U.S. Climate Divisions

U.S. Climate Divisions

History of the U.S. Climate Divisional Dataset

For many years the Climate Divisional Dataset was the only long-term temporally and spatially complete dataset from which to generate historical climate analyses (1895-2013) for the contiguous United States (CONUS). It was originally developed for climate-division, statewide, regional, national, and population-weighted monitoring of drought, temperature, precipitation, and heating/cooling degree day values. Since the dataset was at the divisional spatial scale, it naturally lent itself to agricultural and hydrological applications.

There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. (Karl and Koss, 1984).

Drd964x Dataset

Traditionally, climate division values have been computed using the monthly values for all of the Cooperative Observer Network (COOP) stations in each division are averaged to compute divisional monthly temperature and precipitation averages/totals. This is valid for values computed from 1931-2013. For the 1895-1930 period, statewide values were computed directly from stations within each state. Divisional values for this early period were computed using a regression technique against the statewide values (Guttman and Quayle, 1996). These values make up the Drd964x division dataset.

nClimDiv Dataset

The nClimDiv dataset is based on the GHCND dataset using a 5km gridded appoach. It is based on a similar station inventory as the Drd964x dataset however, new methodologies are used to compute temperature, precipitation, and drought for United States climate divisions. These new methodologies include the transition to a grid-based calculation, the inclusion of many more stations from the pre-1930s, and the use of NCDC's modern array of quality control algorithms. These have improved the data coverage and the quality of the dataset, while maintaining the current product stream.

The nClimDiv dataset is designed to address the following general issues inherent in the Drd964x dataset:

  1. For the Drd964x dataset, each divisional value from 1931-2013 is simply the arithmetic average of the station data within it, a computational practice that results in a bias when a division is spatially undersampled in a month (e.g., because some stations did not report) or is climatologically inhomogeneous in general (e.g., due to large variations in topography).
  2. For the Drd964x dataset, all divisional values before 1931 stem from state averages published by the U.S. Department of Agriculture (USDA) rather than from actual station observations, producing an artificial discontinuity in both the mean and variance for 1895-1930 (Guttman and Quayle, 1996).
  3. In the Drd964x dataset, many divisions experienced a systematic change in average station location and elevation during the 20th Century, resulting in spurious historical trends in some regions (Keim et al., 2003; Keim et al., 2005; Allard et al., 2009).
  4. Finally, none of the Drd964x dataset station-based temperature records contain adjustments for historical changes in observation time, station location, or temperature instrumentation, inhomogeneities which further bias temporal trends (Peterson et al., 1998).

The first (and most straightforward) improvement to the nClimDiv dataset involves updating the underlying network of stations, which now includes additional station records and contemporary bias adjustments (i.e., those used in the U.S. Historical Climatology Network version 2; Menne et al., 2009).

The second (and far more extensive) improvement is to the computational methodology, which now addresses topographic and network variability via climatologically aided interpolation (Willmott and Robeson, 1995). The outcome of these improvements is a new divisional dataset that maintains the strengths of its predecessor while providing more robust estimates of areal averages and long-term trends.

The NCDC's Climate Monitoring Branch transitioned from the Drd964x dataset to the more modern the nClimDiv dataset in early 2014. While this transition did not disrupt the current product stream, some variances in temperature and precipitation values may be observed throughout the data record. For example, in general, climate divisions with extensive topography above the average station elevation will be reflected as cooler climatology. An assessment of the major impacts of this transition can be found in Fenimore, et. al, 2011.

National Temperature Comparison Table

NCDC and other centers often express a month's, season's or year's temperature anomaly as a rank, or how the period "ranked" among its history (for example, 23rd warmest of 118 on record). Expressing a value as a rank provides an easily-understandable depiction of the relative placement of the month, season or year, but using rankings is very sensitive to even small changes in the values. For example, imagine a footrace with 118 runners. In most cases, many of the runners finish very near to each other ("in a pack"), where the slightest change could result in a "bump" in rank of several positions within the pack. In the same way, annual temperature anomalies feature a few outstanding (warm or cold) years, and a large "pack". Slight changes to any one year can result in a "bump" in rank in the "middle of the pack". This sensitivity to slight changes is one of the criticisms of using the ranking method, despite its known utility for quickly conveying how a single month, season or year compares to others in history.

Contiguous United States Annual Temperature Anomalies (1981-2010 Base Period)
For more on rankings and associated colors, visit Climatological Rankings.
YearCOOP (V1) AnomalyCOOP (V1) Rank
20122.47119
19981.47118
20061.46117
19341.33116
19991.09115
19211.02114
20010.83113
20070.82112
20050.78111
19310.75110
19900.71109
19530.56108
19870.51107
19540.51107
19860.50105
19390.46104
20030.46104
20000.44102
19380.40101
20020.37100
20110.3599
19910.3198
19810.2897
20040.2897
19330.2595
19460.1694
20100.1694
19940.0692
19000.0191
1941-0.0990
1995-0.1589
1988-0.2188
1992-0.2487
1925-0.2487
1977-0.2885
1910-0.3184
1980-0.4483
2013-0.4582
2009-0.4681
1956-0.4880
1952-0.5179
1973-0.5378
2008-0.5477
1974-0.5576
1963-0.5975
1997-0.6074
1927-0.6373
1936-0.6373
1911-0.6771
1908-0.6870
1959-0.7069
1943-0.7069
1896-0.7267
1922-0.7366
1949-0.7465
1957-0.7465
1930-0.7663
1926-0.8262
1984-0.8461
1928-0.8560
1914-0.8658
1901-0.8658
1947-0.8754
1935-0.8754
1918-0.8754
1958-0.8754
1940-0.9153
1944-0.9252
1983-0.9349
1962-0.9349
1942-0.9349
1996-0.9547
1961-0.9547
1906-1.0046
1989-1.0245
1932-1.0344
1967-1.0442
1945-1.0442
1902-1.0941
1923-1.1040
1955-1.1139
1964-1.1338
1971-1.1437
1913-1.1536
1965-1.1635
1948-1.1734
1970-1.2033
1897-1.2231
1937-1.2231
1919-1.2329
1907-1.2329
1915-1.2828
1909-1.3226
1969-1.3226
1975-1.3325
1966-1.3424
1976-1.3623
1898-1.3721
1960-1.3721
1950-1.3920
1972-1.4519
1982-1.4917
1968-1.4917
1985-1.5416
1904-1.5515
1993-1.5914
1951-1.6713
1920-1.7212
1899-1.7310
1905-1.7310
1978-1.789
1916-1.868
1929-1.897
1979-1.926
1903-2.065
1924-2.174
1895-2.293
1912-2.462
1917-2.641
YearGridded (V2) AnomalyGridded (V2) Rank
20122.46119
20061.43118
19981.41117
19341.28116
19991.05115
19210.98114
20010.87113
20070.83112
20050.81111
19310.71110
19900.69109
19530.54108
19870.51107
19860.50106
19540.50106
20000.44104
19390.44104
20030.43102
20020.38101
19380.36100
20110.36100
19910.3398
19810.3097
20040.2796
19330.1795
20100.1694
19460.1293
19940.0492
1900-0.0591
1995-0.1790
1941-0.1790
1988-0.1988
1992-0.2287
1977-0.2786
1925-0.3185
1910-0.4084
2013-0.4084
1980-0.4382
2009-0.4382
1956-0.4880
2008-0.5379
1973-0.5478
1952-0.5577
1974-0.5676
1963-0.5775
1997-0.6274
1936-0.6773
1927-0.6872
1959-0.7271
1908-0.7570
1943-0.7669
1957-0.7868
1949-0.8067
1911-0.8067
1922-0.8067
1896-0.8364
1984-0.8563
1930-0.8563
1926-0.8761
1958-0.8960
1928-0.9158
1947-0.9158
1962-0.9257
1935-0.9356
1996-0.9453
1940-0.9453
1983-0.9453
1901-0.9552
1961-0.9650
1918-0.9650
1914-0.9849
1942-0.9946
1989-0.9946
1944-0.9946
1967-1.0645
1945-1.0744
1932-1.0943
1906-1.1042
1955-1.1341
1965-1.1440
1964-1.1539
1971-1.1738
1923-1.1837
1970-1.2136
1948-1.2235
1902-1.2434
1937-1.2732
1897-1.2732
1913-1.2830
1919-1.2830
1969-1.3228
1975-1.3228
1966-1.3327
1907-1.3426
1976-1.3625
1960-1.3823
1915-1.3823
1909-1.3921
1898-1.3921
1950-1.4320
1972-1.4519
1982-1.4818
1968-1.5017
1985-1.5216
1993-1.5615
1904-1.6714
1951-1.7113
1920-1.7512
1978-1.7811
1899-1.8210
1905-1.839
1979-1.948
1929-1.977
1916-1.986
1903-2.205
1924-2.244
1895-2.493
1912-2.592
1917-2.761

Discovery Tool

A visualization toolkit was created to help users examine snapshots of both datasets for the comparison period (i.e., through December 2013). The tool allows the user to select criteria which are of interest and investigate the comparisons themselves. Parameters included in the toolkit are temperature, precipitation, degree days and a variety of drought indices. Changes in monthly, seasonal and annual variability can be examined through the use of the interactive time series plots. In addition, slope (trend) values by decade and 30-year period may also be added to the output plots. This allows the user to take a closer look at the behavior of the data at a variety of smaller time scales throughout the record.

References

  • Allard, J., B.D. Keim, J.E. Chassereau, D. Sathiaraj. 2009. Spuriously induced precipitation trends in the southeast United States. Theoretical and Applied Climatology. DOI: 10.1007/s00704-008-0021-9.
  • Guttman, N. V. and R. G. Quayle, 1996: A historical perspective of U.S. climate divisions. Bull. Amer. Meteor. Soc., 77, 293-303.
  • Karl, T.R., C.N. Williams, Jr., P.J. Young, and W.M. Wendland, 1986: A model to estimate the time of observation bias associated with monthly mean maximum, minimum, and mean temperature for the United States, J. Climate Appl. Meteor., 25, 145-160.
  • Karl T. R. and Koss W. J., 1984: Historical Climatology Series 4-3: Regional and National Monthly, Seasonal and Annual Temperature Weighted by Area, 1895-1983
  • Keim, B. D., A. Wilson, C. Wake, and T. G. Huntington, 2003: Are there spurious temperature trends in the United States Climate Division Database? Geophys. Res. Lett.,30, 1404, doi:10.1029/ 2002GL016295
  • Keim, B.D., M.R. Fischer, and A.M. Wilson, 2005: Are there spurious precipitation trends in the United States Climate Division database? Geophys. Res. Lett., 32, L04702, doi: 10.1029/2004GL021985.
  • Menne, M.J., C.N. Williams, and R.S. Vose, 2009: The United States Historical Climatology Network Monthly Temperature Data - Version 2. Bulletin of the American Meteorological Society, 90, 993-1107.
  • Peterson, T.C., T.R. Karl, P.F. Jamason, R. Knight, and D.R. Easterling, 1998: The first difference method: maximizing station density for the calculation of long-term global temperature change. J. Geophys. Res., Atmospheres, 103 (D20), 25967-25974.
  • Willmott, C.J. and S.M. Robeson, 1995. Climatologically aided interpolation (CAI) of terrestrial air temperature. International Journal of Climatology, 15(2), 221-229.
  • Vose, R.S., Applequist, S., Durre, I., Menne, M.J., Williams, C.N., Fenimore, C., Gleason, K., Arndt, D. 2014: Improved Historical Temperature and Precipitation Time Series For U.S. Climate Divisions Journal of Applied Meteorology and Climatology. DOI: http://dx.doi.org/10.1175/JAMC-D-13-0248.1