Many studies have sought to constrain climate projections based on recent observations. Until recently, these constraints had limited impact, and projected warming ranges were driven primarily by model outputs. Here, we use the newest climate model ensemble, improved observations, and a new statistical method to narrow uncertainty on estimates of past and future human-induced warming. Cross-validation suggests that our method produces robust results and is not overconfident. We derive consistent observationally constrained estimates of attributable warming to date and warming rate, the response to a range of future scenarios, and metrics of climate sensitivity. We find that historical observations narrow uncertainty on projected future warming by about 50%. Our results suggest that using an unconstrained multimodel ensemble is no longer the best choice for global mean temperature projections and that the lower end of previous estimates of 21st century warming can now be excluded.
A realistic simulation of the Atlantic Multidecadal Variability (AMV) and related teleconnections is essential to resolve and understand the potential predictability over Europe at decadal timescale. Based on a large ensemble of state-of-the-art climate models, we show that a considerable intermodel spread exists in the spatiotemporal properties of the simulated AMV and teleconnections with European summer temperature. The greater the persistence, variance, and basin-scale spatial coherence, the stronger the teleconnection. We demonstrate that only a few members of a few models produce a teleconnection that is consistent with observational estimates over the instrumental period. This highlights the possible extreme nature of the last century teleconnection and/or a detrimental underestimation of ocean-land teleconnectivity in many climate models. Yet we emphasize the considerable uncertainties due to methods used to disentangle internal and externally forced variations in observations, and to sampling, which must be correctly accounted when analyses are performed on short temporal records.
Political decisions, adaptation planning, and impact assessments need reliable estimates of future climate change and related uncertainties. In order to provide these estimates, different approaches to constrain, filter, or weight climate model projections into probabilistic distributions have been proposed. However, an assessment of multiple such methods to, for example, expose cases of agreement or disagreement, is often hindered by a lack of coordination, with methods focusing on a variety of variables, time periods, regions, or model pools. Here, a consistent framework is developed to allow a quantitative comparison of eight different methods; focus is given to summer temperature and precipitation change in three spatial regimes in Europe in 2041-2060 relative to 1995-2014. The analysis draws on projections from several large ensembles, the CMIP5 multi-model ensemble, and perturbed physics ensembles, all using the high-emission scenario RCP8.5. The methods’ key features are summarized, assumptions are discussed and resulting constrained distributions are presented. Method agreement is found to be dependent on the investigated region but is generally higher for median changes than for the uncertainty ranges. This study, therefore, highlights the importance of providing clear context about how different methods affect the assessed uncertainty, particularly the upper and lower percentiles that are of interest to risk-averse stakeholders. The comparison also exposes cases where diverse lines of evidence lead to diverging constraints; additional work is needed to understand how the underlying differences between methods lead to such disagreements and to provide clear guidance to users.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.