Abstract
The two main methods for determining the average daily near-surface air temperature, twice-daily averaging (i.e., [Tmax+Tmin]/2) and hourly averaging (i.e., the average of 24 hourly temperature measurements), typically show differences associated with the asymmetry of the daily temperature curve. To quantify the relative influence of several land surface and atmosphere variables on the two temperature averaging methods, we correlate data for 215 weather stations across the Contiguous United States (CONUS) for the period 1981–2010 with the differences between the two temperature-averaging methods. The variables are land use-land cover (LULC) type, soil moisture, snow cover, cloud cover, atmospheric moisture (i.e., specific humidity, dew point temperature), and precipitation. Multiple linear regression models explain the spatial and monthly variations in the difference between the two temperature-averaging methods. We find statistically significant correlations between both the land surface and atmosphere variables studied with the difference between temperature-averaging methods, especially for the extreme (i.e., summer, winter) seasons (adjusted R2 > 0.50). Models considering stations with certain LULC types, particularly forest and developed land, have adjusted R2 values > 0.70, indicating that both surface and atmosphere variables control the daily temperature curve and its asymmetry. This study improves our understanding of the role of surface and near-surface conditions in modifying thermal climates of the CONUS for a wide range of environments, and their likely importance as anthropogenic forcings—notably LULC changes and greenhouse gas emissions—continues.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adegoke JO, Pielke RA Sr, Eastman J, Mahmood R, Hubbard KG (2003) Impact of irrigation on midsummer surface fluxes and temperature under dry synoptic conditions: a regional atmospheric model study of the US High Plains. Mon Weather Rev 131:556–564
Arguez A, Durre I, Applequist S, Vose RS, Squires MF, Yin X, Owen TW (2012) NOAA’s 1981-2010 US climate normals: an overview. Bull Am Meteorol Soc 93:1687–1697
Barry RG, Blanken PD (2016) Microclimate and local climate. Cambridge University Press, New York 316 pp
Bernhardt J, Carleton A, LaMagna C (2018) A comparison of daily temperature-averaging methods: spatial variability and recent change for the CONUS. J Clim 31:979–996
Bonan GB, Pollard D, Thompson SL (1992) Effects of boreal forest vegetation on global climate. Nature 359:716–718
Carlson TN, Arthur ST (2000) The impact of land use—land cover changes due to urbanization on surface microclimate and hydrology: a satellite perspective. Glob Planet Chang 25:49–65
Claussen M, Kubatzki C, Brovkin V, Ganopolski A, Hoelzmann P, Pachur HJ (1999) Simulation of an abrupt change in Saharan vegetation at the end of the mid Holocene. Geophys Res Lett 26:2037–2040
Cotton WR, Pielke RA Sr (2007) Human impacts on weather and climate. Cambridge University Press, Cambridge
Dai A, Trenberth KE, Karl TR (1999) Effects of clouds, soil moisture, precipitation, and water vapor on diurnal temperature range. J Clim 12:2451–2473
Dai A, Karl TR, Sun B, Trenberth KE (2006) Recent trends in cloudiness over the United States: a tale of monitoring inadequacies. Bull Am Meteorol Soc 87:597–606
Fry J, Xian G, Jin S, Dewitz J, Homer C, Yang L, Barnes C, Herold N, Wickham J (2011) Completion of the 2006 National Land Cover Database for the conterminous United States. Photogramm Eng Remote Sens 77:858–864
Gallo KP, Easterling DR, Peterson TC (1996) The influence of land use/land cover on climatological values of the diurnal temperature range. J Clim 9:2941–2944
Gough WA (2008) Theoretical considerations of day-to-day temperature variability applied to Toronto and Calgary, Canada data. Theor Appl Climatol 94:97–105
Gough WA, He D (2015) Diurnal temperature asymmetries and fog at Churchill, Manitoba. Theor Appl Climatol 121:113–119
Hardwick SR, Toumi R, Pfeifer M, Turner EC, Nilus R, Ewers RM (2015) The relationship between leaf area index and microclimate in tropical forest and oil palm plantation: forest disturbance drives changes in microclimate. Agric For Meteorol 201:187–195
Kalverla PC, Duine GJ, Steeneveld GJ, Hedde T (2016) Evaluation of the weather research and forecasting model in the Durance Valley complex terrain during the KASCADE field campaign. J Appl Meteorol Climatol 55:861–882
Kelly DL, Schaefer JT, Doswell CA III (1985) Climatology of nontornadic severe thunderstorm events in the United States. Mon Weather Rev 113:1997–2014
Leathers DJ, Palecki MA, Robinson DA, Dewey KF (1998) Climatology of the daily temperature range annual curve in the United States. Clim Res 9:197–211
Lebassi B, Gonzalez J, Fabris D, Maurer E, Miller N, Milesi C, Switzer P, Bornstein R (2009) Observed 1970–2005 cooling of summer daytime temperatures in coastal California. J Clim 22:3558–3573
Li Z, Wang K, Zhou C, Wang L (2016) Modelling the true monthly mean temperature from continuous measurements over global land. Int J Climatol 36:2103–2110
Mahmood R, Boyles R, Brinson K, Fiebrich C, Foster S, Hubbard K, Robinson D, Andersen J, Leathers D (2017) Mesonets: meso-scale weather and climate observations for the US. Bull Am Meteorol Soc 98:1349–1361
Mesinger F, DiMego G, Kalnay E, Mitchell K, Shafran PC, Ebisuzaki W, Ek MB (2006) North American regional reanalysis. Bull Am Meteorol Soc 87:343–360
Orlanski I (1975) A rational subdivision of scales for atmospheric processes. Bull Am Meteorol Soc 56:527–530
Portmann RW, Solomon S, Hegerl GC (2009) Spatial and seasonal patterns in climate change, temperatures, and precipitation across the United States. Proc Natl Acad Sci 106:7324–7329
PRISM Climate Group, Oregon State University. PRISM Climate Data. http://prism.oregonstate.edu. Created 1 June 2016
Rydzik M, Desai AR (2014) Relationship between snow extent and midlatitude disturbance centers. J Clim 27:2971–2982
Trenberth KE (2004) Climatology (communication arising): rural land-use change and climate. Nature 427:213–213
Van den Dool H, Huang J, Fan Y (2003) Performance and analysis of the constructed analogue method applied to US soil moisture over 1981–2001. J Geophys Res Atmos 108:8617
Wang K (2014) Sampling biases in datasets of historical mean air temperature over land. Sci Rep 4:4637
Wilson AM, Jetz W (2016) Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol 14:3
Zhou L, Dickinson R, Dirmeyer P, Chen H, Dai Y, Tian Y (2008) Asymmetric response of maximum and minimum temperatures to soil emissivity change over the Northern African Sahel in a GCM. Geophys Res Lett 35:5
Acknowledgments
This paper represents a portion of the first author’s PhD dissertation at Penn State University (PSU). We are grateful for the guidance and insight provided by dissertation committee members Drs. Brent Yarnal, Jon Nese, and Martin Tingley.
Funding
Partial funding for this research was provided by the Pennsylvania Space Grant Consortium as part of its Graduate Research Fellowship Program at PSU.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Bernhardt, J., Carleton, A.M. Comparing daily temperature averaging methods: the role of surface and atmosphere variables in determining spatial and seasonal variability. Theor Appl Climatol 136, 499–512 (2019). https://doi.org/10.1007/s00704-018-2504-7
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00704-018-2504-7