ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-2559-2016Using statistical models to explore ensemble uncertainty in climate impact
studies: the example of air pollution in EuropeLemaireVincent E. P.vincent.lemaire-etudiant@ineris.frColetteAugustinMenutLaurenthttps://orcid.org/0000-0001-9776-0812Institut National de l'Environnement Industriel et des
Risques (INERIS), Verneuil en Halatte, FranceLaboratoire de Météorologie Dynamique, UMR
CNRS8539, Ecole Polytechnique, Ecole Normale Supérieure, Université
P.M. Curie, Ecole Nationale des Ponts et Chaussées, Palaiseau,
FranceVincent E. P. Lemaire (vincent.lemaire-etudiant@ineris.fr)2March20161642559257429July201521October201511February201622February2016This 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/16/2559/2016/acp-16-2559-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/2559/2016/acp-16-2559-2016.pdf
Because of its sensitivity to unfavorable weather patterns, air
pollution is sensitive to climate change so that, in the future, a climate
penalty could jeopardize the expected efficiency of air pollution mitigation
measures. A common method to assess the impact of climate on air quality
consists in implementing chemistry-transport models forced by climate
projections. However, the computing cost of such methods requires optimizing
ensemble exploration techniques.
By using a training data set from a deterministic projection of climate and
air quality over Europe, we identified the main meteorological drivers of
air quality for eight regions in Europe and developed statistical models that
could be used to predict air pollutant concentrations. The evolution of the
key climate variables driving either particulate or gaseous pollution allows
selecting the members of the EuroCordex ensemble of regional climate
projections that should be used in priority for future air quality
projections (CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and
MPI-ESM-LR/CCLM following the EuroCordex terminology).
After having tested the validity of the statistical model in predictive mode,
we can provide ranges of uncertainty attributed to the spread of the regional
climate projection ensemble by the end of the century (2071–2100) for the
RCP8.5.
In the three regions where the statistical model of the impact of climate
change on PM2.5 offers satisfactory performances, we find a climate
benefit (a decrease of PM2.5 concentrations under future climate) of
-1.08 (±0.21), -1.03
(±0.32), -0.83
(±0.14) µg m-3, for respectively Eastern Europe, Mid-Europe and Northern Italy. In the British-Irish Isles, Scandinavia, France, the
Iberian Peninsula and the Mediterranean, the statistical model is not
considered skillful enough to draw any conclusion for PM2.5.
In Eastern Europe, France, the Iberian Peninsula, Mid-Europe and Northern
Italy, the statistical model of the impact of climate change on ozone was
considered satisfactory and it confirms the climate penalty bearing upon
ozone of 10.51 (±3.06), 11.70
(±3.63), 11.53 (±1.55),
9.86 (±4.41), 4.82
(±1.79) µg m-3, respectively. In the British-Irish Isles,
Scandinavia and the Mediterranean, the skill of the statistical model was not
considered robust enough to draw any conclusion for ozone pollution.
Introduction
The main drivers of air pollution are (i) emission of primary pollutants and
precursors of secondary pollutants, (ii) long-range transport,
(iii) atmospheric chemistry and (iv) meteorology (Jacob and Winner, 2009). We
can thus anticipate that air quality is sensitive to climate change taking as
example the link between heat waves and large-scale ozone episodes (Vautard
et al., 2005). But in addition to the direct impact of climate change on air
pollution through the change in frequency and severity of synoptic conditions
conducive to the accumulation of air pollutants we must also note that
climate can have an impact on anthropogenic and biogenic emission of
pollutants and precursors (Langner et al., 2012b) as well as on changes in
the global background of pollution, and therefore long-range transport (Young
et al., 2013). There is therefore a concern that in the future, climate
change could jeopardize the expected efficiency of pollution mitigation
measures, even if the available studies indicate that if projected emission
reductions are achieved they should exceed the magnitude of the climate
penalty (Colette et al., 2013; Hedegaard et al., 2013).
The most widespread technique used to assess the impact of climate change on
air quality consists in implementing regional climate projections in
Chemistry Transport Models (CTM) (Jacob and Winner, 2009). The computational
cost of such technique is substantial given that it involves multi-annual
global climate simulations, dynamical downscaling through regional climate
simulations and ultimately CTM simulations. Besides the computational cost,
it also raises technical difficulties in collecting, transferring, and
managing large amounts of model data. Unlike many climate impact studies, CTM
projections require Regional Climate Model fields in three dimensions and at
high temporal frequency, whereas many regional climate modelling groups only
store a few vertical levels in compliance with the CORDEX data archiving
protocols. Altogether, these difficulties led to the use of a single source
of climate projections in the majority of future air quality projections
(Meleux et al., 2007; Katragkou et al., 2011; Jiménez-Guerrero et al.,
2012; Langner et al., 2012b; Colette et al., 2013, 2015; Hedegaard et al.,
2013; Varotsos et al., 2013) or two at most in published studies (Huszar et
al., 2011; Juda-Rezler et al., 2012; Langner et al., 2012a; Manders et al.,
2012). There are examples where more than two climate forcings are used, but
then they are implemented with different CTMs, so that the uncertainties in
the spread of RCM and CTMs is aggregated, thereby offering a poor
understanding of the climate uncertainty. In addition, it should be noted
that the choice of the climate driver is generally a matter of opportunity
rather than an informed choice. These studies capture trends and variability
but their coverage of uncertainty is not satisfactory in the climate change
context. This unsatisfactory handling of uncertainties is well illustrated by
the divergence in the very sign of the impact of climate change on
particulate matter (e.g. Lecœur et al., 2014, find a climate benefit for
PM2.5 in Europe while Manders et al., 2012, suggest the opposite). Thus
the lack of multi-model approach in air quality projections is a serious
caveat that needs to be tackled in order to comply with best practices in the
field of climate impact research, where ensemble approaches is state of the
art.
Hence, in order to assess the climate uncertainties on surface ozone and
particulate matter over Europe in a changing climate, we developed an
alternative method which does not require forcing a CTM with an ensemble of
climate models. It consists in developing a statistical model fitted to a
deterministic CTM simulation forced by a single RCM that can be subsequently
applied to a larger ensemble of regional climate projections. This method
allows selecting the members of the RCM ensemble that offer the widest range
in terms of air quality response, somehow the “air quality sensitivity to
climate change projections”. These selected members should be used in
priority in future air quality projections. A byproduct of our statistical
air quality projections is that we explore an unprecedented range of climate
uncertainty compared to the published literature that relies, at best, on two
distinct climate forcings. The confidence we can have in these statistical
projections is of course limited by the skill of the statistical model. Our
approach of using a simplified air quality impact model but with a larger
range of climate forcing can therefore be considered complementary with the
more complex CTMs used with a limited number of climate forcings. The use of
such a methodology is inspired from earlier work in the field of hydrology,
where Vano and Lettenmaier (2014) estimated future stream-flow by using
a sensitivity-based approach which could be applied to generate ensemble
simulations. Such a hybrid statistical and deterministic approach has also
been used in the past in the field of air quality, but mostly for near-term
and local forecasting, relying on statistical models of various complexity
(i.e. Land Use Regression, Neural Network, Nonlinear regression, Generalized
Additive Models etc.) (Prybutok et al., 2000; Schlink et al., 2006; Slini et
al., 2006). The most relevant example in the context of future air quality
projection is that of Lecœur et al. (2014), who used the technique of
wind regime analogues, although they did not apply their approach to an
ensemble of climate projections.
This paper deals with all the steps needed to build the proxy of ensemble.
First (Sect. 2) we present the methods and input data: the design of the
statistical model of the air quality response to meteorological drivers is
presented as well as the deterministic modelling framework used to create our
training data set. Section 3 focuses on results. The deterministic air quality
projections are presented for ozone peaks and PM2.5 in Sect. 3.1. The
selected statistical models for each region are evaluated in Sect. 3.2 for
ozone, PM2.5 and each sub-constituent of the particulate matter mix. The
relevance of the statistical method to evaluate climate uncertainties and
optimize the exploration of the ensemble of climate projections is discussed
in Sect. 4.
MethodologyDesign
We consider ozone and PM2.5 as the main pollutants of interest for both
purposes: public health (Dockery and Pope, 1994; Jerrett et al., 2009) and
climate interactions (IPCC, 2013). For both of them, we investigated the best
correlation that can be found for various European subregions using the
following meteorological variables as predictants: near-surface temperature
(T2m), daily precipitation, incoming short-wave radiation, planetary boundary layer (PBL) depth, surface wind (U10m) and
specific humidity.
The choice of these meteorological variables is based on an analysis of the
literature on the chemical and physical processes linking air pollution and
meteorology. For PM2.5, turbulent mixing, often related to the depth of
the planetary boundary layer, dominates (McGrath-Spangler et al., 2015). A
decrease of the PBL depth lead to either (i) an increase of the concentration
of pollutants because of the lower mixing volume (Jiménez-Guerrero et al.,
2012) or (ii) a decrease of their concentrations because of their faster dry
deposition to surface receptors (Bessagnet et al., 2010). The wind plays also
multiple roles for PM2.5. High wind speed favours the dilution of
particulate matter (Jacob and Winner, 2009) but enhances sea-salt and dust
mobilization (Lecœur and Seigneur, 2013). Precipitation is often reported
as a major sink of PM2.5 through wet scavenging (Jacob and Winner,
2009). Water vapour participates in aerosol formation during nucleation
processes. Moreover, it can have an impact on the rates of certain chemical
reactions, similarly to temperature. The overall impact of temperature on
PM2.5 is difficult to isolate because of the mix of components
contributing to PM2.5 (organic, inorganic, dust, sea-salt…) and
possible compensating effects. For instance, according to Jacob and Winner (2009),
a temperature rise has opposite effects for sulphate and nitrate
(respectively an increase and a decrease of concentrations). But for the
overall PM2.5 mass, an increase in temperature will decrease the
concentration as a result of higher volatility and subsequent higher aerosol
to gas phase conversion (Megaritis et al., 2014).
As far as ozone is concerned, temperature is expected to play a major role as
it catalyses atmospheric chemistry (Doherty et al., 2013). Moreover
increasing temperature and solar radiation enhance isoprene emission which is
a biogenic precursor of ozone (Langner et al., 2012b; Colette et al., 2013).
Finally changing the amount of incoming short-wave radiation will play a role
on ozone photochemistry, either by enhancing its photolysis by the hydroxyl
radical in the presence of water vapour and short-wave radiation or by
enhancing its production in the presence of photolysed nitrogen dioxide
(Doherty et al., 2013). The impact of the PBL depth on ozone varies with the
meteorological conditions. Increasing the depth of the PBL dilutes ozone
concentrations, but it may also favour the dilution of nitrogen oxides close
to the sources, therefore leading to an increase in ozone concentrations in
NOx saturated areas (Jacob and Winner, 2009). The amount of water vapour in
the atmosphere mostly drives the abundance of the hydroxyl radical (OH). OH
is involved in ozone destruction through several processes (i.e. photolysis,
HNO3 production) (Varotsos et al., 2013). It is also involved in ozone
production via the formation of NO2 and radicals (Seinfeld and Pandis,
2008).
Starting from the above list of meteorological predictants, we aim to develop
a statistical model of ozone and particulate matter for each of the eight
European climatic regions defined in the PRUDENCE project (Christensen and
Christensen, 2007). These regions are: British-Irish Isles (BI), Iberian Peninsula
(IP), France (FR), Mid-Europe (ME), Scandinavia (SC), Northern Italy (NI –
referred to as the Alps in Climate studies but chiefly influenced by the
polluted Po-Valley in the air quality context), Mediterranean (MD) and
Eastern Europe (EA). For each of these regions, a spatial average of
predictants (meteorological variables) and pollutant concentrations values is
taken. The statistical model is based on daily averages for all
meteorological and air pollutant concentrations except ozone for which the
daily maximum of 8 h running means is used. The seasonality is removed by
subtracting the average seasonal cycle over the historical period. It should
be noted that focusing on aggregated quantities greatly improves the skill of
the statistical model that would struggle in capture higher temporal
frequency and spatial resolution. An analogy is presented in Thunis et
al. (2015) who demonstrated that annual mean ozone and particulate matter
responses to incremental emission changes were much more linear than
previously thought.
For each region and each pollutant, we first select the two most
discriminating predictants by testing all the possible couple of
meteorological variable and selecting those that reach the highest
correlation. In a second stage we design the actual statistical model that
consists of a Generalized Additive Model based on the two most discriminating
perdictants (Wood, 2006).
It is to facilitate the geophysical interpretation that we use two
meteorological variables instead of a linear combination of multiple
variables (i.e. prior principal component analysis axes). Limiting their
number to two also allows remaining in a 2-D physical parameter space that
supports the discussion as will be illustrated below.
Training and validation data sets
The data sets used to fit and test the statistical models are produced by the
regional climate and air quality modelling framework presented in Colette et
al. (2013). By using deterministic climate and chemistry models from the
global to the regional scale, they could produce long-term air quality
projections over Europe. The Earth System Model (ESM) which drives these
simulations is the IPSL-CM5A-MR (Dufresne et al., 2013). The global data used
in this study were produced for the Coupled Model Intercomparison Project
Phase 5 initiative (CMIP5) (Taylor et al., 2012; Young et al., 2013). Then
the climate data obtained by the ESM are dynamically downscaled with the
regional climate model WRF (Skamarock et al., 2008). The spatial resolution
is 0.44∘ over Europe (Colette et al., 2013). These simulations were
part of the low-resolution simulations performed within the framework of the
European-Coordinated Regional Climate Downscaling Experiment program
(EURO-CORDEX) (Jacob et al., 2014). Whereas higher spatial resolution
simulations are available in the EuroCordex ensemble, the 0.44 resolution
was considered appropriate for air quality projections in agreement with
other publications (Meleux et al., 2007; Langner et al., 2012a, b; Manders et
al., 2012; Colette et al., 2013; Hedegaard et al., 2013; Watson et al.,
2015), and also because higher RCM resolution are not specifically performed
to improve the climate features that are most sensitive for air quality
purposes (temperature, solar radiation, stagnation events, triggering of
low-intensity precipitation events etc.). Finally the regional climate fields
are used to drive the CTM CHIMERE (Menut et al., 2013), for the projection of
air quality under changing climate. Since we are only interested in the
effect of climate change, pollutant emissions remain constant at their level
of 2010, as prescribed in the ECLIPSE-V4a data set (Klimont et al., 2013).
Similarly, chemical boundary conditions prescribed with the INCA model
(Hauglustaine et al., 2014) as well as the land-use are also kept constant.
The Chemistry and Transport Model CHIMERE has been used in numerous studies:
daily operational forecast (Rouïl et al., 2009), emission scenario
evaluation (Cuvelier et al., 2007), evaluation in extreme events (Vautard et
al., 2007), long-term studies (Colette et al., 2011, 2013; Wilson et al.,
2012) and inter-comparisons models and ensembles (Solazzo et al., 2012a, b).
The model performances depend on the setup but general features include a
good representation of ozone daily maxima and an overestimation of night-time
concentrations, leading to a small positive bias in average ozone (van Loon
et al., 2007). Concerning particulate matter, similarly to most
state-of-the-art CTMs, the CHIMERE model presents a systematic negative bias
(Bessagnet et al., 2014). Regarding more specifically its implementation in
the context of a future climate, evaluations of the CHIMERE model are
presented in Colette et al. (2013, 2015) and also Watson et al. (2015) and
Lacressonniere et al. (2016).
The training data set used to build the statistical models consists of the
historical air quality simulations (1976 to 2005), while projections of air
quality under a future climate (RCP8.5 2071–2100) will be used for testing
purposes.
In order to evaluate the uncertainty related to climate change, the
statistical models should be skillful for both pollutant concentrations over
the historical period (training period) and in predictive mode. Alternative
RCM forcing of the CHIMERE CTM could be used to test the approach.
Unfortunately, such alternatives are not available at this stage. The
statistical ensemble exploration technique presented here will ultimately
allow selecting the RCM that should be used in priority to cover the range of
uncertainties in air quality and climate projections. When such simulations
become available, we will be able to further test the skill of the
statistical model. However, so far, the only validation that could be
included here was to rely on a future time period as a validation data set. The
underlying hypothesis is that the historical range of meteorological
parameters used to train the model will be exceeded in the future, therefore
offering an appropriate testing data set. The results of this validation are
presented in Sect. 3.2.
Projection data set
To evaluate the uncertainty related to the climate forcing, and identify the
RCM that should be used in priority for future air quality projections, the
statistical model of air quality is used in predictive mode using the
regional climate projections performed in the framework of the EURO-CORDEX
experiment (Jacob et al., 2014). The combinations of global and regional
climate models used here are the following: CanESM2/RCA4; CSIRO-Mk3-6-0/RCA4;
CNRM-CM5-LR/RCA4; EC-EARTH/RACMO2; EC-EARTH/RC4; GFDL-ESM2M/RCA4;
IPSL-CM5A-MR/RCA4; IPSL-CM5A-MR/WRF; MIROC5/RCA4; MPI-ESM-LR/RCA4;
MPI-ESM-LR/CCLM; NorESM1-M/RCA4 (see Jacob et al., 2014, for details on the
model nomenclature).
The performances of the global models used to drive the regional projections
have been evaluated in Jury (2012) and Cattiaux et al. (2013). In the general
EuroCordex evaluation, Kotlarski et al. (2014) finds a good reproduction of
the spatial temperature variability even if the models exhibit an
underestimation of temperature during the winter in the north Eastern Europe.
In addition to this general feature, the specificity of the WRF-IPSL-INERIS
member is an overestimation of winter temperatures in the southeast. In terms
of precipitations, most of the models exhibit a pronounced wet bias over most
subdomains.
When focusing on WRF members of the EuroCordex ensemble, Katragkou et
al. (2015) points out that the IPSL-INERIS member offers one of the best
balance between precipitation and temperature skills. Both studies are
limited to the evaluation of RCM used with perfect boundary conditions
(ERA-Interim forcing) and no published study has yet evaluated the various
global and/or regional combinations. It should also be noted that the ensemble is
poorly balanced in terms of GCM/RCM combinations (see the larger weight of
the RCA regional model which raise important question regarding the
representativeness of the ensemble).
The left column represents daily average PM2.5 concentrations
for the historical (1976–2005) (a), the end of the century (RCP8.5
–
2071–2100) (b) and the difference between the future and the historical (c).
The statistical significance of this difference is evaluated by a t test and
represented by a black point. The right column presents the same figure for
daily maximum ozone projections. For both pollutants, the CTM CHIMERE has
been used to predict the concentration (Sect. 2.2).
Development and validation of the statistical model
In this part we studied the end (2071–2100) of the century, for one scenario
(RCP8.5) which is an energy-intensive scenario (van Vuuren et al., 2011).
This 30-year period is chosen to be representative regardless of the
inter-annual variability (Langner et al., 2012a). We focus on the RCP8.5 and
the end of the century on purpose to reach a strong climate signal.
Air quality projectionsFine particulate matter
Figure 1a shows the 30 years average PM2.5 concentrations over the
historical period (1976 to 2005). Higher concentrations are modelled over
European pollution hotspots: the Benelux, the Po Valley, Eastern Europe and
large cities. A similar pattern is found in the future (RCP8.5 – average
over the period 2071–2100) albeit with lower concentrations (Fig. 1b). The
difference (future minus historical) is given in Fig. 1c where the
statistical significance of the changes was represented by black points at
each grid points and evaluated by a Student t-test with Welch variant at the
95 % confidence level based on annual mean. The decrease is statistically
significant over most of the domain.
Overall, we identify a climate benefit on particulate matter pollution
similarly to Colette et al. (2013) and Lecœur et al. (2014) but in
opposition to Manders et al. (2012). Hedegaard et al. (2013) find a decrease
in high latitude and an increase in low latitude. The role of future
precipitation projections and more efficient wet scavenging has often been
pointed out to explain such a future evolution of particulate matter (Jacob
and Winner, 2009). However, the lack of robustness in precipitation evolution
over major European particulate pollution hotspots in regional climate models
(Jacob et al., 2014) challenges the confidence we can have in single model
air quality and climate projection, supporting again the need for ensemble
approaches.
Ozone peaks
Figure 1d represents the summer (JJA) average ozone daily maximum
concentrations over the historical period (1976 to 2005). A north–south
gradient appears with lower concentration in the north and higher
concentration fields over the Mediterranean Sea. Figure 1e corresponds to the
summer average ozone projection of the RCP8.5 at the end of the century
(2071–2100) predicted by the model suite presented in Sect. 2.2. A similar
pattern is found, with higher concentrations in the southern part of the
domain (Fig. 1e). The map of the difference (RCP8.5 – actual), Fig. 1f,
indicates an increase of ozone concentrations over Eastern Europe,
Mediterranean land surfaces, and North Africa and a decrease over British-Irish Isles and Scandinavia. Most of the changes are statistically significant
except over Western Europe. This concentration rise is frequently associated to
an increase of temperature in the literature (Meleux et al., 2007; Katragkou
et al., 2011), see Sect. 2.1 above for a review of physical and chemical
processes underlying this association.
Following Langner et al. (2012b), Manders et al. (2012), Colette et
al. (2013, 2015) these results confirm the fact that climate change
constitutes a penalty for surface ozone in Europe.
Statistical models per region that explain the average PM2.5
concentrations during 1976–2005.
Here we introduce the statistical models trained over the historical period
and their evaluation over the future testing period. First we discuss the
impact of key meteorological processes on pollutants concentration on the
basis of the model correlation and put our results in perspective with the
key driving factors reported in the literature. Then we evaluate the
performance of statistical models over the future period in order to discard
regions and pollutants where the skill of the statistical model is too small
to draw robust conclusions on the uncertainties of projections.
Fine particulate matter
The skill and predictors for generalized additive models fitted for each
region are given in Table 1. The depth of the planetary boundary layer is
identified as the major meteorological driver for PM2.5 which is a
different finding compared to Megaritis et al. (2014) who reported a smaller
impact for the PBL depth. Near-surface temperature is often selected as
second predictor. The wind is pointed out as a relevant predictor twice but
only for coastal regions (respectively BI and MD) where sea-salt is
important. Last, precipitation is selected only once and as 2nd variable for
the Iberian Peninsula (IP). It could be partly due to our choice of
statistical model whereas a logical regression would have been more efficient
given that PM correlations are sensitive to the presence and/or absence of
precipitation rather than their intensity. It is difficult to assess
objectively whether the larger role of temperature than precipitation in our
findings is an artifact related to the design of the statistical model. The
importance of precipitation in the impact of climate change on particulate
pollution is often speculated in the literature, with little quantitative
evidence. The statistical model used here offers an objective quantification
of that role. It should be added that the importance of temperature is well
supported by the volatilization process for Secondary Inorganic Aerosol and
Secondary Organic Aerosol. Moreover in the CTM CHIMERE, the volatile species
in the gas and aerosol phases are assumed to be in chemical equilibrium. This
thermodynamic equilibrium, computed by ISORROPIA (Fountoukis and Nenes,
2007), is driven by temperature and humidity and conditions the concentration
of several aerosol species (ammonium, sodium, sulphate, nitrate and so on).
This feature could explain the major role of temperature. It is also
supported by the pattern of projected PM2.5 change, which is spatially
correlated with present-day PM2.5 concentration. This spatial
correlation suggests an impact of a uniform driver which points towards
temperature rather than precipitation change that exhibits a strong
north-south gradient in Europe.
Then the predictive skill of these models is tested over the period
2071–2100 by computing the Normalized Root Mean Squared Error (NRMSE)
between the statistically predicted PM2.5 (concentrations estimated with
the statistical models), and the results of the deterministic regional air
quality and climate modelling suite presented in Sect. 2.2 for 2071–2100.
The NRMSE is defined as the root mean square error between statistically
predicted and deterministically modelled concentrations changes aggregated by
region and at daily temporal frequency, normalized by the standard deviation
of the deterministic model. It allows describing the predictive power of a
model, if the NRMSE is lower or equal to 1 then the model is a better
predictor of the data than the data mean (Thunis et al., 2012).
Figure 2 shows, for each region, the scatter between R2 over the
historical period and the NRMSE in predictive mode for the RCP8.5 at the end
of the century. We expect regions where the correlation over the historical
period is low to be poorly captured by the statistical model in the future.
The fact that the good correlation for EA and ME are associated with an NRMSE
around 0.6 in the future indicates either that the main meteorological
drivers in the future will remain within their range of validity or that
extrapolation is a viable approximation. This feature gives confidence in
using statistical models for these regions in predictive mode. For the NI
region, the NRMSE is acceptable (below 0.8) even if the
R2 is low.
Statistical model evaluation for PM2.5 (left) and ozone
(right). The x axis represents the Normalized Mean Square Error applied to
the delta (future minus historical) of the generalized additive model and
CHIMERE. The y axis represents the R2 of the statistical model
(training period).
Considering that the model skill was satisfactory for the EA, ME and NI
regions, we decided to focus on these regions for the uncertainty assessment
in the remainder of this paper. The fine particulate matter concentrations
have been poorly captured for the region BI, SC, FR, IP and MD. The
associated bad NRMSE are explained by the poor performances of model over
the historical. They are thus excluded from the uncertainty assessment.
Statistical model evaluation for each particulate matter
constituent (from left to right: Dust, Primary Particulate Matter, Sea-salt,
Ammonium, Organic fraction, Nitrate, Sulphate). The x axis represents the
Normalized Mean Square Error applied to the delta (future minus historical)
of either the generalized additive model or CHIMERE. The y axis represents
the R2 of the statistical model (training period).
Particulate matter composition
Because total PM2.5 is constituted by a mix of various aerosol species,
there is a risk of compensation of opposite factors in the statistical model.
In order to assess that risk, we developed such models for each individual PM
constituent in the chemistry-transport model. The performances of these
statistical models in terms of correlation for the historical (training)
period or in predictive mode for the future period (testing) are presented in
Fig. 3.
For all regions, the statistical models are not able to capture the
variability of mineral dust. This is because the design of the statistical
model is exclusively local (i.e. average concentrations over a given region
are related to average meteorological variables over the same region),
whereas most of the mineral dust over any European region is advected from
the boundaries of the domain, in North Africa. It should be noted however,
that except for the regions IP and MD, the dust represents only a small
fraction of the PM concentrations (Fig. 4). That could explain why the
statistical model for PM2.5 performs poorly over IP and MD, but it will
not undermine the confidence we can have in concluding about the robustness
of the PM2.5 model for the region selected above: ME, EA and NI.
Average particulate matter composition for the historical period
per region.
All over Europe, primary particulate matter (PPM) is one of the smallest
particulate matter fractions. Their variability is well captured by the
statistical model for all regions except SC. But because of their small
abundance in that region, they should have a limited impact on the PM2.5
model performance.
The sea salts are well reproduced by the statistical model for all
regions except NI and EA. These two regions have no maritime area, therefore
sea-salt concentrations are lower and exclusively due to advection which, as
a non-local factor, is not well captured by the statistical model.
Ammonium (NH4+) aerosols are satisfactorily captured by the statistical
models for five regions out of eight including those selected for the overall
PM2.5 model (ME, EA, and NI).
The organic aerosol fraction (ORG) is well reproduced over the historical
period and the predictive skill is satisfactory (NRMSE around 0.7) for ME,
EA, and NI.
The statistical models are efficient to reproduce the nitrate (NO3-)
concentrations over the historical period for ME, EA, AL, MD, FR, and BI
regions but the predictive skills are only considered satisfactory for ME,
EA, FR and NI, where nitrate constitutes a large fraction of PM2.5.
Sulphate aerosols (SO42-) are well represented by the statistical
models for BI, EA, and ME. The performances are low in the NI region, but
sulphates constitute one of the smallest particulate matter fractions for
that region.
This analysis of the skill of statistical models for each compound of the
particulate matter mix confirms that there is no compensation of opposite
factors in the selection of skillful models for total PM2.5 proposed in
Sect. 3.2.1. The only cases were one of the particulate matter compound was
not well captured by a statistical model, could be attributed to a low, and
often non-local contribution of the relevant particulate matter constituent
for the considered regions. We conclude that the selection of ME, EA, and NI
as regions where it is possible to build a statistical model of PM2.5
variability using Generalized Additive Models based on meteorological
predictants would hold if the model had been built for each constituent of
the particulate matter mix.
Ozone peaks
For summertime ozone peaks, as expected, near-surface temperature and
incoming short-wave radiation are identified as the two main meteorological
drivers for most regions (Table 2). Concerning the region EA, the drivers
which give the best results are near-surface temperature and specific
humidity. Nevertheless, when using specific humidity as second predictor, the
statistical model is overfitted and has a low predictive skill
(NRMSE = 0.9). Thus the use of short-wave radiation as second predictor
appears much more robust (NRMSE = 0.6) even if the R2 is lower. The
skill of the statistical model is very low over the British-Irish Isles and
Scandinavia. This is because ozone pollution in these regions is largely
influenced by non-local contributions (long-range transport of air
pollution). The poor performances of the statistical model over the
Mediterranean region are more surprising. The lower variability of
temperature and incoming short-wave radiation in this region compared to other
parts of Europe (standard deviation of 12.5 ∘C and 150 W m-2
for MD; from 15 to 20 ∘C and from 220 to 300 W m-2 for the
other regions) makes them less relevant as statistical predictants of ozone
concentrations.
Statistical models per region that explain the daily maximum summer
ozone levels during 1976–2005.
We conclude that the generalized additive models that can be considered
efficient enough in terms of correlation to capture the ozone variability
over the historical period are those of the following regions: EA, FR, IP,
ME and NI.
This selection is further supported by investigating the predictive skill of
the models assessed by computing their NRMSE against deterministic CTM
simulations available for a future period. The regions mentioned above where
the correlation of the statistical model is low (BI, SC and MD) also exhibit
a large NRMSE (Fig. 2). So that, only the regions EA, FR, IP, ME and NI are
selected for the remainder of this paper.
Exploring the ensemble of climate projections with the statistical
model
The statistical models introduced in Sect. 2, developed in Sect. 3 and tested
in Sect. 3.2 are applied here to the ensemble of regional climate projections
presented in Sect. 2.3 to develop a proxy of ensemble of air quality and
climate projections for each selected region. This proxy of ensemble will be
used to identify the subset of regional climate projections that should be
used in priority in the deterministic modelling suite, but it can also give
an indication on the robustness of the climate impact on air quality where
the skill of the statistical model is considered satisfactory.
Fine particulate matter
In order to assess qualitatively the robustness of the evolution of regional
climate variables having an impact on air quality, we first design a 2-D
parameter space where the isopleths of statistically predicted pollutant
concentrations are displayed (background of Fig. 5). Then the distributions
of historical and future meteorological variables as extracted from the
regional climate projections are added to this parameter space. For each
Regional Climate Projection, we show the average of the two driving
meteorological variables as well as the 70th percentile of their 2-D-density
plot, i.e. the truncation at the 70th quantile of their bi-histogram which
means that 70 % of the simulated days lies within the contour. Both
historical and future climate projections (here for the RCP8.5 scenario and
the 2071–2100 period) are displayed on the parameter space. The climate
projections are all centred on the IPSL-CM5A-MR/WRF member so that only the
distribution of the latter is shown for the historical period.
Predicted concentrations evolution of summertime ozone and
PM2.5 (expressed in µg m-3) per selected regions and per
model. The ensemble mean and standard deviation are also calculated.
RCP8.5 2071–2100Delta (future – historical) Ozone max PM2.5GCM/RCM\RegionsEAFRIPMENIEAMENICNRM-CM5-LR/RCA48.006.969.754.823.69-0.77-0.82-0.71CSIRO-Mk3-6-0/RCA411.2616.0313.3014.155.81-1.39-1.72-1.06CanESM2/RCA417.9719.0315.0721.207.46-1.29-1.56-1.03EC-EARTH/RACMO27.7711.3710.798.556.77-1.16-0.98-0.77EC-EARTH/RCA410.8814.4311.4512.115.15-0.92-0.92-0.75GFDL-ESM2M/RCA47.267.7910.285.854.54-1.04-0.90-0.70IPSL-CM5A-MR/RCA413.7613.4612.8811.024.43-1.28-1.12-1.04IPSL-CM5A-MR/WRF10.116.059.085.190.01-1.32-1.30-0.86MIROC5/RCA412.3011.2911.619.623.85-1.16-0.86-0.85MPI-ESM-LR/CCLM6.409.6311.036.015.58-0.81-0.58-0.62MPI-ESM-LR/RCA49.5611.7511.519.645.54-1.02-0.79-0.83NorESM1-M/RCA410.8812.6011.5810.125.02-0.79-0.88-0.76Ensemble Mean10.5111.7011.539.864.82-1.08-1.03-0.83Ensemble Standard Deviation3.063.631.554.411.790.210.320.14
The left figure presents the proxy of ensemble projections for
daily average de-seasonalized PM2.5 concentrations in Eastern Europe.
The right figure represents the proxy for daily maximum de-seasonalized
summer ozone for Eastern Europe. For both figures, the shaded background
represents the evolution of pollutants estimated by the statistical models.
The contours are representing the regional climate projections and the
triangles their mean. The black dashed contour represents the historical –
IPSL-CM5A-MR/WRF – and the square its mean.
As pointed out in Table 1, the main meteorological drivers are the depth of
the PBL and near-surface temperature for the example of PM2.5 over
Eastern Europe region displayed in Fig. 5. The statistically modelled
isopleths in the background of the figure show that PM2.5 concentration
decrease when the depth of the PBL increases (x axis), or when temperatures
increase (y axis). The interactions captured by the GAM exhibit the strong
influence of high vertical stability events (with low surface temperature and
PBL depth) in increasing PM2.5 concentrations. On the contrary, for high
temperature ranges, the depth of the PBL becomes a less discriminating
factor. The comparison of historical and future distributions shows that both
meteorological drivers evolve significantly in statistical terms (Student
t test with Welch variant at the 95 % confidence level based on annual
mean). However, even though the PBL depth constitutes the most important
meteorological driver for PM2.5, it does not evolve notably compared to
the surface temperature in the future (Fig. 5). Thus the largest increase of
the secondary driver (surface temperature) leads to a decrease of PM2.5
concentrations. The largest and the smallest PM2.5 concentrations
decrease are found for CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM, respectively.
But the overall spread of RCMs in terms of both the evolution of PBL depth
and temperature is limited, suggesting that this climate benefit on
particulate pollution is a robust feature. Those isopleths present the same
characteristics for ME and NI regions (Figs. S1, S4 in the Supplement). The
qualitative evolution represented in Fig. 5 is further quantified by applying
the GAM to the future meteorological variables in the regional climate
projections. These results are represented by the probability density
functions of the predicted concentrations of each GCM/RCM couple minus the
estimated values for the historical simulation (e.g. 2071–2100 vs.
1976–2005, Fig. 6). For EA and ME, the longer tail of the probability
density function of MPI-ESM-LR/CCLM compared to the average of the models
reflects that stronger pollution episodes will occur in the future even if
the mean of the concentrations is lower than the average of the ensemble
(Fig. 6 for EA and Fig. S2 for ME).
The left figure represents, for each regional climate model the
probability density function (PDF) of the concentrations estimated with the
generalized additive model at the end of the century minus the estimated
concentrations of the historical period for daily average de-seasonalized
PM2.5 concentrations in Eastern Europe. The right figure presents the
results for daily maximum de-seasonalized summer ozone for Eastern Europe.
Besides the distribution, the ensemble mean and standard deviation of the
estimated projected change in PM2.5 concentrations has been quantified
(Table 3). All the selected regions depict a significant decrease of the
PM2.5 concentrations across the multi-model proxy ensemble indicating
that according to the GAM model, the climate benefit on particulate matter is
a robust feature in these regions. The magnitude of the decrease depends on
the region, its ensemble mean (± standard deviation) is -1.08
(±0.21), -1.03 (±0.32),
-0.83 ± (0.14) µg m-3, for respectively EA, ME and NI
(Table 3).
In order to explain the differences in the response of individual RCM in the
ensemble, we need to explore the historical meteorological variables
probability density functions (PDF, Fig. 7) and to compare them with the
evolution of IPSL-CM5A-MR/WRF (Fig. 7). The comparison of the historical
distribution for the temperature reflects the stronger extremes of
IPSL-CM5A-MR/WRF (e.g. colder than the others when it is cold). It is only
for the NI region that IPSL-CM5A-MR/WRF lies in the mean of the ensemble.
Concerning the PBL depth, the values are similar to the average of the
ensemble for ME even if MPI-ESM-LR/RCA4 and EC-EARTH/RACMO2 present the
largest values. IPSL-CM5A-MR/WRF has a thinner boundary layer for NI and a
deeper than the average for EA but the differences are limited Fig. 7).
It is for CSIRO-Mk3-6-0/RCA4 that we find the most important decrease of
PM2.5 for the selected regions (Table 3). This is related to a larger
temperature rise compared to the other models and a larger boundary layer
height increase compared to the other member of the ensemble for these
regions Fig. 5). CanESM2/RCA4 and CSIRO-Mk3-6-0/RCA4 exhibit the same
features for the ME region.
MPI-ESM-LR/CCLM presents the smallest decrease of PM2.5 for each of the
selected regions (e.g. over ME is almost 3 times smaller than the largest
decrease) except EA where CNRM-CM5-LR/RCA4 presents a smaller decrease
(-0.77 µg m-3 vs. -0.81 µg m-3). As
already mentioned above, the particular tails of the statistically modelled
PM2.5 distributions for EA and ME indicate a larger contribution of
large pollution episodes in the future for that RCM. But the historical
distributions exhibit a larger boundary layer than the average models of the
ensemble and a similar temperature. Thus, the low PM2.5 concentration
decrease is explained by the limited average evolution of the meteorological
drivers as shown in Fig. 5.
Overall we conclude that a climate benefit is identified for the PM2.5
for each of the selected regions. To the extent that the statistical model is
skillful, as demonstrated in Sect. 3.2.1, this result is robust across the
range of available climate forcings since the whole ensemble of regional
climate projection present consistent features. The regional climate models
that exhibit the largest and smallest responses are CanESM2/RCA4;
CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM, which should therefore be considered
a priority for further evaluation using explicit deterministic projections
involving full-frame regional climate and chemistry models.
The first column of the panel represents the historical distribution
of the meteorological variables identified by our statistical models as the
two major drivers (a PBL Height; b near-surface
temperature) for PM2.5 in Eastern Europe. The second column represents
the historical JJA distribution of the two main drivers for summer ozone
(a near-surface temperature; b incoming short-wave
radiation).
Ozone peaks
For most of the selected regions (FR, IP, ME and NI), the main drivers are
the same (i.e. near-surface temperature and short-wave radiation). The
isopleth in the background of Fig. 5 show that temperature and short-wave
radiation have a similar impact on ozone peaks, except in the larger range of
short-wave radiation anomalies, where temperature becomes less
discriminating. All the isopleths (Fig. 5 for EA and Figs. S1, S4 and S7 for
ME, NI, FR and IP) exhibit an increase in the distribution of temperatures
because the projected future is warmer than the historical period. According
to the ozone peak concentrations predicted by the GAM (displayed in the
background of Fig. 5) these increases will lead to more ozone episodes. This
trend appears for the entire models ensemble so that we can conclude with
confidence that the climate penalty bearing upon ozone is a robust feature
even if the specific distribution of some of the models stand out
(CanESM2/RCA4; CNRM-CM5-LR/RCA4; CSIRO-Mk3-6-0/RCA4; IPSL-CM5A-MR/WRF).
The ozone increase of the ensemble reaches +10.51 (±3.06), +11.70
(±3.63), +11.53 (±1.55), +9.86 (±4.41), +4.82
(±1.79) µg m-3 for EA, FR, IP, ME and NI (Table 3). These
values confirm the statistically significant climate penalty (the mean is at
least two times larger than the standard deviation). However, as already
mentioned for Fig. 5, we find minor differences among the models. The
meteorological distributions are marginally different between the models of
the ensemble: the summertime temperature predicted by IPSL-CM5A-MR/WRF has
stronger extremes than the other models. Moreover, it is warmer than the
ensemble in EA. Concerning incoming short-wave radiation, IPSL-CM5A-MR/WRF
lies in the average (Figs. S3, S6, S9) except for the region EA where the
amount of incoming radiation is the highest among the ensemble (Fig. 7). Note
that, only EC-EARTH/RACMO2 and MPI-ESM-LR/RCA4 exhibits lower values (around
half of the average for MPI-ESM-LR/CCLM). The lower amount of summertime
incoming short-wave radiation for the couple MPI-ESM-LR/CCLM is relevant for
all the selected regions.
The magnitude of the ozone rise differs between the models and the regions.
Note that CanESM2/RCA4 exhibits the largest difference (i.e. around 1.5
times the ensemble mean) followed by CSIRO-Mk3-6-0/RCA4 for each selected
regions. This is explained by the larger temperature increase during
summertime which is the major driver, as identified by the statistical
models, of ozone concentration. Note that the value is skyrocketing for the
region ME, 5 times the value of IPSL-CM5A-MR/WRF which shows one of the
lowest increases. CNRM-CM5-LR/RCA4 presents the lowest increase.
On the contrary, the lower increase of the summer temperature and sometimes a
decrease of the incoming short-wave radiation amount (e.g. IPSL-CM5A-MR/WRF
in NI) lead to lower ozone concentration changes for IPSL-CM5A-MR/WRF and
CNRM-CM5-LR/RCA4 for FR, IP, ME and NI (Table 3). Note the specific evolution
for the region NI, where the IPSL-CM5A-MR/WRF model yields almost no increase
of the ozone concentration compared to the other models while on the map of
the differences in the deterministic model (Fig. 1f), the evolution was
statistically significant. This absence of evolution reflects the limitation
of the statistical models.
In Fig. S5, we can point out an outstanding pattern of the MPI-ESM-LR/CCLM
distribution for the NI region with particularly large tails. The ozone rise
would be more pronounced for the upper quantile which depicts more extreme
ozone pollution episodes (note that
this was also the case for that model in terms of PM2.5 pollution).
Overall the climate penalty is confirmed even if some regional climate
models stand out of the distribution, such as CanESM2/RCA4; CNRM-CM5-LR/RCA4
and CSIRO-Mk3-6-0/RCA4 which should therefore be considered for further
deterministic projections.
Conclusions
An alternative technique to assess the robustness of projections of the
impact of climate change on air quality has been introduced. Using a
training data set consisting of long-term deterministic regional climate and
air quality projections, we could build statistical models of the response
of ozone and particulate pollution to the main meteorological drivers for
several regions of Europe. Applying such statistical models to an ensemble
of regional climate projections leads to the development of an ensemble of
proxy projections of air quality under various future climate forcings. The
assessment of the spread of the ensemble of proxy projections allows
inferring the robustness of the impact of climate change, as well as
selecting a subset of climate models to be used in priority for future
explicit air quality projections, therefore proposing an optimized
exploration of the ensemble.
The main meteorological drivers that were identified are (i) for PM2.5:
the boundary layer depth and the near-surface temperature and (ii) for ozone:
the near-surface temperature and the incoming short-wave radiation. The skill
of the statistical models depends on the regions of Europe and the pollutant.
For PM2.5 and the regions Eastern Europe (EA) and Mid-Europe (ME), a
generalized additive model captures about 60 % of the variance and 40 % for
Northern Italy. But for the British-Irish Isles (BI) and Scandinavia (SC),
where air pollution is largely driven by long-range transport, such a local
approach is not able to reproduce the variability of pollutant
concentrations.
The ozone concentrations are well reproduced by the statistical model for the
following regions: Eastern Europe (EA), France (FR), Iberian Peninsula (IP),
Mid-Europe (ME) and Northern Italy (NI). The meteorological variables are not
discriminating enough for the Mediterranean region. For the regions where the
performances of the statistical model were considered satisfactory, a proxy
of the future pollutant concentrations could be estimated (i.e. (i). EA,
ME, and NI; (ii). EA, FR, IP, ME and NI).
An overall climate benefit for PM2.5 was found in the proxy ensemble of
climate and air quality projections. The ensemble mean change is -1.08
(±0.21), -1.03 (±0.32),
-0.83 ± (0.14) µg m-3, for EA, ME and NI,
respectively. This beneficial impact of climate change for particulate matter
pollution is in agreement with the deterministic projections of Huszar et
al. (2011), Juda-Rezler et al. (2012) and Colette et al. (2013) but in
opposition to Manders et al. (2012). These differences could be partly
explained by the different time windows (i.e. 2060–2041 vs. 2100–2071),
climate scenario (i.e. A1B which is similar to RCP6.0 vs. RCP8.5), and
pollutant (i.e. PM10 vs. PM2.5). This impact of climate change on
particulate pollution should be put in perspective with the magnitude of the
change that is expected from the current air quality legislation. Such a
comparison was performed by Colette et al. (2013) who found (on average over
Europe) a climate benefit by the middle of the century of the order of
0–1 µg m-3, therefore in line with our estimate but also
much lower than the expected reduction of 7–8 µg m-3 that
they attributed to air quality policies.
For all the selected regions a robust climate penalty on ozone was
identified: +10.51 (±3.06), +11.70 (±3.63), +11.53 (±1.55),
+9.86 (±4.41), +4.82 (±1.79) µg m-3 for EA, FR, IP, ME, and
NI, respectively. This finding is in line with previous
studies (Meleux et al., 2007; Huszar et al., 2011; Katragkou et al., 2011;
Jiménez-Guerrero et al., 2012; Juda-Rezler et al., 2012; Langner et al.,
2012a, b; Colette et al., 2013, 2015; Hedegaard et al., 2013; Varotsos et
al., 2013). It should be noted that when comparing the impact of climate
change and emission reduction strategies, Colette et al. (2013) found a
climate penalty of the order of 2–3 µg m-3 (which is broadly
consistent with our results given that they focused on the middle of the
century) that could be compensated with the expected magnitude of the
reduction of 5–10 µg m-3 brought about by air quality
policies.
The major strength of our approach is to account for the climate uncertainty
in the recent EuroCordex ensemble of regional climate projections, whereas
all the published literature relied on a very limited subset of RCM forcing
(at best two for a given chemistry-transport modelling study). We therefore
propose an unprecedented view in the robustness of the impact of climate
change on air quality across an ensemble of climate forcing. However, this
achievement is limited by the quality of the underlying statistical model
that does not capture all the variance of the air quality response to
climate change. These results should thus be ultimately compared with
further deterministic projections using a range of climate forcings. Then,
our approach can yield precious information in pointing out which regional
climate models should be investigated in priority, therefore proposing a
smart exploration of the ensemble of projections. The following models:
CanESM2/RCA4; CNRM-CM5-LR/RCA4 and CSIRO-Mk3-6-0/RCA4 and MPI-ESM-LR/CCLM,
have been identified as the climate models that should be used in priority
for future air quality.
Finally, we should add that the method applied here for air quality
projection also opens the way for other climate impact studies, where
quantifying uncertainties using low computational demand is desirable.
The Supplement related to this article is available online at doi:10.5194/acp-16-2559-2016-supplement.
Acknowledgements
The climate and air quality projections used in the present study were
produced as part of the European Union's Seventh Framework Programme
(FP7/2007–2013) under grant agreement no. 282687 (ATOPICA) and computing
resources of the TGCC/CCRT/CEA.
Edited by:
J. Brandt
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