(REGIONAL) CLIMATE MODELLING
A tipping element (TE) is a subsystem prone to crossing a tipping point (Lenton 2008, 2013). The soil is a subsystem of the Amazonian rainforest which has been tagged as TE of the global climate system (Lenton et al. 2008, Boers et al. 2017, Boy & Wilcke 2008). The Amazonian rainforest may change into a seasonal forest (Lenton et al., 2008; Malhi et al., 2009) or a Savannah ecosystem (Bush et al., 2017). Crucial drivers are global warming and unsustainable land use practices (Fearnside 2008, Schellnhuber et al., 2009). Hence, the integrative tipping element is the regional climate system, with a particular focus on the effect of land use change on land surface energy and moisture fluxes that can have significant consequences for the topo-climatic settings and the diurnal cycle of moist convection (Rieck 2014). A systematic analysis of interlinkages between land use change and climate response, however, is thwarted by the lack of operational knowledge and the suitable climate modelling approaches, capable of representing and simulating climate responses to land use changes at commensurate (local to regional) scales.
In Prodigy, we implement a modelling approach for the MAP region that combines regional climate and land use models to analyze the nature of climate feedbacks on alternative land use change scenarios.
Crossing the regional climate system tipping point, land use change might trigger a drastic change in the occurrence of drought events, which in turn increases the stress on soil ecosystems. The primary task is modelling regional climate change to provide quantitative knowledge on the external stressor drought. For this, dynamical climate modelling will be performed using the mesoscale climate model of WRF (Weather Research and Forecasting) in a convection-permitting non-hydrostatic setup forced with ERA5 re-analyses in hindcast mode (Langkamp and Böhner 2011; Böhner et al. 2013; Kilian 2017; Böhner et al. 2020). The WRF model setup explicitly aims at improving otherwise oversimplified representations of land surface dynamics and associated processes. Modelling results will be validated against historical station and gridded observations. The validated setup will be used to analyze local climate feedbacks on series of alternative land use scenarios to identify a land use pattern that might trigger or disturb the convection systems. The same WRF model system will be used to dynamically downscale the MPI-ESM-LR based future climate change projections for the SSP3-7.0 and SSP5-8.5 scenarios for the MAP region.
Böhner, J., Dietrich, H., Fraedrich, K., Kawohl, T., Kilian, M., Lucarini, V., & Lunkeit, F. (2013). Development and implementation of a hierarchical model chain for modelling regional climate variability and climate change over southern Amazonia. Interdisciplinary Analysis and Modeling of Carbon-Optimized Land Management Strategies for Southern Amazonia, 119. https://d-nb.info/1154360652/34#page=125
Böhner, J. Hasson, S. & Kilian, M. (2020): Evaluation of spatial variation characteristics of dynamically modelled precipitation and temperature fields – A comparative analysis of WRF simulations over western Amazonia and the central Himalayas. – GeoÖko 41: 41-66.
Boers, N., Marwan, N., Barbosa, H. M., & Kurths, J. (2017). A deforestation-induced tipping point for the South American monsoon system. Scientific reports, 7(1), 41489. https://doi.org/10.1038/srep41489
Boy, J., & Wilcke, W. (2008). Tropical Andean forest derives calcium and magnesium from Saharan dust. Global Biogeochemical Cycles, 22(1). https://doi.org/10.1029/2007GB002960
Bush, M. B. (2017). The resilience of Amazonian forests. Nature, 541(7636), 167-168. https://doi.org/10.1038/541167a
Fearnside, P. M. (2008). The roles and movements of actors in the deforestation of Brazilian Amazonia. Ecology and society, 13(1). https://www.jstor.org/stable/26267941
Kilian, M. (2017). Climate variability and potential future climate change in southern Amazonia: sensitivity of the hydrological cycle to land use changes (Doctoral dissertation, Staats-und Universitätsbibliothek Hamburg Carl von Ossietzky).
Langkamp, T., & Böhner, J. (2011). Influence of the compiler on multi-CPU performance of WRFv3. Geoscientific Model Development, 4(3), 611-623. https://doi.org/10.5194/gmd-4-611-2011
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S., & Schellnhuber, H. J. (2008). Tipping elements in the Earth’s climate system. Proceedings of the national Academy of Sciences, 105(6), 1786-1793. https://doi.org/10.1073/pnas.0705414105
Lenton, T. M. (2013). Environmental tipping points. Annual Review of Environment and Resources, 38, 1-29. https://doi.org/10.1146/annurev-environ-102511-084654
Malhi, Y., Aragão, L. E., Galbraith, D., Huntingford, C., Fisher, R., Zelazowski, P., … & Meir, P. (2009). Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proceedings of the National Academy of Sciences, 106(49), 20610-20615. https://doi.org/10.1073/pnas.0804619106
Rieck, M., Hohenegger, C., & van Heerwaarden, C. C. (2014). The influence of land surface heterogeneities on cloud size development. Monthly Weather Review, 142(10), 3830-3846. https://doi.org/10.1175/MWR-D-13-00354.1
Schellnhuber, H. J. (2009). Tipping elements in the Earth System. Proceedings of the National Academy of Sciences, 106(49), 20561-20563. https://doi.org/10.1073/pnas.0911106106