that suggests warming rates are amplified by elevation, with high mountain areas experiencing more rapid changes in temperature than lower elevation environments. This has serious implications for both mountain ecosystems and areas downstream.
However, the phenomenon of elevation-dependent warming, or EDW, as one of the regional expressions of climate change is still not completely understood. A new paper published in the journal Climate Dynamics
, ‘Elevation-dependent warming in global climate model simulations at high spatial resolution,’
aims to shed further light on this topic. Statistically significant change
“EDW occurs when a systematic and statistically significant change in warming rates with elevation is found,” says Dr. Elisa Palazzi of the National Research Council of Italy, who co-authored the paper. “And though this change may, in principle, not always be positive – meaning that warming rates can both increase or decrease with elevation – this is often the case.”
The paper uses climate model simulations to analyze EDW in three different mountain areas in the Northern Hemisphere: The Colorado Rocky Mountains, the Greater Alpine Region, and the Tibetan Plateau-Himalayas. “The use of climate models to study EDW overcomes some of the inadequacies inherent in all observing systems – especially when trying to identify the main mechanisms at work, and to disentangle the role of the different possible drivers,” Palazzi explains. “Specifically, we analyzed a set of runs performed with a state-of-the-art global climate model, the EC-Earth model, at five atmospheric horizontal resolutions from about 125km down to about 16km.”
By analysing this comprehensive set of simulations using the EC-Earth model – which is able to span a large range of spatial resolutions, including high resolutions not generally achievable with state-of-the-art global climate models – the paper’s authors were able to test the sensitivity of EDW to the spatial resolution of the data.
“A peculiarity of these simulations is that several different members were run for each resolution, with half of the members including standard model physics while the other half used stochastic parameterizations,” Palazzi adds. “This gave us the opportunity to gauge the internal variability of the EC-Earth model, and thus also climate variability in EDW, as well as the uncertainty associated with the specific model chosen.”Mountains as early-warning indicators
So what did these simulations uncover? “Our results show that the more frequent EDW drivers in all regions and seasons are the changes in albedo and in downward thermal radiation, and this is reflected in both daytime and nighttime warming,” says Palazzi. “In the Tibetan Plateau-Himalayas and in the Alpine Region, an additional driver is the change in specific humidity. Moreover, we verified that the role of internal climate variability can be significant in modulating the EDW signal. We also found that, although generally the climate model shows no clear resolution dependence in its ability to simulate the existence of EDW in the different regions, specific EDW characteristics such as its intensity and the relative role of different driving mechanisms may be different in simulations performed at different spatial resolutions.”
But while increasing the spatial resolution in climate models may thus offer a greater understanding of EDW, particularly in areas of complex topography, Palazzi stresses that other improvements could also be crucial. “Enhancements in model parameterizations, particularly those involving surface processes in high-mountain areas, such as snow albedo and cloud-radiation feedbacks, may also improve the simulation of EDW.”
“Considering the importance that mountains have as early-warning indicators of the consequences of global warming, EDW is a phenomenon that calls for further research and efforts - both in terms of observations, such as establishing networks of ground stations along an altitudinal gradient for example, and model simulations.”READ THE PAPER IN FULL: Palazzi, E., Mortarini, L., Terzago, S. et al. Elevation-dependent warming in global climate model simulations at high spatial resolution. Climate Dynamics (2018).
GEO-GNOME: ADDRESSING A NEED FOR MOUNTAIN DATA
An initiative co-led by the MRI and the National Research Council of Italy (CNR) is working to meet the need for mountain data. The Group on Earth Observations Global Network for Observations and Information in Mountain Environments (GEO-GNOME) seeks to connect and facilitate access to diverse sources of mountain observation data and information regarding drivers, conditions, and trends in biophysical and socio-economic processes of change at different scales.Find out more.