Home Archived April 13, 2016

Climate Analysis Tools


 Plot showing the daily maximum temperature for Bozeman, MT since pre-1900.

Project Abstract

The physical science linking human-induced increases in greenhouse gasses to the warming of the global climate system is well established, but the implications of this warming for ecosystem processes and services at regional scales is still poorly understood. Thus, the objectives of this work were to: (1) describe rates of change in temperature averages and extremes for western Montana, a region containing sensitive resources and ecosystems, (2) investigate associations between Montana temperature change to hemispheric and global temperature change, (3) provide climate analysis tools for land and resource managers responsible for researching and maintaining renewable resources, habitat, and threatened/endangered species and (4) integrate our findings into a more general assessment of climate impacts on ecosystem processes and services over the past century. Over 100 years of daily and monthly temperature data collected in western Montana, USA are analyzed for long-term changes in seasonal averages and daily extremes. In particular, variability and trends in temperature above or below ecologically and socially meaningful thresholds within this region (e.g., -17.8°C (0°F), 0°C (32°F), and 32.2°C (90°F)) are assessed. The daily temperature time series reveal extremely cold days (≤-17.8°C) terminate on average 20 days earlier and decline in number, whereas extremely hot days (≥32°C) show a three-fold increase in number and a 24-day increase in seasonal window during which they occur. Results show that regionally important thresholds have been exceeded, the most recent of which include the timing and number of the 0°C freeze/thaw temperatures during spring and fall. Finally, we close with a discussion on the implications for Montana’s ecosystems. Special attention is given to critical processes that respond non-linearly as temperatures exceed critical thresholds, and have positive feedbacks that amplify the changes.

 Side-by-side comparison diagram - MSU Bozeman and Fortine


Within Pederson et al. (2009), and in the example R code provided here, the selected temperature thresholds were intended to highlight several potentially important points beyond which biological systems and physical systems may become increasingly impacted. However, the R code is easy to modify, and different temperature thresholds or annualization periods may be set for applicability in different climates, or for different known sensitivities of biological organisms or physical processes. The program also produces a series of summary graphics that helps in quality assurance and control of the raw data to aid in detection of a host of potential problems (e.g. missing data, outliers).

We urge potential users to also exercise caution when interpreting patterns and trends from any individual station due to the complexity of global climate change impacts within and across particular regions, the ‘weather’ noise associated with looking at trends and patterns on progressively smaller and smaller spatial scales, and the problems that may arise from any single station record when taken alone. Thus, we urge future program users to perform the analyses using multiple stations across a climatically similar region. DO NOT expect to find proof or disproof of greenhouse gas caused global warming from analyzing an individual station record, or even a particular pattern or trend that may arise from a limited region. Evidence for or against greenhouse-gas caused warming relies on careful detection and attribution studies that assess the role of climate forcings using climate models and large observational datasets. For climate change detection and attribution across the Western U.S. readers are urged read a recent paper from Bonfils et al. (2008).

Bonfils, C., B.D. Santer, D.W. Pierce, H.G. Hidalgo, G. Bala, T. Das, T.P. Barnett, D.R. Cayan, C. Doutriaux, A.W. Wood, A. Mirin, and T. Nozawa, 2008: Detection and Attribution of Temperature Changes in the Mountainous Western United States. J. Climate, 21, 6404–6424. (http://ams.allenpress.com/perlserv/?request=get-abstract&doi=10.1175%2F2...)

R Code written for single station daily temperature analysis may be obtained by right clicking on the following links:

HCNtempAnalysis.R: original script used in the manuscript.
HCNdaily_analysis_5-5-08.r: A modified script with different extremes more relevant for the southwestern U.S., plus it tracks annual extremes.
HCNtemp-visualization.r: Produces 3-D contour plots of daily temperature data.

Link to image gallery of analyses the script will perform.

This R code is free from the authors and comes with ABSOLUTELY NO WARRENTY AND NO GAURENTEE OF SUPPORT. That said, users often have to make adjustments to the following sections depending on what modifications are desired:

## Create the output dataframe

  • Sta.T.summary <- data.frame(matrix(NA, nrow = (numyears), ncol = 19)) names(Sta.T.summary) = c("sYear", "firstmin32", "lastmin32",
  • "countmin32", "firstmin15", "lastmin15", "countmin15",
    "firstmax32","lastmax32","countmax32","yearlymin", "Year95",
    "firstmax95", "lastmax95", "countmax95", "firstmax100", "lastmax100", "countmax100", "yearlymax")

  • Sta.T.summary$sYear <- seq(startyear, (endyear), by=1)
  • Sta.T.summary$Year95 <- seq((startyear2-1), (endyear2), by=1)
  • Sta.T.summary[1:10,]

  • You sometimes have to add or subtract a year from the frame to get the data to fit, depending on the observations. Just then make sure to cutoff extraneous or partial years in this section:

    Sta.T.summary <- subset(Sta.T.summary, sYear >= (StartYearCutOff+1) & Year95 <= 2004)

    We kindly request that if you use this R code in support of analysis that you please provide the following source citation:

    Pederson, G.T., L.J. Graumlich, D.B. Fagre, T. Kipfer and C.C. Muhlfeld. 2009. What do recent temperature trends portend? A view from western Montana, USA. Climatic Change 96: DOI 10.1007/s10584-009-9642-y, 22pp.

    Click here for Full Text in PDF (965KB)

    For inquiries, please contact Greg Pederson.