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Avoiding catastrophic climate change requires rapid decarbonization and improved ecosystem stewardship. To achieve the latter, ecosystems should be prioritized by responsiveness to direct, localized action and the magnitude and recoverability of their carbon stores. Here, we show that a range of ecosystems contain ‘irrecoverable carbon’ that is vulnerable to release upon land use conversion and, once lost, is not recoverable on timescales relevant to avoiding dangerous climate impacts. Globally, ecosystems highly affected by human land-use decisions contain at least 260 Gt of irrecoverable carbon, with particularly high densities in peatlands, mangroves, old-growth forests and marshes. To achieve climate goals, we must safeguard these irrecoverable carbon pools through an expanded set of policy and finance strategies. In order to limit warming and the most severe consequences of climate change, net global carbon emissions must reach zero by 2050. Many ecosystems contain carbon that would be irrecoverable on this timescale if lost and must be protected to meet climate goals.

Limiting warming to 1.5 °C is expected to lessen the risk of extreme events, relative to 2 °C. Considering Australia, this work shows a decrease of about 25% in the likelihood of record heat, both air and sea surface, if warming is limited to 1.5 °C. To avoid more severe impacts from climate change, there is international agreement to strive to limit warming to below 1.5 °C. However, there is a lack of literature assessing climate change at 1.5 °C and the potential benefits in terms of reduced frequency of extreme events1,2,3. Here, we demonstrate that existing model simulations provide a basis for rapid and rigorous analysis of the effects of different levels of warming on large-scale climate extremes, using Australia as a case study. We show that limiting warming to 1.5 °C, relative to 2 °C, would perceptibly reduce the frequency of extreme heat events in Australia. The Australian continent experiences a variety of high-impact climate extremes that result in loss of life, and economic and environmental damage. Events similar to the record-hot summer of 2012–2013 and warm seas associated with bleaching of the Great Barrier Reef in 2016 would be substantially less likely, by about 25% in both cases, if warming is kept to lower levels. The benefits of limiting warming on hydrometeorological extremes are less clear. This study provides a framework for analysing climate extremes at 1.5 °C global warming.

Two decades of international negotiations show that agreeing on emission levels for climate change mitigation is a hard challenge. However, if early warning signals were to show an upcoming tipping point with catastrophic damage, theory and experiments suggest this could simplify collective action to reduce greenhouse gas emissions. At the actual threshold, no country would have a free-ride incentive to increase emissions over the tipping point, but it remains for countries to negotiate their emission levels to reach these agreements. We model agents bargaining for emission levels using strategic reasoning to predict emission bids by others and ask how this affects the possibility of reaching agreements that avoid catastrophic damage. It is known that policy elites often use a higher degree of strategic reasoning, and in our model this increases the risk for climate catastrophe. Moreover, some forms of higher strategic reasoning make agreements to reduce greenhouse gases unstable. We use empirically informed levels of strategic reasoning when simulating the model.

The increased use of wind turbines for power generation could play an important role in climate change mitigation efforts. This study shows that, assuming greenhouse gas emissions are kept in check and energy-use efficiency increases, crossing the 2 °C warming threshold could be significantly delayed or even avoided altogether depending on how aggressively wind energy is taken up over coming decades.

Understanding climate sensitivity is critical to projecting climate change in response to a given forcing scenario. Recent analyses have suggested that transient climate sensitivity is at the low end of the present model range taking into account the reduced warming rates during the past 10-15 years during which forcing has increased markedly. In contrast, comparisons of modelled feedback processes with observations indicate that the most realistic models have higher sensitivities. Here I analyse results from recent climate modelling intercomparison projects to demonstrate that transient climate sensitivity to historical aerosols and ozone is substantially greater than the transient climate sensitivity to CO2. This enhanced sensitivity is primarily caused by more of the forcing being located at Northern Hemisphere middle to high latitudes where it triggers more rapid land responses and stronger feedbacks. I find that accounting for this enhancement largely reconciles the two sets of results, and I conclude that the lowest end of the range of transient climate response to CO2 in present models and assessments (less than 1.3 C) is very unlikely.

The response of the carbon cycle to climate change, including carbon fluxes, is now shown to be the second largest source of uncertainty in projections of temperature. A simplified climate model using temperature records and historical estimates of CO2 concentrations demonstrates that considering these two factors together reduces uncertainty further than treating them as individual parameters.

A tipping point occurs when an external forcing causes a qualitative change in a system. Human-induced climate change could push several large elements of the climate system, such as the Greenland ice sheet, past a tipping point. Given the severity of the potential impacts, early warning of these changes would be advantageous. This Review discusses the most promising approaches to early warning of tipping points.

Recent observed global warming is significantly less than that simulated by climate models. This difference might be explained by some combination of errors in external forcing, model response and internal climate variability.

Accurately determining the warming associated with scenarios of greenhouse gas emissions remains an overarching aim of climate modelling. Research now shows that contemporary measurements significantly reduce uncertainty bounds and indicate that some more extreme warming predictions may be less likely.

Permafrost loss can be projected by considering its distribution against warming air temperatures. Using observations to constrain loss estimates, this study investigates loss under different levels of warming. Permafrost, which covers 15 million km2 of the land surface, is one of the components of the Earth system that is most sensitive to warming1,2. Loss of permafrost would radically change high-latitude hydrology and biogeochemical cycling, and could therefore provide very significant feedbacks on climate change3,4,5,6,7,8. The latest climate models all predict warming of high-latitude soils and thus thawing of permafrost under future climate change, but with widely varying magnitudes of permafrost thaw9,10. Here we show that in each of the models, their present-day spatial distribution of permafrost and air temperature can be used to infer the sensitivity of permafrost to future global warming. Using the same approach for the observed permafrost distribution and air temperature, we estimate a sensitivity of permafrost area loss to global mean warming at stabilization of million km2 °C−1 (1σ confidence), which is around 20% higher than previous studies9. Our method facilitates an assessment for COP21 climate change targets11: if the climate is stabilized at 2 °C above pre-industrial levels, we estimate that the permafrost area would eventually be reduced by over 40%. Stabilizing at 1.5 °C rather than 2 °C would save approximately 2 million km2 of permafrost.