The role of the snow-albedo feedback
in simulated regional climate change
over the Rocky Mountains
Justin Minder & Ted Letcher
University at Albany
Roy Rasmussen,
Kyoko Ikeda & Changhai Liu
NCAR-RAP-HAP
http://www2.ametsoc.org/stac/index.cfm/committees/committee-on-mountain-meteorology/
my
v
How will temperature change over mountains under climate change?
v
(spatial patterns,
seasonality,
variability)
A tricky problem:
• The character of climate change on large-scales is
uncertain
• Mountains shape their own climates (in complex ways),
potentially modifying the impacts of large scale changes
This talk will focus on the role of the snow-albedo
feedback in modifying climate change over mountains
…using climate models.
… and what are the
uncertainties?
Global climate models (GCMs)
Break the globe into a grid
• Grid spacing ~100km
Prescribe initial state & “forcings”
Solve governing equations
• parameterize unresolved
processes
ΔT
IPCC AR5
Uncertainties :
• Unknown forcing
• Answer depends on model
• Answer depends on random natural
variability
• Unresolved processes (like orographic
effects!)
Global climate
models
Uncertainties
• Answer depends on model
Global climate models
Uncertainties
Answer depends on random natural variability
T-trend (2010-2060)
40 simulations with same
GCM, same forcing,
slightly different initial
Deser et al. (2014)
Regional climate models (RCMs)
GCM Uncertainties:
• Unresolved processes (like orographic effects!)
Regional climate models
• Run a higher resolution simulation forced
at boundaries by GCM or analysis
• Better capture small-scale processes (like
terrain effects… hopefully)
• Grid spacing: ~1-50km
• Much more computationally expensive
RCM Uncertainties
• Inherits all the errors/uncertainties of
the parent GCM!!
• Different RCM’s give different answers
• Hard to evaluate, but still have bias
• Can’t afford numerous simulations
Our strategy
Use a RCM as a tool to learn general lessons about how
mountains modulate the large-scale climate warming signal
How does the snow-albedo feedback (SAF) work over mountains?
Conduct simplified & controlled experiments to isolate and
understand processes
Isolate terrain effects from natural variability and uncertainties in large-scale
climate. Learn general lessons.
Develop diagnostic tools to compare & evaluate models
How strong is the SAF and what controls it?
Apply lessons learned to better understand fully complex problem
Why do models differ? How do model biases affect the SAF?
Our strategy:
Use a RCM to learn how mountains modulate large-scale climate warming
Headwaters Domain
State-of-the-art RCM
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•
•
•
Weather Research & Forecast (WRF) model
Noah 3.2 Land surface model (LSM)
Domain centered on CO Rockies
Horiz. grid spacing: 4km (12km, 36km)
Rasmussen et al. (2011, 2014)
“Pseudo-global warming” (PGW)
experiment
Control simulation
• 2000-2008
• Boundary conditions (BC’s) from NARR
reanalysis
• Excellent agreement with SNOTEL
precip.
• 2000-2008 NARR BC’s with monthly
perturbations (wind, humidity, temperature)
• Perturbations from GCM experiment of midcentury climate (AR4-SRES A2)
• Same large scale circulation (i.e., same
weather) but shifted climate
• Neglect uncertain changes in large-scale
circulation & storms
• Focus on regional modulation of
Resolution dependence of Rocky
Mountain terrain
Topography
KMResolution
Resolution
12KM
436
Approximate size of
GCM Grid Cell
(“flat high place”)
Breckenridge
Sawatch
San Juan
Breckenridge
Sawatch
Sangre de Cristo
San Juan
Sangre de Cristo
Snow cover and albedo from control simulation
(winter-spring 2003)
Terrain
Frac. Snow cover (%)
Albedo
Modeled and satellite-observed
fractional snow cover (winter-spring 2003)
Terrain
WRF model
MODIS obs.
Modeled and satellite-observed
fractional snow cover (winter-spring 2003)
Terrain
WRF model
MODIS obs.
Spatial patterns of warming & snow loss (PGW-control)
contours of elev.:
2.7km, 4km
contour of control
climate 25% snow cover
Seasonality of
warming &
albedo
(domain-average)
T (control)
albedo (control)
Albedo changes appear to:
• Strongly enhance warming
• Enhance variability of
warming
…depends on season
ΔT
Boundary perturbation
Δ albedo
Correlation between springtime
warming and albedo change
MAM, all Years
Close linear relationship between
albedo and warming
Quantifying the snow-albedo feedback:
linear feedback analysis
units: W m-2
System response
(e.g., change in top of
atmosphere [TOA] radiation )
(e.g., CO2 , boundary warming)
Roe (2008)
(e.g., loss of snow changes surface albedo)
Incoming solar
radiation
Strength of the Snow
Albedo Feedback (SAF)
Dependence of surf.
albedo on temp.
W m-2 K-1
Dependence of TOA
(planetary) albedo on
surface albedo
Qu & Hall (2007)
Quantifying the snow-albedo feedback:
Dependence of Surface Albedo on Surface Temperature
• Coupling strength between
albedo and temperature
• Quantify this by taking
difference between simulations
• Albedo-temperature
correlations support “linearity”
Qu and Hall 2013
MAM, all Years
Quantifying the snow-albedo feedback:
Dependence of “Planetary” Albedo on Surface albedo
• Change in planetary (TOA) albedo for a
given change in surface albedo
• Depends on degree of “masking” by
clouds, aerosols, water vapor
• Tricky to calculate
“Single Layer Model”
Donohoe and Battisti (2011)
Quantifying the snow-albedo feedback:
Putting it all together (over “headwaters” domain)
(incoming sunlight)
year-to-year variability
(dependence of albedo on temp.)
Large year-to-year variability;
resolution dependent in April-June
(dependence of TOA
albedo on surface albedo)
Similar for all months, years: ~0.6
(strength of the SAF)
Quantifying the snow-albedo feedback:
Dependence on model grid spacing
(incoming sunlight)
(dependence of albedo on temp.)
resolution dependent in April-June
(dependence of TOA
albedo on surface albedo)
Similar for all model resolutions: ~0.6
FMAMJ Average:
~2.4 W m-2 K -1
GCM ensemble FMAMJ average over Colorado
Rockies: ~1.9 W m-2 K -1. (Qu and Hall 2013)
36km simulation is strongest
in early to mid spring
4km simulation is strongest
in late spring to early
summer
Non-local snow-albedo feedback:
snow cover loss [%-pts.] (snowy pixels)
“What happens in the mountains doesn’t stay in the mountains”
15
10
5
0
2
2.4
ΔT [°C] (snow free pixels)
Up to 1 C extra warming
due to remote snow loss!
2.8
Current/Future work
• How do model biases affect the SAF?
• What role do other feedbacks (e.g., water vapor) play in
shaping climate warming over the Rockies?
• How does SAF interact with local circulations?
• How does SAF depend on land-surface (& snow) model?
• How does SAF vary between mountain ranges?
• How does SAF interact with dust radiative forcing?
Conclusions
The SAF is the dominant mechanism that
imparts spatial structure to RCM-simulated
climate warming over the CO Rockies
The strength of the SAF can be quantified and
understood using linear feedback analysis
• Processes
• Seasonality
• Resolution-dependence
The SAF has significant non-local effects
• Warming in nearby snow-free lowlands
• Buffered by atmospheric transport