1. Effects of Temperature and Precipitation Variability on Snowpack Trends in the Western U.S.
JISAO/SMA Climate Impacts Group and the Department of Civil and Environmental Engineering, University of Washington
Center for Science and Technology Policy Research, University of Colorado Boulder
Alan F. Hamlet Philip W. Mote Martyn P. Clark Dennis P. Lettenmaier
Description of Experiment 1:
Temperature and precipitation are unperturbed and the results are not composited. These results are a continuous time series from and reflect observed trends in the forcings over that time period.
Description of Experiment 2:
Precipitation for each month is held constant at the climatological value for each grid cell. Temperature varies as in the unperturbed time series. These results reflect the SWE trends attributable to temperature trends only.
Description of Experiment 3:
Temperature for each month is held constant at the climatological value for each grid cell. Precipitation varies as in the unperturbed time series. These results reflect the SWE trends associated with precipitation trends only.
Description of Experiment 4:
Data used in Experiment 1 is composited for warm PDO epochs ( and ). Relative trends are based on this new 42-year time series.
Overview
Decadal and interannual climate variability and (at longer timescales) global warming all affect trends in snowpack in the West. The figure below, for example, shows the effects of the Pacific Decadal Oscillation (PDO) and the El Niño Southern Oscillation (ENSO) on summer streamflow in the Columbia River, which is highly correlated with winter snowpack in the PNW.
Here we analyze trends in snow water equivalent (SWE) on April 1 (commonly the spring peak) over the West using a high quality long-term data set of precipitation and temperature and the VIC macro-scale hydrologic model (Liang et al, 1994; see schematic below) implemented at 1/8th degree resolution. Separate runs are made holding either temperature or precipitation fixed for each month, which allows us to isolate the effects of trends in temperature and precipitation. In addition, the results are composited according to warm PDO epochs to examine the role of decadal scale climate variability in the overall trends.
Linear trends are extracted from the model simulations of SWE for each grid cell. For cells with more than 50 mm of average SWE, relative trends (i.e. [raw trend]/ [lt mean]) are then summarized in the plots shown to the right.
Cool
Warm
Effects of PDO and ENSO on Summer Streamflows in the Columbia River.
Red dots =warm ENSO
Green dots = ENSO Nut.
Blue dots=cool ENSO
Relative Trend in April 1 SWE
(% per year)
Relative Trend in April 1 SWE
(% per year)
Relative Trend in April 1 SWE
(% per year)
Relative Trend in April 1 SWE
(% per year)
Effects of temperature and precipitation
DJF avg T (C)
Trend %/yr
DJF AVG T (C)
DJF AVG T (C)
DJF AVG T (C)
Effects of temperature only
DJF avg T (C)
Trend %/yr
Effects of precipitation only
Liang, X., Lettenmaier, D.P., Wood, E.F. and Burges, S. J., 1994, A Simple Hydrologically Based Model of Land Surface Water and Energy Fluxes for General Circulation Models, J. Geophys. Res., 99, D7, pp14,415-14,428
Relative Trend in April 1 SWE
(% per year)
Relative Trend in April 1 SWE
(% per year)
Relative Trend in April 1 SWE
(% per year)
DJF avg T (C)
DJF Temp (C)
b) Max Accumulation
c) 90 % Melt
a) 10 % Accumulation
Change in Date
Trend %/yr
Discussion EX 1:
Positive trends in SWE are apparent, particularly in areas with colder DJF temperatures, suggesting increasing trends in precipitation. Cells with warmer DJF temperatures have predominantly negative trends in SWE, suggesting that temperature effects are the dominant driver in these locations.
Key to Scatter Plots:
Red = PNW
Blue = CA
Green = CRB
Black = GB
PNW CA
CRB GB
Discussion EX 4:
Because the two periods are climatologically similar (particularly in the PNW), the influence of decadal variability on the trends is reduced. The trends shown are therefore estimates of the trends associated with other sources of variability, such as global warming. The results show that the period from was warmer than the period from and generally wetter despite similar North Pacific ocean variability. Trends in SWE due to temperature alone are similar to those in EX 2.
Discussion EX 3:
With temperature variability removed from the analysis, precipitation variability becomes the primary determinant of trends. Note that the majority of cells have experienced increasing precipitation over the period of analysis, but that warmer cells show negative trends in SWE when the effects of temperature are added (see EX 1).
Discussion EX 2:
With precipitation variability removed from the analysis, temperature variability becomes the primary determinant of trends. Note that although many high elevation cells do not experience strong trends in SWE (as expected), almost all of the trends are negative in sign. This reflects a general warming trend experienced by the entire region over the period of analysis. Areas with warm winter temperatures are most sensitive to warming
DJF Temp (C)
Elevation (m)
DJF Temp (C)
Fixed Precip
Fixed Precip
Fixed Precip
April 1 SWE (mm)
Trend in Calendar Day of SWE Accumulation and Melt FT