3.2.2 Multiple drivers of tipping processes

Most Earth system tipping processes have multiple drivers. Prevention of ESTPs requires tackling all of them. Given this multi-causality, the term prevention is related to, but not synonymous with, mitigation. The familiar concept of climate mitigation in the narrow sense of reducing GHG emissions can be applied to ESTPs; emission reductions serve to limit atmospheric GHG concentrations and correspondingly limit future increases in global average temperature, which is key for reducing the general risks of climate change. Since global temperature increase is a causal variable for most Earth system tipping processes of interest here, mitigation in the sense of reducing emissions of GHG will be the most important approach to preventing the crossing of ESTPs. This includes the management of SLCPs.

Most ESTPs have multiple interacting causes (see Table 3.2.1), and effective prevention strategies will also have to attend to causes other than warming. It is important to distinguish between a primary cause, which in many cases is GHG-induced climate change (through atmospheric or ocean warming pathways and precipitation changes, which we categorise as ‘direct climate’ drivers), and secondary causes. Some secondary causes, such as ice sheet meltwater effects on ocean currents, land ‘greening’ due to warming and CO2 fertilisation, or ocean acidification, are second-order effects of climate change or other effects of GHG emissions (i.e. ‘Climate-Associated’ drivers). Others are independent of climate change – e.g. pollution affecting coral reefs or deforestation of the Amazon rainforest (i.e. ‘non-climate’ drivers). These secondary causal drivers can bring forward a system’s tipping point, hence tackling them can help prevent tipping. The importance and number of additional causes differs across tipping elements. 

Table 3.2.1: Multiple drivers of ESTPs
Primary and secondary drivers of the ESTPs identified in this report. DC: Direct climate driver (direct impact of emissions on meteorological variables via radiative forcing); CA: Climate-associated driver (including second-order and associated effects of climate change); NC: Non-climate driver. Drivers can enhance (↗) or counter (↘) tipping.

Tipping pointPrimary driversSecondary drivers
Cryosphere
Ice sheet collapse
(Greenland, West/East Antarctica)
DC: atmospheric warming (↗)
DC: ocean warming and circulation changes (↗ GrIS, WAIS, EA marine / ↘ GrIS)
DC: precipitation increase (↘)
DC: black carbon deposition (↗)
CA: sea ice decline (↗)
CA: atmospheric circulation (?)
Sea ice loss
(N.B. tipping unlikely in this report, but affects other key ESTPs)
DC: atmospheric warming (↗)DC: atmospheric circulation shifts (↗/↘)
DC: ocean warming (↗)
DC: ocean circulation shifts (↗/↘)
DC: black carbon deposition (↗)
DC: storminess increase (↗)
CA: ocean stratification increase (↘)
Glacier retreat
(regional)
DC: atmospheric warming (↗)DC: deposition of dust, black carbon etc. (albedo) (↗)
DC: reduced snow (input & albedo) (↗)
DC: local thermokarst (↗)
Permafrost thaw
(regional; and subsea)
DC: atmospheric warming (↗)
DC: ocean warming (subsea, ↗)
CA: vegetation change (↗/↘)
CA: wildfire intensity increase (↗)
CA: precipitation change (rain extremes, snow cover albedo (↗)
CA: sea ice loss (subsea, ↗)
CA: water pressure reduction (subsea, ↗)
Biosphere
Tropical forest dieback
(regional: Amazon, maybe Congo)
DC: atmospheric warming (↗)
NC: deforestation/degradation (↗)
DC: drying (↗)
CA: increasing fire frequency/intensity (↗)
DC: heatwaves (↗)
CA: ENSO intensification (e.g. Amazon, SE Asia ↗)
CA: AMOC/SPG weakening/collapse (e.g. Amazon, ↗)
CA: terrestrial greening (↘ declining)
Boreal forest southern dieback/
northern expansion
DC: drying (↗)
CA: fire frequency/intensity increase (↗)
DC: atmospheric warming (↗)
CA: permafrost thaw (↗)
CA: insect outbreaks (↗)
NC: deforestation & degradation (↗)
DC: heatwaves (↗)
CA: terrestrial greening (↘)
CA: vegetation albedo (↗)
CA: sea ice albedo decline (↗)
DC: precipitation changes (?)
Temperate forest dieback
(N.B. uncertain in this report)
DC: atmospheric warming (↗)
DC: droughts (↗)
DC: heatwaves (↗)
CA: insect outbreaks (↗)
CA: windthrow (↗)
NC: deforestation & degradation (↗)
CA: fire frequency increase (↗)
NC: fragmentation (↗)
Savanna degradationNC: fire suppression (↗)
NC: overgrazing (↗)
DC: increased precipitation intensity (↗)
CA: terrestrial greening (↗)
NC: afforestation (↗)
CA: regional circulation changes (e.g. Sahel) (↗)
Dryland degradationDC: drying (↗)
DC: atmospheric warming (↗)
NC: land use intensification (↗)
DC: extreme events (heatwaves, floods) (↗)
DC: increased rainfall variability (↗)
CA: terrestrial greening (↘)
CA: insect outbreaks (↗)
CA: invasive species (↗)
Lake eutrophication/browningNC: nutrient pollution (↗)
CA: terrestrial greening (↗)
NC: afforestation (↗)
DC: atmospheric warming (↗)
DC: precipitation changes (↗)
Coral reef die-offDC: ocean warming (↗)
DC: marine heatwaves (↗)
CA: disease spread (↗)
CA: ocean acidification (↗)
NC: water pollution (nutrient / sediment) (↗)
NC: disruption (ships, over-harvesting) (↗)
CA: disease spread (↗)
CA: invasive species (↗)
DC: storm intensity (↗)
CA: sea level rise (↗)
Mangrove and seagrass meadow die-offDC: climate extremes increase (↗) 
NC: habitat loss/degradation (↗)
CA: sea level rise (esp. mangroves (↗)
NC: nutrient pollution (↗)
NC: shoreline change (↗)
DC: ocean warming (seagrass, ↗)
CA: disease spread (seagrass, ↗)
NC: invasive species (seagrass, ↗)
Marine regime shifts
(some fisheries, kelp, lipid pump, hypoxia)
NC: over-exploitation (↗)
DC: ocean warming (↗)
NC: water pollution (nutrients / sediment) (↗)
NC: habitat loss (↗)
DC: marine heatwaves (↗)
Ocean/atmosphere circulation
Ocean overturning collapse
(AMOC, SPG, Southern Ocean)
DC: ocean warming (↗)
DC: precipitation increase (↗)
CA: ice sheet meltwater increase (SMOC ↗, in future for AMOC/SPG ↗)
CA: river discharge increase (AMOC/SPG ↗)
CA: sea ice extent & thickness decrease (↗)
DC: regional aerosol forcing increase (↘)
CA: regional ocean circulation changes (?)
CA: wind trends (SO, ?)
CA: sea ice formation (SO, ?)
Monsoon collapse/strengthening
(West African, maybe Indian summer and South American)
DC: increased water vapour in atmosphere (ISM ↘, WAM/SAM ↗)
NC: increased summer insolation (↘)
DC/NC: increased aerosols, dust (↗, ?)
NC: land-cover change, e.g. deforestation (↗)
CA: desertification (↗)
CA: regional SST variations (?)
CA/NC: regional soil moisture/veg variation(?)
CA: ENSO / Indian Ocean Dipole change (?)
CA: AMOC slowdown (SAM, WAM ↗)
CA: low cloud reduction (ISM ↘) 
CA: ocean warming (ISM ↗)

Given this multi-causality of ESTPs, prevention requires tackling all of the drivers. The familiar concept of climate mitigation in the sense of reducing GHG emissions applies to ESTPs. Emission reductions serve to limit atmospheric GHG concentrations and correspondingly limit future increases in global average temperature, which is key for reducing the general risks of climate change. Since global temperature increase is a causal variable for most Earth system tipping processes of interest here, mitigation in the sense of reducing emissions of GHG will be the most important approach to preventing the crossing of ESTPs. This includes the management of SLCPs.

At the same time, conceiving of prevention only in terms of climate mitigation is too narrow. Prevention of most tipping points will involve a combination of mitigation and measures to address other drivers. Different tipping processes have distinct causal profiles requiring a tailored approach to prevention. Some tipping processes share characteristics that might allow developing prevention strategies for groups of tipping points (e.g. for major ice sheets or forest biomes). However, even within a cluster of similar tipping systems, significant differences might exist that affect the design of effective prevention approaches (e.g. different threshold temperatures for different ice sheets or different secondary drivers for forest dieback).

Prevention strategies that consider multiple causes might be more challenging because different causal variables can operate at different scales, both spatially and temporally. Correspondingly, effective governance approaches will have to be multi-scale and capable of taking cross-scale dynamics into account (see Chapter 3.1). For example, preventing Amazon dieback requires not only limiting temperature and precipitation changes, but also regional and national land management and other policies. Such a multi-causal approach to prevention could be advanced within the current framework of global sustainability governance with adjustments of existing institutions and strategic efforts to link and coordinate efforts across different scales. 

Box
3.2.2

Multiple drivers of Amazon rainforest dieback

The Amazon rainforest plays an important role as a climate regulator and biodiversity hotspot, but is at risk of dieback. If tipped, large parts of the Amazon could change relatively quickly (over multiple decades) into either a degraded forest or dry savannah-like state, leading to impacts that would be catastrophic for natural and human systems. These impacts include increases in regional and global temperature, decrease in precipitation across the Amazon and southern South America, droughts, fires and biodiversity loss, to name a few (see Chapter 1.3 & 2.2.3.1). Recent scientific evidence based on remotely sensed vegetation data suggests that more than three-quarters of the Amazon rainforest has been losing resilience since the early 2000s, which is consistent with parts of the forest nearing a tipping point (Boulton, Lenton, and Boers, 2022). Resilience is being lost faster in regions with less rainfall (which are more at risk of dieback) and in parts of the rainforest that are closer to human activity.

Global atmospheric temperature increase leading to drying is a key driver of potential tipping in the Amazon (see Chapter 1.3.2.1). Deforestation and forest fragmentation are also important drivers that contribute to and accelerate the shift from rainforest to degraded forest or savanna, raising the probability of crossing a tipping point during the 21st Century. Given these multiple drivers operating at global (temperature increase), regional (forest fragmentation), national and even lower (deforestation) scales, the Amazon tipping system is amenable to prevention efforts at multiple scales. Global mitigation efforts to limit atmospheric GHG concentrations present one approach, but other governance efforts need to address regional-scale drivers beyond the climate sphere. Slowing deforestation and forest fragmentation requires strong governance efforts outside the international climate change regime – e.g. collaboration among, and national policies in, Amazon states, changes in global investor behaviour and shifts in global consumption patterns. Strategic prevention efforts need to consider, and ideally coordinate, dynamics across these multiple scales.

Deforestation is an insightful example. Trends in the Amazon over the last decade have been a major concern. The annually deforested area increased by about 75 per cent between 2016 and 2022, but decreased during the first seven months of 2023 by more than 40 per cent compared to the same period in the previous year (Reuters, 2023). Deforestation in the Amazon has many interacting drivers linked to the global economy, but it is influenced primarily by national-scale policies, especially in Brazil. Between 2005 and 2016, Brazil experienced a notable reduction in deforestation rates (approximately 70 per cent (Global Forest Watch 2016)), demonstrating the effectiveness of the government’s efforts to combat it during that period. A combination of factors contributed to this, including increased law enforcement and the implementation of sustainable land use policies and programmes in the Amazon region.

In particular, Brazil’s Action Plan for the Prevention and Control of Deforestation in the Legal Amazon (PPCDAm) played a crucial role in driving down deforestation rates and promoting sustainable practices. Its application was effectively suspended in recent years, leading to an increase in deforestation, but reinstated in 2023 by the incoming Brazilian presidency. In addition, the Amazon Fund, established in 2008, is a financial mechanism to support local communities, NGOs and governmental initiatives in their efforts to reduce deforestation, increase recognition of land rights for Indigenous peoples, and promote sustainable development in the Amazon region. Actions taken during the previous Brazilian government resulted in significant changes to the Amazon Fund, leading to its temporary suspension, which may have contributed to the increase in deforestation. New pledges have been made in 2023 with the incoming presidency of Brazil.

The successful deforestation programmes in Brazil, as well as the dramatic impacts accompanying their suppression over recent years, demonstrate the importance of national-level politics for tipping point prevention in addition to, and largely independent of, global-scale climate governance institutions. Effective approaches to prevent a tipping point of the Amazon rainforest have to address deforestation locally and nationally in the Amazon states, but also global temperature change in the UNFCCC to protect and maintain this critical biome.

For some, especially biosphere-related, tipping points, one could conceive of tipping point prevention more broadly as efforts to build social-ecological resilience of a tipping system in its current stable state. Beyond countering the destabilisation of tipping systems by reducing tipping drivers, resilience-building measures can increase the capacity of the system to withstand disturbances. Fostering resilience can be achieved with a variety of strategies, including restoring diversity and redundancy in a system (e.g. species diversity in forests), reducing stressors and fostering sustainable land use. Efforts to protect and at least partly restore biosphere tipping systems such as the Amazon rainforest or coral reefs can both reduce pressures on them and increase their resilience to tipping event drivers like climate change. For example, restoring degraded or lost rainforest and protecting remaining rainforest (through, for example, improved land rights for Indigenous peoples, promoting agroforestry, and improved governance) can reduce deforestation and lead to substantial recovery of a degraded forest within a couple of decades (Poorter et al., 2021; Science Panel for the Amazon, 2021). This can maintain moisture-recycling feedbacks (see 1.3.2.1), thereby helping to maintain rainfall in at-risk forests downwind, as well as improving local resilience to climate change-induced droughts.

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