Abstract
Potential climate tipping points pose a growing risk for societies, and policy is calling for improved anticipation of them. Satellite remote sensing can play a unique role in identifying and anticipating tipping phenomena across scales. Where satellite records are too short for temporal early warning of tipping points, complementary spatial indicators can leverage the exceptional spatial-temporal coverage of remotely sensed data to detect changing resilience of vulnerable systems. Combining Earth observation with Earth system models can improve process-based understanding of tipping points, their interactions, and potential tipping cascades. Such fine-resolution sensing can support climate tipping point risk management across scales.
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Introduction
Climate change could drive some critical parts of the Earth system towards tipping points—triggering a ‘tipping event’ of abrupt and/or irreversible change into a qualitatively different state, self-propelled by strong amplifying feedback1,2. Crossing tipping points—triggering ‘regime shifts’3 or ‘critical transitions’4—may occur in systems across a range of spatial scales, from local ecosystems to sub-continental ‘tipping elements’1,2. Here, we refer to these collectively as tipping systems. The resulting magnitude, abruptness, and/or irreversibility of changes in system function may be particularly challenging for human societies and other species to adapt to, worsening the risks that climate change poses. Passing tipping points can feedback to climate change by e.g., triggering carbon release5, reducing surface albedo6, or altering ocean heat uptake7. Tipping one system can alter the likelihood of tipping another, with a currently poorly quantified risk that tipping can cascade across systems3,8,9 (meaning here that tipping one system makes tipping of another more likely10).
For all these reasons, an improved observational and modelling framework to sense where and when climate tipping points can be triggered, and how tipping systems interact, could have considerable societal value. Remote sensing data can make a unique contribution because of its global coverage at fine temporal and spatial resolution. It has played an increasingly important role in tipping point science. Early identification of tipping elements in the Earth’s climate system1 drew on remotely sensed evidence of accelerating loss of Arctic sea ice11, Antarctic Peninsula ice shelves12, and the Greenland13,14 and Antarctic13,15 ice sheets. Subsequently, remote sensing has provided key evidence on the location and proximity of tipping points in the polar ice sheets16,17, overturning classical assumptions on the pace of their response to climate change, with measurements of ice speedup18, thinning19, and grounding line retreat16,20, proving critical to identifying destabilisation of the West Antarctic ice sheet21 (WAIS). Satellite data has also been used to detect new candidate tipping elements including a strong shift in cloud feedbacks22, to reveal alternative stable states of boreal23,24 and tropical25,26,27 vegetation, and to track how vegetation resilience varies over space and time28,29.
Resilience is the ability of a system to recover from perturbations, which can be measured as the recovery rate. Resilience declines when approaching a tipping point30 providing potential early warning signals (EWS) due to critical slowing down4 (CSD) of system dynamics. However, resilience can also be lost in the absence of a tipping point31. Hence it is essential to independently identify tipping systems, e.g., using theory and evidence of alternative stable states and/or abrupt shifts in the past32, in spatial data, or in model simulations. Existing work30 proposed a resilience monitoring system for terrestrial ecosystems, irrespective of tipping, whereas here we focus on tipping systems throughout the Earth system. Previously identified tipping systems1,2,32 include the Greenland ice sheet33 (GrIS), the Atlantic Meridional Overturning Circulation34,35 (AMOC), and the Amazon rainforest25. Recently, empirical evidence of resilience loss has been detected in all three29,36,37, which for the Amazon was based on remotely sensed vegetation optical depth38 (VOD). In fast-responding tipping systems, there is a clear opportunity to leverage remote sensing data to look more widely for resilience changes. For slower-responding tipping systems, the relatively short satellite era of ~50 years is insufficient39. However, space-for-time substitution25,26,40 and spatial stability indicators41 can leverage the fine spatial resolution of satellite records to help forewarn of approaching tipping points. Combining Earth observations and models can improve predictions of, e.g., abrupt droughts to avert food security crises42,43, or abrupt loss of ecosystem function and services to inform regional policy-making and land-use planning44.
Here we start by highlighting policy needs for improved and sustained information on climate change tipping points and remote sensing requirements to help address those needs. Then we delve deeper into how remote sensing can help identify potential tipping points, improve resilience monitoring and early warning of tipping events, and assess the potential for tipping systems to interact and possibly cascade. In the outlook, we suggest potential ways forward and future research avenues.
Policy needs
There are strong societal and policy drivers for improved information on potential climate tipping points because abrupt and/or irreversible, large-scale changes pose considerable risks. The risk of crossing a West Antarctic ice sheet tipping point45 has been recognised since the 1970s, and the IPCC’s ‘reasons for concern’ have included ‘large-scale discontinuities in the climate system’ since 2001. Over successive IPCC Reports their likelihood has been repeatedly revised upwards, such that there are now reasons for concern at present levels of global warming46. Figure 1 summarises currently identified climate tipping elements and their estimated sensitivity to global warming2, indicating that several major systems are at risk of being tipped below 2 °C. Considerable uncertainties remain and remote sensing data can help constrain them by, e.g., comparing model results to empirical evidence including identifying emergent constraints47, and estimating proximity to tipping points using CSD applied to remotely sensed data. Overall, interactions between tipping elements, including feedback to global temperature, are assessed to further increase the likelihood of tipping events9,48, although some specific interactions may decrease it48. The desire to avoid crossing climate tipping points has already informed mitigation policy targets including the 2015 Paris Agreement to limit warming to “well below 2 °C” and subsequent ‘net zero’ emissions pledges49. In our view, the risk of tipping was previously underestimated2,46, and this gives a compelling reason to strengthen such mitigation pledges and action to meet them.
Tipping elements are categorised as cryosphere (blue), biosphere (green), or circulation (purple). Colours of labels denote temperature thresholds categorised into three levels of global warming above pre-industrial (key on the right), with darker red indicating lower temperature thresholds (greater urgency). Permafrost appears twice as some parts are prone to abrupt thaw (at lower temperatures) and some (organic-rich Yedoma) to self-propelling collapse (at higher temperatures).
At national-regional scales, climate tipping points could have severe impacts, such as stronger and more frequent extreme events, accelerated sea-level rise, and fundamental changes in climate variability50. These impacts are often distinct in their pattern and/or magnitude from those expected due to global warming alone, thus posing distinct adaptation challenges. Whether or not tipping points can be avoided by stronger mitigation policy, improved information on where and when they could occur can help guide stronger and more targeted adaptation policy. This can be aimed at reducing impacts of tipping points, exposure, and/or vulnerability to those impacts, and therefore risk51. Where biosphere tipping systems are (at least partly) within a national jurisdiction (e.g., boreal forests, Amazon rainforest, tropical coral reefs), remotely sensed information on an approaching tipping point could help inform national efforts to increase ecosystem resilience30. At local scales, the risk of climate change triggering tipping events, e.g., in ecosystems or glaciers, is a challenge for regional policy and management, which again can benefit from improved risk assessment and resilience monitoring30.
Remote sensing targets and requirements
Given these needs, how can remote sensing of tipping systems help support policy-making and environmental management across scales? Table 1 summarises the tipping systems discussed herein, their key properties, the current utility of remote sensing for probing tipping processes, pertinent variables sensed, and methods of remotely sensing them. Figure 2 summarises the capacity of different remote sensing methods to monitor tipping systems and pertinent variables in different domains. Scientific targets for remote sensing of tipping systems include: monitoring relevant feedback processes to improve process understanding52; detecting alternative stable states and associated abrupt changes53; establishing links from alternative states and their stability to climate variables25,26,28; observing system dynamics over time including changes in stability or resilience, and associated early warning signals on regional16,29 and global54 scales, and; calibrating, constraining and evaluating models of tipping systems to improve predictions17,22.
Summarises information in Table 1 (grouping ocean circulation and ocean biosphere together as the ‘ocean’ realm).
Building on previous work42, we propose a minimum set of ideal criteria for remotely sensed datasets to be useful in tipping point applications: (1) Salient variables correlated with key processes underlying tipping dynamics and their possible interactions. (2) Accurate, analysis-ready data. (3) Spatial coverage of the tipping systems of interest. (4) Spatial resolution sufficient to resolve key feedbacks involved in tipping dynamics. (5) Temporal resolution sufficient to resolve timescales of tipping or recovery (Table 1). (6) Temporal duration sufficient to estimate system resilience, and ideally to detect changes in forcing and resilience. (7) Low data latency to support timely detection and/or early warning of tipping points.
Box 1 expands on current remote sensing capabilities and limitations in relation to these criteria.
Remote sensing opportunities
Having established these criteria, we now identify and elaborate key opportunities for remote sensing to advance the understanding and detection of different tipping phenomena, of changing resilience, and of interactions between tipping systems.
Detecting different tipping phenomena
Remote sensing can advance the detection of different types of tipping phenomena across scales (Table 1), which pose different remote sensing challenges and opportunities.
Crossing scales
The most impactful tipping points can be divided into four categories: Impacts can result from tipping inherently large-scale tipping elements (macro tipping), or from localised tipping points that interact to cause larger-scale change (propagating tipping) or are crossed coherently across a large area (clustered tipping) or initiate significant consequences in social systems (societal impact tipping). Large-scale tipping elements have generally been identified1,2 from the ‘top down’, e.g., from conceptual models, understanding of key feedbacks, and/or paleoclimate records of large-scale past abrupt changes32. Meanwhile, localised tipping systems have principally been identified from the ‘bottom up’ by direct observations55,56. Remote sensing can simultaneously identify and monitor tipping systems, phenomena, and their interactions across scales.
Macro tipping
For tipping elements involving atmospheric circulation (e.g., monsoons), ocean circulation (e.g., AMOC, sub-polar gyre; SPG), or ice sheets (e.g., GrIS, WAIS), the crucial reinforcing feedback mechanisms that can propel tipping operate across large spatial scales. The global coverage of remote sensing uniquely enables comprehensive observation at the large scale of those feedbacks. Even where a system is only partially observable, remotely sensed data can reveal underlying (in)stability. For example, remote sensing provides unique opportunities to identify large-scale expressions of SPG and AMOC circulation strength and associated stability changes in fingerprint patterns in sea surface temperature (SST), salinity (SSS), or height (SSH) in specific areas (e.g., Labrador Sea and Nordic Seas) where models suggest a link between these observable fingerprints and proximity to tipping points57. Remote sensing of deep ocean pressure from gravity field changes also reveals below-surface characteristics relating to AMOC strength58. Remote sensing of fine-scale properties across large areas can be used to recalibrate process-based models to improve assessments of large-scale tipping potential. For example, assimilating remotely sensed rainfall data can improve short-term monsoon forecasts59. Correcting modelled cloud ice particle content has revealed the possibility of much higher long-term climate sensitivity22. Furthermore, progress is being made assimilating remotely sensed ice-surface velocity and elevation changes into high-resolution models of Antarctica60.
Propagating tipping
Large-scale tipping elements can in some cases (e.g., WAIS, Amazon rainforest), be considered as networks of smaller, coupled components within which propagating tipping may occur due to causal interactions. In rare cases, tipping of the most sensitive components may ultimately destabilise the rest in a ‘domino cascade’10. The comprehensive coverage of remote sensing at high spatial and temporal resolution is uniquely able to detect propagating tipping by monitoring pertinent localised phenomena and larger-scale responses. For example, several localised tipping points of the Pine Island glacier are theoretically able to destabilise the Amundsen basin61, in turn risking the whole West Antarctic ice sheet62. Satellite-based radar altimetry has detected both the localised grounding line retreat of glaciers16 and confirmed that ice dynamical imbalance has spread to one-quarter of the WAIS since the 1990s19. Another example is the Amazon rainforest, where if dieback starts in the northeast it may propagate southwest—along the prevailing low-level wind and moisture transport direction—through the reduction of rainfall recycling by the forest63. Alternatively, dieback or deforestation starting in the drier southeast may propagate through drying the local climate and enhancing fires. Continuous satellite-based drought42 and fire monitoring are crucial to detect where the forest is at risk of tipping and any propagating tipping. Remote sensing can also track human activities of deforestation, land-use change64, and associated forest fragmentation65 that may trigger tipping. At ecosystem scales, remote sensing can detect propagating tipping, e.g., in the form of propagating ‘invasion fronts’ where one bi-stable ecosystem state replaces another66. It can also monitor potential inhibition of propagating tipping by damping feedback at larger scales, for example in patterned vegetation systems67,68.
Clustered tipping
Where spatial coupling is less strong, localised tipping may still occur in clusters near-synchronously across a large area, due to a spatially coherent climate or anthropogenic forcing reaching a common threshold, e.g., widespread coral bleaching, thermokarst, and lake formation in degrading permafrost, or synchronous forest disturbances69 and dieback. Remote sensing is key to detecting clustered tipping and assessing its spatial scale, for example through the application of abrupt change detection algorithms70—e.g., change point analysis applied to dryland ecosystems53,71 or general trend retrieval applied to thaw lakes across the Arctic72. Remote sensing across environmental gradients is also key to assessing where clustered tipping could occur, helping detect multiple attractors and thus the potential for local tipping points, using e.g., tree cover with respect to rainfall. Early studies suggested widespread multi-stability of tree cover along rainfall gradients in tropical25,26 and boreal23 regions. However, other potential causal drivers of multimodality—notably human activities—can shrink the areas of true bistability24,27,73. Remote sensing of the Global Climate Observing System’s Essential Climate Variables74 (GCOS ECV) can also provide evidence of pertinent feedbacks, e.g., localised forest-cloud feedbacks75 or large-scale alteration of carbon sinks76 (e.g., by permafrost thaw or forest dieback).
Societal impact tipping
Remote sensing can detect localised tipping points in the provision of ecosystem services, which can have substantial impacts on societal systems, where tipping intersects with high human population density. For example, the abrupt loss of glaciers that feed dry-season runoff can have severe impacts on agricultural irrigation downstream77, and agricultural systems may exhibit their own tipping points in the delivery of ecosystem services78. Another example is the amplification of persistent heatwaves by land surface drying and atmospheric heat storage79, with potentially severe impacts—e.g., Europe 2003, Russia 2010, and North America 2021. Remote sensing data are already an essential part of ensemble forecasting of atmospheric blocking events and helped detect amplifying feedbacks and resultant impacts on the biosphere80, including wildfires81, affecting air pollution and human health82. Remote sensing is also used for early warning of droughts and food security crises42,43. Impacts of heatwaves and drought can further cascade through social systems, e.g., when the 2010 drought in Russia harmed wheat production, exports were restricted, contributing to an escalating global wheat price, which is implicated in the ‘Arab Spring’83. However, empirical research is needed to establish what is a social tipping point to avoid misuse of the concept84,85.
Resilience monitoring and tipping point early warning
Relatively long remote sensing records, and new techniques to harmonise continuous observations over time for Essential Climate and Biodiversity Variables86,87, offer new opportunities for monitoring resilience (Box 2) and providing early warning signals (EWS) of some tipping points (Fig. 3).
Examples over different time intervals using directly and remotely sensed data: a Trends in lag-1 temporal autocorrelation (AR(1)) of global vegetation30 from monthly MODIS NDVI for 2001-2020, and of global sea surface temperatures (following the approach of ref. 166) from monthly HadISST for 1982-2021 (which includes AVHRR data). AR(1) trends are measured with Kendall’s τ rank correlation coefficient, with darker green (vegetation) and darker purple (SST) indicative of greater loss of resilience. Light grey areas correspond to pixels with sea ice and dark grey areas to those with low NDVI ( < 0.18) values. b Trends in AR(1) in the Amazon rainforest (Kendall τ) from AVHRR NDVI for 2003-2016 (redrawn from ref. 29). c Changes in SST in the Sub-Polar Gyre region from HadISST for 1870-2019 (upper panel) and associated upward trend in AR(1) (lower panel) suggesting loss of resilience of the AMOC (redrawn from ref. 37). d Fluctuations in patterned vegetation connectedness and rainfall at a site in the Sahel68 (11°37’12”N, 27°51’36”E) from Sentinel-2 and ERA5 precipitation data for 2016–2019 (upper panel), between seasonal extremes of (i) maximum and (ii) minimum connectedness (lower panels). Connectedness is quantified from feature vectors with an ‘Offset50’ metric defined in ref. 68. By measuring the decay rate of connectedness between maxima and minima, averaged over years, and compared across sites, the resilience of these dryland systems is found to decline with rainfall68.
Critical slowing down
Established early warning methods hinge on the phenomenon of critical slowing down (CSD): relatively slow forcing towards a tipping point where a system’s state loses stability, causing overall negative feedback to get weaker, slowing system dynamics including the recovery rate from small perturbations—i.e., loss of resilience. Non-tipping systems may also lose resilience31, hence additional, independent evidence of strongly amplifying feedback and/or empirical or paleoclimate evidence of past tipping, should be used to identify tipping systems. For candidate biosphere tipping systems such as tropical rainforests, boreal forests, and possibly drylands53, remote sensing provides unique opportunities to monitor resilience changes that have only begun to be exploited. Vegetation Optical Depth (VOD) data recently enabled the first global-scale empirical confirmation of CSD theory, by comparing recovery rates from perturbations with estimates based on the CSD indicators variance and lag-1 autocorrelation54.
Temporal resilience indicators
Where repeated perturbations are known to occur, changes in recovery rate can be directly monitored54,88,89. However, in most cases, resilience can only be inferred from changes in temporal autocorrelation (e.g., at lag-1; AR(1)) and variance90. These temporal EWS all require a separation of timescales: a system should be forced slower than its intrinsic response timescale for it to remain close to equilibrium. Furthermore, to detect CSD, a system must be monitored over the forcing timescale, and at a higher frequency than its response timescale. The intrinsic recovery timescales of different tipping systems range from the order of days (atmospheric circulation) or months (vegetation) to millennia (ice sheets). Climate forcing is occurring on multi-decadal to centennial timescales hence some intrinsically ‘slow’ tipping systems may not show CSD in practice. The longest ~50-year remote sensing records (i.e., Landsat) manage to capture the forcing timescale, but only the responses of relatively ‘fast’ tipping systems are monitorable with temporal EWS (those in Table 1 with a timescale of change ~10 years).
Resilience sensing of vegetation
Ecosystems are highly complex and only a subset are tipping systems, but they risk abrupt losses of functionality, with the potential to find CSD in remotely sensed data. The reliability of temporal resilience indicators, given measurement noise and data gaps, has been carefully assessed for NDVI and similar optical indices across major biomes91 and at the global scale92. The predicted relationships between recovery rate and autocorrelation or variance resilience indicators have also recently been confirmed for vegetated ecosystems at a global scale54, based on VOD and NDVI. Hence autocorrelation and variance can be used to measure vegetation resilience changes over time at high spatial resolution using remotely sensed data (Fig. 3a, b). Globally, during the last two decades, the fraction of land surface exhibiting resilience losses has increased54 compared to the 1990s. Focusing on tipping systems: The Amazon rainforest shows a large-scale loss of resilience29 over the past 20 years in VOD and NDVI (Fig. 3b), which peaked during two severe Amazon drought events in 2005 and 2010 and is greatest in drier parts of the forest and places closer to human activities (whereas during the 1990s resilience was being gained29,54). For boreal forests, NDVI fluctuations have a poor fit to an autoregressive model across large areas93, whilst VOD fluctuations54 suggest slow recovery rates (low resilience), with a heterogeneous pattern of resilience losses and gains across space. Smaller scale, societal impact tipping systems (Table 1) include forest regions subject to dieback, and analysis of Californian forests has shown CSD in NDVI prior to forest dieback events94. Opportunities for future progress depend crucially on improvements in remote sensing datasets highlighted in Box 2.
Application to other tipping systems
Although some ocean and cryosphere tipping elements are expected to be too slow to show temporal EWS in current Earth observation records, changes in the mean state, more localised tipping events, and some crucial feedbacks may be faster, more detectable, and informative of resilience changes. CSD has been detected in the analysis of ice-core-derived height variations of the central-western Greenland ice sheet36 over the last ~170 years—although contrasting patterns of mass loss acceleration in different basins indicate a complex picture95,96. As remote sensing records get longer, they can play a key role in comprehensively monitoring the dynamic state of the Greenland ice sheet. CSD has also been detected in proxies of AMOC strength37 from Atlantic SST (Fig. 3c) and SSS fluctuations observed over the last ~150 years and reconstructed over the last millennium97. Remote sensing can offer additional process-based monitoring of tipping processes in the Atlantic circulation. For instance, a large change in the sub-polar gyre (SPG) should be preceded98 by large and characteristic changes in SST and SSS, followed by changes in SSH as regional circulation changes. The same may be true for deep convection in the Nordic Seas, a key part of the AMOC. Other relatively fast tipping systems with the potential for remotely sensed temporal EWS include coral reefs99, monsoons, and atmospheric blocking events100 (Table 1). Although highly uncertain, the risks from very fast tipping in atmospheric circulation systems, including monsoons, demand continuous monitoring that remote sensing can provide59. Moreover, consistent increases in lag-1 autocorrelation of soil moisture have recently been found prior to drought-related changes in food security43, demonstrating EWS as an important potential driver of societal impact tipping.
Noise-induced tipping
Where the resilience of a tipping system is low and short-term variability in forcing (‘noise’) is sufficiently high, noise-induced tipping may occur without forewarning101. This includes cases of fast forcing of slower tipping systems (where CSD is not expected or detectable) and of increasing climate variability and extremes triggering tipping102. Remote sensing can help assess the statistical likelihood of noise-induced tipping103 through monitoring both system resilience and forcing variability (see ‘Deriving tipping probabilities’, below).
Leveraging spatial data
The spatial coverage and fine resolution of remotely sensed data offer additional underutilized opportunities for resilience sensing and potential tipping point early warning, especially where the temporal duration of data is limited.
Space-for-time substitution
Space-for-time substitution assumes that changes in properties along spatial environmental gradients are equivalent to the response of a system to temporal changes in the same environmental driver(s). For example, where temporal remotely sensed data is sufficient to estimate resilience indicators (e.g., AR(1)) at each location, but not to detect changes in them, looking across gradients in environmental drivers can reveal how resilience varies, e.g., how resilience of tropical forests is generally lower in regions with less mean annual precipitation28. It is important to account (where possible) for other factors that also vary spatially and may influence resilience, e.g., using a linear additive model28, recognising that data for some of these factors may not be remotely sensed, e.g., soil fertility28. Also, additional information should be used to determine whether declining resilience may indicate an approach to a tipping point.
Deriving tipping probabilities
Using space-for-time substitution, remotely sensed data can be used to derive probability density functions for vegetation states, for different climate boundary conditions, e.g., mean annual precipitation. Characterising how weather variability drives vegetation variability, the resilience and size of the basin of attraction (of a current stable state) can be inferred, and from that, probabilities of leaving that state (through noise-induced tipping). Applying this approach to remotely sensed annual tree cover fraction reveals that the most resilient parts of the Amazon rainforest are those that have experienced stronger interannual rainfall variability in their long-term past40. In cases where available time series show frequent transitions between alternative attractors, tipping probabilities can be estimated directly103,104, e.g., using paleoclimate data and lake data103. Hence, remote sensing data for systems that have undergone multiple abrupt shifts, such as lakes105, could be used to estimate tipping probabilities.
Spatial early warning indicators
EWS in spatially extended systems depends on the nature of spatial interactions, which remote sensing can help resolve. For macro tipping, responses are expected to be spatially homogeneous, whereas, for other tipping phenomena involving heterogeneous feedbacks and spatial interactions, these can determine the scale of tipping106 and where EWS are expected107. For example, reduced rainfall could lead to vegetation change at different times in different places, but the locations may be causally linked via moisture recycling feedbacks107,108,109. Spatial EWS can be expected as increases in spatial variance or skewness4,110,111 and cross-correlations41. The detection of spatial EWS requires that a tipping system is monitored at least at the spatial resolution over which its reinforcing feedbacks manifest spatially112. Fine-resolution remote sensing can enable this, as demonstrated across rainfall gradients in the tropics where spatial EWS have been found before the switch of savanna/forest vegetation types113,114.
Spatially patterned systems
In systems with regular spatial structure, such as the patterned vegetation found in drylands68 (Fig. 3d), spatial self-organisation (creating multiple stable patterns states at low rainfall) may enable ecosystems to evade a tipping point and associated abrupt loss of ecosystem services67. Plant-soil feedback can be key to spatial self-organisation and affect community assembly and resilience above and below ground66. Fine spatial resolution remote sensing data that are continuous in time across large areas has enabled quantification of pattern connectedness and monitoring of its temporal variation as an aboveground resilience indicator68 (Fig. 3d). However, to fully unravel ecosystem complexity also requires complementary approaches, including on-site field studies.
Combining data and models
Combining remotely sensed data and Earth system models offers opportunities to improve forecasting of tipping points, which is crucial given persistent parametric and structural errors in the ability of models to predict tipping points32.
Designing remote sensing strategies
Earth system models can guide where spatially to look for temporal EWS, for example in ocean circulation39 or ice sheets61. Model simulations could also help identify which processes and where best to remotely monitor for spatial EWS of a tipping point. For example, examining simulated SSH, SST, and SSS data prior to modelled abrupt shifts in the sub-polar gyre57,98, incorporating known uncertainties in remote sensing, could determine which remotely sensed data are most informative for EWS and where additional monitoring could add value.
Emergent constraints
Emergent constraints47 describe a semi-empirical method whereby models identify observable targets that can constrain future predictions115. Emergent constraints allow observational (including remotely sensed) data to constrain the distribution of long-term projections from a large multi-model ensemble. Most focus has been on linear responses, e.g., precipitation forecasts116, but emergent constraints can be developed and applied to tipping responses, as has been done for SPG instability57,98 and Amazon dieback117. Theoretical progress is needed to build confidence in this relatively sensitive and empirical approach.
Decadal predictions
Decadal climate predictions already highlight the benefit of initialising climate models from observed states118, assimilating the best available observations, including from remote sensing for spatial-temporal coverage119. This approach greatly improves the predictability of the North Atlantic Oscillation120. There is a clear opportunity to apply it to tipping elements with a decadal memory component. For instance, the SPG shows abrupt changes in several CMIP5 and CMIP6 model simulations57,98, but it is unclear how close this tipping point is or how it is mechanistically related to the AMOC. Initialising those models with remotely sensed data could provide an improved assessment of tipping event timing, tipping system interactions, and, through large ensembles, a statistical assessment of likelihood. Decadal prediction systems are now going beyond climate models to full Earth system models121,122, opening further opportunities for the assimilation of remotely sensed data to improve forecasting of ‘fast’ biogeochemical tipping systems e.g., Sahel vegetation.
Assessing tipping interactions
Remote sensing can provide critical information to improve the assessment of interactions between tipping systems, including the potential for cascades10, by regularly and consistently observing multiple modelled variables across space and time.
Current understanding
Current assessments of tipping interactions come from paleoclimate proxy data8, expert elicitation for a subset of tipping elements123, model studies of specific tipping element interactions124, or qualitative assessment across different scales of tipping system3. Idealised models have been used to assess the transient48 or eventual equilibrium9 response to the combined effects of interactions amongst a subset of tipping elements—suggesting they increase risk overall9,48, lowering tipping point thresholds48—but this is based on a dated expert elicitation123. Large uncertainties remain over whether particular interactions are net stabilising or destabilising123.
Detecting interactions
Remotely sensed data (Table 1) can provide crucial information to detect or validate tipping system interactions predicted by Earth system models and constrain their signs and strengths. Taking a previously identified example of a key interaction chain46, backed up by detailed model studies125: Rapid melting of the GrIS is already well-observed by altimetry and gravimetry and is predicted to increase the likelihood of crossing tipping points in the sub-polar gyre (SPG) circulation and the AMOC124—albeit dependent on model and resolution126,127,128. Associated changes in North Atlantic SSS, SST, and the SPG circulation should be observable through passive microwave, thermal infrared, and altimetry, respectively. Effects on AMOC strength should also become detectable in SST and SSS spatial fingerprints. Models, paleo-data129,130 and the observational record show that AMOC weakening shifts the intertropical convergence zone (ITCZ) southwards, affecting tropical monsoon systems, but whether this has a destabilising9,123 or stabilising131,132 effect on the Amazon rainforest is currently unclear. Remotely sensed data can help resolve this through detecting movements in the position of the ITCZ, variations in tropical Atlantic SSTs, and resulting changes in precipitation, water level, and storage over the Amazon region in outgoing longwave radiation, radar, radar altimetry, and gravimetry. Destabilisation of the Amazon rainforest is already identifiable in VOD and NDVI29. Amazon deforestation and/or climate change-induced dieback might trigger monsoon shifts because the South American monsoon depends critically on evapotranspiration from the rainforest108, which can be probed with a combination of remote sensing and models.
Inferring causality
Applying methods of data-driven causality detection133 to time series134 and across spatial135 remotely sensed data can help establish causal relationships between tipping systems and eliminate confounding factors in apparently coupled changes. Remotely sensed data provide a promising basis to recover the network of interactions between faster tipping systems, building on the successful recovery of causal connections between geophysical variables136, including ice cover in the Barents Sea tipping element2 and mid-latitude circulation134 and Walker circulation couplings in the equatorial Pacific137. Remotely sensed data could also be used to infer causal effects of climate on this network, building on success for vegetation138,139. For fast tipping systems that have undergone abrupt shifts, convergent cross-mapping140 could be attempted to establish whether a deterministic nonlinear attractor can be recovered from remotely sensed data.
Outlook
We have highlighted the unique value that satellite remote sensing, with its global coverage, fine resolution, and increasing diversity of variables, can bring to advancing the understanding, detection and anticipation of climate change tipping points, and their interactions, across scales. Given the risk that tipping points pose this should be urgently informing both future missions and the extraction of information from existing remotely sensed data. Here we recommend key areas for advancing research to remotely sense climate change tipping points across scales.
Sensing system
Establishing a tipping point sensing system would provide a unifying research framework to bring together the Earth system and Earth observation communities. It would combine data and models to identify and anticipate potential tipping points, demanding advances in data, methods, and analysis. This should start with a systematic scan of existing remotely sensed data to detect abrupt shifts and regions of multi-stability with the potential for future tipping, and a systematic analysis of the potential for temporal and spatial EWS in faster tipping systems, given current data or prospective missions. Models should guide what and where to monitor tipping processes and temporal and spatial EWS in remotely sensed data. Conversely, remote sensing data should be used to constrain model projections of the location and timing of tipping points under specific forcing scenarios. The resulting tighter integration of observations, models, and theory would address the urgent need for improved scientific information on tipping point risks to inform policy.
Improving data
The veracity of a tipping point sensing system depends crucially on improving the salience, accuracy, continuity, and consistency of remotely sensed data. We recommend revising acquisition strategies and exploiting special constellations (e.g., synchronised orbits, bistatic or multi-static radar) toward smarter use of existing remotely sensed data. To make ecosystem monitoring more salient, calls for improved vegetation indices that link to key properties such as biomass (e.g., utilising active microwave). Enhancing data accuracy calls for ongoing utilisation of cloud-insensitive wavelengths (e.g., SAR) with improved consistency of coverage. Temporal resilience sensing would benefit from enhanced access to single-sensor data, and improved multi-instrument records that minimise introducing artefacts141 (see Box 2). Spatial resilience sensing would benefit from open access to very high-resolution spatial data and computational power to analyse it.
Refining methods
The veracity of a tipping point sensing system also depends on refining methods of analysing remotely sensed data. Recent advances in training deep learning to detect and provide early warning of tipping points142,143 should be applied to remotely sensed data, including using segmentation algorithms to complement edge detection in spatial data144. Methods of estimating resilience based on spatial statistics should be applied to the high spatial but low temporal resolution of some existing (e.g., Landsat) and new (e.g., GEDI) data, and duly refined. New data (e.g., GEDI, Sentinel-6, EnMAP) demand resilience sensing methods that limit the impacts of data discontinuities, of merging signals from different sensors, and of low temporal resolution. The comparison of recovery rates measured after perturbation and inferred from AR(1) and variance should be extended beyond vegetation indexes54 to underpin the wider application of temporal EWS. Multivariate Earth observations should be used to help resolve different mechanistic explanations for observed increases in autocorrelation and variance, e.g., combining vegetation indexes (such as NDVI and VOD), rainfall statistics, and deforestation data to understand signals of changing Amazon rainforest resilience29.
Conclusion
The resulting fine-resolution spatial-temporal sensing of tipping systems can support policy-making and risk management at regional, national, and international scales. It can actively help to protect numerous human lives and livelihoods that are at risk from climate change tipping points. Return on investment is also expected to be good, as the framework for Earth observation technology is largely in place and expanding rapidly with commercial partnerships with public agencies. Key opportunities lie in smarter use and combination of existing remote sensing data to detect and forewarn of tipping points across scales.
Data availability
All data used in Fig. 3 are freely available from the following sources: MODIS data from NASA: https://modis.gsfc.nasa.gov/data/. HadISST data from the Met Office: https://www.metoffice.gov.uk/hadobs/. AVHRR NDVI data from USGS: https://www.usgs.gov/centers/eros/science/usgs-eros-archive-avhrr-normalized-difference-vegetation-index-ndvi-composites. Sentinel-2 data from Copernicus: https://dataspace.copernicus.eu/explore-data/data-collections/sentinel-data/sentinel-2. ERA5 precipitation data from Copernicus: https://doi.org/10.24381/cds.adbb2d47. The AMOC SST Index can be found as ‘SST_SG_GM’ in ref. 37.
Change history
01 March 2024
A Correction to this paper has been published: https://doi.org/10.1038/s41467-024-45881-0
References
Lenton, T. M. et al. Tipping elements in the Earth’s climate system. Proc. Natl Acad. Sci. USA 105, 1786–1793 (2008). Defines climate tipping points, identifies a shortlist of ‘tipping elements’ and their temperature thresholds, and highlights the potential for early warning signals of them.
Armstrong McKay, D. I. et al. Exceeding 1.5 C global warming could trigger multiple climate tipping points. Science 377, eabn7950 (2022).
Rocha, J. C., Peterson, G., Bodin, Ö. & Levin, S. Cascading regime shifts within and across scales. Science 362, 1379–1383 (2018).
Scheffer, M. et al. Early warning signals for critical transitions. Nature 461, 53–59 (2009). Shows how generic early warning signals can precede tipping points in many complex systems, raising the possibility of resilience monitoring of the Earth system.
Gasser, T. et al. Path-dependent reductions in CO2 emission budgets caused by permafrost carbon release. Nat. Geosci. 11, 830–835 (2018).
Wunderling, N., Willeit, M., Donges, J. F. & Winkelmann, R. Global warming due to loss of large ice masses and Arctic summer sea ice. Nat. Commun. 11, 5177 (2020).
Liu, W., Fedorov, A. V., Xie, S.-P. & Hu, S. Climate impacts of a weakened Atlantic Meridional Overturning Circulation in a warming climate. Sci. Adv. 6, eaaz4876 (2020).
Brovkin, V. et al. Past abrupt changes, tipping points and cascading impacts in the Earth system. Nat. Geosci. 14, 550–558 (2021).
Wunderling, N., Donges, J. F., Kurths, J. & Winkelmann, R. Interacting tipping elements increase risk of climate domino effects under global warming. Earth Syst. Dynam. 12, 601–619 (2021).
Klose, A. K., Karle, V., Winkelmann, R. & Donges, J. F. Emergence of cascading dynamics in interacting tipping elements of ecology and climate. R. Soc. Open Sci. 7, 200599 (2020).
Comiso, J. C., Parkinson, C. L., Gersten, R. & Stock, L. Accelerated decline in the Arctic sea ice cover. Geophys. Res. Lett. 35, L01703 (2008).
Cook, A. J. & Vaughan, D. G. Overview of areal changes of the ice shelves on the Antarctic Peninsula over the past 50 years. Cryosphere 4, 77–98 (2010). A comprehensive review of ice shelf collapse events that have occurred in Antarctica during the satellite era.
Velicogna, I. & Wahr, J. Measurements of time-variable gravity show mass loss in Antarctica. Science 311, 1754–1756 (2006).
Rignot, E. & Kanagaratnam, P. Changes in the velocity structure of the Greenland ice sheet. Science 311, 986–990 (2006).
Thomas, R. et al. Accelerated sea-level rise from West Antarctica. Science 306, 255–258 (2004).
Rignot, E., Mouginot, J., Morlighem, M., Seroussi, H. & Scheuchl, B. Widespread, rapid grounding line retreat of Pine Island, Thwaites, Smith, and Kohler glaciers, West Antarctica, from 1992 to 2011. Geophys. Res. Lett. 41, 3502–3509 (2014).
Joughin, I., Smith, B. E. & Medley, B. Marine ice sheet collapse potentially under way for the Thwaites Glacier Basin, West Antarctica. Science 344, 735–738 (2014).
Rignot, E. et al. Four decades of Antarctic ice sheet mass balance from 1979–2017. Proc. Natl Acad. Sci. USA 116, 1095–1103 (2019).
Shepherd, A. et al. Trends in Antarctic ice sheet elevation and mass. Geophys. Res. Lett. 46, 8174–8183 (2019).
Konrad, H. et al. Net retreat of Antarctic glacier grounding lines. Nat. Geosci. 11, 258–262 (2018). The first complete picture of grounding line retreat in Antarctica, an indicator of marine ice shelf instability.
Mouginot, J., Rignot, E. & Scheuchl, B. Sustained increase in ice discharge from the Amundsen Sea Embayment, West Antarctica, from 1973 to 2013. Geophys. Res. Lett. 41, 1576–1584 (2014).
Bjordal, J., Storelvmo, T., Alterskjær, K. & Carlsen, T. Equilibrium climate sensitivity above 5 °C plausible due to state-dependent cloud feedback. Nat. Geosci. 13, 718–721 (2020).
Scheffer, M., Hirota, M., Holmgren, M., Van Nes, E. H. & Chapin, F. S. Thresholds for boreal biome transitions. Proc. Natl Acad. Sci. USA 109, 21384–21389 (2012).
Abis, B. & Brovkin, V. Environmental conditions for alternative tree-cover states in high latitudes. Biogeosciences 14, 511–527 (2017).
Hirota, M., Holmgren, M., Van Nes, E. H. & Scheffer, M. Global resilience of tropical forest and savanna to critical transitions. Science 334, 232–235 (2011).
Staver, A. C., Archibald, S. & Levin, S. A. The global extent and determinants of Savanna and forest as alternative biome states. Science 334, 230–232 (2011).
Wuyts, B., Champneys, A. R. & House, J. I. Amazonian forest-savanna bistability and human impact. Nat. Commun. 8, 15519 (2017).
Verbesselt, J. et al. Remotely sensed resilience of tropical forests. Nat. Clim. Change 6, 1028–1031 (2016). Uses remotely sensed vegetation data to demonstrate that as mean annual precipitation declines, temporal autocorrelation increases, indicating loss of resilience.
Boulton, C. A., Lenton, T. M. & Boers, N. Pronounced loss of Amazon rainforest resilience since the early 2000s. Nat. Clim. Change 12, 271–278 (2022).
Lenton, T. M. et al. A resilience sensing system for the biosphere. Philos. Trans. R. Soc. B: Biol. Sci. 377, 20210383 (2022).
Kéfi, S., Dakos, V., Scheffer, M., Van Nes, E. H. & Rietkerk, M. Early warning signals also precede non-catastrophic transitions. Oikos 122, 641–648 (2012).
Boers, N., Ghil, M. & Stocker, T. F. Theoretical and paleoclimatic evidence for abrupt transitions in the Earth system. Environ. Res. Lett. 17, 093006 (2022).
Robinson, A., Calov, R. & Ganopolski, A. Multistability and critical thresholds of the Greenland ice sheet. Nat. Clim. Change 2, 429–432 (2012).
Liu, W., Xie, S.-P., Liu, Z. & Zhu, J. Overlooked possibility of a collapsed Atlantic Meridional Overturning Circulation in warming climate. Sci. Adv. 3, e1601666 (2017).
Jackson, L. C. & Wood, R. A. Hysteresis and Resilience of the AMOC in an Eddy-Permitting GCM. Geophys. Res. Lett. 45, 8547–8556 (2018).
Boers, N. & Rypdal, M. Critical slowing down suggests that the western Greenland ice sheet is close to a tipping point. Proc. Natl Acad. Sci. USA 118, e2024192118 (2021).
Boers, N. Observation-based early-warning signals for a collapse of the Atlantic Meridional Overturning Circulation. Nat. Clim. Change 11, 680–688 (2021).
Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).
Boulton, C. A., Allison, L. C. & Lenton, T. M. Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model. Nat. Commun. 5, 5752 (2014).
Ciemer, C. et al. Higher resilience to climatic disturbances in tropical vegetation exposed to more variable rainfall. Nat. Geosci. 12, 174–179 (2019).
Dakos, V., van Nes, E., Donangelo, R., Fort, H. & Scheffer, M. Spatial correlation as leading indicator of catastrophic shifts. Theor. Ecol. 3, 163–174 (2010). Shows how spatially extended systems can show specific early warning signals of collapse, opening possibilities for applications to Earth observations.
Krishnamurthy R, P. K., Fisher, J. B., Schimel, D. S. & Kareiva, P. M. Applying tipping point theory to remote sensing science to improve early warning drought signals for food security. Earth’s Future 8, e2019EF001456 (2020).
Krishnamurthy R, P. K., Fisher, J. B., Choularton, R. J. & Kareiva, P. M. Anticipating drought-related food security changes. Nat. Sustain. 5, 956–964 (2022).
Thellmann, K. et al. Tipping points in the supply of ecosystem services of a mountainous watershed in Southeast Asia. Sustainability 10, 2418 (2018).
Mercer, J. H. West Antarctic ice sheet and CO2 greenhouse effect: a threat of disaster. Nature 271, 321–325 (1978). The seminal paper on marine ice sheet instability.
Lenton, T. M. et al. Climate tipping points—too risky to bet against. Nature 575, 592–595 (2019).
Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).
Cai, Y., Lenton, T. M. & Lontzek, T. S. Risk of multiple interacting tipping points should encourage rapid CO2 emission reduction. Nat. Clim. Change 6, 520–525 (2016).
Committee on Climate Change. Net Zero—The UK’s Contribution to Stopping Global Warming. (Committee on Climate Change, 2019).
Lenton, T. M. & Ciscar, J.-C. Integrating tipping points into climate impact assessments. Clim. Change 117, 585–597 (2013).
Collins, M. et al. in The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change (eds H.-O. Pörtner et al.) 589–656 (Cambridge University Press, 2019).
Sellers, P. J., Schimel, D. S., Moore, B., Liu, J. & Eldering, A. Observing carbon cycle-climate feedbacks from space. Proc. Natl Acad. Sci. USA 115, 7860–7868 (2018).
Berdugo, M., Gaitán, J. J., Delgado-Baquerizo, M., Crowther, T. W. & Dakos, V. Prevalence and drivers of abrupt vegetation shifts in global drylands. Proc. Natl Acad. Sci. USA 119, e2123393119 (2022).
Smith, T., Traxl, D. & Boers, N. Empirical evidence for recent global shifts in vegetation resilience. Nat. Clim. Change 12, 477–484 (2022). Uses remotely sensed vegetation data to confirm theory that resilience indicators based on continuous temporal statistics accurately capture recovery rate from perturbations.
Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).
Biggs, R., Peterson, G. D. & Rocha, J. C. The Regime Shifts Database: a framework for analyzing regime shifts in social-ecological systems. Ecol. Soc. 23, 9 (2018).
Swingedouw, D. et al. On the risk of abrupt changes in the North Atlantic subpolar gyre in CMIP6 models. Ann. N. Y. Acad. Sci. 1504, 187–201 (2021).
Swingedouw, D. et al. Early warning from space for a few key tipping points in physical, biological, and social-ecological systems. Surv. Geophys. 41, 1237–1284 (2020). First review of the potential for remote sensing to provide early warning signals of some climate tipping points.
Kumar, P., Kishtawal, C. M. & Pal, P. K. Impact of satellite rainfall assimilation on Weather Research and Forecasting model predictions over the Indian region. J. Geophys. Res.: Atmos. 119, 2017–2031 (2014).
Pattyn, F. & Morlighem, M. The uncertain future of the Antarctic ice sheet. Science 367, 1331–1335 (2020).
Rosier, S. H. R. et al. The tipping points and early warning indicators for Pine Island Glacier, West Antarctica. Cryosphere 15, 1501–1516 (2021).
Feldmann, J. & Levermann, A. Collapse of the West Antarctic ice sheet after local destabilization of the Amundsen Basin. Proc. Natl Acad. Sci. 112, 14191–14196 (2015).
Staal, A. et al. Forest-rainfall cascades buffer against drought across the Amazon. Nat. Clim. Change 8, 539–543 (2018).
Hansen, M. C. et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science 342, 850–853 (2013).
Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).
Inderjit, Callaway, R. M. & Meron, E. Belowground feedbacks as drivers of spatial self-organization and community assembly. Phys. Life Rev. 38, 1–24 (2021).
Rietkerk, M. et al. Evasion of tipping in complex systems through spatial pattern formation. Science 374, eabj0359 (2021).
Buxton, J. E. et al. Quantitatively monitoring the resilience of patterned vegetation in the Sahel. Glob. Change Biol. 28, 571–587 (2021).
Druckenbrod, D. L. et al. Redefining temperate forest responses to climate and disturbance in the eastern United States: New insights at the mesoscale. Glob. Ecol. Biogeogr. 28, 557–575 (2019).
Boulton, C. & Lenton, T. A new method for detecting abrupt shifts in time series. F1000Research 8, 746 (2019).
Bernardino, P. N. et al. Global-scale characterization of turning points in arid and semi-arid ecosystem functioning. Glob. Ecol. Biogeogr. 29, 1230–1245 (2020).
Nitze, I., Grosse, G., Jones, B. M., Romanovsky, V. E. & Boike, J. Remote sensing quantifies widespread abundance of permafrost region disturbances across the Arctic and Subarctic. Nat. Commun. 9, 5423 (2018).
Kumar, S. S. et al. Alternative vegetation states in tropical forests and Savannas: the search for consistent signals in diverse remote sensing data. Remote Sens. 11, 815 (2019).
Zeng, Y. et al. Towards a traceable climate service: assessment of quality and usability of essential climate variables. Remote Sens. 11, 1186 (2019).
Duveiller, G. et al. Revealing the widespread potential of forests to increase low level cloud cover. Nat. Commun. 12, 4337 (2021).
Deng, Z. et al. Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions. Earth Syst. Sci. Data 14, 1639–1675 (2022).
Sorg, A., Bolch, T., Stoffel, M., Solomina, O. & Beniston, M. Climate change impacts on glaciers and runoff in Tien Shan (Central Asia). Nat. Clim. Change 2, 725–731 (2012).
Watson, S. C. L. et al. Does agricultural intensification cause tipping points in ecosystem services? Landsc. Ecol. 36, 3473–3491 (2021).
Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C. & Vilà-Guerau de Arellano, J. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).
Reichstein, M. et al. Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis. Glob. Change Biol. 13, 634–651 (2007).
Witte, J. C. et al. NASA A-Train and Terra observations of the 2010 Russian wildfires. Atmos. Chem. Phys. 11, 9287–9301 (2011).
Shaposhnikov, D. et al. Mortality related to air pollution with the Moscow heat wave and wildfire of 2010. Epidemiology 25, 359–364 (2014).
Hunt, E. et al. Agricultural and food security impacts from the 2010 Russia flash drought. Weather Clim. Extremes 34, 100383 (2021).
Kopp, R. E., Shwom, R. L., Wagner, G. & Yuan, J. Tipping elements and climate–economic shocks: Pathways toward integrated assessment. Earth’s Future 4, 346–372 (2016).
Milkoreit, M. Social tipping points everywhere?—Patterns and risks of overuse. WIREs Clim. Change 14, e813 (2022).
Mortimer, C. et al. Benchmarking algorithm changes to the Snow CCI+ snow water equivalent product. Remote Sens. Environ. 274, 112988 (2022).
Paul, S., Hendricks, S., Ricker, R., Kern, S. & Rinne, E. Empirical parametrization of Envisat freeboard retrieval of Arctic and Antarctic sea ice based on CryoSat-2: progress in the ESA Climate Change Initiative. Cryosphere 12, 2437–2460 (2018).
Wissel, C. A universal law of the characteristic return time near thresholds. Oecologia 65, 101–107 (1984). Pioneering study of the relationship between linear stability and recovery rate, which is at the heart of the theory underlying resilience monitoring.
De Keersmaecker, W. et al. Evaluating recovery metrics derived from optical time series over tropical forest ecosystems. Remote Sens. Environ. 274, 112991 (2022).
Kubo, R. The fluctuation-dissipation theorem. Rep. Prog. Phys. 29, 255–284 (1966).
De Keersmaecker, W. et al. How to measure ecosystem stability? An evaluation of the reliability of stability metrics based on remote sensing time series across the major global ecosystems. Glob. Change Biol. 20, 2149–2161 (2014). Rigorously examines how different remote sensing data quality issues affect different ecosystem stability metrics.
Forzieri, G., Dakos, V., McDowell, N. G., Ramdane, A. & Cescatti, A. Emerging signals of declining forest resilience under climate change. Nature 608, 534–539 (2022).
De Keersmaecker, W. et al. A model quantifying global vegetation resistance and resilience to short-term climate anomalies and their relationship with vegetation cover. Glob. Ecol. Biogeogr. 24, 539–548 (2015). Pioneering study of global vegetation resilience in response to drought and temperature anomalies utilising remotely sensed vegetation data.
Liu, Y., Kumar, M., Katul, G. G. & Porporato, A. Reduced resilience as an early warning signal of forest mortality. Nat. Clim. Change 9, 880–885 (2019).
King, M. D. et al. Dynamic ice loss from the Greenland ice sheet driven by sustained glacier retreat. Commun. Earth Environ. 1, 1 (2020).
Khan, S. A. et al. Accelerating ice loss from peripheral glaciers in North Greenland. Geophys. Res. Lett. 49, e2022GL098915 (2022).
Michel, S. L. L. et al. Early warning signal for a tipping point suggested by a millennial Atlantic Multidecadal Variability reconstruction. Nat. Commun. 13, 5176 (2022).
Sgubin, G., Swingedouw, D., Drijfhout, S., Mary, Y. & Bennabi, A. Abrupt cooling over the North Atlantic in modern climate models. Nat. Commun. 8, 14375 (2017). Identifies a tipping point of deep convection collapse in the North Atlantic subpolar gyre occurring in several climate models at low levels of global warming.
Knudby, A., Jupiter, S., Roelfsema, C., Lyons, M. & Phinn, S. Mapping coral reef resilience indicators using field and remotely sensed data. Remote Sens. 5, 1311–1334 (2013).
Tantet, A., van der Burgt, F. R. & Dijkstra, H. A. An early warning indicator for atmospheric blocking events using transfer operators. Chaos: Interdiscip. J. Nonlinear Sci. 25, 036406 (2015).
Lenton, T. M. Early warning of climate tipping points. Nat. Clim. Change 1, 201–209 (2011).
Turner, M. G. et al. Climate change, ecosystems and abrupt change: science priorities. Philos. Trans. R. Soc. B: Biol. Sci. 375, 20190105 (2020).
Arani, B. M. S., Carpenter, S. R., Lahti, L., Nes, E. H. V. & Scheffer, M. Exit time as a measure of ecological resilience. Science 372, eaay4895 (2021).
Hassanibesheli, F., Boers, N. & Kurths, J. Reconstructing complex system dynamics from time series: a method comparison. N. J. Phys. 22, 073053 (2020).
Gilarranz, L. J., Narwani, A., Odermatt, D., Siber, R. & Dakos, V. Regime shifts, trends, and variability of lake productivity at a global scale. Proc. Natl Acad. Sci. USA 119, e2116413119 (2022).
van Nes, E. H. & Scheffer, M. Implications of spatial heterogeneity for catastrophic regime shifts in ecosystems. Ecology 86, 1797–1807 (2005).
Bathiany, S., Claussen, M. & Fraedrich, K. Detecting hotspots of atmosphere-vegetation interaction via slowing down—Part 1: a stochastic approach. Earth Syst. Dynam. 4, 63–78 (2013).
Boers, N., Marwan, N., Barbosa, H. M. J. & Kurths, J. A deforestation-induced tipping point for the South American monsoon system. Sci. Rep. 7, 41489 (2017).
Claussen, M., Bathiany, S., Brovkin, V. & Kleinen, T. Simulated climate–vegetation interaction in semi-arid regions affected by plant diversity. Nat. Geosci. 6, 954–958 (2013).
Guttal, V. & Jayaprakash, C. Spatial variance and spatial skewness: leading indicators of regime shifts in spatial ecological systems. Theor. Ecol. 2, 3–12 (2009). Uses a spatial ecological model to demonstrate the potential for spatial early warning indicators of an approaching tipping point.
Kéfi, S. et al. Early warning signals of ecological transitions: methods for spatial patterns. PLoS ONE 9, e92097 (2014).
Nijp, J. J. et al. Spatial early warning signals for impending regime shifts: a practical framework for application in real-world landscapes. Glob. Change Biol. 25, 1905–1921 (2019).
Eby, S., Agrawal, A., Majumder, S., Dobson, A. P. & Guttal, V. Alternative stable states and spatial indicators of critical slowing down along a spatial gradient in a savanna ecosystem. Glob. Ecol. Biogeogr. 26, 638–649 (2017).
Majumder, S., Tamma, K., Ramaswamy, S. & Guttal, V. Inferring critical thresholds of ecosystem transitions from spatial data. Ecology 100, e02722 (2019).
Hegerl, G. C. et al. Toward consistent observational constraints in climate predictions and projections. Front. Clim. 3, 678109 (2021).
Shiogama, H., Watanabe, M., Kim, H. & Hirota, N. Emergent constraints on future precipitation changes. Nature 602, 612–616 (2022).
Cox, P. M. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013).
Keenlyside, N. S., Latif, M., Jungclaus, J., Kornblueh, L. & Roeckner, E. Advancing decadal-scale climate prediction in the North Atlantic sector. Nature 453, 84–88 (2008).
Counillon, F., Sakov, P. & Bertino, L. Application of a hybrid EnKF-OI to ocean forecasting. Ocean Sci. 5, 389–401 (2009).
Smith, D. M. et al. North Atlantic climate far more predictable than models imply. Nature 583, 796–800 (2020).
Séférian, R. et al. Multiyear predictability of tropical marine productivity. Proc. Natl Acad. Sci. USA 111, 11646–11651 (2014).
Li, H., Ilyina, T., Müller, W. A. & Sienz, F. Decadal predictions of the North Atlantic CO2 uptake. Nat. Commun. 7, 11076 (2016).
Kriegler, E., Hall, J. W., Held, H., Dawson, R. & Schellnhuber, H. J. Imprecise probability assessment of tipping points in the climate system. Proc. Natl Acad. Sci. USA 106, 5041–5046 (2009).
Bakker, P. et al. Fate of the Atlantic meridional overturning circulation: strong decline under continued warming and Greenland melting. Geophys. Res. Lett. 43, 12,252–212,260 (2016).
Madsen, M. S. et al. The role of an interactive Greenland ice sheet in the coupled climate-ice sheet model EC-Earth-PISM. Clim. Dyn. 59, 1189–1211 (2022).
Stouffer, R. J. et al. Investigating the causes of the response of the thermohaline circulation to past and future climate changes. J. Clim. 19, 1365–1387 (2006).
Swingedouw, D. et al. Decadal fingerprints of freshwater discharge around Greenland in a multi-model ensemble. Clim. Dyn. 41, 695–720 (2013).
Swingedouw, D. et al. AMOC Recent and Future Trends: A crucial role for oceanic resolution and Greenland melting? Front. Clim. 4, (2022). Shows that Greenland ice sheet melt can significantly weaken deep convection in the North Atlantic subpolar gyre, but this is not captured in the latest coupled climate models.
Mosblech, N. A. S. et al. North Atlantic forcing of Amazonian precipitation during the last ice age. Nat. Geosci. 5, 817–820 (2012).
Jomelli, V. et al. In-phase millennial-scale glacier changes in the tropics and North Atlantic regions during the Holocene. Nat. Commun. 13, 1419 (2022).
Ciemer, C., Winkelmann, R., Kurths, J. & Boers, N. Impact of an AMOC weakening on the stability of the southern Amazon rainforest. Eur. Phys. J. Spec. Top. 230, 3065–3073 (2021).
Good, P., Boers, N., Boulton, C. A., Lowe, J. A. & Richter, I. How might a collapse in the Atlantic Meridional Overturning Circulation affect rainfall over tropical South America? Clim. Resil. Sustain. 1, e26 (2022).
Runge, J. Causal network reconstruction from time series: From theoretical assumptions to practical estimation. Chaos: Interdiscip. J. Nonlinear Sci. 28, 075310 (2018).
Runge, J. et al. Inferring causation from time series in Earth system sciences. Nat. Commun. 10, 2553 (2019). Excellent introduction to methods of inferring causal interactions in the Earth system from time series data.
Reich, B. J. et al. A review of spatial causal inference methods for environmental and epidemiological applications. Int. Stat. Rev. 89, 605–634 (2021).
Pérez-Suay, A. & Camps-Valls, G. Causal inference in geoscience and remote sensing from observational data. IEEE Trans. Geosci. Remote Sens. 57, 1502–1513 (2019).
Kretschmer, M., Coumou, D., Donges, J. F. & Runge, J. Using causal effect networks to analyze different Arctic drivers of midlatitude winter circulation. J. Clim. 29, 4069–4081 (2016).
Papagiannopoulou, C. et al. A non-linear Granger-causality framework to investigate climate–vegetation dynamics. Geosci. Model Dev. 10, 1945–1960 (2017).
Reygadas, Y., Jensen, J. L. R., Moisen, G. G., Currit, N. & Chow, E. T. Assessing the relationship between vegetation greenness and surface temperature through Granger causality and Impulse-Response coefficients: a case study in Mexico. Int. J. Remote Sens. 41, 3761–3783 (2020).
Sugihara, G. et al. Detecting causality in complex ecosystems. Science 338, 496–500 (2012).
Smith, T. et al. Reliability of resilience estimation based on multi-instrument time series. Earth Syst. Dyn. 14, 173–183 (2023).
Bury, T. M. et al. Deep learning for early warning signals of regime shifts. Proc. Natl Acad. Sci. USA 118, e2106140118 (2021).
Dylewsky, D. et al. Universal early warning signals of phase transitions in climate systems. J. R. Soc. Interface 20, 20220562 (2023).
Bathiany, S., Hidding, J. & Scheffer, M. Edge detection reveals abrupt and extreme climate events. J. Clim. 33, 6399–6421 (2020).
Popp, T. et al. Consistency of satellite climate data records for Earth system monitoring. Bull. Am. Meteorol. Soc. 101, E1948–E1971 (2020).
Plummer, S., Lecomte, P. & Doherty, M. The ESA Climate Change Initiative (CCI): a European contribution to the generation of the Global Climate Observing System. Remote Sens. Environ. 203, 2–8 (2017). Describes how satellite observations are used to document essential climate variables.
White, H. J. et al. Quantifying large-scale ecosystem stability with remote sensing data. Remote Sens. Ecol. Conserv. 6, 354–365 (2020).
Bousquet, E. et al. Influence of surface water variations on VOD and biomass estimates from passive microwave sensors. Remote Sens. Environ. 257, 112345 (2021).
Tao, S. et al. Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts. Proc. Natl Acad. Sci. USA 119, e2116626119 (2022).
Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).
Green, H. L., Findlay, H. S., Shutler, J. D., Land, P. E. & Bellerby, R. G. J. Satellite Observations Are Needed to Understand Ocean Acidification and Multi-Stressor Impacts on Fish Stocks in a Changing Arctic Ocean. Front. Marine Sci. 8, 635797 (2021).
Melet, A. et al. Earth observations for monitoring marine coastal hazards and their drivers. Surv. Geophys. 41, 1489–1534 (2020).
Foo, S. A. & Asner, G. P. Scaling up coral reef restoration using remote sensing technology. Front. Marine Sci. 6, 79 (2019).
Staal, A., Dekker, S. C., Hirota, M. & van Nes, E. H. Synergistic effects of drought and deforestation on the resilience of the south-eastern Amazon rainforest. Ecol. Complex. 22, 65–75 (2015).
Zemp, D. C. et al. Self-amplified Amazon forest loss due to vegetation-atmosphere feedbacks. Nat. Commun. 8, 14681 (2017).
van Belzen, J. et al. Vegetation recovery in tidal marshes reveals critical slowing down under increased inundation. Nat. Commun. 8, 15811 (2017).
Alibakhshi, S., Groen, T., Rautiainen, M. & Naimi, B. Remotely-sensed early warning signals of a critical transition in a wetland ecosystem. Remote Sens. 9, 352 (2017).
Tehrani, N. A. & Janalipour, M. Predicting ecosystem shift in a Salt Lake by using remote sensing indicators and spatial statistics methods (case study: Lake Urmia basin). Environ. Eng. Res. 26, 200225–200220 (2021).
Lees, K. J. et al. Using remote sensing to assess peatland resilience by estimating soil surface moisture and drought recovery. Sci. Total Environ. 761, 143312 (2021).
Lees, K. J., Buxton, J., Boulton, C. A., Abrams, J. F. & Lenton, T. M. Using satellite data to assess management frequency and rate of regeneration on heather moorlands in England as a resilience indicator. Environ. Res. Commun. 3, 085003 (2021).
Miner, K. R. et al. Permafrost carbon emissions in a changing Arctic. Nat. Rev. Earth Environ. 3, 55–67 (2022).
Talib, J. et al. The sensitivity of the West African monsoon circulation to intraseasonal soil moisture feedbacks. Q. J. R. Meteorol. Soc. 148, 1709–1730 (2022).
IPCC. Global Warming of 1.5 °C. An IPCC Special Report on the Impacts of Global Warming of 1.5 °C above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. (IPCC, 2018).
IPCC. IPCC Special Report on the Ocean and Cryosphere in a Changing Climate. (IPCC, 2019).
IPCC. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, 2021).
Boulton, C. A. & Lenton, T. M. Slowing down of North Pacific climate variability and its implications for abrupt ecosystem change. Proc. Natl Acad. Sci. USA 112, 11496–11501 (2015).
Acknowledgements
This paper is an outcome of the ‘Tipping Points in the Earth’s Climate’ Forum held at the International Space Science Institute (ISSI), Bern, Switzerland (26-29 January 2021). T.M.L., C.A.B., and J.E.B. were supported by the Leverhulme Trust (RPG-2018-046). T.M.L., J.F.A., C.A.B., and J.E.B. were also supported by DARPA. A.B. was supported by the European Space Agency through Permafrost_cci (4000123681/18/INB) and AMPAC-Net (4000137912/22/I-DT), and the European Research Council project No. 951288 (Q-Arctic). S.B. and N.B. acknowledge funding from the Volkswagen Stiftung, the European Union’s Horizon 2020 research and innovation programme under grant agreement number 820970 (TiPES contribution #273) and under the Marie Sklodowska-Curie grant agreement number 956170, as well as from the Federal Ministry of Education and Research under grant number 01LS2001A. A.M.C was supported by the Oppenheimer Programme in African Landscape Systems co-funded by Oppenheimer Generations Research and Conservation. T.S. acknowledges support from the DFG STRIVE project (SM 710/2-1). D.S. received financial support from the French government in the framework of the University of Bordeaux’s IdEx “Investments for the Future” programme/RRI Tackling Global Change and from the UKRI DECADAL project.
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T.M.L. led the writing with J.F.A., A.B., S.B., C.A.B., J.E.B., A.C., A.M.C., S.H., T.L., B.P., A.S., T.S., D.S., R.W., and N.B. all inputting. A.B. and A.S. helped refine the table. J.F.A., C.A.B. and J.E.B. produced the figures.
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Lenton, T.M., Abrams, J.F., Bartsch, A. et al. Remotely sensing potential climate change tipping points across scales. Nat Commun 15, 343 (2024). https://doi.org/10.1038/s41467-023-44609-w
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DOI: https://doi.org/10.1038/s41467-023-44609-w
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