Draft proposal for BIOE 504 Project:
Analysis of past to present spatial climate trends in the GYE and uncertainty in regional downscaled
climate models
Tony Changa,1
a
Department of Ecology, Montana State University, Landscape Biodiversity Lab, 310 Lewis Hall, Bozeman, MT 59717, USA
1
Tel: 406-997-2670 E-mail address: tony.chang@msu.montana.edu
Purpose:
Quantify spatial trends and evaluate uncertainty in down-scaled regional climate data.
1. Introduction
Climate driven shifts in the mid to high latitudes have been predicted to catalyze disturbance events for
dominant vegetation species. These disturbance events may exceed the resilience of ecosystems resulting in
changes to the community structure and function. Predicting changes to ecosystems requires an understanding of
the system’s exposure to climate change. However, the rate of climate change can vary based on topographic
attributes of a landscape. Consideration of this variability at a regional scale is not well understood and may be
used as a predictive indicator of future change in vegetation distribution.
For my project I propose to examine the spatial patterns in climate change within the Greater Yellowstone
Ecosystem (GYE). The Greater Yellowstone Ecosystem (GYE) is characterized by large environmental complexity of
mountainous terrain and vast elevation gradients that provide central habitat for communities of high biodiversity,
including rare and endangered species (Hansen et al. 2000). Heterogeneous landscapes such as GYE, mark the
edge habitat that may be highly affected by climate shifts with events such as range contraction and changes in
phenology (Wilson et al 2005, Parmesan and Yohe 2003). This study will examine spatial patterns of climate
change in two phases. The first phase will consist of using existing weather station data (i.e. COOP, SNOTEL, RAW),
over a historic time period. The climate factors of the monthly average maximum temperature (Tmax), minimum
temperature (Tmin) and precipitation (ppt) will be assessed given their strong abiotic effects on ecosystems
primary production. The second phase will utilize an interpolated regional climate model to observe the range of
spatial variability of climate trend and validate cells containing weather stations to determine mean absolute error
of the interpolation model.
Phase 1: Currently, there exists ~76 weather stations within the GYE that are available for analysis having a
temporal coverage between 1895-2012. I will use a pilot study with an 8 weather station subset that covers a wide
range of spatial variability across the GYE. Due to variability within weather station installation and inconsistencies
of continuous data coverage, a histogram was developed to determine sufficient temporal coverage for climate
trend analysis (Fig.1)
150
100
50
0
1894
1900
1906
1912
1918
1924
1930
1936
1942
1948
1954
1960
1966
1972
1978
1984
1990
1996
2002
2008
Frequency of Data Points
Histogram of GYE Station Data Points by Year
Year
Figure 1. Histogram of temporal coverage of station observations for 8 stations within the GYE. In this example, sufficient
coverage for trend analysis was arbitrarily determined beginning at the year 1970.
The weather station subset for this pilot study consists of: Gardiner, Tower Falls, Mammoth, Jackson, Old Faithful,
Cooke City, Lake Yellowstone, and Darwin Ranch. These stations were considered due to their complete temporal
data coverage from the periods of ~1970-2011 and a robust spatial variability within the GYE including an elevation
gradient from 5304-8160 ft (Fig.2).
Figure 2. Select weather stations for pilot analysis of spatial climate trends within the Greater Yellowstone
Ecosystem.
2. Initial regression patterns
Initial climate trend analysis consists of a simple linear regression of the climate factor time series at the
individual weather station level (Fig. 3 and Fig. 4). Minimum temperature trends display significant correlation with
time for all stations except Tower Falls (Table 1). Maximum temperature and precipitation did not display a
significant trend with time for the period of 1970-2011. It is anticipated that this pattern will be consistent for
other stations within the GYE. These results suggest that annual winter temperatures (months of December,
January, February), when minimum temperatures are at their lowest, are increasing over time.
Table 1. Significant climate trends were found within the minimum temperature factor. Maximum temperatures
and precipitation do not display temporal correlation across the GYE.
Station Name
Elevation
Latitude
Longitude
Tmin Grad
p-value
Tmax Grad
p-value
ppt Grad
p-value
Gardiner
5305
45.2
-110.42
0.085
1e-5*
0.044
0.084
-0.003
0.151
Towerfalls
6266
44.916
-110.42
0.027
0.225
0.034
0.115
-0.003
0.203
Mammoth
6300
44.977
-110.69
0.031
0.05*
0.031
0.139
-0.002
0.361
Jackson
6450
43.6
-110.73
0.061
0.02*
0.065
0.011
-0.004
0.372
Old Faithful
7320
44.456
-110.85
0.101
2e-4*
-0.007
0.833
-0.002
0.822
CookeCity
Lake
Yellowstone
Darwin
Ranch
7460
45
-109.97
0.068
1e-4*
0.016
0.448
0
Nan
7835
44.544
-110.42
0.086
0.0005*
0.029
0.116
0.008
0.066
8160
43.416
-110.15
0.074
2e-4*
0.004
0.84
-0.002
0.692
Furthermore, regression analysis of weather stations by elevation suggest a potential relationship of
climate trends with elevations (Fig. 5). Giorgi et al (1996) and Rangwala and Miller (2012) suggest this trend may
be consistent with depletion of high elevation snowpack and increased surface snow-albedo feedback.
Tmin Gradient (~1970-2011) vs. Elevation
∆Tmin (C)/year
0.12
Old Faithful
0.1
0.08
Gardiner
Jackson
0.06
0.04
Cooke City Darwin Ranch
Mammoth
0.02
Lake Yellowstone
y = 0.0044x + 0.0469
R² = 0.1681
Tower Falls
0
5305
6266
6300
6450
7320
7460
7835
8160
Station Elevation
Figure 5. Potential elevation dependency of surface climate change signal within the Greater Yellowstone
Ecosystem.
Figure 3. Minimum temperature rates of change with time display a significant trend for 7 of 8 of the subset stations.
Figure 4. Maximum temperature rates of change with time are do not display a significant change within subset stations.
Principal component analysis of average monthly Tmin observations also display a potential relationship of
elevation with climate signal within the GYE (Fig. 6).
Figure 6. Principal component analysis of average monthly Tmin suggests elevation dependency of climate
change signal.
Examination of total variance by month for all years display greatest variability of Tmin in the winter season (Table
2). Future analysis will consist of principal component analysis examination of variation of climate change signal
using calendar months as independent variables to observe for explanation of variability.
Table 2. Variance in average Tmin by month are at their greatest during winter season.
Station
Gardiner
Cooke City
Darwin Ranch
Jackson
Lake Yellowstone
Moran Junction
Old Faithful
Snake River
Tower Falls
YNP Mammoth
Jan
31.1
24.0
24.8
56.1
34.3
48.8
25.3
44.3
34.3
28.4
Feb
21.9
16.7
23.9
34.9
31.4
35.9
32.4
28.2
25.5
28.7
Mar
20.3
17.1
18.8
31.8
26.9
37.2
17.4
22.4
22.7
22.5
Apr
9.0
12.4
15.8
6.3
14.3
14.0
13.6
10.3
8.6
8.2
May
5.5
5.0
6.5
5.1
9.2
6.0
6.4
4.2
4.4
5.4
Jun
5.1
3.9
2.0
4.5
5.8
6.4
3.3
3.4
4.7
6.2
Jul
7.6
3.6
3.4
4.3
9.3
6.9
8.1
5.7
7.1
6.3
Aug
6.7
4.1
5.1
6.0
11.5
10.0
7.3
11.2
7.4
7.2
Sep
8.9
5.2
6.3
7.4
8.7
8.8
5.3
7.9
8.2
7.2
Oct
7.4
7.1
6.0
8.1
10.3
7.8
10.2
11.1
8.9
8.3
Nov
16.8
19.1
21.1
20.9
17.1
22.3
29.2
30.3
23.4
19.6
Dec
16.5
24.2
26.2
37.2
29.4
37.3
31.8
47.0
22.2
21.2
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
10
9
8
7
6
5
4
3
2
1
0
Jan
10
9
8
7
6
5
4
3
2
1
0
Figure 7. Depictions of mean standard deviations for all weather stations in subset A) minimum temperature ( oF)
displays high variability for winter seasons and B) maximum temperature (oF) displaying greatest variability in
the spring and fall seasons.
Phase 2: I will use the Daymet or PRISM climate model to quantify the change in climate factors within a finite
spatial extent from the time period of 1970 – 2011(Daly et al. 1994,Thornton et al 1997). This project will the
change in climate factors that include; (Tmax, Tmin, Ppt) within the Greater Yellowstone Ecosystem extent at a
800mx800m resolution with data at a temporal resolution of 1 daily intervals. Trends in temperature change will
be preliminarily evaluated using an Ordinary Least Squares method and then explore autoregressive models.
Statistical tests (Student’s t/ or others) will then be applied to the models to determine significant change.
Due to non-existent climate data for many grid cells, RCMs interpolate many of their values based on
either weighted linear regressions (PRISM) or the spatial convolution of a truncated Gaussian weighting filter
(DAYMET) with a set of station locations. Currently the DAYMET/PRISM data set does not provide for regional
specific accuracy information. This may bring into consideration use of a Kriging method for spatial interpolation
to generate a climate surface.
Once a surface is decided upon, then the projects goals are twofold, to understand the temporal trend in
the data and to develop an uncertainty grid. To analyze the climate time series, we can draw a trend using a
standard linear fit model, given that the data is monotonous (which appears to be the case). These trends will be
displayed spatially to demonstrate spatial relationships with temporal climate change(Fig 8.)
Objectives:
1. Gather climate recording station data across the GYE from 1900-2011, including their spatial location in terms of
latitude, longitude, and elevation.
Requirements:
Collect datasets from NCDC and USDA ftp sites.
Format datasets for 3 specific factors (Tmax, Tmin, and ppt) over time periods
Utility:
Provides summary of climate in a limited extent with high resolution to
quantify levels of change across the landscape.
2. Estimate parameters for the temporal and spatial models and develop a probability distribution for each
parameter to observe confidence intervals for parameters.
Requirements:
Estimate parameters given a linear model for temporal relationships. Linear
models will be fit in terms of year and by specific month in year.
Utility:
Analysis in year will identify that a trend is occurring over a 12 month period.
Specific month in year trend will examine potential climate changes that may
have impact on phenology, assuming that temperature is a trigger for
vegetation lifecycles.
Spatial models assuming a specified distribution implies that data is sampled at
a single moment within a set of probability density functions. This distribution
of the process needs to be assessed provide a reality check of the level of
“truth”, or how much “information” we can gain for the sample.
4. Use developed distributions for parameters to create confidence intervals for climate data interpolations
spatially and temporally.
Requirements:
Generate multiple realizations of each parameter using random subsets of the
sample dataset (bootstrapping).
Utility:
Bootstraping provides a measurable quantity of confidence from which the
data is derived from. This implies that some observations (spatially located),
may have narrower confidence intervals than others, meaning that certainty
across the landscape is not homogenous.
5. Once model is developed, examine for spatial trends in temporal relationships with climate.
Requirements:
Map change over time parameter spatially using color gradients.
Utility:
Application of the above analysis, land resource managers can observe spatial
trends in climate change to focus their resources on for future mitigations, with
some known levels of confidence and uncertainty.
Methods: (Workflow diagram)
1.
2.
3.
4.
Data collection FormattingQuality ControlStratification
Model selections Parameter estimates
Bootstrap of estimates Analysis of process distribution Confidence intervals applied to individual
parameters
Temporal trends applied to spatial visualization  comparison to statistical interpretation models.?
Fig 8. Demonstration of climate trend grid generated using simple linear regression model at the cell level to determine spatial relationships
to climate change. Project is directed at assigning cell level uncertainty for future application of drawing climate trend relationships with
ecological datasets.
Phase 3: Vegetation overlay analysis
A recent land manager workshop was performed in September this year identified, Whitebark Pine as a
resource of concern for vulnerability assessment given potential climate changes in the recent decades (Table 3).
Structural changes in sub-alpine forests have been reported in the GYE due to pine beetle outbreaks in the past 30
years have resulting in severe mortality of whitebark pine (Jewett et al 2011, Logan et al., 2010 Logan and Bentz
1999). This mortality event has been thought to be related to climatic shifts in minimum temperatures, preventing
larval mortality (Regniere and Bentz 2007).
Resource
SCORE
Cutthroat Trout
4.29
Whitebark Pine
4.26
Gray Wolves
4.1
Ungulates
3.97
Grizzly Bear
3.94
Bison
3.81
Elk
3.75
Table 3. Scoring of GYE conservation targets for resource vulnerability assessment based on park and forest manager valuation during
climate workshop in July 30, 2012 (Oliff et al in prep). Score value range from 1-5, with 1 being low priority and 5 being highest.
Vegetation overlay analysis combined with the spatial climate change maps may reveal strong correlations of spatial mortality patterns
with whitebark pine (Fig. 9).
Figure 9. Macfarlane et al. (2010) landscape assessment of mountain pine beetle mortality in the GYE via aerial photography analysis.
Mortality rating range from (1-6) indicating level of health of white bark pine from 0= green (healthy), 3 = red needle (infestation), 6 =
grey(complete mortality).
Literature Cited
Giorgi, F., Hurrell, J.W., and Marinucci, M.R. 1996. Elevation dependency of the surface climate change
signal: a model study. Journal of Climate, 10:288-296.
Rangwala, I. and Miller, J.R. 2012. Climate change in mountains: a review of elevation-dependent
warming and its possible causes. Climatic Change, 114:527-547.
Régnière, J. and Bentz, B.J. 2007. Modeling cold tolerance in the mountain pine beetle, Dendroctonus
ponderosae. Journal of Insect Physiology. 53: 559-572.
Appedices
A1. Flow model of DAYMET interpolation scheme using weather station observations.
A2. Principal component analysis of independent months (jan =1, feb = 2….) displaying variability during the
winter seasons.
A3. Individual spatial climate trend map by month from 1970-2011, illustrating minimum temperature factor.