Elevation Dependent Warming: Where, When, and Why James R. Miller1, Imtiaz Rangwala1, Catherine Naud2, and Yonghua Chen2

1Department of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ 2Columbia University, New York, NY

  Observations and model simulations indicate that the positive snow/albedo feedback loop has been, in part, responsible for enhanced warming at higher elevations in some mountain regions and will continue during the 21st century.

  Observations and model simulations indicate that the positive water vapor feedback loop contributes to enhanced DLR at higher elevations in some mountain regions (e.g., Tibetan Plateau, Alps), particularly during winter, and will continue throughout the 21st century.

REFERENCES Liu, X., and B. Chen, 2000: Climatic warming in the Tibetan Plateau during recent decades. Internat. J. Climatology, 20: 1729-1742. Naud, C.M., J. R. Miller, and C. Landry, 2012: Using satellites to investigate the sensitivity of longwave radiation to water vapor at high

elevations, J. Geophys. Res., 117, D0510.doi10.1029/2011JD016917. Rangwala, I., J. Miller, G. Russell and M. Xu, 2010: Using a global climate model to evaluate the influences of water vapor, snow cover

and atmospheric aerosol on warming in the Tibetan Plateau during the 21st century, Climate Dynamics, DOI 10.1007/s00382-009-0564-1.

Rangwala, I. and J. Miller, 2012: Climate Change in Mountains: A Review of Elevation Dependent Warming and its Possible Causes. Climatic Change, 114(3-4): 527-547.

Ruckstuhl, C., R. Philipona, J. Morland and A. Ohmura, 2007: Observed relationship between surface specific humidity, integrated water vapor, and longwave downward radiation at different altitudes. J. Geophys. Res., 112: L19809, doi:10.1029/2005GL023624.

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The figure above shows the positive feedback loop associated with snow albedo. It is one of the primary feedbacks responsible for enhanced rates of warming at high latitudes. Several studies have indicated that a similar feedback occurs in high elevation regions (Rangwala and Miller, 2012). Shown below is another positive feedback loop that is associated with water vapor that appears to be partially responsible for the enhanced rates of warming in some mountain regions. As temperature increases, atmospheric water vapor increases, which in turn increases the downward longwave radiation (DLR), which increases the surface temperature. As shown in the next column, this feedback is stronger during winter when the atmospheric water vapor is low and the rate of change of DLR with respect to changes in water vapor is large.

The figures above are the decadally averaged seasonal values of the modeled surface specific humidity (q) and DLR over the Tibetan Plateau from 1950-2100 are plotted on the power law relationship (DLR = 181.4 x q0.29) described in Ruckstuhl et al. (2007) for all sky conditions. Seasonally observed values of q are denoted by the arrows on the x-axis. The atmospheric water vapor is lower at higher elevations, hence, the water vapor feedback is stronger at higher elevations because the slopes of the curves are steeper when the atmosphere is drier (from Rangwala et al., 2010). The figure below on the left is obtained from satellite retrievals and indicates that satellites can be used to extend the observational database in high elevation regions and obtain the same non-linear relationship between water vapor and DLR, hence can be used to investigate the water vapor feedback (from Naud et al., 2012). The figure below on the right is based on a neural network analysis that shows the same non-linear shape which suggests that this analysis can be used to investigate sensitivities among climate variables.

ABSTRACT In some high elevation regions there is evidence that temperatures are warming faster than the global average, and the warming appears to be elevation dependent in some cases. Among the factors that can contribute to this enhanced warming are clouds, atmospheric water vapor, snow cover, aerosols, and the land surface. One of the difficulties in trying to quantify the relationships and feedbacks among climate variables is that observations are often sparse or non-existent in these complex topographic regions. Another difficulty is that the enhanced warming is usually occurring in response to more than one climate variable, and furthermore, these variables are often correlated with each other. We provide examples of enhanced high-elevation warming, discuss seasonal and inter-decadal variability, and suggest mechanisms that might be responsible for the temperature enhancements. We also demonstrate that a neural net scheme can be used to quantify sensitivities between pairs of climate variables (e.g., the relationship between surface downward longwave radiation and atmospheric water vapor) and that satellite retrievals can help to expand the observational database.

SNOW AND WATER VAPOR FEEDBACKS

One region where elevation dependent warming has been found is in the Tibetan Plateau. The figure below (from Rangwala et al., 2010) shows observed (1961–1990; from Liu and Chen 2000; # stations = 178) and modeled (1961–1990 and 2000–2090; # grid cells = 29) trends in surface temperature (C/decade) in the Tibetan Plateau as related to the elevation of the observing station and the model grid, respectively. The observed elevation dependence is represented well in the GISS climate model. The model projects that this elevation dependence will continue during the 21st century and will be enhanced at all elevations.

TIBETAN PLATEAU

SUMMARY

Surface Temperature

Absorbed Solar Radiation

Albedo

Snow Cover

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+ –

DLF

+

+

+

Downward LongwaveRadiation

AtmosphericWater Vapor

SurfaceTemperature

SATELLITE NEURAL NETWORK