1. Introduction
The increase in atmospheric greenhouse gas concentrations (i.e., CO2, CH4, and NO2) derived from anthropogenic sources has produced measurable changes in global climate since preindustrial times (Houghton et al. 2001). Increases in global mean annual temperature and decreasing Arctic sea ice are two examples of changes attributed to anthropogenic emissions (Johannessen et al. 1999; Jones et al. 2000). The Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC; Houghton et al. 2001) presented the most compelling and complete climate change research to date. However, a deficiency in our understanding is the nature of regional-scale climate changes within the broader context of global climate change.
Coupled atmosphere–ocean general circulation models (AOGCMs) have been the primary tools for investigating future climate change through the end of this century in the IPCC TAR. AOGCMs are run with relatively coarse horizontal resolution (∼280 km × 280 km), which is not adequate for accurate climate simulation in regions of complex topography such as western North America (Bell et al. 2004). However, regional climate models (RCMs) have been used to address the study of climate at higher resolutions (40 km × 40 km – 100 km × 100 km) for a number of topographically complex regions. RCMs have been shown to improve the representation of climate, over global climate models, based on comparisons of model output to observations of monthly average temperature and precipitation in regions such as western North America, the European Alps, the Aral Sea region, and Scandinavia (Bell et al. 2004; Christensen et al. 1998; Giorgi et al. 1997; Kim et al. 2002; Leung and Ghan 1999a; Leung and Ghan 1999b; Small et al. 1999; Snyder et al. 2002).
Simulations of future climate at the regional scale have been performed for the entire continental United States (Giorgi et al. 1994; Giorgi et al. 1998; Pan et al. 2001). For the topographically complex western United States, some studies have focused broadly on future climate change over the entire region (Leung and Ghan 1999a; Leung and Ghan 1999b; Thompson et al. 1998), while others have focused on a smaller region at higher spatial resolution centered over California (Bell et al. 2004; Kim 2001; Kim et al. 2002; Leung et al. 2004; Snyder et al. 2004; Snyder et al. 2002).
California is of interest for a number of reasons; the complex topography and latitudinal extent of the state create many different climate regions that support a wide variety of ecosystems, ranging from desert in the south, to temperate rainforest in the north, and to alpine tundra in the mountainous regions. The state is highly populated, and as such, water and climate issues are constantly scrutinized. Water resources in California are highly tuned to the interannual and longer-term variability of precipitation. The state’s water delivery system is designed to transport water from northern California, where the majority of precipitation occurs, to the large population centers, particularly in southern California (California Department of Water Resources 1998). California relies on accumulated snow stored in the mountainous regions to provide a steady supply of runoff during the dry summer and fall months. Analysis of observational records shows that historically the year-to-year snow accumulation is highly variable (Cayan 1996; Dettinger and Cayan 1995) because of the natural variability of precipitation in this region. Changes to the mean climate and variability due to increasing greenhouse gas concentrations could drastically alter the water resources of California and western North America.
Previous studies of future climate in this region have found increased temperature and decreased snow accumulation but are inconsistent in terms of changes in precipitation in response to future climate change (some find increases while others decreases; Kim 2001; Kim et al. 2002; Knowles and Cayan 2002; Leung et al. 2004; Snyder et al. 2002). Snyder et al. (Snyder et al. 2002) used boundary conditions derived from a global climate model (GCM) with monthly varying sea surface temperatures (SSTs) to examine the sensitivity of California climate to doubled preindustrial CO2 concentrations (560 ppm). That study found that under future climate conditions, the water resources of the state were dramatically affected. The results showed that snow accumulation decreased by up to 50% and that the snow season terminated approximately one month earlier in the future climate case. Mean monthly temperatures increased throughout the state, with the largest increases in the higher-elevation regions. Mean monthly precipitation increased in the northern half of the state and decreased in the southern half during the wet season (November–May).
Leung et al. (Leung et al. 2004), Kim (Kim 2001), and Kim et al. (Kim et al. 2002) used transient forcing to drive regional climate models over the western United States. In both studies by Kim, the Second Hadley Centre Coupled Ocean–Atmosphere General Circulation Model (HadCM2) was used to force the MAS regional model (36-km resolution) for a total of 10 yr (2040–49). Results from those studies show increased monthly temperatures of up to 3°C, a large increase in cold-season precipitation, and decreased snowfall. In Leung et al. (Leung et al. 2004), the Parallel Climate Model (PCM) was used to force the fifth-generation Pennsylvania State University (Penn State)–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; 40-km resolution) for 20 yr of a transient scenario (2040–60). Their study found increased temperature of 1° to 2.5°C, no overall change in precipitation except for some summer drying, and a reduction of snowpack by up to 70%. For Leung et al. (Leung et al. 2004), the temperature change of the GCM and RCM was about the same magnitude over the entire domain. However, the spatial pattern of the RCM temperature change was more detailed because of better representation of topography. For precipitation, the magnitude of decrease in the GCM was not as great as in the RCM, and the spatial pattern of the RCM reflects the more detailed topography. For both Kim (Kim 2001) and Kim et al. (Kim et al. 2002), the change in the GCM results for the future case was not reported. A separate examination of future snow accumulation using statistically downscaled output from an AOGCM found decreases of up to 50% by 2090 (with 2.5 times the preindustrial CO2 concentrations; Knowles and Cayan 2002).
In this study, we examine climate change over California using an RCM driven by a transient climate change scenario from an AOGCM (both models are described below). While equilibrium and transient forcing of an RCM have been previously explored for this region (Kim et al. 2002; Leung et al. 2004; Snyder et al. 2002), our study looks further into the future by examining climate at the end of this century (2080–99); we also use a different pair of global and regional models than previous transient studies. The use of multiple global and regional model pairs for the same region is important as it provides a range of results for future climate, something policy makers have repeatedly requested (Franco and Sanstad 2004).
2. Methods and models
Output from the NCAR Climate System Model version 1.2 (CSM1.2; Boville and Gent 1998) was used to drive the regional climate model (RegCM2.5) for two experiments. Two archived segments of a continuous run, each 20 yr in length and corresponding to the years 1980–99 and 2080–99, were obtained from NCAR at the appropriate time resolution for driving the regional climate model (6 hourly). This continuous run is the CSM IPCC A1 scenario, which is a branch of another CSM run that began with the year 1870 and used time-dependent atmospheric SO2 and greenhouse gases (Dai et al. 2001). For the time period 1980–99, CSM used observed values of greenhouse gases and atmospheric SO2 that were updated in each year of the run. A comparison of the 1980–99 time period with observations shows that the CSM compares well for surface temperature and precipitation (Dai et al. 2001). For the 2080–99 period, values of greenhouse gases and atmospheric SO2 were updated yearly in the model using calculated values based on the IPCC A1 scenario.
2.1. The AOGCM
The CSM1.2 is a component model with dynamic atmosphere, ocean, sea ice, and land surface models (Boville and Gent 1998). The atmospheric component model is the Community Climate Model (CCM3), the ocean model is the NCAR Ocean Model (NCOM), the sea ice model is the NCAR Community Sea Ice Model (CSIM), and the land surface model is NCAR Land Surface Model (LSM). The components are coupled together through a flux coupling component and run in tandem. For this study, the horizontal resolution of the CCSM was T42 (2.8° × 2.8° latitude by longitude) and the vertical resolution was 18 levels.
2.2. The RCM
RegCM2.5 was originally developed at NCAR for climate studies as a modified version of the Penn State–NCAR MM4 weather model (Giorgi et al. 1993a; Giorgi et al. 1993b). RegCM2.5 includes the CCM3 radiation package (Giorgi and Shields 1999), and is coupled to the Biosphere–Atmosphere Transfer Scheme (BATS) land surface model (Dickinson et al. 1993). The model domain for the experiments in this paper is centered over California with 60 latitudinal grid points by 55 longitudinal grid points and a horizontal resolution of 40 km (Figure 1). The vertical resolution is 14 levels and is represented by terrain-following sigma coordinates. The model has been validated against observational data for modern-day climate for this region and does a reasonable job of simulating the spatial and temporal features (Bell et al. 2004).
2.3. Experiments
Two cases, “Modern” (1980–99) and “Future” (2080–99), were run using RegCM2.5. The range of CO2 concentrations in the AOGCM runs was 338–369 ppm for the period 1980–99 and 635–686 ppm for the period 2080–99. RegCM2.5 was run for 20 yr for each case, with CO2 values in the RCM set to 353 and 660 ppm in the Modern and Future cases, respectively. These values are the midpoint values of the AOGCM runs, chosen because the RCM does not have an option for time-varying greenhouse gases.
3. Results
3.1. Comparison of the Modern case RCM and GCM results to observations
Monthly average temperature and precipitation output from the RCM for the entire model domain, excluding the boundary forcing zone, was compared with gridded monthly observational datasets generated by the Climate Research Unit (CRU; New et al. 2000) in order to assess the model performance in our domain. The RCM results for the Modern case (1980–99) show a negative difference (RCM minus observations) in surface temperature of between 2° and 4°C on a monthly basis compared to CRU monthly average temperature data for 1980–99 (Figure 2). The driving GCM data (CSM1.2) are 1°–2°C cooler than the observations (1979–98) on an annual average basis for western North America (Dai et al. 2001). It is, therefore, likely that some of the colder-than-observed temperatures in the RCM are inherited from the driving conditions.
A comparison of monthly average precipitation from the RCM for the Modern case shows positive precipitation differences of up to 2.0 mm day−1 (RCM minus observations) from October through April (the wet season; Figure 3). The RCM has a negative difference of up to 0.6 mm day−1 (drier than observations) for July through September. The differences between the GCM driving data (CSM1.2) and observations of precipitation on an annual average basis are up to +5 mm day−1 over western North America (Dai et al. 2001). CSM also overestimates cloud cover over land.
Figure 4 shows the average temperature for June–July–August (JJA) over the domain from the RCM Modern case, GCM Modern case, and observations of surface temperature from CRU. The RCM is cooler than observations in each month, especially over the Sierra Nevada and the Central Valley. The GCM is cooler than observations over some of California, but a few grid cells in southern California show temperatures warmer than the observations. The RCM pattern of temperature for these months matches much better with the observations than the GCM (Figure 4). We find that the RCM is cooler than the observations in all months over much of California (not shown). The RCM pattern continues to agree well with the observed patterns of temperature for the other months. The GCM is generally cooler than the observations over California for all other months, and the pattern is consistently a poor match to the observed pattern (not shown).
Precipitation over the RCM domain is shown in Figure 5 for December–January–February (DJF) from the RCM, GCM, and CRU observations of precipitation. The RCM tends to overestimate precipitation in all three months over the Sierra Nevada and along the coast of northern California (Figure 5). There is good agreement between the pattern of precipitation produced by the RCM and the observations. The GCM tends to underestimate precipitation over California and does not capture the precipitation highs in the Sierra Nevada and along the coast of northern California (Figure 5). For all other months, the RCM continues to underestimate the amount of precipitation over California, while reproducing the pattern of the observations quite well (not shown). The GCM underestimates the amount of precipitation in most months, except for the summer when it overestimates precipitation in central California (not shown). The GCM pattern of precipitation is poorly matched to the observations in all months.
3.2. RegCM2.5 temperature results
We defined three regions within California, based on the similarity of climate and geographic features in those regions, to aid in our analysis (Figure 1). The northern region includes coastal and inland California north of the Los Angeles area. The mountain region includes the Sierra Nevada, Cascade Range, and higher-elevation regions in northern California. The southern region includes coastal, inland, and high desert regions of southern California.
The mean monthly temperature results for the entire domain show increases of up to 6°C in the “Future” relative to the “Modern” case, with the greatest warming in the Sierra Nevada in May (not shown). Temperatures along the coast increased by 1°–2.5°C on a month-to-month basis. Inland temperatures increased up to 3°C, with the greatest increases at higher elevations (not shown).
For the three regions that we defined, temperature increased in every month. The mean temperature in each month in the Future case is greater than in the Modern case (Figure 6). The maximum and minimum monthly temperatures, calculated from all months for each month of each case and then spatially averaged for each region (Figure 1) are also greater in the Future case relative to the Modern case. In many months, for each region, the increase in mean temperature in the Future case exceeds the maximum temperature in the Modern case. Additionally, there are months where the increase in minimum temperature in the Future case is greater than the mean temperature in the Modern case. Changes in the overall temperature range (from maximum to minimum) occur in some months as well. In April, for all three regions, there is an increase in the temperature range in the Future case relative to the Modern. We see the opposite occur in May, where the temperature range decreases in all regions.
We tested monthly temperature changes for statistical significance in the northern, mountain, and southern regions. We used the Mann–Whitney U test, as in Snyder et al. (Snyder et al. 2002), to compare the values of monthly temperature, spatially averaged, for the Modern and Future cases to determine if they are from the same distribution. This test is similar to the t test, but with one important difference: the data does not need to have a normal distribution (Gaussian). This makes the Mann–Whitney U test well suited to climate data such as precipitation and snow accumulation that may not have a normal (Gaussian) distribution. For consistency, we also used the Mann–Whitney U test for temperature. Specifically, we are applying the test to monthly average values to determine if the values from the Modern and Future cases could have come from the same distribution. The method used here is as follows: 1) Perform a spatial average for each month, for each variable, for each of the regions through the time series. This produces 12 months × 20 yr × 3 regions × 3 variables × 2 cases. 2) For each variable, perform the Mann–Whitney U test on the 20 yr of monthly averages, for each region. The result of the test is the median difference between the Modern and Future datasets and the p level. If the p level is less than 0.05, then the two datasets (Modern and Future) are significantly different at the 95% confidence interval. Table 1 shows the differences in the median temperature (Future minus Modern). We found that for all three regions in all months, the changes in temperature from the Modern to Future case are statistically significant at the 95% confidence interval.
3.3. RegCM2.5 precipitation results
Mean monthly precipitation results show increases in some months and decreases in others for the Future case relative to the Modern case (not shown). Precipitation increases in northern and central California in both January and February by up to 3.5 mm day−1 (21% increase) in the Future case (not shown). In January, the largest increases are 2.5 mm day−1 over the Sierra Nevada (19% increase; not shown). In February, the greatest increases are 3.5 mm day−1 along the north coast and northern Sierra Nevada (21% increase). Decreases in Future precipitation of 2 mm day−1 occur in the Sierra Nevada in March and May (30% decrease; not shown). Decreased precipitation of 3 mm day−1 along the north coast occurs in December (25% decrease).
By region, we again find increases and decreases in precipitation on a monthly basis (Figure 7). In general, the mean precipitation values change very little between the Future and Modern cases; there are, however, exceptions. In the mountain and northern regions in May, the mean precipitation decreases in the Future case. There are also changes in the maximum monthly precipitation in March, April, November, and December; in the northern region, the maximum precipitation decreases in the Future case relative to the Modern case.
Table 2 shows the results of the Mann–Whitney U test as applied to monthly precipitation for the northern, mountain, and southern regions. We found that only the mountain and southern regions in May showed statistically significant differences in precipitation between the two cases at the 95% confidence interval.
3.4. RegCM2.5 snow accumulation results
Monthly mean snow accumulation is dramatically decreased by up to 400-mm (70%) snow water equivalent in all months in the Future case (not shown). The decreases are concentrated in the Sierra Nevada and southern Cascade Range. In the Future case, the end of the snow season occurs about one month earlier than in the Modern case, with almost all snow melted by May.
Figure 8 shows the large decreases in mean, maximum, and minimum monthly snow accumulation in the mountain region. In most months, the maximum monthly value in the Future case is less than the mean in the Future case. We find that the variability of the snow accumulation is also greatly diminished, as shown by the decreased range of monthly snow accumulation values.
Since the other two regions lack measurable snow accumulation (Table 3), we applied the Mann–Whitney U test to monthly snow accumulation for just the mountain region. We found that for the months of December through June, the changes in monthly snow accumulation between the Future and Modern cases are significant at the 95% confidence interval.
3.5. Trends through time
In addition to monthly average results, we examined the trends through the length of the runs to determine rates of change for the variables (Table 4). Annual average temperature trends of + 0.019°C yr−1 were found in the Modern case, and in the Future case the trend was +0.011°C yr−1. We found annual average precipitation trends in the Modern case to be 0.006 (mm day−1) yr−1 and in the Future case to be 0.018 (mm day−1) yr−1. For snow accumulation in the Modern case, model results indicate a trend of + 0.13 mm yr−1 in annual average snow accumulation. In comparison, in the Future case, there is a trend of 0.037 mm yr−1 in annual average snow accumulation. The trends, for all three variables, were found to not be statistically significant when compared to the annual variability.
4. Discussion
This study provides an examination of modeled climate over California for two time periods using transient forcing. It fills an important gap in transient climate studies of California by modeling the climate at the end of the century (2080–99) using a different global and regional climate model than previous studies. Leung et al. (Leung et al. 2004) pointed out the need to use multiple global and regional models to adequately explore the uncertainty of future climate change.
For our domain, RegCM2.5 does a reasonable job of reproducing monthly average temperature and precipitation for the time period 1980–99. Differences in temperature and precipitation apparent in the RCM compared to observations can be attributed largely to the boundary conditions. The seasonal cycle of temperature is reproduced quite well, after removing the difference. Seasonal precipitation is underestimated in the winter and spring but overestimated in the summer. Overall, the seasonal cycle of precipitation is not as well represented as temperature.
This study also provides a good comparison to other studies using transient forcing for the midcentury. Leung et al. (Leung et al. 2004) found increased temperatures of 1°–2.5°C for a midcentury scenario (2040–60), which is about half the increase that we find for the end of the century. They also found that cold season (December–January–February) precipitation decreased by up to 0.9 mm day−1. We found decreases in December precipitation of up to 1 mm day−1 in northern California and increases in both January and December of up to 4 mm day−1.
Our snow accumulation model results show statistically significant decreases on a monthly basis for the mountain region. These decreases are of the same magnitude as those reported by Snyder et al. (Snyder et al. 2002), Knowles and Cayan (Knowles and Cayan 2002), and Leung et al. (Leung et al. 2004). Since precipitation is changing very little on a monthly basis, the total quantity of water (rain and snow) input into the system is not changing. However, the increased temperatures cause less of the precipitation to fall as snow at higher elevations, and this produces increased winter runoff and decreased spring and summer runoff. Under such conditions, the anthropogenic water storage and delivery system in this region would need to adapt to the new conditions of increased winter runoff from higher elevations and decreased runoff in the spring and summer months.
The comparison of the RCM and the GCM results for the Modern case showed some similarities and differences between the models. While both the RCM and GCM underestimated temperature compared to observations, the RCM reproduced the spatial pattern better than the GCM. For precipitation, the RCM generally overestimated monthly values but reproduced the spatial pattern, while the GCM underestimated them and was poorly matched spatially with the observational data. The more detailed topography of the RCM is clearly contributing to the better spatial match with the observations.
In general, the choice of a particular GCM climate change scenario that is used to drive the RCM plays the greatest role in differences between RCM simulations (i.e., rate of CO2 increase varies across scenarios). In addition, differences in the driving GCM used, model physics (both RCM and GCM), domain size and location, and RCM horizontal resolution are most likely contributing to differences between the simulations.
By examining the monthly distributions of temperature, precipitation, and snow accumulation (Figures 6–8) by region, we can learn something about the probabilities of future climate. For example, with temperature in every month, we find that the maximum, minimum, and mean increased in the Future case relative to the Modern. We can view the increase as a shift in the probability distribution of temperature for the Modern climate toward warmer temperatures overall. The Future distribution for temperature indicates a very small probability that monthly mean temperature in the Future will be the same as the Modern. For monthly precipitation, the Modern and Future distributions are very similar, meaning it is probable that precipitation in the Future climate will be indistinguishable from the Modern climate. Future snow accumulation values are much less than those in the Modern. The probability of large decreases in snow accumulation in the Future climate is therefore very high.
The monthly precipitation values from both cases were found to be statistically similar. Therefore, any changes due to CO2 forcing are indistinguishable from the natural variability of precipitation. Snyder et al. (Snyder et al. 2002) found increased precipitation in northern California and decreases in southern California for a future climate scenario, and that the differences in precipitation between a 560-ppm CO2 case and a 280-ppm CO2 case were not statistically significant. Based on the results in this study, the year-to-year variability of precipitation is greater than any changes that occur because of increased CO2 forcing over the 20-yr time period that we examined. Given that precipitation in this region is modulated by oscillations in the climate system over long time scales [i.e., the El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO)], climate model runs of greater duration may be needed to determine the actual effect of climate change on precipitation. Several periods of these oscillations are likely needed to get a complete result. Additionally, longer runs would be needed to assess whether changes in the frequency of these oscillations are occurring as a result of greenhouse gas forcing and its feedbacks. However, it is also possible that the amount of increase in CO2 concentration in this study may not produce statistically significant changes in precipitation in this region for any length of experiment. The frequency, intensity, and spatial patterns of natural variability are not adequately represented in both global and regional climate models to the point where we can say that there is no substantial difference between the models and observations. The issue of natural variability in these models needs further consideration given the importance of variability to the climate of many regions, including western North America.
Based on the temperature results, we find a greater sensitivity of climate at higher elevations to increased atmospheric CO2 concentrations. Previous modeling work using an RCM with doubled modern-day CO2 found a strong relation between elevation and temperature in the European Alps (Giorgi et al. 1997). In that region, they found that the greatest increases in temperature occurred at higher elevations. They concluded that the increases are due primarily to decreased snow accumulation resulting in more absorbed solar radiation at the surface. The mountain region, the region with the highest elevations in our domain, had the largest temperature increases on a monthly basis (Table 1). We also found that the decrease in the amount of snow on the ground in March through May and also in December, in the Future case relative to the Modern, results in an increase of about 20 W m−2 (10 W m−2 in December) in mean absorbed solar radiation in each of those months (not shown). It is likely that the increased absorbed solar flux is contributing to the greater increase in temperature in the mountain region relative to the other regions.
The trends calculated for temperature, precipitation, and snow accumulation are a unique result that we can examine only in studies using transient forcing. The year-to-year increase in greenhouse gases in the driving data allows us to assess the response of the climate system to a more realistic forcing. The positive temperature trend from the Modern case is consistent with published results from observational data (Jones et al. 2000). Other modeling studies have shown the existence of these upward trends as well. The small positive trends in precipitation may suggest that the atmosphere is indeed storing more moisture and thereby increasing the potential for more precipitation, as has been observed in the twentieth century (New et al. 2000). The positive trends in snow accumulation could be due to increased snow accumulation at elevations that are still above the freezing line in both the Modern and Future cases. This has been observed in the western United States in the present day, where, despite warming temperatures, snow accumulation has actually increased (Mote 2003).
In forcing the RCM with output from an AOGCM, we are including important feedbacks from the global climate system. The interactions between the dynamic ocean, sea ice, land surface, and atmospheric models can generate nonlinear feedbacks that may not be captured by using output from a GCM lacking these other models (ocean and sea ice models). The RCM does not include a dynamic ocean model, and therefore dynamic feedbacks between the ocean and the atmosphere at the regional scale are missing.
5. Conclusions
Model results from an RCM indicate statistically significant increases in monthly average temperature and decreases in snow accumulation due to increased atmospheric CO2 concentrations for California. Precipitation differences between the cases were determined to be statistically indistinguishable. Given no net increase in precipitation, decreases in snow accumulation due to increasing temperature will likely lead to a significant alteration of the current spatial and temporal pattern of runoff in California. This research lends further support to other studies (described above) that have reached the same conclusions.
We also found evidence of an elevation signal in the temperature response that is likely due to snow–albedo feedbacks. Reduction of snowpack, due to CO2-induced warming, leads to increased absorption of solar radiation by the land surface, leading to further increases in temperature in high elevation areas. The transition from snow-covered ground to bare ground in the winter months leads to increases in the land surface temperature of up to 7°C.
Positive trends in temperature, precipitation, and snow accumulation in the Modern case support observations made of these variables in the California region. A comparison of the trends in the Modern and Future cases shows that, while the trends remain positive in the Future case, the rate of change in the trends decreases for temperature and snow accumulation and increases for precipitation. The decreased rate of change for temperature could be an indication that the contribution of CO2 to warming is reaching a saturation point, where increasing the CO2 further will not lead to the same amount of warming per unit increase in CO2 (Kothavala et al. 1999). The decreased rate in the positive trend of snow accumulation is a combination of increased moisture in the atmosphere and the warming of high-elevation regions. This leads to increased snow accumulation above the snow line, while at the same time the snow line is at a higher elevation in the Future case, which leads to less area being covered by snow. The increased rate of change for precipitation may indicate that the atmosphere is holding more moisture and that, as temperature increases, the increase in moisture is nonlinear.
While variability plays a key role in the climate of this region, it is difficult to determine the effects that future global climate change will have on climate variability. With experiments of only 20 yr in length in this study, we can only hope to resolve half of one period of the PDO and possibly two or three periods of ENSO. Our runs are not long enough to adequately address the issue for a region with such large interannual variability. Future studies will need to employ longer runs, preferably of the same length as fully coupled global climate model runs (100 yr or greater) in order to adequately represent the natural variability of this region. In addition, ENSO in the RCM will depend in part on the degree to which ENSO events are captured in GCMs.
These results provide one estimate of the potential impact of climate change on California. More work is needed to determine the influence of using different AOGCMs as driving conditions for the RCM. Further work in examining the effects of increased spatial resolution in the RCM should also be done. Even at 40-km horizontal resolution, the topography is still underrepresented and some smaller-scale processes may be neglected as a result.
This study shows that using time-varying climate forcing, and given continually increasing greenhouse gases, monthly temperature will increase by substantial amounts by the end of this century with a high degree of certainty. In California, this temperature increase will affect the hydrologic budget of the state by decreasing snow accumulation in the winter months, which will lead to a deficit of runoff in the late spring and summer.
Acknowledgments
The authors wish to thank J. Bell, S. Bryant, L. Kueppers, and J. Sewall for helpful comments. We also thank the two anonymous reviewers. The David and Lucile Packard Foundation provided funding for this research.
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Topography in meters of the full RCM domain and map of California showing the location and extent of the northern, mountain, and southern regions.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Monthly and domain average surface temperature for the Modern run compared with CRU monthly temperature data (1980–99). Units are degrees Celsius. The CRU mean is plotted as the red solid line, with the standard deviation plotted as the red dashed lines. The RCM mean is plotted as the blue solid line, with the standard deviation plotted as the blue dashed lines. The difference (CRU minus RCM) is plotted as the black long dashed–short dashed line.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Same as in Figure 2, but for precipitation.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Average temperature for JJA for the RCM Modern case, GCM Modern case, and CRU observations (1980–99). Units are degrees Celsius.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Same as in Figure 4, but for average precipitation for DJF.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Monthly mean, maximum, and minimum temperature by region for the Future and Modern cases. Units are in kelvins.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Same as in Figure 6, but units are in mm day−1.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Monthly mean, maximum, and minimum snow accumulation by region for the Future and Modern cases. Units are in mm snow water equivalent.
Citation: Earth Interactions 9, 11; 10.1175/EI148.1
Monthly median surface temperature difference (Future − Modern) and significance by region (°C). Values in bold indicate significance at the 95% confidence interval based on the Mann–Whitney U test. Values shown are the differences of the medians of the cases for each region.
Monthly median snow accumulation difference (Future − Modern) and significance for the mountain region (mm snow water equivalent). Values in bold indicate significance at the 95% confidence interval based on the Mann–Whitney U test. Values shown are the differences of the medians of the cases.
Trend in annual average values of temperature, precipitation, and snow accumulation.
