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Seasonal Prediction for Vietnam using RegCM4.2
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Phan Van Tan1, Hiep Van Nguyen2, Long Trinh-Tuan1, Trung Nguyen-
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Quang1, Thanh Ngo-Duc1, Thanh Nguyen-Xuan1, Patrick Laux3
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VNU Hanoi University of Science
Vietnam Institute of Meteorology Hydrology and Environment
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research
Email: tanpv@vnu.edu.vn
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Abstract
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Operational seasonal climate predictions for Vietnam are currently conducted by
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statistical methods. Official dynamical seasonal climate predictions with high resolution
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Regional Climate Models (RCMs) for Vietnam are still not available. To investigate the
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ability of dynamical seasonal climate predictions for Vietnam, in this study, the Regional
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Climate Model version 4.2 (RegCM4.2) is used to perform seasonal prediction of 2-m air
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temperature for the January, 2012 to November, 2013 period. Initial and time-dependent
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boundary conditions are from the Climate Forecast System (CFS).
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RegCM4.2 is configured with a single domain with 36-km horizontal resolution. A model
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experiment using the CFS reanalysis data for the period 1980-2010 is firstly performed to
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construct model climatology. The observed temperatures at 64 meteorological stations
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over Vietnam for the same period are used to construct observed climatology for model
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bias correction.
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RegCM4.2 forecast is run four times per month from the current month up to next six
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months. A model ensemble prediction initialized from the current month is computed from
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the mean of the four runs within the month. A total of 768 months or 64 years of model
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runs was conducted to investigate the model performance. Primarily results showed that
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without bias correction, the RegCM model forced by CFS has a very little or no skill in
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both phases and value predictions. With bias correction (BAS) constructed from the 30-
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year model climatology applied to the raw output, RegCM predictions show some initial
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successes in seasonal prediction of 2-m mean and extreme air temperature. The SUC
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experiment in which the results from BAS experiment are further successively adjusted
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with model bias at one month lead time of the previous run show some further
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improvement of the seasonal predictions with skill score of phase forecast greater than
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0.3 at most of target months.
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1. Introduction
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Seasonal predictions are crucial for planning as well as for disaster prevention.
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While short-range weather forecasts are valid for timescales of hours to days, seasonal
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predictions focus on long-term averages of meteorological variables (Wang and Zhu,
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2001). Basic products of seasonal predictions are often monthly or seasonal means.
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Seasonal predictions can be performed by statistical and dynamical methods (Stockdale,
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2000). Statistical method in which the predictions are conducted based on the statistical
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relationships between predictants such as surface climate elements and predictors such as
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atmospheric variables, sea surface temperature (SST), and soil moisture has been widely
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applied for predicting tropical cyclone activities, seasonal mean temperature, precipitation
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(Annamalai et al., 2005; Duffy et al., 2006; Kloizbach et al., 2003; Krishnamurti et al.,
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2001).
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The dynamical method uses mathematical models to perform climate predictions. These
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models can predict evolutions of the climate system for several months in advance
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(Doblas-Reyes et al., 2006). The models can be in the form of General Circulation
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Models (GCMs) (Wang et al., 2001; Stockdale, 2000) or Regional Climate Models
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(RCMs) (Castro et al., 2012; Yuan and Liang, 2011). Dynamical methods using GCMs
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have shown advantages over statistical methods in predicting large-scale phenomena (eg.
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Saha et al., 2006; Kirtman et al., 2009; Castro et al., 2012; Kim et al., 2012). Specifically,
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with a relatively coarse horizontal resolution, the Climate Forecast System (CFS) has skill
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in forecasting the Nino-3.4 SST compared to an operational statistical method (Saha et al.,
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2006). The CFS skill in representing SST results in reasonable predictions of large-scale
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circulation such as monsoon and El Niño–Southern Oscillation (ENSO) events (Kim et
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al., 2012; Drbohlav et al., 2010; Sooraj et al., 2012; Wang et al., 2010). One of the most
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disadvantages of the global models is the expensive computational cost. Therefore, global
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models usually run with relatively coarse horizontal resolutions in which the effects of
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complex terrains as well as sub-grid scale features on local weather and climates are
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normally not well represented.
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Focusing on a limited area, RCMs can perform high-resolution seasonal predictions with
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a relatively low computational cost. Running with higher resolution, RCMs normally
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have advantages over GCMs in generating relatively smaller scale features such as
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convections (Castro et al., 2012; Yuan and Liang, 2011) or climate features over complex
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terrain areas (Yihui et al., 2006; Frumkin and Misra, 2012). Because RCMs run on limited
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areas and require GCM outputs as initial and boundary conditions, the quality of a RCM
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prediction depends not only on the RCM itself but also on the quality of the GCM forcing
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data and on the RCM configuration such as domain size, frequency of time-dependent
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boundary forcing, etc. Castro et al. (2012) showed that the Weather Research and
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Forecasting model (WRF) downscaling from CFS for North America adds value in
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precipitation prediction skill only during the early warm season at which CFS has good
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skill of predicting large-scale atmospheric circulation. According to Yuan and Liang
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(2011), during the cold seasons over the United States, WRF reduces errors of mean
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seasonal forecasts of CFS precipitation by about 22%. They also showed that the
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downscaling of WRF improves forecast of extreme rainfall events.
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With its complex topography, land surface conditions, long coastlines, and location within
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the Asian monsoon region, Vietnam has a complex climate, largely influenced by
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mesoscale phenomena. Climate of Vietnam is strongly affected by Asian monsoon
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systems, tropical perturbations embedded in the Inter-tropical Conversion Zone (ITCZ),
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typhoon activities. During the summer time (May to August), almost entire the country
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experiences high-temperature conditions, including long hot spells except the high
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mountain areas. In the winter time, the Northern part of Vietnam, including North
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Central, is affected by cold surges orginated from Siberian high, which might cause the
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damaged cold spells. In term of precipitation, rainfall from May to October contributes
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about 80% to the annual total rainfall over the Northern and Southern Vietnam, while in
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the Central Vietnam, the rainy season is from August to December (Nguyen and Nguyen,
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2004). Therefore, the seasonal prediction one of the most important issues for natural
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disaster prevention in Vietnam, especially in the present context of climate change, in
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which unusual weather events occur more frequently.
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Operational seasonal predictions for Vietnam are currently only conducted with statistical
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methods. Dynamical seasonal predictions with high resolution RCMs for Vietnam are still
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not available. Recently, the RegCM model (Giorgi et al., 1993a; Pal et al., 2000) has been
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successfully used for climate researches in Vietnam including investigation of the
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seasonal and interannual variations of climate surface variables (Phan at al., 2009), and
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climate extremes over Vietnam (Ho et al., 2011). Currently, CFS outputs are available in
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real time mode (Saha et al., 2006), that provide initial and time-dependent boundary
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conditions for RCMs such as RegCM to run operationally. In this study, the Regional
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Climate Model version 4.2 (RegCM4.2) is employed as a RCM driven by CFS to perform
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downscaling seasonal predictions for 2-m air temperature over Vietnam during the years
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of 2012 and 2013. The main objectives of this study are (1) to evaluate the RegCM4.2
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seasonal predictions over Vietnam region and (2) to examine the role of initial soil
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moisture and soil temperature in RCMs on seasonal predictions. In the rest of this paper,
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model configuration is presented in Section 2. Section 3 is experiment design. The results
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are showed in Section 4. Summary and discussion are given in Section 5.
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2. Model configuration
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In this study, RegCM4.2, a primitive equation, hydrostatic, compressible, limited-area
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model (Giorgi et al., 2011, 1993a; Pal et al., 2000) is used. Model configuration is the
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same as in Ho et al. (2011) which includes the Biosphere–Atmosphere Transfer Scheme
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(BATS) surface scheme, a nonlocal vertical diffusion boundary layer scheme (Giorgi et
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al., 1993a), the Community Climate Model version 3 (CCM3) (Giorgi et al., 1999)
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radiation scheme, Grell convective schemes (Grell, 1993). There are 18 vertical σ-levels
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with 6 levels in the planetary boundary layer (under 850 mb). The top level pressure is 50
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mb. Model runs with a single 36-km resolution domain (Fig. 1a) centered at 11.5oN and
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108.0oE with 145 and 131 grid-points in west-east and south-north directions,
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respectively, with a lateral buffer zone of 12 grid points. The lateral boundary conditions
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are provided by the NCEP Climate Forecast System (CFS) reanalysis (CFSR) with
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resolution of 0.5 degree for the climatology simulation and the CFS forecast with
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resolution of 1 degree for real-time seasonal predictions. Time-dependent boundary
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conditions for the RegCM are updated every 6 hours.
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Because this study requires 30 years (1981-2010) of inland historical station data to
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compute observation climatology at stations, only 64 stations of more than 171 stations
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from Vietnam National Hydro-Meteorological Service are used for model verification.
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Locations of the 64 stations are shown in Fig. 1b.
(Figure 1 around here)
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3. Experimental design
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RegCM4.2 is firstly run for the period of 30 years from 1980 to 2010 with initial and
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boundary conditions from CFSR to construct model climatology and model bias at
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stations. The model is run from 0000Z 01 January 1980 to 0000Z 01 January 2011. The
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first year (1980) is not used for analysis to allow model to spin up. The model simulated
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data are interpolated to the stations. Thirty values of monthly mean for every variable
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from 30 years of model simulation are used to calculate the simulated 33rd (q33) and 66th
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(q66) percentiles for each month at each station. The same procedure is applied to
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observed data to compute the observed q33 and q66.
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For the seasonal prediction experiment, RegCM4.2 is driven by CFS forecast to conduct
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seasonal prediction for the months from February 2012 to November 2013. RegCM4.2 is
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initialized every 7 days (4 times per month) from January to March 2012 and run for a
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six-month period. Model forecasts initialized within a month are averaged to form an
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ensemble forecast for next 6 months. The schematic diagram of prediction experiment is
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shown in Fig. 2.
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Temperature prediction at a station is performed in two forms, phase predictions and
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value predictions. In the phase predictions, the observed phases are firstly defined by
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relatively comparing the current observed monthly mean temperature with the observed
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q33 and q66 of the same month. The forecast phases are then defined by relatively
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comparing the current simulated monthly mean temperature with the simulated q33 and
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q66 of the same month (CTL). To reduce the model systematic error (bias), two additional
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experiments are performed: (1) the model outputs are adjusted with model climatology
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bias computed from 30 year model simulation and observation at each station (BAS); and
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(2) the results from BAS runs are further successively adjusted with model bias at one
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month lead time of the previous run (SUC).
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To compute bias from model climatology, technically RegCM needs to run with initial
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and boundary conditions from hindcast CFS for the period 1980-2010. Because the
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hindcast of CFS are not available, the CFSR data has been used instead. Because of using
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CFSR, some model bias may still be included in the predictions. An additional successive
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adjustment in the SUC experiment is expected to further reduce model bias. The forecast
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phase of the month at different forecast lead time is compared with the observed phase for
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verifying model skill in phase predictions. In the value predictions, observed (predicted)
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monthly means of 2-m air temperature are computed from observed (predicted) daily
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mean 2-m air temperature. The predicted daily mean is then compared with observed
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mean for model verification. The skill score (SS) is computed as a ratio of number of
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corrected phases stations to all 64 stations. In this work, a total of 768 months or 64 years
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of model runs was conducted to investigate the model performance in seasonal prediction
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for Vietnam.
(Figure 2 around here)
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4. Results
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4.1 Value prediction of 2-m air temperature
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The model overall trend in forecasting 2m temperature are investigated in Figure 3.
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Figure 3a shows that without bias correction in CTL experiment, RegCM forced by CFS
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has significant cold bias in temperature forecast. The cold bias is more significant at low
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temperature than at higher temperature. Besides cold bias, forecasted 2m temperature in
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the CTL also shows a large dispersal. For the same observed value at 17oC for instance,
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the model forecast range is from 6-20oC (Fig. 3a). Errors for value prediction of 2-m air
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temperature for the different runs at the different months for lead time from 1 to 6 at all
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stations are shown in Fig. 4. In the CTL runs, it is clear that the model seasonal forecasts
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show mostly over 3oC colder than observed without bias correction. The error is larger in
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the winter (more than 5oC colder than observed) than in the summer and larger at stations
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located over the high complex terrain (North of Viet Nam and at central highland) than at
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others (Figs. 4a, b, c, top). The significantly large errors of 3-5oC in the CTL imply that
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the model raw outputs are not possible to directly used for seasonal forecast and that some
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bias correction is required.
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With climatology bias correction, the linear trend of forecast temperature (Fig. 3b, red) in
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the BAS experiments is much closer to the perfect line (black) than in the CTL.
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Moreover, the dispersal of the forecast is also reduced. For the observed 17oC mentioned
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above, the forecast range is now only from 14-20oC (Fig. 3b). Although the cold bias is
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significantly reduced in the BAS, the model forecast still show a systematic cold bias
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presenting by the linear trend line (Fig. 3b, red) entirely above the perfect line (Fig. 3b,
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black). In BAS experiments, cold bias at each station is also noticeably reduced. The
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absolute errors are also reduced to less than 2.5oC at almost all stations. About half of
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stations have absolute errors less than 1oC (Figs. 4a, 4b, 4c, middle). It is interesting that
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the errors are not significantly increased with increase in forecast lead time. The feature
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implies possibility for longer seasonal forecast with CFS/RegCM. Although BAS has
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significantly reduced the errors in comparison to the CTL, errors in BAS case still show
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cold bias for almost all stations at all lead time (Figs. 4a, 4b, 4c, middle). Further bias
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correction may be applied to reduce the cold bias in model forecast.
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With further successive correction described on Section 2, the overall cold bias in linear
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trend has been mostly canceled in SUC. The bias in SUC shows cold bias at lower
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(observed T2m < 20oC) temperature and warm bias at higher (observed T2m > 25oC)
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temperature (Fig. 3c). Comparing to the BAS, the errors at stations for all lead time, all
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target months (Figs. 4a, 4b, and 4c, bottom) are further noticeably reduced at southern
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stations. For the northern stations, there are some increases in warm bias in the summer
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target months at most of the forecast lead times.
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Concerning to the dependence of prediction error on different regions of Vietnam, it can
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be seen on Figure 4 that prediction errors are larger in the North of Vietnam than in
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Central and Southern Vietnam for all runs. The complex terrain and larger seasonal
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variation of temperature in the north of Vietnam may be the main reasons for relatively
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larger errors.
(Figure 3, 4a,b,c around here)
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4.2 Phase prediction of 2-m air temperature
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Figure 5 shows the observed phases for different months at 64 stations. and predicted
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phases for different experiments. The figure shows clearly that 2012-2013 period is a hot
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phase over the south of Vietnam (station from Hue to Ca Mau). For station in the North
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and Northern Central of Vietnam, there is a relative hot period from Apr 2012 to June
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2013 at most of stations. Because the RegCM prediction shows a significant cold bias of -
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2 to -6oC (Fig. 4), the model usually predicts below normal phases without bias correction
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in the CTL run (Fig. 6, top), resulting in mostly zero skill in phase prediction (Fig. 7, top).
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In the BAS experiment, bias correction with 30-year model climatology allows RegCM to
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capture some observed phases, especially for the northern part of Vietnam (Fig. 6,
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middle). The model still shows below normal phases over most part of Vietnam in the
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BAS experiment with relatively low SS. The SS in the BAS is mostly below 0.2 (Fig. 7,
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middle).
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The SUC experiment shows advantages over the other two experiments and promise some
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potential skill of phase prediction of 2-m air temperature. With correction from both
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model climatology bias and from previous model run, the phase forecast can reasonably
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captures most of the observed above normal phases (Fig. 6, bottom). The SS values
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increase significantly with most of the value from 0.3 to 0.6 regardless of forecast lead
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time (Fig. 7, bottom).
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(Figure 5, 6a,b,c, 7 around here)
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4.3 Prediction of extreme air temperature
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Forecast errors of maximum 2-m temperature (Tx) for different lead time are showed on
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Figure 8. It can be seen that similar to the forecasted 2-m temperature, the forecasted Tx
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in the CTR experiment also experienced with overall cold bias. At all lead time, most of
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the forecast errors (forecast-observation) are from -6oC to -1oC (Fig. 8, white bar). After
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adjusting with model climatology, the BAS experiment shows significant reducing in
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model cold bias in Tx (Fig. 8, gray bar). In the SUC forecast, the accuracy has improved
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significantly with errors in most cases in the range of ± 1.0oC (Fig. 8, black bar).
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Although the errors are overall reduced in the SUC experiment, there is an increase in
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warm bias of forecasted Tx for lead time larger than three months (Figs. 8d, 8e, and 8f).
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The forecast errors of minimum daily 2-m temperature (Tn) on Figure 9 show very
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similar features with that of Tx on Figure 8. The CTR experiment shows significant cold
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bias in Tn forecast with most values from -10oC to -1oC (Fig. 9, white bar). The BAS
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experiment reduces the errors to ± 2.0oC (Fig. 9, gray bar). The SUC experiment for lead
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time 1 and 2 has most error in the range of ± 1.0oC. For longer forecast lead time, the
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forecast errors in the SUC experiment show a warm bias with some values from 2oC to
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3oC (Fig. 9, black bar). In the future, successive adjustment with error of the same month
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in the previous year may be applied to investigate possibility to reduce the warm bias in
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the SUC experiment.
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(Figure 8, 9 around here)
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5. Summary and discussion
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In this study, the Regional Climate Model version 4.2 (RegCM4.2) has been used to
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perform seasonal prediction of 2-m air temperature for Vietnam from for January 2012 to
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November 2013 in form of operational prediction for the first time. Initial and time-
257
dependent boundary conditions are from the Climate Forecast System (CFS).
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RegCM4.2 was firstly used to simulate for the period 1980-2010 to construct model
259
climatology for phase prediction as well as for model bias corrections. For seasonal
260
prediction, RegCM4.2 is run four times per month from the current month up to next six
261
months. A model ensemble prediction initialized from current month is computed from
262
mean of the four runs within the month. Temperature predictions are performed in two
263
forms, phase predictions and value predictions. The predictions were compared with the
264
observed temperatures at 64 meteorological stations over Vietnam for seasonal forecast
265
verification.
266
Primarily results of this study showed that without bias correction, the RegCM model
267
forced by CFS has a very little or no skill in both phases and value predictions. With bias
268
correction constructed from the 30-year model climatology applied to the raw output,
269
RegCM predictions show some initial successes in seasonal prediction of 2-m mean and
270
extreme air temperature in operational mode for Vietnam. The SUC experiment in which
271
the results from BAS experiment are further successively adjusted with model bias at one
272
month lead time of the previous run show some further improvement of the seasonal
273
predictions, especially in the first three lead times with SS at most of target months are
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larger than 0.3.
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Overall, the model has higher skill in the South of Vietnam than in the North. The relative
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difficulty in seasonal prediction for the North of Vietnam may be due to the effect of
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more complex terrain and larger seasonal variations of temperature. Because of the
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limited availability of the CFS global forecast, only prediction for about two years was
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conducted in this study. More predictions should be performed in the future to further
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verify the ability of RegCM driven by CFS in dynamical seasonal prediction in Vietnam.
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In addition to monthly temperature, other climate variables and phenomena such as
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rainfall, hot days, cold days should be also considered for model verification.
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Acknowledgement
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This work was supported by the Vietnam Ministry of Science and Technology Foundation
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under the Project No: DT.NCCB-DHUD.2011-G/09.
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