Introduction
Vegetation fires, either anthropogenic or ignited naturally by
lightning, affect the climate through complex interactions between the
biosphere and the atmosphere. Wildfires impact soil, vegetation and
ecosystems directly. In addition, aerosols and trace gases emitted into the
atmosphere are key parameters of the overall fire climate impact
. Aerosol particles emitted from
fires are known to impact a wide range of atmospheric processes including
radiative transfer, atmospheric chemistry and cloud micro-physical processes
. A crucial parameter that has been
identified to influence the lifetime of aerosols and thus potentially also
their climate impact is the fire emission height, i.e., the altitude
above the surface at which fire smoke plumes release emissions into the
atmosphere. The terms “fire emission height”, “injection
height” and “plume height” have been used as equivalent terms in the
literature, although they do not always have the same meaning. In this
study, we use the term “plume height” to describe the top level above
the surface at which emissions are injected, i.e., the “plume-top
height”. In contrast, “emission profiles” specify the entire vertical
emission profiles from the surface to the top of the smoke plume.
Theories and models, which describe the process of plume rise, have been
developed since the 1970s. Today various semi-empirical
e.g., and
analytical-numerical plume height models e.g., are available. In addition to these plume height
models which take into account fire properties and atmospheric conditions to
calculate plume heights, other parametrizations are solely based on fire
brightness temperature or fire intensity
. The review papers of and
provide extensive summaries of various plume height models. Although
a reasonable performance of the plume models has been demonstrated for
selected case studies on local or regional scales, the knowledge about smoke
plume heights on a global scale is very limited due to a lack of observational
data sets. Besides a small number of airborne in situ and ground-based remote-sensing studies, e.g., or , satellite data
sets provide observations of potentially global coverage. Although smoke
plume measurement uncertainties are only ±200 m for well-constrained
plumes , only a limited number of plumes are
available on the global scale, because a partly manual analysis is required
for each individual plume. The most comprehensive data set of individual
smoke plume heights is provided by the “Multiangle Imaging Spectroradiometer (MISR) Plume Height Project” (MPHP)
that has been analyzed in the framework of several regional studies
. These studies indicate
a large variability of smoke plume heights all over the globe. Various case
studies demonstrated that particularly intense fires can, under favorable
meteorological conditions, result in emission injections into the upper
troposphere or even the lower stratosphere . Very rare cases of pyro-cumulonimbus events caused by
particularly strong fires may even be comparable to small volcanic eruptions
. However, the majority of emission
injections are limited to the planetary boundary layer (PBL) .
The studies of , and showed that the transport
of wildfire emissions crucially depends on an appropriate implementation of
smoke plume heights that consider the free tropospheric injection of
a certain emission fraction. Nevertheless, due to computational costs and the
lack of complexity regarding the representation of fire processes in global
models, standard versions of state-of-the-art global climate and Earth system
models respectively currently make use of simple latitude- and region-dependent
vertical emission distributions or prescribe injections
at the surface e.g.,.
In order to step forward towards a better representation of smoke plume
heights in climate models, we implement the simple, semi-empirical plume
height parametrization by into the general circulation
model ECHAM6. In a first step, we evaluate the performance of the plume
height parametrization for selected plumes reported in the MPHP data set. We
use fire radiative power (FRP) reported in the MPHP based on MODIS (Moderate Resolution Imaging Spectroradiometer) data to
test different versions of the parametrization on the global scale and
constrain uncertainties introduced by uncertainties in fire-related and
meteorological variables. Furthermore, the Sofiev plume height
parametrization is adjusted to the ECHAM6-HAM2 aerosol–climate modeling
system by the application of a statistical–empirical tuning. In a second step
we simulate plume heights in ECHAM6-HAM2 globally for the years 2005–2011.
For these experiments the global fire assimilation system (GFAS;
) FRP is used as input. We carry out a number of
simulations that cover the standard Sofiev parametrization as well as
a modified version of the Sofiev parametrization optimized for application in
ECHAM6-HAM2. Moreover, effects of the implementation of a prescribed diurnal
cycle are investigated. A sensitivity simulation with a global doubling of
FRP assesses the implications of a potential climate-induced increase in fire
intensity.
The impact of changes in plume heights regarding aerosol burden, transport
and radiative forcing is presented in the second part of this two-paper
series (Veira et al., 2015).
Section 2 in this paper introduces the ECHAM6 global circulation model
extended by the HAM2 aerosol model, configurations of the Sofiev plume height
parametrization and the GFAS data set. Section 3 provides a statistical
analysis of the global plume height parametrization performance and the
application of an statistical–empirical FRP correction. In Sect. 4, we
present global plume height patterns simulated by ECHAM6-HAM2 and enhanced by the
Sofiev parametrization, and compare these to plume height distributions in the
standard version of the ECHAM6-HAM2 model. Furthermore, we discuss the
influence of the diurnal cycle in fire intensity. In Sect. 5 we compare plume
heights simulated by the Sofiev parametrization to results from a more
complex 1-D plume model. The conclusions of this study are summarized in
Sect. 6, where we discuss our results in the context of climate and Earth system model development.
Methodology
In the following, our general setup of the global circulation model ECHAM6,
the aerosol model extension HAM2 and the semi-empirical plume height
parametrization are described. We introduce the MPHP data set which we use
for the evaluation of the plume height parametrization. Moreover, the
implementation of the GFAS fire intensity data set in ECHAM6-HAM2 is
explained. The last two sections present details on the specific model setup
used for the plume height evaluation and the simulation of global plume
height patterns.
ECHAM6-HAM2
ECHAM6 is a general circulation model and serves as the
atmospheric and land component of the Max Planck Institute Earth system model
(MPI-ESM). A detailed model description is provided by
and . For all our simulations we apply a T63
grid (spectral space) which corresponds to a Gaussian grid of approximately
1.875∘×1.875∘. In the vertical, we use 47 vertical
layers ranging from the surface to 0.01 hPa. A computing time step of
10 min is chosen for all simulations. The plume height
parametrization evaluation experiments only apply prescribed sea surface
temperature, which originates from the Atmospheric Model Intercomparison
Project (AMIP). No nudging against observations is applied for these
simulations, because we aim to investigate the basic skills of the
ECHAM6-HAM2 model (extended by the plume height parametrization) to capture
the spectrum of plume heights, not to reproduce individual plume
observations.
For all other simulations, the atmospheric model is additionally nudged
against observational data every 6 h. Thus, the model dynamics is
forced to stay close to the ERA-Interim reanalysis fields and
changes in global plume height patterns between different plume height
parametrizations stay comparable. For these simulations, the ECHAM6 model is
extended by the aerosol module HAM2, modeling the dynamics, micro-physics,
transport and radiative impact of aerosol species . The
aerosol module represents the aerosol spectrum by superposition of seven
lognormal distributions including nucleation, Aitken, accumulation and
coarse mode. Vegetation fire emissions, here referred to as “wildfire
emissions”, are represented by three species: black carbon (BC), organic
carbon (OC) and sulfur dioxide (SO2). A description of the changes in the
HAM model configuration from the original model version HAM1
to HAM2, used in this study, has been published by
. The term “ECHAM6-HAM2” in this paper refers to
model version ECHAM6.1.0-HAM2.2.
Plume heights Hp in the standard version of ECHAM6-HAM2.2 are
generally prescribed as the PBL height plus two
model layers:
Hp=PBLHeight+2model layers.
For the large majority of plume heights lower than 4 km,
75 % of the released wildfire emissions are vertically distributed
with a constant mass mixing ratio from the surface to the level below the
PBL, 17 % are injected into the next model layer above the PBL and
8 % are injected in the layer of height Hp. If the PBL
height exceeds 4 km, the plume heights are set to PBL height and the
emissions are equally distributed with constant mass mixing ratio from the
surface to the first model layer below the PBL height. The upper limit of
4 km is an arbitrary value, but it represents the standard plume
height implementation of ECHAM6-HAM2 described by . In order
to ensure comparability of our results to previous studies, we apply this
standard implementation for one reference simulation, whereas improved
plume height parametrizations are used for all other simulations.
Implementation of an improved plume height parametrization
To improve the representation of plume heights in
ECHAM6-HAM2, we implement the simple, semi-empirical plume height
parametrization by , henceforth named “Sofiev
Parametrization” (SP). The original SP predicts plume heights as a function
of PBL height, HPBL, Brunt–Väisälä frequency of the
free troposphere (FT), NFT, at 2× PBL height, and the total FRP of a fire, Pf:
Hp=αHPBL+βPfPf0γexp-δNFT2/N02.
Here, α is that part of the PBL passed freely, β is a scaling
factor for the fire intensity, γ describes the power-law dependence on
Pf, δ scales the dependence on the stability of the FT, N0 is the reference Brunt–Väisälä frequency
and Pf0 is the reference FRP. N0 and Pf0 are
a priori chosen as N0=2.5x 10-4s-1
and Pf0=106W, respectively. The constants α,
β, γ and δ have been determined by
using a computational learning data set:
α=0.24,β=170m,γ=0.35,δ=0.6.
As the use of NFT at 2× PBL height is, from a physical
point of view, not most appropriate for plumes which do not reach the
FT, proposed a two-step iteration scheme,
with separate tuning constants for PBL and FT plumes.
For PBL plumes
α=0.15,β=102m,γ=0.49,δ=0.0,
and for FT plumes
α=0.93,β=298m,γ=0.13,δ=0.7.
In this study, the performance of the one-step as well as the two-step SP are
tested. successfully applied the one-step SP for CO
modeling in the CHIMERE model. For stable PBL layers,
achieved improved plume height predictions with the SP when replacing the
Brunt–Väisälä frequency of the FT by the
inversion layer Brunt–Väisälä frequency. Thus, for all nighttime
plumes (18:00–08:00 LT) we replace NFT in Eq. () by
NPBL which describes the Brunt–Väisälä frequency of
the stable nocturnal boundary layer at the second lowest model layer
approximately 150 m above the surface.
Visualization of gridded mean plume heights of the MPHP data set for the year
2006. The height of each column shows the injection height above the surface. The
highest columns represent maximum injection heights of approximately
6 km; we apply a linear scaling of the plume heights. Colors indicate
the related total fire radiative power (FRP) detected by MODIS.
The implementation of this simple plume height parametrization is
a significant improvement compared to prescribed plume heights, because it
takes into account fire activity as well as ambient meteorological conditions
at the time of the fire. However, various parameters, such as fire size and wind
drag or entrainment and multiple core fire
structure , are known to impact plume heights and are not
explicitly represented in the SP. On the other hand, studies by
, , and indicate that
neither of the more complex plume models shows an outstanding model
performance. Moreover, the input parameters required for plume models on the
global scale, such as fire size and fire intensity, are still very uncertain
. Although FRP is strongly correlated with the heat flux
of a fire and thus with fire-induced atmospheric convection, the reliance of
plume heights on FRP measured by remote-sensing techniques is much more
uncertain than the theoretical relationship between FRP and heat fluxes
might suppose. Therefore, the use of a more advanced, more analytical
plume model driven by original MODIS or derived FRP data cannot be expected
to increase the accuracy of plume height predictions for global climate models with coarse resolution.
MPHP satellite data set
The MPHP represents a synthesis of MISR smoke aerosol data and MODIS MOD14 thermal anomaly
data . This unique plume height data set has
been accomplished by application of the MISR INteractive eXplorer (MINX)
software tool which retrieves wind-corrected plume heights from MISR data. In
contrast to the plume heights provided in the MPHP, no further processing is
applied to the manually selected MODIS MOD14 thermal anomalies which are
attributed to individual MISR plumes. The latest release of the MPHP (April 2012)
includes data of wildfire smoke plumes in North and South America,
Eurasia, Africa and Southeast Asia, observed between 2001 and 2009. The
MPHP data set used in this study is based on red band retrievals only as no
blue band data were available. For future studies, an explicit validation
of red and blue band retrievals is highly desirable, because for thin plumes
blue band retrievals are expected to provide more accurate plume height
estimations than red band retrievals. Various
studies made use of specific parts of this data set: the assignment of plume
height distributions to vegetation types in North America , peat fire plumes in Borneo and Sumatra and
the analysis of Australian bush fire plumes . Each individual
plume data set provides extensive information about FRP, optical smoke
properties, plume height statistics and wind profiles. For more detailed
information, see the official product description
at http://www-misr.jpl.nasa.gov/getData/accessData/MisrMinxPlumes/. As
stated in the MPHP data quality statement and the error analysis therein,
important biases are introduced by pyro-cumulus clouds which hide below-cloud
fire activity, by shortcomings in the manual digitization of the plumes and
by large uncertainties in the MODIS fire pixels. By excluding plumes of
poor or fair retrieval quality and
incomplete individual data files, the MPHP provides 6942 plumes which we use
for the evaluation of the SP. A visualization of mean annual MPHP plume
height values for the year 2006 is presented in
Fig. . This visualization illustrates the
heterogeneous plume height distribution in the MPHP data set and gives
a qualitative sense of plume height distributions and FRP diversity. On
average, fires of small fire intensity feature lower plume heights, but for
presumably favorable meteorological conditions, even low-intensity fires
reach plume heights of several kilometers. According to the official MPHP
product description and , an observational plume height
accuracy of ±200 m can be assumed. Due to the fact that MISR detects
aerosol plumes that have been aged for a certain period of time, the measured
plume heights do not in some cases adequately represent the convection
generated by the thermal anomalies at the time of a specific satellite
overpass. Thus, MODIS FRP values that correspond to MPHP plume heights can
only be seen as a rough approximation.
FRP bin scheme used for ECHAM6-HAM2 simulations. Individual GFAS fires of
0.1∘×0.1∘ resolution are assigned to FRP bins
1–41 according to their FRP value. See text for more detailed information.
FRP bin no.
1–10
11–15
16–21
22–26
27–41
FRP range [MW]
0–100
100–200
200–500
500–1000
> 1000
Bin width [MW]
10
20
50
100
–
GFAS fire intensity data
The investigation of global plume height patterns and the subsequent climate
impact requires fire intensity data of full global coverage which the MPHP
does not provide. In the current standard setup of ECHAM6-HAM2, wildfire
emissions from the Aerosol Comparisons between Observations and Models
(AEROCOM) project are prescribed , but
no data on FRP are provided. Therefore, we extended the model to use FRP
information from an external data set as a boundary condition. The GFASv1.1
data set offers not only global FRP data but also
corresponding wildfire emissions of BC, OC and SO2. Thus, a consistent
framework for this study and subsequent investigations of the emission height
climate impact is provided. GFASv1.1 applied in this study has a spatial
resolution of 0.1∘×0.1∘ and a daily temporal
resolution. We assume that each 0.1∘×0.1∘ grid
cell includes only one individual fire if a non-zero FRP value is reported in
GFAS.
Relative frequency of total FRP per fire for MODIS, MPHP and GFAS data. MODIS
refers to total FRP of grouped MOD14 level 2 thermal anomalies which feature
distances of 3 km or less to neighboring fire pixels. GFAS data
(version v1.1) are provided as daily mean total FRP of 0.1∘×0.1∘ individual grid cells. MPHP data refer to the total FRP of
manually selected MODIS daytime fires.
The 0.1∘×0.1∘ grid information of GFAS for the
years 2005–2011 is transferred to the ECHAM6-HAM2 T63 grid by combining GFAS
FRP values for each individual fire to fixed FRP bins.
Table illustrates the used FRP bin scheme. The plume
height parametrization is run only once within a grid cell for each FRP bin.
With a maximum of 41 FRP bins considered instead of running the plume height
parametrization for each individual fire of the GFAS data set at every grid
cell, the application of the FRP bin scheme reduces the computational costs
for the plume height parametrization calculations by more than 95 %.
The FRP bins scheme represents a conceptual approach to implement
a simplified fire intensity distribution into a global model. The limitation
of 41 FRP bins was chosen for technical reasons related to the specific input
data format of the ECHAM6 model. The FRP value of bin 1 (0–10 MW)
represents the individual daily mean for the FRP in a particular grid cell,
because the variations of the FRP bin 1 values cover several orders of
magnitude (10-6 to 9.9 MW). For the FRP bins 2–26, a mean FRP
value is applied which represents the mean FRP of all fires in this data set
for the entire period 2005–2011. Due to the importance of intense fires with
FRP values larger than 1000 MW, each of these fires is treated
individually and the specific GFAS FRP value is used to calculate the plume
height (FRP bins 27–41). For 2 days in the 2005–2011 period, more than 15
fires with FRP values larger than 1000 MW could be found in one
specific grid cell and thus the FRP bins 27–41 are not sufficient. In this
case the redundant fires were shifted to neighboring grid cells. Due to the
damping factor γ in Eq. (), the small changes in FRP on the order of 0–5 % introduced by application of the FRP bin scheme do
not alter plume heights simulated by the SP by more than a few meters. Thus,
although the FRP bin scheme represents a simplification of the FRP
distribution, the loss of accuracy in global plume height distributions is
negligible.
In contrast to the GFASv1.1 data set we apply in this study,
used MODIS MOD14 level 2 FRP data for the preparation of
a plume height climatology. The relative frequency of total FRP per fire for
GFAS and MODIS MOD14 level 2 thermal anomalies is presented in
Fig. together with the frequency distribution of the MPHP. We
group individual MODIS FRP pixels, which have a distance smaller than
3 km to the next fire pixel, to one fire, because in many cases
individual MODIS MOD14 level 2 thermal anomalies are not connected although
they belong to the same fire. The method of grouping individual MODIS pixels
has successfully been applied by . The advantage of GFAS
over MODIS FRP is the assimilation technique applied in GFAS that produces
a considerable fraction of fires which are below the MODIS FRP detection
limit and thus not included in the MODIS MOD14 data set.
Figure clearly demonstrates the large number of low-intensity
fires which is included in GFAS, but not represented in MODIS. MPHP plumes
are based on MODIS fire counts, but have been selected manually. Therefore,
the FRP frequency distribution of small fires in the MPHP data set is shifted
towards more intense fires which are easier to identify by eye. As such the
evaluation of the SP using the MPHP data set is of limited significance,
because small fires are underrepresented.
Setup of simulations for evaluation of various implementations of the Sofiev
plume height parametrization. Each version of the parametrization is
additionally run with FRP values of ±30 % to estimate the impact
of FRP uncertainties on the plume heights. See text for a detailed
description of the individual simulation setups.
Simulation name
Plume height parametrization
Meteorology
FRP from MPHP
EVAL-SOFIEV-1
Sofiev one-step
day of observation
original
EVAL-SOFIEV-2
Sofiev two-step
day of observation
original
EVAL-SOFIEV-1-METEO
Sofiev one-step
25 % most favorable conditions
original
EVAL-SOFIEV-MODIFIED
Sofiev one-step + FRP correction
day of observation
tuning for plumes > Hdeep
Model setup for evaluation of the plume height parametrization
have already shown that their
plume height parametrization offers a generally reasonable individual
performance, if the parametrization is forced with meteorological input data
from ECMWF reanalysis data. Here, we evaluate the SP implemented into the
ECHAM6 general circulation model. For long-term climate simulations, the
individual plume height performance is less important than the statistical
performance of the global plume height distribution. Therefore, we do not
force the ECHAM6 model with reanalysis data, but apply free model runs with
prescribed sea surface temperature. Moreover, we quantify FRP uncertainties
in more detail than previously done by . The SP is run
offline based on the meteorological parameters from the ECHAM6 output and FRP
from the MPHP data set as described in Sect. . We run
ECHAM6 simulations with prescribed AMIP-II sea surface temperature for
2000–2010 to generate a climatology of meteorological input parameters
required in the SP. As we expect only a minor impact of aerosol emissions on
the meteorological parameters which determine the plume height and as
GFASv1.1 data are only available for 2005–2011, we do not use the HAM2
aerosol module for the SP evaluation experiments. In total, the SP is run for
a selection of 6942 MPHP plumes. To take into account the FRP uncertainties
of 30 % in the MPHP data set, we run the SP additionally for fire
intensity values of 0.7 × FRP and 1.3 × FRP.
For each plume we test the standard SP (EVAL-SOFIEV-1) as well as the two-step
iteration scheme (EVAL-SOFIEV-2) described in Sect. . The SP
is run at the particular day when the plume was reported in the MPHP. To
estimate plume heights in favorable meteorological conditions, we
additionally simulate the plume heights at each day of the month and analyze
the upper 25 % of all plumes within a month. This simulation is
called EVAL-SOFIEV-1-METEO. A summary of all simulations for the evaluation
of the SP is provided in Table .
found a tendency of the SP to underestimate particularly
high plumes, although the plume height spectrum was not subject to a more
detailed analysis. There might be various factors which contribute to an
underestimation of high plumes including low fire emissivity at 4 µm and
an underestimation of FRP due to the smoke opacity effect. Investigations by
who compared MODIS FRP data to the Autonomous Modular Sensor-Wildfire (AMS) airborne multi-spectral imaging system indicate that
MODIS underestimates the FRP of high-intensity fires. For a particular fire
of approximately 500 MW, the underestimation of surface FRP was
found to be nearly 50 %. For a smaller fire of 72 MW
(detected by AMS), the surface FRP bias was roughly 20 %. There is
a general tendency of MODIS to underestimate FRP for high plumes due to the
smoke which decreases the detectability of the thermal anomalies below the
smoke. This opacity effect of smoke plumes has been described by
. As we use direct MODIS FRP for our plume height
simulations, we expect similar underestimations of FRP in our plume height
calculations.
Setup of global plume height pattern simulations. All simulations are nudged towards observations every 6 h; simulation period is 2005–2011.
Simulation name
Plume height parametrization
Diurnal cycle of FRP
Emission distribution
HAM2.2-STANDARD
PBL +2 model layers
NO
25 % into FT, 75 % into PBL
SOFIEV-ORIGINAL
SOFIEV (original)
NO
constant mass mixing ratio
SOFIEV-DCYCLE
SOFIEV (original)
YES
constant mass mixing ratio
SOFIEV-2X-FRP
SOFIEV (original, 2xFRP)
NO
const. mass mixing ratio
SOFIEV-MODIFIED
SOFIEV (modified)
YES
constant mass mixing ratio
and investigated MODIS FRP data and found
sub-pixel information to be useful for the prediction of high-altitude
injections. However, so far there is no global data set available that
provides this sub-pixel data for a wide range of fire sizes and intensities.
Even though the magnitude of the underestimation cannot be quantified on the
global scale, satellite pictures of the MPHP data set clearly indicate that
the underestimation of MODIS FRP tends to increase with plume height. This
holds especially for calm conditions and pyro-cumulus events as one can see
for a number of plumes in the MPHP data set (personal communication with
David Nelson). To take into account this significant FRP underestimation of
particularly strong fires, we apply an empirical FRP correction of the SP
which tunes deep plumes higher than a threshold Hdeep towards the
observations by replacing the FRP Pf in
Eq. () with Pf*, where
Pf*=Pf×HpHdeepε.
We empirically vary ε and define Hdeep based on the
statistical performance of EVAL-SOFIEV-1 evaluated with the MPHP data set.
The empirically determined best performance values of ε are
subsequently used for the simulation EVAL-SOFIEV-MODIFIED (see
Table ).
Model setup for simulation of global plume height patterns
We run the ECHAM6-HAM2 general circulation model as described
in Sect. in nudged mode (ERA-INTERIM data) for the years
2004–2011 to simulate global plume height patterns. Due to the limited
availability of GFASv1.1 (years 2005–2011) plume heights for 2004 are driven
by 2008 GFAS fire intensity data. The year 2004 serves thereby solely as model
spin-up and is excluded from our analysis. In total we run five ECHAM6-HAM2
simulations: one reference simulation “HAM2.2-STANDARD”, for which we use the
standard plume height distribution scheme and four simulations which represent
different configurations of the SP
(Table ). Simulation SOFIEV-ORIGINAL is based
on the original SP as described in and evaluated in
simulation EVAL-SOFIEV-1. In SOFIEV-DCYCLE, we apply a simplified diurnal
cycle according to , which distributes 80 % of the
FRP constantly during daytime (08:00–18:00 LT) and the remaining
20 % during nighttime (18:00–08:00 LT). In simulation
SOFIEV-MODIFIED, we use the results from the plume height parametrization
evaluation to tune the SP. Vertical emission distributions in experiment
HAM2.2-STANDARD are implemented as described in Sect. , while
all SOFIEV simulations apply a constant mass mixing ratio from the surface to
the top of the plume.
Simulation SOFIEV-2X-FRP is a sensitivity scenario of more intense fires in
a warmer climate and serves as a sensitivity test. A climate-change-induced
increase in fire activity has been found based on climate projections for the
end of the 21st century particularly for boreal regions,
e.g.,. Since no global
estimates of a future intensification in FRP are available, we only consider
a hypothetical global doubling in fire intensity in simulation SOFIEV-2X-FRP.
Statistical analysis of different versions of the Sofiev plume height
parametrization implemented in ECHAM6. The KS tests describe results for
a Kolmogorov–Smirnov test, the square root error is shown as cumulative sum
over the cumulative probability function. Uncertainties of mean heights
indicate 1 SD (Standard Deviation).
Data set
MPHP OBS
EVAL-SOFIEV-1
EVAL-SOFIEV-2
EVAL-SOFIEV-1-METEO
EVAL-SOFIEV-MODIFIED
Mean plume height [m]
1382±702
1389±572
1517±637
1651±599
1411±646
Mean plume height FRP +30 % [m]
–
1478±616
1603±668
1750±649
1511±717
Mean plume height FRP -30 % [m]
–
1279±519
1403±596
1554±616
1292±567
10th percentile [m]
651
789
834
1011
789
25th percentile [m]
892
988
1048
1231
988
50th percentile [m]
1248
1280
1402
1544
1280
75th percentile [m]
1713
1666
1834
1937
1688
90th percentile [m]
2271
2123
2282
2421
2218
95th percentile [m]
2671
2465
2675
2782
2621
99th percentile [m]
3709
3193
3629
3576
3556
Max plume height [m]
11 986
6153
5620
7404
7786
Mean top 10 plumes [m]
6122±2008
5153±596
5129±308
5521±908
6235±881
KS test d value
–
0.081
0.117
–
0.081
KS test d value upper 40 %
–
0.075
0.249
–
0.034
Cumulative square root error
–
0.161
0.300
–
0.034
Plume height parametrization performance
This chapter presents the evaluation of the various
versions of the SP described in Sect. .
Table provides statistical values of the global plume
height distribution for all versions of the SP and the observational MPHP
data set. Parametrization EVAL-SOFIEV-1 shows basic agreement with the
observed spectrum for a wide range of plume heights. The global mean plume
height of EVAL-SOFIEV-1 (1389±572 m) is very close to the
observed global mean of 1382±702 m. However, there is
a general tendency of the SP to overestimate low plumes and to underestimate
high plumes. Similar problems to reproduce particularly high as well as low
plumes have been reported for other plume rise parametrizations by
.
The uncertainties in plume heights introduced by the ±30 %
uncertainty in the FRP impact the mean plume heights by less than
100 m. The two-step SP (EVAL-SOFIEV-2) provides a slightly better
representation of the plume height variations, but the one-step SP holds
a smaller positive model bias for low plumes and a better representation of
extraordinarily high plumes. For favorable meteorological conditions
(parametrization EVAL-SOFIEV-1-METEO), the increase in plume heights compared
to EVAL-SOFIEV-1 ranges between 200 and 400 m except for the highest plumes
which significantly exceed this range (1250 m for the maximum plume
height). Compared to the FRP uncertainty, the meteorological parameters turn
out to be more important for plume heights on the global scale. Due to the
simplified representation of plume buoyancy in the Sofiev formula,
the interpretation of these findings has to be taken with care, but the
setup of our simulations does not allow for a more detailed analysis.
A Kolmogorov–Smirnov test (KS test) indicates that the best statistical
performance is provided by EVAL-SOFIEV-1, for both, the complete
distribution, as well as the uppermost 40 percentiles. The uppermost 40
percentiles serve best for the KS test, because for these percentiles the
cumulative probability distribution of EVAL-SOFIEV-1 continuously exceeds the
MPHP distribution.
Global mean plume height distribution for different plume height
parametrizations and MPHP observations. Blue shading represents uncertainties
of ±200 m in the plume height observations, red shading
represents a ±30 % FRP uncertainty applied for the plume height
parametrizations.
Performance of the one-step Sofiev plume height parametrization (EVAL-SOFIEV-1)
for plumes below 6 km. Honeycomb colors indicate the number of plumes
in a specific 100 m height bin for EVAL-SOFIEV-1. Red honeycombs
represent plumes for EVAL-SOFIEV-MODIFIED. For reasons of clarity, only
EVAL-SOFIEV-MODIFIED plumes above 4 km are shown.
Mean plume height bias of simulation EVAL-SOFIEV-1 for 2001–2009 compared to
the observational MPHP data set (EVAL-SOFIEV-1 minus MPHP). Blue colors
indicate underestimation of plume heights by the model, red colors indicate
overestimation of plume heights by the model. The large majority of grid
boxes contain more than one individual plume; in these cases averaged biases
are shown. The large areas of white colors, e.g., in Europe and
Australia, represent the limited global coverage of the MPHP data set as no
plumes are available in these regions.
Maximum plume heights for simulations HAM2.2-STANDARD (a),
SOFIEV-ORIGINAL (b), SOFIEV-DCYCLE (c) and SOFIEV-MODIFIED (d).
Plume heights for (a) represent standard plume heights
in ECHAM6-HAM2.2, plume heights in (b) to (d) are based on
various versions of the Sofiev plume height parametrization. For a detailed
description, see Sect. .
Figure visualizes the vertical plume height
distribution for the different versions of the SP. While the
EVAL-SOFIEV-1-METEO parametrization lies significantly above the observations
for the entire plume height range below 4 km, EVAL-SOFIEV-1 matches
the uncertainty range of the observations for a large part of the plume
height spectrum. The spikes in the EVAL-SOFIEV-2 distribution originate from
the two-step algorithm which tends to shift plumes away from levels of the PBL
height.
As particularly high plumes are in many cases linked to large emission
injections, these plumes require special attention in the context of global
climate modeling. Based on empirical variations of the tuning parameters in
parametrization EVAL-SOFIEV-MODIFIED (see Eq. ), we found the best
statistical performance for ε=0.5 and
Hdeep=1500m. The correction of FRP for deep plumes
significantly improves the overall plume height parametrization performance
on the global scale (see Table ). The cumulative square
error of the entire distribution is decreased from 0.16 for parametrization
EVAL-SOFIEV-1 to 0.03 for parametrization EVAL-SOFIEV-MODIFIED. While the
mean plume height of EVAL-SOFIEV-MODIFIED (1411±646 m) does
not change substantially compared to EVAL-SOFIEV-1
(1389±572 m), the maximum plume heights are increased from
6.1 to 7.8 km and the KS test d value for the uppermost 40
percentiles is reduced by ≈50 %. Figure
shows the frequency of plume heights in specific 100 m bins for
parametrization EVAL-SOFIEV-1 (0–6 km) and EVAL-SOFIEV-MODIFIED
(4–6 km only). The large majority of low plumes are adequately
represented by EVAL-SOFIEV-1, but for high plumes >4 km, the FRP
correction applied in EVAL-SOFIEV-MODIFIED is particularly important. While
the number of plume heights >4 km is 38 in the observational MPHP
data set (out of 6942 plumes in total), the number of plumes
>4 km is increased from 12 in simulation EVAL-SOFIEV-1 to 33 in
simulation EVAL-SOFIEV-MODIFIED (see Fig. ).
Figure presents a global map of the mean plume height
bias simulated by EVAL-SOFIEV-1 compared to MPHP observations for all
analyzed plumes. Very similar patterns apply for parametrization
EVAL-SOFIEV-MODIFIED as the FRP correction introduced in EVAL-SOFIEV-MODIFIED
only marginally effects the mean plume heights. Although significant
individual over- and underestimations on the grid box scale are observable,
there is no clear region-specific bias pattern observable in the
extratropics. In tropical South America, plumes generally tend to be
slightly overestimated, but in other parts of the tropics (e.g., Southeast Asia) tropical plumes are captured very well by the SP. A more
detailed analysis shows that the positive model bias in tropical South
America is primarily related to plumes with heights smaller than
3 km. Due to the vast majority of these tropical low plumes injecting
emissions into the well-mixed PBL, this bias is
generally of limited importance for the emission height climate impact.
Plume height characteristics for various plume height implementations. All
values represent global means for 2005–2011. Uncertainties for mean top 100
plumes represent 1 SD. A description of the simulation setups is provided
in Table .
Simulation Name
HAM2.2-STANDARD
SOFIEV-ORIGINAL
SOFIEV-DCYCLE
SOFIEV-MODIFIED
SOFIEV-2X-FRP
mean height [m]
2798±813
1327±457
1526±517
1559±577
1500±549
10th percentile [m]
1784
833
956
956
924
25th percentile [m]
2173
1012
1164
1164
1128
50th percentile [m]
2733
1256
1449
1459
1406
75th percentile [m]
3364
1552
1790
1827
1754
90th percentile [m]
3883
1892
2167
2255
2169
95th percentile [m]
4199
2161
2461
2607
2511
99th percentile [m]
4798
2831
3195
3543
3356
max height [m]
14 408
6386
7121
8701
7788
Mean top 100 plumes [m]
9510±1027
4786±389
5676±477
6782±632
5755±485
Global plume height patterns
In the next sections, global plume height patterns simulated by the various
plume height implementations in ECHAM6-HAM2 are presented. All simulations
are based on FRP data as reported by the GFASv1.1 data set. We analyze global
and regional differences in plume heights, impacts of a diurnal and seasonal
cycle and the fraction of free tropospheric injections.
Global patterns of mean and maximum plume heights
Table shows a comparison of global plume
height statistics for all five simulations introduced in
Sect. . We apply a linear weighting of plume heights
with FRP. The weighting becomes particularly important for global mean plume
height values as the large number of small fires in GFASv1.1 dominates the
plume height spectrum. Thus, intense fires injecting large amounts of
emissions are more adequately represented in global plume height statistics.
The ECHAM6-HAM2 standard plume height implementation (PBL height +2 model
layers) results in a mean global plume height of 2798±813 m.
This plume height value is considerably higher than all mean plume heights in
the various versions of the SP, which range from 1327±457 m
(SOFIEV-ORIGINAL) to 1559±577 m (SOFIEV-MODIFIED). The
introduction of a diurnal cycle in FRP (SOFIEV-DCYCLE), as well as the
additional ECHAM6-HAM2.2-specific FRP correction for high plumes, does not
impact mean plume heights by more than 450 m except for the 99th
percentile. For a doubling in FRP (simulation SOFIEV-2X-FRP), mean plume
heights range between the SOFIEV-ORIGINAL simulation and the SOFIEV-DCYCLE
simulation. Daytime plumes in SOFIEV-DCYCLE and SOFIEV-MODIFIED are weighted
approximately 6 times greater than nighttime plumes due to their higher FRP
values. However, although the differences between the various versions of the
SP are very limited for 99 % of all plumes, the disproportionately
important 1 % of the highest plumes show larger differences.
Figure presents maximum values of
hourly plume heights for all simulations from 2005 to 2011. On average, plume
heights simulated by the SP show significantly smaller maximum plume heights
than the plume heights simulated by HAM2.2-STANDARD. By taking into account
not only the PBL height but also the fire intensity, the SP represents a more
heterogeneous pattern of plume heights (see
Fig. a–d). The HAM2.2-STANDARD plume
heights follow a distinct gradient from the Equator to the poles due to their
dependence on PBL height. In contrast to
the HAM2.2-STANDARD scenario, SP maximum plume heights are generally lower
than 4 km in many regions. Plume heights greater than 4 km
are simulated in the subtropical and tropical savannah, in remote mid-latitudes
and in boreal regions. The differences in plume heights between the various
versions of the SP (Fig. b–d) are
much smaller than the differences to HAM2.2-STANDARD
(Fig. a). The implementation of
a diurnal cycle (SOFIEV-DCYCLE,
Fig. c) introduces a significant mean
plume height increase in regions of high fire intensity. For SOFIEV-MODIFIED,
the FRP correction for plumes >1500 m leads to a further increase
in plume heights. In contrast to the SOFIEV-ORIGINAL simulation, for
SOFIEV-MODIFIED a very small fraction of individual plumes reaches plume
heights of more than 7 km. In parts of Australia, boreal Canada and
Siberia, some high plumes simulated by SOFIEV-MODIFIED rise substantially
above the HAM2.2-STANDARD plume heights.
Globally averaged vertical emission distribution of wildfire emissions for
different emission height parametrizations 2005–2011. All emission
distributions are weighted with FRP, i.e., strong fires contribute
disproportionately high to the distribution (see text Sect. ).
Red and blue shadings indicate 1 SD of simulation HAM2.2-STANDARD
and SOFIEV-ORIGINAL, respectively. Note that the SOFIEV-DCYCLE and SOFIEV-MODIFIED
lines largely overlie for pressure levels >700 hPa. For
a detailed description of the simulation setups (see
Table ).
Global mean diurnal cycle of plume heights for different plume height implementations. Shadings indicate 1 SD.
presented zonal mean injection profiles and regional
maximum plume heights of 5.5 km with the majority of plumes injecting
into the lowest 1000 m. In contrast to our study, the results by
were based on MODIS MOD14 FRP data and were therefore
lacking a significant fraction of small fires which is included in GFASv1.1.
However, the dominance of emission injections into the lowest 1–2 km
is observable in both studies. The tuning of high plumes applied in
SOFIEV-MODIFIED leads to a small fraction of plume heights above
6–7 km which is not included in , although such
high plumes are reported in the MPHP data set. In the MPHP data set, one
single, particularly high plume even exceeds a plume height of 10 km
and lies thus beyond the spectrum of our SP simulations.
applied a modified version of the 1-D plume rise model by
for global modeling of plume heights for the year 2006
and analyzed regional plume height distributions in Indonesia, North America,
Africa and Siberia. The authors found a very limited number of plumes
(approximately 10–100 plumes for 2006) which exceeded injection heights by
more than 5 km above the PBL height. Due to differences in model
resolution, FRP inventories and temporal resolution, the study by
is not directly comparable to our simulations.
Nevertheless, the magnitude of the highest plumes shows basic agreement with
simulation SOFIEV-MODIFIED.
Seasonal cycle of daily mean plume heights for North America (a) and Central Africa (b). Shading represents
1 SD; crosses indicate maximum individual mean daily values in these
regions. See text and Table for a more
detailed description of model simulations HAM2.2-STANDARD, SOFIEV-ORIGINAL
and SOFIEV-DCYCLE.
Vertical emission distributions
For all SP simulations we assume a vertical emission distribution of constant
mass mixing ratio in all levels below the top plume height. In the
HAM2.2-STANDARD simulation a fixed fraction of the emissions (25 %)
is injected in the next two layers above the PBL (see
Sects. and ).
Figure illustrates the vertical emission distributions of
all simulations as 7 year global means. All versions of the SP are
emitting the major fraction of the emissions below 800 hPa with small
differences between these simulations. For simulation HAM2.2-STANDARD,
a considerably larger emission fraction is injected into layers
3–5 km above the surface.
The SP simulations are basically in line with the observational study of
who found on average only 45 % of smoke MISR pixels
above 1 km. showed that, for prescribed standard
emission profiles, ECHAM6-HAM2 generally overestimates BC in the upper
troposphere over the Pacific. This model bias might to some extent be related
to too high plumes in ECHAM6-HAM2 standard. A doubling of FRP is not found to
considerably change the vertical emission distributions compared to
simulation SOFIEV-ORIGINAL.
Diurnal and seasonal cycles
The purely PBL-related plume height variations in HAM2.2-STANDARD result in
a distinct diurnal cycle of plume heights (see Fig. ). The
SP, which also takes into account the FRP and Brunt–Väisälä
frequency, shows a less pronounced diurnal cycle. Overall, simplified diurnal
variations in FRP (simulation SOFIEV-DCYCLE) turn out to impact the overall
diurnal cycle by 200–500 m and are therefore of similar importance
as diurnal variations in PBL and Brunt–Väisälä frequency. The
limited impact of a diurnal cycle in FRP in all SOFIEV simulations coincides
with the results from who showed that the differences in CO
profiles are only marginally influenced except for the lowest 1–2 km
when diurnal FRP variations are accounted for.
For analysis of the simulated seasonal cycle in plume heights, we choose
North America (30–60∘ N, 90–120∘ W) as
a region with a distinct fire activity peak during the northern hemispheric
summer and tropical Africa (0–15∘ N,
15∘ W–45∘ E) as a region of maximum fire activity in
southern hemispheric summer conditions. Figure shows
seasonal variations of the HAM2.2-STANDARD, SOFIEV-ORIGINAL and SOFIEV-DCYCLE
simulations. In both regions, seasonal variations of area averaged plume
heights are not very pronounced, since a large number of small fires
dominates the mean plume heights. There is a distinct seasonal cycle in the
top plume heights observable in the SOFIEV simulations for North America and
– though less pronounced – also for Africa. This seasonal cycle in plume
heights is mainly related to the seasonal cycle in individual FRP values
peaking in the summer season. For HAM2.2-STANDARD, the seasonal cycle is not
represented because PBL heights do not show distinct seasonal patterns in
those regions.
Global fraction of FT plumes for all day (00:00–24:00), daytime
(08:00–18:00) and nighttime (18:00–08:00) plume heights. Uncertainties
indicate SDs of day to day variations.
Simulation name
FT Fraction 00:00–24:00 [%]
FT Fraction 08:00–18:00 [%]
FT Fraction 18:00–08:00 [%]
SOFIEV-ORIGINAL
11.9 ± 1.7
3.7 ± 0.7
17.8 ± 2.3
SOFIEV-DCYCLE
9.7 ± 1.4
5.2 ± 0.9
12.8 ± 1.7
SOFIEV-2X-FRP
15.0 ± 2.0
5.5 ± 1.0
21.6 ± 2.7
SOFIEV-MODIFIED
9.7 ± 1.4
5.2 ± 1.0
12.8 ± 1.7
Fraction of free tropospheric injections
In HAM2.2-STANDARD, all plumes are prescribed to reach or exceed PBL height.
In all versions of the SP, the fraction of plumes that reach the FT is significantly smaller than 100 % (see
Table ). The simulated global fraction of FT
plumes ranges between 9.7±1.4 and 15.0±2.0 %. For
daytime plumes, which emit 80 % of the total wildfire emissions, the
fraction of FT plumes is substantially smaller (3.7–5.5 %).
A similar fraction of 4 % daytime plumes reaching the FT was
presented by for Indonesia. For North America
found a fraction of 4–12 %. In contrast,
and found values of 26 % for North America,
and 30 % for Australia, respectively. A slightly smaller fraction of
North American FT injections (14–22 %) has also been identified by
.
Comparison to other plume height parametrizations
The SP represents a simple, semi-empirical plume height parametrization that
takes into account fire intensity as well as meteorological parameters for
calculation of plume heights. This parametrization does not explicitly
account for fire size, wind drag, entrainment and the number of updraft cores
which have been shown to influence plume heights
. But for long-term climate
modeling, the computational costs for the implementation of more complex
analytical models are disproportionate to the benefits. Nevertheless we
compare the plume heights calculated by the SP to the widely used 1-D plume
height model by for a limited period of time. We use fire
and meteorological data from the Monitoring Atmospheric Composition and
Climate (MACC-II) project for the period 1 January 2014 to 13 July 2014. The
MACC-II data were most suitable for this comparison, because a modified
version of the Freitas plume rise model (PRM-MODEL;
) had already been implemented in MACC-II.
Therefore, the required additional effort for the implementation of the SP was
very limited. As the PRM-MODEL requires the fire size of each fire which is
not provided in GFASv1.1, it was unfortunately not possible to run the
PRM-MODEL simulations for our ECHAM6-HAM2.2 experiment setups.
The PRM-MODEL provides entire detrainment profiles, but for comparability to
the SP we only analyze the mean height of maximum injections. In the
PRM-MODEL, maximum injection heights are defined as the average of the levels
for which the detrainment is >50 % of the maximum
detrainment. Moreover, the PRM-MODEL output is assimilated to fill
observational gaps as described by . The implementation of
the PRM model for all individual fires entailed roughly a doubling in
computation time for the MACC-II system, whereas the additional computational
costs of the SP implementation are negligible. A comparison of global mean
daily plume height distributions of the PRM model vs. SP implemented in the
MACC-II system is shown in Fig. . For this period, the PRM
model provides a mean plume height of 1287±807 m, for SP the
mean is 1392±506 m; the 10th percentile in PRM is only
273 m, whereas it is 809 m in SP; the 95th percentile is
2663 m in PRM and 2322 m in SP; the mean plume height of the
highest 100 plumes in the PRM is 7251±466 m, for the SP it is
4406±329 m. Overall the differences between the models are
largely restricted to the lowest 500–1000 m within the well-mixed
PBL and furthermore to the upper 97–99 percentile. However, a modified SP in
MACC to improve occasionally high plumes (which would require additional
tuning similar to our FRP correction in simulation SOFIEV-MODIFIED) would
shift the plume height distribution to a better agreement with the PRM (see
Table ).
Plume height distributions calculated by the PRM-MODEL and the original
version of the SP (SOFIEV-ORIGINAL) implemented in MACC-II for 1 January 2014
to 13 July 2014. Each line represents the global mean plume height
distribution of a particular day.
Summary and conclusions
In this study prescribed plume heights
in ECHAM6-HAM2.2 have been replaced by the implementation of different
versions of a simple, semi-empirical plume height parametrization after
. In a first step we evaluated the modeled plume height
distribution against 6942 plumes of the Multiangle Imaging Spectroradiometer (MISR) Plume Height Project (MPHP) data set.
Overall the semi-empirical parametrization shows a reasonable performance
within the uncertainty range, although low plumes tend to be slightly
overestimated and high plumes tend to be underestimated.
A statistical–empirical correction for the fire radiative power (FRP) of high
plumes turned out to significantly improve the uppermost 10 % of the
plume height spectrum, because this correction compensates the smoke opacity
effects which reduce the detectability of FRP for intense fires.
For the plume height parametrization used in this
study, meteorological conditions impact the plume heights more effectively
than uncertainties in FRP. The reliance of plume heights on FRP
in the Sofiev parametrization represents a very simplified approach
which provides reasonable statistics on the global scale, but it might
fail for the prediction of individual plumes.
In a second step we simulated plume heights for fire activity of global
coverage for 2005–2011 using FRP reported in the global fire inventory
GFASv1.1 as input for the plume height parametrization.
The application of the fire-intensity-dependent plume height parametrization
introduced considerable changes to global plume height patterns compared to
the ECHAM6-HAM2 standard plume height implementation which solely depends on
PBL height. The global mean plume height simulated by the modified Sofiev
plume height parametrization is 1559±577 m with a fraction of
3.7±0.7 % of daytime plumes emitting into the FT. The highest 100 plumes reach altitudes of 6.1–8.7 km
above the surface. On the global scale, plume heights simulated by the Sofiev
plume height parametrization are significantly lower than for ECHAM6-HAM2
prescribed plume heights and show a much more heterogeneous spatial
distribution. As a results of the strong damping in the FRP impact on plume
heights described by the Sofiev plume height parametrization, a hypothetical
doubling in future fire intensity, as well as the implementation of a
diurnal cycle in FRP, only marginally increases the vast majority of
emission heights. Basic global plume height patterns are rarely affected
by these changes in FRP, but for the uppermost 10 percent of all plumes,
an average increase in plume heights by 300–500 m is simulated.
The lack of high-resolution plume height data sets of full global coverage
remains a limiting factor for the evaluation of plume height parametrizations
in climate and Earth system models. Nevertheless, the implementation of an
advanced plume height representation into a climate model is an essential
step forward to advance the progress in our understanding of the overall fire
emission climate impact. The simulations presented in this study form the
basis for the investigation of the fire emission height impact on black
carbon long-range transport and radiation which we show in the second part of
this two-paper series. For subsequent studies without observational FRP data,
we will couple the implemented plume height parametrization to a mechanistic
interactive fire model within a global vegetation model.
This will enable the investigation of climate-change-induced past and future
changes in fire intensity and related changes in plume heights.