Fig. 3: Sensing the changing resilience of tipping systems directly from observations.
From: Remotely sensing potential climate change tipping points across scales
Examples over different time intervals using directly and remotely sensed data: a Trends in lag-1 temporal autocorrelation (AR(1)) of global vegetation30 from monthly MODIS NDVI for 2001-2020, and of global sea surface temperatures (following the approach of ref. 166) from monthly HadISST for 1982-2021 (which includes AVHRR data). AR(1) trends are measured with Kendall’s τ rank correlation coefficient, with darker green (vegetation) and darker purple (SST) indicative of greater loss of resilience. Light grey areas correspond to pixels with sea ice and dark grey areas to those with low NDVI ( < 0.18) values. b Trends in AR(1) in the Amazon rainforest (Kendall τ) from AVHRR NDVI for 2003-2016 (redrawn from ref. 29). c Changes in SST in the Sub-Polar Gyre region from HadISST for 1870-2019 (upper panel) and associated upward trend in AR(1) (lower panel) suggesting loss of resilience of the AMOC (redrawn from ref. 37). d Fluctuations in patterned vegetation connectedness and rainfall at a site in the Sahel68 (11°37’12”N, 27°51’36”E) from Sentinel-2 and ERA5 precipitation data for 2016–2019 (upper panel), between seasonal extremes of (i) maximum and (ii) minimum connectedness (lower panels). Connectedness is quantified from feature vectors with an ‘Offset50’ metric defined in ref. 68. By measuring the decay rate of connectedness between maxima and minima, averaged over years, and compared across sites, the resilience of these dryland systems is found to decline with rainfall68.
