Lin, XiaomaoPielke, R. A.Mahmood, R.Fiebrich, C. A.Aiken, Robert2016-09-202016-09-202016-01-25http://hdl.handle.net/2097/33971Citation: Lin, X., Pielke, R. A., Mahmood, R., Fiebrich, C. A., & Aiken, R. (2016). Observational evidence of temperature trends at two levels in the surface layer. Atmospheric Chemistry and Physics, 16(2), 827-841. doi:10.5194/acp-16-827-2016Long-term surface air temperatures at 1.5m screen level over land are used in calculating a global average surface temperature trend. This global trend is used by the IPCC and others to monitor, assess, and describe global warming or warming hiatus. Current knowledge of near-surface temperature trends with respect to height, however, is limited and inadequately understood because surface temperature observations at different heights in the surface layer of the world are rare especially from a high-quality and long-term climate monitoring network. Here we use high-quality two-height Oklahoma Mesonet observations, synchronized in time, fixed in height, and situated in relatively flat terrain, to assess temperature trends and differentiating temperature trends with respect to heights (i.e., near-surface lapse rate trend) over the period 1997 to 2013. We show that the near-surface lapse rate has significantly decreased with a trend of -0.18 +/- 0.03 degrees C (10 m)(-1) per decade indicating that the 9m height temperatures increased faster than temperatures at the 1.5m screen level and/or conditions at the 1.5m height cooled faster than at the 9m height. However, neither of the two individual height temperature trends by themselves were statistically significant. The magnitude of lapse rate trend is greatest under lighter winds at night. Nighttime lapse rate trends were significantly more negative than daytime lapse rate trends and the average lapse rate trend was three times more negative under calm conditions than under windy conditions. Our results provide the first observational evidence of near-surface temperature changes with respect to height that could enhance the assessment of climate model predictions.Attribution 3.0 United States (CC BY 3.0 US)https://creativecommons.org/licenses/by/3.0/Historical Climatology NetworkAir-TemperatureSeriesMeteorology & Atmospheric SciencesText