Publications of Klaus Klingmüller
All genres
Meeting Abstract (2)
21.
Meeting Abstract
Combined machine learning model of aeolian dust and surface soil moisture. In EGU General Assembly 2024, Vienna, Austria & Online, EGU24-9208. EGU General Assembly 2024, Vienna, April 14, 2024 - April 19, 2024. (2024)
22.
Meeting Abstract
Data-Driven Aeolian Dust Emission Scheme for Climate Modelling. In AGU Fall Meeting 2022, Board 0368. AGU Fall Meeting, Chicago, IL, December 12, 2022 - December 16, 2022. (2022)
Poster (1)
23.
Poster
Implementation and Evaluation of the Land Surface Model JSBACH in the ECHAM/MESSy Atmospheric Chemistry Model. EGU General Assembly 2024, Vienna (2024)
Working Paper (6)
24.
Working Paper
Weaker cooling by aerosols due to dust-pollution interactions. Atmospheric Chemistry and Physics Discussions 20 (2020)
25.
Working Paper
Direct radiative effect of dust-pollution interactions. Atmospheric Chemistry and Physics Discussions 18 (2018), 20 pp.
26.
Working Paper
Revised mineral dust emissions in the atmospheric chemistry-climate model EMAC (based on MESSy 2.52). Geoscientific Model Development Discussions 10 (2017), 46 pp.
27.
Working Paper
Chemical aging of atmospheric mineral dust during transatlantic transport. Atmospheric Chemistry and Physics Discussions 16 (2016)
28.
Working Paper
Aerosol optical depth trend over the Middle East. Atmospheric Chemistry and Physics Discussions 16 (2016)
29.
Working Paper
Aerosol water parameterization: a single parameter framework. Atmospheric Chemistry and Physics Discussions 15 pp. 33493 - 33553 (2015), 60 pp.
Other (1)
30.
Other
Supplement of Evaluation of the coupling of EMACv2.55 to the land surface and vegetation model JSBACHv4, Geoscientific Model Development 17, (2024)
Preprint (3)
31.
Preprint
Evaluation of the coupling of EMACv2.55 and the land surface and vegetation model JSBACHv4. EGUsphere (2024)
32.
Preprint
15 (2022)
Data-driven aeolian dust emission scheme for climate modelling, evaluated with EMAC 2.54. Geoscientific Model Development Discussions 33.
Preprint
14 (2020)
Climate model-informed deep learning of global soil moisture distribution. Geoscientific Model Development Discussions