TY  - JOUR
A1  - Ciemer, Catrin
A1  - Boers, Niklas
A1  - Hirota, Marina
A1  - Kurths, Jürgen
A1  - Müller-Hansen, Finn
A1  - Oliveira, Rafael S.
A1  - Winkelmann, Ricarda
T1  - Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall
JF  - Nature geoscience
N2  - With ongoing global warming, the amount and frequency of precipitation in the tropics is projected to change substantially. While it has been shown that tropical forests and savannahs are sustained within the same intermediate mean annual precipitation range, the mechanisms that lead to the resilience of these ecosystems are still not fully understood. In particular, the long-term impact of rainfall variability on resilience is as yet unclear. Here we present observational evidence that both tropical forest and savannah exposed to a higher rainfall variability-in particular on interannual scales-during their long-term past are overall more resilient against climatic disturbances. Based on precipitation and tree cover data in the Brazilian Amazon basin, we constructed potential landscapes that enable us to systematically measure the resilience of the different ecosystems. Additionally, we infer that shifts from forest to savannah due to decreasing precipitation in the future are more likely to occur in regions with a precursory lower rainfall variability. Long-term rainfall variability thus needs to be taken into account in resilience analyses and projections of vegetation response to climate change.
Y1  - 2019
U6  - https://doi.org/10.1038/s41561-019-0312-z
SN  - 1752-0894
SN  - 1752-0908
VL  - 12
IS  - 3
SP  - 174
EP  - 179
PB  - Nature Publ. Group
CY  - New York
ER  - 
TY  - GEN
A1  - Ciemer, Catrin
A1  - Rehm, Lars
A1  - Kurths, Jürgen
A1  - Donner, Reik Volker
A1  - Winkelmann, Ricarda
A1  - Boers, Niklas
T1  - An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic sea surface temperatures
T2  - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1207 
KW  - complex networks
KW  - droughts
KW  - prediction
KW  - Amazon rainforest
Y1  - 2019
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-525863
SN  - 1866-8372
IS  - 9
ER  - 
TY  - JOUR
A1  - Ciemer, Catrin
A1  - Rehm, Lars
A1  - Kurths, Jürgen
A1  - Donner, Reik Volker
A1  - Winkelmann, Ricarda
A1  - Boers, Niklas
T1  - An early-warning indicator for Amazon droughts exclusively based on tropical Atlantic sea surface temperatures
JF  - Environmental Research Letters
N2  - Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.
KW  - complex networks
KW  - droughts
KW  - prediction
KW  - Amazon rainforest
Y1  - 2019
VL  - 15
IS  - 9
PB  - IOP - Institute of Physics Publishing
CY  - Bristol
ER  -