The challenge to downscale
present and future climates
John Horel
University of Utah
John.horel@utah.edu
Observations
Defining Mountain Climates
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Dynamical Modeling
Observations
Defining Mountain Climates
Data Assimilation
Dynamical Modeling
Creating an Analysis
• Models or observations cannot
independently define mountain climates
and climate processes effectively
• Data assimilation methods
– Horizontally/vertically interpolate coarse
resolution model output to finer scale grid
– Use observations to adjust downscaled model
guidance at every gridpoint
Caveats
• Number of conventional observations
much less than number of gridpoints
• Models and observations contain errors
that must be taken into account (either
specified or predicted)
Recognition of Sources of Errors
Smooth terrain
Inaccurate ICs
Representative
Incomplete
Physics
Dynamical Model
Errors
Instrumental
Analysis
Errors
Observational
Errors
Need for balance…
Spatial & Temporal
Continuity
Dynamical Model
Climates
Specificity
Analysis
Climates
Observational
Climates
Analyses of Record (AOR)
• There is considerable demand for AORs: Best realtime and retrospective analyses at high spatial and
temporal resolution (Horel and Colman, BAMS, 2005)
• Planning led by Lee Anderson, NWS Office of
Science and Technology
• Clear applications:
– Contribute to generation of NWS operational gridded
forecasts and verification of those products
– Support efforts in regional and local climate modeling
and prediction
• Initial states for climate predictions
• Verification of climate models
• Process studies of past and ongoing climate
NWS Proposed AOR Program
• Phase I: Real Time Mesoscale Analysis
• Analyses to be produced hourly within 30 minutes
after valid time
• Phase II – Ongoing Analysis of Record
– Use state-of-the-art data assimilation
methods to obtain best analysis possible a
day or so after valid time
• Phase III – Reanalysis
– Apply mature AOR retrospectively
– 30 year time history of AORs
THE REAL TIME MESOSCALE ANALYSIS (RTMA):
Progress towards a National Mesoscale Analysis of Record
Manuel Pondeca,
Geoff Manikin, David Parrish, Jim Purser, Wan-Shu Wu,
Geoff DiMego, John Derber, Stan Benjamin, John Horel,
Lee Anderson, Brad Colman, Stephen Jascourt
Environmental Modeling Center
National Centers for Environmental Prediction
http://www.met.utah.edu/mesowest
http://www.met.utah.edu/mesowest
Moving Beyond the RTMA to Analyses of Record
• RTMA 2D variational assimilation approach is
insufficient for Analyses of Record
• Requires 4 dimensional data assimilation system
• Ensemble (vs. single deterministic) approaches
are as relevant to analyses as model forecasts
• Resources not available yet to support R&D
• Significant opportunity to advocate improving data
assimilation methods appropriate for mountain
climate applications
Observations
Downscaling Future Mountain Climates
Statistical downscaling
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Dynamical Modeling
Premise
• Resolution of global general circulation models (and
regional models) is too coarse to provide detailed
information on response of mountain precipitation or
surface temperature to increasing greenhouse gas
emissions
• Relate in physically-based manner observed mountain
precipitation to year-to-year variations in regional
tropospheric circulation features likely simulated by
GCMs with greater fidelity
• Assume relationships between present-day circulation
features & mountain precipitation continue during next
100 years
GFDL GCM 700 hPa winter (DJF) temperature
response** in northern Utah to A1B greenhouse
gas emission scenario
** bias removed
Separating the Inseparable…
• Nonlinear feedbacks are critical in the climate
system
• These feedbacks make it difficult to separate the
many physical mechanisms responsible for
future changes in snow pack
• One of the many ways precipitation may be
affected by increased greenhouse gas
emissions:
– Increased elevation of rain/snow line during winter as
a result of increased tropospheric temperature
Pilot Study Area:
East Slope Transect near Utah’s Ben Lomond Peak
Approach
• Estimate during each storm the variability in
elevation of rain/snow line over past 27 winters
using simplified physically-based approach
• Relate year-to-year variability in fraction of
precipitation above snow line during winter to
variability in 700 hPa winter temperature from
nearby rawinsonde
• Assume present relationship between 700 hPa
temperature & snow fraction will continue during
next 100 years
• Assess sensitivity of snow fraction to evolving
700 hPa temperature estimated by GCMs
Sensitivity to Tw: for each 1oC
increase, snow level rises 166 m
~3000 m
Tw observed ~ -5oC
700 hPa
Ben Lomond Peak 2970 m
BLP
BLT
Rawinsonde
Salt Lake City Airport
~65 km SSW
1288 m
Tw=2oC
Ogden Valley ~1500 m
Sensitivity to Tw: for each 1oC
increase, snow level rises 166 m
~3000 m
Tw observed ~ -4oC
700 hPa
Ben Lomond Peak 2970 m
BLP
BLT
Rawinsonde
Salt Lake City Airport
~65 km SSW
1288 m
Tw=2oC
Ogden Valley ~1500 m
Regression: Winter SLC 700 hPa Temperature vs.
Ben Lomond Transect Winter Snow Fraction
DJF Seasonal Snow Fraction
R= -0.65
Slope= -7.1% per oC
DJF 700 hPa Temperature (oC) at SLC
Snowpack Temperature Sensitivity
• Sierras
– Howat and Tulaczyk (2005)
– ~6-10% decrease in SWE per oC
• Cascades
– Casola et al. (2008)
– ~20% loss in snowpack per oC
• Ben Lomond Transect
– ~7% decrease in snow fraction per oC
DJF Seasonal Snow Fraction
Estimated Winter Snow Fraction
Based on GFDL A1B Simulation
Summary
• Analysis of Record proposed program provides
framework for improving downscaling for present
climate
• Funding for that program not clear and support
from broader community would be of great help
• Downscaling for present day or future climates
requires using entire suite of tools
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Observations
Surface state information
Statistical modeling
Dynamical modeling