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Article

Role of Aerosols on Atmospheric Circulation in Regional Climate Experiments over Europe

by
Ginés Garnés-Morales
1,
Juan Pedro Montávez
1,*,
Amar Halifa-Marín
1 and
Pedro Jiménez-Guerrero
1,2,*
1
Regional Atmospheric Modeling Lab (G-MAR), Department of Physics, Regional Campus of International Excellence Campus Mare Nostrum (CEIR), University of Murcia, 30100 Murcia, Spain
2
Biomedical Research Institute of Murcia (IMIB-Arrixaca), 30120 Murcia, Spain
*
Authors to whom correspondence should be addressed.
Atmosphere 2023, 14(3), 491; https://doi.org/10.3390/atmos14030491
Submission received: 9 February 2023 / Revised: 27 February 2023 / Accepted: 28 February 2023 / Published: 2 March 2023
(This article belongs to the Special Issue Aerosol Cloud Radiation Interactions)
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Abstract

:
Aerosols can strongly influence atmospheric circulation, and categorizing it into circulation types (CTs) helps in understanding the relationship between atmospheric forcing and surface conditions. However, few studies have considered the impact of interactive aerosols on atmospheric dynamics from a climatic perspective. This contribution aims to assess whether simulations with interactive aerosols (online solving of aerosol–radiation interactions, ARI, and aerosol–radiation–cloud interactions, ARCI) significantly impact atmospheric dynamics over Europe during winter compared to conventional regional climate models with prescribed aerosols. For that, Principal Component Analysis (PCA) has been applied to reduce the dimensionality of the problem in order to cluster different weather patterns. Results showed significant differences in the two predominant patterns, characterized by a western zonal flow (CT1) and a low-pressure system centered in Italy (CT2). The ARI experiment revealed a substantial reduction of surface level pressure over central-eastern Europe for CT1, resulting in a southward shift of the flux direction, and an increase in pressure over Scandinavia for CT2. The ARCI experiment exhibited a similar, but weaker effect. Furthermore, the study demonstrated the impact of aerosols on the frequency of different CTs and on the concentration of black and white aerosols. The findings of this study emphasize the significant role of aerosols in the atmospheric system and the need for further research to reduce uncertainty in meteorological and climatic experiments, particularly in the context of mitigating climate change.

1. Introduction

The atmospheric circulation is highly variable due to its dynamic nature. Categorizing the dynamic atmospheric circulation into classes of circulation types (CTs) helps in understanding the relationship between atmospheric forcing and surface conditions, especially during extreme events [1]. Previous studies (e.g., ([2,3,4]) have attempted to characterize the CTs over Europe and found a high internal variability in the projected changes of these circulation types. Despite climate models projecting warmer and drier summer conditions in Central Europe, only small and inconsistent changes were observed, with no clear shift to a persistent summer or winter circulation [5].
In this sense, aerosols can have an impact on CTs in the atmosphere. Aerosols, including both natural and human-made particles, can affect the energy balance of the Earth’s atmosphere by absorbing or scattering incoming solar radiation (aerosol–radiation interactions, ARI). On a global scale, this scattering has a net cooling effect on the Earth’s surface [6,7]. However, their behavior may differ significantly on a regional scale (e.g., Europe), depending on the type of aerosol [8,9,10]. Additionally, aerosols can interact with clouds (aerosol–cloud interactions, ACI), acting as cloud condensation nuclei (CCN), affecting cloud albedo (Twomey effect [11]) and lifetime (Albrecht effect [12]), as well as precipitation [13,14], which can also impact atmospheric circulation. For example, the presence of more aerosols can lead to increased cloud cover, which can reduce incoming solar radiation and cause cooling at the surface, potentially altering the position and strength of high and low pressure systems. Both ARI and ACI interactions can lead to regional changes in temperature and atmospheric stability [15], which in turn can influence the position and strength of high and low pressure systems, as well as the direction and speed of winds [16].
Overall, the influence of aerosols on CTs is complex and depends on various factors such as the type, distribution, and other physico-chemical properties of the aerosols (e.g., diameter, color, shape or hygroscopicity, among others), as well as their interactions with other atmospheric processes. For example, black carbon, which comes from combustion processes, absorbs radiation and heats the atmospheric layer it is in [17]. This causes an increase in temperature and a decrease in relative humidity, leading to cloud evaporation by thermodynamic causes [16]. On the other hand, aerosols such as sea salt and sulfates, which scatter a lot of radiation due to their light color, reflect solar radiation and prevent it from reaching the Earth’s surface [10,18]. Despite the inherent difficulty, understanding the role of aerosols in modulating atmospheric circulation is important for improving our ability to predict and understand the Earth’s climate and weather patterns.
Despite the increasing number of studies on aerosol interactions with the atmospheric system from a regional and climatic perspective [14,19], few studies have considered the impact of interactive aerosols on atmospheric dynamics. Currently, regional climate models used to describe weather patterns do not consider interactive aerosols (e.g., [5]), which could lead to significant changes in atmospheric dynamics and modify the frequency and intensity of circulation types [20,21].
Therefore, this contribution aims to determine if simulations with interactive aerosols significantly impact atmospheric dynamics during winter (December-January-February, DJF) over Europe, compared to simulations used in conventional regional climate models with prescribed, non-dynamic aerosols.

2. Materials and Methods

2.1. Data and Experiments

The simulations used in this contribution were taken from the database generated in the project’s “Air quality–climate interactions and their impact on renewable energies under climate change scenarios” (REPAIR). The objective of REPAIR lies in studying the impact of air quality–climate interactions and potential future emission reductions due to the increased use of renewable energies on climate change in Europe through its mitigating role in radiative forcing and air quality. Further information and validation of REPAIR’s database can be found in a number of contributions (e.g., [7,9,14,22,23]). The experiments included consisting of regional climate simulations run with the Weather Research and Forecasting (WRF) model coupled to interactive chemistry (WRF-Chem model, version 3.9.1). The simulations were either uncoupled from atmospheric chemistry (in the WRF-alone version; [24]) or included a complete online coupling with atmospheric chemistry and pollutant transport, incorporating aerosol–radiation and aerosol–cloud interactions [25].
Three different experiments were included in this work, covering the period from 1991 to 2010. The first experiment uses a prescribed/constant concentration of aerosols in order to estimate aerosol optical depth (AOD) and CCN. Afterward, this experiment is referred to as BASE. The second experiment includes only aerosol–radiation interactions coupled online (direct and semidirect effects), and is denoted as ARI experiment. The third experiment includes aerosol–radiation–cloud interactions (direct, semidirect, and indirect effects) and is hereafter referred to as ARCI experiment. In both ARI and ARCI runs, aerosols are computed in a coupled way, meaning they are solved online interactively by WRF-Chem. These experiments allow for the quantification of aerosol effects on radiation and clouds from a climatic perspective.
In the BASE experiment, aerosols are not treated interactively but are prescribed in the WRF-Chem model. This is achieved by setting the AOD to a constant 0.12 value and considering 250 CCN per cubic centimeter in every cell of the domain. In the ARI experiment, aerosols are treated online, and their interactions with radiation are activated in the model [26], henceforth affecting the estimation of AOD with respect to the BASE run. However, in ARI CCN remains as prescribed in the BASE configuration. The ARCI experiment includes the aerosol–radiation interactions and also allows aerosols to interact online with cloud microphysics [27,28]. A comprehensive description of the aerosol–radiation–cloud interactions implemented in the WRF simulations can be found in [9], as well as the aerosol validation.
The spatial configuration of the WRF simulations in the three experiments consists of two unidirectionally nested domains (one-way nesting). The inner domain meets the Euro-CORDEX standards [29] and covers Europe with a spatial resolution of 0.44 ∘ (∼50 km). The outer domain has a spatial resolution of approximately 150 km and extends southwards to a latitude of approximately 20 ∘ N. This domain is intended to cover the most significant dust emission areas originating from the Saharan desert. Dust is introduced into the inner domain through the boundary conditions [8]. Vertically, 29 non-uniform sigma levels were used, with a higher resolution near the surface, and the atmospheric upper limit was set at the 50 hPa level.
The physics configuration includes the Lin microphysics scheme [27], the Noah land surface layer [30], the RRTM radiative scheme [31], the Grell 3D ensemble cumulus scheme [32,33], and the University of Yonsei boundary layer scheme [34].
The WRF-Chem model uses the GOCART aerosol module [35], which considers five species of aerosols: black carbon, organic carbon, dust, sea salt, and sulfates. The gas-phase chemistry option was coupled with the RACM-KPP kinetics preprocessor [36] using the GOCART scheme. The Fast-J module was used for photolysis [26] and biogenic emissions were calculated using the Guenther scheme [37]. Anthropogenic emissions were taken from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP) [38] and kept unchanged during the simulations. Natural emissions, however, depend on climate conditions and therefore vary with meteorological situations [39].
The overall refractive index for a specific size bin was calculated by volume averaging and by linking each chemical component of the aerosols with its own complex index of refraction. The Mie theory and the summation over all size bins were employed to find the composite aerosol optical properties. This was done while assuming the wet particle diameters and taking into consideration the variations in humidity, which allows for changes in the optical properties. Finally, the aerosol optical properties were incorporated into the shortwave radiation scheme. The interaction between aerosols and clouds was modeled by linking the calculated number of cloud droplets with the microphysics scheme [28], which affected both the mean radius of the droplets and the optical depth of the clouds.
This version of WRF-Chem does not allow for a full coupling with aerosol–cloud interactions, which is the main source of the aerosols’ influence on the climate. However, the simulations incorporate the Lin scheme as the microphysics option, which functions as a single-moment scheme but can be modified to a two-moment scheme for the ARCI experiments. Single-moment microphysics schemes, like the Lin scheme, are inadequate for studying aerosol–cloud interactions as they only predict the mass of cloud droplets and not their number or concentration [40]. The prediction of two moments offers a more comprehensive treatment of the particle size distribution, which is essential for computing microphysical process rates and cloud-precipitation evolution. In this sense, the modified Lin double-moment microphysics is used for the ARCI simulations, where both the mass and the number of cloud droplets are predicted. The prognostic approach for cloud droplet number involves the treatment of water vapor, cloud water, rain, cloud ice, snow, and graupel [41]. The conversion of cloud droplets to rain droplets depends on droplet number [42]. Droplet nucleation and evaporation rates represent the aerosol activation and resuspension rates, but the simulation does not consider ice nuclei based on predicted particulates. However, ice clouds are included using a prescribed ice nuclei distribution following the Lin scheme. Finally, the interaction between clouds and incoming solar radiation has been implemented by linking the simulated cloud droplet number with the Goddard shortwave radiation scheme [43], which represents the first indirect effect (i.e., an increase in droplet number associated with increases in aerosols) and with the Lin microphysics, representing the second indirect effect (i.e., a decrease in precipitation efficiency associated with increases in aerosols).

2.2. Principal Components Analysis (PCA)

Principal component analysis (PCA), also referred to as empirical orthogonal function (EOF) analysis, is a multivariate statistical method that transforms the original data into a new orthogonal space of functions, representing the main variation modes of the system [44]. The purpose of this transformation is to eliminate the correlation existing in the original data, thereby reducing noise for other analyses such as clustering. These variation modes, known as EOFs, are the eigenvectors of the correlation or covariance matrix of the anomalies of the original data. The projections of the data onto these new functions are called principal components (PCs), which are uncorrelated with one another. For more information on PCA techniques, please refer to [45].
The study was conducted by focusing on only the most significant empirical orthogonal functions (EOFs), which are the ones that have a significant impact on the global variance. The importance of an EOF is determined by its eigenvalue. The most significant EOFs correspond to those with the highest eigenvalues. By limiting the analysis to these key EOFs, the complexity of the problem was reduced, thus, reducing the dimensionality.

2.3. Calculation of Circulation Types

PCA is a useful tool for identifying the main circulation patterns, so a PCA was performed using daily sea level pressure (SLP) from the BASE experiment as the classification variable. The spatial window used for classification covered most of Europe, between 10 ∘ W to 30 ∘ E and 35 ∘ N to 60 ∘ N. Since the number of grid points is higher than the number of temporal steps (days), T-mode was adopted in the PCA configuration. This made the matrix smaller by treating temporal stages as variables and grid points as observations. The T-mode EOFs represent temporal variability patterns, so days with similar weight contributions to those modes also exhibit similar spatial patterns.
When T-mode is set, PCA makes use of this property to assign the samples to different clusters and no clustering method is required [44,46]. So, the days whose highest weight presents the same sign in the same EOF are grouped together. Thus, the positive or negative sign of the weights defines the number of clusters (=number of CTs) to consider, twice as many as the number of PCs retained. In this study, the number of PCs was determined by the number of EOFs whose cumulative variance reaches 90%. Finally, once all days are grouped, a temporal average is applied to each one of them to get the main pattern (centroid) of that CT.
An analogous method was applied to determine the CTs of ARCI and ARI experiments, except for the way the days were grouped. The clustering was made by matching the samples (their projection in PCs-space) to the closest BASE centroid. In other words, the days of ARCI/ARI classified as a certain CT were those whose PCs had minimal (Euclidean) distance, in PCs-space, to the BASE centroid of that CT. This method is not a free classification but helps to analyze the differences between CTs of different simulations.
In this study, the R package synoptReg [46] was used to carry out the calculation. The pca_decision function was applied first to determine the number of EOFs required to explain 90% of the SLP variance. Then, the synoptclas function was used to classify each day into a group based on its closest BASE centroid in PCs-space, which was determined by finding the minimum Euclidean distance. The synoptclas function was modified to classify the days of ARCI and ARI experiments into groups.

2.4. Characterization of the Differences between CTs

The differences between the CTs of ARCI and ARI and their equivalents in the BASE experiment were evaluated by calculating the statistical significance of the difference at each grid point. This was done using a two-sided Mann–Whitney test as described in the reference [45]. The test involves calculating a statistic U, which is defined as the minimum of U 1 and U 2 .
U 1 = R 1 − n 1 2 ( n 1 + 1 )
U 2 = R 2 − n 2 2 ( n 2 + 1 )
where R i is the sum of the ranks held by the members of the sample i when putting the observations of both samples in one set; n i is the number of observations in sample i. Thus, the difference at a grid point was considered significant when the Mann–Whitney test output a p value of less than 0.05.
The Mann–Whitney test was performed using the R function wilcox.test (included in the coin package [47]). This function is capable of carrying out the Mann–Whitney test on two data vectors. In this case, a data vector was created using the SLP values that a grid point adopted in the days classified as a certain CT.
Given that ARCI and ARI experiments differ from the BASE only by the inclusion of dynamic aerosols, the AOD anomaly was computed for each ARI’s and ARCI’s CT to establish a connection between the differences in SLP pattern and variations in aerosol concentration. The anomalies were calculated by considering all types of aerosols as a whole and then separately to determine which species of aerosol is responsible for the AOD variations and, in turn, the changes in CTs.
In addition, to collate the AOD results, the differences in the thickness of atmospheric layers between the ARCI/ARI and BASE experiments were also computed. These calculations were performed at a single latitude where the greatest variations had been observed. If a CT presents an AOD anomaly caused by an increase in radiation-absorbing aerosols, such as black carbon, the layers should broaden due to the increase in temperature. On the other hand, if the anomaly is caused by an increase in radiation-reflective aerosols, such as sea salt, the layers should narrow due to a decrease in temperature.

3. Results and Discussion

3.1. Circulation Types

The results of the PCA analysis reveal that six PCs are required to explain 90% of the SLP variance. This translates to 2 × 6 = 12 weather types, or circulation types (Figure 1).
It is noticeable that most CTs exhibit low-pressure areas in the north of the continent, while high pressures predominate in the south of Europe. This results in the west and north fluxes being common in Europe.
Some of these CTs have been identified in previous studies (e.g., [48,49]). However, comparing CTs can be a challenging task due to the different spatial windows, classification variables, time period considered, and methodology employed [44].

3.2. Differences in Circulation Types

In this section, a special focus is set on the first two CTs because they are the two most important types, with the greatest frequency of occurrence, and the largest differences are found in them.
As shown in Figure 1, CT1 is characterized by an anticyclone in the south of the continent and a low-pressure area in the north. This dipole creates a western zonal flux with a particularly high-pressure gradient in western-central Europe. CT2, on the other hand, has a low center in Italy and a weak pressure gradient across the entire continent.
Figure 2 depicts the differences in SLP between the interactive-aerosols experiments (ARCI and ARI) and the BASE experiment for CT1 and CT2, respectively. Only the grid points that show a statistically significant difference are highlighted in the figure. In the ARCI experiment, a decrease in SLP is depicted in the center-east region of Europe for CT1, whereas in CT2, the situation is almost reversed with an increase in SLP observed, particularly in the eastern part of the continent. The differences between the two CTs range from 3 to 5 hPa in magnitude, although the positive differences in CT2 can reach up to 7 hPa in the region between the Balkans and the Baltic Sea. Additionally, the differences in CT2 are more widespread, extending to Great Britain and higher latitude regions. The differences in ARI are similar to those in ARCI. Again, a decrease in SLP is observed in CT1 in experiments with online coupled aerosols, and an increase is seen in CT2, both in the center-east region of the continent. However, the differences are more substantial, reaching up to 10 hPa.
Therefore, two important conclusions can be drawn from the changes introduced by the interactive-aerosol simulations: (1) both ARI and ARCI experiments result in differences of the same direction (decrease of SLP in CT1 and increase in CT2), and (2) ARI modifications are more intense than those in ARCI. In other words, the ARI and ARCI simulations produce CTs that have differences in the same direction compared to the BASE CTs, but with different magnitudes. When aerosol–cloud interactions are activated, the impacts of aerosol–radiation interactions on the SLP pattern are reduced. The physical mechanisms behind this counteracting behavior are related to changes in AOD and will be later deployed in Section 3.3.
With regards to the impact of these changes on atmospheric circulation, in CT1, the decrease in SLP results in a southward shift of the air flux. This shift is more significant in the ARI experiment due to its larger differences. As air now originates from higher latitudes, it could cause a decrease in temperatures over Europe, as observed when examining the differences in surface temperature. Conversely, in CT2, the increase in SLP tends to weaken the low-pressure system centered over the Mediterranean Sea and amplify the high-pressure area in northern Europe. These changes are more pronounced in ARI due to its larger differences.
With regards to the impact on the frequency of appearance (Table 1), the frequencies of CT1 and CT2 are lower in both the ARI and ARCI experiments. In other words, there are fewer days classified as CT1 and CT2 when interactive aerosols are taken into consideration. Once again, the ARI simulation shows greater differences. While in the ARCI simulation the frequencies of CT1 and CT2 decrease by 1.17% and 1.93%, respectively, in the ARI experiment they decrease by 2.33% and 4.37% respectively. This serves as another indication that the ARI simulation diverges more from BASE compared to the ARCI experiment.

3.3. AOD Anomaly

Figure 3 illustrates the anomalies in AOD for CT1 and CT2 in the ARCI experiment. These anomalies are calculated both considering all aerosol types collectively (TOTAL) and separately, including black carbon (BC), dust, organic carbon (OC), sea salt (SEAS), and sulfates (SULF). The range of the AOD anomalies in the ARI experiment is very similar to those in the ARCI experiment, with correlation coefficients ranging from 0.85 to 0.97 (therefore, they are not presented in this contribution). The similarities in the AOD in both experiments can be attributed to the similar treatment of aerosol transport in both simulations and the limited impact of aerosol–cloud interactions on aerosol concentrations.
In CT1, there is a negative anomaly of all aerosol types over Europe, which is caused by the zonal flux, a characteristic of this circulation type, that cleans the atmosphere leading to a decrease in aerosol load, mainly BC and OC (the most important contributors to total AOD), but slight increases of SEAS and SULF coming from the Atlantic Ocean. In the ARI experiment, as only radiation effects are calculated online (aerosol–cloud interactions are solved from prescribed aerosols), scattering of solar radiation is enhanced due to the high presence of reflecting aerosols in these areas (direct effect) [7]. As absorbing aerosols play a negligible role here, the semi-direct effect is not important in the radiative budget. Therefore, as less shortwave radiation reaches the troposphere, a cooling of the atmosphere is produced, reducing the thickness of the atmospheric layers (see Section 3.4 for further details). Hence, a lower geopotential height is depicted for a similar pressure level, and therefore a reduction of the SLP is produced. Conversely, in the ARCI experiment, the existence of SEAS enables an early, rapid and strong latent heat release in the lower troposphere when acting as CCN [50,51]. That heat release counteracts the cooling observed in the ARI experiment, leading to a smaller decrease of the SLP in the ARCI experiment with respect to ARI, as observed in Figure 2.
On the other hand, in CT2, the opposite occurs due to the weak pressure gradient of this weather type that leads to the stagnation of aerosols and thus, an increase in AOD, particularly in Western and Central Europe and the coast of Libya. Opposite to CT1, here a strong increase is observed in absorbing aerosols (BC and OC) and a strong decrease in radiation-reflecting aerosols (SEAS and SULF, mainly). Here, in the ARI experiment, the absorption of radiation leads to a warming of the low troposphere [16], with a strong impact of the semi-direct effect, decreasing cloudiness [7,14] and hence increasing air temperature. In the troposphere, the layer thicknesses are consequently increased, with a higher geopotential height for the same pressure level, and therefore a higher SLP. In the ARCI experiment, the introduction of a larger number of CCN with respect to the prescribed aerosols [8,9] enhances the formation of clouds, reducing the shortwave radiation reaching the ground through the albedo effect and henceforth the temperature with respect to the ARI experiment. That leads to a smaller increase of the SLP in the ARCI experiment with respect to ARI (Figure 2).
Henceforth, when considering the driving factors of these variations, dust and black/ organic carbon anomalies have the largest impact. In fact, dust explains the anomalies along the Libyan coast, while organic carbon anomalies match very well with the anomalies found in West-Central Europe in both CT1 and CT2. The computation of the Pearson correlation coefficient of the TOTAL anomaly with the DUST + OC anomaly results in about 0.98 for all cases. The physical mechanism described previously is also corroborated by the changes in the atmospheric layer thicknesses previously indicated and explained in detail below in Section 3.4.

3.4. Thickness

Figure 4 shows the cross section at 50 ∘ N latitude of the layer thickness differences from 1000 to 200 hPa between the interactive-aerosol experiments and BASE, in CT1 and CT2.
In both ARCI and ARI experiments, the results show that CT1 has a reduction in thickness across all atmospheric layers, except for the highest layer (200–300 hPa). The most significant differences can be observed between 750 and 300 hPa and between 0 and 25 ∘ longitude. Examining the AOD anomalies (Section 3.3), as previously commented, CT1 shows a decrease in black and organic carbon aerosols, while there is an increase in sea salt and sulfate aerosols. This means there is a reduction in radiation-absorbing aerosols and an increase in radiation-reflecting aerosols. This results in a cooling of the atmospheric layers due to less absorbed radiation, causing the tropospheric layers to become narrower.
On the other hand, both ARI and ARCI experiments lead to an increase in thickness for CT2, particularly in the middle layers and in west-central longitudes. The AOD anomalies for CT2 show the opposite trend as compared to CT1, with a positive anomaly in black and organic carbon aerosols, but a negative anomaly in sea salt and sulfates. This results in higher absorption of solar radiation, causing an increase in temperature and an expansion of the atmospheric layers.
While ARI and ARCI simulations both lead to changes of equal signs (as for SLP), the changes introduced by ARI simulations are more significant. The differences in ARCI are contained between ±6 hPa, while the differences in ARI reach ±15 hPa. The inclusion of aerosol–cloud interactions results in a reduction of the effects of aerosols on the storage of energy in the atmosphere.

4. Conclusions

The results of this study demonstrate the significant differences in winter European CTs when simulations include aerosol interactions with radiation and clouds (ARI and ARCI experiments, respectively) compared to non-coupled simulations. The two main weather types are characterized by a western zonal flux (CT1) and a low-pressure system centered in Italy with a slight pressure gradient (CT2). The effect of both interactive aerosol experiments (ARI and ARCI) on CT1 is primarily a reduction in SLP over central-eastern Europe, which results in a southward shift of the flux direction. The effect of online aerosols on CT2 is an increase in SLP, also in the central-eastern part of the continent, leading to a weakening of the Mediterranean low and an intensification of the pressure field over Scandinavia. Although both ARCI and ARI simulations result in variations of the same sign, those of ARI are considerably stronger. In other words, the inclusion of aerosol interactions with cloud microphysics mitigates the effects of ARI. The inclusion of interactive aerosols can also cause changes in the frequency of different CTs. For example, both CT1 and CT2 decrease in frequency in both the ARI and ARCI experiment. Once again, these changes are more pronounced in ARI.
The anomalies in AOD suggest that the negative differences in SLP in CT1 in online coupled experiments are associated with a decrease in temperature in the troposphere. CT1 exhibits a reduction in the concentration of black aerosols (radiation-absorbent aerosols) and an increase in the concentration of white aerosols (radiation-reflective aerosols) in the ARCI experiment. These changes in aerosol concentrations lead to a higher scattering of radiation and lower storage of energy in the atmosphere, causing a decrease in temperature and, consequently, a decrease in the thickness of atmospheric layers. Conversely, the positive differences in SLP observed in CT2 when interactive aerosols are included seem to be linked to an increase in temperature. The differences in SLP in CT2 correspond to an increase in the concentration of absorbing aerosols and a decrease in the concentration of reflective particles. As a result, the temperature in atmospheric layers increases and so does their thickness.
Overall, the results indicate that aerosols play a significant role in the atmospheric system and, as a result, their impact on radiation and clouds should not be ignored in regional climate simulations. Further research is necessary to address the lack of information on the effects of aerosols not only on climate dynamics but also on microphysical processes, in order to reduce uncertainty when dealing with meteorological and climatic experiments. This is particularly crucial in the context of mitigating climate change.

Author Contributions

Conceptualization, G.G.-M. and A.H.-M.; methodology, G.G.-M.; formal analysis, G.G.-M., A.H.-M., J.P.M. and P.J.-G.; investigation, G.G.-M. and A.H.-M.; writing—original draft preparation, G.G.-M. and A.H.-M.; writing—review and editing, J.P.M. and P.J.-G.; visualization, G.G.-M.; supervision, J.P.M. and P.J.-G.; project administration, P.J.-G.; funding acquisition, J.P.M. and P.J.-G. All authors have read and agreed to the published version of the manuscript.

Funding

The authors acknowledge the ECCE project (PID2020-115693RB-I00) of the Ministerio de Ciencia e Innovación/Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033/).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are available upon reasonable request from the corresponding author ([email protected]).

Conflicts of Interest

The author declares no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AOD Aerosol optical depth
ARCIAerosol–radiation–cloud interactions
ARIAerosol–radiation interactions
BCBlack carbon
CCNCloud condensation nuclei
CTCirculation type
EOFEmpirical orthogonal function
OCOrganic carbon
PCPrincipal component
PCAPrincipal components analysis
SEASSea salt
SLPSea level pressure
SULFSulfates
WRFWeather Research and Forecasting model

References

  1. Hertig, E.; Russo, A.; Trigo, R.M. Heat and Ozone Pollution Waves in Central and South Europe—Characteristics, Weather Types, and Association with Mortality. Atmosphere 2020, 11, 1271. [Google Scholar] [CrossRef]
  2. Kučerová, M.; Beck, C.; Philipp, A.; Huth, R. Trends in frequency and persistence of atmospheric circulation types over Europe derived from a multitude of classifications. Int. J. Climatol. 2017, 37, 2502–2521. [Google Scholar] [CrossRef]
  3. Richardson, D.; Kilsby, C.G.; Fowler, H.J.; Bárdossy, A. Weekly to multi-month persistence in sets of daily weather patterns over Europe and the North Atlantic Ocean. Int. J. Climatol. 2019, 39, 2041–2056. [Google Scholar] [CrossRef]
  4. Mittermeier, M.; Weigert, M.; Rügamer, D.; Küchenhoff, H.; Ludwig, R. A deep learning based classification of atmospheric circulation types over Europe: Projection of future changes in a CMIP6 large ensemble. Environ. Res. Lett. 2022, 17, 084021. [Google Scholar] [CrossRef]
  5. Huguenin, M.F.; Fischer, E.M.; Kotlarski, S.; Scherrer, S.C.; Schwierz, C.; Knutti, R. Lack of Change in the Projected Frequency and Persistence of Atmospheric Circulation Types Over Central Europe. Geophys. Res. Lett. 2020, 47, e2019GL086132. [Google Scholar] [CrossRef]
  6. Glassmeier, F.; Hoffmann, F.; Johnson, J.S.; Yamaguchi, T.; Carslaw, K.S.; Feingold, G. Aerosol–cloud-climate cooling overestimated by ship-track data. Science 2021, 371, 485–489. [Google Scholar] [CrossRef]
  7. Jerez, S.; Palacios-Peńa, L.; Gutiérrez, C.; Jiménez-Guerrero, P.; López-Romero, J.M.; Pravia-Sarabia, E.; Montávez, J.P. Sensitivity of surface solar radiation to aerosol–radiation and aerosol–cloud interactions over Europe in WRFv3.6.1 climatic runs with fully interactive aerosols. Geosci. Model Dev. 2021, 14, 1533–1551. [Google Scholar] [CrossRef]
  8. Palacios-Pe na, L.; Jiménez-Guerrero, P.; Baró, R.; Balzarini, A.; Bianconi, R.; Curci, G.; Landi, T.C.; Pirovano, G.; Prank, M.; Riccio, A.; et al. Aerosol optical properties over Europe: An evaluation of the AQMEII Phase 3 simulations against satellite observations. Atmos. Chem. Phys. 2019, 19, 2965–2990. [Google Scholar] [CrossRef] [Green Version]
  9. Palacios-Peña, L.; Montávez, J.P.; López-Romero, J.M.; Jerez, S.; Gómez-Navarro, J.J.; Lorente-Plazas, R.; Ruiz, J.; Jiménez-Guerrero, P. Added Value of Aerosol–cloud Interactions for Representing Aerosol Optical Depth in an Online Coupled Climate-Chemistry Model over Europe. Atmosphere 2020, 11, 360. [Google Scholar] [CrossRef] [Green Version]
  10. Li, J.; Carlson, B.E.; Yung, Y.L.; Lv, D.; Hansen, J.; Penner, J.E.; Liao, H.; Ramaswamy, V.; Kahn, R.A.; Zhang, P.; et al. Scattering and absorbing aerosols in the climate system. Nat. Rev. Earth Environ. 2022, 3, 363–379. [Google Scholar] [CrossRef]
  11. Twomey, S. The Influence of Pollution on the Shortwave Albedo of Clouds. J. Atmos. Sci. 1977, 34, 1149–1152. [Google Scholar] [CrossRef]
  12. Albrecht, B.A. Aerosols, Cloud Microphysics, and Fractional Cloudiness. Science 1989, 245, 1227–1230. [Google Scholar] [CrossRef]
  13. Sun, Y.; Zhao, C. Distinct impacts on precipitation by aerosol radiative effect over three different megacity regions of eastern China. Atmos. Chem. Phys. 2021, 21, 16555–16574. [Google Scholar] [CrossRef]
  14. López-Romero, J.M.; Montávez, J.P.; Jerez, S.; Lorente-Plazas, R.; Palacios-Pe na, L.; Jiménez-Guerrero, P. Precipitation response to aerosol–radiation and aerosol–cloud interactions in regional climate simulations over Europe. Atmos. Chem. Phys. 2021, 21, 415–430. [Google Scholar] [CrossRef]
  15. Su, T.; Li, Z.; Li, C.; Li, J.; Han, W.; Shen, C.; Tan, W.; Wei, J.; Guo, J. The significant impact of aerosol vertical structure on lower atmosphere stability and its critical role in aerosol–planetary boundary layer (PBL) interactions. Atmos. Chem. Phys. 2020, 20, 3713–3724. [Google Scholar] [CrossRef] [Green Version]
  16. Baró, R.; Lorente-Plazas, R.; Montávez, J.P.; Jiménez-Guerrero, P. Biomass burning aerosol impact on surface winds during the 2010 Russian heat wave. Geophys. Res. Lett. 2017, 44, 1088–1094. [Google Scholar] [CrossRef]
  17. Bond, T.C.; Doherty, S.J.; Fahey, D.W.; Forster, P.M.; Berntsen, T.; DeAngelo, B.J.; Flanner, M.G.; Ghan, S.; Kärcher, B.; Koch, D.; et al. Bounding the role of black carbon in the climate system: A scientific assessment. J. Geophys. Res. Atmos. 2013, 118, 5380–5552. [Google Scholar] [CrossRef]
  18. Fei, K.c.; Wu, L.; Zeng, Q.c. Aerosol Optical Depth and Burden From Large Sea Salt Particles. J. Geophys. Res. Atmos. 2019, 124, 1680–1696. [Google Scholar] [CrossRef]
  19. Boé, J.; Somot, S.; Corre, L.; Nabat, P. Large discrepancies in summer climate change over Europe as projected by global and regional climate models: Causes and consequences. Clim. Dyn. 2020, 54, 2981–3002. [Google Scholar] [CrossRef]
  20. Palacios-Pe na, L.; Lorente-Plazas, R.; Montávez, J.P.; Jiménez-Guerrero, P. Saharan Dust Modeling Over the Mediterranean Basin and Central Europe: Does the Resolution Matter? Front. Earth Sci. 2019, 7, 290. [Google Scholar] [CrossRef] [Green Version]
  21. Nabat, P.; Somot, S.; Cassou, C.; Mallet, M.; Michou, M.; Bouniol, D.; Decharme, B.; Drugé, T.; Roehrig, R.; Saint-Martin, D. Modulation of radiative aerosols effects by atmospheric circulation over the Euro-Mediterranean region. Atmos. Chem. Phys. 2020, 20, 8315–8349. [Google Scholar] [CrossRef]
  22. Tarín-Carrasco, P.; Im, U.; Geels, C.; Palacios-Pe na, L.; Jiménez-Guerrero, P. Contribution of fine particulate matter to present and future premature mortality over Europe: A non-linear response. Environ. Int. 2021, 153, 106517. [Google Scholar] [CrossRef]
  23. Tarín-Carrasco, P.; Im, U.; Geels, C.; Palacios-Pe na, L.; Jiménez-Guerrero, P. Reducing future air-pollution-related premature mortality over Europe by mitigating emissions from the energy sector: Assessing an 80% renewable energies scenario. Atmos. Chem. Phys. 2022, 22, 3945–3965. [Google Scholar] [CrossRef]
  24. Skamarock, W.C.; Klemp, J.B.; Dudhia, J.; Gill, D.O.; Barker, D.M.; Wang, W.; Powers, J.G. A Description of the Advanced Research WRF Version 3. NCAR Technical Note -475+STR; National Center for Atmospheric Research: Boulder, CO, USA, 2008.
  25. Grell, G.A.; Peckham, S.E.; Schmitz, R.; McKeen, S.A.; Frost, G.; Skamarock, W.C.; Eder, B. Fully coupled online chemistry within the WRF model. Atmos. Environ. 2005, 39, 6957–6975. [Google Scholar] [CrossRef]
  26. Fast, J.D.; Gustafson, W.I., Jr.; Easter, R.C.; Zaveri, R.A.; Barnard, J.C.; Chapman, E.G.; Grell, G.A.; Peckham, S.E. Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model. J. Geophys. Res. Atmos. 2006, 111. [Google Scholar] [CrossRef]
  27. Lin, Y.L.; Farley, R.D.; Orville, H.D. Bulk Parameterization of the Snow Field in a Cloud Model. J. Appl. Meteorol. Climatol. 1983, 22, 1065–1092. [Google Scholar] [CrossRef]
  28. Chapman, E.G.; Gustafson, W.I., Jr.; Easter, R.C.; Barnard, J.C.; Ghan, S.J.; Pekour, M.S.; Fast, J.D. Coupling aerosol–cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources. Atmos. Chem. Phys. 2009, 9, 945–964. [Google Scholar] [CrossRef] [Green Version]
  29. Jacob, D.; Petersen, J.; Eggert, B.; Alias, A.; Christensen, O.; Bouwer, L.; Braun, A.; Colette, A.; Déqué, M.; Georgievski, G.; et al. EURO-CORDEX: New high-resolution climate change projections for European impact research. Reg. Environ. Chang. 2014, 14, 563–578. [Google Scholar] [CrossRef]
  30. Tewari, M.; Wang, W.; Dudhia, J.; LeMone, M.; Mitchell, K.; Ek, M.; Gayno, G.; Wegiel, J.; Cuenca, R. Implementation and verification of the united NOAH land surface model in the WRF model. In Proceedings of the 20th Conference on Weather Analysis and Forecasting/16th Conference on Numerical Weather Prediction, Seattle, WA, USA, 12–16 January 2016; pp. 11–15. [Google Scholar]
  31. Iacono, M.J.; Delamere, J.S.; Mlawer, E.J.; Shephard, M.W.; Clough, S.A.; Collins, W.D. Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models. J. Geophys. Res. Atmos. 2008, 113. [Google Scholar] [CrossRef]
  32. Grell, G.A. Prognostic Evaluation of Assumptions Used by Cumulus Parameterizations. Mon. Weather Rev. 1993, 121, 764–787. [Google Scholar] [CrossRef]
  33. Grell, G.A.; Dévényi, D. A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys. Res. Lett. 2002, 29, 38-1–38-4. [Google Scholar] [CrossRef] [Green Version]
  34. Hong, S.Y.; Noh, Y.; Dudhia, J. A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Mon. Weather Rev. 2006, 134, 2318–2341. [Google Scholar] [CrossRef] [Green Version]
  35. Ginoux, P.; Chin, M.; Tegen, I.; Prospero, J.M.; Holben, B.; Dubovik, O.; Lin, S.J. Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res. Atmos. 2001, 106, 20255–20273. [Google Scholar] [CrossRef]
  36. Ahmadov, R.; McKeen, S.A.; Robinson, A.L.; Bahreini, R.; Middlebrook, A.M.; de Gouw, J.A.; Meagher, J.; Hsie, E.Y.; Edgerton, E.; Shaw, S.; et al. A volatility basis set model for summertime secondary organic aerosols over the eastern United States in 2006. J. Geophys. Res. Atmos. 2012, 117. [Google Scholar] [CrossRef]
  37. Guenther, A.; Karl, T.; Harley, P.; Wiedinmyer, C.; Palmer, P.I.; Geron, C. Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature). Atmos. Chem. Phys. 2006, 6, 3181–3210. [Google Scholar] [CrossRef] [Green Version]
  38. Lamarque, J.F.; Bond, T.C.; Eyring, V.; Granier, C.; Heil, A.; Klimont, Z.; Lee, D.; Liousse, C.; Mieville, A.; Owen, B.; et al. Historical (1850–2000) gridded anthropogenic and biomass burning emissions of reactive gases and aerosols: Methodology and application. Atmos. Chem. Phys. 2010, 10, 7017–7039. [Google Scholar] [CrossRef] [Green Version]
  39. Jiménez-Guerrero, P.; Gómez-Navarro, J.; Baró, R.; Lorente-Plazas, R.; Ratola, N.; Montávez, J. Is there a common pattern of future gas-phase air pollution in Europe under diverse climate change scenarios? Clim. Chang. 2013, 121, 661–671. [Google Scholar] [CrossRef]
  40. Li, G.; Wang, Y.; Zhang, R. Implementation of a two-moment bulk microphysics scheme to the WRF model to investigate aerosol–cloud interaction. J. Geophys. Res. Atmos. 2008, 113, D15211. [Google Scholar] [CrossRef]
  41. Ghan, S.J.; Leung, L.R.; Easter, R.C.; Abdul-Razzak, H. Prediction of cloud droplet number in a general circulation model. J. Geophys. Res. Atmos. 1997, 102, 21777–21794. [Google Scholar] [CrossRef] [Green Version]
  42. Liu, Y.; Daum, P.H.; McGraw, R.L. Size truncation effect, threshold behavior, and a new type of autoconversion parameterization. Geophys. Res. Lett. 2005, 32, L11811. [Google Scholar] [CrossRef]
  43. Chou, M.D.; Suarez, M. A Solar Radiation Parameterization (CLIRAD-SW) Developed at Goddard Climate and Radiation Branch for Atmospheric Studies; NASA Technical Memorandum NASA/TM-1999-104606; NASA: Washington, DC, USA, 1999; 51p.
  44. García-Valero, J.; Montávez, J.; Jerez, S.; Gómez-Navarro, J.; Lorente-Plazas, R.; Jiménez-Guerrero, P. A seasonal study of the atmospheric dynamics over the Iberian Peninsula based on circulation types. Theor. Appl. Climatol. 2012, 110, 291–310. [Google Scholar] [CrossRef]
  45. Wilks, D.S. Statistical Methods in the Atmospheric Sciences; Elsevier Academic Press: Amsterdam, The Netherlands; Boston, MA, USA, 2011. [Google Scholar]
  46. Lemus-Canovas, M.; Lopez-Bustins, J.A.; Martin-Vide, J.; Royé, D. SynoptReg: An R package for computing a synoptic climate classification and a spatial regionalization of environmental data. Environ. Model. Softw. 2019, 118, 114–119. [Google Scholar] [CrossRef]
  47. Hothorn, T.; Hornik, K.; van de Wiel, M.A.; Zeileis, A. Implementing a class of permutation tests: The coin package. J. Stat. Softw. 2008, 28, 1–23. [Google Scholar] [CrossRef]
  48. Casado, M.; Pastor, M.; Doblas-Reyes, F. Euro-Atlantic circulation types and modes of variability in winter. Theor. Appl. Climatol. 2008, 96, 17–29. [Google Scholar] [CrossRef]
  49. Esteban, P.; Martin-Vide, J.; Mases, M. Daily atmospheric circulation catalogue for Western Europe using multivariate techniques. Int. J. Climatol. 2006, 26, 1501–1515. [Google Scholar] [CrossRef]
  50. Pravia-Sarabia, E.; Gómez-Navarro, J.J.; Jiménez-Guerrero, P.; Montávez, J.P. Influence of sea salt aerosols on the development of Mediterranean tropical-like cyclones. Atmos. Chem. Phys. 2021, 21, 13353–13368. [Google Scholar] [CrossRef]
  51. Pravia-Sarabia, E.; Halifa-Marín, A.; Gómez-Navarro, J.J.; Palacios-Peña, L.; Jiménez-Guerrero, P.; Montávez, J.P. On the role of aerosols in the production of orographically-induced extreme rainfall in near-maritime environments. Atmos. Res. 2022, 268, 106001. [Google Scholar] [CrossRef]
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Figure 1. Circulation Types (CTs) over Europe for wintertime (DJF) in the BASE experiment, ordered by their frequency. These 12 CTs explain 90.58% of the total variance.
Figure 1. Circulation Types (CTs) over Europe for wintertime (DJF) in the BASE experiment, ordered by their frequency. These 12 CTs explain 90.58% of the total variance.
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Figure 2. Differences in sea level pressure (SLP): ARCI−BASE (top) and ARI−BASE (bottom) in CT1 and CT2. Only the grid points with statistically significant differences ( p < 0.05 ) are shown.
Figure 2. Differences in sea level pressure (SLP): ARCI−BASE (top) and ARI−BASE (bottom) in CT1 and CT2. Only the grid points with statistically significant differences ( p < 0.05 ) are shown.
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Figure 3. AOD anomalies in CT1 and CT2 in ARCI experiments, computed considering all types of aerosols (TOTAL, top left) and by species. The species of aerosols involved are black carbon (BC; top right), dust (center left), organic carbon (OC; center right), sea salt (SEAS; bottom left) and sulfates (SULF; bottom right).
Figure 3. AOD anomalies in CT1 and CT2 in ARCI experiments, computed considering all types of aerosols (TOTAL, top left) and by species. The species of aerosols involved are black carbon (BC; top right), dust (center left), organic carbon (OC; center right), sea salt (SEAS; bottom left) and sulfates (SULF; bottom right).
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Figure 4. Differences in layer thickness ARCI−BASE (top) and ARI−BASE (bottom) at 50 ∘ N in CT1 and CT2.
Figure 4. Differences in layer thickness ARCI−BASE (top) and ARI−BASE (bottom) at 50 ∘ N in CT1 and CT2.
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Table
Table 1. Relative frequency of appearance (%) of CT1 and CT2 in each experiment. Difference between ARCI/ARI and BASE experiments.
Table 1. Relative frequency of appearance (%) of CT1 and CT2 in each experiment. Difference between ARCI/ARI and BASE experiments.
CTBASEARCIARIARCI-BASEARI-BASE
CT1 21.72 20.55 19.39 − 1.17 − 2.33
CT2 17.17 15.24 12.8 − 1.93 − 4.37
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Garnés-Morales, G.; Montávez, J.P.; Halifa-Marín, A.; Jiménez-Guerrero, P. Role of Aerosols on Atmospheric Circulation in Regional Climate Experiments over Europe. Atmosphere 2023, 14, 491. https://doi.org/10.3390/atmos14030491

AMA Style

Garnés-Morales G, Montávez JP, Halifa-Marín A, Jiménez-Guerrero P. Role of Aerosols on Atmospheric Circulation in Regional Climate Experiments over Europe. Atmosphere. 2023; 14(3):491. https://doi.org/10.3390/atmos14030491

Chicago/Turabian Style

Garnés-Morales, Ginés, Juan Pedro Montávez, Amar Halifa-Marín, and Pedro Jiménez-Guerrero. 2023. "Role of Aerosols on Atmospheric Circulation in Regional Climate Experiments over Europe" Atmosphere 14, no. 3: 491. https://doi.org/10.3390/atmos14030491

APA Style

Garnés-Morales, G., Montávez, J. P., Halifa-Marín, A., & Jiménez-Guerrero, P. (2023). Role of Aerosols on Atmospheric Circulation in Regional Climate Experiments over Europe. Atmosphere, 14(3), 491. https://doi.org/10.3390/atmos14030491

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