Schaphoff, S.; von Bloh, W.; Rammig, A.; Thonicke, K.; Biemans, H.; Forkel, M.; Gerten, D.; Heinke, J.; Jägermeyr, J.; Knauer, J.et al.; Langerwisch, F.; Lucht, W.; Müller, C.; Rolinski, S.; Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description. Geoscientific Model Development 11 (4), pp. 1343 - 1375 (2018)
Knauer, J.; Zaehle, S.; Reichstein, M.; Medlyn, B. E.; Forkel, M.; Hagemann, S.; Werner, C.: The response of ecosystem water-use efficiency to rising atmospheric CO2 concentrations: sensitivity and large-scale biogeochemical implications. New Phytologist 213 (4), pp. 1654 - 1666 (2017)
Filippa, G.; Cremonese, E.; Migliavacca, M.; Galvagno, M.; Forkel, M.; Wingate, L.; Tomelleri, E.; di Cella, U. M.; Richardson, A. D.: Phenopix: A R package for image-based vegetation phenology. Agricultural and Forest Meteorology 220, pp. 141 - 150 (2016)
Sippel, S.; Otto, F. E. L.; Forkel, M.; Allen, M. R.; Guillod, B. P.; Heimann, M.; Reichstein, M.; Seneviratne, S. I.; Thonicke, K.; Mahecha, M. D.: A novel bias correction methodology for climate impact simulations. Earth System Dynamics 7 (1), pp. 71 - 88 (2016)
Thurner, M.; Beer, C.; Carvalhais, N.; Forkel, M.; Santoro, M.; Tum, M.; Schmullius, C.: Large-scale variation in boreal and temperate forest carbon turnover rate is related to climate. Geophysical Research Letters 43 (9), pp. 4576 - 4585 (2016)
Forkel, M.; Migliavacca, M.; Thonicke, K.; Reichstein, M.; Schaphoff, S.; Weber, U.; Carvalhais, N.: Codominant water control on global interannual variability and trends in land surface phenology and greenness. Global Change Biology 21 (9), pp. 3414 - 3435 (2015)
Forkel, M.; Carvalhais, N.; Schaphoff, S.; Bloh, W. v.; Migliavacca, M.; Thurner, M.; Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model-data integration. Biogeosciences 11 (23), pp. 7025 - 7050 (2014)
Urban, M.; Forkel, M.; Eberle, J.; Hüttich, C.; Schmullius, C.; Herold, M.: Pan-arctic climate and land cover trends derived from multi-variate and multi-scale analyses (1981–2012). Remote Sensing 6 (3), pp. 2296 - 2316 (2014)
Urban, M.; Forkel, M.; Schmullius, C.; Hese, S.; Hüttich, C.; Herold, M.: Identification of land surface temperature and albedo trends in AVHRR pathfinder data from 1982 to 2005 for northern Siberia. International Journal of Remote Sensing 34 (12), pp. 4491 - 4507 (2014)
Forkel, M.; Carvalhais, N.; Verbesselt, J.; Mahecha, M. D.; Neigh, C. S.R.; Reichstein, M.: Trend change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sensing 5 (5), pp. 2113 - 2144 (2013)
Forkel, M.; Thonicke, K.; Beer, C.; Cramer, W.; Bartalev, S.; Schmullius, C.: Extreme fire events are related to previous-year surface moisture conditions in permafrost-underlain larch forests of Siberia. Environmental Research Letters 7 (4), 044021 (2012)
Forkel, M.: Controls on Global Greening, Phenology and the Enhanced Seasonal CO2 Amplitude: Integrating Decadal Satellite Observations and Global Ecosystem Models. Dissertation, 323 pp., Friedrich Schiller University Jena, Jena (2015)
Thanks to FLUXCOM-X, the next generation of data driven, AI-based earth system models, scientists can now see the Earth’s metabolism at unprecedented detail – assessed everywhere on land and every hour of the day.
David Hafezi Rachti was awarded twice: for his EGU poster with this year’s “Outstanding Student and PhD candidate Presentation” (OSPP) and for his Bachelor thesis, he received the 1st prize of the “Young Climate Scientist Award 2024”.
A recent study by scientists from the Max Planck Institute for Biogeochemistry and the University of Leipzig suggests that increasing droughts in the tropics and changing carbon cycle responses due to climate change are not primarily responsible for the strong tropical response to rising temperatures. Instead, a few particularly strong El Niño events could be the cause.
EU funds the international research project AI4PEX to further improve Earth system models and thus scientific predictions of climate change. Participating scientists from 9 countries met at the end of May 2024 to launch the project at the MPI for Biogeochemistry in Jena, which is leading the project.
From the Greek philosopher Aristotle to Charles Darwin to the present day, scientists have dealt with this fundamental question of biology. Contrary to public perception, however, it is still largely unresolved. Scientists have now presented a new approach for the identification and delimitation of species using artificial intelligence (AI).
When it comes to studying climate change, we generally assume that the total amount of carbon emissions determines how much the planet will warm. A new study suggests that not only the amount, but also the timing of those emissions controls the amount of surface warming that occurs on human time-scale.
Tropical forests are continuously being fragmented and damaged by human influences. Using remote sensing data and cutting-edge data analysis methods, researchers can now show for the first time that the impact of this damage is greater than previously estimated.
The new research project "PollenNet" aims to use artificial intelligence to accurately predict the spread of pollen. In order to improve allergy prevention, experts are bringing together the latest interdisciplinary findings from a wide range of fields.
If rivers overflow their banks, the consequences can be devastating. Using methods of explainable machine learning, researchers at the Helmholtz Centre for Environmental Research (UFZ) have shown that floods are more extreme when several factors are involved in their development.
Europe is the fastest warming continent in the world. According to the European Environment Agency’s assessment, many of these risks have already reached critical levels and could become catastrophic without urgent and decisive action.
Plant observations collected with plant identification apps such as Flora Incognita allow statements about the developmental stages of plants - both on a small scale and across Europe.