I have yet to touch upon how scientists identify or detect tipping points and how they determine where a critical threshold is located in time. In this post, I will outline the major characteristics of a system approaching critical thresholds and how scientists determine them.
General properties
Three general properties characterizes the point at which a critical threshold is surpassed:
- Rate of change increase sharply then what prevailed over previous stable periods
- System state exceeds range of historical variations
- Rate of change increased at a pace which exceeds the abilities of nations to respond
The timing, type of transition and magnitude of change depends on the nature of interactions and heterogeneity within the system. Recent increases in attempts to predict the type and temporal onset of tipping points can mainly be characterized in two ways (Thompson and Seiber 2010):
1. Models
Climate models have been widely used to predict the presence, timing and magnitude/extent of changes in identified tipping elements. However, the nature of climate models means that the magnitude of threshold effects and when feedback induced thresholds are reached are highly uncertain and varies among model specifications and parameterization (Maslin and Austin 2012). Models also have varying interpretations of mechanisms behind the earth system and varying sophistication in processes represented. Climate models are often used to predict future changes (eg. Arctic summer ice loss seen in Holland et.al. 2006) and validated by its ability to adequately simulate past abrupt changes (eg. 'dangerous climate change' prediction model in Hansen et.al. 2006).
2. Time Series - statistical analysis from observational climatological or ecological time series data to identify statistical characteristics that precedes tipping points/bifurcation
A prime example of this is early warning systems for natural hazards risk management. As Earth have underwent a long history of abrupt climatic changes, scientists often make use of natural archives (eg. ice cores; diatoms; pollen etc.) to infer past climatic change and fluctuations (Thomas 2016). Paleo records of Earth surpassing ancient critical thresholds provides scientists with an observational records from which early warning signals can be identified. Scientists using time series statistical analysis to identify early warning signals showed universal signals across multiple ancient abrupt climate shifts (icehouse -> greenhouse; Younger Dryas; N.Africa climatic shift) (Davos et.al. 2008).
Early warning signals
Tipping points could affect decision making if adequate knowledge on timing, occurrence, and impacts were available. There is therefore a whole field of research aimed at finding preceding signals and characteristics of tipping points from historical abrupt transitions. Early warning identification can take the path of qualitative assessment or quantitative prediction of timing of impending thresholds (Lenton 2011). Listed below are early warning signals identified in past abrupt tipping points (Scheffer et.al. (2012). These are true for a range of complex systems, ranging from climatic systems to financial and social systems.
1. Models
Climate models have been widely used to predict the presence, timing and magnitude/extent of changes in identified tipping elements. However, the nature of climate models means that the magnitude of threshold effects and when feedback induced thresholds are reached are highly uncertain and varies among model specifications and parameterization (Maslin and Austin 2012). Models also have varying interpretations of mechanisms behind the earth system and varying sophistication in processes represented. Climate models are often used to predict future changes (eg. Arctic summer ice loss seen in Holland et.al. 2006) and validated by its ability to adequately simulate past abrupt changes (eg. 'dangerous climate change' prediction model in Hansen et.al. 2006).
2. Time Series - statistical analysis from observational climatological or ecological time series data to identify statistical characteristics that precedes tipping points/bifurcation
A prime example of this is early warning systems for natural hazards risk management. As Earth have underwent a long history of abrupt climatic changes, scientists often make use of natural archives (eg. ice cores; diatoms; pollen etc.) to infer past climatic change and fluctuations (Thomas 2016). Paleo records of Earth surpassing ancient critical thresholds provides scientists with an observational records from which early warning signals can be identified. Scientists using time series statistical analysis to identify early warning signals showed universal signals across multiple ancient abrupt climate shifts (icehouse -> greenhouse; Younger Dryas; N.Africa climatic shift) (Davos et.al. 2008).
Early warning signals
Tipping points could affect decision making if adequate knowledge on timing, occurrence, and impacts were available. There is therefore a whole field of research aimed at finding preceding signals and characteristics of tipping points from historical abrupt transitions. Early warning identification can take the path of qualitative assessment or quantitative prediction of timing of impending thresholds (Lenton 2011). Listed below are early warning signals identified in past abrupt tipping points (Scheffer et.al. (2012). These are true for a range of complex systems, ranging from climatic systems to financial and social systems.
1. Critical slowing down - Decreased rates of change as system approaches critical thresholds; increasingly sluggish system response; increase in amplitude and reduction in fluctuations2. Skewness and kurtosis - Asymmetric fluctuations; presence of extreme values measured through high skewness and kurtosis as system approaches bifurcation3. Increased autocorrelation - Decreased rates of change lead to state of system being more and more like its past state prior to critical threshold, thus increase in correlation4. Spatial patterns - often ecological; signals derived from spatial/temporal persistence and presence of species
Thank you for reading! In the next post, I will continue this discussion by looking at identified tipping points in Earth's history.
I like how you have mentioned that the concept of tipping point may also be applied to financial and social systems, which when I come to think of it, that makes perfect sense.
ReplyDeleteAlso, I like how you have clearly listed out the two main methods of evaluating tipping points: climate models and time series analysis.The use of diagram again helps me understand the concept and mechanism of tipping points so much more clearly!