Substantial changes in anthropogenic aerosols and precursor gas emissions
have occurred over recent decades due to the implementation of air pollution
control legislation and economic growth. The response of atmospheric aerosols
to these changes and the impact on climate are poorly constrained,
particularly in studies using detailed aerosol chemistry–climate models. Here
we compare the HadGEM3-UKCA (Hadley Centre Global Environment Model-United Kingdom Chemistry and Aerosols) coupled chemistry–climate model for the period
1960–2009 against extensive ground-based observations of sulfate aerosol
mass (1978–2009), total suspended particle matter (SPM, 1978–1998),
PM
Aerosols can cause acid deposition, degradation of atmospheric
visibility, changes to Earth's radiative balance, and are also a major
source of air pollution, which affects human health. Aerosols interact with
climate by absorbing and reflecting incoming solar radiation and by modifying
the microphysical properties of clouds. These effects have been defined in
the latest Intergovernmental Panel on Climate Change (IPCC) report
Here we use a global coupled chemistry–climate model to improve our understanding of changes in aerosols over Europe from 1960 to 2009. The climate impact of aerosols over this period, in response to emission changes, was calculated as an aerosol radiative forcing. An assessment of the confidence in this effect was obtained from the ability of the model to reproduce observed long-term changes in a number of aerosol properties including mass concentrations and aerosol optical depth (AOD).
Anthropogenic emissions of aerosol particles and their precursors have
increased significantly since pre-industrial times. For example, global
SO
Changes in anthropogenic emissions and aerosol concentrations affect
Earth's climate
Ground-based monitoring networks providing observations of aerosol
concentrations and physiochemical properties were established following air
pollution control legislation. The longest continuous measurements of
aerosols are available in North America and Europe from the 1970s to present
day. In Europe, observations of aerosol mass concentrations (both sulfate and
total) are available from the European Monitoring and Evaluation Programme
(EMEP) network
Several studies have analysed long-term trends in observed aerosols.
Evaluating the ability of chemistry–climate models to reproduce observed
trends is necessary in order to reliably predict the climate effects of
aerosols over this period. Many studies qualitatively match the direction of
observed trends in aerosol but underestimate both absolute concentrations and
the magnitude of observed trends
Several studies
Changes in surface solar radiation (SSR) do not provide a direct measurement
of aerosols but they can be used to infer their influence on the surface
radiation balance.The Global Energy Balance Archive (GEBA) provides long-term
observations of SSR from the 1950s until present day over a large part of
Europe
These previous studies have either simplified their treatment of aerosols or the model was evaluated against a limited range of aerosol properties over a relatively coarse spatial and temporal scale. The importance of studies at regional spatial scales was also highlighted as changes in aerosols at this level can potentially be masked by compensatory changes observed on the global scale. Here we simulate monthly-mean aerosol concentrations from 1960 to 2009 using the HadGEM3-UKCA (Hadley Centre Global Environment Model-United Kingdom Chemistry and Aerosols) global chemistry–climate model, which includes aerosol microphysics (aerosol number and mass size distributions). We evaluate the ability of the model to consistently capture observed changes in bulk in situ aerosol properties (PM, aerosol size distributions and chemical components) as well as radiative properties (AOD, SSR) over Europe. We also calculate the regional top of atmosphere radiative perturbations due to simulated changes in aerosols. This has enabled a detailed regional analysis and evaluation of the changing radiative impact of aerosols due to variations in emissions.
Section 2 describes the HadGEM3-UKCA model, the simulations performed and the long-term observations used. Section 3 discusses and evaluates the simulated changes to European aerosols and surface solar radiation. Section 4 presents aerosol radiative forcing over Europe. Conclusions are presented in Sect. 5.
We used the coupled chemistry–climate model HadGEM3-UKCA to study the
interaction between chemistry, aerosols and the impacts on the radiation
balance of the climate system. HadGEM3-UKCA is part of the third generation
of the Met Office's Hadley Centre Global Environment Model (HadGEM) family,
which incorporates an online treatment of chemistry and aerosols through the
United Kingdom Chemistry and Aerosols (UKCA) programme. The Met Office
Unified Model (UM) acts as the dynamical core and provides the components for
atmospheric transport and tracer mixing. This is based on the dynamics
implemented by
HadGEM3-UKCA is used here in atmosphere-only mode. We output monthly 3-D
aerosol and radiation fields for the years 1960–2009 at a resolution of
1.875
Tropospheric ozone, HO
The Fast-J photolysis scheme is implemented within UKCA to calculate online
photolysis rates based on the distribution of clouds, ozone and aerosols
HadGEM3-UKCA uses the modal aerosol scheme of the Global Model of Aerosol
Processes (GLOMAP mode)
The Edwards–Slingo radiation code
The radiation scheme was called twice for each time step (every 30 m) in a double-call radiation configuration
Sea salt emissions are calculated using the surface wind speed and the
parameterisation of
Monthly mean anthropogenic emissions of CO, SO
Figure
Location of measurements used in this study for
Annual European emissions (Gg yr
Figure
Ground-based measurements of aerosols used in this study are listed in
Table
Details of the ground-based observations used in this study.
Temporal variation in the number of locations measuring surface
solar radiation (SSR), sulfate aerosol mass (SO
The EMEP network has reported the concentrations of sulfate and total aerosol
mass at locations across Europe from 1978 until present day
We used sulfate aerosol mass, PM
Aerosol size distribution data over Europe from the EUSAAR and GUAN ground-based monitoring sites have previously been collected and processed by
The AERONET program is a ground-based network of sun photometers, currently
with more than 200 sites providing aerosol optical, microphysical and
radiative properties
GEBA contains worldwide measurements of energy fluxes at the surface from
more than 2000 sites, with the highest density over Europe. Monthly mean
values of incident SSR (expressed as mean irradiance, in W m
Comparisons were made using monthly and annual mean values at individual
monitoring locations and also across Europe as a whole. Model values were
linearly interpolated to each measurement site using the relative
contribution from the four closest surrounding model grid squares. The
absolute and percentage change in the simulated and observed values of
sulfate, SPM, PM
The temporal trend in simulated and observed data was calculated by fitting
an ordinary least squares linear model to the data using the function
The simulated temporal trends were evaluated by comparing against observed
trends; if the gradient of the simulated and observed trends are within
An assessment of model accuracy is provided here by calculating the
normalised mean bias factor (NMBF) of the model when compared to the
observations
The goodness of fit between the model and observations is obtained by calculating the square of the linear Pearson correlation coefficient. A measure of the difference between model and observational values is provided by calculating the root mean square error (RMSE).
Figure
Simulated European annual mean
Figure
Simulated European AOD (Fig.
Figure
Statistical summary of modelled and observed annual and seasonal
(DJF and JJA) mean sulfate at all long-term (
Annual mean sulfate aerosol mass concentrations
(
The model underestimates European annual mean sulfate aerosol mass
concentrations in the period 1978–2009 (NMBF
European and sub-regional normalised mean bias factors for
summertime (red) and wintertime (blue) sulfate aerosol mass concentrations
across all the years that data were available. The solid line shows the
median value, the boxes show the 25th and 75th percentile values with the
error bars showing the maximum and minimum values and the circles
representing outliers (values
An underprediction of wintertime European sulfate concentrations has been
previously reported and may be due to an underestimation of oxidants in the
model
Although the model underestimates absolute sulfate concentrations, the
simulated trend over the period 1978–2009
(
Figure
The model underpredicts the observed European annual mean SPM mass
concentrations, with a NMBF of
Annual (black), summertime (red) and wintertime (blue) trends in
modelled and observed sulfate aerosol mass concentrations
(
Annual mean SPM mass concentrations (
Statistical summary of modelled and observed annual and seasonal
(DJF and JJA) mean SPM concentrations at all long-term (
European and sub-regional normalised mean bias factors for
summertime (red) and wintertime (blue)
Figure
Calculated annual (black), summertime (red) and wintertime (blue)
linear trends in modelled and observed
Figure
The model generally underestimates observed PM
The temporal changes in PM
The model underpredicts SPM mass concentrations by up to
20
Underprediction of aerosol mass could be due to underestimation of aerosol
processes, as well as missing aerosol sources from the model. The model does
not include nitrate aerosol which could account for
1–3
Uncertainty in aerosol precursor emissions will also contribute to the
model–observation discrepancy. In particular, domestic wood burning and
wildfires could contribute up to 50 % of OC locally over Europe and may be
underestimated in emission data sets
Nevertheless, even using the upper estimates of some of these potential missing sources, there still appears to be a model underprediction of total aerosol mass particularly during the early period (1980–1990), suggesting that additional sources or processes are missing within the model or that removal processes are overestimated.
Figure
Annual mean observed vs. simulated aerosol number concentrations for
The model is able to reproduce the observed aerosol number concentrations
within a factor of 2 at the majority of European monitoring locations, which
is in agreement with the recent intercomparison and evaluation of global
aerosol microphysical models
This suggests that N50 concentrations (a proxy for CCN concentrations) are
slightly underpredicted by the model in the present day but have declined
across Europe (Fig.
Figure
Statistical summary of modelled and observed annual and seasonal
(DJF and JJA) mean AOD at all long-term (
Annual mean AOD at 440 nm with observations
from the AERONET sites overplotted for
Observed and simulated AOD has declined over the period 2000–2009
(Fig.
Figure
Statistical summary of simulated and observed annual mean SSR for three different time periods (1960–1974, 1975–1989 and 1990–2009) at the 20 measurement sites across Europe which have a continuous 50-year data record. Absolute and relative (%) changes in SSR are calculated as the difference between the mean of the initial 5 years of data minus the mean of the last 5 years of data for the time period considered. Trends that are above or below the value of twice of the standard error of the trend (95 % confidence) are highlighted in bold.
Observed (black line) and simulated (red line) European annual mean
all-sky SSR anomalies (Wm
Between 1990 and 2009 both the observed and simulated European SSR increases
by 5.8 and 4.0 W m
In the period 1975–1989, both the modelled and observed SSR anomalies are
generally negative, which coincides with the maximum anthropogenic emissions
and atmospheric aerosol loading. Over this period the observed SSR decreases
by an average of 2.2 W m
Over the period 1960–1974, simulated European annual mean SSR remained
relatively constant (Fig.
European normalised mean bias factors for sulfate aerosol mass,
SPM mass, PM
Understanding the discrepancy in simulated SSR prior to 1980 is difficult
because aerosol observations are not available. Possible causes of model
discrepancy include errors in simulated aerosol, problems with the
observations, or the ECMWF reanalysis product. With regards to observational
uncertainties, there were fewer observations of SSR before 1970
(Fig.
Figure
Taylor diagram comparing simulated and observed European sulfate
mass concentrations, SPM, PM
The observed negative trends in sulfate, PM
Figure
Global and European shortwave top of atmosphere all-sky and
clear-sky aerosol RF, relative to a 1980–2000 mean. Values for 1972 are
included as this is when the simulated minimum aerosol RF occurs over Europe.
For comparison we include global carbon dioxide RF (relative to 1750) from
the IPCC fifth assessment report
European and sub-regional top of atmosphere aerosol radiative
forcing (Wm
The simulated clear-sky aerosol TOA RF (Fig.
The changes in aerosol RF we simulate over Europe are slightly larger than
those calculated for the USA by
The calculated changes in all-sky TOA RF indicate the extent to which changes
in anthropogenic emissions over the last 50 years have affected the European
radiative balance. Reductions in anthropogenic aerosols have resulted in a
positive response in the European radiative balance. We estimate that the
magnitude of these emission reductions has caused European mean all-sky RF to
increase by
The agreement between the model and observations in the changes in aerosols
and in the brightening period of the surface radiation balance between the
1990 and 2009 improve our confidence in the magnitude and temporal change of the
simulated TOA RF over this period when most of the change occurs
(2.0 W m
We used the HadGEM3-UKCA coupled chemistry–climate model to
simulate changes in aerosols between 1960 and 2009, a period over which
anthropogenic sources of aerosol changed substantially. We evaluated the
model against European observations of sulfate aerosol mass, total SPM, PM
The model underpredicts sulfate aerosol mass concentrations (NMBF
Observed trends in surface aerosol mass and AOD were generally well
represented by the model. Sulfate aerosol mass declines by 68 % in the
observations and by 78 % in the model between 1978 and 2009, consistent
with the decrease in SO
The all-sky European SSR was shown to increase between 1990 and 2009 in both
the model (4.0 W m
Prior to 1990, there are discrepancies between observed and simulated all-sky
SSR anomalies. Specifically, the model is unable to reproduce the magnitude
and timing of the observed reduction in SSR values (dimming). These
errors in SSR coincide with the largest model bias in observed SPM
concentrations between 1978 and 1980 (Fig.
From the peak in aerosol loading in the early 1970s, European all-sky aerosol
TOA radiative forcing has increased by
Steven Turnock would like to acknowledge the funding for his PhD studentship from the Natural Environment Research Council (NERC) and Met Office. For making their data available to be used in this study we would like to acknowledge the EMEP, GEBA and AERONET measurement networks along with any data managers involved in data collection. We would also like to acknowledge Ari Asmi for providing the aerosol size distribution data from the EUSAAR and GUAN networks and Carly Reddington for pre-processing this data set for use in the model evaluation. Anthropogenic and biomass-burning emissions from the MACCity data set were retrieved from the ECCAD emissions server. This work was also made possible by participation in the EU Framework 7 PEGASOS project (no. 265148). We acknowledge use of the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, a strategic partnership between the Met Office and the Natural Environment Research Council. Matthew Woodhouse would like to thank the Royal Society for support via the International Exchange Scheme. Arturo Sanchez-Lorenzo was supported by a postdoctoral fellowship JCI-2012-12508 and projects CGL2014-55976-R, CGL2014-52135-C3-1-R financed by the Spanish Ministry of Economy and Competitiveness. Edited by: A. Hofzumahaus