Myles R. Allen’s research while affiliated with University of Oxford and other places

In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. Here we compile monitoring datasets (published here https://doi.org/10.5281/zenodo.15327155 Smith et al., 2025a) to produce updated estimates for key indicators of the state of the climate system: net emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. This year, we additionally include indicators for sea-level rise and land precipitation change. We follow methods as closely as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. The indicators show that human activities are increasing the Earth’s energy imbalance and driving faster sea-level rise compared to the AR6 assessment. For the 2015–2024 decade average, observed warming relative to 1850–1900 was 1.24 [1.11 to 1.35] °C, of which 1.23 [1.0 to 1.5] °C was human-induced. The 2024 observed record in global surface temperature (1.52°C best estimate) is well above the best estimate of human-caused warming (1.36°C). However, the 2024 observed warming can still be regarded as a typical year, considering the human induced warming level and the state of internal variability associated with the phase of El Niño and Atlantic variability. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.27 [0.2–0.4] °C per decade over 2015–2024. This high rate of warming is caused by a combination of greenhouse gas emissions being at an all-time high of 53.6 ± 5.2 GtCO2e per year over the last decade (2014–2023), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track decreases or increases in the rate of the climatic changes presented here.

Plain Language Summary In 2020, human activities caused global warming to reach 1.2°C since pre‐industrial times, leaving us with 0.3 and 0.8°C to meet the objectives of the Paris Agreement. Multiple model simulations are conducted under the adaptive emission scenarios to explore the relevant mitigation pathways, similar to the United Nations Framework Convention on Climate Change (UNFCCC) strategy. The effects of CO2 and non‐CO2 factors (like non‐CO2 greenhouse gases and aerosols) are analyzed through extensive ensemble simulations with various climate sensitivities. Our simulations illustrate that both forcing contributions are substantial, and the uncertainty in climate response to aerosols is the key hindrance to accurate climate projections under the Paris Agreement targets. The climate simulations exposed to the additional CO2 emissions display substantial and measurable global and regional differences in temperature and precipitation. For example, according to the ensemble‐averaged results, 1.1 trillion tons of additional CO2 emissions correspond to 0.5°C and 0.7% increases in global mean temperature and precipitation, highlighting the urgency to achieve carbon emission neutrality as soon as possible. Our findings accentuate that any further CO2 emission in the future increases the chance of the emergence of unfavorable climate characteristics jeopardizing current ecosystems and human well‐being.

While international climate policies now focus on limiting global warming to well below 2 °C or pursuing a 1.5 °C level of global warming, the climate modelling community has not provided an experimental design in which all Earth system models (ESMs) converge and stabilize at the same prescribed global warming levels. This gap hampers accurate estimations based on comprehensive ESMs of the carbon emission pathways and budgets needed to meet such agreed warming levels and of the associated climate impacts under temperature stabilization. Here, we apply the Adaptive Emission Reduction Approach (AERA) with ESMs to provide such simulations in which all models converge at 1.5 and 2.0 °C warming levels by adjusting their emissions over time. These emission-driven simulations provide a wide range of emission pathways and resulting atmospheric CO2 projections for a given warming level, uncovering uncertainty ranges that were previously missing in the traditional Coupled Model Intercomparison Project (CMIP) scenarios with prescribed greenhouse gas concentration pathways. Meeting the 1.5 °C warming level requires a 40 % (full model range: 7 % to 76 %) reduction in multi-model mean CO2-forcing-equivalent (CO2-fe) emissions from 2025 to 2030, a 98 % (57 % to 127 %) reduction from 2025 to 2050, and a stabilization at 1.0 (-1.7 to 2.9) PgC yr⁻¹ from 2100 onward after the 1.5 °C global warming level is reached. Meeting the 2.0 °C warming level requires a 47 % (8 % to 92 %) reduction in multi-model mean CO2-fe emissions until 2050 and a stabilization at 1.7 (-1.5 to 2.7) PgC yr⁻¹ from 2100 onward. The on-average positive emissions under stabilized global temperatures are the result of a decreasing transient climate response to cumulative CO2-fe emissions over time under stabilized global warming. This evolution is consistent with a slightly negative zero emissions commitment – initially assumed to be zero – and leads to an increase in the post-2025 CO2-fe emission budget by a factor of 2.2 (-0.8 to 6.9) by 2150 for the 1.5 °C warming level and a factor of 1.4 (0.9 to 2.4) for the 2.0 °C warming level compared to its first estimate in 2025. The median CO2-only carbon budget by 2150, relative to 2020, is 800 GtCO2 for the 1.5 °C warming level and 2250 GtCO2 for the 2.0 °C warming level. These median values exceed the median IPCC AR6 estimates by 60 % for the 1.5 °C warming level and 67 % for 2.0 °C. Some of the differences may be explained by the choice of the mitigation scenario for non-CO2 radiative agents. Our simulations highlight shifts in carbon uptake dynamics under stabilized temperature, such as a cessation of the carbon sinks in the North Atlantic and in tropical forests. On the other hand, the Southern Ocean remains a carbon sink centuries after temperatures stabilize. Overall, this new type of warming-level-based emission-driven simulation offers a more coherent assessment across climate models and opens up a wide range of possibilities for studying both the carbon cycle and climate impacts, such as extreme events, under climate stabilization.

Simple climate models (also known as emulators) have re-emerged as critical tools for the analysis of climate policy. Emulators are efficient and highly parameterised, where the parameters are tunable to produce a diversity of global mean surface temperature (GMST) response pathways to a given emission scenario. Only a small fraction of possible parameter combinations will produce historically consistent climate hindcasts, a necessary condition for trust in future projections. Alongside historical GMST, additional observed (e.g. ocean heat content) and emergent climate metrics (such as the equilibrium climate sensitivity) can be used as constraints upon the parameter sets used for climate projections. This paper describes a multi-variable constraining package for the Finite-amplitude Impulse Response (FaIR) simple climate model (FaIR versions 2.1.0 onwards) using a Bayesian framework. The steps are, first, to generate prior distributions of parameters for FaIR based on the Coupled Model Intercomparison Project (CMIP6) Earth system models or Intergovernmental Panel on Climate Change (IPCC)-assessed ranges; second, to generate a large Monte Carlo prior ensemble of parameters to run FaIR with; and, third, to produce a posterior set of parameters constrained on several observable and assessed climate metrics. Different calibrations can be produced for different emission datasets or observed climate constraints, allowing version-controlled and continually updated calibrations to be produced. We show that two very different future projections to a given emission scenario can be obtained using emissions from the IPCC Sixth Assessment Report (AR6) (fair-calibrate v1.4.0) and from updated emission datasets through 2022 (fair-calibrate v1.4.1) for similar climate constraints in both cases. fair-calibrate can be reconfigured for different source emission datasets or target climate distributions, and new versions will be produced upon availability of new climate system data.

Achieving net-zero global emissions of carbon dioxide (CO2), with declining emissions of other greenhouse gases, is widely expected to halt global warming. CO2 emissions will continue to drive warming until fully balanced by active anthropogenic CO2 removals. For practical reasons, however, many greenhouse gas accounting systems allow some 'passive' CO2 uptake, such as enhanced vegetation growth owing to CO2 fertilization, to be included as removals in the definition of net anthropogenic emissions. By including passive CO2 uptake, nominal net-zero emissions would not halt global warming, undermining the Paris Agreement. Here we discuss measures to address this problem, to ensure residual fossil fuel use does not cause further global warming: land management categories should be disaggregated in emissions reporting and targets to better separate the role of passive CO2 uptake; where possible, claimed removals should be additional to passive uptake; and targets should acknowledge the need for Geological Net Zero, meaning one tonne of CO2 permanently restored to the solid Earth for every tonne still generated from fossil sources. We also argue that scientific understanding of Net Zero provides a basis for allocating responsibility for the protection of passive carbon sinks during and after the transition to Geological Net Zero.

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by climate change 1,2 , climate models struggled to confirm this 3,4 . Using a high-resolution operational seasonal forecasting system that successfully predicted the extreme wet conditions, we perform counterfactual experiments simulating pre-industrial and future conditions. Both perturbed experiments show only minor rainfall changes, suggesting a limited role of CO₂ forcing for the event. Historical rise in CO₂ and ocean warming enhanced the rainfall by less than 10%, while simulations with increased CO₂ and warmer oceans fail to show a clear signal but increase the range of possible outcomes. By decomposing rainfall and underlying large-scale circulation into atmospheric CO 2 and SST-induced components, we illustrate how their relative changes control future dynamical responses. Accurately capturing the local dynamics is crucial for reliable regional climate adaptation and informing loss and damage discussions.

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by climate change 1,2 , climate models struggled to confirm this 3,4 . Here we perform two high-resolution seasonal climate forecast experiments for June-to-August 2022 with reduced and increased CO₂, complementing the successful operational forecasts issued in May 2022. Both experiments predict extreme wet conditions, suggesting that the historical rise in CO₂ slightly enhanced the rainfall. In contrast, simulations with increased CO₂ fail to show a further rise in mean rainfall but increased the range of possible outcomes. A decomposition of the rainfall and underlying large-scale circulation signals into their atmospheric CO 2 and SST-induced responses reveals how the balance of their relative changes controls the future dynamical response, which leads to the non-linear rainfall climate change signals over Pakistan. Accurately capturing these dynamics is crucial for reliable regional climate adaptation and assessing climate change-induced losses and damages.

The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.-Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and U.S. Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO 2 concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO 2 -induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO 2 signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a six-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.

Methane is an important greenhouse gas¹, but the role of trees in the methane budget remains uncertain². Although it has been shown that wetland and some upland trees can emit soil-derived methane at the stem base3,4, it has also been suggested that upland trees can serve as a net sink for atmospheric methane5,6. Here we examine in situ woody surface methane exchange of upland tropical, temperate and boreal forest trees. We find that methane uptake on woody surfaces, in particular at and above about 2 m above the forest floor, can dominate the net ecosystem contribution of trees, resulting in a net tree methane sink. Stable carbon isotope measurement of methane in woody surface chamber air and process-level investigations on extracted wood cores are consistent with methanotrophy, suggesting a microbially mediated drawdown of methane on and in tree woody surfaces and tissues. By applying terrestrial laser scanning-derived allometry to quantify global forest tree woody surface area, a preliminary first estimate suggests that trees may contribute 24.6–49.9 Tg of atmospheric methane uptake globally. Our findings indicate that the climate benefits of tropical and temperate forest protection and reforestation may be greater than previously assumed.

Intergovernmental Panel on Climate Change (IPCC) assessments are the trusted source of scientific evidence for climate negotiations taking place under the United Nations Framework Convention on Climate Change (UNFCCC). Evidence-based decision-making needs to be informed by up-to-date and timely information on key indicators of the state of the climate system and of the human influence on the global climate system. However, successive IPCC reports are published at intervals of 5–10 years, creating potential for an information gap between report cycles. We follow methods as close as possible to those used in the IPCC Sixth Assessment Report (AR6) Working Group One (WGI) report. We compile monitoring datasets to produce estimates for key climate indicators related to forcing of the climate system: emissions of greenhouse gases and short-lived climate forcers, greenhouse gas concentrations, radiative forcing, the Earth's energy imbalance, surface temperature changes, warming attributed to human activities, the remaining carbon budget, and estimates of global temperature extremes. The purpose of this effort, grounded in an open-data, open-science approach, is to make annually updated reliable global climate indicators available in the public domain (10.5281/zenodo.11388387, Smith et al., 2024a). As they are traceable to IPCC report methods, they can be trusted by all parties involved in UNFCCC negotiations and help convey wider understanding of the latest knowledge of the climate system and its direction of travel. The indicators show that, for the 2014–2023 decade average, observed warming was 1.19 [1.06 to 1.30] °C, of which 1.19 [1.0 to 1.4] °C was human-induced. For the single-year average, human-induced warming reached 1.31 [1.1 to 1.7] °C in 2023 relative to 1850–1900. The best estimate is below the 2023-observed warming record of 1.43 [1.32 to 1.53] °C, indicating a substantial contribution of internal variability in the 2023 record. Human-induced warming has been increasing at a rate that is unprecedented in the instrumental record, reaching 0.26 [0.2–0.4] °C per decade over 2014–2023. This high rate of warming is caused by a combination of net greenhouse gas emissions being at a persistent high of 53±5.4 Gt CO2e yr-1 over the last decade, as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that the rate of increase in CO2 emissions over the last decade has slowed compared to the 2000s, and depending on societal choices, a continued series of these annual updates over the critical 2020s decade could track a change of direction for some of the indicators presented here.