Interactions between land eocsystems, atmosphere and the climate system.
The aim of the Biogeochemical Signals Department is to improve our understanding of the interactions between biogeochemical element cycles on land surface and the atmosphere on local, regional and global scales. In addition to the climate-relevant cycles of carbon and water, our research focuses on the essential nutrients nitrogen (N) and phosphorus (P) and their importance for plant growth, soil dynamics and feedbacks between biospheric processes and climate.
We utilise atmospheric greenhouse gas observations and transport modelling and remote sensing data to to understand regional variations in the terrestrial greenhouse gas balance and identify underlying biospheric signals. We combine knowledge about eco-physiological processes with observations and modelling of biogeochemical cycles at different spatial scales to understand the underlying drivers of these biospheric signals.
We develop complex models to simulate terrestrial biogeochemical element cycles and their dependence on vegetation and soil properties as well as local climate. Based on detailed knowledge of physiological principles of ecosystem processes, we seek to improve these models and adapt them better to capture biological processes at climate relevant scales. We test the improved models with different ecosystem and atmospheric observations. Our new findings also feed into global models of the Earth system to estimate the impact of increasing human influence on terrestrial ecosystems.
Latest Key Results
a–f, The contributions of CO2 (a), N2O (b), CH4 (c), aerosols (d), O3 (e) and the net effect (f) (that is, sum of a–e) were derived in the GEOS-Chem-RRTMG model by calculating differences in all-sky top-of-atmosphere radiative forcing between CTRL_2019 and No_allNr experiments. The radiative forcing of aerosols is the sum of the direct radiative forcing contributed by ammonium, nitrate and sulfate aerosols. Numbers in parentheses represent the global area-weighted averages, whereas numbers in the brackets indicate the uncertainty ranges based on sensitivity experiments with GEOS-Chem-RRTMG using ±1 standard deviation among NMIP2 ensembles as well as ±30% uncertainty in OH and O3 concentrations (Supplementary Information Section 1.2). Note the Nr effects on global CO2, N2O and CH4 are assumed to be evenly distributed, so that the patterns of these three greenhouse gases are mostly determined by other forcing agents, including the distribution of clouds. This work is available here: https://www.nature.com/articles/s41586-024-07714-4 and is licensed under the Creative Commons Attribution 4.0 International License. Reference: Gong, C.; Tian, H.; Liao, H.; Pan, N.; Pan, S.; Ito, A.; Jain, A. K.; Kou-Giesbrecht, S.; Joos, F.; Sun, Q.; Shi, H.; Vuichard, N.; Zhu, Q.; Peng, C.; Maggi, F.; Tang, F. H. M.; Zaehle, S.: Global net climate effects of anthropogenic reactive nitrogen. Nature 632, pp. 557 - 563 (2024)
a–f, The contributions of CO2 (a), N2O (b), CH4 (c), aerosols (d), O3 (e) and the net effect (f) (that is, sum of a–e) were derived in the GEOS-Chem-RRTMG model by calculating differences in all-sky top-of-atmosphere radiative forcing between CTRL_2019 and No_allNr experiments. The radiative forcing of aerosols is the sum of the direct radiative forcing contributed by ammonium, nitrate and sulfate aerosols. Numbers in parentheses represent the global area-weighted averages, whereas numbers in the brackets indicate the uncertainty ranges based on sensitivity experiments with GEOS-Chem-RRTMG using ±1 standard deviation among NMIP2 ensembles as well as ±30% uncertainty in OH and O3 concentrations (Supplementary Information Section 1.2). Note the Nr effects on global CO2, N2O and CH4 are assumed to be evenly distributed, so that the patterns of these three greenhouse gases are mostly determined by other forcing agents, including the distribution of clouds. This work is available here: https://www.nature.com/articles/s41586-024-07714-4 and is licensed under the Creative Commons Attribution 4.0 International License. Reference: Gong, C.; Tian, H.; Liao, H.; Pan, N.; Pan, S.; Ito, A.; Jain, A. K.; Kou-Giesbrecht, S.; Joos, F.; Sun, Q.; Shi, H.; Vuichard, N.; Zhu, Q.; Peng, C.; Maggi, F.; Tang, F. H. M.; Zaehle, S.: Global net climate effects of anthropogenic reactive nitrogen. Nature 632, pp. 557 - 563 (2024)
Latest Publications
Abramowitz, G.; Ukkola, A.; Hobeichi, S.; Page, J. C.; Lipson, M.; Kauwe, M. G. D.; Green, S.; Brenner, C.; Frame, J.; Nearing, G.et al.; Clark, M.; Best, M.; Anthoni, P.; Arduini, G.; Boussetta, S.; Caldararu, S.; Cho, K.; Cuntz, M.; Fairbairn, D.; Ferguson, C. R.; Kim, H.; Kim, Y.; Knauer, J.; Lawrence, D.; Luo, X.; Malyshev, S.; Nitta, T.; Ogee, J.; Oleson, K.; Ottlé, C.; Peylin, P.; de Rosnay, P.; Rumbold, H.; Su, B.; Vuichard, N.; Walker, A. P.; Wang-Faivre, X.; Wang, Y.; Zeng, Y.: On the predictability of turbulent fluxes from land: PLUMBER2 MIP experimental description and preliminary results. Biogeosciences 21 (23), pp. 5517 - 5538 (2024)
Towers, I. R.; O'Reilly-Nugent, A.; Sabot, M.; Vesk, P. A.; Falster, D. S.: Optimising height-growth predicts trait responses to water availability and other environmental drivers. Plant, Cell and Environment 47 (12), pp. 4849 - 4869 (2024)
Gier, B. K.; Schlund, M.; Friedlingstein, P.; Jones, C. D.; Jones, C.; Zaehle, S.; Eyring, V.: Representation of the terrestrial carbon cycle in CMIP6. Biogeosciences 21 (22), pp. 5321 - 5360 (2024)
Karbasi, S.; Abdi, A. H.; Malakooti, H.; Orza, J. A. G.: Atmospheric CO2 column concentration over Iran: Emissions, GOSAT satellite observations, and WRF-GHG model simulations. Atmospheric Research 314, 107818 (2024)
