Large-scale emergence of regional changes in year-to-year temperature variability by the end of the 21st century.

Global warming is expected to not only impact mean temperatures but also temperature variability, substantially altering climate extremes. Here we show that human-caused changes in internal year-to-year temperature variability are expected to emerge from the unforced range by the end of the 21st century across climate model initial-condition large ensembles forced with a strong global warming scenario. Different simulated changes in globally averaged regional temperature variability between models can be explained by a trade-off between strong increases in variability on tropical land and substantial decreases in high latitudes, both shown by most models. This latitudinal pattern of temperature variability change is consistent with loss of sea ice in high latitudes and changes in vegetation cover in the tropics. Instrumental records are broadly in line with this emerging pattern, but have data gaps in key regions. Paleoclimate proxy reconstructions support the simulated magnitude and distribution of temperature variability. Our findings strengthen the need for urgent mitigation to avoid unprecedented changes in temperature variability.

Volcanic-induced global monsoon drying modulated by diverse El Niño responses.

There remains large intersimulation spread in the hydrologic responses to tropical volcanic eruptions, and identifying the sources of diverse responses has important implications for assessing the side effects of solar geoengineering and improving decadal predictions. Here, we show that the intersimulation spread in the global monsoon drying response strongly relates to diverse El Niño responses to tropical eruptions. Most of the coupled climate models simulate El Niño-like equatorial eastern Pacific warming after volcanic eruptions but with different amplitudes, which drive a large spread of summer monsoon weakening and corresponding precipitation reduction. Two factors are further identified for the diverse El Niño responses. Different volcanic forcings induce systematic differences in the Maritime Continent drying and subsequent westerly winds over equatorial western Pacific, varying El Niño intensity. The internally generated warm water volume over the equatorial western Pacific in the pre-eruption month also contributes to the diverse El Niño development.

Natural drivers of multidecadal Arctic sea ice variability over the last millennium.

The climate varies due to human activity, natural climate cycles, and natural events external to the climate system. Understanding the different roles played by these drivers of variability is fundamental to predicting near-term climate change and changing extremes, and to attributing observed change to anthropogenic or natural factors. Natural drivers such as large explosive volcanic eruptions or multidecadal cycles in ocean circulation occur infrequently and are therefore poorly represented within the observational record. Here we turn to the first high-latitude annually-resolved and absolutely dated marine record spanning the last millennium, and the Paleoclimate Modelling Intercomparison Project (PMIP) Phase 3 Last Millennium climate model ensemble spanning the same time period, to examine the influence of natural climate drivers on Arctic sea ice. We show that bivalve oxygen isotope data are recording multidecadal Arctic sea ice variability and through the climate model ensemble demonstrate that external natural drivers explain up to third of this variability. Natural external forcing causes changes in sea-ice mediated export of freshwater into areas of active deep convection, affecting the strength of the Atlantic Meridional Overturning Circulation (AMOC) and thereby northward heat transport to the Arctic. This in turn leads to sustained anomalies in sea ice extent. The models capture these positive feedbacks, giving us improved confidence in their ability to simulate future sea ice in in a rapidly evolving Arctic.

Possible causes of data model discrepancy in the temperature history of the last Millennium.

Model simulations and proxy-based reconstructions are the main tools for quantifying pre-instrumental climate variations. For some metrics such as Northern Hemisphere mean temperatures, there is remarkable agreement between models and reconstructions. For other diagnostics, such as the regional response to volcanic eruptions, or hemispheric temperature differences, substantial disagreements between data and models have been reported. Here, we assess the potential sources of these discrepancies by comparing 1000-year hemispheric temperature reconstructions based on real-world paleoclimate proxies with climate-model-based pseudoproxies. These pseudoproxy experiments (PPE) indicate that noise inherent in proxy records and the unequal spatial distribution of proxy data are the key factors in explaining the data-model differences. For example, lower inter-hemispheric correlations in reconstructions can be fully accounted for by these factors in the PPE. Noise and data sampling also partly explain the reduced amplitude of the response to external forcing in reconstructions compared to models. For other metrics, such as inter-hemispheric differences, some, although reduced, discrepancy remains. Our results suggest that improving proxy data quality and spatial coverage is the key factor to increase the quality of future climate reconstructions, while the total number of proxy records and reconstruction methodology play a smaller role.

Importance of the Pre-Industrial Baseline in Determining the Likelihood of Exceeding the Paris Limits.

During the Paris Conference in 2015, nations of the world strengthened the United Nations Framework Convention on Climate Change by agreeing to holding "the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C"1. However, "pre-industrial" was not defined. Here we investigate the implications of different choices of the pre-industrial baseline on the likelihood of exceeding these two temperature thresholds. We find that for the strongest mitigation scenario RCP2.6 and a medium scenario RCP4.5 the probability of exceeding the thresholds and timing of exceedance is highly dependent on the pre-industrial baseline, for example the probability of crossing 1.5°C by the end of the century under RCP2.6, varies from 61% to 88% depending on how the baseline is defined. In contrast, in the scenario with no mitigation, RCP8.5, both thresholds will almost certainly be exceeded by the middle of the century with the definition of the pre-industrial baseline of less importance. Allowable carbon emissions for threshold stabilisation are similarly highly dependent on the pre-industrial baseline. For stabilisation at 2°C, allowable emissions decrease by as much as 40% when earlier than 19th century climates are considered as a baseline.