Mark R. England

Human-induced warming is amplified in the Arctic, but its causes and consequences are not precisely known. Here, we review scientific advances facilitated by the Polar Amplification Model Intercomparison Project. Surface heat flux changes and feedbacks triggered by sea-ice loss are critical to explain the magnitude and seasonality of Arctic amplification. Tropospheric responses to Arctic sea-ice loss that are robust across models and separable from internal variability have been revealed, including local warming and moistening, equatorward shifts of the jet stream and storm track in the North Atlantic, and fewer and milder cold extremes over North America. Whilst generally small compared to simulated internal variability, the response to Arctic sea-ice loss comprises a non-negligible contribution to projected climate change. For example, Arctic sea-ice loss is essential to explain projected North Atlantic jet trends and their uncertainty. Model diversity in the simulated responses has provided pathways to observationally constrain the real-world response.

Antarctic sea ice area exhibited an abrupt decline in 2015–2016, transitioning from a near record maximum state to a then‐record minimum state. The underlying drivers are still being studied, raising questions whether this marks the onset of a long‐term decline, or an isolated internal climate variability event. We identify extreme events in CMIP6 pre‐industrial control simulations that are comparable to the observed extreme event in 2015–2016 and explore their atmospheric and oceanic drivers. Results show these events are rare but possible. The most robust association we find is between a negative Southern Annular Mode transition and extreme Antarctic sea ice loss. Most models show sea ice recovery after extreme loss, differing from the persistent decline observed in recent years. This contrast suggests anthropogenic forcing may now be playing a role. Our results underscore the role of internal variability while improving understanding of extreme events and their relevance for future sea ice predictability.

Over the past two decades, Arctic sea ice loss has slowed considerably, with no statistically significant decline in September sea ice area since 2005. This pause is robust across observational data sets, metrics, and seasons. Large‐ensemble CMIP5 and CMIP6 simulations reveal that such periods with minimal sea ice decline under increasing greenhouse gas emissions are not unusual. Analysis of ensemble members that simulate analogs of the observed pause indicates that the current slowdown could plausibly persist another five to 10 years, although the chances of a faster‐than‐average decline are increased in the near‐future. The modeling evidence suggests that internal variability has substantially offset anthropogenically forced sea ice loss in recent decades. Overall, this observed pause in Arctic sea ice decline is consistent with simulated internal variability superimposed on the long‐term trend according to the bulk of the climate modeling evidence.

Antarctic sea ice area exhibited an abrupt decline in 2015-16, transitioning from a near record maximum state to a then-record minimum state. The underlying drivers are still being studied, raising questions whether this marks the onset of a long-term decline, or an isolated internal climate variability event. We identify extreme events in CMIP6 pre-industrial control simulations that are comparable to the observed extreme event in 2015-2016 and explore their atmospheric and oceanic drivers. Results show these events are rare but possible. The most robust association we find is between a negative SAM transition and extreme Antarctic sea ice loss. Most models show sea ice recovery after extreme loss, differing from the persistent decline observed in recent years. This contrast suggests anthropogenic forcing may now be playing a role. Our results underscore the role of internal variability while improving understanding of extreme events and their relevance for future sea ice predictability.

Over the past two decades, Arctic sea ice loss has slowed considerably, with no statistically significant decline in September sea ice area since 2005. This pause is robust across observational datasets, metrics, and seasons. Large-ensemble CMIP5 and CMIP6 simulations reveal that such periods with no sea ice decline under increasing greenhouse gas emissions are not unusual. Analysis of ensemble members that simulate analogues of the observed pause indicates that the current slowdown could plausibly persist another five to ten years. The modelling evidence suggests that internal variability has substantially offset anthropogenically forced sea ice loss in recent decades, although possible contributions from changes in the forced response remain uncertain. Overall, this observed pause in Arctic sea ice decline is consistent with simulated internal variability superimposed on the long term trend according to the bulk of the climate modelling evidence.

Since the beginning of the satellite era in 1979, September sea ice area over the Arctic has nearly halved, and the rapid sea ice decline in the early 2000s fueled concerns that the first ice-free Arctic summer could occur before 2020. In light of these dire forecasts, therefore, it is remarkable that no statistically significant decline in September Arctic sea ice coverage has occurred over past two decades, as we demonstrate here.This 20-year-long hiatus in pan-Arctic sea ice decline is robust across observational datasets, and also robust to the choice metric (sea ice area or extent). In fact, the present hiatus is seen in all months of the year, not only in September.One is immediately led to ask whether climate models are able to capture multi-decadal-long periods with no Arctic sea ice decline in the presence of strong anthropogenic radiative forcing. To answer this, we analyze multiple large ensembles of CMIP5 and CMIP6 simulations over the first half of the 21st century. We find that 20-year-long periods with no significant Arctic in sea ice loss occur rather frequently in climate models, even under high emissions scenarios. Roughly 20-30% of the hundreds of model runs analyzed here show 2005-2024 trends smaller than the small observed trend, although the intermodel spread is considerable. Sub-selecting models based on their ability of simulating observed climatological sea ice, or on their equilibrium climate sensitivity, or on the specific scenario forcings, does not alter the key result.Our analysis also reveals that the simulated forced response, in most models, is considerably larger than the observed trends. This highlights the important role of internal variability in enhancing and, more recently, in reducing Arctic sea ice decline. In fact, our analysis of model output reveals that a different configuration of internal climate variability could have caused an overall growth in Arctic sea ice over the past 20 years, surprisingly enough.Looking at the coming decades, and focusing on those climate simulation which are consistent with the present observed trends, we find the hiatus is 50% likely to persist for another five years, and could plausibly last another full decade (with roughly 30% chance).Taken together, our findings lead us to conclude that the current hiatus in Arctic sea ice decline, even as emissions of greenhouse gases continue to rise, is not an unexpected event that questions our basic understanding of the Arctic climate system. Climate models show that it can occur rather frequently, and that it could plausibly persist for years to come.

Methane is a potent greenhouse gas which has substantially contributed to climate change since the pre-industrial era, second only in importance to carbon dioxide. Due to its short atmospheric lifetime and high global warming potential, methane emissions have disproportionately large impacts on near-term climate change. Beyond its direct role as a greenhouse gas, methane also has other important implications for climate, human health, air quality and vegetation, largely due to its impact on tropospheric ozone. Thus, reducing methane emissions has been identified as a key policy lever for delaying the worst impacts of near-term climate change with expected co-benefits for health and air quality. The most notable of these efforts is the Global Methane Pledge which aims to achieve a 30% reduction in global anthropogenic methane emissions by 2030 as compared to 2020. And yet, in many respects, methane mitigation has been overlooked relative to other climate mitigation strategies. Existing modelling evidence for the estimating the potential climate benefits of methane mitigation rely extensively on idealised climate emulators or comprehensive modelling studies based on a limited number of models and ensemble members. Both approaches have important limitations. Hence, there is a pressing need for a co-ordinated intermodel comparison project which uses state-of-the-art ESMs in which all modelling groups prescribe identical reductions in methane concentrations or emissions, all modelling groups use the same baseline scenario, and sufficient ensemble members are simulated to investigate the broader climate and health impacts of methane mitigation. MethaneMIP has been envisioned to undertake these tasks.In this talk I will introduce the MethaneMIP protocol and the two new methane reduction scenarios ‘Technical Measures’ and ‘Ambitious’, which are both branched from SSP2-4.5 and cover the period 2020-2050. The overarching aim of MethaneMIP is to provide a policy-relevant state-of-the-art estimate of the climate and health impacts of methane mitigation, and a robust quantification of the uncertainties, as well as furthering our understanding of methane’s role in the climate system. Over ten modelling centres from across the world are participating in MethaneMIP, with simulations for the core MethaneMIP experiments currently underway. For the first time, I will present the preliminary results of MethaneMIP as pertaining to the research questions it was set up to address, including: What are the best estimates of the expected climate and health benefits of plausible methane mitigation by mid-century? Which near-term climate events projected to occur may be delayed or avoided by curbing methane emissions? What are potential impacts of successful implementation of the Global Methane Pledge? When should we expect the climate or health signal from reduced methane to be detectable in the presence of internal variability? I will finish by discussing the implications of MethaneMIP for climate policy, as well as introducing the flagship emissions-driven MethaneMIP simulations which will be performed later this year.

Antarctic sea ice cover experienced an abrupt decline in 2016, transitioning from a record maximum state to a record minimum state. However, the drivers of this rapid retreat are currently not well understood. Therefore, it is difficult to determine whether this signals the start of a long term melting trend, as has been long anticipated by climate models, or is an isolated episode of internal climate variability. In this study, we utilise the CMIP6 archive to understand if internal climate variability could be responsible for this Antarctic sea ice anomaly, and if so what the primary atmospheric and oceanic drivers are. This involves examining composites of the tropical teleconnections, subsurface ocean heat content, and high latitude atmospheric variability preceding extreme Antarctic sea ice anomalies in CMIP6 simulations. The primary objective is to elucidate the multifaceted factors influencing these extreme events, specifically addressing the 2016-2017 sea ice retreat, with lessons for 2023’s extreme Antarctic sea ice state. Initial results indicate that such events are possible in the absence of anthropogenic emissions in some climate models, although the occurrences are considered rare. We also show that that using the limited observed record alone will underestimate the interannual variability of the Antarctic sea ice cover and therefore overestimate how rare such an anomaly would be. In fact, if we extend the observed record further back using statistical reconstructions, rapid declines of sea ice extent occurred in the early and mid 20th century. Our results highlight the importance of internal climate variability in the Southern high latitudes and advance our understanding of the drivers and predictability of Antarctic sea ice changes. We discuss the implications of this work for 2023’s record Antarctic sea ice anomaly.

A fundamental divide exists between previous studies which conclude that polar amplification does not occur without sea ice and studies which find that polar amplification is an inherent feature of the atmosphere independent of sea ice. We hypothesise that a representation of climatological ocean heat transport is key for simulating polar amplification in ice-free climates. To investigate this we run a suite of targeted experiments in the slab ocean aquaplanet configuration of CESM2-CAM6 with different profiles of prescribed ocean heat transport, which are invariant under CO2 quadrupling. In simulations without climatological ocean heat transport, polar amplification does not occur. In contrast, in simulations with climatological ocean heat transport, robust polar amplification occurs in all seasons. What is causing this dependence of polar amplification on ocean heat transport? Energy-balance model theory is incapable of explaining our results and in fact would predict that introducing ocean heat transport leads to less polar amplification. We instead demonstrate that shortwave cloud radiative feedbacks can explain the divergent polar climate responses simulated by CESM2-CAM6. Targeted cloud locking experiments in the zero ocean heat transport simulations are able to reproduce the polar amplification of the climatological ocean heat transport simulations, solely by prescribing high latitude cloud radiative feedbacks. We conclude that polar amplification in ice-free climates is underpinned by ocean-atmosphere coupling, through a less negative high latitude shortwave cloud radiative feedback that facilitates enhanced polar warming. In addition to reconciling previous disparities, these results have important implications for interpreting past equable climates and climate projections under high emissions scenarios.