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).

In March 2015, historical data for thirteen Alaskan climate divisions were added to the nClimDiv database and will be updated each month with the CONUS nClimDiv data. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nClimDiv dataset. More information on this new dataset can be access here: Alaska FAQ's

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 NCEI'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 NCEI's 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.

In March 2015, historical data for thirteen Alaskan climate divisions were added to the nClimDiv database and will be updated each month with the CONUS nClimDiv data. The Alaska nClimDiv data were created and updated using similar methodology as that for the CONUS, but with a different approach to establishing the underlying climatology. The Alaska data are built upon the 1971-2000 PRISM averages whereas the CONUS values utilize a base climatology derived from the nClimDiv dataset. More information on this new dataset can be access here: Alaska FAQ's

National Temperature Comparison Table

NCEI often expresses 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
20122.48118
19981.47117
20061.45116
19341.31115
19991.09114
19211.00113
20010.83112
20070.82111
20050.78110
19310.74109
19900.71108
19530.57107
19540.51106
19870.50105
19860.49104
19390.45103
20000.44102
20030.44102
19380.39100
20020.3799
20110.3598
19810.3197
19910.3197
20040.2795
19330.2394
19460.1693
20100.1693
19940.0591
1900-0.0290
1941-0.1089
1995-0.1488
1988-0.2187
1992-0.2586
1977-0.2685
1925-0.2685
1910-0.3383
1980-0.4382
2009-0.4681
1956-0.4880
1952-0.5079
1973-0.5378
1974-0.5477
2008-0.5576
1997-0.6075
1963-0.6075
1936-0.6573
1927-0.6672
1943-0.7071
1911-0.7071
1959-0.7169
1908-0.7268
1949-0.7467
1957-0.7467
1922-0.7565
1896-0.7864
1930-0.7963
1984-0.8362
1926-0.8461
1958-0.8760
1928-0.8858
1947-0.8858
1914-0.8955
1901-0.8955
1918-0.8955
1935-0.9054
1983-0.9153
1940-0.9252
1944-0.9349
1942-0.9349
1962-0.9349
1961-0.9547
1996-0.9547
1989-1.0346
1967-1.0444
1945-1.0444
1932-1.0542
1906-1.0542
1955-1.1041
1902-1.1239
1923-1.1239
1971-1.1338
1964-1.1437
1965-1.1636
1948-1.1735
1970-1.1933
1913-1.1933
1919-1.2432
1937-1.2531
1897-1.2729
1907-1.2729
1915-1.3028
1969-1.3127
1975-1.3226
1966-1.3325
1909-1.3623
1976-1.3623
1960-1.3722
1950-1.3821
1898-1.4020
1972-1.4419
1982-1.4618
1968-1.4917
1985-1.5316
1993-1.5815
1904-1.5914
1951-1.6613
1920-1.7412
1899-1.7710
1978-1.7710
1905-1.789
1916-1.898
1979-1.916
1929-1.916
1903-2.085
1924-2.194
1895-2.363
1912-2.492
1917-2.671
YearGridded (V2) AnomalyGridded (V2) Rank
20122.46121
20151.58120
20061.43119
19981.41118
19341.28117
19991.05116
19210.98115
20010.87114
20070.83113
20050.81112
19310.71111
19900.69110
19530.54109
19870.51108
19540.50107
19860.50107
19390.44105
20000.44105
20030.43103
20020.38102
20110.36101
19380.36101
19910.3399
19810.3098
20040.2797
19330.1796
20100.1695
19460.1294
19940.0493
1900-0.0592
1995-0.1791
1941-0.1791
1988-0.1989
1992-0.2288
1977-0.2787
2014-0.2986
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
1911-0.8067
1949-0.8067
1922-0.8067
1896-0.8364
1930-0.8563
1984-0.8563
1926-0.8761
1958-0.8960
1928-0.9158
1947-0.9158
1962-0.9257
1935-0.9356
1983-0.9453
1996-0.9453
1940-0.9453
1901-0.9552
1918-0.9650
1961-0.9650
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
1897-1.2732
1937-1.2732
1913-1.2830
1919-1.2830
1975-1.3228
1969-1.3228
1966-1.3327
1907-1.3426
1976-1.3625
1960-1.3823
1915-1.3823
1898-1.3921
1909-1.3921
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