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The Amazon rainforest is threatened by both anthropogenic climate and land use change. Observations and some modelling results suggest that the Amazon rainforest might abruptly transition to a savanna-like state and that this transition would be practically irreversible. Here, we analyze observations of precipitation, temperature and tree cover together with high-resolution simulations of several comprehensive climate models to investigate the combined effect global warming and a potential collapse of the Atlantic Meridional Overturning Circulation (AMOC) will have on the Amazon. Our results show that, while high levels of warming lead to forest dieback, an AMOC collapse would tend to stabilize the forest by increasing rainfall and decreasing temperature in most of the Amazon basin. Although an AMOC collapse would have devastating impacts globally, our results hence suggest that it may delay or even prevent parts of the Amazon rainforest from transitioning to a savanna-like state.

The resilience of tropical forests against climate change and deforestation is vital for biodiversity and carbon drawdown. This resilience is hard to measure directly, but is suspected to be decreasing. There is particular concern that the Amazon rainforest may be approaching a “tipping point” where the large-scale loss of species and carbon pools amplifies substantially. Candidate mechanisms for such threshold effects often involve positive feedbacks and span a large range of scales. For example, individual trees can die from hydraulic failure when soil moisture decreaseses, forest fires can mediate a regional transition to a savanna state, and by synchronising remote regions, the moisture recycling feedback could cause a continental-scale forest dieback. Conceptual dynamical systems suggest that the loss of resilience that accompanies such transitions can be measured by statistical indicators like increasing autocorrelation. Satellite observations of vegetation indices related to greenness and biomass seem to support these theoretical expectations.Here we analyse dynamic global vegetation models (DGVMs) from CMIP6, as well as idealised simulations with LPJ, in order to bridge the complexity gap between conceptual models and the real world. First, we assess how resilience of terrestrial carbon pools in the tropics depends on mean annual rainfall (MAP). We find that this relationship differs between models, and can also differ substantially from the observed positive relationship, depending on how the models capture carbon pool dynamics on the grid-cell level. Second, we show that changes in resilience do not necessarily require any atmosphere-vegetation feedbacks, fire feedback or ecological interactions, suggesting that observed relationships may capture physiological effects in individual trees rather than the stability of the entire forest. Third, we also find that the coexistence of vegetation types affects vegetation resilience in DGVMs. In particular, plant types with faster dynamics can replace slower ones (e.g., grass replacing trees), leading to decreased autocorrelation but not necessarily larger sensitivity to MAP. We conclude that suitable indicators of tropical vegetation resilience should be determined by (i) using DGVMs to understand better what mechanisms are at play, and (ii) using observations to rule out certain model approaches (e.g. area-averaged versus individual-based models).

The Amazon rainforest acts as a carbon sink and is one of the most bio-diverse ecosystems of our planet. As such, it is an important but vulnerable subsystem of the Earth System. Studies suggest that the region is bi-stable with respect to mean annual precipitation. Thus, it is considered a Tipping Element of the Earth System.In this work, we investigate several statistics which, according to dynamical system theory, change in the advent of a Tipping Point. To assess the state of the Amazon rainforest, various remotely sensed vegetation indices (VIs) exist. Multiple single-sensor VIs are considered and analyzed if they show reasonable behavior. The results reveal an ongoing loss of resilience in several parts of the Amazon rainforest.

The Amazon forest is at risk of dieback due to climate change, in particular decreasing mean annual precipitation (MAP) and increasing mean annual temperature (MAT). This study assesses the influence on South American vegetation under two possible future climate change scenarios: global warming, and global warming combined with an AMOC collapse. We consider MAT and MAP as control parameters and use their projected changes from climate model simulations with the Earth System Model HadGEM3. We then estimate the most probable states of vegetation based on empirical relationships between these parameters and tree cover. Our results suggest that an AMOC collapse would not contribute to further rainforest dieback over most of the Amazon basin. Instead, in parts of tropical South America, MAP increases and MAT decreases after AMOC collapse, which tends to stabilize the Amazon forest and hence delay the Amazon dieback compared to the default global warming scenario.

In recent years, sea-surface temperature (SST) and salinity-based indices have been used to detect critical slowing down (CSD) indicators for a possible collapse of the Atlantic Meridional Overturning Circulation (AMOC). However, these observational SST and salinity datasets have inherent uncertainties and biases which could influence the CSD analysis. Here we present an in-depth uncertainty analysis of AMOC CSD indicators. We first use uncertainties provided with the HadSST4 and HadCRUT5 datasets to generate uncertainty ensembles and estimate the uncertainty of SST-based AMOC fingerprints, and we then calculate stringent significance measures on the CSD indicators in the EN4.2.2, HadISST1 and HadCRUT5 datasets.

National and international climate services support adaptation to climate change. Climate services should aim to deliver climate service products, which are useable, useful, accurate and suitable for users. Good practices for the design of climate services include transdisciplinary approaches and co-creation of climate service products.The advancement of effective adaptation measures to climate change requires continuous interactions between climate service providers and users of the products. To assess the effectiveness of this co-creation endeavours evaluation is crucial. At present, output and outcome assessments are conducted occasionally in the climate services research field but they are performed ex-post and do not help to adjust the ongoing process of co-creation.Thus, the focus of the presented work is on the formative evaluation of the co-creative development of science-based climate service products. It is done during the run-time of a project with the intention to reflect and readjust the processes. To find the related evaluation criteria, we analysed in detail the process of co-creation of climate service products in the knowledge transfer project ADAPTER (ADAPT tERrestrial systems, https://adapter-projekt.org/). In ADAPTER, simulation-based climate service products have been developed together with key partners and practitioners from the agricultural sector, to support decision-making in the context of climate change adaptation.Starting with the empirical identification of the main characteristics of the product development process, six sub-processes were determined. Then, questions for a formative evaluation were assigned to the different steps and sub-processes. Finally, a literature review on quality criteria in transdisciplinary research delivered additional qualitative aspects and served as theoretical basis. As a result, a scheme of quality criteria and related assessment questions for the different sub-processes in climate service development was created, based on both empirical and theoretical work. Of course, this formative evaluation scheme needs validation and testing but, at least, will be particularly helpful to reflect on and to improve the co-creation processes in climate services and enhance their quality.

Climate change increasingly affects all parts of society. Different economic sectors such as the agricultural sector have to adapt to climate change. More and more climate services are being developed in order to support this adaptation to climate change with accurate and suitable products. Good practises for the design of climate services include transdisciplinary approaches and co-creation of climate service products. The development of usable and useful climate service products and effective adaptation measures requires constant interactions between climate service providers and users of the products. To assess the effectiveness of these co-creation endeavours, continuous evaluation is crucial. At present, output and outcome assessments are conducted occasionally in this research field. However, these summative evaluations that are preformed ex-post do not help to adjust the ongoing process of co-creation. Therefore, the focus of the presented work is on formative evaluation of the co-creative development of science-based climate service products. A formative evaluation is done during the run-time of a project with the intention to reflect and readjust it. For this purpose, we analysed in detail the process of co-creation of climate service products in the knowledge transfer project ADAPTER (ADAPT tERrestrial systems, https://adapter-projekt.org/) and combine this analysis with a systematic literature review. In ADAPTER, simulation-based climate service products are developed together with key partners and practitioners from the agricultural sector, with the aim of supporting decision making in the context of climate change adaptation.As a first step, main characteristics of the product development process were identified empirically and six sub-processes of product development were determined. Secondly , questions for a formative evaluation were assigned to the different steps and sub-processes. Thirdly, a literature review including fields other than climate services delivered additional qualitative aspects. As a result, a scheme of quality criteria and related assessment questions for the different sub-processes in climate service development was created, based on both empirical and theoretical work. Subsequently, this scheme needs validation and testing. The resulting formative evaluation scheme will be particularly helpful to reflect on and to improve the co-creation processes in climate services and beyond.

Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned to adapt to interannual climate variability and extremes. In the ADAPTER project, we collaborate with stakeholders from these groups and generate practically relevant information, tailored climate change indices and usable information products. The challenges of climate change for agriculture are manifold. The genetic traits of crops need to be adapted to a new climatic average, for instance by breeding new sorts of crops that are specialised for warmer and dryer conditions (i.e. maximising average yields). Agricultural practises need to be adapted to changing seasonal weather patterns under changing climate conditions. It is also vital to ensure the resilience to climate extremes by aiming for a low inter-annual yield variability, in order to prevent price shocks or food shortages. In order to adequately determine the optimal balance between specialisation and risk diversification, the agricultural sector hence requires knowledge not only about changes in the mean climate, but also on the variance around the changing mean. In this contribution, we focus on this second aspect by analysing the potential impact of forced changes in climate variability on the stability of crop yields in central Europe. We analyse the changing climate variability in 85 regional climate model projections from Coordinated Downscaling Experiments over Europe (EURO-CORDEX). We first show how the projections indicate a general increase in climate variability during critical development stages of wheat, rapeseed and maize in Europe. Second, we determine several more specific agronomic climate indices that capture events that have previously been shown to be critical for yields, for instance the occurrence of high daily maximum temperature, the seasonal sum of rainfall, the number of dry days, or the occurrence of compound events with simultaneous drought and increased temperatures. Finally, we illustrate how the results can be made accessible to practitioners in the agriculture sector by co-designing interactive browser applications, thus directly supporting the adaptation of the agricultural system to climate change.

Agriculture is among the sectors that are most vulnerable to extreme weather conditions and climate change. In Germany, the subsequent dry and hot summers 2018, 2019, and 2020 have brought this into the focus of public attention. Agricultural actors like farmers, advisors or companies are concerned with such interannual variability and extremes. Yet, it often remains unclear what long-term adaptation options are most suitable in the context of climate change, mainly because climate projections have uncertainties and are usually not tailored to meet requirements, measures and scales of the individual practicioners. In the ADAPTER project, we explore regional and local change on the weather- and climate-related time scales and together with stakeholders (administration, plant breeders, educators, agricultural advisors), we co-design tailored climate change indices and usable products.In this contribution, we provide a snapshot view of our stakeholders' requirements regarding information about climate change over the next decades. We then focus on the analysis of three groups of indices based on 85 regional climate model simulations from Coordinated Downscaling Experiments over Europe - EURO-CORDEX: (i) changes in daily temperature variability, (ii) occurrence of agricultural droughts in summer, (iii) compound events of combined dryness and elevated temperatures during the same events. We show that these user-oriented, newly constructed indices can capture relevant changes during important phenological development states of typical crops. Finally, we discuss first implications of our findings for different adaptation strategies in Mid-Europe, such as alternating crop rotations, irrigation strategies or plant breeding. The analysis products presented are interactively and publicly available through a product platform (www.adapter-projekt.de) for agricultural stakeholders.

Climate change and its impacts at local scales, such as the more frequent occurrence of extreme weather events like droughts or floods, pose an increasing problem for agriculture. Our aim is to support farmers with soil condition and weather forecasting products that provide the basis for optimal adaptation to short-term weather variability and extremes as well as to long-term, regional climate change.For this purpose, a prototypical monitoring and real-time forecasting system was established. The monitoring networks consist of a novel cosmic ray neutron sensor (Styx Neutronica), soil moisture and temperature sensors in four depths between 5 and 60 cm (SoilNet) and an all-in-one weather station (ATMOS-41, METER Environment) to measure the atmospheric conditions including air temperature, humidity, pressure, solar irradiance, wind speed and precipitation at 2 meter height above ground. The observation data are transmitted in real time to a cloud server via the cellular solution NBIoT (Narrow Band Internet of Things). After data post-processing the meteorological and hydrological parameters measured on site are directly assimilated into the fully coupled multi-physical numerical model system TSMP (Terrestrial Systems Modeling Platform, www.terrsysmp.org) at Forschungszentrum Jülich. ParFlow hydrologic model (www.parflow.org) is used in combination with the Community Land Model (CLM) to predict hourly, high-resolution (near plot level) information on soil moisture or other soil and meteorological parameters for the next 10 days. A special feature here is the prediction on the temporal development of plant-available water between 0-60cm depth for the sites of our monitoring network partners.Observation data as well as the forecasting products are published in near real time on the digital product platform www.adapter-projekt.de. Users thus have direct access to relevant information that support them in planning agricultural management, e.g. irrigation and fertilization requirements, trafficability or workability.

The geological record shows that abrupt changes in the Earth system can occur on timescales short enough to challenge the capacity of human societies to adapt to environmental pressures. In many cases, abrupt changes arise from slow changes in one component of the Earth system that eventually pass a critical threshold, or tipping point, after which impacts cascade through coupled climate–ecological–social systems. The chance of detecting abrupt changes and tipping points increases with the length of observations. The geological record provides the only long-term information we have on the conditions and processes that can drive physical, ecological and social systems into new states or organizational structures that may be irreversible within human time frames. Here, we use well-documented abrupt changes of the past 30 kyr to illustrate how their impacts cascade through the Earth system. We review useful indicators of upcoming abrupt changes, or early warning signals, and provide a perspective on the contributions of palaeoclimate science to the understanding of abrupt changes in the Earth system. A synthesis of intervals of rapid climatic change evident in the geological record reveals some of the Earth system processes and tipping points that could lead to similar events in the future.

Die Landwirtschaft ist ein Sektor mit unmittelbarer Abhängigkeit von Wetter und Klima, und daher in besonderem Maße von Extremwetterereignissen und Klimaveränderungen betroffen. Die seit 2018 anhaltende Dürrephase in weiten Teilen Deutschlands hat uns dies besonders vor Augen geführt. Viele Akteure in der Landwirtschaft reagieren nur kurzfristig auf stattfindende Schwankungen der Witterungsbedingungen. Welche langfristigen Anpassungsmaßnahmen nötig sind, bleibt oft unklar, da sowohl Klimaprojektionen als auch deren Bedeutung und Relevanz in der Praxis mit Unsicherheiten behaftet sind. Im Projekt ADAPTER erkunden wir diese Unsicherheiten, um Klimaprojektionen passgerecht in praxisrelevante Informationen zu überführen. Den inhaltlichen Fokus erarbeiten wir dabei im Dialog mit Praxispartnern, die in Verwaltungsbehörden, Landwirtschaftskammern, im Ackerbau und in Pflanzenzuchtunternehmen aktiv sind. In unserem Beitrag gehen wir sowohl auf die Methodik als auch die Ergebnisse dieser Vorgehensweise ein. Insbesondere identifizieren und analysieren wir praxisrelevante Klima-Indizes, die auf bestimmte Entwicklungsstadien von Feldfrüchten und auf ausgewählte Klima-Boden-Räume in Deutschland zugeschnitten sind. Die Indizes erfassen z.B. das Auftreten extremer, einander gegenseitig beeinflussender Bedingungen in mehreren Variablen gleichzeitig (sogenannter "compound events"), die Statistik der Dauer von Extremereignissen und die Stärke der täglichen Variabilität. Unsere Datenanalyse umfasst dabei Beobachtungen, Reanalysen und 85 regionale Klimasimulationen des EURO-CORDEX-Ensembles. Beispielsweise finden wir Tendenzen für eine zunehmende Temperaturvariabilität, ein zunehmendes Risiko von kombiniertem Trocken- und Hitzestress, und Änderungen der potentiellen Befahrbarkeit der Böden. Um zu beurteilen, wie diese Änderungen den Ertrag beeinträchtigen können, verschneiden wir die projizierten Klimaänderungen mit physiologischen Schwellwerten der heute angebauten Feldfrüchte. Auf Grundlage unserer Auswertung erörtern wir abschließend, wie sich die Häufigkeit und Dauer von Trockenperioden im Klimawandel verändert, und inwiefern diese Änderungen aus der Klimavariabilität heraustreten.

Like other complex systems, sand dunes alternate between stable states, which have coexisted for 12,000 years in northern China., Dune systems can have alternative stable states that coexist under certain environmental conditions: a vegetated, stabilized state and a bare active state. This behavior implies the possibility of abrupt transitions from one state to another in response to gradual environmental change. Here, we synthesize stratigraphic records covering 12,000 years of dynamics of this system at 144 localities across three dune fields in northern China. We find side-by-side coexistence of active and stabilized states, and occasional sharp shifts in time between those contrasting states. Those shifts occur asynchronously despite the fact that the entire landscape has been subject to the same gradual changes in monsoon rainfall and other conditions. At larger scale, the spatial heterogeneity in dune dynamics averages out to produce relatively smooth change. However, our results do show different paths of recovery and collapse of vegetation at system-wide scales, implying that hysteretic behavior occurs in spatially extended systems.

The most discernible and devastating impacts of climate change are caused by events with temporary extreme conditions (“extreme events”) or abrupt shifts to a new persistent climate state (“tipping points”). The rapidly growing amount of data from models and observations poses the challenge to reliably detect where, when, why, and how these events occur. This situation calls for data-mining approaches that can detect and diagnose events in an automatic and reproducible way. Here, we apply a new strategy to this task by generalizing the classical machine-vision problem of detecting edges in 2D images to many dimensions (including time). Our edge detector identifies abrupt or extreme climate events in spatiotemporal data, quantifies their abruptness (or extremeness), and provides diagnostics that help one to understand the causes of these shifts. We also publish a comprehensive toolset of code that is documented and free to use. We document the performance of the new edge detector by analyzing several datasets of observations and models. In particular, we apply it to all monthly 2D variables of the RCP8.5 scenario of the Coupled Model Intercomparison Project (CMIP5). More than half of all simulations show abrupt shifts of more than 4 standard deviations on a time scale of 10 years. These shifts are mostly related to the loss of sea ice and permafrost in the Arctic. Our results demonstrate that the edge detector is particularly useful to scan large datasets in an efficient way, for example multimodel or perturbed-physics ensembles. It can thus help to reveal hidden “climate surprises” and to assess the uncertainties of dangerous climate events.

Climate change research often focuses on trends in the mean and variance. However, analyses of palaeoclimatic and contemporary dynamics reveal that climate memory - as measured for instance by temporal autocorrelation - may also change substantially over time. Here, we show that elevated temporal autocorrelation in climatic variables should be expected to increase the chance of critical transitions in climate-sensitive systems with tipping points. We demonstrate that this prediction is consistent with evidence from forests, coral reefs, poverty traps, violent conflict and ice sheet instability. In each example, the duration of anomalous dry or warm events elevates chances of invoking a critical transition. Understanding the effects of climate variability thus requires research not only on variance, but also on climate memory.

The dynamics of complex systems, such as ecosystems, financial markets and the human brain, emerge from the interactions of numerous components. We often lack the knowledge to build reliable models for the behaviour of such network systems. This makes it difficult to predict potential instabilities. We show that one could use the natural fluctuations in multivariate time series to reveal network regions with particularly slow dynamics. The multidimensional slowness points to the direction of minimal resilience, in the sense that simultaneous perturbations on this set of nodes will take longest to recover. We compare an autocorrelation-based method with a variance-based method for different time-series lengths, data resolution and different noise regimes. We show that the autocorrelation-based method is less robust for short time series or time series with a low resolution but more robust for varying noise levels. This novel approach may help to identify unstable regions of multivariate systems or to distinguish safe from unsafe perturbations.

The heart of the scientific enterprise is a rational effort to understand the causes behind the phenomena we observe. In large-scale complex dynamical systems such as the Earth system, real experiments are rarely feasible. However, a rapidly increasing amount of observational and simulated data opens up the use of novel data-driven causal methods beyond the commonly adopted correlation techniques. Here, we give an overview of causal inference frameworks and identify promising generic application cases common in Earth system sciences and beyond. We discuss challenges and initiate the benchmark platform causeme.net to close the gap between method users and developers., Nature Communications, 10 (1), ISSN:2041-1723

Complex models of the atmosphere show that increased carbon dioxide (CO2) concentrations, while warming the surface and troposphere, lead to lower temperatures in the stratosphere and mesosphere. This cooling, which is often referred to as “stratospheric cooling”, is evident also in observations and considered to be one of the fingerprints of anthropogenic global warming. Although the responsible mechanisms have been identified, they have mostly been discussed heuristically, incompletely, or in combination with other effects such as ozone depletion, leaving the subject prone to misconceptions. Here we use a one-dimensional window-grey radiation model of the atmosphere to illustrate the physical essence of the mechanisms by which CO2 cools the stratosphere and mesosphere: (i) the blocking effect, associated with a cooling due to the fact that CO2 absorbs radiation at wavelengths where the atmosphere is already relatively opaque, and (ii) the indirect solar effect, associated with a cooling in places where an additional (solar) heating term is present (which on Earth is particularly the case in the upper parts of the ozone layer). By contrast, in the grey model without solar heating within the atmosphere, the cooling aloft is only a transient blocking phenomenon that is completely compensated as the surface attains its warmer equilibrium. Moreover, we quantify the relative contribution of these effects by simulating the response to an abrupt increase in CO2 (and chlorofluorocarbon) concentrations with an atmospheric general circulation model. We find that the two permanent effects contribute roughly equally to the CO2-induced cooling, with the indirect solar effect dominating around the stratopause and the blocking effect dominating otherwise.

The presence of multiple stable states has far-reaching consequences for a system's susceptibility to disturbances, including the possibility of abrupt transitions between stable states. The occurrence of multiple stable states of vegetation is supported by ecological theory, models, and observations. Here we describe the occurrence of multiple stable states of tree cover in a global dynamic vegetation model and provide the first global picture on multiple stable states of tree cover due to a fire-vegetation feedback. The multiple stable states occur in the transition zones between grasslands and forests, mainly in Africa and Asia. By sensitivity simulations and simplifying the relevant model equations we show that the occurrence of multiple states is caused by the sensitivity of the fire disturbance rate to the presence of woody plant types.

The prospect of finding generic early warning signals of an approaching tipping point in a complex system has generated much interest recently. Existing methods are predicated on a separation of timescales between the system studied and its forcing. However, many systems, including several candidate tipping elements in the climate system, are forced periodically at a timescale comparable to their internal dynamics. Here we use alternative early warning signals of tipping points due to local bifurcations in systems subjected to periodic forcing whose timescale is similar to the period of the forcing. These systems are not in, or close to, a fixed point. Instead their steady state is described by a periodic attractor. For these systems, phase lag and amplification of the system response can provide early warning signals, based on a linear dynamics approximation. Furthermore, the Fourier spectrum of the system's time series reveals harmonics of the forcing period in the system response whose amplitude is related to how nonlinear the system's response is becoming with nonlinear effects becoming more prominent closer to a bifurcation. We apply these indicators as well as a return map analysis to a simple conceptual system and satellite observations of Arctic sea ice area, the latter conjectured to have a bifurcation type tipping point. We find no detectable signal of the Arctic sea ice approaching a local bifurcation.

Temperature variability will increase in poor countries and decrease in rich countries, creating a new climate injustice., Extreme events such as heat waves are among the most challenging aspects of climate change for societies. We show that climate models consistently project increases in temperature variability in tropical countries over the coming decades, with the Amazon as a particular hotspot of concern. During the season with maximum insolation, temperature variability increases by ~15% per degree of global warming in Amazonia and Southern Africa and by up to 10%°C−1 in the Sahel, India, and Southeast Asia. Mechanisms include drying soils and shifts in atmospheric structure. Outside the tropics, temperature variability is projected to decrease on average because of a reduced meridional temperature gradient and sea-ice loss. The countries that have contributed least to climate change, and are most vulnerable to extreme events, are projected to experience the strongest increase in variability. These changes would therefore amplify the inequality associated with the impacts of a changing climate.

The notion that small changes can have large consequences in the climate or ecosystems has become popular as the concept of tipping points. Typically, tipping points are thought to arise from a loss of stability of an equilibrium when external conditions are slowly varied. However, this appealingly simple view puts us on the wrong foot for understanding a range of abrupt transitions in the climate or ecosystems because complex environmental systems are never in equilibrium. In particular, they are forced by diurnal variations, the seasons, Milankovitch cycles and internal climate oscillations. Here we show how abrupt and sometimes even irreversible change may be evoked by even small shifts in the amplitude or time scale of such environmental oscillations. By using model simulations and reconciling evidence from previous studies we illustrate how these phenomena can be relevant for ecosystems and elements of the climate system including terrestrial ecosystems, Arctic sea ice and monsoons. Although the systems we address are very different and span a broad range of time scales, the phenomena can be understood in a common framework that can help clarify and unify the interpretation of abrupt shifts in the Earth system.

The end of the African Humid Period about 6,000 years ago was associated with vegetation change and decreased precipitation. Conceptual modelling suggests that the nature of the feedback between climate and vegetation is dependent on vegetation type and diversity.

Early warning signals (EWS) have become a popular statistical tool to infer stability properties of the climate system. In Part 1 of this two-part paper we have presented a diagnostic method to find the hotspot of a sudden transition as opposed to regions that experience an externally induced tipping as a mere response. Here, we apply our method to the atmosphere–vegetation model PlanetSimulator (PlaSim) – VECODE using a regression model. For each of two vegetation collapses in PlaSim-VECODE, we identify a hotspot of one particular grid cell. We demonstrate with additional experiments that the detected hotspots are indeed a particularly sensitive region in the model and give a physical explanation for these results. The method can thus provide information on the causality of sudden transitions and may help to improve the knowledge on the vulnerability of certain subsystems in climate models.

International audience

Complex models of the atmosphere show that increased carbon dioxide (CO2) concentrations, while warming the surface and troposphere, lead to lower temperatures in the stratosphere and mesosphere. This cooling, which is often referred to as "stratospheric cooling", is evident also in observations and considered to be one of the fingerprints of anthropogenic global warming. Although the responsible mechanisms have been identified, they have mostly been discussed heuristically, incompletely, or in combination with other effects such as ozone depletion, leaving the subject prone to misconceptions. Here we use a one-dimensional window-grey radiation model of the atmosphere to illustrate the physical essence of the mechanisms by which CO2 cools the stratosphere and mesosphere: (i) the blocking effect, associated with a cooling due to the fact that CO2 absorbs radiation at wavelengths where the atmosphere is already relatively opaque, and (ii) the indirect solar effect, associated with a cooling in places where an additional (solar) heating term is present (which on Earth is particularly the case in the upper parts of the ozone layer). By contrast, in the grey model without solar heating within the atmosphere, the cooling aloft is only a transient blocking phenomenon that is completely compensated as the surface attains its warmer equilibrium. Moreover, we quantify the relative contribution of these effects by simulating the response to an abrupt increase in CO2 (and chlorofluorocarbon) concentrations with an atmospheric general circulation model. We find that the two permanent effects contribute roughly equally to the CO2-induced cooling, with the indirect solar effect dominating around the stratopause and the blocking effect dominating otherwise.

It has been widely debated whether Arctic sea-ice loss can reach a tipping point beyond which a large sea-ice area disappears abruptly. The theory of dynamical systems predicts a slowing down when a system destabilises towards a tipping point. In simple stochastic systems this can result in increasing variance and autocorrelation, potentially yielding an early warning of an abrupt change. Here we aim to establish whether the loss of Arctic sea ice would follow these conceptual predictions, and which trends in sea ice variability can be expected from pre-industrial conditions toward an Arctic that is ice-free during the whole year. To this end, we apply a model hierarchy consisting of two box models and one comprehensive Earth system model. We find a consistent and robust decrease of the ice volume's annual relaxation time before summer ice is lost because thinner ice can adjust more quickly to perturbations. Thereafter, the relaxation time increases, mainly because the system becomes dominated by the ocean water's large heat capacity when the ice-free season becomes longer. Both trends carry over to the autocorrelation of sea ice thickness in time series. These changes are robust to the nature and origin of climate variability in the models and hardly depend on the balance of feedbacks. Therefore, characteristic trends can be expected in the future. As these trends are not specific to the existence of abrupt ice loss, the prospects for early warnings seem very limited. This result also has implications for statistical indicators in other systems whose effective mass changes over time, affecting the trend of their relaxation time. However, the robust relation between state and variability would allow an estimate of sea-ice variability from only short observations. This could help one to estimate the likelihood and persistence of extreme events in the future., Comment: Discussion Paper, currently (Jan. 2016) under review in The Cryosphere

Over the past 10 years the use of the term ‘tipping point’ in the scientific literature has exploded. It was originally used loosely as a metaphor for the phenomenon that, beyond a certain threshold, runaway change propels a system to a new state. Although several specific mathematical definitions have since been proposed, we argue that these are too narrow and that it is better to retain the original definition.

We examine the relationship between the mean and the variability of Arctic sea-ice coverage and volume in a large range of climates from globally ice-covered to globally ice-free conditions. Using a hierarchy of two column models and several comprehensive Earth system models, we consolidate the results of earlier studies and show that mechanisms found in simple models also dominate the interannual variability of Arctic sea ice in complex models. In contrast to predictions based on very idealised dynamical systems, we find a consistent and robust decrease of variance and autocorrelation of sea-ice volume before summer sea ice is lost. We attribute this to the fact that thinner ice can adjust more quickly to perturbations. Thereafter, the autocorrelation increases, mainly because it becomes dominated by the ocean water's large heat capacity when the ice-free season becomes longer. We show that these changes are robust to the nature and origin of climate variability in the models and do not depend on whether Arctic sea-ice loss occurs abruptly or irreversibly. We also show that our climate is changing too rapidly to detect reliable changes in autocorrelation of annual time series. Based on these results, the prospects of detecting statistical early warning signals before an abrupt sea-ice loss at a "tipping point" seem very limited. However, the robust relation between state and variability can be useful to build simple stochastic climate models and to make inferences about past and future sea-ice variability from only short observations or reconstructions.

The authors examine the transition from a seasonally ice-covered Arctic to an Arctic Ocean that is sea ice free all year round under increasing atmospheric CO2 levels. It is shown that in comprehensive climate models, such loss of Arctic winter sea ice area is faster than the preceding loss of summer sea ice area for the same rate of warming. In two of the models, several million square kilometers of winter sea ice are lost within only one decade. It is shown that neither surface albedo nor cloud feedbacks can explain the rapid winter ice loss in the climate model MPI-ESM by suppressing both feedbacks in the model. The authors argue that the large sensitivity of winter sea ice area in the models is caused by the asymmetry between melting and freezing: an ice-free summer requires the complete melt of even the thickest sea ice, which is why the perennial ice coverage decreases only gradually as more and more of the thinner ice melts away. In winter, however, sea ice areal coverage remains high as long as sea ice still forms, and then drops to zero wherever the ocean warms sufficiently to no longer form ice during winter. The loss of basinwide Arctic winter sea ice area, however, is still gradual in most models since the threshold mechanism proposed here is reversible and not associated with the existence of multiple steady states. As this occurs in every model analyzed here and is independent of any specific parameterization, it is likely to be relevant in the real world.

Research on the possibility of future abrupt climate change has been popularized under the term ‘tipping points’ and has often been motivated by using simple, low-dimensional concepts. These include the iconic fold bifurcation, where abrupt change occurs when a stable equilibrium is lost, and early warning signals of such a destabilization that can be derived based on a simple stochastic model approach. In this paper, we review the challenges and limitations that are associated with this view, and we discuss promising research paths to explore the causes and the likelihood of abrupt changes in future climate. We focus on several climate system components and ecosystems that have been proposed as candidates for tipping points, with an emphasis on ice sheets, the Atlantic Ocean circulation, vegetation in North Africa and Arctic sea ice. In most example cases, multiple equilibria found in simple models do not appear in complex models or become more difficult to find, while the potential for abrupt change still remains. We also discuss how the low-dimensional logic of current methods to detect and interpret the existence of multiple equilibria can fail in complex models. Moreover, we highlight promising methods to detect abrupt shifts and to obtain information about the mechanisms behind them. These methods include linear approaches such as statistical stability indicators and radiative feedback analysis as well as non-linear approaches to detect dynamical transitions and infer the causality behind events. Given the huge complexity of comprehensive process-based climate models and the non-linearity and regional peculiarities of the processes involved, the uncertainties associated with the possible future occurrence of abrupt shifts are large and not well quantified. We highlight the potential of data mining approaches to tackle this problem and finally discuss how the scientific community can collaborate to make efficient progress in understanding abrupt climate shifts.

Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change.

The existence and productivity of vegetation is the basis for food and energy supply in the Sahel. Past changes in climate and vegetation abundance have raised the question whether the region could become greener in the future as a result of higher CO2 levels. By analyzing three Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) with dynamic vegetation, the authors demonstrate why an answer to this question remains elusive in contrast to more robust projections of vegetation cover in the extratropics. First, it depends on the location and the time scale whether vegetation expands or retreats. Until the end of the twenty-first century, the three models agree on a substantial greening in the central and eastern Sahel due to increased CO2 levels. This trend is reversed thereafter, and vegetation retreats in particular in the western Sahel because the beneficial effect of CO2 fertilization is short lived compared to climate change. Second, the vegetation cover changes are driven by different processes in different models (most importantly, precipitation change and CO2 fertilization). As these processes tend to oppose each other, the greening and browning trends are not a reliable result despite the apparent model agreement. The authors also find that the effect of vegetation dynamics on the surface energy balance crucially depends on the location. In contrast to the results of many previous studies, the Sahel appears as a hotspot where the physiological effects of CO2 can exert a cooling because vegetation structure and distribution overcompensate for the decreased stomatal conductance.