Modeling and Projection of the North American Monsoon Using a High-Resolution Regional Climate Model

Observations of the springtime maximum of snowpack across the western United States can provide a useful indicator for seasonal predictability of the North American Monsoon. Therefore, quantifying the accuracy of these observations is crucial in order to better understand the limitations when using such observations to predict NAM onset and seasonal intensity. Here, we investigated the accuracy of snow water equivalent (SWE) observations when compared to accumulated precipitation (AP) observations compiled by 748 SNOwpack TELemetry stations. Our analysis suggests a regionally dependent systematic bias between SWE measurements and AP. Often observed, SWE outpaces accumulated precipitation (AP) which can be statistically and physically explained through (1) precipitation under-catchment and/or (2) drifting snow. Forty-four percent of the 748 stations studied reported at least one year where the maximum SWE was greater than AP, while sixteen percent of the stations showed this inconsistency for at least 20% of the observed years. Regions with a higher likelihood of inconsistency contained drier snow and are exposed to higher winds speeds, both of which are positively correlated to drifting snow potential. Days when SWE increased but AP remained constant occurred on an average of 10.75 days per year for all stations, with 31% greater wind speeds at 10 meters for such days (using reanalysis winds). Findings suggest accurate SWE 25 observations throughout the Cascade Mountains and lower elevations of the Interior West while indicating a potential for overestimated SWE observations throughout the higher elevations of the Rocky Mountains, Utah mountain ranges, and the Sierra Nevada. 1. Seasonal prediction of the North American monsoon using snowpack Antecedent wintertime conditions across the western United States can influence the timing of onset and seasonal intensity of the North American Monsoon (NAM; Gutzler and Preston 1997). Variability in the region’s snow cover, soil moisture and temperature influences the timing of NAM onset by modulating the partitioning of net radiation through the surface energy budget (Zhu et al. 2005). As such, the observations of the region’s snow cover are often used as a predictor for the impending NAM season. Therefore, it is important to acknowledge the uncertainties and biases associated with these observations. This study investigates sources responsible for a frequently observed inconsistency between measurements of snow water equivalent (SWE) and accumulated precipitation (AP) reported by the SNOwpack TELemetry (SNOTEL) network (Schaefer et al. 1996). The inconsistency occurs in the sense that SWE exceeds AP (which is physically unlikely) at some point in time or throughout the snow season. Possible biases of the SNOTEL observations have been attributed to the two distinct measuring devices in observing SWE and AP (Johnson and Marks 2004): While SWE is measured using a snow pillow sensor equipped with a pressure transducer, AP is measured with a standard 30.5-cm orifice storage-type gauge (equipped with an alter shield to reduce turbulence around the gauge orifice and maximize catch efficiency). Bias in SWE measurement occurs when temperature differences exist between surrounding ground cover and the 26 pillow sensor, creating uneven distribution of snow between the sensor and the surrounding ground cover. However, these conditions can lead to both underand overmeasurement depending on the snowmelt conditions and the snow density rate of change (Johnson and Marks, 2004), so the bias should be random throughout the whole network. Regarding AP, it is known that the accuracy of precipitation catchment decreases with increasing wind speed (Neff, 1977). The types of gauge similar to those of the SNOTEL network have a margin of error between 10% and 50%, possibly more in mountainous regions (WMO, 2008). Losses in AP due to wetting or evaporation and trace underestimates may also contribute to the bias. The SNOTEL network was designed to observe surface meteorological variables for hydrologic and climatic uses. Given the now 30-year data period, SNOTEL observations have been used extensively used for the depiction of decadal trends in surface hydrological conditions (Serreze et al., 1999, 2001; Fassnacht et al., 2003; Mote et al., 2004; Hamlet et al., 2005; Carson, 2007; Pederson et al., 2011) and to evaluate against climate model simulations (e.g., Jin and Miller 2011). Thus, a comprehensive understanding of possible biases influencing the SNOTEL data is paramount. This paper examines the full archive of SNOTEL data and addresses one potential cause for the frequently observed systematic inconsistency between SWE and AP. 2. Methodology and datasets SNOTEL stations operate on a water year (October 1 – September 30) and report at least daily values of SWE and AP (near surface air temperature is also measured but is 27 not examined in this study). SWE and AP values are reset to zero on October 1 of each year. SWE and AP are reported in inches with a sensor resolution of 0.1 inch (2.54 mm). In addition to the ~800 SNOTEL stations currently in operation (NRCS http://www.wcc.nrcs.usda.gov/nwcc/inventory), we also utilize six-hourly 10-m winds from the North American Regional Reanalysis (NARR) for investigation of drifting snow. NARR assimilates meteorological and land-surface observations and outputs data on a 32-km horizontal resolution grid (Mesinger et al., 2006). Accurate representation of 10-m wind conditions over complex terrain on daily scales is not possible; however, comparison of ambient wind conditions of each SNOTEL site under various synoptic scenarios is still useful. To avoid undue biases in the daily measurements of SWE and precipitation due to reporting errors, data used in this study was subjected to two quality control (QC) filters. QC filters address the following issues: (1) accurate reset and appropriate daily corrections of SWE and AP for new water years and (2) allowable number of missing data points in a usable year for either SWE or AP. These filters ensure the most reliable daily measurements as well as remove years with large gaps in the data where accurate observations are questionable. A final count of 748 stations remained after exclusion of stations throughout the state of Alaska and unfit stations removed through QC filtering. Here, ice-bridging conditions leading to uneven distribution of snow are assumed to have negligible effects on daily observations as they typically occur during the transition from late winter to spring and this study focuses on the snow accumulation period before melt