An Assessment of Global Climate Model-Simulated Climate for the Western Cordillera of Canada (1961–90)

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HYDROLOGICAL PROCESSES Hydrol. Process. 17, 3703–3716 (2003) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/hyp.1393

An assessment of global climate model-simulated climate for the western cordillera of Canada (1961–90)

Barrie R. Bonsal,1* Terry D. Prowse2 and Alain Pietroniro1 1 Aquatic Ecosystem Impacts Research Branch, National Water Research Institute, Saskatoon, Saskatchewan, S7N 3H5, Canada 2 National Water Research Institute, Water & Climate Impacts Research Centre, Victoria, British Columbia, V8W 3P5, Canada

Abstract: Climate change is projected to signiﬁcantly affect future hydrologic processes over many regions of the world. This is of particular importance for alpine systems that provide critical water supplies to lower-elevation regions. The western cordillera of Canada is a prime example where changes to temperature and precipitation could have profound hydro-climatic impacts not only for the cordillera itself, but also for downstream river systems and the drought-prone Canadian Prairies. At present, impact researchers primarily rely on global climate models (GCMs) for future climate projections. The main objective of this study is to assess several GCMs in their ability to simulate the magnitude and spatial variability of current (1961–90) temperature and precipitation over the western cordillera of Canada. In addition, several gridded data sets of observed climate for the study region are evaluated. Results reveal a close correspondence among the four gridded data sets of observed climate, particularly for temperature. There is, however, considerable variability regarding the various GCM simulations of this observed climate. The British, Canadian, German, Australian, and US GFDL models are superior at simulating the magnitude and spatial variability of mean temperature. The Japanese GCM is of intermediate ability, and the US NCAR model is least representative of temperature in this region. Nearly all the models substantially overestimate the magnitude of total precipitation, both annually and on a seasonal basis. An exception involves the British (Hadley) model, which best represents the observed magnitude and spatial variability of precipitation. This study improves our understanding regarding the accuracy of GCM climate simulations over the western cordillera of Canada. The ﬁndings may assist in producing more reliable future scenarios of hydro-climatic conditions over various regions of the country. Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. KEY WORDS global climate models; simulations; western cordillera; Canada; temperature; precipitation; gridded climate data

INTRODUCTION Climate change is expected to considerably alter future hydrologic processes over many regions of the globe. Of particular concern are alpine areas that provide runoff to adjacent lower-elevations, where water supply is important for ecological processes and various human activities. Changes to the occurrences of ﬂoods and/or droughts, or dramatic shifts to the timing of the spring freshet and the length of the ice-covered season, could have profound effects on the amount and timing of the downstream water supply. The western cordillera of Canada is a prime example of an alpine region with numerous downstream uses dependent on its water resources. Within and directly adjacent to the cordillera are major hydro-electric reservoirs whose generating capacity are wholly dependent on alpine runoff. The Bennett Dam on the Peace River, which is one of the world’s ten largest reservoirs (70 ð 109 m3; International Commission on Large Dams, 1988), is a major example. Further downstream, the drought-prone Canadian Prairies with their extensive agricultural industries and municipal infrastructure (i.e. combined urban population of >3 million) are especially dependent on river ﬂow originating from the eastern slopes of the cordillera. Similarly, the ecological health of major alpine-fed

* Correspondence to: Barrie R. Bonsal, Aquatic Ecosystem Impacts Research Branch, National Water Research Institute, Saskatoon, Saskatchewan S7N 3H5, Canada. E-mail: [email protected] Received 8 August 2002 Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Accepted 12 May 2002 3704 B. R. BONSAL, T. D. PROWSE AND A. PIETRONIRO rivers and their riparian systems, such as the Athabasca, Liard, Peace (three principal headwater tributaries of the Mackenzie River) and Saskatchewan rivers (mean annual flows ¾0Ð5–2Ð5 ð 103 m3 s1), is significantly controlled by flow from the alpine headwaters. The Peace–Athabasca delta (which was internationally recognized by the Ramsar Convention in 1982, and as a UNESCO World Heritage Site in 1983), located almost 1200 km downstream of the cordillera, is an extensive riparian system (¾6 ð 103 km2) that is particularly sensitive to the timing and magnitude of the flow on two of these major rivers (Prowse and Conly, 2000). Recent changes to western Canadian temperature and precipitation have already had discernible impacts on several hydro-climatic processes affecting the western cordillera. For instance, Bonsal and Prowse (2003) observed that, during the 20th century, the timing of the spring 0 °C isotherm has become significantly earlier (10 to 20 days) over most of British Columbia and the southern Yukon. This warming has also been associated with a rapid thinning of mountain glaciers in Alaska, the Yukon, and northern British Columbia during the last 50 years (Arendt et al., 2002). Additionally, an inter-decadal shift in atmospheric patterns that produce winter alpine precipitation (Keller, 1997; L. Romolo, personal communication) has been cited as being partly responsible for a reduction in spring ice-jam floods that are essential to recharging of aquatic systems in the Peace–Athabasca delta (e.g. Prowse and Conly, 1998). Future projections of large-scale increases in temperature, along with uncertainties in regional variations of precipitation, suggest even further changes to hydro-climatic parameters over western Canada. To assess the potential climatologic impacts on hydrologic processes over this region, accurate future climate scenarios at the appropriate temporal and spatial scales are required. At present, the impacts community primarily relies on coupled global climate models (GCMs) for projections of future temperature and precipitation. Finer resolution regional climate models (RCMs) nested within the GCMs are becoming increasingly available. For example, Lueng and Ghan (1999) simulated 7-year climatologies of temperature and precipitation over the Pacific Northwest (a region topographically similar to the western cordillera of Canada) using the Pacific Northwest Laboratory Regional Climate Model nested within a GCM. However, the use of RCMs is currently limited to specific areas for selected periods, whereas GCM output is available over the entire globe for extensive time periods. The IPCC–TGCIA (1999) suggests that a main criterion in the selection of GCMs for future climate scenarios is the ability of the model to simulate current climate both globally and for the region in question. However, most future climate impacts research has employed GCM-simulated changes in temperature and precipitation with little attention on how these GCMs replicate current climate over the study regions in question. This is likely due in part to the lack of suitable observed, regional-scale data sets for which to carry out these comparisons. Recently, however, several gridded data sets of observed monthly temperature and precipitation over Canada have become available and are used in this investigation. The main objective of this study is to provide a relative assessment of several GCMs in their ability to simulate the magnitude and spatial variability of current (1961–90) temperature and precipitation over the western cordillera of Canada. The analysis focuses on temperature and precipitation, since they are generally considered as the most primary surface parameters, are more easily validated with observational data, and are generally made available in the archives of global model simulations. Given the complexity of the study region, and the fact that most climate stations are within valley bottoms, there is uncertainty regarding the best realizations of current climate in this region. Therefore, several gridded data sets of observed temperature and precipitation over the area are also compared. The goal of the GCM assessment is to ascertain which models are better at replicating current climate over the western cordillera at seasonal and annual time scales. This will aid in determining the models that are best suited for future hydro-climatic impact studies in this region.

DATA AND METHODOLOGY GCM data Data from nine GCM simulations representing seven modelling centres (Table I) were used in this investigation. The majority of values were obtained from the Intergovernmental Panel on Climate Change

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) GCM-SIMULATED CLIMATE FOR THE WESTERN CORDILLERA, CANADA 3705

Table I. GCM simulations used in this investigation

Modelling centre Version(s) Resolution (lat/long)

Canadian Centre for Climate Modelling and Analysis CGCM1 3Ð75° ð 3Ð75° CGCM2 Hadley Centre for Climate Prediction and Research HadCM2 2Ð5° ð 3Ð75° HadCM3 Australian Commonwealth Scientiﬁc and Industrial Research Organisation CSIROMk2b 5Ð6° ð 3Ð2° German Climate Research Centre ECHAM4 2Ð8° ð 2Ð8° USA Geophysical Fluid Dynamics Laboratory GFDL-R15 4Ð5° ð 7Ð5° Japanese Centre for Climate Research Studies CCSR-98 5Ð6° ð 5Ð6° USA National Centre for Atmospheric Research NCAR-DOE 4Ð5° ð 7Ð5°

(IPCC) Data Distribution Centre (DDC) (http://ipcc-ddc.cru.uea.ac.uk/), which was established in 1998 to facilitate the distribution of consistent climate change scenarios for impact researchers (IPCC-TGCIA, 1999). Since then, some modelling centres have released output from more recent versions of their models. At the time of this study, two readily available data sets included those from the Canadian (CGCM2) and Hadley (HadCM3) models. These data were obtained from the Canadian Climate Impacts Scenario (CCIS) Web site (http://www.cics.uvic.ca/scenarios). The current-climate simulations for each of the nine model runs consisted of warm-start, transient experiments that incorporated historic equivalent CO2 and sulphate (SO4) concentrations. The variables extracted included 1961–90 climatological values of mean, minimum, and maximum monthly temperature, as well as monthly total precipitation. For this study, comparisons of mean temperature and total precipitation are carried out on seasonal and annual time scales. Seasons are deﬁned as winter (Dec–Jan–Feb), spring (Mar–Apr–May), summer (Jun–Jul–Aug) and autumn (Sep–Oct–Nov). Note that mean temperature refers to the average of the minimum and maximum values (which matches that used in the climatological archives). Minimum and maximum temperatures, however, were not available for the GFDL, NCAR and HadCM3 model runs. As a result, the model mean temperatures (i.e. temperature averaged for all time steps in a day) were incorporated.

Observed data The western cordillera is a complex region consisting of large variations in topography. Most climate stations are located in valleys and, therefore, are not fully representative of GCM data, which are averaged over the elevation of an entire grid cell. As a result, the comparisons require observed climate data that account for elevation. Recently, several gridded data sets of observed monthly temperature and precipitation for the 1961–90 period have become available (Table II). They all consider topographical features such as elevation and aspect; however, each is different in terms of their gridding procedures and/or number of input climate stations. The Climatic Research Unit (CRU) global climate data set (available from the IPCC DDC) consists of monthly climatologies of mean temperature and precipitation over global land areas on a0Ð5° latitude/longitude grid. The climate surfaces were constructed from station normals made available from various national climate centres. Station data were interpolated as a function of latitude, longitude, and elevation using thin-plate splines (New et al., 1999). The next data set incorporates the Parameter-elevation Regressions on Independent Slopes Model (PRISM) that was developed at Oregon State University (Daly et al., 1994). The procedure utilizes all available station data and a digital elevation model (DEM) to calculate linear parameter-elevation relationships that change locally with elevation. The model also creates zones of different climatological regimes based on various barrier, slope, and aspect characteristics. PRISM is, therefore, well suited to mountainous terrains. For Canada, PRISM data are available for British Columbia, Alberta, Saskatchewan, Manitoba and the Yukon at a resolution of 2Ð5 arc minutes.

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) 3706 B. R. BONSAL, T. D. PROWSE AND A. PIETRONIRO

Table II. Gridded data sets of observed climate used in this investigation

Data set Methodology Resolution

Climatic Research Unit Thin-plate splines; function of latitude, 0Ð5° ð 0Ð5° longitude, elevation PRISM DEM; linear parameter–elevation 2Ð5 arc minutes relationships; creates zones of different climatological regimes ANUSPLIN Thin-plate splines; more stations than CRU 5 arc minutes data set Square-grid Multivariate regression; elevation, local 1 arc minute slope, distance to ocean; rehabilitated data

ANUSPLIN temperature and precipitation exist for all of Canada at a 5 arc minute resolution. As with the CRU data, these values are based on the thin-plate spline surface fitting technique; however, the ANUSPLIN database incorporates many more climate stations in the gridding procedure (McKenney et al., 2001). The method involves fitting a surface described by mathematical functions onto the data points. The degree of smoothing is optimized objectively by minimizing the predictive error of the fitted function as measured by cross-validation. It incorporates elevation, latitude, and longitude while fitting the surface. The final data set utilizes the square-grid method of interpolation. Multivariate regression of climate normals is performed on the station’s geographic coordinates, elevation, local slope, distance to ocean, and other physiographic parameters. Values are interpolated to a 1 arc minute resolution (Seglenieks et al., 2000). Contrary to the other data sets (which use the Meteorological Service of Canada’s archived temperature and precipitation), the square-grid values utilize rehabilitated data that were adjusted to account for changes in instrumentation, observing techniques and station location (Mekis and Hogg, 1999; Vincent and Gullett, 1999). Precipitation amounts are generally higher than those in the archive, since the adjustments take into account, for example, wetting loss and the inclusion of trace precipitation events. The rehabilitated data sets also consist of a smaller subset of stations compared with the archive.

Study area and methodology The study area approximates the western cordillera of Canada, including most of the Yukon Territory, British Columbia and western Alberta (Figure 1). Owing to the varying resolutions of the GCM and observed data, all monthly values are interpolated to a common 2Ð5° latitude/longitude grid resulting in 33 grids over the study area. This resolution is comparable to the larger GCM grids and has been used in several GCM intercomparison studies (e.g. Gates et al., 1998). Seasonal and annual values of mean temperature and precipitation are then derived from the monthly gridded data. An important criterion in the GCM assessment involves the models’ ability to simulate the magnitude of current climate over the study area. To facilitate this comparison, areally averaged values (over the entire study region) for each variable and season are determined for all GCM and observed data sets. Differences between the average values are assessed using the standard t-test (e.g. Ebdon, 1985) at the 5% level. Areal averages provide useful information on magnitude differences, but reveal little regarding the spatial consistency of the data. Recently, Taylor (2001) devised a diagram that provides a statistical summary of pattern similarities in terms of their correlation coefﬁcients, their root-mean-square errors (RMSEs), and the ratio of their standard deviations. These statistics can all be presented as a single point on a two-dimensional plot (i.e. the ‘Taylor’ diagram). Taylor diagrams (described below) are used in this study to compare pattern similarities of the nine GCM runs with observed climate over the cordilleran region.

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) GCM-SIMULATED CLIMATE FOR THE WESTERN CORDILLERA, CANADA 3707

Figure 1. Study area used in this investigation (shaded region). All data are interpolated to a 2.5° latitude/longitude grid whose centres are represented by the black dots

The statistic most often used to quantify pattern similarity is the spatial pattern correlation coefficient R, defined as 1 N f f r r N n n R D nD1 fr where f and r are the modelled and observed values respectively, n is the number of grids, and is the spatial standard deviation. Pattern similarity can also be quantified by the RMSE, defined as 1 1 N 2 E D f r 2 N n n nD1 To isolate the differences in the patterns from differences in the means of the two fields, E can be resolved into two components. The overall bias is E D f r and the pattern RMSE (PRMSE) is defined by 1 1 N 2 E0 D [f f r r]2 N n n nD1

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The two components add quadratically to yield the full mean square difference:

E2 D E2 C E02

The PRMSE, therefore, is indicative of the RMSE once the overall bias (i.e. the difference in the means) has been removed. The value approaches zero as the two patterns become more alike (Taylor, 2001). The correlation coefficient and PRMSE supply complementary statistical information describing the correspondence between two patterns. A high correlation coefficient is generally associated with a low PRMSE and vice versa. To provide a more complete characterization of the fields, the spatial standard deviations of the modelled and observed data are also given. The closer these two parameters, the better the models are at simulating the magnitude of spatial variability over the study area. All of the aforementioned pattern statistics (i.e. correlation coefficient, PRMSE, and standard deviations) are geometrically related, and thus can be easily displayed on the Taylor diagram (e.g. see Figure 2a). In these diagrams, the radial distance from the origin is proportional to the standard deviations of the data (the solid semi-circles). The PRMSEs between modelled and observed values are proportional to their distance apart, and have the same units as standard deviation (the grey dashed semi-circles). Correlation coefficients are given by the azimuthal position of the plotted model points with respect to the origin (black dashed lines). A model point that is closer to that of the observed indicates that it has a similar standard deviation, a low PRMSE, and a high correlation (i.e. the two spatial patterns are similar). In Figure 2a, the average observed annual temperature (shown as the ‘O’ on the bottom axis) has a standard deviation of 4 °C. The HadCM2 model for example (H2), has a standard deviation of just over 4 °C. With respect to the observed temperature, the PRMSE of the HadCM2 model is 1 °C, and the correlation coefficient is 0Ð97.

RESULTS Observed data comparisons An initial step is to evaluate the various gridded data sets of observed climate over the western cordillera. Comparisons involving mean temperature and total precipitation are given in Tables III and IV. Annual and seasonal averages and standard deviations, as well as correlation coefficient matrices, are provided. Regarding temperature, Table III shows that average values are similar for all seasons (especially summer) and annually. PRISM averages are slightly warmer than the other data sets, particularly during winter. The t-test, however, reveals no significant differences in mean values between any of the data sets during all time periods. Standard deviations are also consistent, although PRISM tends to be a little lower during winter and spring. Correlation coefficients are strong and significant for all seasons (although slightly lower in association with the square-grid technique during summer). Total precipitation comparisons in Table IV display somewhat higher variations. This is likely due to the higher degree of spatial variability inherent in precipitation, and to the different methodologies incorporated in the gridding procedures. The most notable differences include higher averages associated with PRISM (and, to a lesser extent, the square-grid method) and lower values for the CRU data. Annually, this amounts to nearly 175 mm (35%) more precipitation for PRISM when compared with CRU. Significant differences occur between PRISM and CRU for every period except winter. Reasons for the higher PRISM precipitation are not evident but may be related to the more detailed methods of incorporating elevational differences in the interpolation procedure. This includes the use of spatially and temporally varied boundary layers to account for increasing precipitation with elevation (Daly et al., 1994). Square-grid values are also somewhat higher compared with CRU (significant during autumn and annually). A probable explanation involves the use of rehabilitated data in the square-grid interpolation procedure, which generally have higher precipitation amounts than those in the archive (Mekis and Hogg, 1999). The square-grid data also have lower standard deviations compared with the other data. This could also be related to the rehabilitated data set, which

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Table III. Mean temperature comparisons for the gridded data sets of observed climate over the western cordillera of Canada. The left portion of the table displays averages and standard deviations (SDs) for the data sets, while the right portion provides correlation coefﬁcient matrices

° ° Period Data set Average ( C) SD ( C) CRU PRISM ANUSPLIN Square-grid

Annual CRU 2Ð74Ð2—0Ð99 0Ð99 0Ð99 PRISM 1Ð83Ð60Ð99 — 0Ð99 0Ð99 ANUSPLIN 2Ð64Ð10Ð99 0Ð99 — 0Ð98 Square-grid 2Ð14Ð20Ð99 0Ð99 0Ð98 — Winter CRU 16Ð97Ð2—0Ð99 0Ð99 0Ð99 PRISM 15Ð05Ð90Ð99 — 0Ð99 0Ð98 ANUSPLIN 16Ð56Ð80Ð99 0Ð99 — 0Ð98 Square-grid 16Ð37Ð10Ð99 0Ð98 0Ð98 — Spring CRU 2Ð74Ð6—0Ð99 0Ð99 0Ð99 PRISM 1Ð73Ð70Ð99 — 0Ð99 0Ð94 ANUSPLIN 2Ð74Ð60Ð99 0Ð99 — 0Ð96 Square-grid 1Ð94Ð50Ð99 0Ð94 0Ð96 — Summer CRU 11Ð11Ð7—0Ð88 0Ð97 0Ð82 PRISM 11Ð41Ð70Ð88 — 0Ð95 0Ð77 ANUSPLIN 11Ð21Ð70Ð97 0Ð95 — 0Ð82 Square-grid 11Ð41Ð80Ð82 0Ð77 0Ð82 — Autumn CRU 2Ð54Ð4—0Ð99 0Ð99 0Ð99 PRISM 1Ð94Ð00Ð99 — 0Ð99 0Ð99 ANUSPLIN 2Ð54Ð30Ð99 0Ð99 — 0Ð98 Square-grid 1Ð43Ð90Ð99 0Ð99 0Ð98 — has fewer stations. Owing to the complexity of the study region and associated high spatial variations in precipitation, a decrease in the number of representative stations would likely result in lower variability over the entire region. Correlation coefficients in Table IV are lower than those associated with temperature but, nonetheless, are all significant. Lowest correlations are associated with the square-grid data. The preceding discussion indicates that, for the western cordillera of Canada, there is a close correspondence among the four gridded data sets, particularly for temperature. Precipitation has higher variations, but this can likely be attributed to the complex nature of the study region. It is difficult to discern which data are best representative of climate over the study area, since the majority of observations have been recorded at lower elevations. The fact that all data sets (which incorporate different methodologies and input stations) display consistent values suggests that any one or all of them could be used to represent temperature and precipitation for the entire study region. The next section compares GCM-simulated climate with observations over the cordillera. For reasons outlined above, the observed climate is represented by the average of all four gridded data sets. Note that comparisons were also carried out using each observed data set individually, and, unless noted otherwise, the individual results were statistically similar to those using the four data sets average.

GCM comparisons Mean temperature. Taylor diagrams showing annual and seasonal comparisons of mean temperature simulated by the nine GCM runs with observed values are given in Figure 2a to e. For magnitude comparisons, a table with average observed and GCM temperatures is also provided. Asterisks indicate models with mean values signiﬁcantly different from the observed. Figure 2a shows that the majority of models simulate both

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Table IV. As for Table III, except for total precipitation. Asterisks signify signiﬁcant differences in average precipitation values (with respect to the CRU data) using a two-tailed t-test at the 5% signiﬁcance level

Period Data set Average (mm) SD (mm) CRU PRISM ANUSPLIN Square-grid

Annual CRU 485Ð9 233Ð0—0Ð84 0Ð97 0Ð76 PRISM 657Ð9Ł 215Ð30Ð84 — 0Ð90 0Ð80 ANUSPLIN 550Ð4 206Ð40Ð97 0Ð90 — 0Ð69 Square-grid 593Ð8Ł 151Ð60Ð76 0Ð80 0Ð69 — Winter CRU 110Ð889Ð5—0Ð92 0Ð98 0Ð79 PRISM 153Ð890Ð30Ð92 — 0Ð95 0Ð78 ANUSPLIN 128Ð384Ð30Ð98 0Ð95 — 0Ð77 Square-grid 149Ð579Ð70Ð79 0Ð78 0Ð77 — Spring CRU 88Ð354Ð2—0Ð93 0Ð98 0Ð83 PRISM 115Ð5Ł 47Ð80Ð93 — 0Ð95 0Ð89 ANUSPLIN 101Ð549Ð00Ð98 0Ð95 — 0Ð87 Square-grid 109Ð333Ð10Ð83 0Ð89 0Ð87 — Summer CRU 175Ð148Ð0—0Ð70 0Ð96 0Ð62 PRISM 214Ð6Ł 46Ð70Ð70 — 0Ð77 0Ð76 ANUSPLIN 181Ð838Ð10Ð96 0Ð77 — 0Ð67 Square-grid 187Ð837Ð70Ð62 0Ð76 0Ð67 — Autumn CRU 111Ð762Ð7—0Ð76 0Ð92 0Ð58 PRISM 174Ð2Ł 65Ð80Ð76 — 0Ð86 0Ð76 ANUSPLIN 138Ð856Ð30Ð92 0Ð86 — 0Ð69 Square-grid 147Ð2Ł 41Ð00Ð58 0Ð76 0Ð69 — the magnitude and spatial variability of observed average annual temperature (2Ð3 °C) accurately over the study area. In particular, the HadCM2 and ECHAM models have very similar means (2Ð0 °Cand2Ð7 °C) and standard deviations (3Ð6 °Cand4Ð1 °C), relatively low PRMSEs (both near 1Ð0 °C) and high correlation coefficients (0Ð97 and 0Ð98) compared with observed temperature. The HadCM3, GFDL, CGCM2, and CSIRO models are also representative of observations, with slightly larger differences in average values (although not significant) compared with the previous two GCMs. They also have similar variances, low PRMSEs, and high correlations. Three models have mean values that are significantly different from the observed means. Of these, the CGCM1 and CCSR simulate annual temperatures with a mean of 0Ð7 °C and similar standard deviations to those observed. The NCAR model run is too warm (approximately 7 °C higher than observed) and is associated with a lower standard deviation and a higher PRMSE. Note that every model has a very high correlation coefficient, indicating that they accurately represent the spatial pattern of variation over the study region. However, the wider range in standard deviations suggests differing abilities in simulating the magnitude of this variability. Winter (Figure 2b) displays a greater range in model simulations compared with the other periods (note the different temperature scale). This is likely attributable, in part, to the higher degree of variability inherent in winter temperature. In terms of magnitude, models that are not significantly different include the HadCM3, GFDL and ECHAM. In fact, the HadCM3 simulates an identical temperature to that observed (16Ð2 °C). The HadCM3 and ECHAM models also replicate the spatial variability of winter temperature quite well, as evidenced by their standard deviations (near those observed), relatively low PRMSEs, and high correlation coefficients. The GFDL, on the other hand, tends to underestimate standard deviation during winter, whereas the HadCM2 has a spatial pattern very close to that observed. Models with an intermediate level of winter temperature simulation include the CGCM1, CGCM2, CSIRO and CCSR. As with annual temperature, NCAR

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) GCM-SIMULATED CLIMATE FOR THE WESTERN CORDILLERA, CANADA 3711 C. The corresponding ° ved average temperature mer and (e) autumn (see t differences between model and obser C with the exception of winter, which is 2 ° -test at the 5% signiﬁcance level t stern cordillera of Canada for: (a) annual, (b) winter, (c) spring, (d) sum e models. Asterisks signify signiﬁcan values using a two-tailed ons and PRMSEs are in centigrade. Contour intervals are 1 text for explanation of the diagram). Standard deviati tables show average temperatures for the observed data and each of the nin Figure 2. Taylor diagrams showing mean temperature comparisons over the we

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) 3712 B. R. BONSAL, T. D. PROWSE AND A. PIETRONIRO simulates a higher temperature than observed. It also under-predicts standard deviation, has a higher PRMSE, and a lower correlation coefficient. For spring, only the GFDL and NCAR models are associated with significantly different average temperature values. Regarding spatial variability, all models have similar correlation coefficients and PRMSEs. The CGCM1, CGCM2, CSIRO, CCSR, GFDL and ECHAM models are closest in terms of simulating observed standard deviation. For summer, Figure 2d shows that GFDL and CGCM2, and to a lesser extent CSIRO, CGCM1, HadCM3 and CCSR, best represent observed temperature in terms of magnitude. The HadCM2 and ECHAM simulations are, on average, too low. In addition, their standard deviations are, higher than observed, and the PRMSEs are generally greater than the previous six model runs. NCAR is associated with an even higher PRMSE and standard deviation, and greatly overestimates mean temperature. All summer correlation coefficients are lower compared with the other periods. Figure 2e reveals that the HadCM2, HadCM3, ECHAM, GFDL, CSIRO and CCSR models are best representative of the magnitude and spatial variability of autumn temperature. NCAR, CGCM2 and CGCM1 simulate temperatures that are higher and variances that are lower than those observed over the region. In summary, the model runs display a varying range in their ability to replicate the magnitude and spatial variability of mean temperature over the western cordillera. It is also shown that models which simulate the magnitude of observed temperature with higher accuracy generally have a similar spatial variability to the observed values (and vice versa). Although some seasonal differences are apparent, the HadCM2, HadCM3, ECHAM, CSIRO, GFDL, CGCM1 and CGCM2 models generally outperform the other runs. CCSR tends to be in the middle of the pack. NCAR is consistently warmer during all seasons and, with the exception of summer, under-predicts the standard deviation of observed temperature. Of note, the more recent versions of the Canadian (CGCM2) and Hadley (HadCM3) models show little differences from the CGCM1 and HadCM2 runs in their ability to simulate the magnitude and spatial pattern of mean temperature over the cordillera. In fact, the two versions from both models tend to represent mean temperature accurately with some seasonal differences.

Total precipitation. The precipitation comparisons in Figure 3 show much higher variations than those for mean temperature. The most prominent feature includes the fact that nearly all the models significantly over-predict precipitation on an annual basis and during each season. Focusing on average annual values (Figure 3a), the CGCM1, CGCM2 and NCAR model runs simulate more than twice the amount of observed precipitation (572 mm). The CCSR, GFDL, HadCM2, CSIRO and ECHAM models are slightly better, but overestimate by a factor of 1Ð5to1Ð75. The best GCM in terms of magnitude is the 728Ð4 mm simulated by the HadCM3 (25% higher than observed). There are also substantial variations with regard to the pattern similarity of annual precipitation. In terms of PRMSEs, GFDL and HadCM3 (and to a lesser extent CCSR, NCAR, ECHAM and CSIRO) show the lowest values, whereas CGCM1, CGCM2 and HadCM2 have the highest errors. Most correlation coefficients are between 0Ð4and0Ð6, whereas the HadCM3 and NCAR models are closest to the observed standard deviation. In terms of both magnitude and spatial variability, the HadCM3 is best representative of annual precipitation over the study area. Of the remaining models, the ECHAM and CSIRO runs have the closest simulations. The winter results are similar to the annual comparisons, particularly in terms of spatial variability. The most noteworthy finding involves the HadCM3, whose magnitude is almost identical to the observed precipitation value. All other model runs significantly over-predict winter precipitation. The HadCM3 also has one of the highest correlation coefficients and lowest PRMSEs compared with the other models, and a similar standard deviation to observed winter precipitation over the region. As with annual values, the ECHAM and CSIRO models are next best. Spring simulations of precipitation (Figure 3c) are all significantly higher than the observed value of 103Ð7 mm. Once again, the HadCM3 is closest (168Ð4 mm), whereas CSIRO, ECHAM, CCSR, GFDL and HadCM2 are approximately twice that observed. The CGCM1, CGCM2 and NCAR models over-predict spring values by factors ranging from 2Ð5to>3. The Taylor plot has the same general pattern as the previous two periods, although the spread of model points is considerably smaller. Even though the

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) GCM-SIMULATED CLIMATE FOR THE WESTERN CORDILLERA, CANADA 3713 plot each season and 100 mm for the annual res. Contour intervals are 40 mm for Figure 3. As for Figure 2, except for total precipitation. Units are in millimet

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) 3714 B. R. BONSAL, T. D. PROWSE AND A. PIETRONIRO

NCAR, GFDL and CCSR models are much higher in terms of magnitude, they accurately simulate the spatial variability of spring precipitation, as evidenced by their similar standard deviations (to observed), relatively low PRMSEs and high correlation coefficients. Summer results are somewhat different from the other seasons, in that the average simulated values tend to be closer to those observed. The ECHAM, GFDL, CCSR, HadCM2, HadCM3 and CSIRO precipitation values are only around 25% higher than observations. In fact, when compared with the higher PRISM value of observed summer precipitation over the study area (214Ð6 mm in Table IV), ECHAM, GFDL, CSSR and HadCM2 average precipitation amounts are not significantly different. As with the other seasons, the CGCM1, CGCM2 and NCAR models greatly over-predict summer precipitation totals. With the exception of the NCAR model, all GCMs appear to be very similar in terms of simulated spatial variability. This includes similar standard deviations to observed precipitation and low PRMSEs. Correlation coefficients, however, are much lower than other seasons. Figure 3e shows that autumn closely resembles the annual, winter, and spring comparisons discussed previously. The HadCM3 is again the best representative model, exemplified by the fact that it is the only GCM with no significant difference in mean values. The preceding discussion demonstrates that the vast majority of GCMs significantly overestimate observed precipitation for the western cordillera of Canada. One notable exception is the HadCM3 run, which provides the closest simulation of observed precipitation amounts, particularly during spring and autumn. It is also one of the best models in terms of simulated spatial variability. In general, the next best models appear to be the ECHAM and CSIRO, whereas GFDL, CCSR and HadCM2 are of intermediate skill. The CGCM1, CGCM2 and NCAR model simulations tend to be the least representative. In terms of magnitude, summer appears to be the best season, with many model simulations within 25% of the observed mean value. The spatial variability of precipitation appears to be better simulated than the magnitude and, with the exception of summer, this variability is consistent among the different model runs, as evidenced by the similar clustering patterns in the Taylor diagrams. As with temperature, there are little differences in the CGCM2’s ability to simulate precipitation over the cordillera when compared with CGCM1. The HadCM3, on the other hand, appears to simulate precipitation with a higher accuracy than HadCM2. Another difference from temperature includes the fact that the models with the best representation of precipitation magnitude were not always the best in terms of spatial variability (and vice versa).

DISCUSSION AND SUMMARY A relative assessment of nine GCM runs in their ability to simulate the magnitude and spatial variability of current (1961–90) temperature and precipitation over the western cordillera of Canada was provided. Results indicated considerable variability for the different model simulations, with mean temperature being closer to observations than precipitation. In general, the HadCM2, HadCM3, ECHAM, CSIRO, GFDL, CGCM1 and CGCM2 models are superior at simulating the magnitude and spatial variability of observed mean temperature. The CCSR is of intermediate ability, and the NCAR model is least representative of temperature in this region. Precipitation is substantially overestimated by the majority of the GCM runs for all periods analysed. This result is consistent with GCM comparison studies for Arctic regions of the globe, which found that all GCMs greatly overestimate precipitation when compared with observations (e.g. Walsh et al., 1998, 2002). An exception, however, involves HadCM3, which is by far the best at simulating the observed magnitude and spatial variability of precipitation over the western cordillera. Of the remaining runs, ECHAM and CSIRO are generally the most accurate, whereas GFDL, CCSR and HadCM2 are next best. For the most part, the CGCM1, CGCM2 and NCAR models have the poorest precipitation skill. It should be noted that, owing to the complex nature of the study area, and the fact that most observing stations are located at lower elevations, there is some uncertainty regarding the best estimates of observed precipitation. Measurement biases, such as gauge undercatch (which, for snow in windy environments, can be greater than 50%; Goodison et al., 1998), increase this uncertainty. Even though these uncertainties in

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) GCM-SIMULATED CLIMATE FOR THE WESTERN CORDILLERA, CANADA 3715 observed precipitation may exist, this does not significantly affect the finding that the majority of GCMs vastly overestimate precipitation in this region. Furthermore, it was revealed that there was a close correspondence among the four observed gridded data sets (Tables III and IV). Although precipitation showed more variability than temperature, amounts were generally similar among all the data sets, with few significant differences (including the square-grid values that were adjusted for measurement biases). These similarities increase confidence regarding the reliability of observed climate over the western cordillera. It is realized that this study focused on comparisons over the entire cordilleran region and that examination of finer spatial and temporal scales may show more varying results. For example, Milewska et al. (2002) found some significant differences among ANUSPLIN, PRISM, and square-grid values of observed temperature and precipitation within specific regions of the Canadian Prairies. Additionally, preliminary analyses over the cordillera suggest that GCM simulations of both temperature and precipitation are generally closer to observations over southern portions of the region (south of 60 °N) as opposed to northern areas. Temporally, model simulations for individual months were occasionally closer to observed values than the seasonal comparisons. In addition, the aspect of inter-annual variability was not addressed in this analysis. This investigation was not designed to provide definite answers as to which GCM(s) should be incorporated for future hydro-climatic impact studies over the western cordillera of Canada. Rather, it gives a relative assessment of the current (or near current) generations of climate models in their ability to replicate the magnitude and spatial variability of observed temperature and precipitation on seasonal time scales. It is possible that a certain GCM(s) could realistically simulate future temperature and precipitation changes even if it does not accurately replicate current climate over the region in question. However, it is suggested that more confidence can be placed in those models with better simulations of observed climate. This degree of confidence also depends on the spatial and temporal aspects of the impact study. For example, it was shown here that certain GCM runs are better at simulating the magnitude of observed climate over the region, whereas others are superior at representing the spatial variability. In addition, some models perform better during certain seasons. These factors require consideration for particular hydro-climate impact studies (e.g. changes to the timing of spring freshet or river-ice break-up dates), which require accurate projections of future climate with reliable variability at specific times during the year. The preceding discussion focused on temperature and precipitation, since they are the most frequently observed and readily available climatic variables. For the same reason, most hydro-climatic assessments rely on precipitation and temperature-indexed (e.g. degree-day) models for calculating fluxes, such as evaporation and snowmelt. However, these temperature-indexed approaches may not be applicable for climate-change studies, since future hydro-meteorological conditions could significantly vary from those of current climate (for which these indexed models are calibrated). In the case of snowmelt, for example, a calibrated degree-day model that produces reliable results for high-radiation clear-sky conditions under a current climate will be much less effective in modelling future conditions if cloud cover greatly increases and snowmelt is generated by a significantly different composition of heat fluxes. More reliable results will likely require that future conditions be modelled with more physically based approaches that calculate all the major heat and mass fluxes. This necessitates a broader suite of meteorological variables than simply temperature and precipitation. To date, evaluations of the reliability of such variables in the GCMs have been rare. Accurate modelling of future hydro-climatic conditions will, therefore, require the creation of spatially extensive and reliable sets of observed data, similar to the gridded records of temperature and precipitation employed in this analysis. Note that this assessment was carried out using GCM versions currently available from the IPCC DDC and that ongoing evaluations are required as these models evolve. Data from more recent versions of the Canadian and Hadley models (which were readily available) were also incorporated. Analyses showed small differences in temperature and precipitation simulations between CGCM1 and CGCM2 runs. HadCM3 precipitation is better simulated than HadCM2, whereas mean temperature is projected equally well by both Hadley models. It is suggested that, as data from newer versions of the models become available, regional assessments should be carried out to determine whether improvements are observed. This information can then be fed back to

Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003) 3716 B. R. BONSAL, T. D. PROWSE AND A. PIETRONIRO the GCM community to determine, for example, whether systematic biases are present that may account for consistent errors in the simulations (e.g. the overestimation of precipitation). In conclusion, this study has improved our knowledge regarding GCM simulations of observed (1961–90) temperature and precipitation over the western cordilleran region of Canada. This information, along with similar assessments for other regions of the country, can assist the impacts community regarding the production of more reliable future hydro-climatic scenarios over various portions of Canada.

ACKNOWLEDGEMENTS We are grateful to Krysha Dukacz and Ross Mackay of the National Water Research Institute of Environment Canada for assistance with the data preparation and presentation. Appreciation is also given to Ron Hopkinson of the Meteorological Service of Canada for the provision of the gridded data sets of observed temperature and precipitation. We would also like to thank the two reviewers for their useful suggestions toward an improved version of the manuscript.

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Copyright  2003 Crown in the right of Canada. Published by John Wiley & Sons, Ltd. Hydrol. Process. 17, 3703–3716 (2003)