Electronic Supplementary Material for
Historical Perspective on the Dust Bowl Drought in
the Central United States
Climatic Change
Dorian J. Burnette1 and David W. Stahle2
1
Department of Earth Sciences, 109 Johnson Hall, University of Memphis,
Memphis, TN, 38152
2
Department of Geosciences, 113 Ozark Hall, University of Arkansas,
Fayetteville, AR, 72701
Corresponding Author:
Dorian J. Burnette
Department of Earth Sciences
109 Johnson Hall
University of Memphis
Memphis, TN 38152
Phone: 901-678-4452
Fax: 901-678-2178
E-Mail: djbrntte@memphis.edu
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1. Data collection
The most continuous weather records in Kansas prior to 1870 are located in the
northeastern part of the state, and three clusters of stations that were the most continuous from
the 19th century to present were selected for the reconstruction (Darter 1942). The primary
stations within each cluster were used as a baseline and then supplemented with surrounding
station data to fill gaps and extend the history of the station cluster. The Lawrence cluster was
supplemented with additional data from Burlingame, Globe, Morse, Olathe, Ottawa, and Topeka,
Kansas. Fort Leavenworth and Kansas City were used to augment the record from the city of
Leavenworth, and Manhattan was supplemented with nearby station data available from Fort
Riley and Wamego, Kansas.
The daily precipitation records from Oregon and Miami, Missouri, were also included to
provide additional overlap with the longest stations from northeastern Kansas, and were
supplemented with nearby station data for continuity. The Oregon, Missouri, record was kept by
William Kaucher, but only portions of the daily record were available on microfilm from the
National Archives beginning in 1867 (Darter 1942). Kaucher began systematic daily
temperature and precipitation records upon his arrival in Oregon in 1855 (Hackett 1903), but
most of these data have not been found. In this paper, we use Kaucher’s daily observations
available in the microfilms and an additional one and one-half years of data that were published
in the Missouri Valley Times from 1874-76 (total daily record for Oregon = 1 January 1867 to
31 December 1892, with missing values). These daily data were then supplemented with daily
precipitation observations from Atchison, Kansas, and St. Joseph, Missouri.
Daily precipitation data for Miami, Missouri, running from 1 January 1850 to 31
December 1892 were obtained from the Amos H. Sullivan papers (Sullivan 1934). Sullivan was
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a surgeon who had an interest in the history of the Miami area and kept a diary of precipitation
data beginning in 1847. Daily data were only available back to January 1850, so this study uses
his daily observations beginning on 1 January 1850. Miami does not have a long modern record,
so nearby Brunswick, Missouri, was substituted for the modern Miami record and supplemented
with Carrollton and Marshall, Missouri.
All of these daily precipitation records were screened for legibility and metadata as each
station was digitized. The Web Search Store Retrieve Display system of NOAA’s Climate
Database Modernization Program was consulted for some of these data (Dupigny-Giroux et al.
2007), but the quality of the scanned microfilms can be poor. Therefore, the historical data were
primarily digitized directly from the source (i.e., microfilms and hand-written documents). Any
observations that were too illegible were recorded as missing. All modern records for each
station were obtained from the Global Historical Climatology Network (GHCN, Peterson and
Vose 1997). The augmented station data files available online at
www.djburnette.com/research/kansas/precip/ list the stations that were used for each day.
The degree to which an average of these five-station clusters represents the spatial
variability of warm season rainfall across the Kansas-Missouri study area was examined by
correlating the 1950-2006 seasonal precipitation totals with the 0.5° gridded precipitation
amounts from the Climatic Research Unit (TS3.0; Mitchell and Jones 2005). The growing
season is illustrated in Figure 1, and similar results were found in the spring and summer (not
shown). The primary data used for precipitation in the TS3.0 gridded dataset were those
compiled by Mike Hulme, and these data have stations in common with the GHCN (Mitchell and
Jones 2005). Thus, some correlation is expected, but the strong correlation extends beyond the
stations used in this study (northeastern Kansas and northwestern Missouri area vs. Fig. 1).
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2. Screening for data quality
Histograms of the frequency of daily precipitation totals can be used to assess the quality
of daily precipitation records (e.g., Daly et al. 2007; Burnette 2009). If the quality of the daily
precipitation data is high, then the histogram should resemble a smooth, negative exponential
curve (i.e., a high frequency of daily 0.01 inch amounts and fewer daily 1.00 inch totals). Failure
to record all of the small precipitation events (e.g., 0.01 to 0.05 inches) is revealed by the
truncation of the frequency histogram for the smallest daily totals. In addition to undercount,
Daly et al. (2007) found that “spikes” can occur at amounts ending with a zero or five, which
could bias the precipitation totals. This “5/10 bias” could be due to the coarse scale on the
precipitation gauge, but could also arise from rounding the precipitation totals to “even” values
and other poor observation practices (e.g., estimating the amount of precipitation that was in the
gauge at observation time; Daly et al. 2007). These frequency histograms were constructed for
each of the five augmented stations on a seasonal basis (see Burnette 2009). They were also
constructed for specific time segments based on changes apparent in the metadata (e.g., for
single observers and station locations, when possible). Segments of the historical and modern
station data with possible undercount and 5/10 bias were flagged for further analysis.
3. Calculation of the effective moisture index
The self-calibrating PDSI program of Wells et al. (2004) was used to calculate the
effective moisture values on a monthly basis, which computes the difference between the
precipitation and potential evapotranspiration, where potential evapotranspiration is derived
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using the three possible formulations below from the Thornthwaite method (Thornthwaite 1948;
Willmott et al. 1985):
PE = 0, when T < 0C
(1)
PE = 16[(10T)(I)-1 ]a , when 0C  T  26.5C
(2)
PE = -415.85 + 32.24(T) - 0.43(T)2 , when T  26.5C
(3)
where PE is the monthly potential evapotranspiration in mm and T is the monthly mean
temperature in °C. The variable, I, is the heat index, which is a single value computed for a
station based on modern normal monthly mean temperatures (T) during the number of months
(N) when T ≥ 0°C:
N
I=
 [(T)(5)
-1 1.514
]
(4)
i=1
The variable, a, in Equation 2 is a function of the heat index:
a = (0.00000067)(I)3 - (0.0000771)(I)2 + (0.0179)(I) + 0.49
(5)
Monthly estimates of PE are then adjusted for the length of the month and duration of daylight
hours by:
APE = PE[(d)(30)-1 (h)(12)-1 ]
(6)
where APE is the adjusted monthly potential evapotranspiration in mm, d is the length of the
month in days, and h is the duration of daylight hours on the fifteenth day of the month.
References
Burnette DJ (2009) Reconstruction of the eastern Kansas temperature and precipitation records
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Fig. 1 The five station regional average growing season precipitation totals for 1950-2006 are
positively correlated with the 0.5° gridded precipitation data during the growing season (both
AMJJA) across the central U.S. (gridded precipitation data from Climatic Research Unit TS3.0;
Mitchell and Jones 2005, all shaded areas = P<0.05)
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