Tipping elements in the Earth System

Self Cite

163

141

The authors note that: "Due to a minor technical error in the calculation of the climatological average of the considered atmospheric temperatures for each calendar day, Figs. 2 and 3 appeared incorrectly. The amended figures and their legends are provided below. The main message and the interpretation of our paper remain unaffected by this correction." "The figures in the Supporting Information have been exchanged accordingly. We'd like to add that for the calculation of the climatological average, the leap days have been removed, and in the prediction phase, only the data from the past up to the prediction date have been considered. In addition, we note that for calculating the link strengths S ij (t), not the cross-covariance function C ij (t) (τ) has been considered but the absolute values of the corresponding cross-correlation functions c ij (t) (τ). When averaging over all link strengths, we obtain the time dependent average link strength S(t). In the learning phase, we compare S(t) with decision thresholds above its mean to obtain the optimal threshold used in the prediction phase." Fig. 2. The forecasting algorithm. We compare the average link strength S(t) in the climate network (red curve) with a decision threshold Θ (horizontal line, here Θ = 2.82) (left scale) with the standard NINO3.4 index (right scale), between January 1, 1950 and December 31, 2011. Only thresholds above the average of S(t) in the learning phase are considered. When the link strength crosses the threshold from below outside an El Niño episode, we give an alarm and predict that an El Niño episode will start in the following calendar year. The El Niño episodes (when the NINO3.4 index is above 0.5°C for at least 5 mo) are shown by the filled blue areas. The first half of the record (A) is the learning phase where we optimize the decision threshold. In the second half (B), we use the threshold obtained in (A) to predict El Niño episodes. Correct predictions are marked by green arrows and false alarms by dashed arrows. The index n marks a nonpredicted El Niño episode. To resolve by eye the accurate positions of the alarms, we show in SI Appendix, Fig. S5, magnifications of those parts of Fig. 2 where the crossings or non-crossings are difficult to see clearly without magnification. We also show the alarms for the slightly larger threshold Θ = 2.83 (SI Appendix, Fig. S6), which yields the same performance in the learning phase and one more false alarm in the prediction phase. The lead time between the prediction and the beginning of the El Nino episodes is 1.01 ± 0.28 y, while the lead time to the maximal NINO3.4 value is 1.35 ± 0.47 y.

mentioning

confidence: 99%

Improved El Niño forecasting by cooperativity detection

Ludescher

Gozolchiani

Bogachev

et al. 2013

Self Cite

163

141

“…Considerable effort is currently being put into investigating both the causes of tipping points in Earth systems and uncovering indicators of the proximity to critical thresholds (104). Schellnhuber (105) argues that the tipping elements issue "probably poses one of the toughest challenges for contemporary science" and highlights "social tipping elements" as an important research area. Similarly, Galaz (106) believes that "social connectors," which can lead to tipping points that would not otherwise occur, need to be researched.…”

Section: Discussionmentioning

confidence: 99%

Tracking sustainable development with a national barometer for South Africa using a downscaled “safe and just space” framework

Cole

Bailey

New

2014

120

Nations in the 21st century face a complex mix of environmental and social challenges, as highlighted by the on-going Sustainable Development Goals process. The “planetary boundaries” concept [Rockström J, et al. (2009) Nature 461(7263):472–475], and its extension through the addition of social well-being indicators to create a framework for “safe and just” inclusive sustainable development [Raworth K (2012) Nature Climate Change 2(4):225–226], have received considerable attention in science and policy circles. As the chief aim of this framework is to influence public policy, and this happens largely at the national level, we assess whether it can be used at the national scale, using South Africa as a test case. We developed a decision-based methodology for downscaling the framework and created a national “barometer” for South Africa, combining 20 indicators and boundaries for environmental stress and social deprivation. We find that it is possible to maintain the original design and concept of the framework while making it meaningful in the national context, raising new questions and identifying priority areas for action. Our results show that South Africa has exceeded its environmental boundaries for biodiversity loss, marine harvesting, freshwater use, and climate change, and social deprivation is most severe in the areas of safety, income, and employment. Trends since 1994 show improvement in nearly all social indicators, but progression toward or over boundaries for most environmental indicators. The barometer shows that achieving inclusive sustainable development in South Africa requires national and global action on multiple fronts, and careful consideration of the interplay between different environmental domains and development strategies.

“…Tipping points in the climate system-i.e., values of system parameters at which critical transitions in climate dynamics take place-have gained increasing attention in the course of the ongoing debate on potential impacts of anthropogenic climate change (36)(37)(38). Given the large risk (product of impact and likelihood) associated with the activation of certain climate tipping elements (e.g., the Greenland and West Antarctic ice sheets), understanding and anticipating future qualitative changes in climate tipping elements is of major importance and requires a good knowledge of past transitions recorded in paleoclimate data (39).…”

Section: Discussionmentioning

confidence: 99%

Nonlinear detection of paleoclimate-variability transitions possibly related to human evolution

Donges

Donner²,

Trauth³

et al. 2011

220

163

Potential paleoclimatic driving mechanisms acting on human evolution present an open problem of cross-disciplinary scientific interest. The analysis of paleoclimate archives encoding the environmental variability in East Africa during the past 5 Ma has triggered an ongoing debate about possible candidate processes and evolutionary mechanisms. In this work, we apply a nonlinear statistical technique, recurrence network analysis, to three distinct marine records of terrigenous dust flux. Our method enables us to identify three epochs with transitions between qualitatively different types of environmental variability in North and East Africa during the (i) Middle Pliocene (3.35-3.15 Ma B.P.), (ii) Early Pleistocene (2.25-1.6 Ma B.P.), and (iii) Middle Pleistocene (1.1-0.7 Ma B.P.). A deeper examination of these transition periods reveals potential climatic drivers, including (i) large-scale changes in ocean currents due to a spatial shift of the Indonesian throughflow in combination with an intensification of Northern Hemisphere glaciation, (ii) a global reorganization of the atmospheric Walker circulation induced in the tropical Pacific and Indian Ocean, and (iii) shifts in the dominating temporal variability pattern of glacial activity during the Middle Pleistocene, respectively. A reexamination of the available fossil record demonstrates statistically significant coincidences between the detected transition periods and major steps in hominin evolution. This result suggests that the observed shifts between more regular and more erratic environmental variability may have acted as a trigger for rapid change in the development of humankind in Africa.African climate | Plio-Pleistocene | climate-driven evolution | dynamical transitions | nonlinear time series analysis R ecent comparisons of terrestrial and marine paleoclimate archives have resulted in an intense debate concerning global vs. regional forcing of East Africa's climate and its relationship to human evolution during the past 5 Ma (1-4). The gradual longterm retreat of equatorial rain forests and the emergence of drier environments in East Africa (2, 5, 6) were interrupted by distinct epochs of increased humidity indicated by paleo-lake levels in different basins of the East African Rift System (EARS) displaying synchronous highs at about 2.7-2.5, 1.9-1.7, and 1.1-0.9 Ma B.P. (7). Notably, these epochs coincide well with certain global-scale climate transitions like the final closure of the Panama isthmus (8-10), intensification of the atmospheric Walker circulation (11), and the shift from a predominance of obliquity-driven glacial variability (41 ka period) to glacial-interglacial cycles with a 100 ka period (12, 13). Further reconstructions revealed additional relevant lake periods at 4.7-4.3, 4.0-3.9, 3.4-3.3, and 3.2-2.95 Ma B.P. (3). Previous findings suggest that the dominating summer aridity in the East African climate was controlled mainly by orbitally driven changes in the local irradiation driving regional monsoon activity via changes of sea-surf...