ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus GmbHGöttingen, Germany10.5194/acp-15-13555-2015Impact of future land-cover changes on HNO3 and O3 surface dry
depositionVerbekeT.LathièreJ.juliette.lathiere@lsce.ipsl.frSzopaS.https://orcid.org/0000-0002-8641-1737de Noblet-DucoudréN.Laboratoire des Sciences du Climat et de l'Environnement
– LSCE-IPSL, CEA/CNRS/UVSQ, Gif-sur-Yvette, FranceJ. Lathière (juliette.lathiere@lsce.ipsl.fr)9December2015152313555135683April20158July201518November201519November2015This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/15/13555/2015/acp-15-13555-2015.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/15/13555/2015/acp-15-13555-2015.pdf
Dry deposition is a key component of surface–atmosphere exchange of
compounds, acting as a sink for several chemical species. Meteorological
factors, chemical properties of the trace gas considered and land surface
properties are strong drivers of dry deposition efficiency and variability.
Under both climatic and anthropogenic pressure, the vegetation distribution
over the Earth has been changing a lot over the past centuries and could be
significantly altered in the future. In this study, we perform a modeling
investigation of the potential impact of land-cover changes between
the present day (2006) and the future (2050) on dry deposition velocities at the
surface, with special interest for ozone (O3) and nitric acid
(HNO3), two compounds which are characterized by very different
physicochemical properties. The 3-D chemistry-transport model LMDz-INCA is
used, considering changes in vegetation distribution based on the three
future projections, RCPs 2.6, 4.5 and 8.5, and present-day (2007)
meteorology. The 2050 RCP 8.5 vegetation distribution leads to a rise of up
to 7 % (+0.02 cm s-1) in the surface deposition velocity calculated for
ozone (Vd,O3) and a decrease of -0.06 cm s-1 in the surface deposition
velocity calculated for nitric acid (Vd,HNO3) relative to the present-day values in tropical Africa and up to +18 and -15 %, respectively,
in Australia. When taking into account the RCP 4.5 scenario, which shows
dramatic land-cover change in Eurasia, Vd,HNO3 increases by up to
20 % (annual-mean value) and reduces Vd,O3 by the same magnitude in
this region. When analyzing the impact of surface dry deposition change on
atmospheric chemical composition, our model calculates that the effect is
lower than 1 ppb on annual-mean surface ozone concentration for both the
RCP 8.5 and RCP 2.6 scenarios. The impact on HNO3 surface concentrations
is more disparate between the two scenarios regarding the spatial
repartition of effects. In the case of the RCP 4.5 scenario, a significant
increase of the surface O3 concentration reaching locally by up to 5 ppb (+5 %)
is calculated on average during the June–August period. This
scenario also induces an increase of HNO3 deposited flux exceeding
locally 10 % for monthly values. Comparing the impact of land-cover change
to the impact of climate change, considering a 0.93 ∘C increase of
global temperature, on dry deposition velocities, we estimate that the
strongest increase over lands occurs in the Northern Hemisphere during
winter,
especially in Eurasia, by +50 % (+0.07 cm s-1) for Vd,O3 and
+100 % (+0.9 cm s-1) for Vd,HNO3. However, different regions are
affected by both changes, with climate change impact on deposition
characterized by a latitudinal gradient, while the land-cover change impact
is much more heterogeneous depending on vegetation distribution modification
described in the future RCP scenarios. The impact of long-term land-cover
changes on dry deposition is shown to be significant and to differ strongly
from one scenario to another. It should therefore be considered in
biosphere–atmospheric chemistry interaction studies in order to have a fully
consistent picture.
Introduction
Amongst surface–atmosphere interactions, dry deposition plays a key role in
the exchange of compounds and acts as a significant sink for several
atmospheric species. Performing an intercomparison of 26 state-of-the-art
atmospheric chemistry models, Stevenson et al. (2006) estimated the surface
removal of ozone by dry deposition to be about 1000 ± 200 Tg yr-1 on
average, with values ranging from 720 to 1507 Tg yr-1 amongst models, compared
to 5100, 4650 and 550 Tg yr-1 for chemical production, chemical destruction
and stratospheric input fluxes, respectively. This study also underlined that
although global deposition fluxes are consistent between models, locally
there is a large variability in the ozone deposition velocities (Stevenson
et al., 2006). Since all these models use deposition schemes based on
Wesely's prescription (Wesely et al., 1989), the discrepancies suggest
different hypotheses for the land-type consideration. Based on satellite
measurements from OMI (Ozone Monitoring Instrument) combined with the
Goddard Earth Observing System chemical transport model (GEOS-Chem), Nowlan
et al. (2014) estimated dry deposition to land to be 98 % of total
deposition for NO2 and 33 % for SO2. The deposition fluxes over
land represent 3 % of global NOx emissions and 14 % of global sulfur
emissions. Land surfaces can therefore play a significant role on
deposition, with a highly variable contribution from one chemical compound
to another.
The air–surface exchange of trace compounds has been shown to be strongly
variable, especially between different types of surface vegetation and soil
characteristics (Wesely et al., 2000). Regarding ozone, model data
differences reported in the literature could be attributed to
oversimplifications in the implementation of the dry deposition scheme (Val
Martin et al., 2014) since many models rely on “resistance in series”
schemes developed in the 1980s (Hardacre et al., 2015).
In order to quantify the non-photochemical sink for tropospheric burden at
the regional and global scales, the scientific community uses numerical dry
deposition schemes calibrated with field measurements of dry deposition
velocities (Wesely et al., 1989; Zhang et al., 2002b), implemented usually
in chemistry-transport models. Dry deposition efficiency is influenced by
multiple meteorological factors (temperature, solar radiation, humidity and
especially atmospheric turbulence), chemical properties of the trace gas
considered (solubility, oxidative capacity) and land surface properties
(surface type, surface roughness, foliar surface and ecosystem height in the
case of vegetation surfaces). Some of these factors are poorly constrained
and are thus accounted for in deposition schemes in a very simplistic way.
The vegetation distribution, for instance, is usually prescribed using maps
for the region of interest that are generally kept the same for either past,
present or future studies (e.g., Andersson and Engardt, 2010 or Lamarque et
al., 2013). There is therefore a lack of knowledge regarding the impact of
long-term changes in vegetation distribution on dry deposition chemical
compounds at the surface. Since the beginning of the industrial era, human
activities have modified the use of large surfaces, affecting significantly
the vegetation distribution, especially in the northern temperate latitude
regions. Further land-cover modifications are expected in the 21st
century due to projected increases in energy and food demands, and
vegetation, in tropical regions in particular, could undergo drastic
alterations.
Interactions between vegetation and atmospheric chemistry
potentially affected by land-use changes. In this work, only the red arrows
are investigated.
Only a few studies have been carried out recently on the dry deposition
changes in the future. Some of them focus on the impact of climate change on
the dry deposition (Andersson and Engardt, 2010) while others combine the
effects of several future changes (climate, CO2 levels, land cover) on
atmospheric chemistry in general (Ganzeveld et al., 2010; Wu et al., 2012).
However, considering anthropogenic land-cover changes among other large
modifications of the vegetation/atmospheric chemistry drivers does not allow
us
to identify whether the land-cover change should be considered as a
priority in the studies of future atmospheric chemistry or not. The objective of
this study is to investigate and isolate the potential impact of land-cover
changes between the present day (2006) and the future (2050) on dry
deposition velocities at the surface, using a modeling approach with a 3-D
chemistry-transport model as illustrated in Fig. 1. Changes in vegetation
distribution are based on the three future projections known as
Representative Concentration Pathway (RCP) scenarios (van Vuuren et al.,
2011), developed for the climate model intercomparison project (CMIP5): RCPs
2.6, 4.5 and 8.5. For this work we focus on ozone (O3) and nitric acid
(HNO3), two compounds which are characterized by very different
biophysical properties (e.g., solubility and oxidative capacity). In Sect. 2,
we describe the chemistry-transport model LMDz-INCA, the dry deposition
module and the modeling strategy adopted. In Sect. 3, we describe the
different future land-cover changes as given in the three RCP scenarios 2.6,
4.5 and 8.5 and explain their impacts on surface dry deposition velocities
of ozone and nitric acid. Finally, the magnitude of land-cover effects
related to climate change on dry deposition velocities by 2050 is
discussed.
Modeling setup
In our study, the global chemistry–climate model LMDz-INCA (Hauglustaine et
al., 2004) is used to compute dry deposition. LMDz (v4) is an atmospheric
general circulation model that simulates the transport of trace species. The
model is run with 19 hybrid levels from the surface to 3 hPa at a horizontal
resolution of 1.85∘ in latitude and 3.75∘ in longitude.
It is coupled online to the chemistry and aerosols model INCA (v2) which
computes concentrations of reactive tracers considering their emissions,
chemical transformations, transport and deposition processes. The
atmospheric oxidation reactions of CH4, CO and non-methane hydrocarbons
are documented in Folberth et al. (2006). In order to be able to isolate the
effect of land-cover change only on the atmospheric chemical composition,
through change in surface dry deposition, emissions are prescribed according
to Lamarque et al. (2010) for anthropogenic fluxes and Lathière et al. (2006)
for biogenic volatile organic compounds, as described in Szopa et al. (2013), and are kept
constant between all runs.
Dry deposition in LMDz-INCA
The chemical deposition scheme used in INCA is based on the parameterization
of Wesely (1989) and Wesely and Hicks (2000), computing dry deposition
velocity Vd as a succession of resistances as follows:
|Vd(z)|=[Ra(z)+Rb+Rc]-1,
where Ra is the aerodynamic resistance, Rb the quasi-laminar
resistance and Rc the bulk surface resistance.
Ra determines the ability of the airflow to bring gases or particles
close to the surface and depends mainly on the atmospheric turbulence
structure and on the height considered. In this paper, we will focus on dry
deposition at the surface ground level (z=0). Rb describes the
resistance to the transfer very close to the surface and is driven by the
surface (surface roughness) and the gas or particle (molecular diffusivity)
characteristics. Ra and Rb are calculated based on
Walcek et al. (1986). The surface resistance Rc represents the different pathways
through which the gas or particles can deposit and is determined by the
affinity of the surface for the chemical compound. Deposition can thus
occurs directly on the ground and/or, in the case of vegetative surfaces, on
the different vertical layers of the canopy on trunks, branches and mainly
on leaves, through stomata or cuticles (Wesely, 1989). Vegetation surfaces
in particular cover a large area of the Earth, with a high spatial and
seasonal variability due to species diversity. Environmental conditions such
as atmospheric CO2, pollutant (ozone) concentrations, radiation,
temperature or the occurrence of possible stress (drought for instance) can
strongly affect the vegetation functioning, and the stomatal opening
especially, and therefore impact dry deposition velocity. The impact of
vegetation type, distribution and functioning on dry deposition is still
not well understood and generally very simply, if at all, considered in
chemistry-transport models (Hardacre et al., 2015). For all chemical species
considered in LMDz-INCA, Rc is based on their temperature dependent
Henry's Law effective coefficient and reactivity factor for the oxidation of
biological substances (Folberth et al., 2006). The coefficients for Henry's
Law are taken from Sander (1999) and reactivity factors are taken from
Wesely (1989) and Walmsley and Wesely (1996).
The dry deposition scheme implemented in LMDz-INCA considers eleven surface
categories: (1) urban land, (2) agricultural land, (3) range land, (4) deciduous forest,
(5) coniferous forest, (6) mixed forest including wetland,
(7) water, both salt and fresh, (8) barren land, mostly desert,
(9) non-forested wetland, (10) mixed agricultural and range land and (11) rock
open areas with low-growing shrubs. This scheme was originally developed by
Wesely (1989) and updated by Wesely and Hicks (2000) for northern hemispheric
regions of the USA and southern Canada. Five seasonal
categories are used as proxy of vegetation growth stage (midsummer with lush
vegetation; autumn with unharvested cropland; late autumn after frost, no
snow; winter, snow on ground, and subfreezing; transitional spring with
partially green short annuals). For global-scale study purposes, the scheme
in LMDz-INCA has been modified in order to represent the different seasonal
cycles throughout the world. The latitude dependency of the vegetation
seasonality is described by dividing the globe into three belts: northern
hemispheric regions (latitude > 33∘ N), tropical regions
(33∘ S < latitude < 33∘ N) and southern
hemispheric regions (latitude < 33∘ S). Summer is
considered in the tropics throughout the whole year, describing the
evergreen vegetation. Two opposite seasonal cycles are taken into account in
extra-tropical northern and southern hemispheric regions, with winter being
activated when snow falls. The deposition of atmospheric compounds on plant
leaves, through stomata especially, is determined following the Wesely (1989)
approach. The stomatal resistance depends on vegetation type,
seasonal category, radiation and temperature, but the potential impact of
other environmental conditions such as drought, or atmospheric concentration
of CO2 or ozone, is not considered. The dry deposition velocity over
each grid box is eventually determined by summing deposition velocities
computed over every land-cover types, weighted by their respective
fractional surface coverage (ranging from 0 to 1).
Surface categories considered in LMDz-INCA for dry deposition,
represented as dominant coverage: agricultural land, range land, deciduous
forest, coniferous forest, water, barren land and mostly desert. Regions
discussed in this study are also illustrated: Eurasia, USA, Central America,
tropical South America, South America, tropical Africa, southern
Africa, western Australia, eastern Australia and tropical regions.
The deposition velocities computed by LMDz-INCA based on a different land-cover distribution was evaluated in Hauglustaine et al. (2004). This work
illustrates values generally consistent with typical deposition velocities
highlighted for North America and Europe as presented in Wesely and Hicks (2000)
and monthly values reaching up to 0.6 cm s-1 for ozone and up to 3 cm s-1
for HNO3 over land. In the supplementary material the ozone dry
deposited fluxes simulated by LMDz-INCA in the present-day simulation and
used in this study are compared to other global model and long-term
measurements which are discussed in Hardacre et al. (2015).
Land-use and land-cover changes between 2007 and 2050
The present-day distribution of vegetation categories considered in
LMDz-INCA is illustrated in Fig. 2 as dominant type, covering the largest
fraction of each grid box. Crops are dominant mainly in restricted temperate
regions of North America, central Europe and also in India, while range
lands are largely spread. Deciduous forests dominate in tropical regions of
South America, Africa and Indonesia, together with central and southern
Europe, while coniferous forests have a high occupancy in boreal regions of
North America and Eurasia. Figure 2 also shows the 10 regions of special
interest selected for this study, which will be considered in more detail
when analyzing our results.
Future maps are based on scenarios of land-cover changes derived from four
different RCPs (Moss et al., 2010; van
Vuuren et al., 2011) and four integrated assessment models (one per RCP) (RCP
8.5, RCP 4.5 and RCP 2.6). Those maps were further harmonized to ensure
smooth transitions with past/historical changes (Hurtt et al., 2011). Those
data sets only provide information on human activities (crop land and grazed
pastureland) in each grid cell (at a 0.5∘ resolution) but do not
provide any recommendation regarding the distribution of natural vegetation.
We have therefore combined them with our original present-day land-cover map
(Loveland et al., 2000), which already includes both natural and
anthropogenic vegetation types, following a methodology described in
Dufresne et al. (2013).
Figure 3 illustrates changes in vegetation fraction for agriculture and
grasslands on one hand, and for forests on the other hand, between
present-day (distribution for 2007) and the future RCP scenarios. For most
affected regions, the changes in land surfaces are presented in Fig. 4.
The RCP 4.5 scenario shows the largest surface change with a total of 20.8×106 km2, representing 10.4 % of the 70∘ S–70∘ N
Earth continental surface. According to the RCP 2.6 and
RCP 8.5 scenarios only 15–16.8×106 km2 of land-cover surfaces is converted.
Vegetation fraction difference between 2050 and the present day for
crops and grasses (left column) and forests (right column) according to the
future RCP scenarios 2.6 (upper line), 4.5 (middle line) and 8.5 (lower
line).
Changes between 2007 and 2050 in land-type surfaces (106 km2)
for the nine regions as illustrated in Fig. 1, in the
case of forests (green), crops (orange), grasses (yellow) and bare soil
(brown).
The RCP 2.6 scenario is characterized by a moderate increase of energy
consumption throughout the 21st century together with a decrease in oil
consumption. The energy supply is thus partly ensured by bioenergy
production increase (van Vuuren et al., 2011). Such hypotheses lead to a
strong expansion of agricultural lands (+2.61×106 km2 globally)
at the expense of forests (-1.40×106 km2) and grasslands
(-1.15×106 km2) targeting mainly Eurasia, USA and tropical
South
America.
Simulations performed in our study with the LMDz-INCA
chemistry–climate model: setup description.
Run objectivesLand-cover mapClimateDurationControlPresent-day 2000sWinds and surface temperature nudged1 yearon ECMWF fields for 2007Impact of future land-use changes2050 RCP 8.5Winds and surface temperature1 year2050 RCP 4.5nudged on ECMWF2050 RCP 2.6fields for 2007Impact of future climatePresent-day 2000s2000–2010 fields (GCM mode)10 years2045–2055 fields (GCM mode)
The RCP 8.5 scenario, characterized by the strongest increase in population
and energy consumption (amongst RCPs), assumes a large increase in global
population until 2050. The resulting demand for food leads to a strong
expansion of land used for crops and pastures at the expense of forests. The
tropical belt (from 30∘ N to 30∘ S) undergoes the
largest changes: tropical forests in South America and southern Africa
are partially harvested (1.0×106 km2 totally, i.e.,
13 % of their 2007 extent) and replaced by grassland and crops, while in
eastern Australia, forests lose 7 % (-0.28×106 km2)
of their 2007 area and are replaced by grasslands which gain 0.12×106 km2 on desert.
The “mitigation” RCP 4.5 scenario is a rather contrasting scenario as it
proposes a strong increase in the cover of all forest categories, a small
expansion of grasslands but an important recession of agricultural surfaces
mainly in developed countries. Indeed Eurasia, USA and Canada undergo a
strong conversion from agriculture and grassland to forests with a magnitude
change of ∼0.8×106 km2 in Eurasia and
∼0.4×106 km2 in northern USA and Canada.
Besides, tropical South America loses 0.55×106 km2
of cumulated croplands and grasslands but forests expand by the same surface
between present day and 2050.
Finally, it is important to underline that the three RCP scenarios offer a wide
variety of land-cover change projections. They all are quite different
compared to previous scenarios, such as the SRES-A2 investigated by
Ganzeveld et al. (2010), characterized by a strong north/south contrast,
with the tropical and southern hemispheric countries mainly encountering
deforestation whereas northern areas (> 35∘ N) were
mainly projected to see afforestation.
Simulation strategy
In order to quantify the effects of these land-cover changes on surface dry
deposition, we carried out two sets of simulations (Table 1). The first set
isolates the effect of future possible land-cover changes on dry
deposition without any climate change. It includes one control run (present
day), using 2006 vegetation distribution (Fig. 2) and three future runs
using the 2050 vegetation maps according to the RCPs 8.5, 4.5 and 2.6
scenarios. The same present-day meteorology, biogenic and anthropogenic
emissions are used in these four simulations. These simulations are run for
1 year with wind and temperature fields being relaxed towards the ECMWF
ERA-Interim reanalysis (Dee et al., 2011) with a time constant of 6 h.
Then a second set of two simulations is performed in order to investigate
the effect of future climate change on deposition and compare it with the
impact of future land-cover change: one run for the 2000–2010 period and a
second run for the 2045–2055 period. Those simulations are performed without
nudging and the LMDz general circulation model requires sea surface
temperature (SST), solar constant and long-lived greenhouse gases (LL-GHG)
global-mean concentrations as forcings. For historical simulations, we use
the HADiSST for SST (Rayner et al., 2003) and the
evolution of LL-GHG concentrations compiled in the AR4-IPCC report. For
future projections, we use the SST from IPSL-CM4 simulation for the SRES-A2
scenario, which induce similar climate trajectories in terms of radiative
forcing than RCP 8.5. We use the LL-GHG concentrations distributed by the RCP
database for RCP 8.5 projection for the 2045–2055 period. Eleven years are
run and averaged to allow smoothing of interannual climate variability. The
mean surface temperature change is 0.93 ∘C between future
simulation and present-day simulation. Both experiments use the same
present-day vegetation distribution, anthropogenic and biogenic emissions.
Annual average of surface dry deposition velocities (upper panel)
and surface concentrations (middle panel and deposition fluxes (lower panel)
over continental surfaces (cm s-1) for O3 (left) and HNO3 (right)
for the present day as simulated by LMDz-INCA.
ResultsPresent-day ozone and nitric acid deposition
First of all, we present the deposition over continental regions for
present-day conditions (Fig. 5) by illustrating the annual means of
deposition velocities at the surface, surface concentrations and deposited
fluxes for O3 and HNO3.
The highest ozone deposition velocities (> 0.35 cm s-1) are
simulated over India, Southeast Asia, western coast and center of South
America, Mexico, Europe and sub-Saharan Africa and Australia. Hence, those
areas are mainly covered by crops and grasses, where the highest Vd,O3
occurs, while Europe and Southeast Asia are mainly covered by deciduous
forests, with therefore lower annual Vd,O3. O3 surface dry
deposition is indeed maximal over small canopies vegetation and minimal over
bare soil with deposition affinity ranging from agriculture > grasslands > deciduous > coniferous > bare
soil (see sensitivity tests in the Supplement).
Temperate regions see ozone deposition velocities significantly reduced in
winter (see Supplement for seasonal means) whereas tropical
regions, covered mainly by small canopies, are characterized by surface
deposition velocity exceeding 0.35 cm s-1 throughout the whole year due to the
lack of seasonality in the vegetation phenology in the global model. In
temperate regions of the Northern Hemisphere, the highest deposition
velocities for ozone reach values of 0.4 to 0.6 cm s-1 for Vd,O3
over Europe.
For HNO3, the annual-mean deposition velocities are maximum over
Brazil, western Europe, India, Indochinese Peninsula and southwestern
Africa (> 1.6 cm s-1 in annual mean). Vd,HNO3 reaches maximum
values over deciduous and coniferous forests due to deposition affinity
ranking from deciduous and coniferous > agriculture > grasslands > bare
soil. This is due to the strong dependency of
Vd,HNO3 on surface roughness (Walcek et al., 1986). For the temperate
region and southern Asia, the HNO3 deposition is strongly affected by the
vegetation cycle with maximum in July between 2.5 and 3.5 cm s-1. This is
remarkable over temperate and boreal forests. In the tropics, Amazonian
forest encounters high HNO3 deposition velocity in winter whereas
deposition velocity over African equatorial forest is limited throughout the
whole year (see Supplement for seasonal means of deposition).
Large areas receive high HNO3 deposition fluxes exceeding
0.5 g (N) m-2 yr-1 in annual mean: northeastern USA, western Europe and
eastern Asia. These areas correspond to the ones identified by Dentener et
al. (2006), in which natural vegetation encounters nitrogen deposition higher
than the “critical load” threshold of 1 g (N) m-2 yr-1.
Annual-mean changes (in relative value %) of surface dry
deposition velocity for O3 between the present day and 2050 induced by the
different LCC (left) and related surface ozone concentrations (right) for
the three RCP scenarios. Values in the [-1; +1] % interval are not shown.
Same as Fig. 6 for HNO3.
The repartition of deposited fluxes is strongly affected by the large
variability of atmospheric concentrations of ozone and nitric acid in the
surface layer. For both O3 and HNO3, the deposited fluxes are
maximum over south and eastern Asia and eastern North America and central and
western Europe. For ozone, the maximum in winter is over central Africa
whereas in summer the ozone deposition is maximum over central Europe and
eastern USA. For HNO3, the deposited flux repartition is equally driven
by the deposition velocity and by the HNO3 surface concentration
distribution. In winter, HNO3 is maximally deposited over eastern USA,
central Africa, central Europe, India and eastern Asia. In summer, regions are
the same in the Northern Hemisphere but the extension of deposited HNO3
areas is higher and the deposition in Africa is weak, due to weak HNO3
concentration.
Impact of 2050–2007 land-cover changes on surface dry deposition
velocities
We then analyze the changes in surface dry deposition velocities between
present day and 2050 induced only by land-cover change. Four regions undergo
interesting land-cover changes in terms of intensity or contrast between
scenarios: Eurasia, North America, tropical Africa and Australia. The left
columns of Figs. 6 and 7 show the relative difference in surface dry
deposition velocities distribution for O3 and HNO3, resulting
from the changes in vegetation distribution between 2007 and 2050 for the
three RCP scenarios. We shall first describe the two scenarios projecting weak
land-cover changes for 2050s: RCP 8.5 and RCP 2.6. In the RCP 8.5 scenario,
one main land-cover change is the expansion of agricultural land at the
expenses of forests. According to this scenario, over tropical Africa the
maximal land-cover change occurs locally with fraction of deciduous forests
decreasing by up to 0.2 while cropland fraction increases by up to 0.2 in
the same region. This induces a rise by up to 7 % (+0.02 cm s-1) in
Vd,O3 and a decrease of 0.06 cm s-1 in Vd,HNO3 relative to the
present-day values in this area. These order of magnitude and sign of
changes are consistent with sensitivity tests in which we replaced totally
forests by croplands inducing an increase of 0.1 cm s-1 in Vd,O3 and a
decrease of 0.5 cm s-1 in Vd,HNO3 (during summer and winter). The
strongest LCC occurs in Australia (-0.12 in forest fraction and +0.2 in
grassland fraction in eastern Australian regions), which induces a local
maximum increase of 18 % (+0.05 cm s-1) in Vd,O3 and a maximum
decrease of 15 % in Vd,HNO3 (-0.1 cm s-1). We find the same order of
magnitude in changes induced by land-cover change in western Australia but
with a different sign for Vd,HNO3 changes (+0.1 cm s-1 ; +9 %), due
to a different type of shift in surface covering (+0.12 in grassland
fraction, -0.10 for desert).
As land-cover changes are weak in the RCP 2.6 scenario, a more dispersed and
weaker effect on surface dry deposition velocities is simulated (maximum
absolute difference of 10 %).
According to the RCP 4.5 scenario, the most dramatic land-cover change
occurs in Eurasia where local maximum changes by up to 0.5 in fraction of
vegetation are projected, involving in most cases an increase in forest
surfaces at the expense of agricultural areas. This increases Vd,HNO3
by up to 20 % (annual-mean value) and reduces Vd,O3 by the same
magnitude in this region. The LCC impacts are stronger by a factor 4–6 in
summer both on O3 and HNO3 deposition velocities. This difference
in deposition velocities between winter and summer were highlighted in
sensitivity tests which see a strong decrease in Vd,O3 during the
June–August period (up to 0.15 cm s-1 in absolute) and a strong increase in
Vd,HNO3 (up to 1.5 cm s-1) underlining a total conversion of croplands to
forests. This is due to a higher surface roughness which enhances the
deposition velocity of HNO3 (via the reduction of the aerodynamic
resistance). However, the higher input surface resistance (prescribed in the
model and variable relating to season indexes) reduces Vd,O3 even
combined to a warmer climate which decreases the stomatal resistance (Rs).
Future climate-induced impacts on surface dry deposition
velocities (%) considering a 0.93 ∘C increase of global
temperature.
Impact on atmospheric composition
The objective of this part is to isolate the effects of dry deposition
changes due to land-cover changes on the tropospheric concentration of
O3 and HNO3. Therefore, solely the impact of land-cover changes on
deposition at the surface is considered between the present-day and 2050
simulations. This impact on surface concentrations of O3 and HNO3
is shown in the right columns of Figs. 6 and 7.
For both the RCP 8.5 and RCP 2.6 scenarios, the LCC effects through deposition
are lower than 1 ppb on annual-mean surface ozone concentrations. In term of
relative difference, only the reduction of ozone over Australia when
considering RCP 8.5 hypotheses exceeds 1 %, reaching up to 5 % at
some points. The impact on HNO3 surface concentrations is more
disparate between the two scenarios when considering the spatial repartition
of effects. The RCP 8.5 scenario leads to a local increase of HNO3 due to
the reduction in the deposition velocity. This HNO3 increase is
notable over Mexico, Brazil, western and southern Africa (comprised in the
1–6 % interval). Land-cover change in Australia leads to an increase
exceeding 7 % in the east and a decrease reaching 5 % in the west.
The RCP 4.5 scenario induces the strongest impacts on deposition velocity
with a reduction of Vd,O3 (-0.08 cm s-1) occurring in Eurasia due a
strong reduction in croplands occupancy (-0.6 in fraction of coverage) and a
strong increase in forest distribution (+0.6 in fraction of coverage)
between 2007 and 2050. It induces a significant increase of the surface
O3 concentration reaching locally by up to 5 ppb (+5 %) on average
during the June–August period. This scenario also induces an increase of the
HNO3 deposition flux exceeding locally 10 % for monthly values in
Eurasia and eastern North America. It thus leads to a reduction in the
HNO3 concentration by 0.2 ppbv in Eurasia (-13 %) and in North
America (-8 %), mainly due to changes in nitric acid velocities of +0.5
and +0.2 cm s-1, respectively.
Are the land-cover-induced changes significant compared with the climate
change impact?
The impact of land-use changes on deposition can be compared to that of
climate when discussing their respective strength on deposition
velocities. To this purpose, we consider a 0.93 ∘C increase of
global temperature, corresponding to the temperature increase projected in
the RCP scenarios between the beginning and the middle of the 21st
century. Figure 8 shows the impact of this climate change on the
deposition velocity for O3 and HNO3. We see that the strongest
increase in surface dry deposition velocities over lands occurs in the
Northern
Hemisphere during winter, especially in Eurasia (+50 % (+0.07 cm s-1) for
Vd,O3 and +100 % (+0.9 cm s-1) for Vd,HNO3). The climate
effect on the deposition velocity by affecting stomatal resistance,
sensitive to surface temperature and solar irradiance, can locally reach
values far more important than the LCC. Table 2 presents the effects of
land-cover change considering RCP 4.5 projection and climate change on
deposition velocity averaged over 10 regions for O3 and HNO3. In
several regions, the effect of land-cover change is of the same order of
magnitude than the one of climate. The modification in land-cover
affectation can thus amplify the climate change effect or, when the sign is
the opposite, counterbalances it.
Mean effect on annual-mean surface deposition velocity (%) of
climate and land-cover changes of O3 and HNO3 averaged over
homogeneous regions (values >±1.5 % are highlighted).
Ozone Nitric acid ClimateRCP 4.5 landSum of climate andClimateRCP 4.5 landSum of climate andchangecover changeland-cover changeschangecover changeland-cover changesGlobal0.5-0.7-0.22.21.23.4Eurasia2.1-2.10.04.33.88.1USA1.5-1.30.23.62.05.6Central America-1.1-1.4-2.61.11.72.8Tropical South America-2.3-1.2-3.51.12.63.7Tropical Africa-1.5-0.8-2.30.40.91.3South Africa-1.4-0.6-2.0-0.10.80.8Western Australia-0.4-0.1-0.5-0.40.0-0.4Eastern Australia-0.5-0.6-1.10.20.50.7South America0.4-0.7-0.40.32.02.3Tropics-1.1-0.6-1.70.61.01.7Discussion and conclusions
Using the RCP 2.6, 4.5 and 8.6 scenarios for land-use change between the 2000s
and 2050s, simulations were carried out with the global chemistry-transport
model LMDz-INCA in order to assess the impact of changes in vegetation
distribution on the dry deposition of ozone and nitric acid at the surface
and on atmospheric composition.
Regarding vegetation distribution, the largest change at the global scale is
given in the RCP 4.5 scenario (20.8×106 km2), with
surface converted being 28 and 19 % lower in the RCP 2.6 and RCP 8.5
scenarios, respectively. Projections show major changes in the Northern
Hemisphere in the case of RCP 4.5 scenario, while Australia and Africa are
mostly affected in the RCP 8.5 scenario.
With vegetation type and surface being key drivers of surface dry deposition, any
change in vegetation distribution can potentially affect dry deposition
velocity and therefore atmospheric chemical composition. Considering the
2050 RCP 8.5, vegetation distribution leads to a rise by up to 7 %
(+0.02 cm s-1) in Vd,O3 and a decrease of 0.06 cm s-1 in Vd,HNO3 relative to
the present-day values in tropical Africa and up to +18 and -15 %,
respectively, in Australia. As land-cover changes are weak in the RCP 2.6
scenario, a more dispersed and weaker effect on surface dry deposition
velocities is simulated (maximum absolute difference of 10 %) when
considering the RCP 2.6 scenario, characterized by a moderate change in
vegetation distribution compared to present day. When taking into account
the RCP 4.5 scenario, which shows dramatic land-cover change in Eurasia,
Vd,HNO3 increases by up to 20 % (annual-mean value) and reduces
Vd,O3 by the same magnitude in this region. When analyzing the impact
of dry deposition change on atmospheric chemical composition, our model
calculates that the effect is lower than 1 ppb at the grid-box scale on
annual-mean surface ozone concentration for both of the RCP 8.5 and RCP 2.6
scenarios. The impact on HNO3 surface concentrations is more disparate
between the two scenarios, regarding the spatial repartition of effects. In
the case of the RCP 4.5 scenario, a significant increase of the surface
O3 concentration reaching locally up to 5 ppb (+5 %) is calculated
on average during the June–August period. This scenario also induces an
increase of HNO3 deposited flux exceeding locally 10 % for monthly
values. Investigating the impact of climate change, considering a
0.93 ∘C increase of global temperature, on surface dry deposition
velocities, we calculate that the strongest increase over lands occurs in
the Northern Hemisphere during winter, especially in Eurasia (+50 %
(+0.07 cm s-1) for Vd,O3 and +100 % (+0.9 cm s-1) for Vd,HNO3). The
climate change impact on deposition is characterized by a latitudinal
gradient, while the effect of land-cover change is much more heterogeneous.
Both climate and vegetation distribution changes are of similar amplitude
but sign can differ.
The objective of study is to isolate the impact of land-cover change on
atmospheric chemical composition through modification of surface dry
deposition only rather than to consider comprehensively all the atmospheric
chemistry/vegetation interactions affected by land-cover change. Indeed, as
far as long-term evolution of atmospheric chemistry is investigated (e.g.,
Stevenson et al., 2006; Lamarque et al., 2010), the evolution of biogenic
emissions due to global changes is discussed, if not shared between models,
but the land-cover maps used for dry deposition remain unchanged. Here we
want to assess the importance of this choice. Land-cover changes would go
together with changes in surface emissions, either from anthropogenic,
agricultural or biogenic sources, with changes in climate and possible
strong consequences on the atmospheric chemical mechanism and
surface–atmosphere interactions. In an attempt to quantify all the effects
of land-cover change, those processes would therefore need to be considered
altogether to get a better picture of the overall resulting effect. However,
they all have large uncertainties and, added to error compensation effects,
the dry deposition change can be can masked by other process changed (see
for example Wu et al., 2012). Moreover, the sensitivity of biogenic
emissions to climate and CO2 changes as well as the level of coupling
between vegetation and chemistry are so different from one model to another
that the full land-cover change response is for the moment highly
model dependent.
Fowler et al. (2009) underline an uncertainty of about 50 % in the ability
of models to estimate dry deposition fluxes for main chemical species, the
lack of measurements making a proper and extensive model evaluation
especially difficult. Hardacre et al. (2015), who compared the dry
deposition of ozone of 15 global atmospheric chemistry-transport models with
measurements in Europe and North America, underline discrepancies of up to a
factor of 2, notably in the summer maximum, but do not find a systematic
model bias. Dry deposition in global models is still largely based on the
in-series resistance approach proposed by Wesely (1989) and generally does not
integrate more recent findings demonstrated by field or laboratory studies
(Hardacre et al., 2015).
Vegetation is usually crudely described in chemistry-transport models, with
leaf surface or cuticle and stomatal resistances for instance being
prescribed or very simply parameterized and a lack of the representation of
seasonal variation or stress (water, temperature) impacts. This could lead
to significant uncertainty in model representation and projections of
atmospheric chemical composition and surface–atmosphere interactions. The
work by Wesely and Hicks (2000) underlines that selecting proper input
parameters for dry deposition schemes, such as stomatal, cuticle and soil
resistances, is crucial for a satisfactory determination of dry deposition
efficiency for both simple and multi-layers models. Zhang et al. (2003)
propose a revised parameterization of dry deposition including the leaf area
index in the calculation of aerodynamic and cuticular resistances, which
could give the possibility of a better representation of the impact of
vegetation seasonality in dry deposition estimates. The roles of surface
wetness, soil moisture and the partition between stomatal and non-stomatal
uptake (shown of high importance for dry deposition processes)
are usually not implemented or poorly described in global models (Fowler et
al., 2009; Hardacre et al., 2015). This is also the case of the LMDz-INCA
model in which dry deposition is described through a highly parameterized
approach. Investigating ozone non-stomatal uptake using measurements over
five different vegetation types, Zhang et al. (2002a) show that the O3
uptake by cuticles is affected by friction velocity, relative humidity,
canopy wetness and leaf area index especially and tends to increase with wetness and
high humidity. A new parameterization for non-stomatal uptake is proposed
and is expected to improve this deposition path in existing models, where a
constant value is often considered, and could therefore be tested more
largely in global models. Investigating the impact of coupling dry
deposition to vegetation phenology in the Community Earth System Model
(CESM) on ozone surface simulation, Val Martin et al. (2014) show the
importance of representing the dependence of dry deposition to vegetation
parameters including drivers of stomatal resistance variation (change in
CO2, drought stress), especially when focusing on the impact of past or
future changes of vegetation. Hardacre et al. (2015) recommend providing
more detailed diagnostics of O3 dry deposition in next model intercomparison
exercices to attribute the differences between models to methodology and/or
representation of processes. The next generation of chemistry-transport
models should therefore rely on online coupling with vegetation, with dry
deposition schemes having a consistent and dynamic description of vegetation
distribution and growth and related short-term (seasonal, annual variation)
or long-term (past and future changes) evolutions. However, model
intercomparisons focusing on each process considered in isolation with a
proper shared methodology/setup is crucial if one wants to progress in the
understanding of the complex vegetation/atmospheric chemistry interactions.
In particular the evolution of land-cover maps should be considered as far
as dry deposition is concerned in addition to emission changes in the next model
intercomparison exercices aiming to project future atmospheric chemistry.
The Supplement related to this article is available online at doi:10.5194/acp-15-13555-2015-supplement.
Acknowledgements
We warmly thank Oliver Wild for useful discussions on the model evaluation
of dry deposition. Computer time was provided by the GENCI French
supercomputing program. This research was supported by CNRS via the
INSU-LEFE French program under the project BOTOX.
Edited by: T. Karl
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