Pacific Northwest Climate Sensitivity Simulated by a Regional Climate Model Driven by a GCM. Part II: 2 CO 2 Simulations

Transcription

1 2031 Pacific Northwest Climate Sensitivity Simulated by a Regional Climate Model Driven by a GCM. Part II: 2 CO 2 Simulations L. R. LEUNG AND S. J. GHAN Pacific Northwest National Laboratory, Richland, Washington (Manuscript received 8 May 1998, in final form 3 August 1998) ABSTRACT Global climate change due to increasing concentrations of greenhouse gases has stimulated numerous studies and discussions about its possible impacts on water resources. Climate scenarios generated by climate models at spatial resolutions ranging from about 50 km to 400 km may not provide enough spatial specificity for use in impact assessment. In Parts I and II of this paper, the spatial specificity issue is addressed by examining what information on mesoscale and small-scale spatial features can be gained by using a regional climate model with a subgrid parameterization of orographic precipitation and land surface cover, driven by a general circulation model. Numerical experiments have been performed to simulate the present-day climatology and the climate conditions corresponding to a doubling of atmospheric CO 2 concentration. This paper describes and contrasts the large-scale and mesoscale features of the greenhouse warming climate signals simulated by the general circulation model and regional climate model over the Pacific Northwest. Results indicate that changes in the large-scale circulation exhibit strong seasonal variability. There is an average warming of about 2 C, and precipitation generally increases over the Pacific Northwest and decreases over California. The precipitation signal over the Pacific Northwest is only statistically significant during spring, when both the change in the large-scale circulation and increase in water vapor enhance the moisture convergence toward the north Pacific coast. The combined effects of surface temperature and precipitation changes are such that snow cover is reduced by up to 50% on average, causing large changes in the seasonal runoff. This paper also describes the high spatial resolution (1.5 km) climate signals simulated by the regional climate model. Reductions in snow cover of 50% 90% are found in areas near the snow line of the control simulation. Analyses of the variations of the climate signals with surface elevation ranging from sea level to 4000 m over two mountain ranges in the Pacific Northwest show that because of changes in the alitude of the freezing level, strong elevation dependency is found in the surface temperature, rainfall, snowfall, snow cover, and runoff signals. 1. Introduction Global climate change due to increasing concentrations of greenhouse gases has stimulated numerous studies and discussions about its possible impacts on water resources (e.g., Gleick 1987; Lettenmaier et al. 1992; Frederick and Major 1997). Numerical experiments using general circulation models (GCMs) and regional climate models (RCMs) have been performed to generate climate change scenarios to elucidate the climate sensitivity to greenhouse warming. Climate scenarios defined at spatial resolutions ranging from about 50 km to 400 km are available for assessing the impacts of climate change on water resources. The Intergovernmental Panel on Climate Change report Corresponding author address: Dr. L. Ruby Leung, Pacific Northwest National Laboratory, P.O. Box 999, Richland, WA ruby.leung@pnl.gov (IPCC 1996) described these climate projections produced with different scenarios of greenhouse gas emissions. The utility of these climate change scenarios for use in impact assessment may be limited because of uncertainty in the projections. Even if the uncertainty can be reduced, the lack of spatial specificity in the projections will remain a major barrier in utilizing climate projections for water resource planning (e.g., Hostetler 1994). As Lins et al. (1997) noted, the functional responsibilities of water resource managers cover relatively small geographical areas and necessarily require (climate change) data of relatively high spatial resolution. Both statistical and dynamical downscaling methods have been used to disaggregate climate model outputs to spatial scales that are more appropriate for impact assessment. Wilby et al. (1998) provides a comprehensive review and comparison of different statistical downscaling methods. In Parts I (Leung and Ghan 1999) and II of this paper, 1999 American Meteorological Society

2 2032 JOURNAL OF CLIMATE VOLUME 12 FIG. 1. The difference in the SST between the GFDL 2 CO 2 and control simulations for Jan and Jul. we address the spatial specificity issue by examining the dynamical downscaling method. Our central question is what information on mesoscale and small-scale spatial features can be gained by using an RCM driven by a GCM. In particular, Leung and Ghan (1999) use a subgrid parameterization of orographic precipitation and land surface cover (Leung and Ghan 1995, 1998), which is implemented in an RCM to account for cloud, radiation, turbulence transfer, and surface processes that are due to subgrid heterogeneity in topography and vegetation. Their method is a computationally efficient alternative to modeling climate at the explicit spatial resolution (10 km or less) that may be required for impact assessment. Leung and Ghan (1999) described an extensive evaluation of a control simulation over the U.S. Pacific Northwest generated by driving the RCM and its subgrid parameterization with a GCM. Although their RCM results are affected by errors in the general circulation simulated by the GCM, the RCM simulation shows large improvements in the spatial correlation between the observations and simulations over the GCM. Furthermore, the RCM simulated realistically the relationships between precipitation surface temperature and surface elevation over two mountain ranges with different topographic and climate characteristics. As a continuation to Part I, this paper studies the spatial specificity in the greenhouse warming climate signals that can be achieved with the subgrid parameterization of Leung and Ghan. Climate signals that are of importance to water resource planning include surface temperature, precipitation, snow cover, and runoff, all of which are highly affected by local surface conditions such as topography and vegetation. The climate signals of these variables will be analyzed and highresolution (1.5 km) simulation of the climate signals will be shown to illustrate the spatial details simulated by the subgrid parameterization. Recently, several papers discussed the possible dependence of greenhouse warming signals on surface elevation. For example, Giorgi et al. (1997) analyzed the elevation dependency of the surface climate signal associated with doubling of CO 2 concentration in the mountainous areas of the European Alpine region using RCM simulations. Their results showed a substantial elevation dependency for climate signals in climate variables such as temperature and precipitation. The altitudinal trends in the temperature signals are consistent with long-term records of temperature trends in the Alpine region (Beniston et al. 1997). Wild et al. (1997) also discussed the greenhouse warming climate signals in surface temperature and surface energy fluxes and their variations with surface elevation over the Alps.

3 2033 FIG. 1.(Continued) Because of the limited spatial resolution in the simulations, neither of these papers described the elevation dependency at surface elevation above 2000 m. Although the surface area of regions above 2000 m is small, the simulation of climate signal at such altitudes is important because snow cover in the alpine regions is an important source of water storage and runoff. We will present simulation results over the Pacific Northwest for surface elevation that ranges from sea level to over 4000 m. Section 3a of this paper describes and contrasts the large-scale and mesoscale features of the climate signals simulated by the GCM and RCM. Section 3b describes regional analyses of the seasonal cycle in the climate signals. Section 3c describes the high spatial resolution climate signals simulated by the RCM, with special focus on analyzing the variations of the climate signals with surface elevation over two mountain ranges in the Pacific Northwest. Last, results will be summarized and discussed in section Numerical experiments The simulation setup in the 2 CO 2 experiment largely follows that of the control experiment described by Leung and Ghan (1999). The National Center for Atmospheric Research (NCAR) Community Climate Model (CCM3; Kiehl et al. 1996) is used to simulate the global circulation. The CCM3 simulation is used to provide lateral boundary conditions for driving the Pacific Northwest National Laboratory Regional Climate Model (PNNL-RCM) (Leung and Ghan 1995, 1998). PNNL- RCM is based on the Pennsylvania State University NCAR Mesoscale Model version 5 (Grell 1993), with a subgrid parameterization of orographic precipitation and land cover developed by Leung and Ghan (1995, 1998). Features of these models, as well as detailed evaluation of the present-day climatology simulated by these models, have been described by Leung and Ghan (1999) and hence will be not repeated here. In both the control and 2 CO 2 simulations, CCM3 is run at T42 horizontal resolution and 18 vertical layers, and RCM is run at 90-km explicit horizontal resolution and 23 vertical layers. The RCM simulation is driven by the CCM3 large-scale conditions using a nudging procedure described by Leung and Ghan (1999). In the GCM simulations, CCM3 was initialized using the conditions corresponding to a 1 September atmospheric condition and allowed to run for 1 month before the simulations are used to drive RCM. The RCM simulations were initialized using atmospheric conditions generated by CCM3. Initialization of soil

4 2034 JOURNAL OF CLIMATE VOLUME 12 moisture follows that of Giorgi and Bates (1989), and the whole domain is assumed snow free when the simulation begins on 1 October. Figure 1 of Leung and Ghan (1999) shows the RCM model domain and topography. Areas where the subgrid parameterization of orographic precipitation and vegetation cover is applied are highlighted by the larger rectangle covering the Pacific Northwest. In the control experiment, CO 2 concentration is 340 ppm in CCM3 and RCM, and sea surface temperature (SST) and sea ice thickness were derived from the Atmospheric Model Intercomparison Program (AMIP II) analysis for the simulation period, September 1988 September In the climate change experiment, called 2 CO 2,CO 2 concentration is 680 ppm in CCM3 and RCM, and SST and sea ice thickness were derived from a simulation generated by the Geophysical Fluid Dynamics Laboratory (GFDL) ocean atmosphere coupled GCM (Manabe et al. 1991) in which CO 2 concentration increases at the rate of 1% yr 1 over 100 yr. The SST and sea ice conditions during the 8-yr simulation (years of the GFDL simulation) when the CO 2 concentration reached a doubling (680 ppm) from its initial value (340 ppm) are used for our climate change experiment. For the control experiment, we chose to use the AMIP II SST and sea ice data rather than the GFDL simulation of the present-day SST and sea ice climatology because the former allows a more formal evaluation of the climatology simulated by CCM3 and RCM. The GFDL model uses a Q-flux adjustment to constrain the climatology of its control simulation to match the observed climatological SST. Differences between the annualmean climatological GFDL control and AMIP SSTs are less than 1 C for almost all of the oceans. Furthermore, comparing the GFDL control and AMIP SST using Student s t-test shows no statistically significant difference between them at the 0.01 level for any season. We can therefore assume that the difference between the GFDL 2 CO 2 and AMIP II analyzed conditions is comparable to that between the GFDL 2 CO 2 and GFDL climatological conditions. The latter difference in the SST is shown in Fig. 1 for January and July. A general warming between 2 and 4 C is found over most areas; exceptional warming of 6 8 C is found at the higher latitudes, and particularly along coastal waters (western boundaries). 3. The 2 CO 2 climate signal This section discusses the climate signal corresponding to a doubling of CO 2 concentration as simulated by CCM3 and RCM. The climate signal is defined as the difference between the 2 CO 2 and control simulations (Leung and Ghan 1999). Different variables will be analyzed at different spatial and temporal scales to describe the characteristics of the climate signal. Analyses have been performed to elucidate the physical mechanisms generating the signal. Special discussion will also be devoted to the altitudinal trends in the climate signals. a. Large-scale/mesoscale features To compare the general spatial features of the 2 CO 2 signals simulated by CCM3 and RCM, the CCM3 simulation is interpolated to the RCM model grid cells. For the RCM simulations, the results shown here are aggregated from the subgrid elevation vegetation classes to the 90-km grid cells to show only the mesoscale features. 1) LARGE-SCALE CIRCULATION Figure 2 shows the climate signals in the 500-mb geopotential height as simulated by CCM3 for each season: winter (December February, DJF), spring (March May, MAM), summer (June August, JJA), and fall (September November, SON) The signals are consistent with the warmer air under the 2 CO 2 scenario, which also holds more water vapor in the atmosphere. During fall and winter, the signal is positive everywhere, with lower values over the western United States during fall, and the California coast during winter. The change in the 500-mb geopotential height induces weak cyclonic circulations that oppose the prevailing westerly flow. During spring, anticyclonic circulation, which enhances the northwesterly flow toward the north Pacific coast, is induced because of the high pressure center in the 500-mb geopotential height signal over the Pacific Ocean. During summer, the high pressure center moves south, and a low pressure center is found over the northern Pacific coast. Therefore, changes in the large-scale circulation vary in both strengths and spatial features during different seasons. The RCM-simulated signal (not shown) is similar in pattern to the CCM3 signal, but generally shows stronger gradients in the 500-mb geopotential height signal, which suggests that RCM produces stronger signals in terms of large-scale circulation. Figure 3 shows the signals in the 850-mb moisture transport as simulated by CCM3. During fall and winter, the moisture transport signal shows a convergence of moisture from northwesterly and southerwesterly flow to the north Pacific coast, despite the change in large-scale circulation that opposes the prevailing westerly wind (Fig. 2). This moisture transport signal, therefore, does not reflect the changes in the circulation, but rather is mainly a result of increases in the available moisture under a warmer climate, which is transported toward the coast by the prevailing wind. During spring and summer, both the change in circulation (northwesterly flow) and increase in atmospheric water vapor enhance the moisture transport in the Pacific Northwest. During summer, strong moisture trans-

5 2035 FIG. 2. The 500-mb geopotential height signal as simulated by CCM3 for different seasons (m). port signals that are amplified by surface topography are found along the Rockies. 2) PRECIPITATION As a result of the change in moisture transport, the precipitation signal simulated by CCM3 as shown in Fig. 4 shows higher precipitation during all seasons near the north Pacific coast, and inland over the northern Rockies in Canada. The signal is particularly strong and more widespread during spring when both the change in the circulation and increase in available moisture enhance the moisture transport toward the Pacific coast more than other seasons. There is also a general trend for less precipitation over the southwestern United States during the cold season, but the signal becomes less clear during summer. Hence during most seasons (except summer), an interesting dipole pattern is found

6 2036 JOURNAL OF CLIMATE VOLUME 12 FIG. 3. Similar to Fig. 2, but for 850-mb moisture transport (kg kg 1 ms 1 ). Contours are in 0.5 g kg 1 ms 1 interval. over the southwestern United States and the Pacific Northwest. Figure 5 shows the difference between the RCM- and CCM3-simulated precipitation signals. For simplicity, we will discuss only the winter and summer signals. During winter, the RCM simulated a stronger dipole pattern, with more increase in precipitation over the Pacific Northwest coast, and decrease over the southwest coast. During summer, the RCM simulation shows numerous mesoscale features over high mountain areas along the Cascades and the Rockies. This reflects the stronger convective precipitation over the more resolved

7 2037 FIG. 4. Similar to Fig. 2 but for precipitation. Contours are in 0.1 mm day 1 interval. elevated warm regions in the RCM domain. Differences in the parameterizations of convection and surface processes may also contribute to the differences between the summer precipitation signals as simulated by the two models. 3) SURFACE TEMPERATURE Since the surface temperature signals do not have as strong a seasonal dependence as the precipitation signal, Fig. 6 shows only the surface temperature signals simulated by CCM3 for winter and summer. The warming is up to 6 C during winter and 2.5 C during summer. The temperature signals are amplified over the cold region of Canada during winter. One possible explanation is the snow albedo feedback effect, which increases the absorption of heat at the surface under the 2 CO 2 scenario where snow cover is significantly less. The change in snow cover will be discussed in more detail in sections 3b(3) and 3c(2). The strong warming signal near

8 2038 JOURNAL OF CLIMATE VOLUME 12 FIG. 5. The difference between the RCM and CCM3 simulated precipitation signal for DJF and JJA. Contours are in 0.2 mm day 1 interval. FIG. 6. The mean surface temperature signal as simulated by CCM3 for DJF and JJA. Contours are in 0.2 C interval. the northeast corner of the domain is likely an atmospheric response to the change in sea ice thickness in the nearby ocean. The difference between the CCM3- and RCM-simulated signals in the mean surface temperature are shown in Fig. 7. The RCM warm signal is stronger than the CCM3 signal in most areas during winter. As discussed by Leung and Ghan (1999), snow cover simulated by CCM3 is too small compared with observation because neither surface temperature nor precipitation are well resolved at the T42 resolution to allow the formation of snow. Hence the stronger warming signal simulated by RCM during winter is related to its ability to represent processes such as snow albedo feedback better at the higher elevation. During summer, stronger warming is found in the RCM simulation over the coastal waters due to the more refined description of coastline in the RCM at 90-km resolution. Over land, the pattern

9 2039 are calculated based on averaging the simulation interpolated to the locations of surface weather and SNOTEL stations. For the CCM3 simulation, bilinear interpolation is performed from the CCM3 grid cells to the station locations. The RCM simulation is interpolated based on linear interpolation with elevation from the RCM elevation vegetation classes to the station elevation, and bilinear interpolation from the four RCM grid cells closest to the station, and then averaged to form regional means. 1) PRECIPITATION Figure 8 shows the mean annual cycle of the precipitation signal over different regions. The regional mean signals simulated by both models are very similar, especially during winter and over WA and MT. Larger differences are found in ID during winter and spring, and in OR during summer. This finding is consistent with the results from Giorgi et al. (1994), which showed the same seasonal dependence in the difference between the GCM and RCM-simulated precipitation signals. This suggests that the mechanisms for precipitation formation have major influence on how the GCM and RCM simulations may differ. Finally, the signals are strongest during spring and summer over all regions. There is a high degree of intraseasonal variability during winter and spring over WA and OR in the climate signals. It is interesting to note that intraseasonal variability is also quite high in the control simulation as shown in Fig. 9 of Leung and Ghan (1999), but with fluctuations in the opposite direction. In essence, there is less intraseasonal variability in the 2 CO 2 winter precipitation than the control simulation. 2) SURFACE TEMPERATURE FIG. 7. The difference between the RCM- and CCM3-simulated mean surface temperature signal for DJF and JJA. Contours are in 0.3 C interval. is more complicated, but the difference between the models is generally much less than wintertime. b. Regional analysis To study the seasonal cycle of the climate signals, we focus our analysis on regional averages over four states [Washington (WA), Oregon (OR), Idaho (ID), and Montana (MT)] in the Pacific Northwest. Regional averages For surface temperature, Fig. 9 shows warming between 0 and 4.5 C throughout all seasons and over all regions in both simulations. The RCM signal is higher than the CCM3 signal during winter; the signals are comparable during summer. The difference between the models is as large as 2 C in some cases. The signals are generally higher over ID and MT, ossibly because the increase in moisture and cloudiness under the 2 CO 2 scenario has a more significant impact on surface temperature under the dryer atmospheric conditions over MT and ID than WA and OR. To differentiate the signals between daily maximum and minimum surface temperature, we plotted in Fig. 10 these signals as simulated by RCM. Larger warming is found in the daily minimum surface temperature; the difference is typically as large as 1.5 C during some months. Note also the high degree of intraseasonal variability associated with the temperature signals.

10 2040 JOURNAL OF CLIMATE VOLUME 12 FIG. 8. Mean seasonal cycle of the precipitation signal (% change) as simulated by CCM3 and RCM averaged over four regions. 3) WATER VAPOR AND CLOUD It is interesting to see what determines the intraseasonal fluctuations in the surface temperature and precipitation signals. Figure 11 shows the signals in water vapor path (WVP), cloud liquid water path (LWP), and cloud ice path (IWP) as simulated by RCM. As expected, the atmosphere holds more water vapor under a warmed condition, which results in more water vapor of up to 2 kg m 2 in the atmospheric column. The month-to-month variations in the mean surface temperature signals (Fig. 9) bear a clear correspondence to that in the atmospheric water vapor. In the 2 CO 2 scenario there is a general decrease in cloud ice in the warmer climate. The strength of the signal depends on the large-scale circulation that governs the changes in cloudiness (both liquid and ice phase) as a whole. The decrease in IWP is sometimes reflected in an increase in LWP as cloud changes phase in a warm climate. Intraseasonal variations in the precipitation signal (Fig. 8) follows that in the total cloudiness. Variations in cloud also explain some differences in the daily maximum and minimum surface temperature signals. Clouds trap longwave radiation to increase the daily minimum surface temperature; they also reflect more solar radiation to reduce the daily maximum surface temperature. For example, during March and April, the daily minimum surface temperature signal is 1 2 C

11 2041 FIG. 9. Similar to Fig. 8 but for mean surface temperature. higher than the daily maximum surface temperature signal because the total cloud condensate is much higher. This creates a peak in the daily minimum surface temperature signal in March and a dip in the daily maximum surface temperature in April. Hence the surface energy budget responds readily to changes in water vapor and cloudiness. 4) SNOW COVER AND RUNOFF Changes in precipitation and surface temperature both affect snow cover and runoff. Figures 12 and 13 show the control and 2 CO 2 RCM simulations of snow water equivalent (SWE) and total runoff. Note that the control simulation of SWE in Fig. 12 is much smaller than that shown in Leung and Ghan (1999), who used only the simulation interpolated at the SNOTEL stations, which are typically located at higher elevation than surface weather stations, to form the regional means. There are large reductions in snow cover under the greenhouse warming scenario. The reductions are generally higher during snow accumulation (between November and March) because less precipitation will reach the surface as snow in the warmer climate. The reductions are smaller in MT because surface temperature is well below freezing during much of the cold season in both the control and 2 CO 2 simulations. As a result of changes in precipitation and snowmelt,

12 2042 JOURNAL OF CLIMATE VOLUME 12 FIG. 10. The RCM-simulated signal in the mean daily maximum and minimum surface temperature over four regions. there are also changes in the total runoff (Fig. 13). In the control simulation, the total runoff shows different characteristics over different regions. Typically over WA and OR, runoff is spread over the seasons with the cold season peaks coinciding with peaks in precipitation, and warm season peaks corresponding to peaks in snowmelt. Over ID and MT, because cold season precipitation is much smaller than over WA and OR, there is not much runoff until snowmelt, which peaks in May. In the 2 CO 2 simulation, runoff over WA and OR is generally higher than the control simulation during October April because of the higher precipitation amount and more precipitation falling as rain than snow in the warmer climate. During summer, there is a reduction in runoff because less snow is available under the warmed climate for snowmelt. As snowmelt also begins earlier in the 2 CO 2 simulation, the May June runoff peaks in the control simulation are shifted by 1 2 months earlier to March and April. In ID and MT, the runoff signal can be described by small increases in runoff during the cold season, which result from the higher precipitation, and lower runoff during snowmelt. The timing of the runoff is similar in both simulations. 5) STATISTICAL SIGNIFICANCE OF CLIMATE SIGNALS Table 1 summarizes the difference between the regional mean control and 2 CO 2 -simulated precipita-

13 2043 FIG. 11. Similar to Fig. 10 but for changes in column WVP, cloud LWP, and cloud IWP simulated by RCM. The unit is g m 2, values for WVP shown have been multiplied by 0.1. tion and surface temperature. The table lists, for each season and each region, the signal p and t, the bias in the control simulation p and t, and the interannual variability pc, pg, tc, tg, where the subscripts p and t stand for precipitation and surface temperature, and c and g stand for control and 2 CO 2 simulations, respectively. We use the t test and f test to determine the statistical significance of the signal and difference between the control and 2 CO 2 simulated interannual variability. The numbers highlighted in the table are those that passed the tests at the 0.01 significant level. Due to the high interannual variability associated with precipitation, the precipitation signals are significant mostly only during spring when, as discussed above, the change in circulation strengthens the northwesterly flow that brings moisture to the Pacific coast. Interestingly, although the simulated precipitation signals are smaller than the model biases most of the time, the signals that are statistically signficant during spring are higher than the bias of the control simulation. From a climate change detection point of view, our results suggest that the springtime signal could be more easily detected and reliable because of the high signal-tonoise and low bias-to-signal ratios. Whether this coincidence during one season is a common feature of other climate models as well has important implications for climate change detection. The warm surface temperature signals are statistically significant during all seasons and over all regions. However, the temperature signals are all higher than the model bias except during spring when the control simulation exhibits a large cold bias. This suggests that reliable detection of climate change signals may need to be performed differently for surface temperature and

14 2044 JOURNAL OF CLIMATE VOLUME 12 FIG. 12. The RCM control and 2 CO 2 simulated SWE. precipitation. Finally, there is also a number of regions during certain seasons where the 2 CO 2 -simulated interannual variabilities are significantly different from the control simulation. For precipitation, the change is mostly an increase in interannual variability; for surface temperature, it is the opposite. c. High-resolution climate signals 1) SPATIAL DISTRIBUTION To show the kind of spatial details that can be achieved with the use of the subgrid parameterization, we mapped the simulated signals from each elevation vegetation class of each grid cell based on a 1.5-km elevation dataset to generate spatial distribution of the climate signals at 1.5-km resolution. Figure 14a shows the 1.5-km resolution surface elevation over the Pacific Northwest. Figure 14b shows the annual 2 CO 2 RCM simulation of precipitation as a percentage of the RCM control simulation. Many small-scale features are now resolved in the precipitation simulation. Except for small areas over the northern Rockies, most continental areas experience increases in precipitation under the greenhouse warming scenario. The increase is mostly within 20%, but larger changes of up to 60% are also found over areas of lower elevation where the control simulation of precipitation is small. Figure 14c shows the spatial distribution of the annual surface temperature signal simulated by RCM. The temperature change is between 1 and 3.5 C, with generally larger changes inland than over coastal areas, and over mountains than low lying areas. With the

15 2045 FIG. 13. Similar to Fig. 12 but for total runoff. combination of highly resolved surface temperature and precipitation changes, signals in snow cover can be much better resolved in mountainous areas. Figure 14d shows the 2 CO 2 RCM-simulated SWE as percentage of the RCM control simulation during March when snow cover is the highest during the year. The land areas in white are those that have no SWE even in the control simulation. Note that although precipitation has generally increased under the 2 CO 2 scenario as shown in Fig. 14b, there is a widespread decrease in SWE because of the higher surface temperature in the 2 CO 2 simulation. Larger changes of up to 100% reduction are found mainly at the lower elevation in the basins. Over the Cascades, there are reductions of about 50%. Despite the higher temperature in the greenhouse warming scenario, there is an increase in SWE during March over some areas. These areas are typically above 3000 m over the Cascades and the Rockies where the winter conditions are well below freezing in both the control and 2 CO 2 simulations. Hence the SWE signal merely reflects the precipitation change (increase) in those areas. 2) ELEVATION DEPENDENCY OF CLIMATE SIGNALS Since the climatological relationships between precipitation/surface temperature and altitude are very well simulated by RCM over various mountain ranges in the Pacific Northwest (Leung and Ghan 1999), we will discuss the RCM climate change results to determine if certain relationships exist between the various climate signals and surface elevation. Because the RCM divides each grid cell into a number of surface elevation bands, climate signals are simulated for a

16 2046 JOURNAL OF CLIMATE VOLUME 12 FIG. 14. Spatial distribution of (a) surface elevation (b) annual-mean 2 CO 2 precipitation (%) of the control simulation (c) annual-mean surface temperature signal and (d) 2 CO 2 simulated SWE during March (%) of the control simulation at 1.5-km resolution. The rectangular areas in (a) indicate the Cascades and northern Rockies. Spatial distribution is not shown near the United States Canada border because TABLE 1. Summary of the RCM-simulated signals ( ), the bias in the control simulation ( ), and the interannual variability ( ) for precipitation p and surface temperature t. The subscripts c and g stand for control and 2 CO 2 simulations, respectively. The numbers in bold correspond to the signals or interannual variability, which passed the t test and f test for statistical significance at the 0.01 level. State Months p p pc pg t t tc tg WA OR ID MT DJF MAM JJA SON DJF MAM JJA SON DJF MAM JJA SON DJF MAM JJA SON

17 2047 FIG. 14. (Continued) high-resolution surface elevation data were not available over Canada in the past to generate the elevation information for the subgrid parameterization. wide range of surface elevation from sea level to over 4000 m. To highlight the dependence of climate signals on elevation, we interpolate the simulations to the surface weather and SNOTEL stations and aggregate the signals by surface elevation according to the elevation classification scheme used to define the elevation bands of the subgrid scheme. Climate signals are then plotted against surface elevation to show their altitudinal trends. Following the analysis of Leung and Ghan (1999), we also show results over the Cascades and northern Rockies bounded by the rectangles shown in Fig. 14a. Unlike Leung and Ghan (1999), who further partitioned the analysis by state within each mountain range, for simplicity, the climate signals will be analyzed over the whole region within the Cascades and northern Rockies to illustrate only possible differences in the climate signals over coastal versus continental mountain ranges. Finally, a number of locations have been selected randomly at the higher elevations to complement the altitudes not reached by the surface weather and SNOTEL stations. This way, climate signals over surface elevation that ranges from 40 m to 4015 m over the Cascades and 300 m and 3100 m over the northern Rockies will be discussed. Figure 15 shows the elevation dependence of the surface temperature signals for different seasons. During the warm seasons, fall over the Cascades and summer to fall over the Rockies, when the regions are completely snow free in either the control or 2 CO 2 simulations, there is only a slightly altitudinal trend in the temperature signals. This trend could be due to the differential increase in the water vapor with altitude that causes more warming at the higher elevations. During other seasons, much stronger warmings are found at elevations where there are changes in the freezing level in a warmer climate; that is, some areas at elevation between 1000 and 2000 m become snow free in the 2 CO 2 simulation. The change in surface albedo and the snow albedo feedback effects on the atmosphere can cause these warmings of about 0.5 C on top of the general warming trend. When plotting the daily maximum and minimum surface temperature separately (not shown), we also noticed that the daily

18 2048 JOURNAL OF CLIMATE VOLUME 12 FIG. 14. (Continued) minimum surface temperature signal is higher than the daily maximum surface temperature signal in both regions, and the difference increases slightly with altitude. This results from the small increase in cloudiness at the higher altitudes (not shown), which reduces the daily maximum surface temperature signal by reflection of sunlight, hence increasing diurnal temperature difference. Figure 16 shows the seasonal precipitation signals as functions of surface elevation over the two mountain ranges. During the warm seasons, the signals are all positive and increase with altitude especially at elevations higher than 1500 m. This altitudinal trend can be attributed to more convective precipitation being triggered at the higher elevation in association with the destabilization caused by the stronger surface heating (Fig. 15). The elevation dependence is less definitive during the cold season. For example, while the spring signal over the Cascades increases with altitude, the opposite is true during winter. To help us understand the cold season trend, we plotted in Fig. 17 the signals in cloud water and cloud ice paths during winter and spring over the Cascades. During winter, there is a small increase in cloud water because of the warming that alters the phase of the condensates, but a large reduction in cloud ice is due mainly to changes in largescale circulation, and some to phase change. As a result, precipitation is reduced, especially at the higher elevations where there are more reductions in cloud ice. During spring, we found a large increase in cloud condensates that increases with altitude. The increase in cloud amount (mostly cloud ice beyond 1500 m) with altitude explains why precipitation also increases with altitude because more precipitation can form through the seeder-feeder mechanism (e.g., Cotton and Anthes 1989) when cloud ice is formed above cloud water. Furthermore, precipitation is also enhanced because cloud ice precipitates more readily than cloud water. To further understand the altitudinal trends in precipitation, more studies would be needed to understand the altitudinal trends in cloud. To understand how changes in precipitation and surface temperature affect the SWE, Fig. 18 shows the cold season snow budgets over the two mountain rang-

19 2049 FIG. 14. (Continued) es. Over the Cascades, the increase in snowfall above 2500 m is overcompensated by the increase in snowmelt, which combine to reduce SWE by up to 300 mm at the higher elevations. Evaporation increases under the greenhouse climate. The signal increases with altitude until above freezing; then evaporation remains about the same regardless of altitude. Runoff is greatly increased at the higher altitudes because of increased rainfall and/or snowmelt. Note that the excess in the reduction of snowfall over snowmelt and the increase in evaporation between 1000 and 2000 m are consistent with the change in freezing level that causes the stronger warming shown in Fig. 15. Over the Rockies, both snowfall and snowmelt are reduced until above 2500 m; then both start to increase with snowfall increasing more rapidly, which result in more SWE at the higher elevation. Again, evaporation increases under the warm conditions; however, unlike over the Cascades, the change decreases with altitude. This is consistent with the small altitudinal trend in the precipitation signal. Stronger runoff is found at the higher elevation due to increases in snowmelt and rainfall. 3) LAKE SIMULATION One of the finer spatial features captured by the subgrid parameterization of Leung and Ghan is subgrid lake. In the Pacific Northwest, 14 lakes have been defined in the simulations. Leung and Ghan (1999) evaluated the control simulation of lake surface temperature at Pyramid Lake and Yellowstone Lake and found reasonable agreement with observations. In Fig. 19, we show the mean annual cycles in the control and 2 CO 2 simulations of lake surface temperature at the two lakes. Larger warming of up to 3 C is found at Pyramid Lake during February and March. The warming at Yellowstone Lake is smaller and is being transformed into melting of lake ice during winter. Figure 20 shows the simulated lake ice mass. At Pyramid Lake, although the mean seasonal minimum temperature in the control simulation shown in Fig. 19 is above freezing, there are individual years when freezing condition is met and ice is formed. The ice disappears completely in the 2 CO 2 simulation, which implies freezing never occurs under greenhouse warm-

20 2050 JOURNAL OF CLIMATE VOLUME 12 FIG. 15. Seasonal mean signals ( C) in surface temperature plotted as functions of surface elevation over the Cascades and northern Rockies. FIG. 16. Similar to Fig. 15 but for precipitation signals (mm day 1 ). ing. At Yellowstone Lake, the warming reduces the ice mass by about 30% and causes ice to form about 1 month later. However, not much change is found in the timing of complete ice melt. The vertical profiles of lake temperature have also been examined (not shown). At Pyramid Lake, the warming is well mixed throughout the depth (100 m) of the lake causing the whole vertical profile to shift by about 2.5 C. At Yellowstone Lake, there is a slightly stronger warming in the mixed layer than the thermocline; the warming is less than 1 C throughout the depth (40 m) of the lake. 4. Summary and discussion This paper is the second of two papers that discuss a climate change experiment performed with a regional climate model driven by a general circulation model. It focuses on the climate signal simulated over the Pacific Northwest, where complexity in terrain and vegetation is an important feature. Therefore, this pa-

21 2051 FIG. 17. Winter and fall signals in LWP and IWP (g m 2 ) over the Cascades. per discusses not only the general spatial distribution of the climate signals simulated by CCM3 and RCM, but also detailed spatial structures that can only be simulated using very high explicit spatial resolution or subgrid parameterizations. Here we summarize and discuss the results from the climate change experiment. 1) Changes in the large-scale circulation simulated by CCM3 are consistent with the generally warmer and wetter conditions in the atmosphere under the 2 CO 2 condition. These changes exhibit strong seasonal variability, which suggests that the traditional focus on merely the winter and summer scenarios may not be adequate. Indeed the precipitation change simulated over the Pacific Northwest is only statistically significant during spring, making it the only season where the simulated signal exceeds the noise and stays within the model bias. On the other hand, GCMs tend to have more problems simulating the climate conditions during the transition seasons (e.g., Foster et al. 1996). The control simulations shown in Part I of this paper also indicated a stronger cold bias during spring, making it the only season when the 2 CO 2 surface temperature is still lower than the observed climatology. Considerations of both the signal-to-noise and bias-to-signal ratios are important for early and reliable climate change detection. This study suggests that detecting climate change signals in surface temperature and precipitation may be performed at different seasons. 2) Wintertime precipitation along the Pacific coast depends strongly on large-scale circulation that determines the strength and position of storm tracks that affect the north south partitioning of precipitation along the coast. The climate signal simulated here shows higher precipitation over the Pacific Northwest FIG. 18. The RCM-simulated signals in the snow budgets plotted as functions of surface elevation over the Cascades and northern Rockies. and less precipitation over California. The precipitation signal depends on combined effects of circulation and moisture availability changes. It is no wonder then that different GCMs can produce rather different precipitation signals in the western United States. For example, the results reported here are quite different from that reported by Giorgi et al. (1994), who used GCM simulations generated by the GENESIS model (Thompson and Pollard 1995) to drive the RegCM2 (Giorgi et al. 1993) regional climate model; their cold

22 2052 JOURNAL OF CLIMATE VOLUME 12 FIG. 19. The surface temperature ( C) in the control and 2 CO 2 simulations at the Pyramid and Yellowstone Lakes. FIG. 20. Similar to Fig. 17 but for lake ice mass in kg m 2. season (November March) precipitation is found to increase almost everywhere along the Pacific coast from WA to most of California. Other GCM results also vary significantly in the simulation of the precipitation signal (e.g., IPCC 1996). It is not clear whether the difference in the simulated precipitation signals is due to differences in the GCM resolutions, physical parameterizations, or lower boundary conditions such as SST and sea ice. A coordinated effort is needed to analyze different GCM and RCM simulations of climate sensitivity concurrently to resolve differences among the model-simulated climate scenarios. 3) The surface temperature signal simulated by CCM3 is about 2 C over the western Unites States. This warming is similar in magnitude to that simulated by the GFDL coupled ocean atmosphere model (Manabe et al. 1991), which provides the 2 CO 2 SST and sea ice conditions for driving CCM3. Intraseasonal and seasonal fluctuations in the surface temperature signal are found to match the variations in atmospheric water vapor and cloud water content, which lead to more warming during the cold season than warm season. Excess warming during winter is also found in the higher latitudes where snow albedo feedback effect and changes in sea ice thickness play an important role. 4) By using a higher explicit spatial resolution and a subgrid parameterization that describes climate variations associated with heterogeneous topography and vegetation within grid cells, the climate signals simulated by RCM are inherently more refined and often reveal interesting features that cannot be described by GCMs. For example, the RCM-simulated surface temperature signals show more regional features associated with topography and coastline. With the more resolved precipitation and surface temperature signals, simulation of snow cover and its changes can be obtained with more spatial specificity. The general performance of GCMs in simulating snow cover and snow mass has been discussed by Foster et al. (1996). They concluded that the main sources of model bias can be ascribed to inaccuracies in simulating surface air temperature and precipitation. Furthermore, GCMs cannot capture the finer spatial details in snow cover, which are governed mainly by topographic variations. Indeed, Leung and Ghan (1999) showed that the CCM3 control simulation almost entirely missed the SWE recorded at SNOTEL stations that are commonly located at the higher elevation. Therefore, the CCM3 SWE signals (not shown) are only 20% 50% of the RCM signal estimated at a combination of surface weather and SNOTEL stations. Accurate and spatially resolved simulation of snow cover, surface temperature, and precipitation signal is essential for reasonable simulations of the amount and timing of runoff, which is a critical element for impact assessment of water resource and other sectors such as ecosystem and agriculture that are intimately tied to changes in water supply. The subgrid parameterization also provides useful information about the conditions of smaller lakes that normally cannot be explicitly resolved by regional climate models. 5) Because RCM resolves spatial features corresponding to a wide range of surface elevation, and the relationships between precipitation surface temperature with surface elevation have been validated with observations, it enhances the usefulness and validity of the analysis of elevation dependency of the climate signals. For example, the surface temperature signals are noticeably higher around the elevation where there are changes in the freezing level, but beyond that the

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