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
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 interdecadal 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.
University, New York, NY
SNOW AND WATER VAPOR FEEDBACKS
Surface
Temperature
+
–
Snow
Cover
Absorbed Solar
Radiation
+
–
Albedo
TIBETAN PLATEAU
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.
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.
SATELLITE
NEURAL NETWORK
Surface
Temperature
+
Downward
DLF
Longwave
Radiation
+
+
Atmospheric
Water Vapor
SUMMARY
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.
  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.