Articles | Volume 21, issue 1
https://doi.org/10.5194/acp-21-415-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-415-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2021
Research article |
| 14 Jan 2021
| 14 Jan 2021
Precipitation response to aerosol–radiation and aerosol–cloud interactions in regional climate simulations over Europe
José María López-Romero
Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, University of Murcia, 30100 Murcia, Spain
Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, University of Murcia, 30100 Murcia, Spain
Sonia Jerez
Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, University of Murcia, 30100 Murcia, Spain
Raquel Lorente-Plazas
Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, University of Murcia, 30100 Murcia, Spain
Department of Meteorology, Meteored, 30893 Murcia, Spain
Laura Palacios-Peña
Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, University of Murcia, 30100 Murcia, Spain
Department of Meteorology, Meteored, 30893 Murcia, Spain
Pedro Jiménez-Guerrero
Physics of the Earth, Regional Campus of International Excellence (CEIR) “Campus Mare Nostrum”, University of Murcia, 30100 Murcia, Spain
Biomedical Research Institute of Murcia (IMIB-Arrixaca), 30120 Murcia, Spain
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Cited articles
Albergel, C., Dutra, E., Munier, S., Calvet, J.-C., Munoz-Sabater, J., de Rosnay, P., and Balsamo, G.: ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?, Hydrol. Earth Syst. Sci., 22, 3515–3532, https://doi.org/10.5194/hess-22-3515-2018, 2018. a
Andreae, M. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth-Sci. Rev.,
89, 13–41, 2008. a
Andreae, M. O., Jones, C. D., and Cox, P. M.: Strong present-day aerosol
cooling implies a hot future, Nature, 435, 1187, https://doi.org/10.1038/nature03671, 2005. a
Archer-Nicholls, S., Lowe, D., Schultz, D. M., and McFiggans, G.: Aerosol–radiation–cloud interactions in a regional coupled model: the effects of convective parameterisation and resolution, Atmos. Chem. Phys., 16, 5573–5594, https://doi.org/10.5194/acp-16-5573-2016, 2016. a
Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D.,
Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas,
E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta,
J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos,
N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos,
S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398,
https://doi.org/10.5194/acp-14-317-2014, 2014. a
Baró, R., Jiménez-Guerrero, P., Balzarini, A., Curci, G., Forkel, R.,
Grell, G., Hirtl, M., Honzak, L., Langer, M., Pérez, J. L., Pirovano, G., San José, R., Tuccella, P., Werhahn, J., and Žabkar, R.: Sensitivity analysis of the microphysics scheme in WRF-Chem contributions to AQMEII phase 2, Atmos. Environ., 115, 620–629, 2015. a
Baró, R., Lorente-Plazas, R., Montávez, J. P., and Jiménez-Guerrero, P.: Biomass burning aerosol impact on surface winds during the 2010 Russian heat wave, Geophys. Res. Lett., 44, 1088–1094, https://doi.org/10.1002/2016GL071484, 2017. a
Baró, R., Jiménez-Guerrero, P., Stengel, M., Brunner, D., Curci, G.,
Forkel, R., Nea, L., Palacios-Peña, L., Savage, N., Schaap, M., Tuccella,
P., van der Gon, H. D., and Galmarini, S.: Evaluating cloud properties in an
ensemble of regional online coupled models against satellite observations,
Atmos. Chem. Phys., 18, 15183–15199, https://doi.org/10.5194/acp-18-15183-2018, 2018. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S. K., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols in Climate Change 2013: The Physical Science Basis, in: Contribution of Working Group I to IPCC AR5, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013. a, b
Brunner, D., Savage, N., Jorba, O., Eder, B., Giordano, L., Badia, A.,
Balzarini, A., Baró, R., Bianconi, R., Chemel, C., Curci, G., Forkel, R.,
Jiménez-Guerrero, P., Hirtl, M., Hodzic, A., Honzak, L., Im, U., Knote, C., Makar, P., Manders-Groot, A., van Meijgaard, E., Neal, L., Pérez, J. L., Pirovano, G., Jose, R. S., Schröder, W., Sokhi, R. S., Syrakov, D., Torian, A., Tuccella, P., Werhahn, J., Wolke, R., Yahya, K., Zabkar, R., Zhang, Y., Hogrefe, C., and Galmarini, S.: Comparative analysis of meteorological performance of coupled chemistry-meteorology models in the context of AQMEII phase 2, Atmos. Environ., 115, 470–498,
https://doi.org/10.1016/j.atmosenv.2014.12.032, 2015. a
Chapman, E. G., Gustafson Jr., W. I., Easter, R. C., Barnard, J. C., Ghan, S. J., Pekour, M. S., and Fast, J. D.: Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of
elevated point sources, Atmos. Chem. Phys., 9, 945–964, https://doi.org/10.5194/acp-9-945-2009, 2009. a
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N.,
Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric
aerosol optical thickness from the GOCART model and comparisons with
satellite and Sun photometer measurements, J. Atmos. Sci., 59, 461–483, 2002. a, b
Christensen, M. F., Heaton, M. J., Rupper, S., Reese, C. S., and Christensen,
W. F.: Bayesian Multi-scale Spatio-temporal Modeling of Precipitation in the
Indus Watershed, Front. Earth Sci., 7, 210, https://doi.org/10.3389/feart.2019.00210, 2019. a
Da Silva, N., Mailler, S., and Drobinski, P.: Aerosol indirect effects on summer precipitation in a regional climate model for the Euro-Mediterranean region, Ann. Geophys., 36, 321–335, https://doi.org/10.5194/angeo-36-321-2018, 2018. a
ECMWF: ERA-20C, available at:
https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c
(last access: 3 March 2020), 2014. a
Fan, J., Leung, L. R., Rosenfeld, D., Chen, Q., Li, Z., Zhang, J., and Yan, H.: Microphysical effects determine macrophysical response for aerosol impacts on deep convective clouds, P. Natl. Acad. Sci. USA, 110, E4581–E4590, 2013. a
Fast, J., Gustafson Jr., W., Easter, R., Zaveri, R., Barnard, J., Chapman, E., Grell, G., and Peckham, S.: Evolution of ozone, particulates, and aerosol
direct forcing in an urban area using a new fully-coupled meteorology,
chemistry, and aerosol model, J. Geophys. Res, 111, D21305, https://doi.org/10.1029/2005JD006721, 2006. a
Feingold, G., Cotton, W. R., Kreidenweis, S. M., and Davis, J. T.: The Impact
of Giant Cloud Condensation Nuclei on Drizzle Formation in Stratocumulus:
Implications for Cloud Radiative Properties, J. Atmos. Sci., 56, 4100–4117,
https://doi.org/10.1175/1520-0469(1999)056<4100:TIOGCC>2.0.CO;2, 1999. a, b
Forkel, R., Balzarini, A., Baró, R., Bianconi, R., Curci, G.,
Jiménez-Guerrero, P., Hirtl, M., Honzak, L., Lorenz, C., Im, U., Pérez, J. L., Pirovano, G., José, R. S., Tuccella, P., Werhahn, J., and Žabkar, R.: Analysis of the WRF-Chem contributions to AQMEII phase2 with respect to aerosol radiative feedbacks on meteorology and pollutant distributions, Atmos. Environ., 115, 630–645, 2015. a, b
Fuzzi, S., Baltensperger, U., Carslaw, K., Decesari, S., Denier van der Gon, H., Facchini, M. C., Fowler, D., Koren, I., Langford, B., Lohmann, U., Nemitz, E., Pandis, S., Riipinen, I., Rudich, Y., Schaap, M., Slowik, J. G., Spracklen, D. V., Vignati, E., Wild, M., Williams, M., and Gilardoni, S.: Particulate matter, air quality and climate: lessons learned and future needs, Atmos. Chem. Phys., 15, 8217–8299, https://doi.org/10.5194/acp-15-8217-2015, 2015. a, b
Geiger, H., Barnes, I., Bejan, I., Benter, T., and Spittler, M.: The
tropospheric degradation of isoprene: an updated module for the regional
atmospheric chemistry mechanism, Atmos. Environ., 37, 1503–1519,
https://doi.org/10.1016/S1352-2310(02)01047-6, 2003. a
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the
GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001a. a
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the
GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, 2001b. a
Gong, W., Min, Q., Li, R., Teller, A., Joseph, E., and Morris, V.: Detailed
cloud resolving model simulations of the impacts of Saharan air layer dust on
tropical deep convection – Part 1: Dust acts as ice nuclei, Atmos. Chem. Phys. Discuss., 10, 12907–12952, https://doi.org/10.5194/acpd-10-12907-2010, 2010. a
Goudie, A. and Middleton, N.: Saharan dust storms: nature and consequences,
Earth-Sci. Rev., 56, 179–204, 2001. a
Goudie, A. S. and Middleton, N. J.: Desert dust in the global system, Springer Science & Business Media, Heidelberg, Germany, 2006. a
Grell, G. and Baklanov, A.: Integrated modeling for forecasting weather and air quality: A call for fully coupled approaches, Atmos. Environ., 45,
6845–6851, 2011. a
Grell, G. A.: Prognostic Evaluation of Assumptions Used by Cumulus
Parameterizations, Mon. Weather Rev., 121, 764–787, 1993. a
Grell, G. A. and Devenyi, D.: A generalized approach to parameterizing
convection combining ensemble and data assimilation techniques, Geophys. Res.
Lett., 29, 1693, https://doi.org/10.1029/2002GL015311, 2002. a
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, 2005. a
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006. a
Hartigan, J. A. and Wong, M. A.: Algorithm AS 136: A k-means clustering
algorithm, J. Roy. Stat. Soc. Ser. C, 28, 100–108, 1979. a
Hersbach, H., Peubey, C., Simmons, A., Berrisford, P., Poli, P., and Dee, D.:
ERA-20CM: a twentieth-century atmospheric model ensemble, Q. J. Roy. Meteorol. Soc., 141, 2350–2375, 2015. a
Hong, S.-Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with
an explicit treatment of entrainment processes, Mon. Weather Rev., 134,
2318–2341, 2006. a
Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J.,
Dai, X., Maskell, K., and Johnson, C.: Climate change 2001: the scientific
basis, The Press Syndicate of the University of Cambridge, Cambridge, 2001. a
Hrarsbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF Newslett., 147, 5–6, 2016. a
Huang, Y., Chameides, W. L., and Dickinson, R. E.: Direct and indirect effects of anthropogenic aerosols on regional precipitation over east Asia, J. Geophys. Res.-Atmos., 112, D03212, https://doi.org/10.1029/2006JD007114, 2007. a
Hwang, S.-O., Park, J., and Kim, H. M.: Effect of hydrometeor species on
very-short-range simulations of precipitation using ERA5, Atmos. Res., 218, 245–256, 2019. a
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res., 113,
D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O. B., Bouwer,
L. M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelamnn, N., Jones, C., Leuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechi, D., Rounsevell, M., Samuel, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B., and Yiou, P.: EURO-CORDEX: new high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578, 2014. a, b
Jerez, S., López-Romero, J., Turco, M., Jiménez-Guerrero, P., Vautard, R., and Montávez, J.: Impact of evolving greenhouse gas forcing on the warming signal in regional climate model experiments, Nat. Commun., 9, 1304, https://doi.org/10.1038/s41467-018-03527-y, 2018. a
Jerez, S., López-Romero, J. M., Turco, M., Lorente-Plazas, R.,
Gómez-Navarro, J. J., Jiménez-Guerrero, P., and Montávez, J. P.: On the spin-up period in WRF simulations over Europe: trade offs between length and seasonality, J. Adv. Model. Earth Syst., 12, e2019MS001945, https://doi.org/10.1029/2019MS001945, 2020a. a, b
Jerez, S., Palacios-Peña, L., Gutiérrez, C., Jiménez-Guerrero, P., López-Romero, J. M., and Montávez, J. P.: Gains and losses in surface solar radiation with dynamic aerosols in regional climate simulations for Europe, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-238, in review, 2020b. a, b
Jiménez, P., García-Bustamante, E., González-Rouco, J., Valero,
F., Montávez, J., and Navarro, J.: Surface wind regionalization in complex terrain, J. Appl. Meteorol. Clim., 47, 308–325, 2008. a
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J.,
Montávez, J. P., and García-Bustamante, E.: A revised scheme for the
WRF surface layer formulation, Mon. Weather Rev., 140, 898–918, 2012. a
Jiménez-Guerrero, P., Jerez, S., Montávez, J., and Trigo, R.:
Uncertainties in future ozone and PM10 projections over Europe from a
regional climate multiphysics ensemble, Geophys. Res. Lett., 40, 5764–5769, 2013. a
Johnson, D. B.: The Role of Giant and Ultragiant Aerosol Particles in Warm Rain Initiation, J. Atmos. Sci., 39, 448–460, 1982. a
Lamarque, J. F., Shindell, D. T., Josse, B., Young, P. J., Cionni, I., Eyring, V., Bergmann, D., Cameron-Smith, P., Collins, W. J., Doherty, R., Dalsoren, S., Faluvegi, G., Folberth, G., Ghan, S. J., Horowitz, L. W., Lee, Y. H., MacKenzie, I. A., Nagashima, T., Naik, V., Plummer, D., Righi, M., Rumbold, S. T., Schulz, M., Skeie, R. B., Stevenson, D. S., Strode, S., Sudo, K., Szopa, S., Voulgarakis, A., and Zeng, G.: The Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): overview and description of models, simulations and climate diagnostics, Geosci. Model Dev., 6,
179–206, https://doi.org/10.5194/gmd-6-179-2013, 2013. a
Li, Z., Wang, Y., Guo, J., Zhao, C., Cribb, M. C., Dong, X., Fan, J., Gong, D., Huang, J., Jiang, M., Jiang, Y., Lee, S.-S., Li, H., Li, J., Liu, J., Qian, Y., Rosenfeld, D., Shan, S., Sun, Y., Wang, H., Xin, J., Yan, X., Yang, X., Yang, X.-Q., Zhang, F., and Zheng, Y.: East Asian Study of Tropospheric
Aerosols and their Impact on Regional Clouds, Precipitation, and Climate (EAST-AIRCPC), J. Geophys. Res.-Atmos., 124, 13026–13054, https://doi.org/10.1029/2019JD030758, 2019. a
Lin, Y.-L., Farley, R. D., and Orville, H. D.: Parameterization of the Snow
Field in a Cloud Model, J. Clim. Appl. Meteorol., 22, 1065–1092, 1983. a
Liu, P., Tsimpidi, A. P., Hu, Y., Stone, B., Russell, A. G., and Nenes, A.: Differences between downscaling with spectral and grid nudging using WRF, Atmos. Chem. Phys., 12, 3601–3610, https://doi.org/10.5194/acp-12-3601-2012, 2012. a
López-Romero, J. M., Baró, R., Palacios-Peña, L., Jerez, S.,
Jiménez-Guerrero, P., and Montávez, J. P.: Impact of resolution on
aerosol radiative feedbacks with in online-coupled chemistry/climate
simulations (WRF-Chem) for EURO-CORDEX compliant domains, in: vol. 18, EGU General Assembly Conference Abstracts, 17–22 April 2016, Vienna, Austria, 2016. a
Middleton, N. and Goudie, A.: Saharan dust: sources and trajectories, T. Inst. Brit. Geogr., 26, 165–181, 2001. a
Milelli, M., Turco, M., and Oberto, E.: Screen-level non-GTS data assimilation in a limited-area mesoscale model, Nat. Hazards Earth Syst. Sci., 10, 1129–1149, https://doi.org/10.5194/nhess-10-1129-2010, 2010. a
Montávez, J. P.: Data set of monthly climate and aerosol variables included in https://doi.org/10.5194/acp-21-1-2021 (Version 1.0) [Data set]. Atmospheric Chemistry and Physics, Zenodo, https://doi.org/10.5281/zenodo.4427810, 2021. a
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and natural radiative forcing, Climate Change, 423, 658–740, 2013. a
Nabat, P., Somot, S., Mallet, M., Sevault, F., Chiacchio, M., and Wild, M.:
Direct and semi-direct aerosol radiative effect on the Mediterranean climate
variability using a coupled regional climate system model, Clim. Dynam., 44, 1127–1155, 2015. a
Palacios-Peña, L., Baró, R., López-Romero, J. M.,
López-Villagra, A., Jerez, S., Montávez, J. P., and
Jiménez-Guerrero, P.: Assessment of Aerosol-Radiation (ARI) and
Aerosol-Cloud (ACI) Interactions from Dust: Modelled Dust Optical Properties
and Remote Sensing Observations, in: International Technical Meeting on Air
Pollution Modelling and its Application, Springer Nature Switzerland AG, 183–187, 2016. a
Palacios-Peña, L., Baró, R., Guerrero-Rascado, J. L., Alados-Arboledas, L., Brunner, D., and Jiménez-Guerrero, P.: Evaluating the representation of aerosol optical properties using an online coupled model over the Iberian Peninsula, Atmos. Chem. Phys., 17, 277–296, https://doi.org/10.5194/acp-17-277-2017, 2017. a
Palacios-Peña, L., Baró, R., Baklanov, A., Balzarini, A., Brunner, D., Forkel, R., Hirtl, M., Honzak, L., López-Romero, J. M., Montávez, J. P., Pérez, J. L., Pirovano, G., San José, R., Schroeder, W., Werhahn, J., Wolke, R., Zabkar, R., and Jiménez-Guerrero, P.: An assessment of aerosol optical properties from remote-sensing observations and regional chemistry-climate coupled models over Europe, Atmos. Chem. Phys., 18, 5021–5043, https://doi.org/10.5194/acp-18-5021-2018, 2018. a
Palacios-Peña, L., Jiménez-Guerrero, P., Baró, R., Balzarini, A.,
Bianconi, R., Curci, G., Landi, T. C., Pirovano, G., Prank, M., Riccio, A.,
Tuccella, P., and Galmarini, S.: Aerosol optical properties over Europe: an
evaluation of the AQMEII Phase 3 simulations against satellite observations,
Atmos. Chem. Phys., 19, 2965–2990, https://doi.org/10.5194/acp-19-2965-2019, 2019. a, b, c, d
Palacios-Peña, L., Montávez, J. P., López-Romero, J. M., Jerez, S., Gómez-Navarro, J. J., Lorente-Plazas, R., Ruiz, J., and
Jiménez-Guerrero, P.: Added Value of Aerosol-Cloud Interactions for
Representing Aerosol Optical Depth in an Online Coupled Climate-Chemistry
Model over Europe, Atmosphere, 11, 360, https://doi.org/10.3390/atmos11040360, 2020. a, b, c, d
Pavlidis, V., Katragkou, E., Prein, A., Georgoulias, A. K., Kartsios, S.,
Zanis, P., and Karacostas, T.: Investigating the sensitivity to resolving
aerosol interactions in downscaling regional model experiments with WRFv3.8.1
over Europe, Geosci. Model Dev., 13, 2511–2532, https://doi.org/10.5194/gmd-13-2511-2020, 2020. a, b, c
Prein, A. F., Gobiet, A., Truhetz, H., Keuler, K., Görgen, K., Teichmann, C., Fox Maule, C., van Meijgaard, E., Déqué, M., Nikulin, G., Vautard, R., Colette, A., Kjellström, E., and Jacob, D.: Precipitation in the EURO-CORDEX and 0.44∘ simulations: high resolution, high benefits?, Clim. Dynam., 46, 383–412, 2015. a
Rodríguez, S., Querol, X., Alastuey, A., Kallos, G., and Kakaliagou, O.:
Saharan dust contributions to PM10 and TSP levels in Southern and Eastern Spain, Atmos. Environ., 35, 2433–2447, 2001. a
Seifert, A., Köhler, C., and Beheng, K. D.: Aerosol-cloud-precipitation effects over Germany as simulated by a convective-scale numerical weather prediction model, Atmos. Chem. Phys., 12, 709–725, https://doi.org/10.5194/acp-12-709-2012, 2012. a
Seinfeld, J. H., Bretherton, C., Carslaw, K. S., Coe, H., DeMott, P. J.,
Dunlea, E. J., Feingold, G., Ghan, S., Guenther, A. B., Kahn, R., Kraucunas,
I., Kreidenweis, S. M., Molina, M. J., Nenes, A., Penner, J. E., Prather, K. A., Ramanathan, V., Ramaswamy, V., Rasch, P. J., Ravishankara, A. R., Rosenfeld, D., Stephens, G., and Wood, R.: Improving our fundamental
understanding of the role of aerosol-cloud interactions in the climate
system, P. Natl. Acad. Sci. USA, 113, 5781–5790, https://doi.org/10.1073/pnas.1514043113, 2016. a, b
Shrivastava, M., Berg, L. K., Fast, J. D., Easter, R. C., Laskin, A., Chapman, E. G., Gustafson Jr., W. I., Liu, Y., and Berkowitz, C. M.: Modeling aerosols and their interactions with shallow cumuli during the 2007 CHAPS field study, J. Geophys. Res.-Atmos., 118, 1343–1360, 2013. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Wang,
W., and Powers, J. G.: A description of the Advanced Research WRF version 3, Tech. rep., NCAR Tech. Note TN-475+STR, National Center for Atmospheric Research, Boulder, https://doi.org/10.5065/D68S4MVH, 2008. a
Stockwell, W. R., Kirchner, F., Kuhn, M., and Seefeld, S.: A new mechanism for regional atmospheric chemistry modeling, J. Geophys. Res.-Atmos., 102, 25847–25879, https://doi.org/10.1029/97JD00849, 2001. a
Tewari, M., Chen, F., Wang, W., Dudhia, J., LeMone, M. A., Mitchell, K., Ek,
M., Gayno, G., Wegiel, J., and Cuenca, R. H.: Implementation and verification
of the unified NOAH land surface model in the WRF model, in: 20th conference on weather analysis and forecasting/16th conference on numerical weather
prediction, 14 January 2004, Seattle, WA, USA, 11–15, 2004. a
Turnock, S. T., Spracklen, D. V., Carslaw, K. S., Mann, G. W., Woodhouse, M. T., Forster, P. M., Haywood, J., Johnson, C. E., Dalvi, M., Bellouin, N., and Sanchez-Lorenzo, A.: Modelled and observed changes in aerosols and surface solar radiation over Europe between 1960 and 2009, Atmos. Chem. Phys., 15, 9477–9500, https://doi.org/10.5194/acp-15-9477-2015, 2015. a
Twomey, S.: The influence of pollution on the shortwave albedo of clouds, J. Atmos. Sci., 34, 1149–1152, 1977. a
Von Storch, H.: Misuses of statistical analysis in climate research, in:
Analysis of climate variability, Springer-Verlag, Berlin, Heidelberg, 11–26, 1999. a
Ward Jr., J. H.: Hierarchical grouping to optimize an objective function, J. Am. Stat. Assoc., 58, 236–244, 1963. a
WHO: Review of evidence on health aspects of air pollution – REVIHAAP Project, available at: https://www.euro.who.int/__data/assets/pdf_file/0004/193108/REVIHAAP-Final-technical-report-final-version.pdf (last access: 7 January 2021), 2013. a
Wild, O., Zhu, X., Prather, M., and Fast, J.: Accurate simulation of in-and
below-cloud photolysis in tropospheric chemical models, J. Atmos. Chem, 37,
245–282, 2000. a
Witha, B., Hahmann, A. N., Sile, T., Dörenkämper, M., Ezber, Y.,
Bustamante, E. G., Gonzalez-Rouco, J. F., Leroy, G., and Navarro, J.: Report
on WRF model sensitivity studies and specifications for the mesoscale wind
atlas production runs: Deliverable D4.3, NEWA – New European Wind Atlas, Zenodo, https://doi.org/10.5281/zenodo.2682604, 2019. a
Yahya, K., Wang, K., Campbell, P., Glotfelty, T., He, J., and Zhang, Y.: Decadal evaluation of regional climate, air quality, and their interactions over the continental US and their interactions using WRF/Chem version 3.6.1, Geosci. Model Dev., 9, 671–695, https://doi.org/10.5194/gmd-9-671-2016, 2016. a
Yang, Q., Gustafson Jr., W. I., Fast, J. D., Wang, H., Easter, R. C., Wang, M., Ghan, S. J., Berg, L. K., Leung, L. R., and Morrison, H.: Impact of natural and anthropogenic aerosols on stratocumulus and precipitation in the Southeast Pacific: a regional modelling study using WRF-Chem, Atmos. Chem. Phys., 12, 8777–8796, https://doi.org/10.5194/acp-12-8777-2012, 2012. a
Yu, H., Kaufman, Y. J., Chin, M., Feingold, G., Remer, L. A., Anderson, T. L., Balkanski, Y., Bellouin, N., Boucher, O., Christopher, S., DeCola, P., Kahn, R., Koch, D., Loeb, N., Reddy, M. S., Schulz, M., Takemura, T., and Zhou, M.: A review of measurement-based assessments of the aerosol direct radiative effect and forcing, Atmos. Chem. Phys., 6, 613–666,
https://doi.org/10.5194/acp-6-613-2006, 2006. a
Short summary
The effect of aerosols on regional climate simulations presents large uncertainties due to their complex and non-linear interactions with a wide variety of factors, including aerosol–radiation and aerosol–cloud interactions. We show how these interactions are strongly conditioned by the meteorological situation and the type of aerosol. While natural aerosols tend to increase precipitation in some areas, anthropogenic aerosols decrease the number of rainy days in some pollutant regions.
The effect of aerosols on regional climate simulations presents large uncertainties due to their...
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