1. Introduction
Some of the most pressing scientific challenges today are related to improving our understanding and prediction of anthropogenic or natural climate change, and its impact on climate-sensitive natural resources, such as water, that are essential for the well-being of mankind. Since the water, energy, and carbon cycles in the earth’s climate system are intimately coupled, it is generally agreed that a key to addressing the problem is to enhance our physical knowledge and understanding of the water and energy cycle of the climate system. The Global Energy and Water Cycle Experiment (GEWEX) was thus initiated by the World Climate Research Program (WCRP) to better observe, understand, and model the water and energy cycle of the climate system. There are critical scientific objectives that must be met for GEWEX to attain its goals, and one of these is concerned with the characterization of water and energy fluxes over land areas to provide benchmark values for the present climate. In the GEWEX science plan, this pressing task is to be addressed in the so-called Water and Energy Budget Study (WEBS) that is first being carried out for the individual study basins selected for the GEWEX Continental Scale Experiments (CSEs), and then collectively under the coordination of the GEWEX Hydrometeorology Panel (GHP; Lawford et al. 2004).
WEBS in GEWEX CSEs differs from previous water and energy budget studies (e.g., Berbery et al. 1999; Trenberth et al. 2001; Roads et al. 2002; among many others) mainly in the use of various observational, (re)analysis, and model data to arrive at quasi-independent estimates of a more or less common set of water and energy budget variables that characterize the regional water and energy cycle of the respective CSE regions. In synthesizing the WEBS results, we would like to assess our ability to (i) develop observations of basic climate variables, (ii) simulate those observations with current models, and (iii) develop budgets from observations, models, and blended datasets such as reanalysis data. We would also want to clarify levels of uncertainty, as well as their sources, in these budgets and to recommend future research and data collections to address the problems. To date, the GEWEX Continental-Scale International Project (GCIP) is the only CSE that has completed its WEBS (Roads et al. 2003), although less comprehensive water and energy budget studies that focused on particular aspects or specific components of the water and energy cycle have been conducted in several CSEs (e.g., Heise 1996; Roads and Betts 2000; Xu and Haginoya 2001; Strong et al. 2002; Betts et al. 2003).
The Mackenzie GEWEX Study (MAGS; Stewart et al. 1998) is one of the CSEs approved by GEWEX to improve our understanding and modeling of water and energy cycling in high-latitude continental regions. The Mackenzie River is one of the major river systems of the world. The Mackenzie River basin (MRB) stretches over 15 degrees of latitude and covers about 1.8 million km2 (Fig. 1). Because of the diverse physiographic features and climatic conditions that characterize the basin, 8 of Canada’s 15 major terrestrial ecozones can be found within the basin. The large-scale circulation and synoptic disturbances over the North Pacific and their interactions with the Western Cordillera play a particularly important role in the transport of water and energy into the MRB. More detailed discussions of the basin’s physical characteristics and large-scale processes that affect water and energy cycling in the region can be found in Stewart et al. (1998) and Szeto et al. (2007).
The purpose of MAGS WEBS is to meet the basic MAGS objective of developing state-of-the-art assessments of the water and energy budgets for the MRB. Different observed, remotely sensed, (re)analyzed, and modeled data were utilized to obtain independent estimates of the budgets. Comparisons of the present results to previous budget estimates will be made where possible. The credentials of current models and data assimilation systems in representing aspects of the water and energy cycle of this northern region will also be assessed. The water and energy budget equations and the datasets employed in this study are described next. Budget results and their discussions are then presented and are followed with some concluding remarks in section 5.
2. Water and energy budget equations
The climate system comprises various subsystems such as the atmosphere, ocean, and the land surface. Depending on the degree of details that is desired in the analysis, there are many forms of the water and energy conservation equations that could be used to assess the water and energy budgets. In this preliminary study, we will limit the analysis to the two-dimensional (vertically integrated) horizontal variations of key water and energy processes in the atmosphere–land surface subsystem and adopt the set of budget equations from Roads et al. (2002, 2003):
- Atmospheric water is
- Surface water is
- Atmospheric temperature is
- Surface temperature is
Definitions of the variables are given in Table 1 and {} denotes vertical integrals over either the atmospheric column or soil column. Detailed derivations and discussions of the budget equations, as well as the computational details of the convergence terms, can be found in Roads et al. (2002, 2003) and will not be repeated here. The main assumptions employed include the neglect of kinetic energy in the conservation of atmospheric energy, and the neglect of latent heat of fusion during the formation of ice-phase precipitation in the atmosphere and during snowmelt at the surface. Vertical integrations of atmospheric quantities are evaluated up to the highest data archive level in the different datasets by using the method outlined in Trenberth (1991). Similar to Roads et al. (2002, 2003), the vertically integrated water budget terms (kg m−2 s−1) are multiplied by 8.64 × 104 s day−1 to provide values in kg m2 day−1 or mm day−1 after dividing by the density of water; and all energy flux terms (W m−2) are multiplied by (8.64 × 104 s day−1)/(CpPsg−1), where Ps = monthly surface pressure, to provide normalized values in units of K day−1. The surface energy terms are also multiplied by a constant atmospheric normalization (i.e., CυHs = CpPsg−1, where Hs = depth of soil layer) in order to provide values in K day−1 so that they can easily be compared to the atmospheric counterparts. It is convenient to combine the residual forcing with the tendency term (e.g., RESQ = RESQ′ − ∂Q/∂t) in calculating the budget residuals. This should have little effect on the values of the mean annual residuals since the long-term change in water and energy storages in the atmosphere and surface can be assumed to be negligible. As this set of budget equations and processing of budget results are also used in the GCIP WEBS (Roads et al. 2003), their adoption for use in this study will facilitate the intercomparisons of budget results from different CSE regions in GHP-wide or other global WEBS efforts.
3. Datasets
A complete water and energy budget assessment for the basin would require the evaluation of the individual terms in Eqs. (2.1)–(2.4) over a wide range of spatiotemporal scales, certainly a nontrivial task for a vast, remote, and poorly observed region. In this preliminary study, we will focus on basin-scale water and energy budgets on monthly and longer time scales. Because of the general lack of extensive and detailed observations for the area, we have to by necessity rely heavily on assimilated, modeled, and remotely sensed datasets to evaluate the basin-scale water and energy fluxes.
Since the regional datasets are typically of higher data resolution and are more thoroughly validated for the area than the global datasets, the relative merits of the global datasets in representing components of the water and energy cycle for this northern basin will be assessed by using the regional datasets as reference. This type of assessment is important, as the global datasets are widely used in regional climate studies because of their extensive areal coverage, long data availability periods, and ready accessibility. To facilitate the intercomparison between the budgets evaluated from the global and regional datasets, the budget analysis are performed over the 5-yr period from June 1997 to May 2002 (the MAGS project itself spanned from 1994 to 2005). The adoption of this analysis period is largely dictated by the maximum overlap of availability period of the datasets used in the study (see the summaries of the various datasets in Tables 2 and 3).
a. The Canadian Regional Climate Model
The primary climate model for use within MAGS is the Canadian Regional Climate Model (CRCM; Caya and Laprise 1999). The version of the model used in this study utilizes the third-generation physical parameterization package of the Canadian Centre for Climate Modelling and Analysis (CCCma), which includes the Canadian Land Surface Scheme (CLASS; Verseghy 1991; Verseghy et al. 1993) among other improvements over the previous-generation CCCma physics package. In this study, the CRCM is run at a horizontal resolution of 51 km with 29 levels in the vertical. The simulation was performed in “climate mode” from April 1997 to almost the present. Lateral boundary and initial atmospheric conditions are specified from the Canadian Meteorological Centre (CMC) Global Environmental Multiscale Model (GEM) operational global analysis. Other aspects of the simulation can be found in MacKay et al. (2003).
b. Assimilated datasets
The global reanalysis datasets used in this study include the National Centers for Environmental Prediction Global Reanalysis 2 (NCEP-R2; Kanamitsu et al. 2002; Kistler et al. 2001) and the global 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40; Uppala et al. 2005; Kållberg et al. 2004). Regional (re)analysis data are obtained from both the NCEP North American Regional Reanalysis (NARR; Mesinger et al. 2006) and the CMC GEM operational regional analysis and forecast archive (Côté et al. 1998a, b).
c. Global satellite or blended datasets
The global datasets used in this study include satellite cloud and radiative products and blended satellite and in situ or model global precipitation and water vapor datasets. Radiative fluxes are obtained from the International Satellite Cloud Climatology Project (ISCCP; Rossow and Schiffer 1991) “FD” dataset (Zhang et al. 2004). For global blended precipitation datasets, both the GEWEX-sponsored Global Precipitation Climatology Project (GPCP; Huffman et al. 1997; Adler et al. 2003) and NCEP global precipitation climatology and NCEP Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin 1997) datasets are employed in the study. The blended global water vapor climatology dataset from the National Aeronautics and Space Administration (NASA) Water Vapor Project (NVAP; Randel et al. 1996) is used to compare with estimates of vertically integrated water vapor from other datasets.
d. Regional and in situ observations
There are four regular rawinsonde sites within, and four other sites that are nearby, the Mackenzie Basin (Fig. 2). Launches are conducted every 12 h. There are 10 principal surface synoptic stations within the basin augmented by 43 auto stations (Fig. 2). Hourly cloud fraction information for seven sites are available from 1950. Discharge data are collected at approximately 80 sites in the basin by the Water Survey of Canada (WSC). The farthest downstream measurements, at Arctic Red River, are available on a daily time scale from 1973.
A gridded, monthly climate dataset of precipitation and screen temperature for Canada covering the period 1895–present, known as CANGRID, is used for comparison with the model outputs. Briefly, this dataset is based on operational climate station data that have been homogenized and adjusted for all known measurement errors (Vincent and Gullett 1999; Mekis and Hogg 1999). Regional snow water equivalent (SWE) estimates derived from Special Sensor Microwave Imager (SSM/I) passive microwave satellite data (Derksen et al. 2003) are used to compare against model and analyzed snow cover in the region.
4. Results and discussions
a. Representativeness of the 5-yr study period
Key basin-averaged budget parameters computed for the current (1997–2002) and longer-term (1979–99) periods are given in Table 4. Despite the fact that the record-breaking 1997/98 El Niño event occurred within the 5-yr study period, the 5- and 20-yr climatologies are very similar. In particular, both the observed and modeled 5- and 20-yr mean precipitation show very little difference. The 5- and 20-yr mean basin evaporation also exhibit little difference for both the ERA-40 and NARR data and a slight (5%) increase in the NCEP-R2 estimate. Both the WSC discharge measurements and NCEP-R2 reanalysis show little difference in the 5- and 20-yr mean runoff from the basin, while the 5-yr mean runoff from the ERA-40 (NARR) reanalysis shows an 8% decrease (9% increase) when compared to the longer-term mean. Both the observed and analyzed surface air temperatures show that the basin during the 5-yr study period was 0.6°–0.7°C warmer than the previous 20 yr on average. This warming in the region could be related to the strong 1997/98 El Niño event or it could be part of the strong warming trend that has been observed in the region (Zhang et al. 2000). These intercomparisons show that while the 5-yr period chosen for the study exhibited some abnormalities in its mean hydroclimatic state, many of the water and energy fluxes computed for the period should also be representative of the longer-term climate.
b. Budgets, annual cycles, and the MRB climate system
A brief description of the regional water and energy cycle of the MRB as revealed from the budget results will be given here. Like other major high-latitude continental basins, the MRB acts as a sink region for heat and water in the global climate system, as reflected in the budget results.
During the boreal cold season (November–February) when the mean north–south global temperature contrast is strong and the atmosphere is dynamically active, a large amount of heat is transported into the MRB from the warm southern and oceanic regions (HC in Fig. 3a). As much of the basin receives no or little solar radiation during these months, there is a net radiation deficit at the surface (QRS in Fig. 3c) that cools the surface of the basin to low temperatures during the winter (Fig. 4). Some of the heat that is transported into the basin is lost to the cold underlying surface via sensible heat transfer (SH in Figs. 3a and 3c), hence cooling the lowest levels of the atmosphere. As a result, surface-based temperature inversions are created over much of the basin’s area that limit evaporation and latent heat transfer at the surface during the cold season (see E and LE during the cold season in Figs. 3b and 3c, respectively). Consequently, the atmospheric heat convergence into the basin is largely balanced by thermal radiation loss to outer space (QR in Fig. 3a). Although the strong mean westerly flow entering the continent from the North Pacific is moisture-laden, much of the moisture is depleted from the atmosphere as the flow converges and rises over the coastal mountains. While the enhanced condensation and associated latent heat release in the forced ascent over the western slopes effectively enhance the transport of dry static energy into the basin on the lee side of the mountains, net moisture flux convergence into the basin is reduced and its magnitude is typically small throughout the year (see MC in Fig. 3b). Precipitation (P, and the associated condensational heating LP) is relatively low and comes solely from synoptic systems that pass through or develop within the basin. As surface evaporation is extremely weak, the winter precipitation is largely balanced by the large-scale moisture flux convergence into the basin. Because of the low winter temperatures that characterize the region, the winter precipitation falls almost exclusively in the form of snow over much of the basin. Apart from possible wind transport and enhanced sublimation in the blowing snow over tundra-covered regions, sublimation is typically weak over much of the basin and the snowpack grows through the season and much of the basin is snow covered by the end of winter. The accumulation of snow on the surface is also reflected in the negative RESW during the winter (Fig. 3d) when RESW = RESW′ − ∂W/∂t is largely determined by the positive tendency term associated with the growing snow mass. Expectedly, runoff is extremely low under such conditions (Fig. 3d).
Solar insolation increases during spring (see QRS in Fig. 3c during April and May) and a large portion of the solar input is consumed in melting the surface snow. As much of the basin lies within the continuous and discontinuous permafrost zone and there is abundant snow on the surface, the meltwater often recharges the active soil layer in many areas of the basin to saturation and produces huge runoff during spring (Fig. 3d).
The basin experiences long hours of solar insolation during the summer (see QRS during June–August in Fig. 3c). As a large portion of the basin’s surface is covered with vegetation, wet soil, or surface water bodies, much of the solar insolation is consumed in evapotranspiration processes and induces large evaporation and latent heat flux at the surface (see E and LE during June–August in Figs. 3b and 3c). A relatively smaller portion of the solar radiation is used to warm the surface, which in turn warms the lower atmosphere via sensible heat transfer (SH in Figs. 3a and 3c). The surface sensible and latent heat fluxes destabilize the atmosphere over the basin, and consequently, despite the northern location of the basin, a considerable portion (typically between a third to one-half of total summer rainfall in the ERA-40 data) of its warm-season precipitation comes from moist convection. The considerable evapotranspiration and precipitation (and their strong phase coherence) that characterize the basin during the warm season (Fig. 3b) show that moisture recycling plays an important role in governing the warm-season water cycle of the region as indicated by results in Szeto (2002). Although summer precipitation contributes to warm-season runoff in the basin, runoff tapers off steadily from the spring snowmelt freshet. Because of the strong surface heat flux and condensation heating in the atmosphere (SH and LP in Fig. 3a), the basin is transformed into a heat source region (i.e., HC is negative) for the large-scale airflow during the summer months. However, despite the strong evapotranspiration that occurs in the basin, the basin on the whole remains as moisture sink during the summer (i.e., MC > 0 in Fig. 3b).
Solar input decreases rapidly as the basin progresses into the autumn months of September and October, and the net basin surface radiation heating becomes negative again during October. As the basin surface water and energy processes enter their dormant cold-season states, the atmospheric moisture and energy convergence into the basin increases as the large-scale thermal and moisture gradients intensify during the fall. In particular, the moisture flux convergence into the basin maximizes during October when the moisture contrast between the basin and the upstream region is enhanced and the synoptic processes again become active over the North Pacific.
c. Discussions of budget parameters
Because the evaluations of many budget terms are based on analysis data, it is convenient to discuss the results with reference to the degree by which the source analysis variables are affected by observations. In particular, we will adopt the convention used in describing the NCEP analysis variables (e.g., Kistler et al. 2001) in the following discussion. In this convention, type A variables are those that are strongly influenced by observations, type B variables are those affected by both the model performance and observations, and type C variables are pure forecast variables with no correction from observations. The discussion will focus on assessing the variability of budgets derived from the different datasets, the self-consistency of the budget components within each dataset, and intercomparisons of the current results with previous estimates where available. A comprehensive compilation of the budget results can be found in Szeto and Crawford (2006).
1) Precipitable water
The vertically integrated atmospheric moisture content or precipitable water (Q) gives a measure of the storage of water in the atmosphere. Despite the fact that Q is only a type B analysis variable, the variability of annual Q among the different estimates is relatively small (Table 5). In particular, the annual basin-averaged Q from analyzed datasets agrees reasonably well with estimates from both the global NVAP (1988–99 climatology) and regional rawinsonde datasets. The relative high bias of annual Q from the rawinsonde measurements can be related to the relatively higher number of sites that are located in the warmer southern regions.
2) Soil moisture
Although estimates of top-soil wetness are becoming available from satellite measurements, regular in situ measurements of soil moisture are relatively scarce. As such, M is typically a type C variable in analysis datasets. Since the soil layer depths vary substantially among the different models, it is very difficult to compare their total soil moisture content. Therefore, the depth-to-bedrock information for the region from Soil Landscape of Canada (http://sis.agr.gc.ca/cansis/nsdb/slc/intro.html) was used to normalize the depth of the model soil layers in the calculation of their total soil moisture content. With this normalization, the range of annual basin-averaged M still varies from about 230 mm for CMC to about 324 mm for NCEP-R2. Because of the differences in the soil model configurations and uncertainties that are introduced in normalizing the soil water in these results, it is physically more meaningful and more interesting to intercompare the spatial and temporal variability of M in the different models rather than their annual averages.
3) Snow cover
Snow cover plays an important role in governing the water and energy cycle for this northern basin. Snow depth is measured routinely at various locations in the basin and there are also remote sensing snow cover estimates from satellite (e.g., SSM/I). The analyzed SWE is typically a type B variable that is derived from the observed snow depth, model-generated precipitation, and the snow densities that are assumed in the analysis procedures. The procedure used by the forecast centers to do the SWE analysis can, however, vary substantially between each other, and hence the large variability exhibited in their annual estimates for the region. The annual basin SWE varies with values less than 40 mm in the NCEP-R2 and NARR estimates to high values of almost 50 mm in ERA-40 and exceeding 70 mm in the CRCM (Table 5). The high bias of SWE in the CRCM can be related to the cold bias in the lower troposphere (Fig. 4) and the low sublimation rates (Table 5) in the model during the cold season. Consequently, snow accumulation starts early while snowmelt is delayed in the CRCM. Further discussions of the model snow cover in the CRCM can be found in MacKay et al. (2003). Since snow mass is a type B variable while precipitation is a type C variable in the analysis datasets considered here, the bias of SWE in each dataset usually does not correlate well with their corresponding cold-season precipitation bias. For example, although the ERA-40 cold-season precipitation is biased low, its SWE is biased high while the reverse is true for SWE and precipitation from the CMC analysis.
4) Screen temperatures
The screen temperature (T2m) is presented here instead of the surface skin temperature (Ts) because the two variables are very closely correlated with each other. However, there are extensive observations for surface air temperatures while there are very few observations available for Ts in the region. Consequently, T2m is a type B variable while Ts is a type C variable in the assimilated datasets. There are small systematic differences between the two that vary on the diurnal and seasonal time scales; that is, T2m exceeds Ts during the winter and at nighttime while the reverse is true during the daytime in summer. The differences are typically small with basin-averaged values less than 1 and 0.5 K during the winter and summer, respectively.
In terms of the annual basin average (Table 5), the CMC and NCEP-R2 T2m values are the closest to the CANGRID regional observations while the NARR and ERA-40 estimates showed moderate warm biases (≤1 K). It should however be noted that the good agreement of the annual average of the estimates could be misleading because of compensating biases of different signs in different seasons. For example, NCEP-R2 showed a relatively large warm (>3 K) bias during winter and a weak (<1 K) cold bias during the rest of the year (Fig. 4). Significant winter warm biases over the southern basin, which are also noted in the longer-term surface budgets presented in Betts et al. (2003), are found in ERA-40 (Fig. 4). Quite significant warm biases are found over the southeastern basin in the NARR data during June–August (JJA). Strong cold biases are found in the CRCM data with the strongest bias occurring during the fall and winter [>4 K for September–November (SON); Fig. 4] and less so during the summer (<1 K). An attempt to explain the cold bias in the CRCM can be found in Szeto (2008).
Subfreezing temperatures characterize the whole basin during the winter and most of the basin (roughly north of 60°N) during spring and fall (Fig. 4). The horizontal variation of T2m is controlled by three main factors of latitude, altitude, and continentality. Latitude and continentality effects dominate to create strong southwest–northeast gradients in T2m during the cold season, particularly during December–February (DJF) when the strong climatological Arctic frontal zone is found over the southern basin. During JJA, the surface air temperature is relatively uniform over the basin interior and it decreases rapidly toward the higher-altitude regions in the west.
5) Atmospheric enthalpy
The vertically integrated atmospheric temperatures (or equivalently, the atmospheric enthalpy or heat content H) exhibit little variability among the different estimates (Table 5). It is of interest to note that the basin-averaged H from the CRCM is very close to others despite the strong cold bias that exists at its lower model troposphere. Similar to the high bias in the annual Q estimates from the rawinsonde measurements, the relatively high value of the observed H is related to the relatively higher numbers of southern-located rawinsonde sites that are used in the study (Fig. 2). The relatively low H estimate from NARR is a result of the unavailability of temperature data at levels that are higher than 100 hPa in the archive.
6) Precipitation
Despite the relatively abundant observations for precipitation, it is a type C variable in most of the analysis datasets that are used in this study. NARR is the only exception; it assimilates precipitation observations in its analysis by using observed precipitation to constrain its atmospheric latent heating. Hence, variability among the different estimates is expected. However, as shown in Table 5 and Fig. 5b, the precipitation estimated from the various datasets, with the exception of NCEP-R2 and the global blended datasets, agree relatively well on both their monthly and annual means. The NCEP-R2 precipitation is substantially higher than others while both the CMAP and GPCP global blended precipitation datasets give similar annual P estimates for the region that are substantially lower than those from other datasets. Precipitation exhibits strong seasonal variability in the MRB (Fig. 5b). Depending on the datasets, the ratio of basin summer precipitation to winter precipitation varies from 2.5 for the CRCM to over 6 in NCEP-R2. In fact, the high bias in the NCEP basin-averaged precipitation occurs mainly during the summer (Fig. 5b) when a substantial portion of precipitation over the basin is of convective origin. Neglecting the global satellite and NCEP P estimates, the mean annual precipitation estimated for the basin ranges from 449 mm (NARR) to 507 mm (CRCM), which is substantially higher than the values assessed previously by using older datasets [∼410 mm in Stewart et al. (1998) and ∼421 mm in Louie et al. (2002)].
The CANGRID observations show that maximum precipitation is found over the mountainous western basin during all seasons (Fig. 6). With the exception of NCEP-R2 during the spring and summer, the observed spatial characteristics of seasonal precipitation are reproduced to various degrees in all models, suggesting that the principal precipitation-producing mechanisms in the basin are reasonably well represented in the models. It is however of interest to note that NARR is underpredicting the summer orographic (and subsequently the basin-averaged) rainfall when compared to observations and other analyses, despite the fact that it is the only analysis system considered in this study that assimilates precipitation observations in its analysis.
7) Evapotranspiration
Evapotranspiration is in general much more poorly observed than precipitation, and E is consequently also a type C variable in the analysis datasets. As surface solar radiation exerts a strong control on E, E exhibits strong seasonality over the basin (Fig. 5b). Evaporation is weak over the basin during DJF in general, with basin-averaged E varies from ∼0.1 mm day−1 in ERA-40 and CMC to about 0.5 mm day−1 for NCEP-R2. The previous estimate of a winter surface sublimation rate of 29 mm yr−1 for the MRB by Déry and Yau (2002) is comparable to the NARR, ERA-40, and CMC values, and smaller than the NCEP estimate. The CRCM differs from others in that it is actually characterized by general weak surface condensation during the winter, which only occurs over the extreme northern basin in the other datasets. This enhanced deposition in the CRCM during the cold season might have partially contributed to the high SWE bias in the CRCM that we discussed earlier. With warmer temperatures, surface evaporation increases markedly with snowmelt in the spring and maximizes during summer where the basin-averaged E approaches or even exceeds P in all datasets. The relative low (high) biases that characterize E in the CRCM (NCEP-R2) are consistent throughout all seasons, and they consequently give the lowest and highest values for the annual E estimate, respectively. It is of interest to note that although vastly different treatments of surface and boundary layer models are used in the CMC, NARR, and ERA-40 analyses, their annual E estimates agree within 0.1 mm day−1. A previous MAGS estimate of E was conducted by Louie et al. (2002) by using the empirical model of Morton (1983). The estimated annual basin-averaged E value of about 0.76 mm day−1 is comparable to the CRCM value but substantially lower than others. If we neglect changes in surface water storage over the study period then E ∼ P − N; the observed discharge and CANGRID precipitation data thus suggest that the annual basin-averaged E is about 0.8 mm day−1. Hence, it is believed that, at least on the annual and basin scales, the E estimate from the CRCM might be closer to the “truth” than the estimates from the other analysis data.
8) Moisture flux convergence
As a high-latitude continental basin, the MRB acts as a moisture sink region in the global water balance and large-scale moisture flux convergence (MC) into the basin plays an important role in producing the precipitation over the region. For example, based on the 45-yr archive, the correlation coefficient r between daily ERA-40 MC and precipitation over the central basin (Slave region) is about 0.56 or 0.26 for July or January, respectively. The correlation is even stronger for high-elevation regions such as the Liard subbasin, at r = 0.70 or 0.50 for the same months.
As shown in Fig. 5a, MC from all datasets exhibits a similar weak seasonality, with maximum convergence occurring in October and minimum in August. Both NCEP-R2 and CRCM exhibited a high bias of net moisture flux convergence during the cold season, which is probably a consequence of the underprediction of topographic precipitation over the coastal regions in the two datasets when compared to CMC, NARR, and ERA-40. It is of interest to note that despite the strong evapotranspiration that occurs in the basin, the basin on the whole remains a moisture sink during the summer in most datasets. Exceptions are found in the CRCM and NARR budgets, both of which suggest that the basin becomes a source of moisture for the large-scale circulation during August (Fig. 5a).
The annual-average MC is highest in NCEP-R2 (0.69 mm day−1) and lowest in ERA-40 and NARR (0.46 mm day−1). Previous estimates of annual MC by Walsh et al. (1994; 0.67 mm day−1), Roads et al. (2002; 0.67 mm day−1, estimated using the same NCEP-R2 data but for 1988–99, a value that we have verified by recomputing the NCEP-R2 MC for the same period), and Strong et al. (2002; 0.73 mm day−1) are all closer to the NCEP estimate. It is arguable that MC estimates from the CMC, NARR, and ERA-40 analyses might be closer to the “truth” because of (i) the higher resolutions and generally more sophisticated model physics that are employed in these models, and (ii) the difference between their MC estimates and the observed discharge is the smallest. Although the annual estimates of MC in NARR and the CRCM is close to those from the CMC and ERA-40, it is actually a result of compensating biases during different seasons (relatively high bias during the cold season and low bias during the summer; Fig. 5a).
9) Runoff
Although runoff is an important component in the surface water budgets, it is poorly simulated in the model and analysis datasets that are used in this study. In the CMC analysis, runoff is simply computed as P − E and it will not be discussed further here. Although quite sophisticated surface modules are included in the CRCM, NCEP-R2, NARR, and the ERA-40 models, runoff processes, especially cold-region runoff processes, are only crudely represented. Most river flow in the basin follows a nival regime in which spring melt generates high flows that are orders of magnitude larger than the winter discharge. All datasets (except CMC) exhibit to some degree these runoff patterns (Fig. 7). However, none of the model runoffs replicate faithfully the phase and magnitude of the observed discharge. Such discrepancies between model runoff and measured discharge are expected because of the large size of the basin and the lack of river routing implemented in the models. Both the annual and peak runoff in NARR and ERA-40 are quite comparable to the observed streamflow data. However, similar to the findings of Betts et al. (2003), the maximum runoff in ERA-40 occurs two months earlier than the peak observed discharge and a month before those found in other model datasets. The early runoff in ERA-40 can be related to its cold-season warm biases, and hence early snowmelt, over the southern basin that are identified earlier. Both the peak and annual model runoff from the CRCM are higher than the observed discharge, as one might expect from the high bias exhibited in its model snow cover (Table 5). Although NCEP-R2 exhibits low bias in its snow cover, its spring and summer runoff is the highest among the estimates due to its significantly overpredicted warm-season precipitation.
10) Dry static energy convergence
As discussed earlier, the MRB, as a high-latitude continental basin, is an important heat sink region in the global energy budget. While the coastal mountains shield off much of the moisture transported into the continent from the ocean by precipitation over the windward slope and effectively reduce the moisture influx into the basin, the condensation heating effectively enhances the convergence of dry static energy (HC) into the basin. In fact, HC for the MRB is the highest among the mid- and high-latitude GEWEX CSE basins (see Table 1 and Fig. 11 of Roads et al. 2002). The large volume of atmospheric data that are required to compute HC has prohibited its evaluation for the NARR dataset in this study, and we will focus the discussion on the HCs that are estimated from other analysis datasets. Contrary to the MC for the basin, the basin-averaged HC exhibits strong seasonality (Fig. 8a). As expected, HC into the basin is strongest during the winter when the pole-to-equator and continental–oceanic temperature contrasts are the greatest. Estimates of HC from all datasets suggest that the basin is a heat source for the circulation (i.e., HC <0) during the summer. The annual basin-averaged HC for the basin agrees reasonably well among the analysis datasets (within 0.03 K day−1 or 10% of each other; Table 5) while the annual HC from the CRCM is biased high among the estimates. Figure 8a shows that the high bias of HC in the CRCM is largely found between the months of May and November (i.e., the basin is a much weaker heat source region during the summer in the CRCM when compared to others). The current estimates of HC values for the MRB are lower than the HC value of 0.51 K day−1 estimated by Roads et al. (2002) for the period 1988–99 by using the same NCEP-R2 dataset, suggesting a possible recent decrease in the mean convergence of dry static energy into the basin.
11) Sensible heat flux
Since sensible heat flux is not part of the routine observations, it is a pure forecast variable in all the assimilated datasets and it is thus strongly dependent on the model physics. All datasets agree that there is downward transfer of sensible heat into the surface during the cold season when the basin surface is substantially colder than the overlying air (Fig. 8b). While the magnitude of the basin-averaged cold-season SH is similar among the CMC, NARR, ERA-40, and CRCM results, the NCEP-R2 SH is substantially larger than others (Fig. 8b). For the basin as a whole, upward (positive) sensible heat flux occurs between March and September for all datasets except NCEP-R2, which exhibits negative basin-averaged SH apart from the months of June and July. The weaker upward SH in the CRCM during the spring is consistent with the low bias in Ts and high bias in SWE that are exhibited in its results. The lower than average SH in the NCEP-R2 analysis is reflected in its negative annual SH while all four other annual estimates are positive (Table 5).
12) Clouds
Although cloud amount is not one of the explicit variables that enter into the water and energy budget equations, it is included here for the discussion because of the critical roles it plays in affecting the water and energy cycle of the MRB. Cloud amount is a type C variable in the analysis datasets, and it is also generally agreed to be one of the more poorly simulated variables in current models. Most datasets show a weak seasonality for the mean cloud coverage, and both the models and manual observations suggest that maximum cloud coverage over the basin occurs during the autumn months between September and November and relatively low cloud coverage occurs during the spring between March and May (Szeto and Crawford 2006). The high mean cloud coverage over the basin during the autumn is likely a result of the combined effects of high frequency of synoptic systems that visit the west coast of Canada as well as lee cyclones that commonly develop within the basin during the autumn. Annual basin-averaged cloud cover is lowest in NCEP-R2 with mean basin average below 50% and followed by both CMC and NARR with mean cloud coverage of 56% (Table 5). Annual estimates from the ERA-40, ISCCP, or the CRCM are all relatively close to the observed values of 65% with the CRCM values slightly biased to the low side, especially during the cold seasons.
13) Radiative fluxes
Radiative transfer plays a critical role in affecting water and energy balance in the MRB because of its strong coupling to other hydrometeorological processes in the region. There are very few ground-based radiative flux measurements for the region, and subsequently, most information on radiative fluxes comes from either satellite measurements or model data. Although radiative fluxes are type C variables in the analyses, the variability among their estimates (e.g., measured by the spread among the estimates using the coefficient of variation CV = ratio of standard deviation to sample mean) is typically smaller than those for other flux variables (Table 5) and much of the revealed variability can be related to the variability of cloud cover in the different datasets.
Although all models use fairly standard values in specifying top-of-the-atmosphere (TOA) incoming shortwave radiation (TOA_SWD), the shortwave radiation that reaches the surface (BOA_SWD) exhibits substantial variability among the estimates (Table 5). In particular, the high bias of BOA_SWD in the NCEP-R2 and NARR data can be related to the lower model cloud cover in these datasets. The high surface upward shortwave flux (BOA_SWU) in NCEP-R2 and NARR can be related directly to their high BOA_SWD despite their lower-than-average SWE (and hence lower-than-average mean surface albedo during the spring). Top-of-the-atmosphere SWU (TOA_SWU), on the other hand, is highest in the CRCM, likely a result of the enhanced mean TOA albedo from the above-average cloud cover and more extensive and longer duration of snow cover in the model.
Relatively lower variability is found in the estimates for both the BOA and TOA longwave fluxes. Both the downward and upward longwave fluxes at the surface, as well as the outgoing longwave flux at TOA (TOA_LWU) are slightly lower in the CRCM, presumably a result of the cold bias that is found in its model surface and lower atmosphere. On the other hand, the slightly above average BOA longwave fluxes in NARR and ERA-40 can be related to the warm bias identified for the datasets. The ERA-40 and ISCCP FD radiative flux estimates are very close to each other, which could be partially related to their similar estimates of cloud coverage for the basin. Both of them exhibit a 10% higher than average atmospheric radiative cooling (QR) for the basin, while the CRCM is characterized by the lowest net surface radiative heating (Table 5).
Although all the datasets exhibit very similar seasonal variability of QR and QRS (Figs. 8a and 9a), there is substantial seasonal dependence shown in their relative biases (note that the CMC TOA_SWD has been used in the computations of QR for NARR because this parameter is not available in the NARR archive). In particular, the net atmospheric radiative cooling QR is typically strongest in ERA-40 data during the autumn, weakest in the CMC during the summer, and weaker in the RCM, NARR, and NCEP-R2 during the cold season. For basin-averaged QRS, surface cooling (heating) is the strongest in NCEP-R2 during the winter (summer), which is presumably also related to the reduced cloudiness in the model. The results in Fig. 9a also show that the low bias in the annual QRS from the CRCM is mainly a result of the lower than average QRS calculations during the warm season, particularly between May and September.
d. Budget closure and error analysis
One of the applications of WEBS results is the assessment of the completeness and correctness of our knowledge for the water and energy cycle of a region through examining the accuracies of the budget estimates and the degree by which the budgets can be closed on various spatial–temporal scales. In addition, such assessments often point to the areas where we should focus our effort toward improving the observations or model prediction of water and energy cycling processes.
The intercomparisons of budget estimates with available observations were discussed in the previous section. Not surprisingly, WEBS parameters that are derived from strongly “corrected” variables in the analysis datasets (e.g., atmospheric enthalpy, screen temperatures, and precipitable water) compare the best to observed values. For the purely forecasted fluxes that also have observations, precipitation estimates compare much better with observations than annual runoff values. Precipitation in the MRB is the end result of many strongly coupled hydrometeorological processes that occur either outside or within the basin. The fact that precipitation, including its spatial and temporal (at least on monthly and longer time scales) variability, can be simulated quite successfully in many models (see Table 5; Figs. 5b and 6) should give us a lot of confidence in the representations of northern water and energy processes in current models. On the other hand, the wide discrepancies between modeled and observed snow cover and runoff suggest that we need to improve the cold-region surface and runoff processes in the models before substantial improvements in runoff predictions for the MRB can be achieved.
When no measurement is available to validate the budgets, the spread of the budget estimates among the different datasets will give a measure of the uncertainties in their evaluations. Similar to the results of intercomparison with observations, smaller budget estimate variability is found in parameters that are derived from strongly corrected analysis variables and wider spreads are found in some purely forecasted flux variables (e.g., in evapotranspiration, runoff, and sensible heat flux; see Table 5). Exceptions are found in the estimates of precipitation and radiative fluxes. In particular, when neglecting the NCEP-R2 and global blended precipitation, annual precipitation estimates from the various sources and many of the radiative flux estimates agree with each other to within 10% of the corresponding ensemble mean values.
An accurate and complete quantitative characterization of the water and energy cycle for a region requires both accurate evaluations of the budget components and adequate closure of the budget balance. The degree to which the budgets are closed in the various datasets is given conveniently by the residuals in balancing their corresponding budgets. Theoretically, there should be perfect balance in purely modeled water and energy budgets from a single model. However, budget imbalance might occur as a result of nonconservative numerical schemes that are employed in the model or from errors that might have been introduced in offline budget computations with archived model outputs. Nevertheless, the budget residuals for the CRCM are in general smaller than those for the analysis datasets. Residuals in balancing the budgets from the analysis datasets are generally expected because of the nudging of the forecast variables with observations during an analysis cycle and the neglect or misrepresentation of important processes in modeling the water and energy cycle for the region. For example, ground heat fluxes have been neglected in the surface energy budget assessments because the parameter is not available in most of the datasets considered here. As shown in Fig. 9b, the RESG for NARR (RESG_NARR) was reduced substantially when ground heat fluxes were included in its surface energy budget assessment (RESG_NARR_GF).
Results in Table 5 show that the residuals in balancing annual atmospheric water budgets (RESQ) can range from ∼25% of observed runoff (for CRCM), to >50% for CMC, and over 100% for NCEP-R2. Similarly, residuals in closing the atmospheric energy budgets (REST) are in general comparable in magnitudes to the budget terms themselves. Residuals are generally smaller in the surface budget balances, suggesting that a large part of the inaccuracy in closing the atmospheric budgets might have come from numerical errors that were introduced in the interpolation of data from model grids to the archiving grids, which subsequently propagated into the computation of the convergence terms. In this regard, it is encouraging that most of the offline MC estimates compare favorably to the NARR MC, which was calculated in-line with the model computations, in either their mean values or seasonal variability (Table 5; Fig. 5a). It is also of interest to note that there are characteristic seasonal dependencies exhibited in the residuals that could vary from dataset to dataset (Figs. 5a, 7, 8c and 9c). In particular, there is general atmospheric energy deficits in the models (REST > 0) when compared to observations during the cool and cold seasons (Fig. 8c). In addition, strong atmospheric moisture surplus (RESQ < 0) is found in CMC, NARR, and ERA-40 during the warm season and in NCEP-R2 throughout the year (Fig. 5a). The atmospheric energy deficit, moisture surplus, and high MC bias in NCEP-R2 during the cold season all point to the possible underprediction of orographic precipitation and latent heat release over the western slopes of the Cordillera, which subsequently results in the underprediction of lee-side subsidence and warming over the MRB in the model, that is, similar to the sequence of events that was hypothesized in Szeto (2008) to account for the strong cold bias in the CRCM. While part of the seasonal variations of RESW (Fig. 7) can be accounted for by the seasonal accumulation and melting of surface snow in the models, deficits in predicted total surface water during the snowmelt period over the MRB in the NCEP-R2 and ERA-40 budgets have been noted in Roads et al. (2002) and Betts et al. (2003), respectively. In particular, the deficit in the ERA-40 budgets was related to the warm bias and early snowmelt in the model as discussed earlier.
Since changes in the atmospheric and surface water storage can be neglected in the long term, (i.e., MC ∼ P − E ∼ N), water budget closure is traditionally assessed by the balance between the long-term average atmospheric moisture flux convergence and observed runoff. With this definition, the regional water budget for the MRB is closed within 6%, 8%, and 10% of the observed runoff using the moisture flux convergence from ERA-40/NARR, CMC, and CRCM, respectively. These are substantial improvements over the closure of ∼26% assessed by using the previous generation CMC analysis dataset (Strong et al. 2002), and these improvements possibly reflect the recent advances in the modeling of atmospheric water cycling processes for the region. Nevertheless, as discussed previously, the residuals in balancing the budget equations along with the bias of the budget component estimates in comparing to observations will provide a more useful and complete measure of the accuracy in closing the budgets. In that regard, much improvement in the current models is still needed before we can use model results to improve substantially the water and energy budget assessments for the MRB.
5. Conclusions
This study represents the first attempt at developing a comprehensive climatology of water and energy budgets for the Mackenzie River basin. Apart from the development of state-of-the-art budget estimates for the MRB, the capability of current models and data assimilation systems in capturing the water and energy cycle of this northern and data-sparse region was also assessed.
Although the CRCM simulation was performed in “climate mode,” the model simulated a quite respectable climate for the MRB when compared with observations and analysis data. Noteworthy points for its basin budgets include its substantially weaker than average HC, MC, and E during the warm season and its delayed snowmelt (and subsequently weak SH and overestimated peak runoff) during spring. All of these budget biases can be partially attributed to the strong cold bias in its low-level tropospheric temperatures in the model calculations for the MRB, especially during the cold season. Despite considerable differences between the model resolutions and physics that are employed in the CMC, NARR, and ERA-40 data assimilation systems, the water and energy budgets derived from these datasets for the MRB are very similar, and in general compared the best to available observations. It should however be noted that NARR underpredicts summer orographic rainfall for the region despite the fact that the system assimilates observed precipitation into its analysis. Although the NCEP-R2 reanalysis has been used in numerous previous hydrometeorological studies related to the MRB, the water and energy budgets evaluated for the basin from the datasets exhibit the strongest deviation from the ensemble mean budgets and compare unfavorably to available observations in general. In particular, the NCEP-R2 dataset presents a significantly more intense warm-season water cycle for the MRB than the ones assessed from other datasets. The NCEP model produced consistently higher surface evaporation than others throughout the year. The surface sensible heat fluxes from the dataset also differ from others in that its sensible heat flux into (from) the surface during the cold (warm) season is substantially stronger (weaker) than those from other datasets. These results suggest that NCEP-R2 fluxes should be used with caution for hydrometeorological and climate change studies for the basin.
Although most global satellite or blended datasets are known or expected to perform poorly in northern regions for obvious reasons, it is still of interest to quantify their merits and deficiency in representing the water and energy budgets in northern regions through intercomparisons with other datasets. In brief, the results show that (i) the annual basin-averaged precipitable water estimates from the NVAP dataset compare extremely well with those estimated from analysis datasets; (ii) both the CMAP and GPCP precipitation are lower than others with the low biases particularly worse during the summer; and (iii) the ISCCP FD radiative fluxes compare closely with estimates from others (particularly the ERA-40 fluxes).
The regional water budget for the MRB is closed within 6%, 8%, and 10% of the observed runoff using the moisture flux convergence from ERA-40/NARR, CMC, and CRCM, respectively. While these are noted improvements over previous water closure assessments for the region, magnitudes of the residuals in balancing the budgets are often comparable to the budget terms themselves in all the model and analysis datasets, suggesting that substantial improvements to the models and observations are needed before we can vastly improve the assessments of the water and energy budgets for this northern region.
The climate of the MRB is governed by complex interactions between atmospheric and surface processes that occur on a wide range of spatial–temporal scales. Some of these processes are common among cold regions while some of them are specific to the MRB. A number of these processes are generally not represented (e.g., organic soil and evaporation from the northern lakes) or are only crudely represented (e.g., ground frost processes, orographic precipitation, sublimation from canopy top, and moisture transport from the partially open Beaufort Sea during late summer and early fall) in current climate models. These limitations will certainly affect the representation of the region’s water and energy cycle in the models and thus the water and energy budgets that are estimated from the model results. The improved understanding of these processes in MAGS and the incorporation of the knowledge into the numerical models will certainly enhance our predictive capability for the basin, and the results from this study provide a reference climatology to gauge the progress in future budget estimates from these improved models and newly available remotely sensed data.
Acknowledgments
The authors thank the MAGS community for its support during the course of this work. K. K. Szeto acknowledges insightful discussions with Dr. John Roads on the subject. The manuscript benefited significantly from comments by Dr. Roads and three anonymous reviewers. Alex Wong, Patrick Lui, Niranchala Nithyanandan, and Billy Szeto are acknowledged for their assistance in the WEBS calculations, data gathering, and processing. This study was financially supported by Environment Canada and the Panel on Energy Research and Development (PERD).
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The location and topographic environment of the Mackenzie River basin (with basin boundary indicated by the thick black line).
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Locations of rawinsonde sites (diamonds), automatic (triangles), and manned (cross) synoptic stations in the MRB and vicinity that are used in the study.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Annual cycle of ensemble (NCEP-R2, ERA-40, NARR, CMC, and CRCM) mean basin-averaged budgets for (a) atmospheric energy, (b) atmospheric water, (c) surface energy, and (d) surface water. Note that NARR budgets are not included in calculations of HC and REST.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Spatial distribution of mean seasonal T2m for NCEP-R2, ERA-40, NARR, CMC, CRCM, and CANGRID. Numbers in bottom left of each panel give seasonal means for the whole basin.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Annual cycle of MRB atmospheric water budgets for NCEP-R2, ERA-40, NARR, CMC, and CRCM: (a) MC and RESQ; (b) P and E. Also included in (b) is the P from the CANGRID dataset.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
As in Fig. 4 but for precipitation.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Annual cycle of MRB surface water budgets for NCEP-R2, ERA-40, NARR, CMC, and CRCM: N and RESW. Also shown is the mean discharge from the Mackenzie River at Arctic Red for the same period. Corresponding P and E budgets are given in Fig. 5b.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Annual cycle of MRB atmospheric energy budgets for NCEP-R2, ERA-40, NARR, CMC, and CRCM: (a) HC and QR, (b) LP and SH, and (c) REST.
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Annual cycle of MRB surface energy budgets for NCEP-R2, ERA-40, NARR, CMC, and CRCM: (a) LE and QRS, (b) SH, and (c) RESG. Also included in (c) is the NARR budget residual when ground heat flux is included in its surface energy budget (NARR-RESG_GF).
Citation: Journal of Hydrometeorology 9, 1; 10.1175/2007JHM810.1
Summary of regional (R) and global (G) observations and remote sensing data used in MAGS WEBS. The numbers of data points that are used in the budget computations are also given for each dataset.
Summary of global (G) and regional (R) analysis and model datasets used in MAGS WEBS. Resolution of the operational CMC GEM model changed from 35 to 24 km in September 1998. The numbers of data points that are used in the budget computations are also given for each dataset.
Intercomparison of 1979–99 (20 yr) and 1997–2002 (5 yr) climatologies of precipitation (P, mm day−1), evaporation (E, mm day−1), runoff (N, mm day−1), and screen temperature (T2m, K) for different datasets.
Summary of annual basin-averaged water and energy budgets for the MRB. All water storage terms (Q, M, SWE) are in mm, T2m in K, enthalpy H in 109 J km−2, moisture fluxes (P, E, MC, N) in mm day−1, energy fluxes in K day−1, and cloud fraction in percent. The global Q observation (Glo obs) is the 1988–99 NVAP climatology and the two values of global precipitation are from CMAP (first) and GPCP (second), respectively. Regional observations (Reg obs) are also given for some data. The coefficient of variation (CV) is defined as the ratio of the standard deviation to the absolute value of the mean estimate given in percent.
