An intercomparison of temperature trends in the U.S. Historical Climatology Network and recent atmospheric reanalyses

Abstract This work compares the Weather Research and Forecasting (WRF) and Hadley Centre Regional Model (HadRM) simulations with the observed daily maximum and minimum temperature (Tmax and Tmin) and precipitation at Historical Climatology Network (HCN) stations over the U.S. Pacific Northwest for 2003–07. The WRF and HadRM runs were driven by the NCEP/Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP)-II Reanalysis (R-2) data. The simulated Tmax in WRF and HadRM as well as in R-2 compares well with the observations. Predominantly cold biases of Tmax are noted in WRF and HadRM in spring and summer, while in winter and fall more stations show warm biases, especially in HadRM. Large cold biases of Tmax are noted in R-2 at all times. The simulated Tmin compares reasonably well with the observations, although not as well as Tmax in both models and in the reanalysis R-2. Warm biases of Tmin prevail in both model simulations, while R-2 shows mainly cold biases. The R-2 data play a role in the model biases of Tmax, although there are also clear indications of resolution dependency. The model biases of Tmin originate mainly from the regional models. The temporal correlation between the simulated and observed daily precipitation is relatively low in both models and in the reanalysis; however, the correlation increases steadily for longer averaging times. The high-resolution models perform better than R-2, although the nested WRF domains do have the largest biases in precipitation during the winter and spring seasons.

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Review of the U.S. Climate Change Science Program's Synthesis and Assessment Product on Temperature Trends in the Lower Atmosphere

10.17226/11285 ◽

2005 ◽

Keyword(s):

Climate Change ◽

Lower Atmosphere ◽

Temperature Trends ◽

Climate Change Science ◽

The U.S

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The U.S. Historical Climatology Network Monthly Temperature Data, Version 2

Bulletin of the American Meteorological Society ◽

10.1175/2008bams2613.1 ◽

2009 ◽

Vol 90 (7) ◽

pp. 993-1008 ◽

Cited By ~ 213

Author(s):

Matthew J. Menne ◽

Claude N. Williams ◽

Russell S. Vose

Keyword(s):

Temperature Data ◽

Monthly Temperature ◽

Historical Climatology ◽

The U.S

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Evaluating the impact of U.S. Historical Climatology Network homogenization using the U.S. Climate Reference Network

Geophysical Research Letters ◽

10.1002/2015gl067640 ◽

2016 ◽

Vol 43 (4) ◽

pp. 1695-1701 ◽

Cited By ~ 13

Author(s):

Zeke Hausfather ◽

Kevin Cowtan ◽

Matthew J. Menne ◽

Claude N. Williams

Keyword(s):

Reference Network ◽

Historical Climatology ◽

The Impact ◽

The U.S

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Quality Control and Processing of Cooperative Observer Program Hourly Precipitation Data

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0300.1 ◽

2020 ◽

Vol 21 (8) ◽

pp. 1811-1825

Author(s):

Jay H. Lawrimore ◽

David Wuertz ◽

Anna Wilson ◽

Scott Stevens ◽

Matthew Menne ◽

...

Keyword(s):

Quality Control ◽

Reference Network ◽

Hourly Precipitation ◽

Digital Era ◽

Historical Climatology ◽

Data Stewardship ◽

Global Historical Climatology Network ◽

Automated Methods ◽

The U.S

AbstractThe National Oceanic and Atmospheric Administration (NOAA) has operated a network of Fischer & Porter gauges providing hourly and subhourly precipitation observations as part of the U.S. Cooperative Observer Program since the middle of the twentieth century. A transition from punched paper recording to digital recording was completed by NOAA’s National Weather Service in 2013. Subsequently, NOAA’s National Centers for Environmental Information (NCEI) upgraded its quality assurance and data stewardship processes to accommodate the new digital record, better assure the quality of the data, and improve the timeliness by which hourly precipitation observations are made available to the user community. Automated methods for removing noise, detecting diurnal variations, and identifying malfunctioning gauges are described along with quality control algorithms that are applied on hourly and daily time scales. The quality of the hourly observations during the digital era is verified by comparison with hourly observations from the U.S. Climate Reference Network and summary of the day precipitation totals from the Global Historical Climatology Network dataset.

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Distribution of Landscape Types in the Global Historical Climatology Network

Earth Interactions ◽

10.1175/2010ei371.1 ◽

2011 ◽

Vol 15 (6) ◽

pp. 1-24 ◽

Cited By ~ 10

Author(s):

Laure M. Montandon ◽

Souleymane Fall ◽

Roger A. Pielke ◽

Dev Niyogi

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Past Century ◽

Historical Reconstruction ◽

The Past ◽

Temperature Trends ◽

Historical Climatology ◽

Global Historical Climatology Network ◽

Landscape Types

Abstract The Global Historical Climate Network version 2 (GHCNv.2) surface temperature dataset is widely used for reconstructions such as the global average surface temperature (GAST) anomaly. Because land use and land cover (LULC) affect temperatures, it is important to examine the spatial distribution and the LULC representation of GHCNv.2 stations. Here, nightlight imagery, two LULC datasets, and a population and cropland historical reconstruction are used to estimate the present and historical worldwide occurrence of LULC types and the number of GHCNv.2 stations within each. Results show that the GHCNv.2 station locations are biased toward urban and cropland (>50% stations versus 18.4% of the world’s land) and past century reclaimed cropland areas (35% stations versus 3.4% land). However, widely occurring LULC such as open shrubland, bare, snow/ice, and evergreen broadleaf forests are underrepresented (14% stations versus 48.1% land), as well as nonurban areas that have remained uncultivated in the past century (14.2% stations versus 43.2% land). Results from the temperature trends over the different landscapes confirm that the temperature trends are different for different LULC and that the GHCNv.2 stations network might be missing on long-term larger positive trends. This opens the possibility that the temperature increases of Earth’s land surface in the last century would be higher than what the GHCNv.2-based GAST analyses report.

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Reexamination of instrument change effects in the U.S. Historical Climatology Network

Geophysical Research Letters ◽

10.1029/2006gl027069 ◽

2006 ◽

Vol 33 (15) ◽

Cited By ~ 25

Author(s):

K. G. Hubbard ◽

X. Lin

Keyword(s):

Historical Climatology ◽

The U.S

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Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0248.1 ◽

2014 ◽

Vol 53 (5) ◽

pp. 1232-1251 ◽

Cited By ~ 135

Author(s):

Russell S. Vose ◽

Scott Applequist ◽

Mike Squires ◽

Imke Durre ◽

Matthew J. Menne ◽

...

Keyword(s):

United States ◽

National Scale ◽

Temperature Bias ◽

Temperature And Precipitation ◽

Temperature Trends ◽

Spatial Consistency ◽

Scale Temperature ◽

Historical Climatology ◽

Historical Temperature ◽

Additional Station

AbstractThis paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895–2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas version 1 exhibits less warming as a result of historical changes in observing practices. Divisional errors in version 2 are likely less than 0.5°C for temperature and 20 mm for precipitation at the start of the record, falling rapidly thereafter. Overall, these results indicate that version 2 can supersede version 1 in both operational climate monitoring and applied climatic research.

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