Nonlinear Identification of Critical Transitions in Paleoclimate.

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 (36–38). 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). Previous research revealed precursory signatures associated with climate tipping points (39–41). Predominantly, changes in the variance structure of characteristic observables represent a critical slowing down of intrinsic transient responses within the data prior to the instabilities (42–46).

RN analysis is a promising exploratory tool for detecting structural changes in paleoclimate dynamics potentially associated with certain tipping elements in Earth’s climate system. It has several advantages above other frequently used techniques for detecting (incipient) bifurcations in time series: (i) RN analysis is not explicitly based on temporal correlations but on geometric considerations (47). Hence it is more robust with respect to noisy and nonuniformly sampled paleoclimate time series with uncertain timing than methods relying on temporal correlations (42–44, 48) like degenerate fingerprinting (42) and detrended fluctuation analysis (43). (ii) As a nonlinear technique, RN analysis is not restricted to detecting only changes in linear statistical properties (i.e., the autocovariance structure) of a time series (42, 44–46). In contrast, it characterizes more general variations in the recorded dynamics, which may become particularly relevant close to bifurcation points. (iii) RN analysis has been shown to reliably perform with a comparably small number of data points (29, 32), particularly when contrasting it with linear spectral methods (1, 2) or certain nonlinear stochastic models of climate fluctuations for detecting noisy bifurcations (49, 50).

We emphasize that all existing methods for detecting tipping points from paleoclimate time series (including RN analysis) make use of sliding window techniques. By construction, such methods have a limited temporal resolution of the variations of the statistics of interest. Hence, identified dynamical transitions can be attributed only to epochs rather than distinct points in time. This strategy has the advantage of a higher robustness with respect to uncertainties in the data themselves as well as the associated age models, rendering the results of RN analysis particularly robust and statistically reliable (29).

Climatological Interpretation.

The three identified transition periods (Fig. 2), which are only partially known from previous studies, can be clearly related to distinct and known climatic mechanisms. Specifically, the interval 1.1–0.7 Ma B.P. corresponds to the Middle Pleistocene transition (MPT) characterized by a change from glacial cycles predominantly related to obliquity variations of Earth’s orbit (approximately 41 ka period) to such with an approximately 100 ka periodicity. The timing of this transition and its underlying mechanisms have been extensively studied elsewhere (13, 51). That the MPT is not detected in the record from ODP site 659 by RN analysis does not imply that it did not have any climatic impact in the corresponding dust-source areas in Northwest Africa. It just shows that our technique is not sensitive to the local signature of the transition, if present, e.g., if it manifests itself in some change of trend (1, 2, 17) (see the discussion in Results). Alternatively, the locally available data may be insufficient in quality and/or resolution to reveal the subtle type of events RN analysis is focusing on. This reasoning holds for any other known transition that is not detected by our method in any of the considered records. Regarding the transition period in the Early Pleistocene (2.25–1.6 Ma B.P.), the timing well coincides with known large-scale changes in atmospheric circulation associated with an intensification and spatial shift of the Walker circulation (11).

The third identified transition period includes the Middle Pliocene (3.3–3.0 Ma B.P.), the last long period during which global mean temperatures have been consistently higher than present day (52). This epoch is thus considered as an analog for the future climate of the late 21st century if anthropogenic emissions of greenhouse gases continue to rise (53). It was characterized by a strongly reduced meridional temperature gradient due to an intermittent poleward shift of the North Atlantic overturning resulting in a strong warming of the Arctic by oceanic surface currents (54, 55). At the same time, RN analysis reveals an enhanced regularity of African dust flux variations in both the Arabian Sea and Atlantic Ocean. At ODP site 721/722, this observation is predominantly caused by a well-pronounced epoch of relatively weak and approximately constant dust flux between about 3.36 and 3.17 Ma B.P. (Fig. 3E). A similar—but shorter—feature is found at ODP site 659 between about 3.25 and 3.19 Ma B.P. (Fig. 3D) as well as at ODP sites 661 and 662 in the Eastern Equatorial Atlantic (1, 2). The presence of such very similar features with a clearly different timing suggests the presence of either one common climatological mechanism influencing the Arabian Peninsula much earlier than Northwest Africa or two distinct (but eventually interrelated) factors, where the first affected only the Northeast African and/or Arabian dust flux dynamics.

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Fig. 3.

Comparison of Middle Pliocene (3.5–3.0 Ma B.P.) global ice volume, sea-surface temperature, and dust flux reconstructions: (A) δ¹⁸O benthic stack record LR04 (57); (B) benthic δ¹⁸O record from ODP site 659; (C) alkenone-based SST reconstruction from ODP site 662 (61); (D) terrigenous dust flux at ODP site 659 (34); (E) terrigenous dust flux at ODP site 721/722 (1, 2); (F) alkenone-based SST reconstruction from ODP site 722 (61); (G,H) planktonic δ¹⁸O and SST reconstruction from ensembles of planktonic foraminifera for a composite sequence from Southern Italy (69). The dark gray vertical bar indicates the MIS M2 (preceding an epoch with cold SST in the Mediterranean Sea and reduced dust flux into the Atlantic Ocean); the light gray bar indicates the interval of reduced dust flux into the Arabian Sea.

A first important candidate factor is the marine isotope stage (MIS) M2 (56), an unusually cold period (3.32–3.28 Ma B.P.) prior to the Middle Pliocene warm period (57, 58); cf. Fig. 3A. This cooling was most likely triggered by a significant reduction of northward heat transport via the North Atlantic Current between 3.33 and 3.283 Ma B.P. (59)—possibly due to intermittent closures and reopenings of the Panamanian Seaway (10)—and led to a strong increase in global ice volume and a global sea-level fall to up to 60 m below present-day values (60). As a consequence, global SST decreased by 2–3 K (59), with a 1–2 K decrease in the tropics (61); see Fig. 3 C and F. This low-latitude cooling could be related to the reduced dust flux offshore of Northwest Africa but can hardly explain the much earlier signature in the Arabian Sea.

A second possible triggering mechanism is the successive northward displacement of New Guinea, because of which instead of warmer Equatorial Pacific water presently only less saline and colder North Pacific water can pass the Indonesian throughflow (62). Recent paleoceanographic reconstructions revealed that, during the Pliocene, subsurface waters in the eastern tropical Indian Ocean freshened and cooled by about 4 K, including a major cooling step between 3.5 and 2.95 Ma B.P. (63). Although SST was much less affected in most parts of the Indian Ocean, the southern coast of the Arabian Peninsula as well as the region around the horn of Africa are known as (wind-driven seasonal) upwelling regions (61, 64), which suggests that cold subsurface waters could have (at least intermittently) reached the ocean surface in this area and, thus, have led to a considerable decrease in the average regional SST. Recent alkenone-based SST reconstructions for ODP site 721/722 (61) actually confirm strong SST variations with partially extremely low values between at least 3.33 (the beginning of the respective record) and 3.28 Ma B.P. (see Fig. 3F), which cannot be explained by the known pattern of early glacial activity (57).

In summary, both aforementioned factors would have led to decreasing SSTs and, hence, to lower surface air temperatures and potential changes in the evaporation-precipitation balance. As a consequence, less convective rainfall, but also a shift (and possible weakening) of dominating atmospheric circulation patterns, was likely. Because in the potential source areas of terrigenous dust lower temperatures foster the formation of a closed vegetation cover, erosion and, hence, dust mobilization would have decreased. This interpretation is supported by the existence of large lakes in East Africa between 3.4 and 2.95 Ma B.P. [including the most pronounced lake episode in the Afar region (65) at about 3.33–3.23 Ma B.P.] pointing towards a very humid climate (Fig. 2D). Our considerations above are speculative so far and need confirmation from modeling studies and complementary paleoclimate archives.

Climate Variability and Human Evolution.

To study the influence of African climate variability on human evolution, we compare the major large-scale transitions in African climate identified above with the currently available fossil record (Fig. 2E). Particularly, we observe that transitions in hominin evolution indicated by the appearance and disappearance of hominin species tend to cluster close to the identified climate shifts. These observed coincidences are unlikely to arise by chance even when taking into account dating uncertainties and the inherent incompleteness of the fossil record, as can be shown by using a suitable statistical significance test (SI Text).

The presented results therefore shed some light on the possible interrelationships between long-term climate change and hominin evolution. Specifically, we suggest that shifts between periods of more regular and more erratic environmental variability have been particularly important triggers for the development of humankind in East Africa. On the one hand, epochs of higher regularity could have led to stabilization and gradual increase of a population and, thus, a spread of a species over a larger area. On the other hand, subsequent periods of more irregular climate variability, possibly associated with changes in long-term trends, would have created an additional evolutionary pressure via a fragmentation of habitats and, thus, adaptation and diversification leading to speciation (18).

Supporting and extending Potts’ variability selection hypothesis on environmental control of human evolution (23, 24), our point of view strongly suggests that, in addition to the mean climate conditions and the amplitude of variations of environmental parameters, (intermittent) changes in the regularity of climate fluctuations should be considered in future, more refined theories. In line with Potts’ hypothesis, this mechanism on orbital time scales would benefit generalists like Homo, which are able to cope with strongly varying environmental conditions, but penalize specialists like Paranthropus (23). We must, however, keep in mind that the time scales of variations that are possibly relevant for migration of populations are not yet resolved in the available paleoclimate data. The necessity for further testing the extended variability selection hypothesis put forward here against other hypotheses (see the Introduction) calls for future efforts to obtain high-resolution (decadal to centennial scale) data on Plio-Pleistocene African climate history.