Variability in projected elevation dependent warming in boreal midlatitude winter in CMIP5 climate models and its potential drivers
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The future rate of climate change in mountains has many potential human impacts, including those related to water resources, ecosystem services, and recreation. Analysis of the ensemble mean response of CMIP5 global climate models (GCMs) shows amplified warming in high elevation regions during the cold season in boreal midlatitudes. We examine how the twenty-first century elevation-dependent response in the daily minimum surface air temperature [d(ΔTmin)/dz] varies among 27 different GCMs during winter for the RCP 8.5 emissions scenario. The focus is on regions within the northern hemisphere mid-latitude band between 27.5°N and 40°N, which includes both the Rocky Mountains and the Tibetan Plateau/Himalayas. We find significant variability in d(ΔTmin)/dz among the individual models ranging from 0.16 °C/km (10th percentile) to 0.97 °C/km (90th percentile), although nearly all of the GCMs (24 out of 27) show a significant positive value for d(ΔTmin)/dz. To identify some of the important drivers associated with the variability in d(ΔTmin)/dz during winter, we evaluate the co-variance between d(ΔTmin)/dz and the differential response of elevation-based anomalies in different climate variables as well as the GCMs’ spatial resolution, their global climate sensitivity, and their elevation-dependent free air temperature response. We find that d(ΔTmin)/dz has the strongest correlation with elevation-dependent increases in surface water vapor, followed by elevation-dependent decreases in surface albedo, and a weak positive correlation with the GCMs’ free air temperature response.
KeywordsElevation dependent warming Mountains Rocky Mountains Tibetan Plateau Water vapor Snow albedo Feedbacks Temperature EDW Winter CMIP5 GCM
We thank the two anonymous reviewers for their time and helpful suggestions. This research is supported by the National Science Foundation Grants: AGS-1064326 and AGS-1064281. We acknowledge KNMI and ESGF data portals for access to CMIP5 data. We thank C. Naud and J. Barsugli for helpful comments.
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