• Energy Research

To investigate the ability of dynamical seasonal climate predictions for Vietnam, the RegCM4.2 is employed to perform seasonal prediction of 2 m mean (T2m), maximum (Tx), and minimum (Tn) air temperature for the period from January 2012 to November 2013 by downscaling the NCEP Climate Forecast System (CFS) data. For model bias correction, the model and observed climatology is constructed using the CFS reanalysis and observed temperatures over Vietnam for the period 1980–2010, respectively. The RegCM4.2 forecast is run four times per month from the current month up to the next six months. A model ensemble prediction initialized from the current month is computed from the mean of the four runs within the month. The results showed that, without any bias correction (CTL), the RegCM4.2 forecast has very little or no skill in both tercile and value predictions. With bias correction (BAS), model predictions show improved skill. The experiment in which the results from the BAS experiment are further successively adjusted (SUC) with model bias at one-month lead time of the previous run showed further improvement compared to CTL and BAS. Skill scores of the tercile probability forecasts were found to exceed 0.3 for most of the target months.

Abstract. The bias in atmospheric variables and that in model computation are two major causes of failures in discharge estimation. Attributing the bias in discharge estimation becomes difficult if the forcing bias cannot be evaluated and excluded in advance in places lacking qualified meteorological observations, especially in cold and mountainous areas (e.g., the upper Tarim Basin). In this study, we proposed an Organizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE)-Budyko framework which helps identify the bias range from the two sources (i.e., forcing and model structure) with a set of analytical approaches. The latest version of the land surface model ORCHIDEE was used to provide reliable discharge simulations based on the most improved forcing inputs. The Budyko approach was then introduced to attribute the discharge bias to two sources with prescribed assumptions. Results show that, as the forcing biases, the water inputs (rainfall, snowfall or glacier melt) are very likely underestimated for the Tarim headwater catchments (−43.2 % to 21.0 %). Meanwhile, the potential evapotranspiration is unrealistically high over the upper Yarkand and the upper Hotan River (1240.4 and 1153.7 mm yr−1, respectively). Determined by the model structure, the bias in actual evapotranspiration is possible but not the only contributor to the discharge underestimation (overestimated by up to 105.8 % for the upper Aksu River). Based on a simple scaling approach, we estimated the water consumption by human intervention ranging from 213.50×108 to 300.58×108 m3 yr−1 at the Alar gauge station, which is another bias source in the current version of ORCHIDEE. This study succeeded in retrospecting the bias from the discharge estimation to multiple bias sources of the atmospheric variables and the model structure. The framework provides a unique method for evaluating the regional water cycle and its biases with our current knowledge of observational uncertainties.