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Sheldon Drobot, James Maslanik, Ute Christina Herzfeld, Charles Fowler, and Wanli Wu

to the statistical differences observed in the time series analysis? Analysis methods used in this study include analysis of variance (ANOVA) with post hoc means comparisons to determine significant differences in the datasets, anomaly correlations to examine seasonal cycles, and similarity maps to highlight spatial regions where datasets differ. 2. Datasets Each of the datasets analyzed here is used to validate or force hydrological models in the WALE project. The NCEP1 and ERA-40 are data

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Wanli Wu, Amanda H. Lynch, Sheldon Drobot, James Maslanik, A. David McGuire, and Ute Herzfeld

because of technical and environmental limitations. It has been suggested that an alternative to estimating terrestrial water and energy cycles is to use land surface models (LSMs; Bonan 2002 ) or regional climate models (RCMs; Wu and Lynch 2000 ; Wu et al. 2005 ). The models close the water and energy budget by design. Thus, if the large-scale forcing data, which drive LSMs and RCMs, are accurate, and if model biases are small, these modeled water and energy fluxes might be used in lieu of

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A. D. McGuire, J. E. Walsh, J. S. Kimball, J. S. Clein, S. E. Euskirchen, S. Drobot, U. C. Herzfeld, J. Maslanik, R. B. Lammers, M. A. Rawlins, C. J. Vorosmarty, T. S. Rupp, W. Wu, and M. Calef

driven with either the NCEP2 or WM climate (temperature and precipitation) data. The use of NCEP1 data resulted in basinwide runoff estimates that were approximately twice the observed estimates of runoff. Thus, the accurate simulation of regional water balance is limited by biases in the forcing data. Uncertainties in simulating regional ecosystem dynamics Several studies in WALE examined uncertainties in simulating carbon dynamics of the region ( Kimball et al. 2006 ; Kimball et al. 2007 ; Clein

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M. A. Rawlins, S. Frolking, R. B. Lammers, and C. J. Vörösmarty


Hydrological models require accurate precipitation and air temperature inputs in order to adequately depict water fluxes and storages across Arctic regions. Biases such as gauge undercatch, as well as uncertainties in numerical weather prediction reanalysis data that propagate through water budget models, limit the ability to accurately model the terrestrial arctic water cycle. A hydrological model forced with three climate datasets and three methods of estimating potential evapotranspiration (PET) was used to better understand the impact of these processes on simulated water fluxes across the Western Arctic Linkage Experiment (WALE) domain. Climate data were drawn from the NCEP–NCAR reanalysis (NNR) (NCEP1), a modified version of the NNR (NCEP2), and the Willmott–Matsuura (WM) dataset. PET methods applied in the model were Hamon, Penman–Monteith, and Penman–Monteith using adjusted vapor pressure data.

High vapor pressures in the NNR lead to low simulated evapotranspiration (ET) in model runs using the Penman–Monteith PET method, resulting in increased runoff. Annual ET derived from simulations using Penman–Monteith PET was half the magnitude of ET simulated when the Hamon method was used. Adjustments made to the reanalysis vapor pressure data increased the simulated ET flux, reducing simulated runoff. Using the NCEP2 or WM climate data, along with the Penman–Monteith PET function, results in agreement to within 7% between the simulated and observed runoff across the Yukon River basin. The results reveal the high degree of uncertainty present in climate data and the range of water fluxes generated from common model drivers. This suggests the need for thorough evaluations of model requirements and potential biases in forcing data, as well as corroborations with observed data, in all efforts to simulate arctic water balances.

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J. S. Kimball, K. C. McDonald, and M. Zhao

.3. Limitations of this investigation For this investigation, we utilized daily meteorological inputs from the NCEP reanalysis, satellite optical-infrared remote sensing measures of vegetation parameters, and general assumptions of plant physiological responses to environmental forcings to simulate regional patterns and annual variability in GPP and NPP for Alaska and northwest Canada. The land cover information used for this study and the resulting productivity calculations largely reflect dominant

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