Verification of Gridded Climate Data in Mountainous Terrain
Preliminary Conclusions of Four Years of Monitoring UnderUnder-sampled Regions
David B. Simeral, John T. Abatzoglou,
(David.Simeral@dri.edu;)
Division of Atmospheric Sciences, Desert Research Institute, Reno, NV; Western Regional Climate Center, Reno, NV
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INTRODUCTION:
Present day climate change is a critical, and yet heavily debated
environmental concern facing the planet. The IPCC’s Fourth
Assessment Report (2007) has noted unequivocal warming of the
climate system with projections of future change far outpacing
those experienced to date. While modeling efforts play an important
role in filling in the gaps in the observational record as well as
providing a basis for projections into the future, little effort has
examined whether they are capable of capturing basic climate
fields in regions of complex terrain and mountainous regions.
Recent effort by WRCC/CEC established a set of high altitude
monitoring observations in the Sierra Nevada and White Mountain
ranges to better understanding past, present, and future behavior
of mountain climate.
STUDY AREA:
The study area selected includes a mountain valley and a
mountaintop location. The DRI TREX network is situated in the
southern Sierra Nevada adjacent the tallest, steepest quasi-linear
topographic barrier in the contiguous U.S. (Fig. 1).
Specifically,TREX is located near Independence, California and
includes 16 stations along three elevational transects starting in
the eastern foothills of the Sierra Nevada extending eastward
across the Owens Valley to the Inyo Mountains foothills. The
mountaintop site selected is the highest weather station in the
lower 48 (4342m) on the summit of White Mountain Peak (Fig. 2).
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Model Error – Network wide
§ Summer TMIN (-.12°C bias); Summer TMAX (-0.65°C bias).
§ Winter TMIN (-0.41°C bias); Winter TMAX (-1.14°C bias).
PART I: PRISM Validation
Motivation:
Motivation: The TREX is of one-of-a-kind network that provides an
exceptional opportunity to investigate issues pertinent to
understanding climate gradients across fine spatial scales. With
this in mind, we examine the how well the PRISM climate model
temperature fields represent surface observations.
A Data and Methods
Data
§ Temperature data from the DRI observational stations are
summarized into mean monthly minimum (TMin) and maximum
(TMax) for summer (June – August, 2004 - 07) and winter months
(December – February, 2004 - 2008).
§ PRISM TMax, TMin and elevation encompassing the TREX
study area (Fig. 3) for contemporaneous period. The PRISM model
uses point data and a 4km gridded digital elevational model to
generate gridded estimates climate parameters (Daly et al., 1994).
Methods
§ Pearson’s correlation was utilized as a measure of the linear
relationship between the observations and PRISM model.
§ Model error calculations were made by taking the difference
between model predictions and observations. All station
elevations were <100m difference from grid elevations with
exception of TREX 1 (+404m), #2 (+105m), #8 (+141m), and
#9 (+135m).
B Results
Correlations
§ Strong network-wide correlation for summer & winter TMAX of
r=0.92, r=0.95 (Figure 5).
§ Network-wide summer TMIN correlation r=0.70 and winter
r=0.89.
Figure 6 (a & b) – Mean seasonal model error (deg C) for TMIN and TMAX, respectively
(red=summer; blue=winter) .
Interpretation
C
§ Wind plays an important role (PRISM does not take into account)
in capturing spatial and temporal temperature patterns, especially
near the Owens River and foothills of the Sierra Nevada where the
degree of diurnal temperature variability is directly linked to wind.
§ PRISM had difficulty capturing the small-scale influence exerted
by the region’s complex terrain which is characterized by undulating
topography, alluvial fans cross-cut by perennial and ephemeral
stream channels, and diverse vegetative cover.
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PART II: Climate Change at 14000 feet
Preliminary Conclusions from Four Years of High Altitude
Observations atop White Mountain Peak, California
Motivation: A Bellwether of Climate Change. High altitude
mountain observations provide a living laboratory to
investigate climate change.
§ Their absolute remoteness from competing factors (e.g., landuse change) may provide an opportunity to more clearly detect
the role of enhanced greenhouse gases on climate change.
§ Data provides continuous observations of unbiased “free air”
conditions.
§ Observed and projected climate change might be expected to
impact ecotypes at high altitude locations that may be at risk of
disappearing (e.g., Diaz and Eishceid, 2007).
A Data and Methods
Figure 1 – TREX network
Figure 2 – White Mountain Peak
Figure 5 – Pearson’s correlation coefficients for TMAX
(red=summer; blue=winter)
Figure 3 – TREX network layout on
PRISM 4km-grid
Figure 4 - WRCC White Mountain
observation (white diamond), NARR grid
(black squares), GR grid (red squares).
Figure 6 – Pearson’s correlation coefficients for TMIN
(red=summer; blue=winter)
Data
Mean Daily Temperature (TMEAN):
(TMEAN): Provides perhaps the most
reliable and meaningful metric of temperature change.
Observations:
Observations: WRCC summit station provides over four years
(Sep 2003-present, complete data 70%) of data at high temporal
resolution.
Free Air Temperature:
Temperature: Atmospheric temperature void of landsurface influence. Data is acquired from two sources
• North American Regional Reanalysis (NARR):
(NARR): High
resolution (32km, 3-hrly, 29 levels) dynamically consistent
gridded dataset that provides meteorological modeled
“observations”. Data spans 1979-present.
• Global NCEPNCEP-NCAR Reanalysis (GR):
(GR): Lower resolution (2.5˚
resolution, 17 levels) data analogous to NARR. Modeled
“observations” begin in 1949, here 1958-present is used due
to homogeneity in assimilation of radiosonde data.
Method
§ Geopotential height and temperature (from NARR, GR) are
horizontally interpolated to station location (lat/long)
§ Interpolation (spline) computes temperature at the station
elevation.
§ Bias-correction with a 61-day moving calendar day window.
B Results
§ Strong station-to-free air correlations
of r=0.985 (NARR) and r=0.97 (GR).
§ Correlation strongest (r>.98) in
winter/spring when synoptics dominate,
weaker (r=0.93) in summer when local
Fig 7: Time series of daily
features more important.
TMEAN for 2007 from WRCC
§ >99% free-air TMEAN within 3˚C of obs.
observations (blue), Global
§ Free air temperatures from NARR/GR
Reanalysis (black, 10˚C offset),
provide a sufficient surrogate for TMEAN & and NARR (red, -10˚C offset).
TMIN; however, TMAX is less favorable,
therein requiring continued observations.
§ This represents a best-case example as mountaintops are expected to
correlate well with free-air temperatures; other stations likely require
more complex analysis.
§ Methodology enables the ability to:
i. Perform rigorous QC
ii. Retrospectively develop time series of TMEAN covering period
of GR (1958-present). Correlation only monthly timescales >0.99
Reconstruction of White Mountain Observations
What can we conclude about 5050-years of Change?
I. Temperatures are increasing.
increasing. Over the 50 year period, a linear
trend of 0.24 ˚C/decade is noted. The trend here is over 30%
greater than that for the state of California as a whole, and
consistent with work showing free-air temperature trends over the
Sierra to be most pronounced above 1800m.
Figure 8: Reconstruction of
White Mountain summit annual
TMEAN from GR.
II.
II. Freezing Levels are increasing.
increasing. Consistent with increasing
temperatures, the mean freezing level has increased significantly.
During spring (MAM), there has been a 50m/decade increase in the
altitude of the 0˚C line since 1958. This corresponds to an increase in
the number of days White Mtn. experiences daily mean temperatures
above freezing, and ultimately to the survival of sub-alpine ecozones.
Figure 9: Reconstruction of the
frequency of days per year with
daily mean temperature
exceeding 0˚C.
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Concluding Thoughts
Four years of observations in complex terrain have proved useful in
validating gridded datasets on one hand, while certain inadequacies
have illuminated the need to further the observational campaigns on
the other. Continued meteorological observations in under-sampled
regions provide much needed data to better understand meteorology,
climate variability and climate change in climate-sensitive regions.
Improved understanding of transfer between observations and
gridded datasets can provide both backward looking (see above) and
forward looking (21st century climate change) snapshots of climate
that bridge the observational record.
References
§ Daly, C., R.P. Neilson, and D.L. Phillips. 1994. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology, 33, 140-158.
§ Diaz H. F., J. K. Eischeid (2007), Disappearing “alpine tundra” Köppen climatic type in the western United States, Geophys. Res. Lett., 34, L18707.
§ Intergovernmental Panel on Climate Change (IPCC), 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the
Intergovernmental Panel on Climate Change. Cambridge University Press, United Kingdom and New York, NY, USA, 996 pp.