Assessment of Regional Global Climate Model Water Vapor Bias and Trends Using Precipitable Water Vapor (PWV) Observations from a Network of Global Positioning Satellite (GPS) Receivers in the U.S. Great Plains and Midwest

Abstract

Precipitable water vapor (PWV) observations from the National Center of Atmospheric Research (NCAR) SuomiNet networks of ground-based global positioning system (GPS) receivers and the National Oceanic and Atmospheric Administration (NOAA) Profiler Network (NPN) are used in the regional assessment of global climate models. Study regions in the U.S. Great Plains and Midwest highlight the differences among global climate model output from the Fourth Assessment Report (AR4) Special Report on Emissions Scenarios (SRES) A2 scenario in their seasonal representation of column water vapor and the vertical distribution of moisture. In particular, the Community Climate System model, version 3 (CCSM3) is shown to exhibit a dry bias of over 30% in the summertime water vapor column, while the Goddard Institute for Space Studies Model E20 (GISS E20) agrees well with PWV observations. A detailed assessment of vertical profiles of temperature, relative humidity, and specific humidity confirm that only GISS E20 was able to represent the summertime specific humidity profile in the atmospheric boundary layer (<3%) and thus the correct total column water vapor. All models show good agreement in the winter season for the region. Regional trends using station-elevation-corrected GPS PWV data from two complimentary networks are found to be consistent with null trends predicted in the AR4 A2 scenario model output for the period 2000–09. The time to detect (TTD) a 0.05 mm yr−1 PWV trend, as predicted in the A2 scenario for the period 2000–2100, is shown to be 25–30 yr with 95% confidence in the Oklahoma–Kansas region.

Using AIRS to assess the precipitable water vapor in global climate models (GCMs) with regional validation from SuomiNet

“…Validation data is obtained by estimating regional monthly mean values from the SuomiNet GPS network [4]. Details of this regional GPS estimation are presented in Roman et al [1]. Fig.…”

“…The GCM predicted 100 year trend in PWV for the Oklahoma region is 0.05 mm/yr [1]. In this section we make use of the stability of the GPS system to assess the trend error in the AIRS PWV measurements at the U.S. Southern Great Plains region.…”

“…However, recent work has identified large differences among models on seasonal and regional scales. For example, in the CMIP3 AR4 A2 scenario, the NASA GISS model consistently shows nearly three times the water vapor content of the NCAR Community Climate model (CCSM3) during the summer season over North America [1]. To better understand the water vapor feedback in models, observations are needed that provide good spatial and temporal resolution over both ocean and land areas.…”

Diurnal variations in integrated water vapor derived from a GPS ground network in the Volga–Ural region of Russia

“…Traditional techniques of IWV sounding such as radiosondes and satellite and ground-based radiometric systems give very limited data for the understanding of these processes. Moreover, long time series of high-precision IWV data are necessary for climate change monitoring and calibration of other techniques (Roman et al, 2012). Therefore, in the last two decades, ground-based receivers of GPS signals have begun to be used as IWV sensors.…”

Synthesis Analysis of One Severe Convection Precipitation Event in Jiangsu Using Ground-Based GPS Technology

“…Notably, GPS-derived PWV data can be used as an independent data source to evaluate and correct the output results of a model so as to further improve the output precision. Using GPS-PWV data for the Great Plains in the mid-western United States, Roman et al [12] systematically assessed the water vapor data obtained from various global climate models and determined the water vapor errors under various models through comparison, thus improving the precision in the water vapor outflow in global and regional climate models to a certain extent. Bennitt et al [13] assimilated GPS-derived zenith total delay (ZTD) data into a numerical weather forecast model and found that such assimilation improved the performance of cloud prediction to a certain degree.…”