Hermann Held

Early-warning signals for critical transitions

Complex dynamical systems, ranging from ecosystems to financial markets and the climate, can have tipping points at which a sudden shift to a contrasting dynamical regime may occur. Although predicting such critical points before they are reached is extremely difficult, work in different scientific fields is now suggesting the existence of generic early-warning signals that may indicate for a wide class of systems if a critical threshold is approaching.

Tipping elements in the Earth's climate system

The term “tipping point” commonly refers to a critical threshold at which a tiny perturbation can qualitatively alter the state or development of a system. Here we introduce the term “tipping element” to describe large-scale components of the Earth system that may pass a tipping point. We critically evaluate potential policy-relevant tipping elements in the climate system under anthropogenic forcing, drawing on the pertinent literature and a recent international workshop to compile a short list, and we assess where their tipping points lie. An expert elicitation is used to help rank their sensitivity to global warming and the uncertainty about the underlying physical mechanisms. Then we explain how, in principle, early warning systems could be established to detect the proximity of some tipping points.

Slowing down as an early warning signal for abrupt climate change

In the Earths history, periods of relatively stable climate have often been interrupted by sharp transitions to a contrasting state. One explanation for such events of abrupt change is that they happened when the earth system reached a critical tipping point. However, this remains hard to prove for events in the remote past, and it is even more difficult to predict if and when we might reach a tipping point for abrupt climate change in the future. Here, we analyze eight ancient abrupt climate shifts and show that they were all preceded by a characteristic slowing down of the fluctuations starting well before the actual shift. Such slowing down, measured as increased autocorrelation, can be mathematically shown to be a hallmark of tipping points. Therefore, our results imply independent empirical evidence for the idea that past abrupt shifts were associated with the passing of critical thresholds. Because the mechanism causing slowing down is fundamentally inherent to tipping points, it follows that our way to detect slowing down might be used as a universal early warning signal for upcoming catastrophic change. Because tipping points in ecosystems and other complex systems are notoriously hard to predict in other ways, this is a promising perspective.

Imprecise probability assessment of tipping points in the climate system

Major restructuring of the Atlantic meridional overturning circulation, the Greenland and West Antarctic ice sheets, the Amazon rainforest and ENSO, are a source of concern for climate policy. We have elicited subjective probability intervals for the occurrence of such major changes under global warming from 43 scientists. Although the expert estimates highlight large uncertainty, they allocate significant probability to some of the events listed above. We deduce conservative lower bounds for the probability of triggering at least 1 of those events of 0.16 for medium (2–4 °C), and 0.56 for high global mean temperature change (above 4 °C) relative to year 2000 levels.

Utilizing belief functions for the estimation of future climate change

We apply belief functions to an analysis of future climate change. It is shown that the lower envelope of a set of probabilities bounded by cumulative probability distributions is a belief function. The large uncertainty about natural and socio-economic factors influencing estimates of future climate change is quantified in terms of bounds on cumulative probability. This information is used to construct a belief function for a simple climate change model, which then is projected onto an estimate of global mean warming in the 21st century. Results show that warming estimates on this basis can generate very imprecise uncertainty models.

Basic mechanism for abrupt monsoon transitions.

Monsoon systems influence the livelihood of hundreds of millions of people. During the Holocene and last glacial period, rainfall in India and China has undergone strong and abrupt changes. Though details of monsoon circulations are complicated, observations reveal a defining moisture-advection feedback that dominates the seasonal heat balance and might act as an internal amplifier, leading to abrupt changes in response to relatively weak external perturbations. Here we present a minimal conceptual model capturing this positive feedback. The basic equations, motivated by observed relations, yield a threshold behavior, robust with respect to addition of other physical processes. Below this threshold in net radiative influx, R c, no conventional monsoon can develop; above R c, two stable regimes exist. We identify a nondimensional parameter l that defines the threshold and makes monsoon systems comparable with respect to the character of their abrupt transition. This dynamic similitude may be helpful in understanding past and future variations in monsoon circulation. Within the restrictions of the model, we compute R c for current monsoon systems in India, China, the Bay of Bengal, West Africa, North America, and Australia, where moisture advection is the main driver of the circulation.

Abrupt Changes: The Achilles' Heels of the Earth System

Abstract Records from the past show that abrupt global change is the norm, not the exception, in the Earth System. Many recent changes-such as the globalization of the worlds economy and the formation of the ozone hole over Antarctica-appear rapid even from the perspective of a single lifetime. What do we now know about the nature of abrupt changes in the Earth System and the probability that human actions could trigger them?

Operationalizing climate targets under learning: An application of cost-risk analysis

Cost-Effectiveness Analysis (CEA) determines climate policies that reach a given climate target at minimum welfare losses. However, when applied to temperature targets under climate sensitivity uncertainty, decision-makers might be confronted with normatively unappealing negative expected values of future climate information or even infeasible solutions. To tackle these issues, Cost-Risk Analysis (CRA), that trades-off the costs for mitigating climate change against the risk of exceeding climate targets, has been proposed as an extension of CEA under uncertainty. Here we build on this proposition and develop an axiomatically sound CRA for the context of uncertainty and future learning. The main contributions of this paper are: (i) we show, that a risk-penalty function has to be non-concave to avoid counter-intuitive preferences, (ii) we introduce a universally applicable calibration of the cost-risk trade-off, and (iii) we implement the first application of CRA to a numerical integrated assessment model. We find that for a 2°-target in combination with a 66 % compliance level, the expected value of information in 2015 vs. 2075 is between 0.15 % and 0.66 % of consumption every year, and can reduce expected mitigation costs by about one third. (iv) Finally, we find that the relative importance of the economic over the risk-related contribution increases with the target probability of compliance.

Anticipating Climate Threshold Damages

Several integrated assessment studies have concluded that future learning about the uncertainties involved in climate change has a considerable effect on welfare but only a small effect on optimal short-term emissions. In other words, learning is important but anticipation of learning is not. We confirm this result in the integrated assessment model “model of investment and technological development” for learning about climate sensitivity and climate damages. If learning about an irreversible threshold is included, though, we show that anticipation can become crucial both in terms of necessary adjustments of pre-learning emissions and resulting welfare gains. We specify conditions on the time of learning and the threshold characteristic, for which this is the case. They can be summarized as a narrow “anticipation window.”

A regulatory framework for carbon capturing and sequestration within the post-kyoto process

Publisher Summary This chapter illustrates that environmental authorities have to deal with situations of “hard uncertainty” where probability density functions about leakage rates and other crucial parameters like the learning rate or the investment costs are not known. It has been argued that this type of uncertainty demands the start of a learning process about new emerging ecological and economic risks. Therefore, the chapter proposes two different permit schemes which are designed in such a way that safe deposits are preferred and uncertainty about leakage is actively reduced by CCS-companies. In proposal #1 CCS is considered as a joker if other mitigation options fail from an economic point of view. Proposal #2 is designed in order to avoid the ecological worst-case scenario that the leakage rate of geological formations is much higher than expected. However, these two proposals should not be seen as contradictory because both have the potential to merge to a hybrid version. The risk of violating the climate window and therefore increasing the probability of dangerous climate change must only be considered seriously, if CCS is used at large-scale.