Downscaling hydroclimatic changes over the Western US based on CAM subgrid scheme and WRF regional climate simulations

Transcription

1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 30: (2010) Published online 28 April 2009 in Wiley InterScience ( DOI: /joc.1928 Downscaling hydroclimatic changes over the Western US based on CAM subgrid scheme and WRF regional climate simulations Yun Qian,* Steven J. Ghan and L. Ruby Leung Atmospheric Science and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, USA ABSTRACT: This study examines two dynamical downscaling methods, a subgrid parameterization and a regional climate model, to compare their impacts on simulating orographic precipitation and surface hydrology in mountain regions. A global climate model was first applied at grid resolution with a subgrid orographic precipitation scheme. The global simulations were then used to drive a regional climate model at 15-km grid resolution over the Western United States. By comparing the global and regional simulations for two 10-year periods, and , this study assesses the two downscaling methods in the context of simulating both the present climate and climate change signals, and the implications of the relatively short simulation length to investigate differences in current and future climate simulated by the models are discussed. The model results show that improving the representation of surface topography through higher spatial resolution or a subgrid method has a large impact on the simulations. Both the subgrid scheme and the regional model significantly improved the simulation of snowpack in the mountains. The spatial distributions of precipitation and snowpack are generally consistent between the subgrid and regional simulations, since they were driven by the same large-scale circulation from the global simulations. However, because rain-shadow effects are not represented in the subgrid scheme, the regional simulations produced much more realistic spatial variability in precipitation and snowpack than the subgrid simulations in narrow mountain ranges. In the climate change experiments, both downscaling procedures preserved the large-scale patterns of temperature and precipitation changes in the global simulations. However, the regional simulations show larger changes in precipitation and snowpack along the coastal mountains than the subgrid simulations. This is attributed to the fact that the regional model explicitly simulates the interactions of atmospheric circulation and the underlying topography, so changes in wind directions with respect to the orientations of the mountains may lead to changes in orographic precipitation that cannot be explained by changes in atmospheric temperature and moisture alone. Hence differences between the precipitation changes simulated by the regional model and the subgrid method are larger in narrow mountains such as the Cascades and the Sierra Nevada because the subgrid method does not account for the influence of mountain orientations at the subgrid scale. As precipitation is an important driver of surface hydrological processes, differences between the precipitation changes simulated by the two methods lead to important differences in the surface hydrological processes under climate change. Copyright 2009 Royal Meteorological Society KEY WORDS CAM; WRF; hydroclimate; regional climate change; downscaling Received 27 August 2008; Revised 12 March 2009; Accepted 14 March Introduction Orography exerts a major influence on precipitation and land surface processes including snowpack and runoff. The impacts of large-scale variability on the surface climate can be modulated by the complex terrain to create important regional differences (Leung et al., 2003). Explicit simulation of this spatial structure by a global climate model is limited because of computational constraints. A variety of methods have been developed to downscale global model projections of climate change (Wilby and Wigley, 1997; Giorgi and Mearns, 1999; * Correspondence to: Yun Qian, Pacific Northwest National Laboratory, Richland, WA, USA. yun.qian@pnl.gov Ghan et al., 2002; Leung et al., 2003). Downscaling is expected to have the largest impacts on simulating precipitation and surface hydrology in regions with complex orography (e.g. Leung et al., 2004). Improved representation of orographic effects in climate models can also lead to improved seasonal climate and hydrologic forecasting skill (Roads 2004). Methods of downscaling include regional climate modelling, statistical downscaling, high-resolution time slice or stretched grid, and physically based subgrid modelling of orographic effects. This study compares the regional climate modelling approach with a subgrid modelling approach applied to a global climate model for simulating the current and future climate in an orographically diverse region. A regional (or limited area) climate model (RCM) Copyright 2009 Royal Meteorological Society

2 676 Y. QIAN ET AL. represents the physics and dynamics of the atmosphere in a specific region of interest, rather than a global domain. As RCMs are applied to smaller geographic regions, higher spatial resolution can be achieved to explicitly resolve orographic effects such as rain shadowing. Several studies, however, have suggested a tendency for the simulated regional mean precipitation to increase with increasing spatial resolution (e.g. Mass et al., 2002; Leung and Qian, 2003), leading to wet biases in highresolution simulations. This may reflect inadequacy of physics parameterizations or their dependence on spatial resolution, as they are often tuned based on sensitivity experiments with coarser resolution models. Leung and Ghan (1995, 1998) developed a physically based downscaling technique in which orographic effects are captured using a subgrid parameterization applied to climate models. In this method, each model grid cell is divided into a nominal number of subgrid elevation/vegetation bands based on high-resolution topographic and vegetation data. The subgrid method estimates the vertical displacement of air parcels in each subgrid band based on the elevation difference between the subgrid band and the grid cell mean, and the Froude number, which is used to distinguish whether the air parcel is blocked or lifted by the subgrid topography. The estimated vertical displacement of the air parcel is then used to determine the subgrid vertical profiles of temperature and humidity based on conservation of energy and moisture, and an orographic forcing term is then applied to the prognostic equation of temperature and moisture for each subgrid class through nudging of the temperature and moisture profiles to the diagnosed profiles over a relaxation time constant. The full suite of atmospheric physics and the land surface physics is applied to each elevation band within each grid cell, but atmospheric dynamics are only calculated based on the grid cell mean variables. Model output is written for each elevation band during the simulation and then spatially distributed in postprocessing according to the elevation of the subgrid band and the high-resolution surface elevation data. This method has been implemented and evaluated in a RCM (Leung and Ghan, 1998, 1999) and a global climate model (Ghan et al., 2002). To investigate the hydrologic impact of climate change, many studies using various downscaling methods (Wilby and Wigley, 1997; Giorgi et al., 2001; Ghan et al., 2002; Leung et al., 2004; Maurer et al., 2001; Salathe et al., 2007; Maurer and Hidalgo, 2008) have been conducted in the past two decades over the Western United States (WUS), which is marked by complex terrain and diverse land surface and climate regimes. In the WUS, mountain snowmelt accounts for more than 70% of the annual stream flows that support irrigation in the semi-arid Central Valley and Columbia Basin; and hydropower generation, navigation, recreation, and fishing in the major river basins. Historical climate variability has shown that snowpack levels have dropped considerably throughout the WUS since the 1950s (Mote et al., 2005); in some regions snowpack has reduced between 30% and 60%, between then and today, which significantly altered streamflow in major river basins of the West. Climate change is thus adding another constraint on the existing water management system. Improving our understanding of physical processes controlling snowpack and refining the techniques in downscaling and projecting the hydroclimate change in upcoming decades are of utmost importance for planning future resources, which is a key to maintaining sustainable development of the region. Previous efforts in simulating orographic precipitation using mesoscale models and the subgrid orographic parameterization have identified strengths and weaknesses in both approaches. Research performed as part of the European Union Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects (PRUDENCE) project is particularly relevant because similar challenges are present in simulating orographic effects in the European Alps and the WUS, as both regions are dominated by maritime climate conditions and the surface topography varies strongly within short distances. Frei et al. (2003, 2006) performed detailed analysis of daily and extreme precipitation statistics of multiple regional climate simulations over the European Alps. Interestingly, they find that model errors are very similar between two models that have the same dynamical core but different parameterizations. In contrast, model errors differ considerably between two models that have similar parameterizations but different dynamics. Leung and Ghan (1999) compared regional simulations of current and future climate in the Pacific Northwest with the global climate simulations that provided boundary conditions for the regional model. Ghan et al. (2006) compared global climate simulations with and without the use of the subgrid method. Although these studies have identified advantages of both downscaling techniques, the relative merits of each method have not been investigated through direct comparison of the two methods. This study compares these two approaches using a more consistent framework where global simulations using the subgrid method are used to provide boundary conditions for a RCM. Furthermore, to provide insights on the use of these methods for climate change study, we compare them in the context of simulating both the present climate and climate change signals in mountain regions. This comparison can guide the use and future development of these methods, and provide an estimate of uncertainty in assessing hydroclimatic changes at the regional scale due to different downscaling techniques. Section 2 describes the models and experimental design, and observational data used for evaluation; Section 3 evaluates the downscaled simulations based on the subgrid scheme and a RCM; Section 4 compares the model projections of hydroclimatic changes in the 2040s, and the conclusions are summarized in section 5.

3 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US Model configuration and evaluation data 2.1. CAM subgrid scheme The NCAR Community Atmosphere Model version 3 (CAM3) is an atmospheric general circulation model that includes the Community Land Model (CLM3) (Dickinson et al., 2006), an optional data ocean model, and a data sea ice model (Collins et al., 2006). The simulated climate of the 20th century is described by Collins et al. (2006), Boville et al. (2006) and Hack et al. (2006). The subgrid orographic precipitation scheme of Leung and Ghan (1995, 1998) was first employed in a RCM to simulate the climate response to doubling of CO 2 (Leung and Ghan, 1999), and later Ghan et al. (2002) and Ghan and Shippert (2006) successfully applied it to CAM3 coupled with CLM3. The computational cost of simulation with this subgrid scheme is about a factor of 2 greater than simulations without it, which is far less than the cost of high-resolution time slice or stretched grid methods. Simulations for the year with the subgrid scheme demonstrate remarkable skill for surface air temperature, precipitation and snow water equivalent (SWE) at spatial scales down to 5 km (Ghan et al., 2002, 2006). In the subgrid method, precipitation, among other variables, is simulated for a small number of subgrid elevation classes within each model grid cell. During postprocessing, the subgrid variables are mapped geographically based on the location and surface elevation of the model grid cells and subgrid classes to generate high-resolution spatial distributions of precipitation and snowpack. Ghan et al. (2006) described the detailed seven steps of this downscaling method employed in CAM3. In this study we have completed two 10-year simulations from 10/1/1993 to 12/31/2003 as the control and 10/1/2039 to 12/31/2049 as the future climate, with CAM3 applied at spatial resolution. The boundary conditions (sea surface temperature (SST) and sea ice cover) for the simulations are taken from a separate simulation by CAM3 and CLM3 coupled with the ocean and sea ice components of the Community Climate System Model, with greenhouse gas and aerosol concentrations prescribed by the Intergovernmental Panel on Climate Change SRES A1B scenario; details of these simulations are described by Ghan and Shippert (2006). In postprocessing, the model history is distributed according to the surface elevation at 15-km grid spacing, which is identical to the surface elevation used in the regional climate simulations in this study WRF regional climate model The RCM used in this study is based on the Weather Research and Forecasting (WRF) model version 2.2 (Skamarock et al., 2005). Leung et al. (2005) made several modifications for climate applications, which include the use of a wider buffer zone and a linear-exponential function for the nudging coefficients in the relaxation of the lateral boundary conditions, updating of SST and sea ice from the lower boundary conditions, updating of the background surface albedo and vegetation fraction to include seasonal changes, estimation of cloud fraction based on Xu and Randall (1996), and implementation of the CAM radiation scheme (Collins et al., 2006), which has been part of the standard WRF model since version 2.0. Our simulations used the modified Kain Fritsch convection parameterization (Kain and Fritsch, 2004; Kain, 2004), the WRF Single-Moment 6-class cloud microphysics scheme (Hong et al., 2006), the CAM3 shortwave and longwave radiation (Kiehl et al., 1996), the Yonsei University (YSU) boundary layer scheme (Hong et al., 2006) and the Noah land surface model (Chen and Dudhia, 2001). In the latter, the value of snow emissivity was changed from the default value of This reduces the model warm bias and improves the simulation of mountain snowpack. Figure 1(a) shows the model domain, centred at 25.3 N and W, with a horizontal grid spacing of 15 km. Also outlined with shading in Figure 1(a) are the Columbia River Basin (CRB) and the Sacramento- San Joaquin (SSJ) River Basin, which are defined for analyses in the following sections. Figure 1(b) (d) show the topography at spatial resolutions of from CAM3, 15 km from WRF and 1/8 degree from observations, respectively. We can see that the Rocky Mountain and coastal ranges such as the Sierra Nevada and the Cascades Mountain are well resolved at 15-km resolution. Initial, lateral and lower boundary conditions, including SST and sea ice cover, for both the control and future simulations were derived from the global climate model CAM3 (see Section 2.2). The SST, sea ice and large-scale circulation are therefore consistent between the CAM3 and WRF simulations, to facilitate direct comparison. Note that for the relatively small WUS domain, the largescale circulation of the WRF simulations is comparable to that of CAM3 even though the large-scale conditions are only applied at the lateral boundaries. The control (future) simulation was initialized on 1 October 1993 (2039) with the lateral and lower boundary conditions updated every 6 h through 31 December 2003 (2049). Qian et al. (2009) evaluated a WRF regional simulation using the same model configuration but driven by the National Center for Environmental Prediction/Department of Energy (NCEP/DOE) global reanalysis data Kanamitsu et al., (2002) and Atmospheric Model Intercomparison Project (AMIP) SST from 1993 to Evaluation data Before we examine how the subgrid and explicit downscaling approaches affect model projections of hydroclimatic changes, it is important to determine how well each method captures precipitation, temperature, snowpack and runoff in the control simulation of the present climate. Several observational datasets have been used to evaluate the simulations in WUS. These include the surface air temperature and precipitation data developed by the Surface Water Modelling Group at the University

4 678 Y. QIAN ET AL. Figure 1. Model domain and mask for two river basins (a, top left), showing elevation at (b, top right), 15 km (c, bottom left) and 1/8 degree (d, bottom right), respectively. This figure is available in colour online at of Washington (Maurer et al., 2001). They include daily maximum and minimum surface temperature and precipitation for at 1/8 grid resolution based on surface station data with topographic adjustment applied to precipitation using the PRISM precipitation climatology (Daly et al., 1994). We note, however, that surface stations at very high elevation are rare, so the topographic adjustments at high elevation may reflect only statistical relationships that are extrapolated from lower elevation data. Nevertheless, hydrologic simulations driven by this surface temperature and precipitation data show good agreement with naturalized streamflow data (Maurer et al., 2001). This lends some confidence to the overall spatial distribution of the meteorological dataset. Three different snow observation data are used in this study to illustrate the uncertainty in observing mountain snowpack and the effects of spatial resolution. The first data are point measurements of SWE at more than 550 snow telemetry (SNOTEL) stations in the WUS between 1981 and 2000 (Leung and Qian, 2003). The

5 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 679 second dataset was developed by the National Operational Hydrologic Remote Sensing Center (NOHRSC). NOHRSC uses a statistical methodology (Hartman et al., 1995) to combine station measurements of snow water, satellite estimates of the snow line and a digital surface elevation model to produce a gridded distribution of snow water at a resolution of 1.5 for WUS. The third dataset is daily SWE at 0.25 resolution over North America for the period The gridded data combines in situ daily observations from 8000 US cooperative stations and Canadian climate stations and first-guess fields with an optimum interpolation scheme developed by Brown et al. (2003), which is employed operationally at the Canadian Meteorological Centre (CMC). Runoff data from the University of the New Hampshire (UNH) Global Runoff Data Centre (GRDC) global monthly runoff climatology is also used for model evaluation. The UNH-GRDC dataset is runoff output from a water balance model driven by observed meteorological data, corrected with the runoff fields disaggregated from the observed river discharges. The UNH-GRDC dataset preserves the accuracy of the observed discharge measurements and maintains the spatial and temporal variability of the simulated runoff to provide a best estimate of terrestrial runoff over large domains (Fekete et al., 2000). 3. Evaluation for control simulations for present climate 3.1. Precipitation Figure 2 shows a comparison of the December January February (DJF) mean precipitation for the control simulation ( ), based on the CAM simulations at with and without the subgrid parameterization, and WRF driven by the CAM boundary conditions, and observations. Note that the atmospheric circulations of the CAM simulations with and without the subgrid parameterizations are identical, as the subgrid method is strictly a downscaling technique that preserves the grid-scale properties. The CAM subgrid simulation has been mapped to the same spatial resolution of 15 km Figure 2. Spatial distribution of DJF mean precipitation in mm/day for CAM (a, top left), CAM subgrid (b, top right), WRF (c, bottom left) and observations (d, bottom right). This figure is available in colour online at

6 680 Y. QIAN ET AL. as the WRF simulation for comparison. The mapping was done based on the precipitation simulated for each subgrid elevation band of each model grid cell and the 15-km resolution surface elevation data used in the WRF simulation. During the cold season, the observed precipitation at 1/8 resolution shows distinct spatial distributions that resemble the complex orography in WUS. There are two orographic precipitation bands associated with the coastal range and the Cascades and the Sierra Nevada. East of these mountains, precipitation decreases sharply in the basins and the intermountain zone, before it increases slightly again over the Rockies. Similar to the observed feature, WRF simulated a clear separation between the two precipitation bands along the coastal range and Cascades/Sierra. However, unlike the observed pattern that shows higher precipitation amount along the coastal range, the WRF simulation shows more precipitation along the Cascades than the coastal range, and the coastal region along Southern California is essentially dry. The latter is related to the large-scale circulation bias in CAM, as our previous simulation with WRF driven by the NCEP/DOE global reanalysis shows more precipitation along the Southern California coast (Qian et al., 2009). Table I compared the WRF and CAM subgrid simulated precipitation against the observation. We can find that both WRF and CAM subgrid underpredicted the precipitation over SSJ and overpredicted the precipitation over CRB in DJF, which imply that large-scale circulation in CAM may have imposed a wet bias for WRF and CAM subgrid, both of which are driven by CAM. However, the overprediction of precipitation over the Cascades and the Rockies was also revealed in previous studies based on WRF (Qian et al., 2009) or MM5 (Leung and Qian, 2003) with a similar spatial resolution and driven by analysis data. So the wet bias over Table I. Observed and simulated precipitation (P: mm/day) and surface air temperature (T: K) averaged over CRB and SJJ for December January February (DJF) and March April May (MAM). DJF CRB MAM P T P T CAM subgrid WRF OBS DJF SSJ MAM P T P T CAM subgrid WRF OBS the Cascades and the Rockies is probably contributed by both the physics and the dynamics in WRF and bias of large-scale circulation in CAM. The CAM simulation shown in Figure 2 exhibits a gradual reduction from the maritime coast to the continental west, with slightly overpredicted precipitation in the Rockies. The CAM subgrid simulation follows similar spatial variations as the CAM Both simulations generally have much lower precipitation than the observed or the WRF simulations along the coastal mountains. The CAM subgrid simulation, however, captured some realistic spatial features of cold season precipitation associated with the orographic signature, although the separation between the coastal wet regions and the Rockies is less clear than the observed or the WRF simulation. Overall the orographic signature of precipitation simulated by CAM subgrid is too weak. Previous experience with the subgrid orography parameterization (Leung and Ghan, 1995) suggests that the orographic signature can be increased by reducing the orographic time scale. However, this will also accentuate the orographic signature of the snow water, which is simulated quite well with the 10-h timescale employed here. Ghan et al. (2006) concluded that a timescale of 10 h is optimal for snow water simulated with a horizontal grid size. It is possible that a somewhat shorter timescale is more appropriate for the finer grid employed here (Leung and Ghan, 1995) concluded that a 6-h timescale was optimal for a 90-km grid. Also, the CAM subgrid may not well capture the convective precipitation, which accounts 20 30% in winter over WUS. Figure 3 shows a comparison of the DJF mean precipitation and elevation along two eastwest transects in the Cascades and Sierra Nevada, based on simulations and observation as shown in Figure 2. Along the Pacific Northwest transect, the WRF simulation realistically captured the strong precipitation peaks associated with the coastal range and central Rockies, but slightly overpredicted precipitation in the Cascades. Along the transect across central California, the WRF simulation realistically captured the precipitation peak associated with the Sierra range, but failed to capture the peak associated with the coastal range. Observed precipitation increases rapidly from the western side of mountain ridges such as the Olympic Mountain, the Cascades and the Sierra Nevada, and reaches a maximum approximately 0.5 west of the ridges and decreases eastward. This rain-shadow effect is well reproduced in the WRF simulations. Both CAM simulations have a general dry bias in the mountains. Along both transects, the separation between the two precipitation bands associated with the coastal range and Cascades/Sierra is less clear, especially in central California. The CAM subgrid simulation has a tendency for precipitation to maximize at the highest elevation rather than the upwind slopes. This reflects the neglect of rain-shadow effects in the subgrid orographic precipitation treatment (i.e. areas belonging to the same subgrid elevation class receive the same

7 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 681 Figure 3. DJF mean precipitation for three simulations (a, CAM ; b, CAM subgrid; c, WRF) for both control and future cases and for observation (d), as well as surface elevation along two eastwest transects across mountains and basins of the Western United States. This figure is available in colour online at amount of precipitation). As the rain shadow is resolved at the explicit grid resolution ( ), its effects are captured over the Cascades and Sierra Nevada as well as central and southern Rockies.

8 682 Y. QIAN ET AL. Figure 4. Spatial distribution of differences between WRF and CAM subgrid for DJF mean precipitation (a, top left), surface air temperature (b, top right) and snow water equivalent (c, bottom). Elevation is shown as contours. This figure is available in colour online at Figure 4(a) shows the spatial distribution of differences in precipitation between WRF and CAM subgrid. We can find the largest wet biases along coast hills and windward side of the Cascades and Sierra Nevada and dry biases at the lee side of the mountains, which is consistent with what is shown along two transects in Figure Snowpack Figure 5 compares the DJF mean snowpack of three observations and three simulations. The DJF mean SWE reaches as high as 800 mm in the Northern Cascades, Sierra Nevada and Northern Rockies. From Figure 5 we can see that orography indeed plays a dominant role in snow processes in WUS. The spatial pattern of both CAM subgrid and WRF-simulated SWE is comparable to the CMC data, but the WRF simulation shows less snowpack in Montana and Southern Alberta. The impacts of spatial resolution are clear by comparing the CMC and NOHRSC data. The SNOTEL data is generally consistent with remotely sensed SWE, but reflects biases towards high-altitude sites. Increasing spatial resolution has a large impact on snowpack simulation. Compared to the CAM simulation at resolution, the CAM subgrid scheme significantly improved the simulation of snowpack associated with orographic effects. The increase in snowpack in the CAM subgrid simulations is a result of both increased precipitation (Figures 2 and 3) and colder temperatures (Figure 7) in the mountains. Similar to the CAM subgrid simulation, WRF simulation reproduced the elevation dependence, capturing the deeper snowpack along mountains (e.g. Cascades and the Sierra Ranges, and Rockies) and light snow in the valleys (e.g. California Central Valley) or basins (e.g. CRB). On the basis if the eastwest transects in the Pacific Northwest and central California (Figure 6), a shift of snowpack peaks towards the upwind slope, corresponding to rain-shadow effect, are well reproduced by the WRF simulations in all inland mountain ridges. Again, the CAM subgrid scheme simulated maximum snowpack at the highest elevation rather than the upwind slopes. From Figure 6(a) we also find a shift of snowpack peaks towards the upwind slope side at CAM

9 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 683 Figure 5. Spatial distribution of three simulated (a, CAM ; b, CAM subgrid; c, WRF, top) and three observed (d, CMC; e, NOHRSC; f, SNOTEL) snow water equivalent (SWE, unit: mm) for DJF. This figure is available in colour online at

10 684 Y. QIAN ET AL. Figure 6. DJF mean snow water equivalent (mm) of three simulations (a, CAM ; b, CAM subgrid; c, WRF) for both control and future cases and surface elevation along two eastwest transects across mountains and basins of the Western United States. This figure is available in colour online at simulation over Cascades/Sierra Ranges and Rockies as snow shadow is resolved at the explicit grid resolution ( ) Temperature Figure 7 shows the DJF mean surface air temperature for observation and simulations. The three simulations captured the spatial distribution of large-scale features well. The mesoscale details of temperature over mountains are also captured in the CAM subgrid and WRF simulations due to the better resolved surface elevation at higher spatial resolution. The magnitude of CAM subgrid and WRF-simulated temperature is very close to the observed during the cold season. CAM subgrid slightly underpredicted the surface air temperature over the Rockies, but the cold bias is generally less than 2 C. The bias of basinaveraged temperature (not shown) in the CAM subgrid and WRF simulations is generally less than 1 C over CRB and SSJ basin throughout the year. Because the CAM simulations were driven by SST and sea ice from a coupled atmosphere ocean simulation, the temperature bias in the CAM subgrid and WRF simulations may simply be related to climate variability rather than an indication of systematic model bias. Longer simulations are needed to assess the statistical significance of our results Runoff Figure 8 shows the WRF simulated and observed runoff for DJF and March April May (MAM), respectively.

11 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 685 Figure 7. Same as Figure 2, but for DJF surface air temperature in C. This figure is available in colour online at The spatial distribution of modelled runoff generally agrees well with the GRDC runoff in both seasons. In DJF, both the modelled and observed data show higher runoff in coastal mountains, which rapidly decreases further inland following the precipitation change, as the main contribution to total runoff during winter is surface runoff generated by liquid precipitation. The runoff in both simulation and observation is very small over the Rockies since the dominant precipitation is in the form of snow that contributes to snowpack on the ground rather than runoff. In MAM, both precipitation and snowmelt contribute to runoff. Both simulation and observation capture the maximum runoff over coastal mountains as well as over the Rockies, mainly contributed by snowmelt during March May. 4. Model projections of hydroclimatic changes in 2040s 4.1. Precipitation Figure 9 shows a comparison of DJF mean precipitation change between and , based on simulations of CAM , CAM subgrid and WRF. The spatial distributions of the changes are generally consistent between the CAM subgrid and WRF, since the CAM simulation provides the large-scale circulation for the WRF simulations. During the cold season, precipitation in WUS is mainly driven by the largescale circulation bringing in abundant moisture from the Pacific Ocean. Both downscaling procedures preserved the CAM large-scale features, showing an increase in precipitation in the Southwest and a significant decrease in the Pacific Northwest, with a maximum decrease of more than 2 mm/day (20%) over the Cascades. The WRF simulation, however, indicates larger changes of precipitation along the coastal mountains, i.e. Cascades in the Northwest and Sierra Nevada in California, than the CAM and CAM subgrid simulations. During the spring and summer (not shown), precipitation in WUS is less dependent on the large-scale circulation and differences between the spatial distributions of the CAM subgrid and WRF precipitation changes are more likely due to differences in physics parameterizations rather than regional forcing.

12 686 Y. QIAN ET AL. Figure 8. Spatial distribution of simulated (a and b, top) and observed (c and d, bottom, GRDC) runoff for DJF and MAM, respectively. Unit: mm/day. This figure is available in colour online at Changes in the 95th percentile extreme daily precipitation is calculated (not shown) based on the WRFsimulated daily precipitation excluding no rain days. During DJF, the extreme daily precipitation decreases 2 20 mm/day in the Pacific Northwest mountain areas and increases 5 25 mm/day in the Southwest. Combined with snowpack in the mountains, significant enhanced extreme precipitation can increase the severity of wintertime flooding (e.g. Sierra Nevada) corresponding to rain on snow events (Leung et al., 2004). Figure 3 also compares the change of DJF mean precipitation along two transects to further illustrate the relationship of precipitation change and elevation. The precipitation difference between and does not show an apparent correlation with orography in the CAM and CAM subgrid simulations. The WRF simulation, however, shows a tendency to amplify the change of orographic precipitation on the windward side of mountains, with decrease (increase) in the Pacific Northwest (central California). As the WRF model explicitly simulates the interactions of regional atmospheric circulation and the underlying topography, changes in the future climate (e.g. wind directions) may lead to larger changes in orographic precipitation than that caused by changes in atmospheric moisture and temperature alone that are physically included in CAM subgrid scheme Temperature Figure 10 shows the DJF mean temperature changes between and The surface air temperature increases C over most of areas with maximum warming in the Rockies and the northern region. A weaker warming band along the Pacific Northwest, Montana and Nebraska, which is probably related to the large-scale circulation change in CAM , affected the pattern of temperature change in the CAM subgrid and WRF simulations. Larger areas with stronger warming are found over the Cascades and Rockies ranges in the WRF simulation, which is consistent with reduced snow-albedo feedback effects since less snow is accumulated in the warmer climate. Comparing with CAM simulations, relative weaker warming are found in WRF over the Western Nevada, Southern California and southcentral Washington because of ignorable snow-albedo feedback resulting from minor snowpack changes over these areas (see Figure 10). Smaller mean albedo over a grid cell resulting from less snow cover fraction and shallower snowpack causes the land surface to absorb more solar radiation, increasing

13 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 687 Figure 9. The spatial distribution of DJF precipitation change in mm/day (left) and in percentage (right) between and for CAM (a and b, top), CAM subgrid (c and d, middle) and WRF (e and f, bottom) simulations. This figure is available in colour online at the skin and air temperatures. As a result of the perturbed radiation and warming, snow depth and fraction decrease during winter and spring, further bringing down the albedo over snow-covered areas. DJF mean albedo reduces 14% and 4%, respectively, averaged over CRB and SSJ. WRF can better resolve topographic features and snow than the CAM along the narrow ranges of Cascade and Rockies. The spatial pattern of temperature changes simulated by WRF should physically be more realistic since WRF-simulated snow

14 688 Y. QIAN ET AL. Figure 10. Same as left panels of Figure 9, but for DJF surface air temperature in C. This figure is available in colour online at cover is much more realistic than CAM (see Figure 5). The CAM subgrid simulation captures the orographic forcing of temperature and humidity but is not able to interactively incorporate the snow-albedo feedback effects Snowpack Figure 11 shows the absolute and percent changes of winter mean SWE between and Significant reductions in snowpack are found for the three simulations along the Cascades and the Rockies by mm. A 5 50 mm SWE reduction is found along the Sierra in CAM subgrid, but not found in the CAM simulation because of the coarse spatial resolution. Snowpack even increases in the Southern Sierra due to significant increased precipitation simulated during the cold season. Percentage changes of SWE reach 20 60% in all simulations and are largest along the Cascades and the Rockies. WRF predicts larger reduction of SWE in percentage in mountain regions than CAM and subgrid simulations. Averaged over the CRB, SWE drops 32.6 mm (35.7%) in the WRF simulation. This SWE reduction amount is close to the projection from the accelerated climate prediction initiative (ACPI) project (i.e mm) over CRB based on PCM and MM5 ensemble simulations for (Leung et al., 2004). Large changes in surface albedo reflecting changes in snow cover have also been reported by Salathe et al. (2007) in the Pacific Northwest region. Figure 6 shows a comparison of the DJF SWE and elevation along two eastwest transects in the Cascades and Sierra Nevada. The large reduction of mountain snowpack in the Pacific Northwest is caused jointly by reduced precipitation and warmer temperature projected for the future. Driven by the rapidly decreased temperature and increased precipitation with elevation, WRF simulated a very steep snowpack gradient on the western side of mountain ridges, and reaches a maximum value slightly to the west of the ridge and decreases eastward because of rain-shadow effect. The maximum snowpack occurs slightly east (i.e. higher elevation and lower temperature) of the location of maximum precipitation. In the CAM subgrid simulation, however, both maximum snowpack and precipitation coincide with the ridge of mountains since rain-shadow effect is not captured.

15 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 689 Figure 11. Spatial distribution of DJF snow water equivalent (SWE) change in mm (left) and in percentage (right) simulated by CAM (a and b, top), CAM subgrid (c and d, middle), and WRF (e and f, bottom). This figure is available in colour online at Runoff Figure 12 shows the spatial distribution of WRF projected runoff change for January, April, July and October. Runoff changes show large seasonal differences. In January, runoff shows a significant decrease by 1 5 mm/day in the coastal mountains mainly due to the significant reductions in precipitation. Averaged for DJF, the runoff decreased 24.6% over CRB and 7.5% over SSJ, respectively. Only in the Coast Range of Canada the runoff shows slight increase possibly due to more snowmelt and/or more precipitation coming in the form of rain

16 690 Y. QIAN ET AL. Figure 12. The spatial distribution of change in runoff (mm/day) simulated by WRF for January (a), April (b), July (c) and October (d). This figure is available in colour online at rather than snow, both resulting from rising temperature in winter (Figure 10(c)). The runoff change is negligible in January in the Rockies where the winter temperature is normally much below freezing and most precipitation is in the form of snow, which implies that reduced snowfall results in less snowpack but no instantaneous response in runoff. Warming of C will have a much smaller effect on snowmelt and rain-to-snow ratio in very cold areas. As shown in Figure 12, the runoff decreases over the Coast Range in Canada during July, because of reduced snowpack, and in October, because of reduced precipitation. During April the spatial distribution of runoff changes shows a mixed pattern. Driven by the increased precipitation in the southern states, the runoff increases over the Sierra Nevada and ridges of the southern Rockies. Over deep snow-covered mountain areas in Canada, the runoff also increases because of increased snowmelt caused by a warmer spring. Runoff shows a decrease by mm/day in the Pacific Northwest coastal mountains and the Rockies due to the reductions in snowpack Surface water budget To illustrate the changes in the seasonal cycle of the surface water budgets, Figure 13 shows the basin mean monthly changes of precipitation, total runoff, evapotranspiration, soil moisture and snowpack accumulation for the surface water budget over CRB and SSJ. Note that the snowpack accumulation rate (SP) is denoted positive for increasing snow amount and negative for melting snow. In CRB, precipitation changes are significant, especially in January and February. This suggests that changes in other components of the water budget are driven by both temperature and precipitation. From Figure 13(a), we can find that snowpack accumulation, which shows a decrease in winter and increase in spring (i.e. reduced snowmelt), is well correlated with the seasonal change of precipitation. Meanwhile, warming further reduces the snowpack between November and June, which is reflected in reduced snow accumulation between November and February (peak in January) and less snowmelt between March and June. As discussed earlier, runoff is affected by changes in both precipitation and snow accumulation or melt. As shown in Figure 13, runoff decreases from January through July, as a result of less precipitation during January and February and of less snowmelt during March to July. Changes in soil moisture follow a pattern similar to precipitation.

17 DOWNSCALING HYDROCLIMATIC CHANGES OVER THE WESTERN US 691 Figure 13. Changes in monthly mean surface water budget averaged over the CRB (a, top) and SSJ basin (b, bottom). Shown in the figure are changes in precipitation (P), snowpack accumulation rate (SP), runoff (R), soil moisture accumulation rate (SM) and evapotranspiration (ET) in mm/day. This figure is available in colour online at Driven by the increase of precipitation, changes in the water budget over SSJ are different from changes over CRB. The snowpack accumulates during February and March resulting from increased precipitation, and snowmelt increases during April and May resulting from warmer temperature during spring. As a result of snowmelt change, the runoff increases between March and May. The soil moisture generally decreases from April to July and evapotranspiration increase in most months in SSJ with the warmer climate. 5. Conclusion and discussion Orography exerts a major influence on precipitation and land surface processes including snowpack and runoff. Downscaling is expected to have the largest impacts on simulating precipitation and surface hydrology in regions with complex orography. In this study global and regional climate simulations have been performed to compare two dynamical downscaling methods, a subgrid parameterization and a RCM, both driven by a global climate model, for simulating orographic effects and projecting the hydrologic impacts of climate change in the WUS. Two 10-year simulations were completed for the present ( ) and future ( ) with CAM3 applied at spatial resolution with the subgrid orographic precipitation scheme, and with the WRF RCM at 15 km spatial resolution for the WUS. The downscaling was shown to be important for realistically capturing regional climate in the WUS. The climatic and hydrological variables, such as precipitation, temperature, snowpack and runoff are generally well simulated by WRF and CAM subgrid against observations. Improving the representation of surface topography through a subgrid method or regional model has a large impact on simulating snowpack. Compared with the CAM simulation at resolution, CAM subgrid and WRF simulations significantly improved the simulation of snowpack associated with orographic effects. The spatial distributions of precipitation and snowpack are generally consistent between the CAM subgrid and WRF simulations, as CAM3 provided the large-scale circulation for two simulations. However, as rain-shadow effects and the impacts on snowpack are only resolved at the explicit grid resolution in the CAM subgrid simulation, there are important differences in the locations of maximum precipitation and snowpack between the WRF and CAM subgrid simulations. During the cold season, precipitation in the WUS is mainly driven by the large-scale circulation bringing in abundant moisture from the Pacific Ocean. Both downscaling procedures preserved the CAM largescale features, showing an increase in precipitation in southern states in the WUS and a significant decrease in the Pacific Northwest in the future climate. However, the regional simulations show larger changes in precipitation and snowpack along the coastal mountains than the subgrid simulations. This is attributed to the fact that the regional model explicitly simulates the interactions between the atmospheric circulation and the underlying topography, so changes in wind directions with respect to the orientations of the mountains may lead to changes in orographic precipitation that cannot be explained by changes in atmospheric temperature and moisture alone. Hence differences between the precipitation changes simulated by the regional model and the subgrid method are larger in narrow mountains such as the Cascades and the Sierra Nevada, because the subgrid method does not account for the influence of mountain orientations at the subgrid scale. As precipitation is an important driver of surface hydrological processes, differences between the precipitation changes simulated by the two methods lead to important differences in the surface hydrological processes under climate change. For example, WRF predicts larger reduction of SWE in percentage in mountain regions than the CAM and subgrid simulations. There are differences in the locations of maximum snowpack and snowpack changes between the WRF and CAM simulations.

18 692 Y. QIAN ET AL. Although we analysed the climate change simulated by both the global and regional models, our results are based on relatively short simulations of 10 years each for the current and future climate due to limited resources. The climate of the WUS is strongly influenced by largescale modes such as the El Nino-Southern Oscillation and Pacific Decadal Oscillation, which induce climate variability at the interannual and decadal time scales. However, Ghan and Shippert (2006) showed that the spatial distribution of temperature changes has a spatial structure inconsistent with that of decadal variability. They further showed that time series of area mean snow water for the WUS has natural variability but also a distinct downward trend with time, and examination of the snowpack time series in their simulations showed that 1 3 years are too short to see the signal of climate change, but simulations of 10 years are long enough to contrast the snow of the 1990s with the snow of the 2040s. Despite the robustness of snowpack and temperature change that may be apparent from the CAM simulations, it is known that different global models produced very different changes in Pacific storm tracks that affect the north south partitioning of precipitation. Hence it would be important to use longer simulations and multiple ensemble members, preferably from different global models, to establish the robustness of the climate change simulations (Salathe et al., 2007), as done in PRUDENCE ( or NARCCAP ( For our main purpose of investigating how different downscaling approaches may affect the simulation of climate and climate change in a topographically diverse region, longer simulation length is also more desirable to establish the statistical significance of the differences between the CAM subgrid and WRF simulations and observations. Acknowledgements We thank William I. Gustafson Jr. for his internal review and constructive comments. This study is partly supported by the NOAA Climate Prediction Program for the Americas (CPPA). Computing resources for the simulations were provided by the National Center for Computational Sciences at the Oak Ridge National Laboratory (ORNL) through the Climate-Science Computational End Station Development and Grand Challenge Team. PNNL is operated for the US DOE by Battelle Memorial Institute under Contract DE-AC06-76RLO1830. ORNL is supported by the US DOE under Contract No. DE- AC05-00OR References Boville BA, Rasch PJ, Hack JJ, McCaa JR Representation of clouds and precipitation processes in the Community Atmosphere Model version 3 (CAM3). Journal of Climate 19(11): Brown RD, Brasnett B, Robinson D Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmosphere-Ocean 41(1): Chen F, Dudhia J Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity.monthly Weather Review 129(4): Collins WD, Rasch PJ, Boville BA, Hack JJ, McCaa JR, Williamson DL, Briegleb BP, Bitz CM, Lin SJ, Zhang M The Formulation and Atmospheric Simulation of the Community Atmosphere Model Version 3 (CAM3). Journal of Climate 19(11): DOI: /JCLI Daly C, Neilson RP, Phillips DL A statistical topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meteorology 33: Dickinson RE, Oleson W, Bonan G, Hoffman F, Thornton P, Vertenstein M, Yang Z-L, Zeng X The Community Land Model and its climate statistics as a component of the Community Climate System Model. Journal of Climate 19(11): Fekete, BM, Vorosmarty CJ, Grabs W Global Composite Runoff Fields Based on Observed Discharge and Simulated Water Balance, Rep. 22. Global Runoff Data Centre: Koblenz, Germany. (Available at Frei C, Christensen JH, Déqué M, Jacob D, Jones RG, Vidale PL Daily precipitation statistics in regional climate models: evaluation and intercomparison for the European Alps. Journal of Geophysical Research 108(D3): doi: /2002jd Frei C, Schöll R, Fukutome S, Schmidli J, Vidale PL Future change of precipitation extremes in Europe: intercomparison of scenarios from regional climate models. Journal of Geophysical Research 111: D doi: /2005jd Giorgi F, Hewitson B, Christensen J, Hulme M, Von Storch H, Whetton P, Jones R, Mearns L, Fu C Regional climate information: evaluation and projections (Chapter 10). In Climate Change 2001: The Scientific Basis, Contribution of Working Group I to the Third Assessment Report of the IPCC, Houghton JT, Ding Y, Griggs DJ, Noguer M, van der Linden PJ, Dai X, Maskell K, Johnson CA (eds). Cambridge University Press: Cambridge; Giorgi F, Mearns LO Regional climate models revisited: an introduction to the special issue. Journal of Geophysical Research 104(D6): Special Issue on New Developments and Applications with the NCAR Regional Climate Model (RegCM). Ghan SJ, Bian X, Hunt AG, Coleman A The thermodynamic influence of subgrid orography in a global climate model. Climate Dynamics 20: DOI: /s Ghan SJ, Shippert T Physically based global downscaling: climate change projections for a full century. Journal of Climate 19(9): Ghan SJ, Shippert T, Fox J Physically based global downscaling: regional evaluation. Journal of Climate 19(3): Hack JJ, Caron JM, Yeager SG, Oleson KW, Holland MM, Truesdale JE, Rasch PJ Simulation of the global hydrological cycle in the CCSM Community Atmosphere Model (CAM3): mean features. Journal of Climate 19(11): Hartman RK, Rost AA, Anderson DM Operational processing of multi-source snow data. Proceedings of the Western Snow Conference Sparks, Nevada, Hong S-Y, Noh Y, Dudhia J A new vertical diffusion package with an explicit treatment of entrainment processes. Monthly Weather Review 134(9): Kain JS The Kain Fritsch convective parameterization: An update. Journal of Applied Meteorology 43: Kain JS, Fritsch JM Convective parameterization for mesoscale models: The Kain Fritsch scheme. The Representation of Cumulus Convection in Numerical Models, Meteorological Monographs, No.24, American Meteorological Society, Kanamitsu M, Ebisuzaki W, Woollen J, Yang S-K, Hnilo JJ, Fiorino M, Potter GL NCEP-DOE AMIP-II Reanalysis (R-2). Bulletin of the American Meteorological Society 83: Kiehl JT, Hack JJ, Bonan GB, Boville BA, Briegleb BP, Williamson DL, Rasch PJ Description of the NCAR Community Climate Model (CCM3). NCAR Tech. Note, NCAR/TN-4201STR, 152. Leung LR, Ghan SJ A subgrid parameterization of orographic precipitation. Theoretical and Applied Climatology 52: Leung LR, Ghan SJ Parameterizing subgrid orographic precipitation and surface cover in climate models. Monthly Weather Review 126: Leung LR, Ghan SJ Pacific northwest climate sensitivity simulated by a regional climate model driven by a GCM. Part II: 2xCO(2) simulations. Journal of Climate 12(7):