Submitted
Cherian, R., and J. Quaas, The role of aerosol trends for rainfall over India, Meteorol. Z., submitted.
Choudhury, G., K. Block, M. Haghighatnasab, J. Quaas, T. Goren, and M. Tesche, Pristine oceans control the uncertainty in aerosol-cloud interactions, Atmos. Chem. Phys. Discuss., submitted.
Kraulich, F., S. Dipu, S. Ojha, and J. Quaas, Role of atmospheric and oceanic processes in Arctic amplification: Sensitivity to climate engineering, Climatic Change, submitted.
Lange, C., and J. Quaas, Adjustments to an abrupt solar forcing in the CMIP6 abrupt-solm4p experiment, EGUsphere, preprint, doi:10.5194/egusphere-2024-3229.
Lauer, O., L. Kremper, D. Rosenfeld, M. Franco, M. Andreae, P. Artaxo, R. Braga, C. Dias-Junior, A. De Araujo, F. Ditas, A. Efraim, B. Ervens, B. Holanda, L. Jungandreas, O. Krüger, L. Machado, L. Hernández Pardo, U. Pöschl, G. Pulik, J. Quaas, Y. Zheng, Y. Zhu, C. Pöhlker, and M. Pöhlker, High aerosol sensitivity of convective clouds over the Amazon, Sci. Advances, revised.
Lenhardt, J., J. Quaas, D. Sejdinovic, and D. Klocke, CloudViT: classifying cloud types in global satellite data and in kilometre-resolution simulations using vision transformers, EGUsphere, preprint, doi:10.5194/egusphere-2024-2724.
Lochmann, M., H. Kalesse-Los, B. Haest, T. Vogl, R. Van Klink, F. Addison, M. Maahn, W. Schimmel, C. Wirth, and J. Quaas, Derivation of aerial insect concentration with a 94 GHz FMCW cloud radar, Remote Sens. Ecol. Conserv., submitted.
Mauritsen, T., Y. Tsushima, B. Meyssignac, N. Loeb, M. Hakuba, P. Pilewskie, J. Cole, K. Suzuki, T. Ackerman, R. Allan, T. Andrews, F. Bender, J. Bloch-Johnson, A. Bodas-Salcedo, A. Brookshaw, P. Ceppi, N. Clerbaux, A. Dessler, A. Donohoe, J. Dufresne, V. Eyring, K. Findell, A. Gettelman, J. Gristey, E. Hawkins, P. Heimbach, H. Hewitt, N. Jeevanjee, C. Jones, S. Kang, S. Kato, J. Kay, S. Klein, R. Knutti, R. Kramer, J. Lee, D. McCoy, B. Medeiros, L. Megner, A. Modak, T. Ogura, M. Palmer, D. Paynter, J. Quaas, V. Ramanathan, M. Ringer, K. Von Schuckmann, S. Sherwood, B. Stevens, I. Tan, G. Tselioudis, R. Sutton, A. Voigt, M. Watanabe, M. Webb, M. Wild, and M. Zelinka, Earth's energy accumulation rate more than doubled, and we must pay close attention, submitted.
Papakonstantinou-Presvelou, I., and J. Quaas, Sensitivity experiments with ICON-LAM to test probable explanations for higher ice crystal number over Arctic sea ice vs. ocean, EGUsphere, preprint, doi:10.5194/egusphere-2024-3293.
Saleeby, S., S. Van den Heever, P. Oue, A. Barrett, C. Barthlott, R. Cherian, J. Fan, A. Fridlind, M. Heikenfeld, C. Hoose, T. Matsui, A. Miltenberger, J. Quaas, J. Shpund, P. Stier, B. Vie, B. White, and Y. Zhang, Model intercomparison of the impacts of varying cloud droplet nucleating1 aerosols on the lifecycle and microphysics of deep convection, J. Atmos. Sci., submitted.
Virtanen, A., J. Joutsensaari, H. Kokkola, D. Partridge, S. Blichner, O. Seland, E. Holopainen, E. Tovazzi, A. Lipponen, S. Mikkonen, A. Leskinen, A. Hyvärinen, P. Zieger, R. Krejci, A. Ekman, I. Riipinen, J. Quaas, and S. Romakkaniemi, Revealing uncertainties in defining the sensitivity of cloud formation to aerosol changes, in revision.
Wang, H., Y. Peng, A. Di Noia, H. Shang, H. Letu, B. Van Diedenhoven, O. Hasekamp, Y. Liu, and J. Quaas, Global quantification of dispersion effect with POLDER satellite data, submitted.
2024
178. Block, K., M. Haghighatnasab, D. Partridge, P. Stier, and J. Quaas, Cloud condensation nuclei concentrations derived from the CAMS reanalysis, Earth Syst. Sci. Data, 16, 443-470, doi:10.5194/essd-16-443-2024, 2024.
177. Doktorowski, S., J. Kretzschmar, J. Quaas, M. Salzmann, and O. Sourdeval, Subgrid-scale variability of cloud ice in the ICON-AES, Geosci. Model Devel., 17, 3099-3110, doi:10.5194/gmd-17-3099-2024, 2024.
176. Eisenhauer, N., K. Frank, A. Weigelt, B. Bartkowski, R. Beugnon, K. Liebal, M. Mahecha, M. Quaas, D. Al-Halbouni, A. Bastos, F. Bohn, M. De Brito, J. Denzler, H. Feilhauer, R. Fischer, I. Fritsche, C. Guimaraes-Steinicke, M. Hänsel, D. Haun, H. Herrmann, A. Huth, H. Kalesse-Los, M. Koetter, N. Kolleck, M. Krause, M. Kretschmer, P. Leitao, T. Masson, K. Mora, B. Müller, J. Peng, M. Pöhlker, L. Ratzke, M. Reichstein, S. Richter, N. Rüger, B. Sánchez-Parra, M. Shadaydeh, S. Sippel, I. Tegen, D. Thrän, J. Umlauft, M. Wendisch, K. Wolf, C. Wirth, H. Zacher, S. Zaehle, and J. Quaas, A belowground perspective on the nexus between biodiversity change climate change, and human well-being, J. Sustain. Agric. Environ., 3, e212108, doi:10.1002/sae2.12108, 2024.
175. Gonzalez, J., S. Dipu, O. Sourdeval, A. Simeon, G. Camps-Valls, and J. Quaas, Emulation of forward modelled top-of-atmosphere MODIS-based spectral channels using machine learning, Remote Sens. Environ., in press, 2024.
174. Hodnebrog, O., G. Myhre, C. Jouan, T. Andrews, P. Forster, H. Jia, N. Loeb, D. Olivié, D. Paynter, J. Quaas, S. Raghuraman, and M. Schulz, Recent reductions in aerosol emissions have increased Earth's energy imbalance, Communications Earth & Environment, 5, 166, doi:10.1038/s43247-024-01324-8, 2024.
173. Jia, H., O. Hasekamp, and J. Quaas, Revisiting aerosol-cloud interactions from weekly cycles, Geophys. Res. Lett., 51, e2024GL108266, doi:10.1029/2024GL108266, 2024.
172. Kretzschmar, J., C. Wirth, M. Pöhlker, F. Stratmann, H. Wex, and J. Quaas, From trees to rain: Enhancement of cloud glaciation and precipitation by pollen, Environ. Res. Lett., 19, 104052, doi:10.1088/1748-9326/ad747a, 2024.
171. Lenhardt, J., J. Quaas, and D. Sejdinovic, Marine cloud base height retrieval from MODIS cloud properties using machine learning, Atmos. Meas. Tech., 17, 5655-5677, doi:10.5194/amt-17-5655-2024, 2024.
170. Luo, H., J. Quaas, and Y. Han, Decreased cloud cover partially offsets the cooling effects of surface albedo from deforestation, Nat. Commun., 15, 7345, doi:10.1038/s41467-024-51783-y, 2024.
169. Luo, H., J. Quaas, and Y. Han, Diurnally asymmetric cloud cover trends amplify greenhouse warming, Science Adv., 10, eado5179, doi:10.1126/sciadv.ado5179, 2024.
168. Mahecha, M., A. Bastos, F. Bohn, N. Eisenhauer, H. Feilhauer, T. Hickler, H. Kalesse-Los, M. Migliavacca, F. Otto, J. Peng, I. Tegen, A. Weigelt, M. Wendisch, C. Wirth, D. Al-Halbouni, H. Deneke, D. Doktor, S. Dunker, A. Ehrlich, A. Foth, A. Garcia-Garcia, C. Guerra, C. Guimaraes-Steinicke, H. Hartmann, S. Henning, H. Herrmann, C. Ji, T. Kattenborn, N. Kolleck, M. Kretschmer, I. Kühn, M. Luttkus, M. Maahn, M. Mönks, K. Mora, M. Pöhlker, M. Reichstein, N. Rüger, B. Sánchez-Parra, M. Schäfer, S. Sippel, M. Tesche, B. Wehner, S. Wieneke, A. Winkler, S. Wolf, S. Zaehle, J. Zscheischler, and J. Quaas, Biodiversity and climate extremes: known interactions and research gaps, 12, e2023EF003963, doi:10.1029/2023EF003963, 2024.
167. Mehrdad, S., D. Handorf, I. Höschel, K. Karami, J. Quaas, S. Dipu, and C. Jacobi, Arctic climate response to European radiative forcing: a deep learning study on circulation pattern changes, Weather Clim. Dynam., 5, 1223-1268, doi:10.5194/wcd-5-1223-2024, 2024.
166. Mülmenstädt, J., E. Gryspeerdt, S. Dipu, J. Quaas, A. Ackerman, A. Fridlind, F. Tornow, S. Bauer, A. Gettelman, Y. Ming, Y. Zheng, P. Ma, H. Wang, K. Zhang, M. Christensen, A. Varble, L. Leung, X. Liu, D. Neubauer, D. Partridge, P. Stier, and T. Takemura, General circulation models simulate negative liquid water path-droplet number correlations, but anthropogenic aerosols still increase simulated liquid water path, Atmos. Chem. Phys., 24, 7331-7345, doi:10.5194/acp-24-7331-2024, 2024.
165. Mülmenstädt, J., A. Ackerman, A. Fridlind, M. Huang, P. Ma, N. Mahfouz, S. Bauer, S. Burrows, M. Christensen, S. Dipu, A. Gettelman, L. Leung, F. Tornow, J. Quaas, A. Varble, H. Wang, K. Zhang, and Y. Zheng, Can GCMs represent cloud adjustments to aerosol-cloud interactions?, in press, doi:10.5194/egusphere-2024-778, 2024.
164. Novitasari, M., J. Quaas, and M. Rodrigues, Cloudy with a chance of uncertainty: Autoconversion rates forecasting via evidential regression from satellite data, Environ. Data Sci., in press, 2024.
163. Novitasari, M., J. Quaas, and M. Rodrigues, Cloudy with a chance of precision: Satellite's autoconversion rates forecasting powered by machine learning, Environ. Data Sci., 3, e23, doi:10.1017/eds.2024.24, 2024.
162. Quaas, J., T. Andrews, N. Bellouin, K. Block, O. Boucher, P. Ceppi, G. Dagan, S. Doktorowski, H. Eichholz, P. Forster, T. Goren, E. Gryspeerdt, O. Hodnebrog, H. Jia, R. Kramer, C. Lange, A. Maycock, J. Mülmenstädt, G. Myhre, F. O'Connor, R. Pincus, B. Samset, F. Senf, K. Shine, C. Smith, C. Stjern, T. Takemura, V. Toll, and C. Wall, Adjustments to climate perturbations - mechanisms, implications, observational constraints, AGU Advances, 5, e2023AV001144, doi:10.1029/2023AV001144, 2024.
161. Schwarz, M., J. Savre, D. Sudhakar, J. Quaas, and A. Ekman, The transition from aerosol- to updraft-limited susceptibility regime in large-eddy simulations with bulk microphysics, Tellus B, 76, 32-46, doi:10.16993/tellusb.94, 2024.
160. Stevens, B., S. Adami, T. Ali, H. Anzt, Z. Aslan, S. Attinger, J. Bäck, J. Baehr, P. Bauer, N. Bernier, B. Bishop, H. Bockelmann, S. Bony, V. Bouchet, G. Brasseur, D. Bresch, S. Breyer, G. Brunet, P. Buttigieg, J. Cao, C. Castet, Y. Cheng, A. Choudhury, D. Coen, S. Crewell, A. Dabholkar, Q. Dai, F. Doblas-Reyes, D. Durran, A. El Gaidi, C. Ewen, E. Exarchou, V. Eyring, F. Falkinhoff, D. Farrell, P. Forster, A. Frassoni, C. Frauen, O. Fuhrer, S. Gani, E. Gerber, D. Goldfarb, J. Grieger, N. Gruber, W. Hazeleger, R. Herken, C. Hewitt, T. Hoefler, H. Hsu, D. Jacob, A. Jahn, C. Jakob, T. Jung, C. Kadow, I. Kang, S. Kang, K. Kashinath, K. Kleinen-von Königslöw, D. Klocke, U. Kloenne, M. Klöwer, C. Kodama, S. Kollet, T. Kölling, J. Kontkanen, S. Kopp, M. Koran, M. Kulmala, H. Lappalainen, F. Latifi, B. Lawrence, J. Lee, Q. Lejeun, C. Lessig, C. Li, T. Lippert, J. Luterbacher, P. Manninen, J. Marotzke, S. Matsouoka, C. Merchant, P. Messmer, G. Michel, K. Michielsen, T. Miyakawa, S. Morin, J. Müller, R. Munir, S. Narayanasetti, O. Ndiaye, C. Nobre, A. Oberg, R. Oki, T. Ozkan-Haller, T. Palmer, S. Posey, A. Prein, O. Primus, M. Pritchard, J. Pullen, D. Putrasahan, J. Quaas, K. Raghavan, V. Ramaswamy, M. Rapp, F. Rauser, M. Reichstein, A. Revi, S. Saluja, M. Satoh, V. Schemann, S. Schemm, C. Schnadt Poberaj, T. Schulthess, C. Senior, J. Shukla, M. Singh, J. Slingo, A. Sobel, S. Solman, J. Spitzer, D. Stammer, P. Stier, T. Stocker, S. Strock, H. Su, P. Taalas, J. Taylor, S. Tegtmeier, G. Teutsch, A. Tompkins, U. Ulbrich, P. Vidale, C. Wu, H. Xu, N. Zaki, L. Zanna, T. Zhou, and F. Ziemen, Earth Virtualization Engines (EVE), Earth System Science Data, 16, 2113-2122, doi:10.5194/essd-16-2113-2024, 2024.
159. Stier, P., S. Van den Heever, M. Christensen, E. Gryspeerdt, G. Dagan, M. Bollasina, L. Donner, K. Emanuel, A. Ekman, G. Feingold, P. Field, P. Forster, J. Haywood, R. Kahn, I. Koren, C. Kummerow, T. L'Ecuyer, U. Lohmann, Y. Ming, G. Myhre, J. Quaas, D. Rosenfeld, B. Samset, A. Seifert, G. Stephens, W. Tao, and S. Saleeby, Multifaceted aerosol effects on precipitation, Nature Geosci., 7, 719-732, doi:10.1038/s41561-024-01482-6, 2024.
158. Zhao, J., X. Ma, J. Quaas, and H. Jia, Exploring aerosol-cloud interactions in liquid-phase clouds over eastern China and its adjacent ocean using the WRF-SBM-MOSAIC model, Atmos. Chem. Phys., 24, 9101-9118, doi:10.5194/acp-24-9101-2024, 2024.
2023
157. Bonazzola, M., H. Chepfer, P. Ma, J. Quaas, D. Winkler, A. Feofilov, and N. Schutgens, Incorporation of aerosols into the COSPv2 satellite lidar simulator for climate model evaluation, Geosci. Model. Devel., 16, 1359-1377, doi:10.5194/gmd-16-1359-2023, 2023.
156. Garcia-Garcia, A., F. Cuesta-Valero, D. Miralles, M. Mahecha, J. Quaas, M. Reichstein, J. Zscheischler, and J. Peng, Soil heat extremes outpace their atmospheric counterpart, Nature Clim. Chang., doi:10.1038/s41558-023-01812-3, 2023.
155. Goren, T., J. Kretzschmar, and J. Quaas, Spatial Aggregation of Satellite Observations Leads to an Overestimation of the Radiative Forcing due to Aerosol-Cloud Interactions, Geophys. Res. Lett., 50, e2023GL105282, doi:10.1029/2023GL105282, 2023.
154. Jia, H., and J. Quaas, Non-linearity of cloud response postpones climate penalty of mitigating air pollution, Nature Climate Change, 13, 943-950, doi:10.1038/s41558-023-01775-5, 2023.
153. Linke, O., J. Quaas, F. Baumer, S. Becker, J. Chylik, S. Dahlke, A. Ehrlich, D. Handorf, C. Jacobi, H. Kalesse-Los, L. Lelli, S. Mehrdad, R. Neggers, J. Riebold, P. Saavedra Garfias, N. Schnierstein, M. Shupe, C. Smith, G. Spreen, B. Verneuil, K. Vinjamuri, M. Vountas, and M. Wendisch, Constraints on simulated past Arctic amplification and lapse-rate feedback from observations, Atmos. Chem. Phys., 23, 9963-9992, doi:10.5194/acp-23-9963-2023, 2023.
152. Linke, O., N. Feldl, and J. Quaas, Present-day sea ice amount and seasonality determine future Arctic amplification, Environmental Research: Climate, 2, 045003, doi:10.1088/2752-5295/acf4b7, 2023.
151. Luo, H., J. Quaas, and Y. Han, Examining cloud vertical structure and radiative effects from satellite retrievals and evaluation of CMIP6 scenarios, Atmos. Chem. Phys., 23, 8169-8186, doi:10.5194/acp-23-8169-2023, 2023.
150. Novitasari, M., J. Quaas, and M. Rodrigues, Unleashing the autoconversion rates forecasting: evidential regression from satellite data, NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems, 2023.
149. Novitasari, M., J. Quaas, and M. Rodrigues, ALAS: Active Learning for Autoconversion rates prediction from Satellite data, NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning: Blending New and Existing Knowledge Systems, 2023.
148. Pöhlker, M., C. Pöhlker, J. Quaas, J. Mülmenstädt, A. Pozzer, M. Andreae, P. Artaxo, K. Block, H. Coe, B. Ervens, P. Gallimore, C. Gaston, S. Gunthe, S. Henning, H. Herrmann, O. Krüger, G. McFiggans, L. Poulain, S. Raj, E. Reyes-Villegas, H. Royer, D. Walter, Y. Wang, and U. Pöschl, Global organic and inorganic aerosol hygroscopicity and its effect on radiative forcing, Nat. Commun., 14, 6139, doi:10.1038/s41467-023-41695-8, 2023.
147. Possner, A., J. Quaas, and M. Quante, Impfen von Wolken zur Erhöhung der Reflektivität - Konzepte, Potenziale und Risiken, Warnsignal Klima: Hilft Technik gegen die Erderwärmung? - Climate Engineering in der Diskussion, 5.5, 250-255, doi:10.25592/warnsignal.klima.climate.engineering.38, 2023.
146. Rosenfeld, D., A. Kokhanovsky, T. Goren, E. Gryspeerdt, O. Hasekamp, H. Jia, A. Lopatin, J. Quaas, Z. Pan, and O. Sourdeval, Frontiers in satellite-based estimates of cloud-mediated aerosol forcing, Rev. Geophys., 61, e2022RG000799, doi:10.1029/2022RG000799, 2023.
145. Sanaei, A., H. Herrmann, L. Alshaabi, J. Beck, O. Ferlian, K. Fomba, S. Haferkorn, M. Van Pinxteren, J. Quaas, J. Quosh, R. Rabe, C. Wirth, N. Eisenhauer, and A. Weigelt, Changes in biodiversity impact atmospheric chemistry through plant volatiles and particles, Commun. Earth Environ., 4, 445, doi:10.1038/s43247-023-01113-9, 2023.
144. Stjern, C., P. Forster, H. Jia, C. Jouan, M. Kasoar, G. Myhre, D. Olivié, J. Quaas, B. Samset, M. Sand, T. Takemura, A. Voulgarakis, and C. Wells, The time scales of climate responses to carbon dioxide and aerosols, J. Climate, 36, 3537-3551, doi:10.1175/JCLI-D-22-0513.1, 2023.
143. Wendisch, M., M. Brückner, S. Crewell, A. Ehrlich, J. Notholt, C. Lüpkes, A. Macke, J. Burrows, A. Rinke, J. Quaas, M. Maturilli, V. Schemann, M. Shupe, C. Barrientos-Velasco, K. Barfuss, A. Blechschmidt, K. Block, I. Bougoudis, H. Bozem, C. Böckmann, A. Bracher, H. Bresson, I. Bretschneider, M. Buschmann, D. Chechin, J. Chylik, S. Dahlke, H. Deneke, K. Dethloff, T. Donth, W. Dorn, R. Dupuy, K. Ebell, U. Egerer, R. Engelmann, O. Eppers, R. Gerdes, R. Gierens, I. Gorodetskaya, M. Gottschalk, H. Griesche, V. Gryanik, D. Handorf, B. Altstädter, J. Hartmann, M. Hartmann, B. Heinold, A. Herber, H. Herrmann, G. Heygster, I. Höschel, Z. Hofmann, J. Hölemann, A. Hünerbein, S. Jafariserajehlou, E. Jäkel, C. Jacobi, M. Janout, F. Jansen, O. Jourdan, Z. Juranyi, H. Kalesse-Los, T. Kanzow, R. Käthner, L. Kliesch, M. Klingebiel, E. Knudsen, T. Kovacs, W. Körtke, D. Krampe, J. Kretzschmar, D. Kreyling, B. Kulla, D. Kunkel, A. Lampert, M. Lauer, L. Lelli, A. Von Lerber, O. Linke, U. Loehnert, M. Lonardi, S. Losa, M. Losch, M. Maahn, M. Mech, L. Mei, S. Mertes, E. Metzner, D. Mewes, J. Michaelis, G. Mioche, M. Moser, K. Nakoudi, R. Neggers, R. Neuber, T. Nomokonova, J. Oelker, I. Papakonstantinou-Presvelou, F. Pätzold, V. Pefanis, C. Pohl, M. Van Pinxteren, A. Radovan, M. Rhein, M. Rex, A. Richter, N. Risse, C. Ritter, P. Rostosky, V. Rozanov, E. Ruiz-Donoso, P. Saavedra, M. Salzmann, J. Schacht, M. Schäfer, J. Schneider, N. Schnierstein, P. Seifert, S. Seo, H. Siebert, M. Soppa, G. Spreen, I. Stachlewska, J. Stapf, F. Stratmann, I. Tegen, C. Viceto, C. Voigt, M. Vountas, A. Walbroel, M. Walter, B. Wehner, H. Wex, S. Willmes, M. Zanatta, and S. Zeppenfeld, Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC) Project, Bull. Amer. Meteorol. Soc., 104, E208-E242, doi:10.1175/BAMS-D-21-0218.1, 2023.
2022
142. Arola, A., A. Lipponen, P. Kolmonen, T. Virtanen, N. Bellouin, D. Grosvenor, E. Gryspeerdt, J. Quaas, and H. Kokkola, Aerosol effects on clouds are concealed by natural cloud heterogeneity and satellite retrieval errors, Nat. Commun., 13, 7357, doi:10.1038/s41467-022-34948-5, 2022.
141. Caldas-Alvarez, A., M. Augenstein, G. Ayzel, K. Barfus, R. Cherian, L. Dillenardt, F. Fauer, H. Feldmann, M. Heistermann, A. Karwat, F. Kaspar, H. Kreibich, E. Lucio-Eceiza, E. Meredith, S. Mohr, D. Niermann, S. Pfahl, F. Ruff, H. Rust, L. Schoppa, T. Schwitalla, S. Steidl, A. Thieken, J. Tradowsky, V. Wulfmeyer, and J. Quaas, Meteorological, impact and climate perspectives of the 29 June 2017 heavy precipitation event in the Berlin metropolitan area, Nat. Hazards Earth Syst. Sci., 22, 3701-3724, doi:10.5194/nhess-22-3701-2022, 2022.
140. Christensen, M., A. Gettelman, J. Cermak, G. Dagan, M. Diamond, A. Douglas, G. Feingold, F. Glassmeier, T. Goren, D. Grosvenor, E. Gryspeerdt, R. Kahn, Z. Li, P. Ma, F. Malavelle, I. McCoy, D. McCoy, G. McFarquhar, J. Mülmenstädt, S. Pal, A. Possner, A. Povey, J. Quaas, D. Rosenfeld, A. Schmidt, R. Schrödner, A. Sorooshian, P. Stier, V. Toll, D. Watson-Parris, R. Wood, M. Yang, and T. Yuan, Opportunistic experiments to constrain aerosol effective radiative forcing, Atmos. Chem. Phys., 22, 641-674, doi:10.5194/acp-22-641-2022, 2022.
139. Dipu, S., M. Schwarz, A. Ekman, E. Gryspeerdt, T. Goren, O. Sourdeval, J. Mülmenstädt, and J. Quaas, Exploring satellite-derived relationships between cloud droplet number concentration and liquid water path using large-domain large-eddy simulation, Tellus, 74, 176-188, doi:10.16993/tellusb.27, 2022.
138. Ganske, A., A. Heil, A. Lammert, J. Kretzschmar, and J. Quaas, Publication of Atmospheric Model Data using the ATMODAT Standard, Meteorol. Z., doi:10.1127/metz/2022/1118, 2022.
137. Goren, T., G. Feingold, E. Gryspeerdt, J. Kazil, J. Kretzschmar, H. Jia, and J. Quaas, Projecting stratocumulus transitions on the albedo-cloud fraction relationship reveals linearity of albedo to droplet concentrations, Geophys. Res. Lett., 49, e2022GL101169, doi:10.1029/2022GL101169, 2022.
136. Haghighatnasab, M., J. Kretzschmar, K. Block, and J. Quaas, Impact of Holuhraun volcano aerosols on clouds in cloud-system resolving simulations, Atmos. Chem. Phys., 2, 8457-8472, doi:10.5194/acp-22-8457-2022, 2022.
135. Jia, H., J. Quaas, E. Gryspeerdt, C. Böhm, and O. Sourdeval, Addressing the difficulties in quantifying droplet number response to aerosol from satellite observations, Atmos. Chem. Phys., 22, 7353-7372, doi:10.5194/acp-22-7353-2022, 2022.
134. Krueger, O., B. Holanda, S. Chowdhury, A. Pozzer, D. Walter, C. Pöhlker, M. Hernández, J. Burrows, C. Voigt, J. Lelieveld, J. Quaas, U. Pöschl, and M. Pöhlker, Black carbon aerosol reductions during COVID-19 confinement quantified by aircraft measurements over Europe, Atmos. Chem. Phys., 22, 8683-8699, doi:10.5194/acp-22-8683-2022, 2022.
133. Linke, O., and J. Quaas, The impact of increasing CO2 levels on the Arctic atmospheric energy budget in CMIP6 climate model simulations, Tellus, 74, 106-118, doi:10.16993/tellusa.29, 2022.
132. Ma, P., B. Harrop, V. Larson, R. Neale, A. Gettelman, H. Morrison, H. Wang, K. Zhang, S. Klein, M. Zelinka, Y. Zhang, Y. Qian, J. Yoon, C. Jones, M. Huang, S. Tai, B. Singh, P. Bogenschutz, X. Zheng, W. Lin, J. Quaas, H. Chepfer, M. Brunke, X. Zeng, J. Mülmenstädt, S. Hagos, Z. Zhang, H. Song, X. Liu, H. Wan, J. Wang, Q. Tang, P. Caldwell, J. Fan, L. Berg, J. Fast, M. Taylor, J. Golaz, S. Xie, P. Rasch, and L. Leung, Better calibration of cloud parameterizations and subgrid effects increases the fidelity of E3SM Atmosphere Model version 1, Geosci. Model Devel., 15, 2881-2916, 2022.
131. Mahecha, M., A. Bastos, F. Bohn, N. Eisenhauer, H. Feilhauer, H. Hartmann, T. Hickler, H. Kalesse-Los, M. Migliavacca, F. Otto, J. Peng, J. Quaas, I. Tegen, A. Weigelt, M. Wendisch, and C. Wirth, Biodiversity loss and climate extremes - study the feedbacks, Nature, 612, 30-32, doi:10.1038/d41586-022-04152-y, 2022.
130. Marjani, S., M. Tesche, P. Bräuer, O. Sourdeval, and J. Quaas, Satellite observations of the impact of aviation on ice crystal number in cirrus clouds, Geophys. Res. Lett., 49, e2021GL096173, doi:10.1029/2021GL096173, 2022.
129. Myhre, G., B. Samset, P. Forster, O. Hodnebrog, M. Sandstad, C. Mohr, J. Sillmann, C. Stjern, T. Andrews, O. Boucher, G. Faluvegi, T. Iversen, J. Lamarque, M. Kasoar, A. Kirkevåg, L. Liu, J. Mülmenstädt, J. Quaas, T. Richardson, C. Smith, D. Shawki, A. Voulgarakis, D. Shindell, T. Tang, P. Stier, D. Watson-Parris, T. Takemura, and D. Olivié, Scientific data from Precipitation Driver Response Model Intercomparison Project (PDRMIP), Scientific Data, 9, 123, doi:10.1038/s41597-022-01194-9, 2022.
128. Papakonstantinou-Presvelou, I., O. Sourdeval, and J. Quaas, Strong ocean/sea-ice contrasts observed in satellite-derived ice crystal number concentrations in Arctic ice boundary-layer clouds, Geophys. Res. Lett., 49, e2022GL098207, doi:10.1029/2022GL098207, 2022.
127. Quaas, J., H. Jia, C. Smith, A. Albright, W. Aas, N. Bellouin, O. Boucher, M. Doutriaux-Boucher, P. Forster, D. Grosvenor, S. Jenkins, Z. Klimont, N. Loeb, X. Ma, V. Naik, F. Paulot, P. Stier, M. Wild, G. Myhre, and M. Schulz, Robust evidence for reversal in the aerosol effective climate forcing trend, Atmos. Chem. Phys., 22, 12221-12239, doi:10.5194/acp-22-12221-2022, 2022.
126. Quaas, J., and E. Gryspeerdt, Aerosol-cloud interactions in liquid clouds, In: Aerosols and Climate, K. Carslaw (ed.), 489-544, doi:10.1016/C2019-0-00121-5, 2022.
125. Salzmann, M., S. Ferrachat, C. Tully, S. Münch, D. Watson-Parris, D. Neubauer, C. Siegenthaler-Le Drian, S. Rast, B. Heinold, T. Crueger, R. Brokopf, J. Mülmenstädt, J. Quaas, H. Wan, K. Zhang, U. Lohmann, P. Stier, and I. Tegen, The global atmosphere-aerosol model ICON-A-HAM2.3 - Initial model evaluation and effects of radiation balance tuning on aerosol optical thickness, J. Adv. Model. Earth Syst., 14, e2021MS002699, doi:10.1029/2021MS002699, 2022.
2021
124. Dipu S., ., J. Quaas, M. Quaas, W. Rickels, J. Mülmenstädt, and O. Boucher, Substantial climate response outside the target area in an idealized experiment of regional radiation management, Climate, 4, 66, doi:10.3390/cli9040066, 2021.
123. Gulev, S., P. Thorne, J. Ahn, F. Dentener, C. Domingues, S. Gerland, D. Gong, D. Kaufman, H. Nnamchi, J. Quaas, J. Rivera, S. Sathyendranath, S. Smith, B. Trewin, K. Von Schuckmann, and R. Vose, Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 287--422, doi:10.1017/9781009157896.004, 2021.
122. Jia, H., X. Ma, F. Yu, and J. Quaas, Significant underestimation of radiative forcing by aerosol-cloud interactions derived from satellite-based methods, Nat. Commun., 12, 3649, doi:10.1038/s41467-021-23888-1, 2021.
121. Mülmenstädt, J., M. Salzmann, J. Kay, M. Zelinka, P. Ma, C. Nam, J. Kretzschmar, S. Hörnig, and J. Quaas, An underestimated negative cloud feedback from cloud lifetime changes, Nature Climate Change, 11, 508-513, doi:10.1038/s41558-021-01038-1, 2021.
120. Novitasari, M., J. Quaas, and M. Rodrigues, Leveraging machine learning to predict the autoconversion rates from satellite data, NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning, 2021.
119. Quaas, J., E. Gryspeerdt, R. Vautard, and O. Boucher, Climate impact of aircraft-induced cirrus assessed from satellite observations before and during COVID-19, Environ. Res. Lett., 16, 064051, doi:10.1088/1748-9326/abf686, 2021.
118. Seelig, T., H. Deneke, J. Quaas, and M. Tesche, Life cycle of shallow marine cumulus clouds from geostationary satellite observations, J. Geophys. Res. Atmos., 126, e2021JD035577, doi:10.1029/2021JD035577, 2021.
117. Senf, F., J. Quaas, and I. Tegen, Absorbing aerosol decreases cloud cover in cloud-resolving simulations over Germany, Quart. J. Roy. Meteorol. Soc., 1-18, doi:10.1002/qj.4169, 2021.
116. Trömel, S., C. Simmer, U. Blahak, A. Blanke, F. Ewald, M. Frech, M. Gergely, M. Hagen, S. Hörnig, T. Janjic, H. Kalesse, S. Kneifel, C. Knote, J. Mendrok, M. Moser, G. Möller, K. Mühlbauer, A. Myagkov, V. Pejcic, P. Seifert, P. Shrestha, A. Teisseire, L. Terzi, E. Tetoni, T. Vogl, C. Voigt, Y. Zeng, T. Zinner, and J. Quaas, Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes, Atmos. Chem. Phys., 21, 17291-17314, doi:10.5194/acp-21-17291-2021, 2021.
2020
115. Bellouin, N., W. Davies, K. Shine, J. Quaas, J. Mülmenstädt, P. Forster, C. Smith, L. Lee, L. Regayre, G. Brasseur, N. Sudarchikova, I. Bouarar, O. Boucher, and G. Myhre, Radiative forcing of climate change from the Copernicus reanalysis of atmospheric composition, Earth Syst. Sci. Data, 12, 1649-1677, doi:10.5194/essd-12-1649-2020, 2020.
114. Bellouin, N., J. Quaas, E. Gryspeerdt, S. Kinne, P. Stier, D. Watson-Parris, O. Boucher, K. Carslaw, M. Christensen, A. Daniau, J. Dufresne, G. Feingold, S. Fiedler, P. Forster, A. Gettelman, J. Haywood, U. Lohmann, F. Malavelle, T. Mauritsen, D. McCoy, G. Myhre, J. Mülmenstädt, D. Neubauer, A. Possner, M. Rugenstein, Y. Sato, M. Schulz, S. Schwartz, O. Sourdeval, T. Storelvmo, V. Toll, D. Winker, and B. Stevens, Bounding global aerosol radiative forcing of climate change, Rev. Geophys., 58, e2019RG000660, doi:10.1029/2019RG000660, 2020.
113. Block, K., F. Schneider, J. Mülmenstädt, M. Salzmann, and J. Quaas, Climate models disagree on the sign of total radiative feedback in the Arctic, Tellus A, 72, 1-14, doi:10.1080/16000870.2019.1696139, 2020.
112. Cherian, R., and J. Quaas, Trends in AOD, clouds and cloud radiative effects in satellite data and CMIP5 and CMIP6 model simulations over aerosol source regions, Geophys. Res. Lett., 47, e2020GL087132, doi:10.1029/2020GL087132, 2020.
111. Costa-Surós, M., O. Sourdeval, C. Acquistapace, H. Baars, C. Henken, C. Genz, J. Hesemann, C. Jimenez, M. König, J. Kretzschmar, N. Madenach, C. Meyer, R. Schrödner, P. Seifert, F. Senf, M. Brueck, G. Cioni, J. Engels, K. Fieg, K. Gorges, R. Heinze, P. Siligam, U. Burkhardt, S. Crewell, C. Hoose, A. Seifert, I. Tegen, and J. Quaas, Detection and attribution of aerosol-cloud interactions in large-domain large-eddy simulations with the ICOsahedral Non-hydrostatic model, Atmos. Chem. Phys., 20, 5657-5678, doi:10.5194/acp-20-5657-2020, 2020.
110. Ganske, A., D. Heydebreck, H. Höck, A. Kraft, and J. Quaas, A Short Guide to Increase FAIRness of Atmospheric Model Data, Meteorol. Z., 29, 483-491, doi:10.1127/metz/2020/1042, 2020.
109. Krämer, M., C. Rolf, N. Spelten, A. Afchine, D. Fahey, E. Jensen, S. Khaykin, T. Kuhn, P. Lawson, A. Lykov, L. Pan, M. Riese, A. Rollins, F. Stroh, T. Thornberry, V. Wolf, S. Woods, P. Spichtinger, J. Quaas, and O. Sourdeval, A Microphysics Guide to Cirrus - Part II: Climatologies of Clouds and Humidity from Observations, Atmos. Chem. Phys., 20, 12569-12608, doi:10.5194/acp-20-12569-2020, 2020.
108. Kretzschmar, J., J. Stapf, D. Klocke, M. Wendisch, and J. Quaas, Employing airborne radiation and cloud microphysics observations to improve cloud representation in ICON at kilometer-scale resolution in the Arctic, Atmos. Chem. Phys., 20, 13145-13165, doi:10.5194/acp-20-13145-2020, 2020.
107. Lauer, M., K. Block, M. Salzmann, and J. Quaas, CO2-forced changes of Arctic temperature lapse-rates in CMIP5 models, Met. Z., 29, 79-93, doi:10.1127/metz/2020/0975, 2020.
106. Mülmenstädt, J., C. Nam, M. Salzmann, J. Kretzschmar, T. L'Ecuyer, U. Lohmann, P. Ma, G. Myhre, D. Neubauer, P. Stier, K. Suzuki, M. Wang, and J. Quaas, Reducing the aerosol forcing uncertainty using observational constraints on warm rain processes, Science Adv., 6, eaaz6433, doi:10.1126/sciadv.aaz6433, 2020.
105. Quaas, J., A. Arola, B. Cairns, M. Christensen, H. Deneke, A. Ekman, G. Feingold, A. Fridlind, E. Gryspeerdt, O. Hasekamp, Z. Li, A. Lipponen, P. Ma, J. Mülmenstädt, A. Nenes, J. Penner, D. Rosenfeld, R. Schrödner, K. Sinclair, O. Sourdeval, P. Stier, M. Tesche, B. Van Diedenhoven, and M. Wendisch, Constraining the Twomey effect from satellite observations: Issues and perspectives, Atmos. Chem. Phys., 20, 15079-15099, doi:10.5194/acp-20-15079-2020, 2020.
104. Quaas, J., and U. Lohmann, Clouds and Aerosols, In: Clouds and Climate: Climate Science's Greatest Challenge, A. Siebesma, S. Bony, C. Jakob, and B. Stevens, Eds., Cambridge University Press, 313-328, doi:10.1017/9781107447738.012, 2020.
103. Rickels, W., M. Quaas, K. Ricke, J. Quaas, J. Moreno-Cruz, and S. Smulders, Who turns the global thermostat and by how much?, Energy Economics, 91, 104852, doi:10.1016/j.eneco.2020.104852, 2020.
102. Stevens, B., C. Acquistapace, A. Hansen, R. Heinze, C. Klinger, D. Klocke, W. Schubotz, J. Windmiller, P. Adamidis, I. Arka, V. Barlakas, J. Biercamp, M. Brueck, S. Brune, S. Buehler, U. Burkhardt, G. Cioni, M. Costa-Surós, S. Crewell, T. Crueger, H. Deneke, P. Friederichs, C. Carbajal Henken, C. Hohenegger, M. Jacob, F. Jakub, N. Kalthoff, M. Köhler, T. Van Laar, P. Li, U. Löhnert, A. Macke, N. Madenach, B. Mayer, C. Nam, A. Naumann, K. Peters, S. Poll, J. Quaas, N. Röber, N. Rochetin, H. Rybka, L. Scheck, V. Schemann, S. Schnitt, A. Seifert, F. Senf, M. Shapkalijevski, C. Simmer, S. Singh, O. Sourdeval, D. Spickermann, J. Strandgren, O. Tessiot, N. Vercauteren, J. Vial, A. Voigt, and G. Zängl, Large-eddy and storm resolving models for climate prediction - the added value for clouds and precipitation, J. Meteorol. Soc. Japan, 98, doi:10.2151/jmsj. 2020-021, 2020.
101. Unglaub, C., K. Block, J. Mülmenstädt, O. Sourdeval, and J. Quaas, A new classification of satellite-derived liquid water cloud regimes at cloud scale, Atmos. Chem. Phys., 20, 2407-2418, doi:10.5194/acp-20-2407-2020, 2020.
100. Von Savigny, C., C. Timmreck, S. Buehler, J. Burrows, M. Giorgetta, G. Hegerl, A. Horváth, G. Hoshyaripour, C. Hoose, J. Quaas, E. Malinina, A. Rozanov, H. Schmidt, L. Thomason, M. Toohey, and B. Vogel, The Research Unit VolImpact: Revisiting the volcanic impact on atmosphere and climate - preparations for the next big volcanic eruption, Meteorol. Z., 29, 3-18, doi:10.1127/metz/2019/0999, 2020.
2019
99. Aas, W., A. Mortier, V. Bowersox, R. Cherian, G. Faluvegi, H. Fagerli, J. Hand, Z. Klimont, C. Galy-Lacaux, C. Lehmann, C. Lund Myhre, G. Myhre, D. Olivié, K. Sato, J. Quaas, P. Rao, M. Schulz, D. Shindell, R. Skeie, A. Stein, T. Takemura, S. Tsyro, R. Vet, and X. Xu, Global and regional trends of atmospheric sulfur, Sci. Rep., 9, 953, doi:10.1038/s41598-018-37304-0, 2019.
98. Böhm, C., O. Sourdeval, J. Mülmenstädt, J. Quaas, and S. Crewell, Cloud base height retrieval from multi-angle satellite data, Atmos. Meas. Tech., 12, 1841-1860, doi:10.5194/amt-12-1841-2019, 2019.
97. Gryspeerdt, E., T. Goren, O. Sourdeval, J. Quaas, J. Mülmenstädt, . Dipu S., C. Unglaub, A. Gettelman, and M. Christensen, Constraining the aerosol influence on cloud liquid water path, Atmos. Chem. Phys., 19, 5331-5347, doi:10.5194/acp-19-5331-2019, 2019.
96. Hasekamp, O., E. Gryspeerdt, and J. Quaas, Analysis of polarimetric satellite measurements suggests stronger cooling due to aerosol-cloud interactions, Nat. Commun., 10, 5405, doi:10.1038/s41467-019-13372-2, 2019.
95. Hutchison, K., B. Iisager, . Dipu S., X. Jiang, J. Quaas, and R. Markwardt, Evaluating WRF Cloud Forecasts with VIIRS Imagery and Derived Cloud Products, Atmosphere, 10, 521, doi:10.3390/atmos10090521, 2019.
94. Jia, H., X. Ma, J. Quaas, Y. Yin, and T. Qiu, Is the positive correlation between cloud droplet effective radius and aerosol optical depth over land due to retrieval artifacts or real physical processes?, Atmos. Chem. Phys., 19, 8879-8896, doi:10.5194/acp-19-8879-2019, 2019.
93. Kretzschmar, J., M. Salzmann, J. Mülmenstädt, and J. Quaas, Arctic clouds in ECHAM6 and their sensitivity to cloud microphysics and surface fluxes, Atmos. Chem. Phys., 19, 10571-10589, doi:10.5194/acp-19-10571-2019, 2019.
92. Mülmenstädt, J., E. Gryspeerdt, M. Salzmann, P. Ma, S. Dipu, and J. Quaas, Separating radiative forcing by aerosol-cloud interactions and fast cloud adjustments in the ECHAM-HAMMOZ aerosol-climate model using the method of partial radiative perturbations, Atmos. Chem. Phys., 19, 15415-15429, doi:10.5194/acp-19-15415-2019, 2019.
91. Schacht, J., B. Heinold, J. Quaas, J. Backman, R. Cherian, A. Ehrlich, A. Herber, W. Huang, Y. Kondo, A. Massling, P. Sinha, B. Weinzierl, M. Zanatta, and I. Tegen, The importance of the representation of air pollution emissions for the modeled distribution and radiative effects of black carbon in the Arctic, Atmos. Chem. Phys., 19, 11159-11183, doi:10.5194/acp-19-11159-2019, 2019.
90. Toll, V., M. Christensen, J. Quaas, and N. Bellouin, Weak average liquid-cloud-water response to anthropogenic aerosols, Nature, 572, 51-55, doi:10.1038/s41586-019-1423-9, 2019.
89. Wendisch, M., A. Macke, A. Ehrlich, C. Lüpkes, M. Mech, D. Chechin, C. Barrientos, H. Bozem, M. Brückner, H. Clemen, S. Crewell, T. Donth, R. Dupuy, K. Ebell, U. Egerer, R. Engelmann, C. Engler, O. Eppers, M. Gehrmann, X. Gong, M. Gottschalk, C. Gourbeyre, H. Griesche, J. Hartmann, M. Hartmann, A. Herber, H. Herrmann, G. Heygster, P. Hoor, S. Jafariserajehlou, E. Jäkel, E. Järvinen, O. Jourdan, U. Kästner, S. Kecorius, E. Knudsen, F. Köllner, J. Kretzschmar, L. Lelli, D. Leroy, M. Maturilli, L. Mei, S. Mertes, G. Mioche, R. Neuber, M. Nicolaus, T. Nomokonova, J. Notholt, M. Palm, M. Pinxteren, J. Quaas, P. Richter, E. Ruiz-Donoso, M. Schäfer, K. Schmieder, M. Schnaiter, J. Schneider, A. Schwarzenböck, P. Seifert, M. Shupe, H. Siebert, G. Spreen, J. Stapf, F. Stratmann, T. Vogl, A. Welti, H. Wex, A. Wiedensohler, M. Zanatta, S. Zeppenfeld, K. Dethloff, and B. Heinold, The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multi-Platform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, Bull. Amer. Meteorol. Soc, 100, 841-871, doi:10.1175/BAMS-D-18-0072.1, 2019.
2018
88. Goren, T., D. Rosenfeld, O. Sourdeval, and J. Quaas, Satellite observations of precipitating marine stratocumulus show greater cloud fraction for decoupled clouds in comparison to coupled clouds, Geophys. Res. Lett., 45, 5126-5134, doi:10.1029/2018GL078122, 2018.
87. Grosvenor, D., O. Sourdeval, P. Zuidema, A. Ackerman, M. Alexandrov, R. Bennartz, R. Boers, B. Cairns, C. Chiu, M. Christensen, H. Deneke, M. Diamond, G. Feingold, A. Fridlind, A. Hünerbein, C. Knist, P. Kollias, A. Marshak, D. McCoy, D. Merk, D. Painemal, J. Rausch, D. Rosenfeld, H. Russchenberg, P. Seifert, K. Sinclair, P. Stier, B. Van Diedenhoven, M. Wendisch, F. Werner, R. Wood, Z. Zhang, and J. Quaas, Remote sensing of cloud droplet number concentration in warm clouds: A review of the current state of knowledge and perspectives, Rev. Geophys., 56, 409-453, doi:10.1029/2017RG000593, 2018.
86. Gryspeerdt, E., J. Quaas, T. Goren, D. Klocke, and M. Brueck, An automated cirrus classification, Atmos. Chem. Phys., 18, 6157-6169, doi:10.5194/acp-18-6157-2018, 2018.
85. Gryspeerdt, E., O. Sourdeval, J. Quaas, J. Delanoë, and P. Kühne, Ice crystal number concentration estimates from lidar-radar satellite retrievals. Part 2: Controls on the ice crystal number concentration, Atmos. Chem. Phys., 18, 14351-14370, doi:10.5194/acp-18-14351-2018, 2018.
84. Ma, X., H. Jia, F. Yu, and J. Quaas, Opposite aerosol index-cloud droplet effective radius correlations over major industrial regions and their adjacent oceans, Geophys. Res. Lett., 45, 5771-5778, doi:10.1029/2018GL077562, 2018.
83. Mülmenstädt, J., O. Sourdeval, D. Henderson, T. L'Ecuyer, C. Unglaub, L. Jungandreas, C. Böhm, L. Russell, and J. Quaas, Using CALIOP to estimate cloud-field base height and its uncertainty: The Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset, Earth Syst. Sci. Data, 10, 2279-2293, doi:10.5194/essd-10-2279-2018, 2018.
82. Nam, C., P. Kühne, M. Salzmann, and J. Quaas, A prospectus for constraining rapid adjustments in general circulation models, J. Adv. Model. Earth Syst., 10, 2080-2094, doi:10.1029/2017MS001153, 2018.
81. Petersik, P., M. Salzmann, J. Kretzschmar, R. Cherian, D. Mewes, and J. Quaas, Subgrid-scale variability of clear-sky relative humidity and forcing by aerosol-radiation interactions in an atmosphere model, Atmos. Chem. Phys., 18, 8589-8599, doi:10.5194/acp-18-8589-2018, 2018.
80. Sourdeval, O., E. Gryspeerdt, M. Krämer, T. Goren, J. Delanoë, A. Afchine, F. Hemmer, and J. Quaas, Ice crystal number concentration estimates from lidar-radar satellite remote sensing. Part 1: Method and evaluation, Atmos. Chem. Phys., 18, 14327-14350, doi:10.5194/acp-18-14327-2018, 2018.
2017
79. Cherian, R., J. Quaas, M. Salzmann, and L. Tomassini, Black carbon indirect radiative effects in a climate model, Tellus, 69, 1369342, doi:10.1080/16000889.2017.1369342, 2017.
78. Dipu S., ., J. Quaas, R. Wolke, J. Stoll, A. Muhlbauer, M. Salzmann, B. Heinold, and I. Tegen, Implementation of aerosol-cloud interactions in the regional atmosphere-aerosol model COSMO-MUSCAT and evaluation using satellite data, Geosci. Model Devel., 10, 2231-2246, doi:10.5194/gmd-10-2231-2017, 2017.
77. Gryspeerdt, E., J. Quaas, S. Ferrachat, A. Gettelman, S. Ghan, U. Lohmann, H. Morrison, D. Neubauer, D. Partridge, P. Stier, T. Takemura, H. Wang, M. Wang, and K. Zhang, Constraining the instantaneous aerosol influence on cloud albedo, Proc. Nat. Acad. Sci. USA, 119, 4899-4904, doi:10.1073/pnas.1617765114, 2017.
76. Heinze, R., A. Dipankar, C. Henken, C. Moseley, O. Sourdeval, S. Trömel, X. Xie, P. Adamidis, F. Ament, H. Baars, C. Barthlott, A. Behrendt, U. Blahak, S. Bley, S. Brdar, M. Brueck, S. Crewell, H. Deneke, P. Girolamo, R. Evaristo, J. Fischer, C. Frank, P. Friederichs, T. Göcke, K. Gorges, L. Hande, M. Hanke, A. Hansen, H. Hege, C. Hoose, T. Jahns, N. Kalthoff, D. Klocke, S. Kneifel, P. Knippertz, A. Kuhn, T. Laar, A. Macke, V. Maurer, B. Mayer, C. Meyer, S. Muppa, R. Neggers, E. Orlandi, F. Pantillon, B. Pospichal, N. Röber, L. Scheck, A. Seifert, P. Seifert, F. Senf, P. Siligam, C. Simmer, S. Steinke, B. Stevens, K. Wapler, M. Weniger, V. Wulfmeyer, G. Zängl, D. Zhang, and J. Quaas, Large-eddy simulations over Germany using ICON: A comprehensive evaluation, Quart. J. Roy. Meteorol. Soc., 143, 69-100, doi:10.1002/qj.2947, 2017.
75. Heyn, I., K. Block, J. Mülmenstädt, E. Gryspeerdt, P. Kühne, M. Salzmann, and J. Quaas, Assessment of simulated aerosol effective radiative forcings in the terrestrial spectrum, Geophys. Res. Lett., 44, 1001-1007, doi:10.1002/2016GL071975, 2017.
74. Heyn, I., J. Quaas, M. Salzmann, and J. Mülmenstädt, Effects of diabatic and adiabatic processes on relative humidity in a GCM, and relationship between mid-tropospheric vertical wind and cloud-forming and cloud-dissipating processes, Tellus A, 69, 1272753, doi:10.1080/16000870.2016.1272753, 2017.
73. Kretzschmar, J., M. Salzmann, J. Mülmenstädt, O. Boucher, and J. Quaas, Comment on ``Rethinking the lower bound on aerosol radiative forcing'', J. Clim., 30, 6579-6584, doi:10.1175/JCLI-D-16-0668.1, 2017.
72. Myhre, G., W. Aas, R. Cherian, W. Collins, G. Faluvegi, M. Flanner, P. Forster, O. Hodnebrog, Z. Klimont, M. Lund, J. Mülmenstädt, C. Myhre, D. Olivié, M. Prather, J. Quaas, B. Samset, J. Schnell, M. Schulz, D. Shindell, R. Skeie, T. Takemura, and S. Tsyro, Multi-model simulations of aerosol and ozone radiative forcing due to anthropogenic emission changes during the period 1990-2015, Atmos. Chem. Phys., 17, 2709-2720, doi:10.5194/acp-17-2709-2017, 2017.
71. Patel, P., J. Quaas, and R. Kumar, A new statistical approach to improve the satellite based estimation of the radiative forcing by aerosol- cloud interactions, Atmos. Chem. Phys., 17, 3687-3698, doi:10.5194/acp-17-3687-2017, 2017.
70. Quaas, M., J. Quaas, W. Rickels, and O. Boucher, Are there good reasons against research into solar radiation management? - A model of intergenerational decision-making under uncertainty, J. Environ. Econ. Manage., 84, 1-17, doi:10.1016/j.jeem.2017.02.002, 2017.
69. Wendisch, M., M. Brückner, J. Burrows, S. Crewell, K. Dethloff, K. Ebell, C. Lüpkes, A. Macke, J. Notholt, J. Quaas, A. Rinke, and I. Tegen, The Arctic Amplifier - Novel Science Planned in a New German Research Initiative, EOS, 98, doi:10.1029/2017EO064803, 2017.
2016
68. Bellouin, N., L. Baker, O. Hodnebrog, D. Olivié, R. Cherian, C. Macintosh, B. Samset, A. Esteve, B. Aamaas, J. Quaas, and G. Myhre, Regional and seasonal radiative forcing by perturbations to aerosol and ozone precursor emissions, Atmos. Chem. Phys., 16, 13885-13910, doi:10.5194/acp-16-13885-2016, 2016.
67. Boucher, O., Y. Balkanski, O. Hodnebrog, C. Myhre, G. Myhre, J. Quaas, B. Samset, N. Schutgens, P. Stier, and R. Wang, The jury is still out on the radiative forcing by black carbon, Proc. Nat. Acad. Sci. USA, 113, E5092-E5093, doi:10.1073/pnas.1607005113, 2016.
66. Gryspeerdt, E., J. Quaas, and N. Bellouin, Constraining the aerosol influence on cloud fraction, J. Geophys. Res., 121, 3566-3583, doi:10.1002/2015JD023744, 2016.
65. Quaas, J., M. Quaas, O. Boucher, and W. Rickels, Regional climate engineering by radiation management: Prerequisites and prospects, Earth's Future, 4, 618-625, doi:10.1002/2016EF000440, 2016.
64. Quennehen, B., J. Raut, K. Law, N. Daskalakis, G. Ancellet, C. Clerbaux, S. Kim, M. Lund, G. Myhre, D. Olivié, S. Safieddine, R. Skeie, J. Thomas, S. Tsyro, A. Bazureau, N. Bellouin, M. Hu, M. Kanakidou, Z. Klimont, K. Kupiainen, S. Myriokefalitakis, J. Quaas, S. Rumbold, M. Schulz, R. Cherian, A. Shimizu, J. Wang, S. Yoon, and T. Zhu, Multi-model evaluation of short-lived pollutant distributions over East Asia during summer 2008, Atmos. Chem. Phys. , 16, 10765-10792, doi:10.5194/acp-16-10765-2016, 2016.
2015
63. Aswathy, V., O. Boucher, M. Quaas, U. Niemeier, H. Muri, J. Mülmenstädt, and J. Quaas, Climate extremes in multi-model simulations of stratospheric aerosol and marine cloud brightening climate engineering, Atmos. Chem. Phys., 15, 9593-9610, doi:10.5194/acp-15-9593-2015, 2015.
62. Baker, L., W. Collins, D. Olivié, R. Cherian, O. Hodnebrog, G. Myhre, and J. Quaas, Climate responses to anthropogenic emissions of short-lived climate pollutants, Atmos. Chem. Phys., 15, 8201-8216, doi:10.5194/acp-15-8201-2015, 2015.
61. Eckhardt, S., B. Quennehen, D. Olivié, T. Berntsen, R. Cherian, J. Christensen, W. Collins, S. Crepinsek, N. Daskalakis, M. Flanner, A. Herber, C. Heyes, O. Hodnebrog, L. Huang, M. Kanakidou, Z. Klimont, J. Langner, K. Law, M. Lund, R. Mahmood, A. Massling, S. Myriokefalitakis, I. Nielsen, J. Nøjgaard, J. Quaas, P. Quinn, J. Raut, S. Rumbold, M. Schulz, S. Sharma, R. Skeie, H. Skov, T. Uttal, K. Salzen, and A. Stohl, Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set, Atmos. Chem. Phys., 15, 9413-9433, doi:10.5194/acp-15-9413-2015, 2015.
60. Mülmenstädt, J., O. Sourdeval, J. Delanoë, and J. Quaas, Frequency of occurrence of rain from liquid-, mixed-, and ice-phase clouds derived from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502-6509, doi:10.1002/2015GL064604, 2015.
59. Quaas, J., Approaches to observe effects of anthropogenic aerosols on clouds and radiation, Current Climate Change Reports, 1, 297-304, doi:10.1007/s40641-015-0028-0, 2015.
58. Quaas, J., and P. Stier, Satellite observations of convection and their implications for parameterizations, Parameterization of Atmospheric Convection, Vol. 2: Current Issues and New Theories, World Scientific Publishing, ISBN 978-1-78326-690-6, 47-58, doi:10.1142/9781783266913_0019, 2015.
57. Rosch, J., T. Heus, H. Brueck, M. Salzmann, J. Mülmenstädt, L. Schlemmer, and J. Quaas, Analysis of diagnostic climate model cloud parametrizations using large-eddy simulations, Q. J. R. Meteorol. Soc., 141, 2199-2205, doi:10.1002/qj.2515, 2015.
56. Stohl, A., B. Aamaas, M. Amann, L. Baker, N. Bellouin, T. Berntsen, O. Boucher, R. Cherian, W. Collins, N. Daskalakis, M. Dusinska, S. Eckhardt, J. Fuglestvedt, M. Harju, C. Heyes, O. Hodnebrog, J. Hao, U. Im, M. Kanakidou, Z. Klimont, K. Kupiainen, K. Law, M. Lund, R. Maas, C. MacIntosh, G. Myhre, S. Myriokefalitakis, D. Olivie, J. Quaas, B. Quennehen, J. Raut, S. Rumbold, B. Samset, M. Schulz, O. Seland, K. Shine, R. Skeie, S. Wang, K. Yttri, and T. Zhu, Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants, Atmos. Chem. Phys., 15, 10529-10566, doi:10.5194/acp-15-10529-2015, 2015.
2014
55. Cherian, R., J. Quaas, M. Salzmann, and M. Wild, Pollution trends over Europe constrain global aerosol forcing as simulated by climate models, Geophys. Res. Lett., 41, 2176-2181, doi:10.1002/2013GL058715, 2014.
54. Ma, X., F. Yu, and J. Quaas, Reassessment of satellite-based estimate of aerosol-climate forcing, J. Geophys. Res., 119, 10394-10409, doi:10.1002/2014JD021670, 2014.
53. Nam, C., J. Quaas, R. Neggers, C. Siegenthaler-Le Drian, and F. Isotta, Evaluation of boundary layer cloud parameterizations in the ECHAM5 general circulation model using CALIPSO and CloudSat satellite data, J. Adv. Model. Earth Syst., 6, 300-314, doi:10.1002/2013MS000277, 2014.
52. Peters, K., J. Quaas, P. Stier, and H. Graßl, Processes limiting the emergence of detectable aerosol indirect effects on tropical warm clouds in global aerosol-climate model and satellite data, Tellus B, 66, 24054, doi:10.3402/tellusb.v66.24054, 2014.
51. Rosenfeld, D., M. Andreae, A. Asmi, M. Chin, G. Leeuw, D. Donovan, R. Kahn, S. Kinne, N. Kivekäs, M. Kulmala, W. Lau, S. Schmidt, T. Suni, T. Wagner, M. Wild, and J. Quaas, Global observations of aerosol-cloud-precipitation-climate interactions, Reviews Geophys., 52, 750-808, doi:10.1002/2013RG000441, 2014.
50. Yano, J., J. Geleyn, M. Köhler, D. Mironov, J. Quaas, P. Soares, V. Phillips, R. Plant, A. Deluca, P. Marquet, L. Stulic, and Z. Fuchs, Basic concepts for convection parameterization in weather forecast and climate models: COST Action ES0905 final report, Atmosphere, 6, 88-147, doi:10.3390/atmos6010088, 2014.
2013
49. Bellouin, N., J. Quaas, J. Morcrette, and O. Boucher, Estimates of aerosol radiative forcing from the MACC re-analysis, Atmos. Chem. Phys., 13, 2045-2062, doi:10.5194/acp-13-2045-2013, 2013.
48. Boucher, O., and J. Quaas, Water vapour affects both rain and aerosol optical depth, Nature Geosci., 6, 4-5, doi:10.1038/ngeo1692, 2013.
47. Cherian, R., C. Venkataraman, J. Quaas, and S. Ramachandran, GCM simulations of aerosol extinction, heating and effects on precipitation over India, J. Geophys. Res., 118, 2938-2955, doi:10.1002/jgrd.50298, 2013.
46. Grützun, V., J. Quaas, F. Ament, and C. Morcrette, Evaluating statistical cloud schemes - what can we gain from ground based remote sensing?, J. Geophys. Res., 118, 10507-10517, doi:10.1002/jgrd.50813, 2013.
45. Klocke, D., J. Quaas, and B. Stevens, Assessment of different metrics for physical climate feedbacks, Clim. Dyn., 41, 1173-1185, doi:10.1007/s00382-013-1757-1, 2013.
44. Nam, C., and J. Quaas, Geographical versus dynamically defined boundary layer cloud regimes and their use to evaluate general circulation model cloud parameterisations, Geophys. Res. Lett., 40, 4951-4956, doi:10.1002/grl.50945, 2013.
43. Randles, C., S. Kinne, G. Myhre, M. Schulz, P. Stier, J. Fischer, L. Doppler, E. Highwood, C. Ryder, B. Harris, J. Huttunen, Y. Ma, R. Pinker, B. Mayer, D. Neubauer, R. Hitzenberger, L. Oreopoulos, D. Lee, G. Pitari, G. Genova, J. Quaas, F. Rose, S. Kato, S. Rumbold, I. Vardavas, N. Hatzianastassiou, C. Matsoukas, H. Yu, H. Zhang, and P. Lu, Intercomparison of shortwave radiative transfer schemes in global aerosol modeling: Results from the AeroCom Radiative Transfer Experiment, Atmos. Chem. Phys., 13, 2347-2379, doi:10.5194/acp-13-2347-2013, 2013.
42. Rennó, N., E. Williams, D. Rosenfeld, D. Fischer, J. Fischer, T. Kremic, A. Agrawal, M. Andreae, R. Bierbaum, R. Blakeslee, A. Boerner, N. Bowles, H. Christian, A. Cox, J. Dunion, Ã. Horváth, X. Huang, A. Khain, S. Kinne, M. Lemos, J. Penner, U. Pöschl, J. Quaas, E. Seran, B. Stevens, T. Walati, and T. Wagner, CHASER: An Innovative Satellite Mission Concept to Measure the Effects of Aerosols on Clouds and Climate, Bull. Amer. Meteor. Soc., 94, 685-694, doi:10.1175/BAMS-D-11-00239, 2013.
41. Schemann, V., B. Stevens, V. Grützun, and J. Quaas, Scale dependency of total water variance, and its implication for cloud parameterizations, J. Atmos. Sci., 70, 3615-3630, doi:10.1175/JAS-D-13-09.1, 2013.
40. Schirber, S., D. Klocke, R. Pincus, J. Quaas, and J. Anderson, Parameter estimation using data assimilation in an atmospheric general circulation model: From a perfect towards the real world, J. Adv. Model. Earth Syst., 5, 58-70, doi:10.1029/2012MS000167, 2013.
39. Schneider, N., J. Quaas, M. Claussen, and C. Reick, Satellite-based analysis of clouds and radiation properties of different vegetation types in the Brazilian Amazon region, AIP Conf. Proc. 1531, 428, doi:10.1063/1.4804798, 2013.
38. Tomassini, L., O. Geoffroy, J. Dufresne, A. Idelkadi, C. Cagnazzo, K. Block, T. Mauritsen, M. Giorgetta, and J. Quaas, The respective roles of surface temperature driven feedbacks and tropospheric adjustment to CO in CMIP5 transient climate simulations, Clim. Dyn., 41, 3103-3126, doi:10.1007/s00382-013-1682-3, 2013.
2012
37. Cherian, R., C. Venkataraman, S. Ramachandran, J. Quaas, and S. Kedia, Examination of aerosol distributions and radiative effects over the Bay of Bengal and the Arabian Sea region during ICARB using satellite data and a general circulation model, Atmos. Chem. Phys., 12, 1287-1305, doi:10.5194/acp-12-1287-2012, 2012.
36. Devasthale, A., K. Karlsson, J. Quaas, and H. Graßl, Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function, Atmos. Meas. Tech., 5, 267-273, doi:10.5194/amt-5-267-2012, 2012.
35. Gehlot, S., and J. Quaas, Convection-climate feedbacks in ECHAM5 general circulation model: A Lagrangian trajectory perspective of cirrus cloud life cycle, J. Clim., 25, 5241-5259, doi:10.1175/JCLI-D-11-00345.1, 2012.
34. Nam, C., and J. Quaas, Evaluation of clouds and precipitation in the ECHAM5 general circulation model using CALIPSO and CloudSat , J. Clim., 25, 4975-4992, doi:10.1175/JCLI-D-11-00347.1, 2012.
33. Peters, K., P. Stier, J. Quaas, and H. Graßl, Aerosol indirect effects from shipping emissions: Sensitivity studies with the global aerosol-climate model ECHAM-HAM, Atmos. Chem. Phys., 12, 5985-6007, doi:10.5194/acp-12-5985-2012, 2012.
32. Quaas, J., Evaluating the "critical relative humidity" as a measure of subgrid-scale variability of humidity in general circulation model cloud cover parameterizations using satellite data, J. Geophys. Res., 117, D09208, doi:10.1029/2012JD017495, 2012.
31. Sanchez-Lorenzo, A., P. Laux, H. Hendricks-Franssen, A. Georgoulias, J. Calbó, S. Vogl, and J. Quaas, Assessing large-scale weekly cycles in meteorological variables: a review, Atmos. Chem. Phys., 12, 5755-5771, doi:10.5194/acp-12-5755-2012, 2012.
30. Weber, T., and J. Quaas, Incorporating the subgrid-scale variability of clouds in the autoconversion parameterization, J. Adv. Model. Earth Syst., 4, M11003, doi:10.1029/2012MS000156, 2012.
29. Zhang, K., D. O'Donnell, J. Kazil, P. Stier, S. Kinne, U. Lohmann, S. Ferrachat, B. Croft, J. Quaas, H. Wan, S. Rast, and J. Feichter, The global aerosol-climate model ECHAM5-HAM, version 2: sensitivity to improvements in process representations, Atmos. Chem. Phys., 12, 8911-8949, doi:10.5194/acp-12-8911-2012, 2012.
28. Zygmuntowska, M., T. Mauritsen, J. Quaas, and L. Kaleschke, Artcic clouds and surface radiation - a critical comparison of satellite retrievals and the ERA-INTERIM reanalysis, Atmos. Chem. Phys., 12, 6667-6677, doi:10.5194/acp-12-6667-2012, 2012.
2011
27. Klocke, D., R. Pincus, and J. Quaas, On constraining estimates of climate sensitivity with present-day observations through model weighting, J. Clim., 24, 6092-6099, doi:10.1175/2011JCLI4193.1, 2011.
26. Koch, D., Y. Balkanski, S. Bauer, R. Easter, S. Ferrachat, S. Ghan, C. Hoose, T. Iversen, A. Kirkevåg, J. Kristjánsson, X. Liu, U. Lohmann, S. Menon, J. Quaas, M. Schulz, O. Seland, T. Takemura, and N. Yan, Soot microphysical effects on liquid clouds, a multi-model investigation, Atmos. Chem. Phys., 11, 1051-1064, doi:10.5194/acp-11-1051-2011, 2011.
25. Peters, K., J. Quaas, and N. Bellouin, Effects of absorbing aerosols in cloudy skies: A satellite study over the Atlantic Ocean, Atmos. Chem. Phys., 11, 1393-1404, doi:10.5194/acp-11-1393-2011, 2011.
24. Peters, K., J. Quaas, and H. Graßl, A search for large-scale effects of ship emissions on clouds and radiation in satellite data, J. Geophys. Res., 116, D24205, doi:10.1029/2011JD016531, 2011.
23. Quaas, J., O. Boucher, N. Bellouin, and S. Kinne, Which of satellite- or model-based estimates is closer to reality for aerosol indirect forcing? - Reply to Penner et al., Proc. Nat. Acad. Sci. USA, 108, E1099, doi:10.1073/pnas.1114634108, 2011.
22. Weber, T., J. Quaas, and P. Räisänen, Evaluation of the subgrid-scale variability scheme for water vapor and cloud condensate in the ECHAM5 model using satellite data, Q. J. R. Meteorol. Soc., 137, 2079-2091, doi:10.1002/qj.887, 2011.
2010
21. Kazil, J., P. Stier, K. Zhang, J. Quaas, S. Kinne, D. O'Donnell, S. Rast, M. Esch, S. Ferrachat, U. Lohmann, and J. Feichter, Aerosol nucleation and its role for clouds and Earth's radiative forcing in the aerosol-climate model ECHAM5-HAM, Atmos. Chem. Phys., 10, 10733-10752, doi:10.5194/acp-10-10733-2010, 2010.
20. Kuhlmann, J., and J. Quaas, How can aerosols affect the Asian summer monsoon? Assessment during three consecutive pre-monsoon seasons from CALIPSO satellite data, Atmos. Chem. Phys., 10, 4673-4688, doi:10.5194/acp-10-4673-2010, 2010.
19. Lohmann, U., L. Rotstayn, T. Storelvmo, A. Jones, S. Menon, J. Quaas, A. Ekman, D. Koch, and R. Ruedy, Total aerosol effect: forcing or radiative flux perturbation, Atmos. Chem. Phys., 10, 3235-3246, doi:10.5194/acp-10-3235-2010, 2010.
18. Quaas, J., B. Stevens, U. Lohmann, and P. Stier, Interpreting the cloud cover - aerosol optical depth relationship found in satellite data using a general circulation model, Atmos. Chem. Phys., 10, 6129-6135, doi:10.5194/acp-10-6129-2010, 2010.
2009
17. Jones, T., S. Christopher, and J. Quaas, A six year satellite-based assessment of the regional variations in aerosol indirect effects, Atmos. Chem. Phys., 9, 4091-4114, doi:10.5194/acp-9-4091-2009, 2009.
16. Quaas, J., S. Bony, W. Collins, L. Donner, A. Illingworth, A. Jones, U. Lohmann, M. Satoh, S. Schwartz, W. Tao, and R. Wood, Current understanding and quantification of clouds in the changing climate system and strategies for reducing critical uncertainties, Clouds in the Perturbed Climate System. Proceedings Ernst Strüngmann Forum, 556-573, doi:10.7551/mitpress/9780262012874.003.0024, 2009.
15. Quaas, J., Aerosol direct and indirect climate forcings - Clues from satellite data and global modeling, Current problems in atmospheric radiation, 1100, 573-576, doi:10.1063/1.3117050, 2009.
14. Quaas, J., Y. Ming, S. Menon, T. Takemura, M. Wang, J. Penner, A. Gettelman, U. Lohmann, N. Bellouin, O. Boucher, A. Sayer, G. Thomas, A. McComiskey, G. Feingold, C. Hoose, J. Kristjánsson, X. Liu, Y. Balkanski, L. Donner, P. Ginoux, P. Stier, B. Grandey, J. Feichter, I. Sednev, S. Bauer, D. Koch, R. Grainger, A. Kirkevåg, T. Iversen, O. Seland, R. Easter, S. Ghan, P. Rasch, H. Morrison, J. Lamarque, M. Iacono, S. Kinne, and M. Schulz, Aerosol indirect effects - general circulation model intercomparison and evaluation with satellite data, Atmos. Chem. Phys., 9, 8697-8717, doi:10.5194/acp-9-8697-2009, 2009.
13. Quaas, J., O. Boucher, A. Jones, G. Weedon, J. Kieser, and H. Joos, Exploiting the weekly cycle as observed over Europe to analyse aerosol indirect effects in two climate models, Atmos. Chem. Phys., 9, 8493-8501, doi:10.5194/acp-9-8493-2009, 2009.
2008
12. Quaas, J., O. Boucher, N. Bellouin, and S. Kinne, Satellite-based estimate of the direct and indirect aerosol climate forcing, J. Geophys. Res., 113, D05204, doi:10.1029/2007JD008962, 2008.
2007
11. Lohmann, U., J. Quaas, S. Kinne, and J. Feichter, Different approaches for constraining global climate models of the anthropogenic indirect aerosol effect, Bull. Amer. Meteor. Soc., 88, 243-249, doi:10.1175/BAMS-88-2-243, 2007.
2006
10. Penner, J., J. Quaas, T. Storelvmo, T. Takemura, O. Boucher, H. Guo, A. Kirkevåg, J. Kristjánsson, and O. Seland, Model intercomparison of indirect aerosol effects, Atmos. Chem. Phys., 6, 3391-3405, doi:10.5194/acp-6-3391-2006, 2006.
9. Quaas, J., O. Boucher, and U. Lohmann, Constraining the total aerosol indirect effect in the LMDZ and ECHAM4 GCMs using MODIS satellite data, Atmos. Chem. Phys., 6, 947-955, doi:10.5194/acp-6-947-2006, 2006.
8. Ringer, M., B. McAvaney, N. Andronova, L. Buja, M. Esch, W. Ingram, B. Li, J. Quaas, E. Roeckner, C. Senior, B. Soden, E. Volodin, M. Webb, and K. Williams, Global mean cloud feedbacks in idealized climate change experiments, Geophys. Res. Lett., 33, L07718, doi:10.1029/2005GL025370, 2006.
2005
7. Dufresne, J., J. Quaas, O. Boucher, S. Denvil, and L. Fairhead, Constrast of the climate effects of anthropogenic sulfate aerosols between the 20th and 21st century, Geophys. Res. Lett., 32, L21703, doi:10.1029/2005GL023619, 2005.
6. Quaas, J., and O. Boucher, Constraining the first aerosol indirect radiative forcing in the LMDZ GCM using POLDER and MODIS satellite data, Geophys. Res. Lett., 32, L17814, doi:10.1029/2005GL023850, 2005.
2004
5. Doutriaux-Boucher, M., and J. Quaas, Evaluation of cloud thermodynamic phase parameterizations in the LMDZ GCM by using POLDER satellite data, Geophys. Res. Lett., 31, L06126, doi:10.1029/2003GL019095, 2004.
4. Quaas, J., O. Boucher, and F. Bréon, Aerosol indirect effects in POLDER satellite data and in the Laboratoire de Météorologie Dynamique-Zoom (LMDZ) general circulation model, J. Geophys. Res., 109, D08205, doi:10.1029/2003JD004317, 2004.
3. Quaas, J., O. Boucher, J. Dufresne, and H. Treut, Impacts of greenhouse gases and aerosol direct and indirect effects on clouds and radiation in atmospheric GCM simulations of the 1930 - 1989 period, Clim. Dyn., 23, 779-789, doi:10.1007/s00382-004-0475-0, 2004.
2003
2. Joppich, W., and J. Quaas, Coupling General Circulation Models on a Meta-Computer, Lecture Notes in Computer Science, 2658, 161-170, doi:10.1007/3-540-44862-4_18, 2003.
1. Menon, S., J. Brenguier, O. Boucher, P. Davison, A. Genio, J. Feichter, S. Ghan, S. Guibert, X. Liu, U. Lohmann, H. Pawlowska, J. Penner, J. Quaas, D. Roberts, L. Schüller, and J. Snider, Evaluating aerosol/cloud/radiation process parameterizations with single column models and Second Aerosol Characterization Experiment (ACE-2) cloudy column observations, J. Geophys. Res., 108, 4762, doi:10.1029/2003JD003902, 2003.
Letzte Aktualisierung am 13. Dezember 2022 von J. Quaas