Robustness of critical slowing down indicators to power-law extremes in an Amazon rainforest model

Critical slowing down has recently been detected as an indicator of reduced resilience in remotely sensed data of the Amazon rainforest [1]. Tropical rainforests are frequently hit by disturbances such as fire, windthrow, deforestation or drought, which are known to follow a heavy-tailed amplitude distribution. Early warning signals based on critical slowing down are theoretically grounded for systems under the influence of weak, Gaussian noise. Hence, it is not imminent that they are applicable also for systems like the Amazon rainforest, which are influenced by heavy-tailed noise. Here, we extended a conceptual model of the Amazon rainforest [2] to study the robustness of critical slowing down indicators to power-law extremes. These indicators are expected to increase before a critical transition.