Abstract. Efficient adaptation strategies to climate change require estimating future impacts and the uncertainty surrounding this estimation. Over- or under-estimating future uncertainty may lead to maladaptation. Hydrological impact studies typically use a top-down approach in which multiple climate models are used to assess the uncertainty related to climate model structure and climate sensitivity. Despite ongoing debate, impact modelers have typically embraced the concept of "model democracy" in which each climate model is considered equally fit. The newer CMIP6 simulations, with several models showing a climate sensitivity larger than that of CMIP5 and larger than the likely range based on past climate information and understanding of planetary physics, have reignited the model democracy debate. Some have suggested that hot models be removed from impact studies to avoid skewing impact results toward unlikely futures. This large-sample study looks at the impact of removing hot models on the projections of future streamflow over 3,107 North American catchments. More precisely, the variability of future projections of mean, high, and low flows is evaluated using an ensemble of 19 CMIP6 GCMs, 5 of which are deemed "hot" based on their global equilibrium climate sensitivity (ECS). The results show that the reduced ensemble of 14 climate models provides streamflow projections with reduced future variability for Canada, Alaska, the Southwest US, and along the Pacific coast. Elsewhere, the reduced ensemble has either no impact or results in increased variability of future streamflow, indicating that outlier climate models do not necessarily provide outlier projections of future impacts. These results emphasize the delicate nature of climate model selection, especially based on global fitness metrics that may not be appropriate for local and regional assessments.
<p>Climate change is already impacting different aspects of our lives, creating new risks and exacerbating existing ones. Developing effective adaptation and mitigation strategies requires a robust understanding of the magnitude and uncertainty of climate change impacts. A top-down approach is generally used to study climate change impacts on hydrology, forcing the hydrological models with the projections of multiple climate models and studying the impacts. To this end, typically, the impact researchers have given equal weight to climate models considering them independent and equally plausible, giving rise to the notion of &#8220;model democracy&#8221;. However, model democracy has been criticized fundamentally, and in model ensembles in which the justifiability of some models is challenged, such as CMIP6, model democracy is not a viable option anymore. Some of the CMIP6 models project a warmer future than those predicted by CMIP5 previously. &#160;The climate sensitivity, a measure of the temperature rise in case of increased atmospheric carbon dioxide concentration, of these &#8220;hot models&#8221; is higher than the range that is expected to be plausible based on observations and our knowledge of planetary physics. The use of hot models in Climate change impact studies biases and overestimates the severity of the impacts. In this study, the impact of the inclusion (or exclusion) of hot models in a multi-model ensemble on the findings of large-sample hydrological climate change impact studies is evaluated. For 3107 North American catchments, we quantify this impact in terms of the magnitude and uncertainty of multiple streamflow metrics, such as mean annual streamflow and the hydrological extremes. The results exhibit a distinct spatial pattern in which the hot models' removal results in reduced streamflow metrics variability in northern regions (Canada and Alaska), southeast US, and along the US pacific coast. The reduced variability means that the hot models contribute to the extremes of the distributions in these regions. The variability reduction is highly dependent on the location of the catchments. Our findings emphasize the importance of the appropriate selection of climate models and display some of the dangers of including ill-advised models in climate change impact studies.</p><p><strong>Keywords: </strong>Climate change, GCMs, CMIP6, Impact study,&#160; Hydrology, hot models, climate model selection, Uncertainty&#160;</p>
No abstract
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.