | Accumulation | 100 | 672–2670 | 144–179 | 1.48–1.51 | Brito et al., 2014; Pöhlker et al., 2016; Rizzo et al., 2010, 2018 |
| Aitken | 63–100 | 307–4071 | 71–98 | 1.35–1.78 | ||||
| Nucleation | 19–38 | 6–948 | 14–33 | 1.60–2.50 | ||||
| Dry | Pasture | fresh and aged biomass burning | Accumulation | – | 5214 | 190 | 1.53 | Rissler et al., 2006 |
| Aitken | – | 5213 | 92 | 1.63 | ||||
| Nucleation | – | 1090 | 12 | 1.82 | ||||
| Dry-Wet transition | Pasture | aged biomass burning | Accumulation | – | 785 | 128 | 1.66 | Rissler et al., 2006 |
| Aitken | – | 406 | 61 | 1.39 | ||||
| Nucleation | – | 849 | 12 | 1.82 |
Figure 3.3.2
Airborne observations of submicrometer particle size distributions at five different altitudes above a forest area in Amazonia on 7 March 2014. Observations were taken on board the G-1 aircraft during the GoAmazon2014/15 experiment. Reproduced from Wang et al., 2016.
Figure 3.3.3
Particle number size distribution for boreal forest sites. Seasonal median and 25th–75th percentile ranges in the shaded areas of size distributions observed at Hyytiälä (HYYT) and ZOTTO 2006–2011 are shown. The timing of each analysis is coded as MAM: March–May; JJA: June–August; SON: September-October; DJF: December–February.
Table 3.3.3
Statistical representation of modal parameters derived from fitting, per season, for Hyytiälä 2006–2011. Data presented as 50th percentile of fitted parameters. N represents the number of particles in the mode, Dg is the geometrical diameter, and GSD is the geometric standard deviation.
| MODE | DG-RANGE (µm) | # OBSERVATIONS | N (cm–3) | Dg (µm) | GSD |
|---|---|---|---|---|---|
| March-April-May | |||||
| Nuclei | 0.001–0.025 | 9052 | 321 | 0.012 | 1.4 |
| Aitken | 0.025–0.07 | 6899 | 2131 | 0.044 | 1.6 |
| Acc1 | 0.07–0.25 | 8953 | 854 | 0.148 | 1.6 |
| Acc2 | 0.25–1.0 | 2620 | 10.4 | 0.403 | 1.5 |
| June-July-August | |||||
| Nuclei | 0.001–0.025 | 8269 | 111 | 0.013 | 1.4 |
| Aitken | 0.025–0.07 | 5790 | 1921 | 0.045 | 1.5 |
| Acc1 | 0.07–0.25 | 8995 | 1742 | 0.138 | 1.5 |
| Acc2 | 0.25–1.0 | 3593 | 7.4 | 0.419 | 1.4 |
| September-October-November | |||||
| Nuclei | 0.001–0.025 | 8203 | 167 | 0.013 | 1.4 |
| Aitken | 0.025–0.07 | 6665 | 1458 | 0.046 | 1.6 |
| Acc1 | 0.07–0.25 | 7931 | 549 | 0.155 | 1.6 |
| Acc2 | 0.25–1.0 | 2611 | 8.9 | 0.401 | 1.4 |
| December-January-February | |||||
| Nuclei | 0.001–0.025 | 8878 | 172 | 0.013 | 1.3 |
| Aitken | 0.025–0.07 | 7153 | 1175 | 0.045 | 1.6 |
| Acc1 | 0.07–0.25 | 8913 | 763 | 0.159 | 1.6 |
| Acc2 | 0.25–1.0 | 2187 | 20.1 | 0.375 | 1.5 |
Table 3.3.4
Statistical representation of modal parameters derived from fitting per season at the ZOTTO tower 2006–2011. Data presented as 50th of fitted parameters.
| MODE | DG-RANGE (µm) | # OBSERVATIONS | N(cm–3) | Dg(µm) | GSD |
|---|---|---|---|---|---|
| March-April-May | |||||
| Nuclei | 0.001–0.025 | 2034 | 5.6 | 0.013 | 1.2 |
| Aitken | 0.025–0.07 | 6641 | 229 | 0.045 | 1.3 |
| Acc1 | 0.07–0.25 | 9255 | 716 | 0.144 | 1.5 |
| Acc2 | 0.25–1.0 | 1936 | 76.3 | 0.299 | 1.3 |
| June-July-August | |||||
| Nuclei | 0.001–0.025 | 3210 | 4.9 | 0.014 | 1.3 |
| Aitken | 0.025–0.07 | 6255 | 142 | 0.042 | 1.3 |
| Acc1 | 0.07–0.25 | 9522 | 691 | 0.14 | 1.5 |
| Acc2 | 0.25–1.0 | 2950 | 37.4 | 0.345 | 1.4 |
| September-October-November | |||||
| Nuclei | 0.001–0.025 | 4459 | 5.1 | 0.014 | 1.2 |
| Aitken | 0.025–0.07 | 9928 | 250 | 0.045 | 1.4 |
| Acc1 | 0.07–0.25 | 12566 | 534 | 0.144 | 1.5 |
| Acc2 | 0.25–1.0 | 2772 | 17 | 0.345 | 1.4 |
| December-January-February | |||||
| Nuclei | 0.001–0.025 | 2087 | 5.7 | 0.011 | 1.2 |
| Aitken | 0.025–0.07 | 4999 | 238 | 0.044 | 1.4 |
| Acc1 | 0.07–0.25 | 6767 | 817 | 0.137 | 1.6 |
| Acc2 | 0.25–1.0 | 1162 | 168 | 0.293 | 1.5 |
Figure 3.4.1
Hygroscopic properties of atmospheric aerosols from tropical rainforest air (AMAZE) and rural boreal air (SPB). Three different hygroscopic regimes are identified: (I) a quasi-eutonic deliquescence and efflorescence regime at low-humidity where substances are just partly dissolved and also exist in a non-dissolved phase; (II) a gradual deliquescence and efflorescence regime at intermediate humidity where solutes undergo gradual dissolution or solidification in the aqueous phase; and (III) a dilute regime at high humidity where the solutes are fully dissolved. (a, b) mass growth factors (Gm) were observed as a function of relative humidity compared to a mass-based κ interaction model (KIM) they developed; (c, d) κm calculated as a function of mass growth factor and (e, f) κm plotted against water activity. The data points and error bars (mean value ±standard deviation) are from FDHA experiments of hydration (blue circles) and dehydration (red crosses). The black lines are fits of κm – interaction model (KIM). From Mikhailov et al., 2013.
Table 3.4.1
Summary of hygroscopicity observations at tropical and boreal sites. S, Da, κCCN, and NCCN are the set supersaturation, observed activation diameter, calculated κ, and the number of activated particles as measured with the CCNC. For the HTDMA measurements, set diameter D and calculated κ from the measured growth factor.
| CAMPAIGN, LOCATION, YEAR | INSTRUMENT, TECHNIQUE | SEASON, TIME PERIOD | S[%] | Da[nm] | κCCN | NCCN[cm-3] | D[nm] | κHTDMA | REFERENCE |
|---|---|---|---|---|---|---|---|---|---|
| Tropical forests | |||||||||
| AMAZE-08, observational tower (TT34), Amazon, Brazil, 2008 | Size resolved CCN | wet season, 14 February –12 March 2008, | 0.10 0.19 0.28 0.46 0.82 | 199 128 105 83 55 | 0.2 0.2 0.17 0.12 0.13 | 41 90 114 141 194 | – | – | Gunthe et al., 2011, Martin et al., 2010 |
| OP3 project, Global Atmospheric Watch (GAW) station, tropical rainforest in Borneo, Malaysia, 2008 | Size resolved CCN, HTDMA | 3 July –20 July 2008 | 0.63 0.56 0.51 0.48 0.47 0.43 0.26 0.25 0.18 0.18 | 65 74 84 96 110 129 148 171 199 224 | 0,13 0,1 0,09 0,07 0,05 0,04 0,05 0,6 0,06 0,06 | – | 32 53 104 155 208 259 | 0,20 0,17 0,21 0,24 0,27 0,30 | Irwin et al., 2011 |
| observational tower (TT34), Amazon, Brazil, 2013 | Size resolved CCN, HTDMA | transition season, 4–28 July 2013 | 0.15 0.26 0.47 0.8 1.13 | 152 105 78 56 45 | 0.18 0.18 0.13 0.12 0.12 | 87 161 212 248 268 | 45 69 102 154 249 | 0.09 0.09 0.12 0.15 0.17 | Whitehead et al., 2016 |
| GoAmazon2014/5, ATTO, Amazon, Brazil, 2014–2015 | Size resolved CCN | Annual average, March 2014–February 2015 | 0.11 0.15 0.2 0.24 0.29 0.47 0.61 0.74 0.92 1.1 | 172 136 117 105 98 77 63 57 49 43 | 0.22 0.22 0.21 0.19 0.17 0.13 0.14 0.13 0.13 0.13 | 275 457 571 652 719 883 900 941 987 1013 | – | – | Pöhlker et al., 2016 |
| Boreal forests | |||||||||
| SMEAR II station, Hyytiälä, Finland, 2007 | Size resolved CCN, CFSTGC and HTDMA | Spring, 25 March–15 May 2007 | 1.1 0.61 0.36 | 40 60 80 | 0.2 0.29 0.22 | – | 30 50 | 0.15 0.14 | Cerully et al., 2011 |
| SMEAR II station, Hyytiälä, Finland, 2008–2009 | Size resolved CCN, HTDMA | Summer, July 2008–June 2009 | 0.1 0.2 0.4 0.6 1 | – – – – | – – – – | 169 346 546 678 894 | – | – | Sihto et al., 2011 |
| SMEAR II station, Hyytiälä, Finland, 2009–2012 | Size resolved CCN | Annual average, January 2009–April 2012 | 0.1 0.2 0.4 0.6 1 | 174 114 88 73 57 | 0.26 0.23 0.13 0.1 0.07 | – | – | – | Paramonov et al., 2013 |
| HUMPPA-COPEC, SMEAR II station, Hyytiälä, Finland, 2010 | Size resolved CCN, VH-TDMA | Summer, 12 July–12 August 2010 | 0.09 0.22 0.48 0.74 1.26 | 41 55 70 102 203 | 0.12 0.14 0.17 0.28 0.22 | – | 50 75 110 | 0.12 0.12 0.15 | Hong et al., 2014 |
Figure 3.4.2
Relationship of the composition-derived hygroscopicity parameter κ to the binned and averaged ratio of organic (OA) to inorganic (IA) aerosol components (Schmale et al., 2018) for seven different measurements sites: FIK = Finokalia, Crete; MHD = Mace Head, Ireland; MEL = Melpitz, Germany; SMR = Hyytiälä, Finland; ATT = ATTO tower, Amazon; JFJ = Jungfraujoch, Switzerland. Note that the asymptotic-like approach of the curves towards 0.1 is due to the assumption of κOA = 0.1.
Figure 3.4.3
Relationship between dry particle diameter and κ for boreal and tropical forests, as well as the seasonality of κ for Hyytiälä. In (a), we can observe the comparison among sites; in (b), we can see the comparison among seasons for Hyytiäla. Both panels show the median values, with error bars being 25th and 75th percentiles. Legend entries also indicate the slope of the linear regression y = ax + b fit. Figure from Paramonov et al., 2013).
Figure 3.5.1
Reported ranges for particle light scattering (σsp) and absorption (σap) in the Amazon Basin, during the wet and dry seasons, at a forest and anthropic landscapes (arc of deforestation). This compilation integrates results from intensive and long-term field experiments using different platforms (ground-based and airborne). Data points refer to medians and interquartile ranges. Scattering coefficients were measured at 550 nm. Absorption coefficients were calculated at wavelengths ranging from 370 to 565 nm from 1999 to 2004 and at 637 nm from 2008 onwards. Most measurements were taken under dry conditions (RH < 40%), except for the data points marked in the figure as ‘X.’ References: Artaxo et al., 2013; Chand et al., 2006; Guyon et al., 2003; Kuhn et al., 2010; Rizzo et al., 2011, 2013; Saturno et al., 2018a.
Table 3.5.1
Descriptive statistics of extensive aerosol optical properties scattering and absorption coefficient (σsp and σap) at SMEAR II, Pallas, and ZOTTO at λ = 550 nm. At the ZOTTO tower, σap was measured at λ = 574 nm.
References: 1) Virkkula et al., 2011; 2) Pandolfi et al., 2018; 3) Luoma et al., 2019; 4) Lihavainen et al., 2015; 5) Chi et al., 2013.
| REF. | SITE | LAT | LON | PERIOD | σsp MEAN ± STD | σsp MEDIAN | σap MEAN ± STD | σap MEDIAN | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | SMEAR II | 61° 51′N, | 24° 17′E | 2006–2009 | 18 ± 20 | 12 | 2.2 ± 2.4 | 1.5 | |
| 2 | 2006–2015 | 17 ± 19 | 11 | ||||||
| 3 | 2006–2017 | 15 ± 17 | 10 | 2.2 ± 2.5 | 1.4 | ||||
| 4 | Pallas | 67° 58′N | 24°07′E | 2001–2010 | 7.8 ± 9.6 | 4.4 | 0.74 ± 1.1 | 0.35 | |
| 2 | 2000–2015 | 7.9 ± 16 | 4.3 | ||||||
| 5 | ZOTTO | 61°N | 89°E | 2006–2011 | 50 m | 15 ± 18 | 11 | 1.87 ± 2.54 | 1.15 |
| 2006–2011 | 300 m | 14 ± 16 | 10 | 1.74 ± 2.4 | 1.02 |
Table 3.5.2
Descriptive statistics of intensive aerosol optical properties scattering Ångström exponent (SAE) backscatter fraction (BSF), single-scattering albedo (SSA), and absorption Ångström exponent (AAE) at SMEAR II, Pallas, and ZOTTO. SAE was calculated for the wavelength range λ = 450 – 700 nm, BSF at λ = 550 nm, SSA at λ = 550 for SMEAR II and Pallas and at λ = 574 nm for ZOTTO. The AAE was calculated over the Aethalometer wavelength range λ = 370 – 950 nm.
References: 1) Virkkula et al., 2011; 2) Pandolfi et al., 2018; 3) Luoma et al., 2019; 4) Lihavainen et al., 2015; 5) Chi et al., 2013.
| REF. | SITE | PERIOD | SAE MEAN ± STD | MEDIAN | BSF MEAN ± STD | MEDIAN | SSA MEAN ± STD | MEDIAN | AAE MEAN ± STD | MEDIAN | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | SMEAR II | 2006–2009 | 1.7 ± 0.5 | 1.8 | 0.14 ± 0.03 | 0.14 | 0.88 ± 0.07 | 0.89 | 1.4* ± 0.3 | 1.4 | |
| 2 | 2006–2015 | 1.8 ± 0.5 | 1.84 | 0.15 ± 0.03 | 0.147 | ||||||
| 3 | 2006–2017 | 1.8 ± 0.6 | 1.88 | 0.15 ± 0.03 | 0.15 | 0.86 ± 0.08 | 0.87 | 0.94 ± 0.41 | 0.98 | ||
| 4 | Pallas | 2001–2010 | 1.7 ± 0.7 | 1.8 | 0.13 ± 0.03 | 0.12 | 0.92 ± 0.06 | 0.91 | 1.1 ± 0.6 | 1.1 | |
| 2 | 2000–2015 | 1.63 ± 0.67 | 1.78 | 0.132 ± 0.04 | 0.125 | ||||||
| 5 | ZOTTO | 2006–2011 | 50 m | 1.84 ± 0.47 | 1.89 | 0.112 ± 0.01 | 0.88 ± 0.08 | 0.88 | |||
| 2006–2011 | 300 m | 1.85 ± 0.49 | 1.9 | 0.117 ± 0.01 | 0.88 ± 0.88 | 0.88 |
Figure 3.5.2
Reported ranges for scattering Ångström exponent (SAE), absorption Ångström exponent (AAE), and single scattering albedo (SSA) in the Amazon Basin, during the wet and dry seasons, at a forest and anthropic landscapes (arc of deforestation). This is a compilation of results from intensive and long-term field experiments using different platforms (ground-based, airborne, remote sensing). Data points refer to averages or medians. SAE, AAE, and SSA values are given at dry conditions, with measurements made at 30–40% RH. References: Artaxo et al., 2013; Chand et al., 2006; Guyon et al., 2003; Kuhn et al., 2010; Marenco et al., 2016; Pérez-Ramírez et al., 2017; Reid et al., 1999; Rizzo et al., 2011, 2013; Saturno et al., 2018a; Schafer et al., 2008; Sena et al., 2013.
Figure 3.5.3
Seasonal variations of σsp and σap and SSA at the boreal forest sites SMEAR II, Pallas, and ZOTTO and at the Amazonian site ATTO. All σsp are calculated at λ = 550 nm, at SMEAR II and Pallas, with σap, and SSA are at λ = 550 nm. At ZOTO σap and SSA are at λ = 574 nm and at ATTO σap and SSA are at λ = 637 nm. The figure was prepared from the results presented for SMEAR II by Virkkula et al. (2011), for Pallas by Lihavainen et al. (2015), for ZOTTO by Chi et al. (2013), and for the Amazonian site ATTO by Saturno et al. (2018a). For Pallas, Lihavainen et al. (2015) reported as numbers the highest and lowest monthly averages of the annual cycle of all data and of continental air masses. For ZOTTO, Chi et al. (2013) presented the seasonal averages of σsp and σap and SSA in polluted and clean air masses, and for ATTO, Saturno et al. (2018a) presented averages of σsp and σap and SSA for the dry and wet seasons.
Figure 3.5.4
(a) Scattering coefficient σ550, measured at 550 nm. The gray lines represent ± standard deviation. (b) Ångström exponent, å, calculated based on values of σ at 450 and 550 nm, along with the backscattering fraction, b. The data cover the days of year 120–270 during the period 2000–2006. All quantities have been plotted as a function of the time the air masses have spent over land prior to arriving at the measurement site. From Lihavainen et al. (2009).
Figure 3.5.5
The optical hygroscopicity parameter γ versus the organic mass fraction for boreal aerosol measured in Hyytiäla, Finland (green points). The grey squares show γ in more inorganic dominated aerosol in Melpitz, Germany, during wintertime (Zieger et al., 2014). γ describes the magnitude of the scattering of enhancement factor f(RH), where f(RH)=α(1-RH)–γ. Figure taken from Zieger et al. (2015).
Figure 3.5.6
Time series of aerosol optical depth (AOD) at 500 nm for several sites in Amazonia from 2000 to 2020 measured with AERONET sun photometers.
Figure 3.5.7
Time series of aerosol optical depth at 500 nm from 2004 to 2019 for 3 AERONET stations over Eurasian Boreal forests: Yakutsk, Yekaterinburg, and Tomsk.
Figure 3.5.8
Time series of Aerosol Optical Depth at 500nm from 1997 to 2019 for 4 AERONET sites in the Canadian boreal forest region: Waskesiu, Thompson, Pickle Lake, and Chapais.
Figure 4.1.3
Wet season satellite-based isoprene flux estimates from 2005 to 2014 in Amazonia, derived from OMI formaldehyde columns. A decreasing trend of –0.42% per year for isoprene fluxes was observed during the wet season. Figure from Yáñez-Serrano et al., 2020.
Figure 4.2.1
Histograms of annual net ecosystem exchanges of CO2 and CH4 in boreal forests from 1990 to 2015. (a) annual NEE flux data collected from a literature synthesis. (b) Annual CH4 data were compiled based on Ni and Groffman (2018).
Figure 4.2.2
Annual Net Ecosystem Exchange (NEE) of carbon measured in Amazon flux towers at pristine forests, in the period 1999–2006, compared to measured rainfall and dryness index. Figure adapted from von Randow et al. (2013) (minus sign represents net carbon uptake by the forest here).
Figure 4.2.3
Time series of net biomass change, productivity, and biomass mortality from 1985 to 2010 in Amazonia, showing an increase in tree mortality that brings the net biomass change close to zero in recent years. Shading corresponds to the number of plots included in the calculation of the mean, varying from 25 plots in 1983 (light grey) to a maximum of 204 plots in 2003 (dark grey). Figure from Brienen et al., 2015.
Figure 4.2.4
Long-term carbon dynamics of structurally intact old-growth tropical forests in Africa and Amazonia from 1985 to 2014. In (a) Trends in net aboveground live biomass carbon. In (b), the carbon gains to the system from wood production. In (c), carbon losses from the system from tree mortality. Shading corresponds to the 95% CI, with darker shading indicating a greater number of plots monitored in that year (the lightest shading indicates the minimum 25 plots monitored). Figure from Hubau et al., 2020.
Figure 4.4.1
Schematic diagram showing processes producing fine particles due to convective cloud outflow above Suriname in Northern Amazonia, with enhanced ultrafine number concentrations in regions of cloud outflow, suggesting nucleation (Adapted from Krejci et al., 2003).
Figure 4.5.1
Tropical primary forest loss 2002–2020 in million hectares per year. The countries responsible for most tropical deforestation is Brazil, the Democratic Republic of Congo, Bolivia, Indonesia, Peru, and Colombia. Data source: World Resources Institute, WRI 2021, (https://research.wri.org/gfr/forest-pulse).
Figure 4.5.2
Time series of monthly mean AOD at 550 nm over Alaska retrieved from ATSR data using FMI’s ADV v2.31 aerosol retrieval algorithm (blue, right axis)). The data are grouped for each month (May-September), showing the AOD variation for each of these months from 1995 until 2011. Also shown are the coincident monthly sums (left axis) of the ATSR-derived fire counts (red) and the fire emissions (green).
Figure 4.6.1
Average spatial distribution of the direct radiative forcing (DRF) of biomass burning aerosols in Amazonia during the dry season of 2010. Radiation flux at the TOA was measured by the CERES (Clouds and the Earth’s Radiant Energy System) sensor, and aerosol was determined using the MODIS sensor. The average forcing is negative, so the cooling effect from organic scattering aerosols dominates over the warming from black carbon emissions.
Figure 4.6.2
Monthly mean variation of short-wave aerosol radiative forcing (SWARF) averaged over 24 h at the top of the atmosphere TOA (green), surface SUR (blue), and within the atmosphere ATM (red) for the period 2011–2017 in Central Amazonia. Data from the Manaus/EMBRAPA AERONET site (Palácios et al., 2020).
Figure 4.6.3
Effects of aerosols on carbon uptake by the Amazonian Forest expressed as NEE for dry and wet seasons in an LBA tower in Rondônia (Rebio Jarú). A relative irradiance of 1.0 corresponds to an atmosphere with background aerosols (AOD = 0.05). At increasing biomass burning aerosol loadings, the relative irradiance decreases. Analysis from Cirino et al., 2014.
Figure 4.6.4
Modeled 1998–2007 mean percentage changes in (d, e, f) diffuse radiation, (g, h, i) GPP, and (j, k, l) NPP during the wet (defined here as of December to May) season, dry (June to November) season and August due to biomass burning aerosol (BBA) emissions. For details, see Rap et al., 2015.
Figure 5.1
Illustration of the propagation of aerosol perturbations (idealized through prescribed cloud droplet number concentrations of 50 cm–3, 250 cm–3, and 2500 cm–3) through mixed-phase cloud microphysics in a single-supercell convective cloud. With increasing droplet number concentrations, the warm rain formation is shut down while the freezing level increases (Heikenfeld et al., 2019).
Figure 6.1
Schematic illustration of the processes through which the effects of global warming in the a) Amazon (left panel, blue boxes, and arrows) and b) boreal regions (right panel, red boxes, and arrows) feedback on the regional climate change (Jia et al., 2019, IPCC SRCCL, 2019).
Figure 6.1.1
Schematic of the carbon-based Continental Biosphere-Atmosphere-Cloud-Climate COBACC feedback loops. The temperature-related feedback loop is in green color, while the GPP-related feedback loop is in orange. CO2 – concentration of carbon dioxide, GPP – gross primary production, BVOC – biogenic volatile organic compounds, SOA – secondary organic aerosol, CS – condensation sink, CCN – cloud condensation nuclei, Rd/Rg – fraction of diffuse radiation in global radiation, T – temperature. For an explanation regarding the colors and arrows, please see the text.
Figure 6.2.1
Relative density distributions of aboveground biomass in living woody plants per 1981–2017 mean annual precipitation class for the tropical forest in the Amazon, Congo Basin, and Southeast Asia. The color shading is proportional to 30 m × 30 m pixels in each of the relative density plots. Thus, the more solid the color is, the greater the number of pixels and the larger the area in that precipitation class, while the less solid color represents fewer pixels and smaller areas. Figure from Brando et al. (2019).
Figure 6.2.2
Vapor pressure deficit (VPD) in Southeast (left) and Northwest Amazonia (right) from 1979 to 2016 measured through various techniques. Adapted from Barkhordarian et al., 2019.
Figure 6.2.3
Conceptual model of the aerosol life cycle over the Amazon Basin relating VOC emissions from the forest with vertical cloud transport and production of new particles in the upper troposphere.
Figure 6.2.4
Conceptual framework for fire feedback in Amazonia, with the different fire types associated with the different ecosystem and human-induced drivers (Barlow et al., 2019).