Tony Chang
BIOE 504: Quantitative Biology
Fall 2012
Assignment #1: one paragraph summary of research project for course
Purpose: Quantify spatial trends and evaluate uncertainty in down-scaled regional climate data.
Project: For my project I propose to use the Parameter Elevation Regression and Independent Slopes
Model (PRISM) climate model to quantify the change in temperature within a finite spatial extent from
the time period of 1895-2011 (Daly et al. 2002). This project will the change in climate factors that
include; (Tmax, Tmin, Ppt) within the Greater Yellowstone Ecosystem extent at a 4km x 4km resolution
with data at a temporal resolution of 1 month 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, PRISM interpolates many of its values (Daly et al.
2008). I hope to use existing weather station data to evaluate the level of uncertainty of the PRISM data
at all grid cells.
Literature Cited:
Daly, C., Gibson, W.P., Taylor, G.H., Johnson, G.L., Pasteris, P. 2002. A knowledge-based approach to the
statistical mapping of climate. Climate Research. 22:99-113.
http://www.prism.oregonstate.edu/pub/prism/docs/climres02kb_approach_statistical_mapping-daly.pdf
Daly, C., Halbleib, M., Smith, J.I., Gibson, W.P. Doggett, M.K., Taylor, G.H., Curtis, J., Pasteris, P.P. 2008.
Physiographically sensitive mapping of climatological temperature and precipitation across the
conterminous United States. International Journal of Climatology. DOI: 10.1002/joc.1688
http://www.prism.oregonstate.edu/pub/prism/docs/intjclim08-physiographic_mappingdaly.pdf
SUMMARY UPDATE (9/18/2012)
Purpose: Quantify spatial trends and evaluate uncertainty in down-scaled regional climate data.
Project: For my project I propose to use the Daymet climate model to quantify the change in
temperature within a finite spatial extent from the time period of 1970 – 2011(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, Daymet interpolates many of its values
based on the spatial convolution of a truncated Gaussian weighting filter with a set of station locations.
Sensitivity to the typical heterogeneous distribution of stations is accomplished with an iterative station
density estimation algorithm (NTSG 2012).I hope to use existing weather station data to evaluate the
level of uncertainty of the Daymet data at all grid cells (Fig 1).
Fig 1. 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.
First steps in uncertainty grid construction will be the application of a jack knife cross validation method
to determine the Mean Absolute Error (MAE) and bias at known stations used in model interpolation
(Fig 2). Following this, a validation analysis will be performed on unused stations to determine additional
MAE and bias on the interpolation.
Fig 2: Example of a predicted vs. observed daily climate factor plot. Points closer to slope line represent correctly predicted values.
Numerical Terradynamic Simulation Group (NTSG). 2012. Spatial Bioclimatology.
<http://www.ntsg.umt.edu/project/daymet>. Accessed Sep. 18, 2012.
Thornton, P.E., Running, S.W., and White, M.A. 1997. Generating surfaces of daily meteorological
variables over large regions of complex terrain. Journal of Hydrology 190:214-251