Author Archives: vdakos

Increasing climate variability will hit world’s poorest countries

It has been difficult to predict how weather extremes such as heat waves and cold snaps might change in a future climate. In this paper, we continue our long-term studies on climate variability, by looking at climate model predictions on future temperatures. We find that rich countries that contribute most to climate change will see less temperature fluctuation, whereas in poor countries the fluctuations will become stronger.

Trends in variability and autocorrelation in observed global temperature records

Figure 1Back in 2012, we started exploring how patterns in the magnitude and persistence of fluctuations in instrumental records of temperature and their major global sea and land indices might have changed during the last century. Our objective was to identify regions where climate variability and autocorrelation might have markedly increased potentially due to anthropogenic forcing. We just published the results of this work in a paper titled Observed trends in the magnitude and persistence of monthly temperature variabilityThe main findings are summarised here.

 

Evaluating early-warning indicators of critical transitions in natural aquatic ecosystems

pnas-2016-gsell-1608242113We just published a study on signatures of instability in empirical time series from five freshwater ecosystems with documented sudden, persistent transitions hypothesized to represent critical transitions. We detected strong variation in early warning indicators, and a low agreement between the four indicators we tested. We conclude that the applicability of these tools was strongly limited by the requirement for ecosystem-specific knowledge of transition-generating mechanisms and their drivers to choose relevant state variables for analysis, especially in monitored systems that are not explicitly designed for estimating this type of indicators.

PAGES Issue on Climate Tipping Points

PAGESIn the just published PAGES issue of Past Global Changes there is a science highlight on Tipping points or “Lessons from the Past for the Future” as the editorial suggests. In ten short 2-page contributions the most up-to-date ideas about past climatic transitions are highlighted together with examples from ecological and socio-ecological abrupt shifts. We have contributed in this issue a short piece on how the shift of the Sahara has been shaping our thinking about abrupt change.

Monitoring economic variables to infer ecosystem resilience

profit fluctuationsStrongly coupled socio-ecological systems propagate their effects and disturbances one to the other. This has been demonstrated in most studies of human management of common resources. In recent work, we show that indicators of decreasing resilience can also propagate from one system to the other. For example, an increasingly harvested fish stock might reflect its eroding resilience in the profits of its harvesters. This generally implies that monitoring socio-economic variables that are  linked to natural resources can indicate the resilience of the socio-ecological coupled system as a whole.

Hysteresis and Heteroskedasticity (early view papers)

Only a handful of studies demonstrate the existence of hysteresis in bistable systems. In a follow-up from our earlier work, we study the recovery trajectory of a light-stressed plankton population in a chemostat experiment (early view in Oikos). We find that reverse trajectories can be explained by hysteresis, time-delays and adaptive process, all of which pose interesting questions for the behavior of bistable systems under changing conditions.

Recently, we summarized a set of measures that can be used as spatial indicators for detecting loss of resilience.  We  now add another measure that can be used as early warning of critical transitions in spatially explicit systems: spatial heteroskedasticity. In short, this indicator is the analog of conditional heteroskedasticity in timeseries (the non constant variance along a timeseries). We now expand its use from indicator of critical transitions in timeseries to spatial data (early view in Ecology & Evolution).